000-N04 actual question bank is actual look at, genuine result.

000-N04 practice exam | 000-N04 exam prep | 000-N04 english test questions | 000-N04 exam questions | 000-N04 cheat sheets - bigdiscountsales.com



000-N04 - IBM Commerce Solutions Order Mgmt Technical Mastery Test v1 - Dump Information

Vendor : IBM
Exam Code : 000-N04
Exam Name : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
Questions and Answers : 30 Q & A
Updated On : November 12, 2018
PDF Download Mirror : 000-N04 Brain Dump
Get Full Version : Pass4sure 000-N04 Full Version


No cheaper source of 000-N04 Q&A found yet.

Thankyou bigdiscountsales..I have cleared my 000-N04 examination with ninety two%. Your question economic group have become very beneficial. If all people practices a hundred% certainly from your question set and research all the questions properly, then hes going to definately prevail. Till now ive cleared three extraordinary tests all with the help of your site. Thanks again.

reap these 000-N04 questions.

Thanks lots bigdiscountsales group, for getting ready outstanding practice checks for the 000-N04 examination. It is clear that without bigdiscountsaless examination engine, college students cannot even consider taking the 000-N04 exam. I tried many different assets for my exam coaching, however I couldnt find myself assured sufficient for taking the 000-N04 examination. bigdiscountsaless examination manual makes clean examination instruction, and offers self belief to the scholars for taking examination without problems.

Do not forget to read these real test questions for 000-N04 exam.

Im very glad to have observed bigdiscountsales online, and even extra glad that I bought 000-N04 package just days earlier than my examination. It gave the nice coaching I needed, on the grounds that I didnt have a lot time to spare. The 000-N04 testing engine is definitely exact, and the whole lot objectives the regions and questions they take a look at during the 000-N04 exam. It can also appear strange to pay for a draindump in recent times, when you could discover almost something at no cost on line, but accept as true with me, this one is well worth every penny! I am very happy - both with the instruction method and even greater so with the result. I handed 000-N04 with a completely strong score.

it is surely excellent revel in to have 000-N04 dumps.

I dont feel alone a mid exams any longer in light of the fact that I have a magnificent study accomplice as this bigdiscountsales dumps. I am exceptionally appreciative to the educators here for being so decent and well disposed and helping me in clearing my extremely exam 000-N04. I solved all questions in exam. This same direction was given to me amid my exams and it didnt make a difference whether it was day or night, all my inquiries were replied.

surprised to see 000-N04 actual take a look at questions!

I even have seen numerous things publicized adage utilize this and score the exceptional however your items were absolutely high-quality as contrasted with others. I will return quickly to purchase more observe aids. I really needed to say a debt of gratitude is in order concerning your amazing 000-N04 take a look at manual. I took the exam this week and completed soundly. Nothing had taught me the thoughts the manner bigdiscountsales Questions & solutions did. I solved 95% questions.

am i able to find actual test questions Q & A of 000-N04 exam?

bigdiscountsales has top products for students because these are designed for those students who are interested in the preparation of 000-N04 certification. It was great decision because 000-N04 exam engine has excellent study contents that are easy to understand in short period of time. I am grateful to the great team because this helped me in my career development. It helped me to understand how to answer all important questions to get maximum scores. It was great decision that made me fan of bigdiscountsales. I have decided to come back one more time.

Weekend examine is enough to pass 000-N04 examination with Q&A I got.

bigdiscountsales is the satisfactory and correct way i have ever encounter to put together and pass IT checks. The component is, it offers you accurately and precisely what you need to recognise for 000-N04 exam. My pals used bigdiscountsales for Cisco, Oracle, Microsoft, ISC and other certifications, all exact and valid. completely dependable, my private preferred.

actual 000-N04 exam questions to pass at first strive.

Like many others, ive currently handed the 000-N04 exam. In my case, sizable majority of 000-N04 examination questions got hereexactly from this manual. The solutions are correct, too, so if you are preparing to take your 000-N04 examination, you cancompletely depend upon this internet site.

tremendous source of awesome dumps, accurate answers.

No matter having a complete-time mission along aspect own family obligations, I decided to sit down for the 000-N04 examination. And i used to be looking for clean, quick and strategic guiding principle to make use of 12 days time before examination. I were given these kinds of in bigdiscountsales Q&A. It contained concise solutions that were smooth to dont forget. Thanks masses.

don't forget to examine these real check questions for 000-N04 exam.

I never idea I could be the use of brain dumps for extreme IT exams (I became always an honors student, lol), but as your profession progresses and youve got extra obligations, together with your family, finding time and money to prepare to your tests get harder and more difficult. Yet, to provide for your family, you want to maintain your career and know-how developing... So, perplexed and a little responsible, I ordered this bigdiscountsales bundle. It lived as much as my expectancies, as I passed the 000-N04 examination with a perfectly correct score. The fact is, they do offer you with real 000-N04 examination questions and answers - which is precisely what they promise. But the coolest information also is, that this statistics you cram to your examination stays with you. Dont all of us love the question and solution layout because of that So, some months later, once I received a huge advertising with even bigger obligations, I often discover myself drawing from the information I got from bigdiscountsales. So it additionally allows ultimately, so I dont experience that guilty anymore.

See more IBM dumps

000-597 | C2020-642 | 000-M79 | LOT-986 | 000-094 | 00M-665 | 000-113 | 000-539 | 000-656 | 000-M30 | C2010-658 | 000-543 | LOT-953 | LOT-980 | 000-598 | 000-M06 | 000-280 | C9510-317 | 000-317 | C2180-273 | A2090-303 | 000-965 | 000-793 | 000-415 | 000-314 | A2040-923 | C4060-156 | 000-578 | C2040-958 | 000-643 | C2040-416 | C2010-517 | C2010-503 | C2090-544 | C2140-056 | LOT-985 | C2020-011 | LOT-405 | M2110-670 | M2090-643 | 000-004 | 000-M61 | 000-005 | 000-022 | C4040-221 | P2020-795 | 000-899 | M2080-663 | C2010-511 | 000-484 |

Latest Exams added on bigdiscountsales

1Z0-628 | 1Z0-934 | 1Z0-974 | 1Z0-986 | 202-450 | 500-325 | 70-537 | 70-703 | 98-383 | 9A0-411 | AZ-100 | C2010-530 | C2210-422 | C5050-380 | C9550-413 | C9560-517 | CV0-002 | DES-1721 | MB2-719 | PT0-001 | CPA-REG | CPA-AUD | AACN-CMC | AAMA-CMA | ABEM-EMC | ACF-CCP | ACNP | ACSM-GEI | AEMT | AHIMA-CCS | ANCC-CVNC | ANCC-MSN | ANP-BC | APMLE | AXELOS-MSP | BCNS-CNS | BMAT | CCI | CCN | CCP | CDCA-ADEX | CDM | CFSW | CGRN | CNSC | COMLEX-USA | CPCE | CPM | CRNE | CVPM | DAT | DHORT | CBCP | DSST-HRM | DTR | ESPA-EST | FNS | FSMC | GPTS | IBCLC | IFSEA-CFM | LCAC | LCDC | MHAP | MSNCB | NAPLEX | NBCC-NCC | NBDE-I | NBDE-II | NCCT-ICS | NCCT-TSC | NCEES-FE | NCEES-PE | NCIDQ-CID | NCMA-CMA | NCPT | NE-BC | NNAAP-NA | NRA-FPM | NREMT-NRP | NREMT-PTE | NSCA-CPT | OCS | PACE | PANRE | PCCE | PCCN | PET | RDN | TEAS-N | VACC | WHNP | WPT-R | 156-215-80 | 1D0-621 | 1Y0-402 | 1Z0-545 | 1Z0-581 | 1Z0-853 | 250-430 | 2V0-761 | 700-551 | 700-901 | 7765X | A2040-910 | A2040-921 | C2010-825 | C2070-582 | C5050-384 | CDCS-001 | CFR-210 | NBSTSA-CST | E20-575 | HCE-5420 | HP2-H62 | HPE6-A42 | HQT-4210 | IAHCSMM-CRCST | LEED-GA | MB2-877 | MBLEX | NCIDQ | VCS-316 | 156-915-80 | 1Z0-414 | 1Z0-439 | 1Z0-447 | 1Z0-968 | 300-100 | 3V0-624 | 500-301 | 500-551 | 70-745 | 70-779 | 700-020 | 700-265 | 810-440 | 98-381 | 98-382 | 9A0-410 | CAS-003 | E20-585 | HCE-5710 | HPE2-K42 | HPE2-K43 | HPE2-K44 | HPE2-T34 | MB6-896 | VCS-256 | 1V0-701 | 1Z0-932 | 201-450 | 2VB-602 | 500-651 | 500-701 | 70-705 | 7391X | 7491X | BCB-Analyst | C2090-320 | C2150-609 | IIAP-CAP | CAT-340 | CCC | CPAT | CPFA | APA-CPP | CPT | CSWIP | Firefighter | FTCE | HPE0-J78 | HPE0-S52 | HPE2-E55 | HPE2-E69 | ITEC-Massage | JN0-210 | MB6-897 | N10-007 | PCNSE | VCS-274 | VCS-275 | VCS-413 |

See more dumps on bigdiscountsales

HP2-E40 | 920-482 | 920-320 | HP0-Y26 | A4120-784 | 000-080 | LOT-822 | 000-706 | 000-601 | HP0-402 | 310-019 | 1Z0-034 | 646-656 | 9L0-064 | HP2-K09 | CSET | ST0-304 | MB2-708 | 310-083 | NS0-320 | C2170-008 | 920-271 | 644-906 | HP5-H04D | 712-50 | OMG-OCUP-300 | 1Z0-218 | CCNT | E20-375 | HP0-823 | C2020-612 | HP2-B100 | S10-100 | 117-102 | 1Z0-550 | C8010-241 | 310-102 | 000-014 | 00M-234 | 250-314 | AP0-001 | 000-M07 | 810-440 | C9020-560 | A2040-925 | C2040-929 | 922-020 | HC-711 | LOT-916 | 310-610 |

000-N04 Questions and Answers

Pass4sure 000-N04 dumps | Killexams.com 000-N04 real questions | [HOSTED-SITE]

000-N04 IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Study Guide Prepared by Killexams.com IBM Dumps Experts


Killexams.com 000-N04 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



000-N04 exam Dumps Source : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Test Code : 000-N04
Test Name : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
Vendor Name : IBM
Q&A : 30 Real Questions

Unbelieveable! however right source modern day 000-N04 real test questions.
a few properly men cant bring an alteration to the worlds way however they can most effective inform you whether you have got been the simplest man who knew how to do that and i want to be acknowledged on this world and make my personal mark and ive been so lame my complete way but I realize now that I wanted to get a bypass in my 000-N04 and this could make me well-known perhaps and yes im quick of glory however passing my A+ checks with killexams.com changed into my morning and night glory.


Did you tried these 000-N04 actual query financial institution and study guide.
I cleared all of the 000-N04 exams effortlessly. This internet site proved very useful in clearing the exams as well as understanding the principles. All questions are explanined thoroughly.


first rate source latest high-highexcellent 000-N04 mind dumps, correct answers.
just surpassed the 000-N04 exam with this braindump. i can affirm that it is 99% valid and includes all this years updates. I handiest got 2 question wrong, so very excited and relieved.


wonderful to pay attention that real take a look at questions trendy 000-N04 exam are supplied here.
One of most complex task is to pick fine observe material for 000-N04 certification examination. I never had sufficient religion in myself and therefore idea I wouldnt get into my favourite university given that I didnt have sufficient things to have a look at from. This killexams.Com came into the photo and my perspective modified. I become able to get 000-N04 absolutely prepared and I nailed my check with their help. Thank you.


Belive me or now not! This resource of 000-N04 questions works.
Just passed the 000-N04 exam way to Killexams. The questions are all accurate and actual. This education percent may be very solid and reliable, totally surpassed my expectancies. I actually have already shared my perspectives with colleagues who surpassed the 000-N04 examination,. So in case you are looking for reliable mind dumps for any exam, that is a terrific alternative. At least 000-N04 examination is genuinely reliable


Is there 000-N04 exam new sayllabus?
I knew that I had to cleared my 000-N04 exam to preserve my interest in present day agency and it changed into not smoothactivity with out a few assist. It have become just incredible for me to investigate loads from killexams.Com instruction % in form of 000-N04 questions answers and exam simulator. Now I proud to announce that im 000-N04 licensed. Terrific workkillexams.


in which am i able to download 000-N04 state-of-the-art dumps?
Im very masses satisfied together along with your take a look at papers in particular with the solved issues. Your take a look at papers gave me courage to seem inside the 000-N04 paper with self belief. The result is seventy seven.25%. All all over again I complete heartedly thank the killexams.Com business enterprise. No exclusive manner to bypass the 000-N04 exam other than killexams.Com model papers. I individually cleared distinctive exams with the help of killexams.Com query bank. I advocate it to each one. If you need to skip the 000-N04 exam then take killexams.Com assist.


Little effor required to put together 000-N04 actual examination bank.
I used this sell off to pass the 000-N04 examination in Romania and had been given 98%, so that is a exquisite way to put together for the exam. All questions I got on the examination were exactly what killexams.Com had provided in this mind promote off, this is splendid I relatively advocate this to every body if you are going to take 000-N04 exam.


Passing 000-N04 exam is simply click away!
Like many others, i have currently surpassed the 000-N04 examination. In my case, extensive majority of 000-N04 exam questions got hereexactly from this manual. The answers are correct, too, so if you are making ready to take your 000-N04 examination, you cancompletely rely upon this net site.


All is nicely that ends properly, at final handed 000-N04 with Q&A.
I dont feel alone throughout exams anymore due to the fact i have a high-quality have a look at accomplice in the form of this killexams. not handiest that but I additionally have instructors whore prepared to guide me at any time of the day. This equal guidance turned into given to me for the duration of my assessments and it didnt be counted whether it changed into day or night time, all my queries were replied. im very grateful to the lecturers right here for being so greatand friendly and assisting me in clearing my very difficult exam with 000-N04 have a look at fabric and 000-N04 look at and sureeven 000-N04 self examine is first rate.


IBM IBM Commerce Solutions Order

Cloud Computing: IBM Acquires Sterling Commerce | killexams.com Real Questions and Pass4sure dumps

by PR Newswire

Article rating:

August 27, 2010 02:45 PM EDT

Reads:

20,162

IBM on Friday announced the closing of its acquisition of Sterling Commerce. The enterprise expands IBM's means to help shoppers speed up their interactions with purchasers, companions and suppliers through dynamic company networks the use of both on-premise or cloud beginning models.

groups are searching for how to create extra clever networks of company partners, purchasers and suppliers to be able to enhance effectivity and profitability. These interactions are increasing dramatically because of the proliferation of digital enterprise transactions, from banks replacing transaction facts and producers sourcing uncooked materials electronically, to sellers automating stock replenishment and managing orders online.

Sterling Commerce gives utility for move-channel commerce and integration of customer, associate and agency networks across a big range of industries. The mixture of IBM and Sterling Commerce permits the mixing of key business procedures throughout channels and amongst trading companions - from advertising and selling to order management and achievement.

"We now offer a complete platform for multi-commercial enterprise enterprise transactions," stated Craig Hayman, typical manager, IBM trade options. "In combination with IBM's latest offerings, Sterling Commerce, Coremetrics and Unica are expanding IBM's capability to support companies automate, control and accelerate core business tactics across advertising and marketing, promoting, order administration and fulfillment."

With the acquisition of Sterling Commerce, IBM advances its ability to assist valued clientele integrate and automate business methods, resulting in superior demand technology, customer adventure and achievement. using the combined applied sciences of IBM and Sterling Commerce, valued clientele have the flexibleness to manipulate these approaches - and their networks of enterprise partners - via public or inner most cloud computing environments.

because IBM introduced its intent to acquire the enterprise in can also, Sterling Commerce has considered endured momentum with valued clientele in both its enterprise integration and commerce solutions businesses. Sterling Commerce these days introduced that Hostess manufacturers has carried out its B2B integration solutions each on-premise and as a carrier to increase Hostess' give chain performance. In June, Cengage researching went reside with the newest edition of Sterling Multi-Channel selling to take advantage of latest market segmentation and enhanced promotions functionality that raise the client adventure of its award-profitable web site, CengageBrain.com.

"We view the IBM acquisition of Sterling Commerce as a positive flow," talked about Charles Qian, manager of eCommerce programs at Cengage learning, a leading international company of innovative educating, discovering and research options. "Our recent implementation changed into seamless, and achieved beneath a good timeframe. I predict the fabulous solutions we've got from Sterling Commerce will handiest be more suitable under IBM."

besides improving IBM's integration and commerce choices, Sterling Commerce application additionally complements IBM's trade-focused application including the company's frameworks assisting the retail, manufacturing, communications, health care and banking industries.

more than 18,000 global shoppers rely on Sterling Commerce's choices, including huge companies such as Boston Market, Honeywell, Monsanto and Pitney Bowes. backyard the U.S., Sterling Commerce's customer record comprises leading producers like Toshiba and desirable retailers equivalent to Auchan and John Lewis.

The acquisition builds on IBM's growing to be portfolio of business application options designed to assist organizations automate, manipulate and accelerate core enterprise processes across advertising, promoting, ordering and achievement. IBM's recent acquisitions of Sterling Commerce and Coremetrics and the meant acquisition of Unica will increase the company's capacity to aid customers' wants in this becoming market.

With the closing of this acquisition approximately 2,500 Sterling Commerce personnel be a part of IBM. in keeping with IBM's software strategy, IBM will continue to guide Sterling Commerce's purchasers whereas permitting them to take potential of the broader IBM portfolio.

IoT & wise Cities experiences

by Pat Romanski

Nov. 7, 2018 12:00 AM EST  Reads: three,206

by way of Liz McMillan

Nov. 6, 2018 11:45 PM EST  Reads: 3,one hundred seventy

by Pat Romanski

Nov. 6, 2018 09:45 PM EST  Reads: 2,258

by way of Elizabeth White

Nov. 5, 2018 11:15 PM EST  Reads: 2,064

with the aid of Pat Romanski

Nov. 5, 2018 04:15 PM EST  Reads: 2,282

via Pat Romanski

Nov. four, 2018 11:00 PM EST  Reads: 2,941

via Yeshim Deniz

Nov. 3, 2018 05:00 AM EDT  Reads: 4,026

by way of Yeshim Deniz

Nov. 2, 2018 03:00 PM EDT  Reads: 3,210

by Elizabeth White

Oct. 30, 2018 03:forty five PM EDT  Reads: 14,062

with the aid of Zakia Bouachraoui

Oct. 30, 2018 11:forty five AM EDT


IBM’s Cognitive options income Slumped. What came about? | killexams.com Real Questions and Pass4sure dumps

A key element of overseas enterprise Machines' (NYSE: IBM) turnaround effort is cognitive computing, which encompasses synthetic intelligence (AI) together with linked technologies. Watson, IBM's cognitive computing device than debuted by using successful a online game of Jeopardy! in 2011, has been utilized to fields together with healthcare, financial services, and even myth soccer.

Cognitive computing is a growth enterprise for IBM, however you wouldn't are aware of it searching at the business's third-quarter effects. The cognitive solutions segment suffered a 5% revenue decline, even after adjusting for a foreign money-linked headwind. That seems like terrible news for a company making a bet its future on AI.

The cognitive solutions phase may still truly be called the "cognitive solutions plus a bunch of other unrelated stuff" segment. It includes Watson and different organizations with increase advantage, but additionally stuff like legacy transaction-processing utility. or not it's kind of a grab bag of IBM agencies that don't fairly fit into its other segments.

That makes it difficult to tell how smartly IBM's growth companies are basically doing, and it makes that 5% income decline a whole lot less meaningful.

The IBM Watson logo.© IBM The IBM Watson logo.

CFO James Kavanaugh went into some aspect all through the revenue call related to the efficiency of the cognitive options company. The segment is broken into two components: solutions application and transaction processing software.

solutions software includes application aimed at strategic verticals (Kavanaugh singled out the healthcare industry). It additionally contains some analytics and security choices, AI like Watson, and blockchain. On excellent of all that, "horizontal domains" like collaboration and commerce are additionally protected.

Transaction processing software comprises "utility that runs mission-crucial workloads leveraging our hardware platform," in accordance with Kavanaugh. here is ordinarily on-premises application used by way of industries like banking, airways, and retail.

Transaction processing utility accounted for a minority of cognitive options earnings within the third quarter, but salary from that class declined by way of eight% 12 months over year. Kavanaugh pointed out that, while most of the revenue for transaction processing utility is annuity-based, the timing of massive deals can have an effect on sales. Kavanaugh expects a return to boom, in accordance with a strong pipeline of deals.

The options utility element of the phase suffered a three% sales decline, driven through some areas the place IBM is struggling. Secular shifts in the collaboration, commerce, and ability management markets are causing issues for the enterprise, and or not it's been adding AI and modernizing its choices to combat those adjustments. The shift to application as a carrier is also putting pressure on sales, with revenue being realized over time as opposed to up front.

The elements of this section with long-term growth competencies are the elements that are growing. Watson health, the company's effort to apply AI to the healthcare trade, loved vast-based increase all over the third quarter. protection grew due to the company's vast portfolio of products. And the enterprise made some huge moves in the blockchain market.

IBM introduced TradeLens, a blockchain-based platform for the international transport trade, in August. The solution, collectively developed with Maersk, had 94 members on board at the time of the announcement. IBM meals have confidence, a new blockchain-primarily based platform that allows for meals to be traced from farm to shop, counts Walmart and French grocery store chain Carrefour as participants. IBM's blockchain efforts are still in their infancy, however each of those platforms have the knowledge to develop into meaningful organizations for the company.

With the cognitive solutions phase being dragged down through legacy companies, the headline performance doesn't replicate the efficiency of IBM's more promising corporations.

backed: 10 stocks we like superior than IBM

When investing geniuses David and Tom Gardner have a stock tip, it can pay to hear. in any case, the newsletter they've run for over a decade, Motley idiot stock advisor , has quadrupled the market.*

David and Tom just printed what they believe are the 10 most appropriate shares for investors to buy at the moment... and IBM wasn't considered one of them! it is correct -- they believe these 10 stocks are even more desirable buys.

click right here to learn about these picks!

*inventory guide returns as of August 6, 2018

Timothy green owns shares of IBM. The Motley fool has no place in any of the shares mentioned. The Motley idiot has a disclosure policy.


IBM's options power Bulgaria-primarily based Praktiker's revenue | killexams.com Real Questions and Pass4sure dumps

international enterprise Machines organization IBM lately introduced that Praktiker, a house DIY retail chain based mostly in Bulgaria has tripled both its online earnings and in-shop purchases considering that its adoption of the enterprise’s omnichannel commerce solution five months in the past.   

Praktiker’s new website elements a web catalogue of more than forty,000 items offering useful suggestions to the conclusion consumer. The website has been seen through 750,000 wonderful clients seeing that its launch. peculiarly, the regular variety of visits has tripled from about 1500 per day initially to 5000 per day at current.

Adoption of IBM’s omnichannel commerce options particularly WebSphere Commerce (for both for B2B and B2C) and Sterling Order administration (for offering insights on give and demand, order achievement approaches) through retail merchants has improved in contemporary instances.

We are expecting that the growing to be adoption of IBM options (retail, Watson) will continue to boost the suitable line.

View photographs

in particular, shares of IBM received 0.forty three% on Tuesday. The stock has outperformed the Zacks laptop - integrated methods industry on a 12 months-to-date basis. while the business won best 3.9% all through the period, the inventory liked 5.1%

Is IBM Poised to improvement?

We observe that competitors is intensifying in the application solutions space with the presence of major players reminiscent of salesforce.com’s CRM Salesforce Commerce Cloud, SAP SE’s SAP SAP Hybris and Oracle’s ORCL Oracle Commerce.

We believe that the continuing adoption fashion for IBM’s utility options systems augurs well for the company ultimately.

As of the ultimate reported quarter, IBM’s Cognitive solutions (solutions application and transaction processing utility) revenues grew 1.four% on a year-over-yr groundwork (up 2.2% at consistent currency) to $5.30 billion.

overseas business Machines employer earnings (TTM)

View pictures

international business Machines employer revenue (TTM) | overseas enterprise Machines enterprise Quote

solutions software boom became driven essentially with the aid of analytics. (study extra: IBM Corp (IBM) Beats on q4 salary; FY17 View high quality).

Zacks Rank

At present IBM has a Zacks Rank #3 (grasp). that you would be able to see the complete checklist of nowadays’s Zacks #1 Rank (strong buy) stocks right here.

more stock news: 8 corporations Verge on Apple-Like Run

Did you leave out Apple's 9X inventory explosion after they launched their iPhone in 2007? Now 2017 appears to be a pivotal 12 months to get in on one more emerging technology anticipated to rock the market. Demand may start from well-nigh nothing to $forty two billion by way of 2025. experiences suggest it may store 10 million lives per decade which might in flip keep $200 billion in U.S. healthcare prices.

Story Continues


000-N04 IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Study Guide Prepared by Killexams.com IBM Dumps Experts


Killexams.com 000-N04 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



000-N04 exam Dumps Source : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Test Code : 000-N04
Test Name : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
Vendor Name : IBM
Q&A : 30 Real Questions

Unbelieveable! however right source modern day 000-N04 real test questions.
a few properly men cant bring an alteration to the worlds way however they can most effective inform you whether you have got been the simplest man who knew how to do that and i want to be acknowledged on this world and make my personal mark and ive been so lame my complete way but I realize now that I wanted to get a bypass in my 000-N04 and this could make me well-known perhaps and yes im quick of glory however passing my A+ checks with killexams.com changed into my morning and night glory.


Did you tried these 000-N04 actual query financial institution and study guide.
I cleared all of the 000-N04 exams effortlessly. This internet site proved very useful in clearing the exams as well as understanding the principles. All questions are explanined thoroughly.


first rate source latest high-highexcellent 000-N04 mind dumps, correct answers.
just surpassed the 000-N04 exam with this braindump. i can affirm that it is 99% valid and includes all this years updates. I handiest got 2 question wrong, so very excited and relieved.


wonderful to pay attention that real take a look at questions trendy 000-N04 exam are supplied here.
One of most complex task is to pick fine observe material for 000-N04 certification examination. I never had sufficient religion in myself and therefore idea I wouldnt get into my favourite university given that I didnt have sufficient things to have a look at from. This killexams.Com came into the photo and my perspective modified. I become able to get 000-N04 absolutely prepared and I nailed my check with their help. Thank you.


Belive me or now not! This resource of 000-N04 questions works.
Just passed the 000-N04 exam way to Killexams. The questions are all accurate and actual. This education percent may be very solid and reliable, totally surpassed my expectancies. I actually have already shared my perspectives with colleagues who surpassed the 000-N04 examination,. So in case you are looking for reliable mind dumps for any exam, that is a terrific alternative. At least 000-N04 examination is genuinely reliable


Is there 000-N04 exam new sayllabus?
I knew that I had to cleared my 000-N04 exam to preserve my interest in present day agency and it changed into not smoothactivity with out a few assist. It have become just incredible for me to investigate loads from killexams.Com instruction % in form of 000-N04 questions answers and exam simulator. Now I proud to announce that im 000-N04 licensed. Terrific workkillexams.


in which am i able to download 000-N04 state-of-the-art dumps?
Im very masses satisfied together along with your take a look at papers in particular with the solved issues. Your take a look at papers gave me courage to seem inside the 000-N04 paper with self belief. The result is seventy seven.25%. All all over again I complete heartedly thank the killexams.Com business enterprise. No exclusive manner to bypass the 000-N04 exam other than killexams.Com model papers. I individually cleared distinctive exams with the help of killexams.Com query bank. I advocate it to each one. If you need to skip the 000-N04 exam then take killexams.Com assist.


Little effor required to put together 000-N04 actual examination bank.
I used this sell off to pass the 000-N04 examination in Romania and had been given 98%, so that is a exquisite way to put together for the exam. All questions I got on the examination were exactly what killexams.Com had provided in this mind promote off, this is splendid I relatively advocate this to every body if you are going to take 000-N04 exam.


Passing 000-N04 exam is simply click away!
Like many others, i have currently surpassed the 000-N04 examination. In my case, extensive majority of 000-N04 exam questions got hereexactly from this manual. The answers are correct, too, so if you are making ready to take your 000-N04 examination, you cancompletely rely upon this net site.


All is nicely that ends properly, at final handed 000-N04 with Q&A.
I dont feel alone throughout exams anymore due to the fact i have a high-quality have a look at accomplice in the form of this killexams. not handiest that but I additionally have instructors whore prepared to guide me at any time of the day. This equal guidance turned into given to me for the duration of my assessments and it didnt be counted whether it changed into day or night time, all my queries were replied. im very grateful to the lecturers right here for being so greatand friendly and assisting me in clearing my very difficult exam with 000-N04 have a look at fabric and 000-N04 look at and sureeven 000-N04 self examine is first rate.


Obviously it is hard assignment to pick solid certification questions/answers assets concerning review, reputation and validity since individuals get sham because of picking incorrectly benefit. Killexams.com ensure to serve its customers best to its assets concerning exam dumps update and validity. The vast majority of other's sham report objection customers come to us for the brain dumps and pass their exams cheerfully and effectively. We never trade off on our review, reputation and quality because killexams review, killexams reputation and killexams customer certainty is vital to us. Uniquely we deal with killexams.com review, killexams.com reputation, killexams.com sham report grievance, killexams.com trust, killexams.com validity, killexams.com report and killexams.com scam. In the event that you see any false report posted by our rivals with the name killexams sham report grievance web, killexams.com sham report, killexams.com scam, killexams.com dissension or something like this, simply remember there are constantly terrible individuals harming reputation of good administrations because of their advantages. There are a great many fulfilled clients that pass their exams utilizing killexams.com brain dumps, killexams PDF questions, killexams hone questions, killexams exam simulator. Visit Killexams.com, our specimen questions and test brain dumps, our exam simulator and you will realize that killexams.com is the best brain dumps site.

[OPTIONAL-CONTENTS-2]


CAT-200 questions answers | HP0-660 braindumps | VCPC610 cram | 920-804 questions and answers | HP0-X01 practice questions | HP2-E26 real questions | N10-006 braindumps | 000-M01 braindumps | 642-162 dumps | BAS-012 test questions | 1T6-530 pdf download | C2090-461 test prep | 000-575 study guide | S10-100 brain dumps | 117-102 free pdf download | 1Z0-545 examcollection | 000-420 practice test | HP0-D01 Practice Test | HP2-K16 dumps questions | C2020-700 free pdf |


[OPTIONAL-CONTENTS-3]

Looking for 000-N04 exam dumps that works in real exam?
killexams.com real 000-N04 exam simulator is exceptionally promising for our clients for the exam prep. Gigantically basic questions, references and definitions are highlighted in brain dumps pdf. Get-together the data in a solitary area is a bona fide help and reasons you get readied for the IT affirmation exam inside a fast time span cross. The 000-N04 exam gives key core interests. The killexams.com brain dumps stays up with the latest starting at real test.

killexams.com have its specialists operating ceaselessly for the gathering of real test questions of 000-N04. All the pass4sure Questions and Answers of 000-N04 gathered by our cluster are looked into and updated by our 000-N04 certification cluster. we have an approach to keep related to the candidates showed up within the 000-N04 exam to induce their reviews regarding the 000-N04 exam, we have an approach to gather 000-N04 exam tips and tricks, their expertise regarding the procedures utilized as an area of the important 000-N04 exam, the errors they did and wiped out the important exam and later on enhance our 000-N04 braindumps as required. Click http://killexams.com/pass4sure/exam-detail/000-N04 killexams.com Discount Coupons and Promo Codes are as under; WC2017 : 60% Discount Coupon for all exams on website PROF17 : 10% Discount Coupon for Orders larger than $69 DEAL17 : 15% Discount Coupon for Orders larger than $99 SEPSPECIAL : 10% Special Discount Coupon for All Orders When you expertise our 000-N04 real Questions and Answers, you will feel certain regarding each one of the themes of 000-N04 exam and feel that your information has been considerably captive forward. These Questions and Answers are not merely practice questions, these are real test Questions and Answers that are sufficient to pass the 000-N04 exam first attempt.

killexams.com allows hundreds of thousands of candidates pass the tests and get their certifications. We have thousands of a hit testimonials. Our dumps are reliable, affordable, updated and of truly best nice to conquer the difficulties of any IT certifications. killexams.com exam dumps are cutting-edge updated in noticeably outclass way on regular basis and material is released periodically. Latest killexams.com dumps are available in trying out centers with whom we are preserving our courting to get modern day cloth.

The killexams.com exam questions for 000-N04 IBM Commerce Solutions Order Mgmt Technical Mastery Test v1 exam is particularly based on two handy codecs, PDF and Practice questions. PDF document carries all of the exam questions, answers which makes your coaching less complicated. While the Practice questions are the complimentary function inside the exam product. Which enables to self-determine your development. The assessment tool additionally questions your vulnerable areas, in which you need to put more efforts so that you can enhance all of your concerns.

killexams.com advocate you to should try its free demo, you will observe the intuitive UI and also you will discover it very pass to personalize the instruction mode. But make sure that, the actual 000-N04 product has extra functions than the trial version. If, you are contented with its demo then you should purchase the real 000-N04 exam product. Avail 3 months Free updates upon buy of 000-N04 IBM Commerce Solutions Order Mgmt Technical Mastery Test v1 Exam questions. killexams.com gives you three months loose update upon acquisition of 000-N04 IBM Commerce Solutions Order Mgmt Technical Mastery Test v1 exam questions. Our expert crew is constantly available at back quit who updates the content as and while required.

killexams.com Huge Discount Coupons and Promo Codes are as under;
WC2017 : 60% Discount Coupon for all exams on internet site
PROF17 : 10% Discount Coupon for Orders greater than $69
DEAL17 : 15% Discount Coupon for Orders extra than $99
OCTSPECIAL : 10% Special Discount Coupon for All Orders


[OPTIONAL-CONTENTS-4]


Killexams 00M-530 VCE | Killexams 920-262 bootcamp | Killexams 190-828 practice exam | Killexams LOT-801 braindumps | Killexams C2010-659 dumps | Killexams F50-531 study guide | Killexams 250-270 free pdf | Killexams HP0-803 Practice Test | Killexams BCP-222 braindumps | Killexams MB2-186 test questions | Killexams 1Z0-479 study guide | Killexams C5050-380 brain dumps | Killexams M2020-620 practice questions | Killexams 3X0-104 exam prep | Killexams HP2-H01 cheat sheets | Killexams 000-593 examcollection | Killexams 1Z1-456 free pdf | Killexams C4090-959 practice test | Killexams TA12 practice test | Killexams 70-545-CSharp test prep |


[OPTIONAL-CONTENTS-5]

View Complete list of Killexams.com Brain dumps


Killexams CVPM dumps questions | Killexams HPE0-J79 practice test | Killexams BH0-001 study guide | Killexams 9A0-044 practice exam | Killexams ST0-306 Practice test | Killexams HP2-E48 mock exam | Killexams HP0-536 practice questions | Killexams ST0-94X braindumps | Killexams 000-281 questions and answers | Killexams 650-322 free pdf | Killexams 050-894 examcollection | Killexams 000-442 free pdf | Killexams 156-315.77 practice questions | Killexams 1V0-701 test prep | Killexams NS0-201 braindumps | Killexams 4H0-002 exam prep | Killexams HP0-S15 VCE | Killexams 190-955 cram | Killexams 000-M237 test questions | Killexams LOT-927 pdf download |


IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Pass 4 sure 000-N04 dumps | Killexams.com 000-N04 real questions | [HOSTED-SITE]

Modeled larval connectivity of a multi-species reef fish and invertebrate assemblage off the coast of Moloka‘i, Hawai‘i | killexams.com real questions and Pass4sure dumps

Introduction

Knowledge of population connectivity is necessary for effective management in marine environments (Mitarai, Siegel & Winters, 2008; Botsford et al., 2009; Toonen et al., 2011). For many species of marine invertebrate and reef fish, dispersal is mostly limited to the pelagic larval life stage. Therefore, an understanding of larval dispersal patterns is critical for studying population dynamics, connectivity, and conservation in the marine environment (Jones, Srinivasan & Almany, 2007; Lipcius et al., 2008; Gaines et al., 2010; Toonen et al., 2011). Many coastal and reef species have a bi-phasic life history in which adults display limited geographic range and high site fidelity, while larvae are pelagic and highly mobile (Thorson, 1950; Scheltema, 1971; Strathmann, 1993; Marshall et al., 2012). This life history strategy is not only common to sessile invertebrates such as corals or limpets; many reef fish species have been shown to have a home range of <1 km as adults (Meyer et al., 2000; Meyer, Papastamatiou & Clark, 2010). Depending on species, the mobile planktonic stage can last from hours to months and has the potential to transport larvae up to hundreds of kilometers away from a site of origin (Scheltema, 1971; Richmond, 1987; Shanks, 2009). Knowledge of larval dispersal patterns can be used to inform effective management, such as marine spatial management strategies that sustain source populations of breeding individuals capable of dispersing offspring to other areas.

Both biological and physical factors impact larval dispersal, although the relative importance of these factors is likely variable among species and sites and remains debated (Levin, 2006; Paris, Chérubin & Cowen, 2007; Cowen & Sponaugle, 2009; White et al., 2010). In situ data on pelagic larvae are sparse; marine organisms at this life stage are difficult to capture and identify, and are typically found in low densities across large areas of the open ocean (Clarke, 1991; Wren & Kobayashi, 2016). A variety of genetic and chemistry techniques have therefore been developed to estimate larval connectivity (Gillanders, 2005; Leis, Siebeck & Dixson, 2011; Toonen et al., 2011; Johnson et al., 2018). Computer models informed by field and laboratory data have also become a valuable tool for estimating larval dispersal and population connectivity (Paris, Chérubin & Cowen, 2007; Botsford et al., 2009; Sponaugle et al., 2012; Kough, Paris & Butler IV, 2013; Wood et al., 2014). Individual-based models, or IBMs, can incorporate both biological and physical factors known to influence larval movement. Pelagic larval duration (PLD), for example, is the amount of time a larva spends in the water column before settlement and can vary widely among or even within species ( Toonen & Pawlik, 2001). PLD affects how far an individual can be successfully transported by ocean currents, and so is expected to directly affect connectivity patterns (Siegel et al., 2003; Shanks, 2009; Dawson et al., 2014). In addition to PLD, adult reproductive strategy and timing (Carson et al., 2010; Portnoy et al., 2013), fecundity (Castorani et al., 2017), larval mortality (Vikebøet al., 2007), and larval developmental, morphological, and behavioral characteristics (Paris, Chérubin & Cowen, 2007) may all play a role in shaping connectivity patterns. Physical factors such as temperature, bathymetry, and current direction can also substantially influence connectivity (Cowen & Sponaugle, 2009). In this study, we incorporated both biotic and abiotic components in an IBM coupled with an oceanographic model to predict fine-scale patterns of larval exchange around the island of Moloka‘i in the Hawaiian archipelago.

The main Hawaiian Islands are located in the middle of the North Pacific Subtropical Gyre, and are bordered by the North Hawaiian Ridge current along the northern coasts of the islands and the Hawaii Lee Current along the southern coasts, both of which run east to west and are driven by the prevailing easterly trade winds (Lumpkin, 1998; Friedlander et al., 2005). The Hawai‘i Lee Countercurrent, which runs along the southern perimeter of the chain, flows west to east (Lumpkin, 1998). The pattern of mesoscale eddies around the islands is complex and varies seasonally (Friedlander et al., 2005; Vaz et al., 2013).

Hawaiian marine communities face unprecedented pressures, including coastal development, overexploitation, disease, and increasing temperature and acidification due to climate change (Smith, 1993; Lowe, 1995; Coles & Brown, 2003; Friedlander et al., 2003; Friedlander et al., 2005; Aeby, 2006). Declines in Hawaiian marine resources argue for implementation of a more holistic approach than traditional single-species maximum sustainable yield techniques, which have proven ineffective (Goodyear, 1996; Hilborn, 2011). There is a general movement toward the use of ecosystem-based management, which requires knowledge of ecosystem structure and connectivity patterns to establish and manage marine spatial planning areas (Slocombe, 1993; Browman et al., 2004; Pikitch et al., 2004; Arkema, Abramson & Dewsbury, 2006). Kalaupapa National Historical Park is a federal marine protected area (MPA) located on the north shore of Moloka‘i, an island in the Maui Nui complex of the Hawaiian archipelago, that includes submerged lands and waters up to 1 4 mile offshore (NOAA, 2009). At least five IUCN red-listed coral species have been identified within this area (Kenyon, Maragos & Fenner, 2011), and in 2010 the Park showed the greatest fish biomass and species diversity out of four Hawaiian National Parks surveyed (Beets, Brown & Friedlander, 2010). One of the major benefits expected of MPAs is that the protected waters within the area provide a source of larval spillover to other sites on the island, seeding these areas for commercial, recreational, and subsistence fishing (McClanahan & Mangi, 2000; Halpern & Warner, 2003; Lester et al., 2009).

In this study, we used a Lagrangian particle-tracking IBM (Wong-Ala et al., 2018) to simulate larval dispersal around Moloka‘i and to estimate the larval exchange among sites at the scale of an individual island. We have parameterized our model with biological data for eleven species covering a breadth of Hawaiian reef species life histories (e.g., habitat preferences, larval behaviors, and pelagic larval durations, Table 1), and of interest to both the local community and resource managers. Our goals were to examine patterns of species-specific connectivity, characterize the location and relative magnitude of connections around Moloka‘i, describe sites of potential management relevance, and address the question of whether Kalaupapa National Historical Park provides larval spillover for adjacent sites on Moloka‘i, or connections to the adjacent islands of Hawai‘i, Maui, O‘ahu, Lana‘i, and Kaho‘olawe.

Table 1:

Target taxa selected for the study, based on cultural, ecological, and/or economic importance.

PLD = pelagic larval duration. Short dispersers (3–25 day minimum PLD) in white, medium dispersers (30–50 day minimum PLD) in light gray, and long dispersers (140–270 day minimum PLD) in dark gray. Spawn season and timing from traditional ecological knowledge shared by cultural practitioners on the island. Asterisk indicates that congener-level data was used. Commonname Scientific name Spawn type # of larvae spawned Spawningday of year Spawning hour of day Spawning moon phase Larval depth (m) PLD (days) Habitat ’Opihi/ Limpet Cellana spp. Broadcast1 861,300 1–60 & 121–181 – New 0–5 3–181,2 Intertidal1 Ko’a/ Cauliflower coral Pocillopora meandrina Broadcast3 1,671,840 91–151 07:15–08:00 Full 0–54 5–90*5 Reef He’e/ Octopus Octopus cyanea Benthic6 1,392,096 1–360 – – 50–100 216 Reef, rubble7 Moi/ Pacific threadfin Polydactylus sexfilis Broadcast 1,004,640 152–243 – – 50–1008 259 Sand10 Uhu uliuli/ Spectacled parrotfish Chlorurus perspicillatus Broadcast 1,404,792 152–212 – – 0–120*11 30*12 Reef10 Uhu palukaluka/ Reddlip parrotfish Scarus rubroviolaceus Broadcast 1,404,792 152–212 – – 0–120*11 30*12 Rock, reef10 Kumu/ Whitesaddle Goatfish Parupeneus porphyreus Broadcast 1,071,252 32–90 – – 0–50*11 41–56*12 Sand, rock, reef10 Kole/ Spotted surgeonfish Ctenochaetus strigosus Broadcast 1,177,200 60–120 – – 50–10011 50*12 Rock, reef, rubble10 ‘Ōmilu/ Bluefin trevally Caranx melampygus Broadcast 1,310,616 121–243 – – 0–80*11 140*13,14 Sand, reef10 Ulua/ Giant trevally Caranx ignoblis Broadcast 1,151,040 152–243 – Full 0–80*11 14013,14 Sand, rock, reef10 Ula/ Spiny lobster Panulirus spp. Benthic15 1,573,248 152–243 – – 50–10016 27017 Rock, pavement16 Methods Circulation model

We selected the hydrodynamic model MITgcm, which is designed for the study of dynamical processes in the ocean on a horizontal scale. This model solves incompressible Navier–Stokes equations to describe the motion of viscous fluid on a sphere, discretized using a finite-volume technique (Marshall et al., 1997). The one-km resolution MITgcm domain for this study extends from 198.2°E to 206°E and from 17°N to 22.2°N, an area that includes the islands of Moloka‘i, Maui, Lana‘i, Kaho‘olawe, O‘ahu, and Hawai‘i. While Ni‘ihau and southern Kaua’i also fall within the domain, we discarded connectivity to these islands because they lie within the 0.5° boundary zone of the current model. Boundary conditions are enforced over 20 grid points on all sides of the model domain. Vertically, the model is divided into 50 layers that increase in thickness with depth, from five m at the surface (0.0–5.0 m) to 510 m at the base (4,470 –4,980 m). Model variables were initialized using the output of a Hybrid Coordinate Ocean Model (HYCOM) at a horizontal resolution of 0.04° (∼four km) configured for the main Hawaiian Islands, using the General Bathymetric Chart of the Oceans database (GEBCO, 1/60°) (Jia et al., 2011).

The simulation runs from March 31st, 2011 to July 30th, 2013 with a temporal resolution of 24 h and shows seasonal eddies as well as persistent mesoscale features (Fig. S1). We do not include tides in the model due to temporal resolution. Our model period represents a neutral ocean state; no El Niño or La Niña events occurred during this time period. To ground-truth the circulation model, we compared surface current output to real-time trajectories of surface drifters from the GDP Drifter Data Assembly Center (Fig. S2) (Elipot et al., 2016), as well as other current models of the area (Wren et al., 2016; Storlazzi et al., 2017).

Biological model

To simulate larval dispersal, we used a modified version of the Wong-Ala et al. (2018) IBM, a 3D Lagrangian particle-tracking model written in the R programming language (R Core Team, 2017). The model takes the aforementioned MITgcm current products as input, as well as shoreline shapefiles extracted from the full resolution NOAA Global Self-consistent Hierarchical High-resolution Geography database, v2.3.0 (Wessel & Smith, 1996). Our model included 65 land masses within the geographic domain, the largest being the island of Hawai‘i and the smallest being Pu‘uki‘i Island, a 1.5-acre islet off the eastern coast of Maui. To model depth, we used the one arc-minute-resolution ETOPO1 bathymetry, extracted using the R package ‘marmap’ (Amante & Eakins, 2009; Pante & Simon-Bouhet, 2013).

Each species was simulated with a separate model run. Larvae were modeled from spawning to settlement and were transported at each timestep (t = 2 h) by advection-diffusion transport. This transport consisted of (1) advective displacement caused by water flow, consisting of east (u) and north (v) velocities read from daily MITgcm files, and (2) additional random-walk displacement, using a diffusion constant of 0.2 m2/s−1 (Lowe et al., 2009). Vertical velocities (w) were not implemented by the model; details of vertical larval movement are described below. Advection was interpolated between data points at each timestep using an Eulerian 2D barycentric interpolation method. We chose this implementation over a more computationally intensive interpolation method (i.e., fourth-order Runge–Kutta) because we did not observe a difference at this timestep length. Biological processes modeled include PLD, reproduction timing and location, mortality, and ontogenetic changes in vertical distribution; these qualities were parameterized via species-specific data obtained from previous studies and from the local fishing and management community (Table 1).

Larvae were released from habitat-specific spawning sites and were considered settled if they fell within a roughly one-km contour around reef or intertidal habitat at the end of their pelagic larval duration. Distance from habitat was used rather than water depth because Penguin Bank, a relatively shallow bank to the southwest of Moloka‘i, does not represent suitable habitat for reef-associated species. PLD for each larva was a randomly assigned value between the minimum and maximum PLD for that species, and larvae were removed from the model if they had reached their PLD and were not within a settlement zone. No data on pre-competency period were available for our study species, so this parameter was not included. Mortality rates were calculated as larval half-lives; e.g., one-half of all larvae were assumed to have survived at one-half of the maximum PLD for that species (following Holstein, Paris & Mumby, 2014). Since our focus was on potential connectivity pathways, reproductive rates were calibrated to allow for saturation of possible settlement sites, equating from ∼900,000 to ∼1,7000,000 larvae released depending on species. Fecundity was therefore derived not from biological data, but from computational minimums.

Development, and resulting ontogenetic changes in behavior, is specific to the life history of each species. Broadcast-spawning species with weakly-swimming larvae (P. meandrina and Cellana spp., Table 1) were transported as passive particles randomly distributed between 0–5 m depth (Storlazzi, Brown & Field, 2006). Previous studies have demonstrated that fish larvae have a high degree of control over their vertical position in the water column (Irisson et al., 2010; Huebert, Cowen & Sponaugle, 2011). Therefore, we modeled broadcast-spawning fish species with a 24-hour passive buoyant phase to simulate eggs pre-hatch, followed by a pelagic larval phase with a species-specific depth distribution. For C. ignoblis, C. melampygus, P. porphyreus, C. perspicillatus, and S. rubroviolaceus, we used genus-level depth distributions (Fig. S3) obtained from the 1996 NOAA ichthyoplankton vertical distributions data report (Boehlert & Mundy, 1996). P. sexfilis and C. strigosus larvae were randomly distributed between 50–100 m (Boehlert, Watson & Sun, 1992). Benthic brooding species (O. cyanea and Panulirus spp.) do not have a passive buoyant phase, and thus were released as larvae randomly distributed between 50–100 m. At each time step, a larva’s depth was checked against bathymetry, and was assigned to the nearest available layer if the species-specific depth was not available at these coordinates.

For data-poor species, we used congener-level estimates for PLD (see Table 1). For example, there is no estimate of larval duration for Caranx species, but in Hawai‘i peak spawning occurs in May–July and peak recruitment in August–December (Sudekum, 1984; Longenecker, Langston & Barrett, 2008). In consultation with resource managers and community members, a PLD of 140 days was chosen pending future data that indicates a more accurate pelagic period.

Habitat selection

Spawning sites were generated using data from published literature and modified after input from Native Hawaiian cultural practitioners and the Moloka‘i fishing community (Fig. 1). Species-specific habitat suitability was inferred from the 2013–2016 Marine Biogeographic Assessment of the Main Hawaiian Islands (Costa & Kendall, 2016). We designated coral habitat as areas with 5–90% coral cover, or ≥1 site-specific coral species richness, for a total of 127 spawning sites on Moloka‘i. Habitat for reef invertebrates followed coral habitat, with additional sites added after community feedback for a total of 136 sites. Areas with a predicted reef fish biomass of 58–1,288 g/m2 were designated as reef fish habitat (Stamoulis et al., 2016), for a total of 109 spawning sites. Sand habitat was designated as 90–100% uncolonized for a total of 115 sites. Intertidal habitat was designated as any rocky shoreline area not covered by sand or mud, for a total of 87 sites. Number of adults was assumed equal at all sites. For regional analysis, we pooled sites into groups of two to 11 sites based on benthic habitat and surrounding geography (Fig. 1A). Adjacent sites were grouped if they shared the same benthic habitat classification and prevailing wave direction, and/or were part of the same reef tract.

Figure 1: Spawning sites used in the model by species. (A) C. perspicillatus, S. rubroviolaceus, P. porphyreus, C. strigosus, C. ignoblis, and C. melampygus, n = 109; (B) P. meandrina, n = 129;(C) O. cyanea and Panulirus spp., n = 136; (D) P. sexfilis, n = 115; and (E) Cellana spp., n = 87. Region names are displayed over associated spawning sites for fish species in (A). Regions are made up of two to 11 sites, grouped based on coastal geography and surrounding benthic habitat, and are designated in (A) by adjacent colored dots. Kalaupapa National Historical Park is highlighted in light green in (A). Source–sink dynamics and local retention

Dispersal distance was measured via the distm function in the R package ‘geosphere’, which calculates distance between geographical points via the Haversine formula (Hijmans, 2016). This distance, measured between spawn and settlement locations, was used to calculate dispersal kernels to examine and compare species-specific distributions. We also measured local retention, or the percentage of successful settlers from a site that were retained at that site (i.e., settlers at site A that originated from site A/total successful settlers that originated from site A). To estimate the role of specific sites around Moloka‘i, we also calculated a source–sink index for each species (Holstein, Paris & Mumby, 2014; Wren et al., 2016). This index defines sites as either a source, in which a site’s successful export to other sites is greater than its import, or a sink, in which import from other sites is greater than successful export. It is calculated by dividing the difference between number of successfully exported and imported larvae by the sum of all successfully exported and imported larvae. A value <0 indicates that a site acts as a net sink, while a value >0 indicates that a site acts as a net source. While we measured successful dispersal to adjacent islands, we did not spawn larvae from them, and therefore these islands represent exogenous sinks. For this reason, settlement to other islands was not included in source–sink index calculations.

We also calculated settlement proportion between different regions for each species (Calabrese & Fagan, 2004). We calculated the forward settlement proportion, i.e., the proportion of settlers from a specific settlement site (s) originating from an observed origin site (o), by scaling the number of successful settlers from site o settling at site s to all successful settlers originating from site o. Forward proportion can be represented as Pso = Sos∕∑So. We also calculated rearward settlement proportion, or the proportion of settlers from a specific origin site (o) observed at settlement site (s), by scaling the number of settlers observed at site s originating from site o to all settlers observed at site s. The rearward proportion can be represented as Pos = Sos∕∑Ss.

Graph-theoretic analysis

To quantify connections between sites, we applied graph theory to population connectivity (Treml et al., 2008; Holstein, Paris & Mumby, 2014). Graph theoretic analysis is highly scalable and can be used to examine fine-scale networks between reef sites up to broad-scale analyses between islands or archipelagos, mapping to both local and regional management needs. It also allows for both network- and site-specific metrics, enabling the comparison of connectivity between species and habitat sites as well as highlighting potential multi-generational dispersal corridors. Graph theory also provides a powerful tool for spatial visualization, allowing for rapid, intuitive communication of connectivity results to researchers, managers, and the public alike. This type of analysis can be used to model pairwise relationships between spatial data points by breaking down individual-based output into a series of nodes (habitat sites) and edges (directed connections between habitat sites). We then used these nodes and edges to examine the relative importance of each site and dispersal pathway to the greater pattern of connectivity around Moloka‘i, as well as differences in connectivity patterns between species (Treml et al., 2008; Holstein, Paris & Mumby, 2014). We used the R package ‘igraph’ to examine several measures of within-island connectivity (Csardi & Nepusz, 2006). Edge density, or the proportion of realized edges out of all possible edges, is a multi-site measure of connectivity. Areas with a higher edge density have more direct connections between habitat sites, and thus are more strongly connected. We measured edge density along and between the north, south, east, and west coasts of Moloka‘i to examine possible population structure and degree of exchange among the marine resources of local communities.

The distribution of shortest path length is also informative for comparing overall connectivity. In graph theory, a shortest path is the minimum number of steps needed to connect two sites. For example, two sites that exchange larvae in either direction are connected by a shortest path of one, whereas if they both share larvae with an intermediate site but not with each other, they are connected by a shortest path of two. In a biological context, shortest path can correspond to number of generations needed for exchange: sites with a shortest path of two require two generations to make a connection. Average shortest path, therefore, is a descriptive statistic to estimate connectivity of a network. If two sites are unconnected, it is possible to have infinite-length shortest paths; here, these infinite values were noted but not included in final analyses.

Networks can also be broken in connected components (Csardi & Nepusz, 2006). A weakly connected component (WCC) is a subgraph in which all nodes are not reachable by other nodes. A network split into multiple WCCs indicates separate populations that do not exchange any individuals, and a large number of WCCs indicates a low degree of island-wide connectivity. A strongly connected component (SCC) is a subgraph in which all nodes are directly connected and indicates a high degree of connectivity. A region with many small SCCs can indicate high local connectivity but low island-wide connectivity. Furthermore, component analysis can identify cut nodes, or nodes that, if removed, break a network into multiple WCCs. Pinpointing these cut nodes can identify potential important sites for preserving a population’s connectivity, and could inform predictions about the impact of site loss (e.g., a large-scale coral bleaching event) on overall connectivity.

On a regional scale, it is important to note which sites are exporting larvae to, or importing larvae from, other sites. To this end, we examined in-degree and out-degree for each region. In-degree refers to the number of inward-directed edges to a specific node, or how many other sites provide larvae into site ‘A’. Out-degree refers to the number of outward-directed edges from a specific node, or how many sites receive larvae from site ‘A’. Habitat sites with a high out-degree seed a large number of other sites, and indicate potentially important larval sources, while habitat sites with a low in-degree rely on a limited number of larval sources and may therefore be dependent on connections with these few other sites to maintain population size. Finally, betweenness centrality (BC) refers to the number of shortest paths that pass through a given node, and may therefore indicate connectivity pathways or ‘chokepoints’ that are important to overall connectivity on a multigenerational timescale. BC was weighted with the proportion of dispersal as described in the preceding section. We calculated in-degree, out-degree, and weighted betweenness centrality for each region in the network for each species.

As with the source–sink index, we did not include sites on islands other than Moloka‘i in our calculations of edge density, shortest paths, connected components, cut nodes, in- and out-degree, or betweenness centrality in order to focus on within-island patterns of connectivity.

Results Effects of biological parameters on fine-scale connectivity patterns

The species-specific parameters that were available to parameterize the dispersal models substantially influenced final output (Fig. 2). The proportion of successful settlers (either to Moloka‘i or to neighboring islands) varied widely by species, from 2% (Panulirus spp.) to 25% (Cellana spp.). Minimum pelagic duration and settlement success were negatively correlated (e.g., an estimated −0.79 Pearson correlation coefficient). Species modeled with batch spawning at a specific moon phase and/or time of day (Cellana spp., P. meandrina, and C. ignoblis) displayed slightly higher settlement success than similar species modeled with constant spawning over specific months. On a smaller scale, we also examined average site-scale local retention, comparing only retention to the spawning site versus other sites on Moloka‘i (Fig. 2). Local retention was lowest for Caranx spp. (<1%) and highest for O. cyanea and P. sexfilis (8.1% and 10%, respectively).

Figure 2: Summary statistics for each species network. Summary statistics are displayed in order of increasing minimum pelagic larval duration from left to right. Heatmap colors are based on normalized values from 0–1 for each analysis. Successful settlement refers to the proportion of larvae settled out of the total number of larvae spawned. Local retention is measured as the proportion of larvae spawned from a site that settle at the same site. Shortest path is measured as the minimum number of steps needed to connect two sites. Strongly connected sites refers to the proportion of sites in a network that belong to a strongly connected component. Mean dispersal distance is measured in kilometers from spawn site to settlement site.

We measured network-wide connectivity via distribution of shortest paths, or the minimum number of steps between a given two nodes in a network, only including sites on Moloka‘i (Fig. 2). O. cyanea and P. sexfilis showed the smallest shortest paths overall, meaning that on average, it would take fewer generations for these species to demographically bridge any given pair of sites. Using maximum shortest path, it could take these species three generations at most to connect sites. Cellana spp. and P. meandrina, by comparison, could take as many as five generations. Other medium- and long-dispersing species showed relatively equivalent shortest-path distributions, with trevally species showing the highest mean path length and therefore the lowest island-scale connectivity.

The number and size of weakly-connected and strongly-connected components in a network is also an informative measure of connectivity (Fig. 2). No species in our study group was broken into multiple weakly-connected components; however, there were species-specific patterns of strongly connected sites. O. cyanea and P. sexfilis were the most strongly connected, with all sites in the network falling into a single SCC. Cellana spp. and P. meandrina each had approximately 60% of sites included in a SCC, but both show fragmentation with seven and six SCCs respectively, ranging in size from two to 22 sites. This SCC pattern suggests low global connectivity but high local connectivity for these species. Medium and long dispersers showed larger connected components; 70% of parrotfish sites fell within two SCCs; 40% of P. porphyreus sites fell within two SCCs; 70% of C. strigosus sites, 55% of C. melampygus sites, and 40% of Panulirus sites fell within a single SCC. In contrast, only 26% of C. ignoblis sites fell within a single SCC. It is also important to note that the lower connectivity scores observed in long-dispersing species likely reflect a larger scale of connectivity. Species with a shorter PLD are highly connected at reef and island levels but may show weaker connections between islands. Species with a longer PLD, such as trevally or spiny lobster, are likely more highly connected at inter-island scales which reflects the lower connectivity scores per island shown here.

Figure 3: Dispersal distance density kernels. Dispersal distance is combined across species by minimum pelagic larval duration (PLD) length in days (short, medium, or long). Most short dispersers settle close to home, while few long dispersers are retained at or near their spawning sites.

Minimum PLD was positively correlated with mean dispersal distance (e.g., an estimated 0.88 Pearson correlation coefficient with minimum pelagic duration loge-transformed to linearize the relationship), and dispersal kernels differed between species that are short dispersers (3–25 days), medium dispersers (30–50 days), or long dispersers (140–270 days) (Fig. 3). Short dispersers travelled a mean distance of 24.06 ± 31.33 km, medium dispersers travelled a mean distance of 52.71 ± 40.37 km, and long dispersers travelled the farthest, at a mean of 89.41 ± 41.43 km. However, regardless of PLD, there were essentially two peaks of mean dispersal: a short-distance peak of <30 km, and a long-distance peak of roughly 50–125 km (Fig. 3). The short-distance peak largely represents larvae that settle back to Moloka‘i, while the long-distance peak largely represents settlement to other islands; the low point between them corresponds to deep-water channels between islands, i.e., unsuitable habitat for settlement. Median dispersal distance for short dispersers was substantially less than the mean at 8.85 km, indicating that most of these larvae settled relatively close to their spawning sites, with rare long-distance dispersal events bringing up the average. Median distance for medium (54.22 km) and long (91.57 km) dispersers was closer to the mean, indicating more even distance distributions and thus a higher probability of long-distance dispersal for these species. Maximum dispersal distance varied between ∼150–180 km depending on species, except for the spiny lobster Panulirus spp., with a PLD of 270 d and a maximum dispersal distance of approximately 300 km.

Settlement to Moloka‘i and other islands in the archipelago

Different species showed different forward settlement proportion to adjacent islands (Fig. 4), although every species in the study group successfully settled back to Moloka‘i. P. meandrina showed the highest percentage of island-scale local retention (82%), while C. ignoblis showed the lowest (7%). An average of 74% of larvae from short-dispersing species settled back to Moloka‘i, as compared to an average of 41% of medium dispersers and 9% of long dispersers. A large proportion of larvae also settled to O‘ahu, with longer PLDs resulting in greater proportions, ranging from 14% of O. cyanea to 88% of C. ignoblis. Moloka‘i and O‘ahu were the most commonly settled islands by percentage. Overall, settlement from Moloka‘i to Lana‘i, Maui, Kaho‘olawe, and Hawai‘i was somewhat lower. Larvae of every species settled to Lana‘i, and settlement to this island made up less than 5% of settled larvae across all species. Likewise, settlement to Maui made up less than 7% of settlement across species, with P. meandrina as the only species that had no successful paths from Moloka‘i to Maui. Settlement to Kaho‘olawe and Hawai‘i was less common, with the exception of Panulirus spp., which had 16% of all settled larvae on Hawai‘i.

Figure 4: Forward settlement from Moloka’i to other islands. Proportion of simulated larvae settled to each island from Moloka‘i by species, organized in order of increasing minimum pelagic larval duration from left to right.

We also examined coast-specific patterns of rearward settlement proportion to other islands, discarding connections with a very low proportion of larvae (<0.1% of total larvae of that species settling to other islands). Averaged across species, 83% of larvae settling to O‘ahu from Moloka‘i were spawned on the north shore of Moloka‘i, with 12% spawned on the west shore (Fig. S4). Spawning sites on the east and south shores contributed <5% of all larvae settling to O‘ahu from Moloka‘i. The east and south shores of Moloka‘i had the highest average percentage of larvae settling to Lana‘i from Moloka‘i, at 78% and 20% respectively, and to Kaho‘olawe from Moloka‘i at 63% and 34%. Of the species that settled to Maui from Moloka‘i, on average most were spawned on the east (53%) or north (39%) shores, as were the species that settled to Hawai‘i Island from Moloka‘i (22% east, 76% north). These patterns indicate that multiple coasts of Moloka‘i have the potential to export larvae to neighboring islands.

Temporal settlement profiles also varied by species (Fig. 5). Species modeled with moon-phase spawning and relatively short settlement windows (Cellana spp. and C. ignoblis) were characterized by discrete settlement pulses, whereas other species showed settlement over a broader period of time. Some species also showed distinctive patterns of settlement to other islands; our model suggests specific windows when long-distance dispersal is possible, as well as times of year when local retention is maximized (Fig. 5).

Figure 5: Species-specific temporal recruitment patterns. Proportion densities of settlement to specific islands from Moloka‘i based on day of year settled, by species. Rare dispersal events (e.g., Maui or Lana‘i for Cellana spp.) appear as narrow spikes, while broad distributions generally indicate more common settlement pathways. Regional patterns of connectivity in Moloka‘i coastal waters

Within Moloka‘i, our model predicts that coast-specific population structure is likely; averaged across all species, 84% of individuals settled back to the same coast on which they were spawned rather than a different coast on Moloka‘i. Excluding connections with a very low proportion of larvae (<0.1% of total larvae of that species that settled to Moloka‘i), we found that the proportion of coast-scale local retention was generally higher than dispersal to another coast, with the exception of the west coast (Fig. 6A). The north and south coasts had a high degree of local retention in every species except for the long-dispersing Panulirus spp., and the east coast also had high local retention overall. Between coasts, a high proportion of larvae that spawned on the west coast settled on the north coast, and a lesser amount of larvae were exchanged from the east to south and from the north to east. With a few species-specific exceptions, larval exchange between other coasts of Moloka‘i was negligible.

Figure 6: Coast-by-coast patterns of connectivity on Moloka‘i. (A) Average rearward settlement proportion by species per pair of coastlines, calculated by the number of larvae settling at site s from site o divided by all settled larvae at site s. Directional coastline pairs (Spawn > Settlement) are ordered from left to right by increasing median settlement proportion. (B) Heatmap of edge density for coast-specific networks by species. Density is calculated by the number of all realized paths out of total possible paths, disregarding directionality.

We also calculated edge density, including all connections between coasts on Moloka‘i regardless of settlement proportion (Fig. 6B). The eastern coast was particularly well-connected, with an edge density between 0.14 and 0.44, depending on the species. The southern shore showed high edge density for short and medium dispersers (0.16–0.39) but low for long dispersers (<0.005). The north shore also showed relatively high edge density (0.20 on average), although these values were smaller for long dispersers. The west coast showed very low edge density, with the exceptions of O. cyanea (0.37) and P. sexfilis (0.13). Virtually all networks that included two coasts showed lower edge density. One exception was the east/south shore network, which had an edge density of 0.10–0.65 except for Cellana spp. Across species, edge density between the south and west coasts was 0.12 on average, and between the east and west coasts was 0.04 on average. Edge density between north and south coasts was particularly low for all species (<0.05), a divide that was especially distinct in Cellana spp. and P. meandrina, which showed zero realized connections between these coasts. Although northern and southern populations are potentially weakly connected by sites along the eastern ( P. meandrina) or western (Cellana spp.) shores, our model predicts very little, if any, demographic connectivity.

To explore patterns of connectivity on a finer scale, we pooled sites into regions (as defined in Fig. 1) in order to analyze relationships between these regions. Arranging model output into node-edge networks clarified pathways and regions of note, and revealed several patterns which did not follow simple predictions based on PLD (Fig. 7). Cellana spp. and P. meandrina showed the most fragmentation, with several SCCs and low connectivity between coasts. Connectivity was highest in O. cyanea and P. sexfilis, which had a single SCC containing all regions. Medium and long dispersers generally showed fewer strongly connected regions on the south shore than the north shore, with the exception of C. strigosus. P. porphyreus showed more strongly connected regions east of Kalaupapa but lower connectivity on the western half of the island.

Figure 7: Moloka’i connectivity networks by species. Graph-theoretic networks between regions around Moloka’i by species arranged in order of minimum pelagic larval duration. (A–D) Short dispersers (3–25 days), (E–G) medium dispersers (30–50 days), and (H–J) long dispersers (140–270 days). Node size reflects betweenness centrality of each region, scaled per species for visibility. Node color reflects out-degree of each region; yellow nodes have a low out-degree, red nodes have a medium out-degree, and black nodes have a high out-degree. Red edges are connections in a strongly connected component, while gray edges are not part of a strongly connected component (although may still represent substantial connections). Edge thickness represents log-transformed proportion of dispersal along that edge.

Region-level networks showed both species-specific and species-wide patterns of connectivity (Fig. 8). With a few exceptions, sites along the eastern coast—notably, Cape Halawa and Pauwalu Harbor—showed relatively high betweenness centrality, and may therefore act as multigenerational pathways between north-shore and south-shore populations. In Cellana spp., Leinapapio Point and Mokio Point had the highest BC, while in high-connectivity O. cyanea and P. sexfilis, regions on the west coast had high BC scores. P. meandrina and C. strigosus showed several regions along the south shore with high BC. For Cellana spp. and P. meandrina, regions in the northeast had the highest out-degree, and therefore seeded the greatest number of other sites with larvae (Fig. 8). Correspondingly, regions in the northwest (and southwest in the case of P. meandrina) showed the highest in-degree. For O. cyanea and P. sexfilis, regions on the western and southern coasts showed the highest out-degree. For most species, both out-degree and in-degree were generally highest on the northern and eastern coasts, suggesting higher connectivity in these areas.

Figure 8: Region-level summary statistics across all species. Betweenness centrality is a measure of the number of paths that pass through a certain region; a high score suggests potentially important multi-generation connectivity pathways. In-degree and out-degree refer to the amount of a node’s incoming and outgoing connections. Betweenness centrality, in-degree, and out-degree have all been normalized to values between 0 to 1 per species. Local retention is measured as the proportion of larvae that settled back to their spawn site out of all larvae spawned at that site. Source-sink index is a measure of net export or import; negative values (blue) indicate a net larval sink, while positive values (red) indicate a net larval source. White indicates that a site is neither a strong source nor sink. Gray values for Cellana spp. denote a lack of suitable habitat sites in that particular region.

Several species-wide hotspots of local retention emerged, particularly East Kalaupapa Peninsula/Leinaopapio Point, the northeast point of Moloka‘i, and the middle of the south shore. Some species also showed some degree of local retention west of Kalaupapa Peninsula. While local retention was observed in the long-dispersing Caranx spp. and Panulirus spp., this amount was essentially negligible. In terms of source–sink dynamics, Ki‘oko‘o, Pu‘ukaoku Point, and West Kalaupapa Peninsula, all on the north shore, were the only sites that consistently acted as a net source, exporting more larvae than they import (Fig. 8). Kaunakakai Harbor, Lono Harbor, and Mokio Point acted as net sinks across all species. Puko‘o, Pauwalu Harbor, and Cape Halawa were either weak net sources or neither sources nor sinks, which corresponds to the high levels of local retention observed at these sites. Pala‘au and Mo‘omomi acted as either weak sinks or sources for short dispersers and as sources for long dispersers.

Only four networks showed regional cut-nodes, or nodes that, if removed, break a network into multiple weakly-connected components (Fig. S5). Cellana spp. showed two cut-nodes: Mokio Point in northwest Moloka‘i and La‘au Point in southwest Moloka‘i, which if removed isolated Small Bay and Lono Harbor, respectively. C. perspicillatus, and S. rubroviolaceus showed a similar pattern in regards to Mokio Point; removal of this node isolated Small Bay in this species as well. In C. ignoblis, loss of Pauwalu Harbor isolated Lono Harbor, and loss of Pala‘au isolated Ilio Point on the northern coast. Finally, in Panulirus spp., loss of Leinaopapio Point isolated Papuhaku Beach, since Leinapapio Point was the only larval source from Moloka‘i for Papuhaku Beach in this species.

Figure 9: Connectivity matrix for larvae spawned on Kalaupapa Peninsula. Includes larvae settled on Molokaí (regions below horizontal black line) and those settled on other islands (regions above horizontal black line), spawned from either the east (E) or west (W) coast of Kalaupapa. Heatmap colors represent rearward proportion, calculated by the number of larvae settling at site s from site o divided by all settled larvae at site s. White squares indicate no dispersal along this path. The role of Kalaupapa Peninsula in inter- and intra-island connectivity

Our model suggests that Kalaupapa National Historical Park may play a role in inter-island connectivity, especially in terms of long-distance dispersal. Out of all regions on Moloka‘i, East Kalaupapa Peninsula was the single largest exporter of larvae to Hawai‘i Island, accounting for 19% of all larvae transported from Moloka‘i to this island; West Kalaupapa Peninsula accounted for another 10%. The park also contributed 22% of all larvae exported from Moloka‘i to O‘ahu, and successfully exported a smaller percentage of larvae to Maui, Lana‘i, and Kaho‘olawe (Fig. 9). Kalaupapa was not marked as a cut-node for any species, meaning that full population breaks are not predicted in the case of habitat or population loss in this area. Nevertheless, in our model Kalaupapa exported larvae to multiple regions along the north shore in all species, as well as regions along the east, south, and/or west shores in most species networks (Figs. 9 and 10). The park may play a particularly important role for long-dispersing species; settlement from Kalaupapa made up 18%–29% of all successful settlement in Caranx spp. and Panulirus spp., despite making up only 12% of spawning sites included in the model. In C. strigosus, S. rubroviolaceus, and C. strigosus, Kalaupapa showed a particularly high out-degree, or number of outgoing connections to other regions, and West Kalaupapa was also one of the few regions on Moloka‘i that acted as a net larval source across all species (Fig. 8). Our study has also demonstrated that different regions of a marine protected area can potentially perform different roles, even in a small MPA such as Kalaupapa. Across species, the east coast of Kalaupapa showed a significantly higher betweenness centrality than the west (p = 0.028), while the west coast of Kalauapapa showed a significantly higher source–sink index than the east (p = 2.63e−9).

Figure 10: Larval spillover from Kalaupapa National Historical Park. Site-level dispersal to sites around Moloka‘i from sites in the Kalaupapa National Historical Park protected area, by species. (A–D) Short dispersers (3–25 days), (E–G) medium dispersers (30–50 days), and (H–J) long dispersers (140–270 days). Edge color reflects proportion of dispersal along that edge; red indicates higher proportion while yellow indicates lower proportion. Kalaupapa National Historical Park is highlighted in light green. Discussion Effects of biological and physical parameters on connectivity

We incorporated the distribution of suitable habitat, variable reproduction, variable PLD, and ontogenetic changes in swimming ability and empirical vertical distributions of larvae into our model to increase biological realism, and assess how such traits impact predictions of larval dispersal. The Wong-Ala et al. (2018) IBM provides a highly flexible model framework that can easily be modified to incorporate either additional species-specific data or entirely new biological traits. In this study, we included specific spawning seasons for all species, as well as spawning by moon phase for Cellana spp., P. meandrina, and C. ignoblis because such data was available for these species. It proved difficult to obtain the necessary biological information to parameterize the model, but as more data about life history and larval behavior become available, such information can be easily added for these species and others. Some potential additions to future iterations of the model might include density of reproductive-age adults within each habitat patch, temperature-dependent pelagic larval duration (Houde, 1989), ontogenetic-dependent behavioral changes such as orientation and diel vertical migration (Fiksen et al., 2007; Paris, Chérubin & Cowen, 2007), pre-competency period, and larval habitat preferences as such information becomes available.

In this study, we have demonstrated that patterns of fine-scale connectivity around Moloka‘i are largely species-specific and can vary with life history traits, even in species with identical pelagic larval duration. For example, the parrotfish S. rubroviolaceus and C. perspicillatus show greater connectivity along the northern coast, while the goatfish P. porphyreus shows higher connectivity along the eastern half of the island. These species have similar PLD windows, but vary in dispersal depth and spawning season. Spawning season and timing altered patterns of inter-island dispersal (Fig. 5) as well as overall settlement success, which was slightly higher in species that spawned by moon phase (Fig. 2). While maximum PLD did appear play a role in the probability of rare long-distance dispersal, minimum PLD appears to be the main driver of average dispersal distance (Fig. 2). Overall, species with a shorter minimum PLD had higher settlement success, shorter mean dispersal distance, higher local retention, and higher local connectivity as measured by the amount and size of strongly connected components.

The interaction of biological and oceanographic factors also influenced connectivity patterns. Because mesoscale current patterns can vary substantially over the course of the year, the timing of spawning for certain species may be critical for estimating settlement (Wren et al., 2016; Wong-Ala et al., 2018). Intermittent ocean processes may influence the probability of local retention versus long-distance dispersal; a large proportion of larvae settled to O‘ahu, which is somewhat surprising given that in order to settle from Moloka‘i to O‘ahu, larvae must cross the Kaiwi Channel (approx. 40 km). However, the intermittent presence of mesoscale gyres may act as a stabilizing pathway across the channel, sweeping larvae up either the windward or leeward coast of O‘ahu depending on spawning site. Likewise, in our model long-distance dispersal to Hawai‘i Island was possible at certain times of the year due to a gyre to the north of Maui; larvae were transported from Kalaupapa to this gyre, where they were carried to the northeast shore of Hawai‘i (Fig. S6). Preliminary analysis also suggests that distribution of larval depth influenced edge directionality and size of connected components (Fig. 7); surface currents are variable and primarily wind-driven, giving positively-buoyant larvae different patterns of dispersal than species that disperse deeper in the water column (Fig. S7).

Model limitations and future perspectives

Our findings have several caveats. Because fine-scale density estimates are not available for our species of interest around Moloka’i, we assumed that fecundity is equivalent at all sites. This simplification may lead us to under- or over-estimate the strength of connections between sites. Lack of adequate data also necessitated estimation or extrapolation from congener information for larval traits such as larval dispersal depth and PLD. Since it is difficult if not impossible to identify larvae to the species level without genetic analysis, we used genus-level larval distribution data (Boehlert & Mundy, 1996), or lacking that, an estimate of 50–100 m as a depth layer that is generally more enriched with larvae (Boehlert, Watson & Sun, 1992; Wren & Kobayashi, 2016). We also estimated PLD in several cases using congener-level data (see Table 1). While specificity is ideal for making informed management decisions about a certain species, past sensitivity analysis has shown that variation in PLD length does not greatly impact patterns of dispersal in species with a PLD of >40 days (Wren & Kobayashi, 2016).

Although our MITgcm current model shows annual consistency, it only spans two and a half years chosen as neutral state ‘average’ ocean conditions. It does not span any El Niño or La Niña (ENSO) events, which cause wide-scale sea-surface temperature anomalies and may therefore affect patterns of connectivity during these years. El Niño can have a particularly strong impact on coral reproduction, since the warm currents associated with these events can lead to severe temperature stress (Glynn & D’Croz, 1990; Wood et al., 2016). While there has been little study to date on the effects of ENSO on fine-scale connectivity, previous work has demonstrated increased variability during these events. For example, Wood et al. (2016) showed a decrease in eastward Pacific dispersal during El Niño years, but an increase in westward dispersal, and Treml et al. (2008) showed unique connections in the West Pacific as well as an increase in connectivity during El Niño. While these effects are difficult to predict, especially at such a small scale, additional model years would increase confidence in long-term connectivity estimations. Additionally, with a temporal resolution of 24 h, we could not adequately address the role of tides on dispersal, and therefore did not include them in the MITgcm. Storlazzi et al. (2017) showed that tidal forces did affect larval dispersal in Maui Nui, underlining the importance of including both fine-scale, short-duration models and coarser-scale, long-duration models in final management decisions.

We also limit our model’s scope geographically. Our goal was to determine whether we could resolve predictive patterns at this scale relevant to management. Interpretation of connectivity output can be biased by spatial resolution of the ocean model, since complex coastal processes can be smoothed and therefore impact larval trajectories. To limit this bias, we focused mainly on coastal and regional connectivity on scales greater than the current resolution. We also used the finest-scale current products available for our study area, and our results show general agreement with similar studies of the region that use a coarser resolution (Wren & Kobayashi, 2016) and a finer resolution (Storlazzi et al., 2017). Also, while knowledge of island-scale connectivity is important for local management, it does disregard potential connections from other islands. In our calculations of edge density, betweenness centrality and source-sink index, we included only settlement to Moloka‘i, discarding exogenous sinks that would bias our analysis. Likewise, we cannot predict the proportion of larvae settling to other islands that originated from Moloka‘i, or the proportion of larvae on Moloka‘i that originated from other islands.

It is also important to note scale in relation to measures of connectivity; we expect that long-dispersing species such as Caranx spp. and Panulirus spp. will show much higher measures of connectivity when measured across the whole archipelago as opposed to a single island. The cut-nodes observed in these species may not actually break up populations on a large scale due to this inter-island connectivity. Nevertheless, cut-nodes in species with short- and medium-length PLD may indeed mark important habitat locations, especially in terms of providing links between two otherwise disconnected coasts. It may be that for certain species or certain regions, stock replenishment relies on larval import from other islands, underscoring the importance of MPA selection for population maintenance in the archipelago as a whole.

Implications for management

Clearly, there is no single management approach that encompasses the breadth of life history and behavior differences that impact patterns of larval dispersal and connectivity (Toonen et al., 2011; Holstein, Paris & Mumby, 2014). The spatial, temporal, and species-specific variability suggested by our model stresses the need for multi-scale management, specifically tailored to local and regional connectivity patterns and the suite of target species. Even on such a small scale, different regions around the island of Moloka‘i can play very different roles in the greater pattern of connectivity (Fig. 8); sites along the west coast, for example, showed fewer ingoing and outgoing connections than sites on the north coast, and therefore may be more at risk of isolation. Seasonal variation should also be taken into account, as mesoscale current patterns (and resulting connectivity patterns) vary over the course of a year. Our model suggests species-specific temporal patterns of settlement (Fig. 5); even in the year-round spawner O. cyanea, local retention to Moloka‘i as well as settlement to O‘ahu was maximized in spring and early summer, while settlement to other islands mostly occurred in late summer and fall.

Regions that show similar network dynamics may benefit from similar management strategies. Areas that act as larval sources either by proportion of larvae (high source–sink index) or number of sites (high out-degree) should receive management consideration. On Moloka‘i, across all species in our study, these sources fell mostly on the northern and eastern coasts. Maintenance of these areas is especially important for downstream areas that depend on upstream populations for a source of larvae, such as those with a low source–sink index, low in-degree, and/or low local retention. Across species, regions with the highest betweenness centrality scores fell mainly in the northeast (Cape Halawa and Pauwalu Harbor). These areas should receive consideration as potentially important intergenerational pathways, particularly as a means of connecting north-coast and south-coast populations, which showed a lack of connectivity both in total number of connections (edge density) and proportion of larvae. Both of these connectivity measures were included because edge density includes all connections, even those with a very small proportion of larvae, and may therefore include rare dispersal events that are of little relevance to managers. Additionally, edge density comparisons between networks should be viewed with the caveat that these networks do not necessarily have the same number of nodes. Nevertheless, both edge density and proportion show very similar patterns, and include both demographically-relevant common connections as well as rare connections that could influence genetic connectivity.

Management that seeks to establish a resilient network of spatially managed areas should also consider the preservation of both weakly-connected and strongly-connected components, as removal of key cut-nodes (Fig. S5) breaks up a network. Sites within a SCC have more direct connections and therefore may be more resilient to local population loss. Care should be taken to preserve breeding populations at larval sources, connectivity pathways, and cut-nodes within a SCC, since without these key sites the network can fragment into multiple independent SCCs instead of a single stable network. This practice may be especially important for species for which we estimate multiple small SCCs, such as Cellana spp. or P. meandrina.

Kalaupapa Peninsula emerged as an important site in Moloka‘i population connectivity, acting as a larval source for other regions around the island. The Park seeded areas along the north shore in all species, and also exported larvae to sites along the east and west shores in all species except P. meandrina and Cellana spp. Additionally, it was a larval source for sites along the south shore in the fishes C. perspicillatus, S. rubroviolaceus, and C. strigosus as well as Panulirus spp. Western Kalaupapa Peninsula was one of only three regions included in the analysis (the others being Ki‘oko‘o and Pu‘ukaoku Point, also on the north shore) that acted as a net larval source across all species. Eastern Kalaupapa Peninsula was particularly highly connected, and was part of a strongly connected component in every species. The Park also emerged as a potential point of connection to adjacent islands, particularly to O‘ahu and Hawai‘i. Expanding the spatial scale of our model will further elucidate Kalaupapa’s role in the greater pattern of inter-island connectivity.

In addition to biophysical modeling, genetic analyses can be used to identify persistent population structure of relevance to managers (Cowen et al., 2000; Casey, Jardim & Martinsohn, 2016). Our finding that exchange among islands is generally low in species with a short- to medium-length PLD agrees with population genetic analyses of marine species in the Hawaiian Islands (Bird et al., 2007; Rivera et al., 2011; Toonen et al., 2011; Concepcion, Baums & Toonen, 2014). On a finer scale, we predict some level of shoreline-specific population structure for most species included in the study (Fig. 6). Unfortunately, genetic analyses to date have been performed over too broad a scale to effectively compare to these fine-scale connectivity predictions around Moloka‘i or even among locations on adjacent islands. These model results justify such small scale genetic analyses because there are species, such as the coral P. meandrina, for which the model predicts clear separation of north-shore and south-shore populations which should be simple to test using genetic data. To validate these model predictions with this technique, more fine-scale population genetic analyses are needed.

Conclusions

The maintenance of demographically connected populations is important for conservation. In this study, we contribute to the growing body of work in biophysical connectivity modeling, focusing on a region and suite of species that are of relevance to resource managers. Furthermore, we demonstrate the value of quantifying fine-scale relationships between habitat sites via graph-theoretic methods. Multispecies network analysis revealed persistent patterns that can help define region-wide practices, as well as species-specific connectivity that merits more individual consideration. We demonstrate that connectivity is influenced not only by PLD, but also by other life-history traits such as spawning season, moon-phase spawning, and ontogenetic changes in larval depth. High local retention of larvae with a short- or medium-length PLD is consistent with population genetic studies of the area. We also identify regions of management importance, including West Kalaupapa Peninsula, which acts as a consistent larval source across species; East Kalaupapa Peninsula, which is a strongly connected region in every species network, and Pauwalu Harbor/Cape Halawa, which may act as important multigenerational pathways. Connectivity is only one piece of the puzzle of MPA effectiveness, which must also account for reproductive population size, long-term persistence, and post-settlement survival (Burgess et al., 2014). That being said, our study provides a quantitative roadmap of potential demographic connectivity, and thus presents an effective tool for estimating current and future patterns of dispersal around Kalaupapa Peninsula and around Moloka‘i as a whole.

Supplemental Information Current patterns in the model domain.

Current direction and velocity is displayed at a depth of 55 m below sea surface on (A) March 31st, 2011, (B) June 30th, 2011, (C) September 30th, 2011, and (D) December 31st, 2011. Arrowhead direction follows current direction, and u/v velocity is displayed through arrow length and color (purple, low velocity, red, high velocity). Domain extends from 198.2°E to 206°E and from 17°N to 22.2°N. The island of Moloka‘i is highlighted in red.

Subset of validation drifter paths.

Drifter paths in black and corresponding model paths are colored by drifter ID. All drifter information was extracted from the GDP Drifter Data Assembly Center (Elipot et al., 2016). Drifters were included if they fell within the model domain spatially and temporally, and were tested by releasing 1,000 particles on the correct day where they entered the model domain, at the uppermost depth layer of our oceanographic model (0–5 m).

Selected larval depth distributions.

Modeled vertical larval distributions for Caranx spp. (left), S. rubroviolaceus and C. perspicillatus (middle), and P. porphyreus (right), using data from the 1996 NOAA ichthyoplankton vertical distributions data report (Boehlert & Mundy 1996).

Coast-specific rearward settlement patterns by island

Proportion of simulated larvae settled to each island from sites on each coast of Moloka‘i, averaged across all species that successfully settled to that island.

Regional cut-nodes for four species networks

Mokio Point and La‘au Point were cut-nodes for Cellana spp., Mokio Point was a cut-node for C. perspicillatus and S. rubroviolaceus, Pauwalu Harbor and Pala‘au were cut-nodes for C. ignoblis, and Leinaopapio Point was a cut-node for Panulirus spp.

Selected dispersal pathways for Panulirus spp. larvae

500 randomly sampled dispersal pathways for lobster larvae (Panulirus spp.) that successfully settled to Hawai‘i Island after being spawned off the coast of Moloka‘i. Red tracks indicate settlement earlier in the year (February–March), while black tracks indicate settlement later in the year (April–May). Most larvae are transported to the northeast coast of Hawai‘i via a gyre to the north of Maui, while a smaller proportion are transported through Maui Nui.

Eddy differences by depth layer.

Differences in eddy pattern and strength in surface layers (A, 2.5 m) vs. deep layers (B, 55 m) on March 31, 2011. Arrowhead direction follows current direction, and u/v velocity is displayed through arrow length and color (purple, low velocity, red, high velocity). While large gyres remain consistent at different depths, smaller features vary along this gradient. For example, the currents around Kaho‘olawe, the small gyre off the eastern coast of O‘ahu, and currents to the north of Maui all vary in direction and/or velocity.


Winning the #ArtificialIntelligence War | @ExpoDX #IoT #DigitalTransformation | killexams.com real questions and Pass4sure dumps

There is a war a-brewin’, but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin’s declaration that “the nation that leads in AI (Artificial Intelligence) will be the ruler of the world,” the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet.

Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings / environment. See the blog “Artificial Intelligence is not Fake Intelligence” for a more detailed explanation on artificial intelligence and machine learning.

The Fast Company article “How to Stop Worrying and Love the Great AI War of 2018,” projected that the AI battle would ultimately boil down between the “AI Big 6”:  Alphabet/Google, Amazon, Apple, Facebook, IBM, and Microsoft. However, there are other contenders worthy of consideration including GE, Tesla, Netflix, Baidu, Tencent, and Albaba.

But what are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others “fumble the future”?

I believe that the AI winners will have the following characteristics:

  • Users, not purveyors, of AI technology
  • Embrace open source for technology agility (independence)
  • Mastery of Big Data (and no, Big Data is not dead)
  • Let me state my case.

    #1 Users, Not Purveyors, of AI TechnologyThe Market Capitalization Leaderboard shown in Figure 1 offers important clues as to which organizations will likely be the AI winners. What will set these organizations apart will be not the selling of technology, but their ability leverage AI for “value capture.”

    Figure 1: Marketing Capitalization Leaders as of May 26, 2017.

    By the way, I think Kleiner Perkins was lazy in classifying “Industry Segment.” The market leaders are less purveyors of AI technology than they are users of AI technology.

  • Less than 10% of Amazon’s revenue comes from technology (cloud); $12B in cloud revenue out of a total revenue of $136B in 2016. So what Industry Segment are they in?
  • Google had quarterly revenues (Q1, 2016) of $26B of which digital media/advertising (search) represented $23B. Their “other” businesses (including Google Cloud) were only $3B. So what Industry Segment are they in?
  • Apple’s most recent quarterly (Q3, 2016) revenues were $42B out of which the iPhone (personal communications, information and entertainment) and the associated iPhone ecosystem (iTunes, Apple Music, App Store) comprised an aggregated $37.5B.
  • Finally, I’m not aware of any AI or data technologies that Facebook sells to the general market. Facebook generated $9.3B in revenue in Q2, 2017 of which $9.16B came from Ad revenue. So what Industry Segment are they in?
  • Mastering Value Capture. Just having the technology is not sufficient; it’s how you use the technology to derive and then drive new sources of customer, business, operational, and financial value that matters. Ultimately, the AI war is about “value capture.”

    The companies listed in Figure 1 are trying to dominate markets, not technology. For example:

  • Apple (#1) seeks to dominate personal communications
  • Google/Alphabet (#2) seeks to dominate digital media, advertising and personal communications
  • Amazon (#4) seeks to dominate online commerce
  • Facebook (#5) seeks to dominate social media, and advertising
  • Each of these AI leaders seeks to extend their value capture capabilities into new markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

    Other market leaders are also moving aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on building an AI platform (see “JPMorgan Takes AI Use to the Next Level”) that will allow JPMC to dominate financial trading. And GE (#16) has made a strategic bet with their Predix platform (see “GE’S Big Bet on Data and Analytics”) as the platform for dominating the Industrial Internet of Things.

    Microsoft (#3) is the one exception as Microsoft is a purveyor of technology. But even Microsoft is branching beyond just selling technology into trying to dominate markets such as digital media, entertainment, and social media where their AI “chops” can give them competitive advantages (see “The Jewel of Microsoft’s Earnings”).

    #2 Embrace Open Source for Technology Agility (Independence)AI leaders will exploit open-source business models to establish platform dominance/standardization, and create technology agility and independence. They will develop an enabling technology, and then give it away via open source. This enables them to encourage the growing community of developers, especially those up-and-coming developers in universities and research labs, to build out and create de facto standards around their enabling technologies.

    Open Source Leaders. The Global AI winners are significant contributors to the artificial intelligence and machine learning open source communities. This includes developments such as Amazon Machine Learning, Google TensorFlow, Facebook Caffe2, Microsoft Azure ML Studio, Microsoft Distributed Machine Learning Toolkit, Facebook GraphQL, and Facebook Torch.

    The leadership role that the “Great AI War” combatants are playing can be seen in many open source projects. For example, Torch is an open source machine learning library and scientific computing framework. The “official maintainers” of Torch are:

  • Research Scientist @ Facebook
  • Senior Software Engineer @ Twitter
  • Research Scientist @ Google DeepMind
  • Research Engineer @ Facebook
  • Training and Education. Another strategy from the Global AI leaders the creation of community or industry training and education opportunities around their open source technologies. For example, Google is committing $1 billion to train American workers to build new businesses with Google’s AI tools (see “Google Commits $1 Billion in Grants to Train U.S. Workers for High-Tech Jobs”).

    Avoiding Technology Lock-in.  But equally important is that these AI leaders are seeking to avoid technology and architecture lock-in. They have watched old school organizations struggle with proprietary software packages that took months if not years for upgrades and bug fixes, while paying a burdensome annual maintenance fees (33% of list price means you’re buying the entire software package again every 3 years). In a world where the enabling data and analytic technologies are changing nearly daily, technological and architecture agility (at scale) and independence is mandatory for organizations looking to win the Great AI War.

    #3 Mastery of Big DataEveryone knows about the astounding growth of big data over the last decade as organizations focused on capturing detailed customer, product, operational and market data. Initially fueled by commerce, web and social media data, big data has accelerated with the growth of video, wearables, and the Internet of Things. (See Figure 2).

    However, organizations have struggled to monetize this wealth of data. Enter artificial intelligence.

    Figure 2: Fueling the Insatiable Appetite for Data

    More Data = Better AI. Artificial intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional Business Intelligence “slice and dice” and query technologies. Data is the food that feeds AI. The more data the AI models consume, the smarter AI gets. For example, Facebook is mastering facial recognition via its DeepFace Deep Learning application by virtue of owning the world’s largest repository of photos.

    To illustrate the symbiotic relationship between big data and AI, let’s look at autonomous vehicles (AV). AV require enormous quantities of data to feed the AV machine learning algorithms. It would take tens of thousands of hours of real-world driving data across a variety of driving scenarios to teach cars how to navigate on their own. To address this data volume problem, AV companies are using the video game “Grand Theft Auto” to help generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the Real World”).

    Data Lake. Leading AI organizations are exploiting the data lake concept to not only store the growing wealth of structured and unstructured (internal and publicly-available) data, but to provide an elastic, scalable, self-provisioning data science platform for “collaborative value creation” in building the machine learning and artificial intelligence models (see “Data Lake Business Model Maturity Index” for more details on data lake business model maturation).

    Exploiting the Economic Value of Data. Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. Data and analytics are digital assets that never wear out, never deplete, and can be used simultaneously at near-zero marginal cost across an infinite business and operational use cases. Understanding the true economic value of the organization’s data can help to prioritize technology and business investments that accelerate value capture from these data sources (see University of San Francisco research paper “Determining the Economic Value of Data” for more details).

    Conclusion: How to Become an AI WinnerAs has been discussed many times in my blog series, and explored in detail in my book, “Big Data MBA: Driving Business Strategies with Data Science,” AI winners will ultimately be those organizations that are the most effective at leveraging data and analytics to power their business models (see Figure 3).

    Figure 3: How Effective Is Your Organization at Leveraging Data and Analytics to Power Your Business Models?

    Ultimately, AI winners will master three key characteristics:

  • Focus on Value Capture by identifying, validating and prioritizing the organization’s key business and operational use cases (see “Use Case Identification, Validation and Prioritization”).
  • Avoid technology and architecture lock-in and create technology independence via an open source technology strategy
  • Mastery of Big Data and the Data Lake to exploit the unique economic value of the data and analytic digital assets (see “Data Lake Business Model Maturity Index”).
  • So in conclusion, let’s have some fun with this blog and think outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

  • What would be the business model ramifications to GE if they were to open source Predix and offer Predix training to universities and third party developers?
  • What would be the business model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?
  • What would be the business model ramifications if IBM moved out of the technology purveyor business and instead acquired companies in financial services and healthcare where their Watson AI platform could create market dominance?
  • As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.

    Fortune Favors the Brave.

    Sources

    Figure 1: ScoopNest “2017 global market capitalization leader board: tech is 40% of top 20 companies and 100% of top 5” and Consultancy UK “Market capitalisation of world’s 100 biggest companies hits $17.4 trillion”

    The post 3 Keys to Winning the Great Artificial Intelligence (AI) War! appeared first on InFocus Blog | Dell EMC Services.

    DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the conference tracks for CloudEXPO | DXWorldEXPO 2018 New York.

    DXWordEXPO New York 2018, colocated with CloudEXPO New York 2018 will be held November 11-13, 2018, in New York City.

    Digital Transformation (DX) is a major focus with the introduction of DXWorldEXPO within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term.

    A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throughout enterprises of all sizes.

    Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

    Sponsorship Opportunities ▸ Here

    Speaking Opportunities ▸ Here

    Sponsorship and Speaking Inquiries: [email protected].

    2018 Conference Agenda, Keynotes and 10 Conference Tracks

    DXWordEXPO New York 2018 and Cloud Expo New York 2018 agenda present 222 rockstar faculty members, 200 sessions and 22 keynotes and general sessions in 10 distinct conference tracks.

  • Cloud-Native | Serverless
  • DevOpsSummit
  • FinTechEXPO - New York Blockchain Event
  • CloudEXPO - Enterprise Cloud
  • DXWorldEXPO - Digital Transformation (DX)
  • Smart Cities | IoT | IIoT
  • AI | Machine Learning | Cognitive Computing
  • BigData | Analytics
  • The API Enterprise | Mobility | Security
  • Hot Topics | FinTech | WebRTC
  • Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

    DXWorldEXPO | CloudEXPO 2018 New York cover all of these tools, with the most comprehensive program and with 222 rockstar speakers throughout our industry presenting 22 Keynotes and General Sessions, 200 Breakout Sessions along 10 Tracks, as well as our signature Power Panels. Our Expo Floor brings together the world's leading companies throughout the world of Cloud Computing, DevOps, FinTech, Digital Transformation, and all they entail.

    As your enterprise creates a vision and strategy that enables you to create your own unique, long-term success, learning about all the technologies involved is essential. Companies today not only form multi-cloud and hybrid cloud architectures, but create them with built-in cognitive capabilities.

    Cloud-Native thinking is now the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, as well as the public sector.

    CloudEXPO is the world's most influential technology event where Cloud Computing was coined over a decade ago and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals.

    FinTech Is Now Part of the DXWorldEXPO | CloudEXPO Program!

    Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses.

    Accordingly, attendees at the upcoming 22nd CloudEXPO | DXWorldEXPO November 11-13, 2018 in New York City will find fresh new content in two new tracks called:

  • FinTechEXPO
  • New York Blockchain Event
  • which will incorporate FinTech and Blockchain, as well as machine learning, artificial intelligence and deep learning in these two distinct tracks.

    Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

    Sponsorship Opportunities ▸ Here

    Speaking Opportunities ▸ Here

    Sponsorship and Speaking Inquiries: [email protected].

    FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.

    More than US$20 billion in venture capital is being invested in FinTech this year. DXWorldEXPO | CloudEXPO are pleased to bring you the latest FinTech developments as an integral part of our program.

    DXWorldEXPO | CloudEXPO are accepting speaking submissions for this new track, so please visit Cloud Computing Expo for the latest information or contact us at [email protected]

    Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

    Sponsorship Opportunities ▸ Here

    Speaking Opportunities ▸ Here

    Sponsorship and Speaking Inquiries: [email protected].

    Download Slide Deck ▸ Here

    Only DXWorldEXPO | CloudEXPO bring together all this in a single location:

    Attend DXWorldEXPO | CloudEXPO. Build your own custom experience. Learn about the world's latest technologies and chart your course to Digital Transformation.

    22nd International DXWorldEXPO | CloudEXPO, taking place November 11-13, 2018, in New York City, will feature technical sessions from a rock star conference faculty and the leading industry players in the world.

    Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

    Sponsorship Opportunities ▸ Here

    Speaking Opportunities ▸ Here

    Sponsorship and Speaking Inquiries: [email protected].

    Download Slide Deck: ▸ Here

    Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterprises are using some form of XaaS - software, platform, and infrastructure as a service.

    With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

    Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers.

    Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

    Sponsorship Opportunities ▸ Here

    Speaking Opportunities ▸ Here

    Sponsorship and Speaking Inquiries: [email protected].

    Download Slide Deck: ▸ Here

    Companies are each developing their unique mix of cloud technologies and services, forming multi-cloud and hybrid cloud architectures and deployments across all major industries. Cloud-driven thinking has become the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, and the public sector.

    Sponsorship Opportunities

    DXWorldEXPO | CloudEXPO are the single show where technology buyers and vendors can meet to experience and discus cloud computing and all that it entails. Sponsors of DXWorldEXPO | CloudEXPO will benefit from unmatched branding, profile building and lead generation opportunities through:

  • Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers.
  • Showcase exhibition during our new extended dedicated expo hours
  • Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35-minute technical session
  • Online advertising on 4,5 million article pages in SYS-CON's i-Technology Publications
  • Capitalize on our Comprehensive Marketing efforts leading up to the show with print mailings, e-newsletters and extensive online media coverage.
  • Unprecedented PR Coverage: Unmatched editorial coverage on Cloud Computing Journal.
  • Tweetup to over 100,000 plus Twitter followers
  • Press releases sent on major wire services to over 500 industry analysts.
  • Secrets of Our Most Popular Sponsors and Exhibitors ▸ Here

    For more information on sponsorship, exhibit, and keynote opportunities, contact [email protected].

    Sponsorship Opportunities ▸ Here

    Download Slide Deck: ▸ Here

    Speaking Opportunities

    The upcoming 22nd International DXWorldEXPO | CloudEXPO November 11-13, 2018 in New York City, NY announces that its Call For Papers for speaking opportunities is now open.

    Secrets of Our Most Popular Faculty Members ▸ Here

    Submit your speaking proposal ▸ Here or by email [email protected].

    Download Slide Deck: ▸ Here

    About DXWorldEXPO LLC

    DXWorldEXPO LLC is a Lighthouse Point, Florida-based trade show company and the creator of DXWorldEXPO - Digital Transformation Conference & Expo. The company produces and presents CloudEXPO, DevOpsSummit, FinTechEXPO - Blockchain Event, the world's most influential conferences and trade shows.


    DRM: How To Boil A Frog | killexams.com real questions and Pass4sure dumps

    1405737 story Music Media Your Rights Online

    Posted by timothy on Monday September 23, 2002 @06:53PM from the too-bad-it's-a-decent-artist dept.

    symbolic writes "This article on the Register explains their experience with Creative's first attempt at supporting DRM, and also reviews a sneaky little technique for 'easing' DRM into peoples' lives via a free Costello preview CD. Two of the tracks are free from any DRM, but for the two that are DRM-enabled, you have to activate the right to listen to them (up to four times), by accessing a central server via the net. For those in the know, the doublespeak used to inform users of any actions they need to take to enable their DRM rights might be quite amusing. To wit: 'The content you are accessing requires an additional level of security. In order to play it, you will need to update your Digital Rights Management Installation.' Others, however, will think they're getting something, when they're actually having something taken away from them. It's a matter of time to see if consumers will flat-out reject this new 'enabling' technology, or let it seep into and infect their lives like the disease that it is."


    Direct Download of over 5500 Certification Exams

    3COM [8 Certification Exam(s) ]
    AccessData [1 Certification Exam(s) ]
    ACFE [1 Certification Exam(s) ]
    ACI [3 Certification Exam(s) ]
    Acme-Packet [1 Certification Exam(s) ]
    ACSM [4 Certification Exam(s) ]
    ACT [1 Certification Exam(s) ]
    Admission-Tests [13 Certification Exam(s) ]
    ADOBE [93 Certification Exam(s) ]
    AFP [1 Certification Exam(s) ]
    AICPA [2 Certification Exam(s) ]
    AIIM [1 Certification Exam(s) ]
    Alcatel-Lucent [13 Certification Exam(s) ]
    Alfresco [1 Certification Exam(s) ]
    Altiris [3 Certification Exam(s) ]
    Amazon [2 Certification Exam(s) ]
    American-College [2 Certification Exam(s) ]
    Android [4 Certification Exam(s) ]
    APA [1 Certification Exam(s) ]
    APC [2 Certification Exam(s) ]
    APICS [2 Certification Exam(s) ]
    Apple [69 Certification Exam(s) ]
    AppSense [1 Certification Exam(s) ]
    APTUSC [1 Certification Exam(s) ]
    Arizona-Education [1 Certification Exam(s) ]
    ARM [1 Certification Exam(s) ]
    Aruba [6 Certification Exam(s) ]
    ASIS [2 Certification Exam(s) ]
    ASQ [3 Certification Exam(s) ]
    ASTQB [8 Certification Exam(s) ]
    Autodesk [2 Certification Exam(s) ]
    Avaya [96 Certification Exam(s) ]
    AXELOS [1 Certification Exam(s) ]
    Axis [1 Certification Exam(s) ]
    Banking [1 Certification Exam(s) ]
    BEA [5 Certification Exam(s) ]
    BICSI [2 Certification Exam(s) ]
    BlackBerry [17 Certification Exam(s) ]
    BlueCoat [2 Certification Exam(s) ]
    Brocade [4 Certification Exam(s) ]
    Business-Objects [11 Certification Exam(s) ]
    Business-Tests [4 Certification Exam(s) ]
    CA-Technologies [21 Certification Exam(s) ]
    Certification-Board [10 Certification Exam(s) ]
    Certiport [3 Certification Exam(s) ]
    CheckPoint [41 Certification Exam(s) ]
    CIDQ [1 Certification Exam(s) ]
    CIPS [4 Certification Exam(s) ]
    Cisco [318 Certification Exam(s) ]
    Citrix [47 Certification Exam(s) ]
    CIW [18 Certification Exam(s) ]
    Cloudera [10 Certification Exam(s) ]
    Cognos [19 Certification Exam(s) ]
    College-Board [2 Certification Exam(s) ]
    CompTIA [76 Certification Exam(s) ]
    ComputerAssociates [6 Certification Exam(s) ]
    Consultant [2 Certification Exam(s) ]
    Counselor [4 Certification Exam(s) ]
    CPP-Institue [2 Certification Exam(s) ]
    CPP-Institute [1 Certification Exam(s) ]
    CSP [1 Certification Exam(s) ]
    CWNA [1 Certification Exam(s) ]
    CWNP [13 Certification Exam(s) ]
    Dassault [2 Certification Exam(s) ]
    DELL [9 Certification Exam(s) ]
    DMI [1 Certification Exam(s) ]
    DRI [1 Certification Exam(s) ]
    ECCouncil [21 Certification Exam(s) ]
    ECDL [1 Certification Exam(s) ]
    EMC [129 Certification Exam(s) ]
    Enterasys [13 Certification Exam(s) ]
    Ericsson [5 Certification Exam(s) ]
    ESPA [1 Certification Exam(s) ]
    Esri [2 Certification Exam(s) ]
    ExamExpress [15 Certification Exam(s) ]
    Exin [40 Certification Exam(s) ]
    ExtremeNetworks [3 Certification Exam(s) ]
    F5-Networks [20 Certification Exam(s) ]
    FCTC [2 Certification Exam(s) ]
    Filemaker [9 Certification Exam(s) ]
    Financial [36 Certification Exam(s) ]
    Food [4 Certification Exam(s) ]
    Fortinet [12 Certification Exam(s) ]
    Foundry [6 Certification Exam(s) ]
    FSMTB [1 Certification Exam(s) ]
    Fujitsu [2 Certification Exam(s) ]
    GAQM [9 Certification Exam(s) ]
    Genesys [4 Certification Exam(s) ]
    GIAC [15 Certification Exam(s) ]
    Google [4 Certification Exam(s) ]
    GuidanceSoftware [2 Certification Exam(s) ]
    H3C [1 Certification Exam(s) ]
    HDI [9 Certification Exam(s) ]
    Healthcare [3 Certification Exam(s) ]
    HIPAA [2 Certification Exam(s) ]
    Hitachi [30 Certification Exam(s) ]
    Hortonworks [4 Certification Exam(s) ]
    Hospitality [2 Certification Exam(s) ]
    HP [746 Certification Exam(s) ]
    HR [4 Certification Exam(s) ]
    HRCI [1 Certification Exam(s) ]
    Huawei [21 Certification Exam(s) ]
    Hyperion [10 Certification Exam(s) ]
    IAAP [1 Certification Exam(s) ]
    IAHCSMM [1 Certification Exam(s) ]
    IBM [1530 Certification Exam(s) ]
    IBQH [1 Certification Exam(s) ]
    ICAI [1 Certification Exam(s) ]
    ICDL [6 Certification Exam(s) ]
    IEEE [1 Certification Exam(s) ]
    IELTS [1 Certification Exam(s) ]
    IFPUG [1 Certification Exam(s) ]
    IIA [3 Certification Exam(s) ]
    IIBA [2 Certification Exam(s) ]
    IISFA [1 Certification Exam(s) ]
    Intel [2 Certification Exam(s) ]
    IQN [1 Certification Exam(s) ]
    IRS [1 Certification Exam(s) ]
    ISA [1 Certification Exam(s) ]
    ISACA [4 Certification Exam(s) ]
    ISC2 [6 Certification Exam(s) ]
    ISEB [24 Certification Exam(s) ]
    Isilon [4 Certification Exam(s) ]
    ISM [6 Certification Exam(s) ]
    iSQI [7 Certification Exam(s) ]
    ITEC [1 Certification Exam(s) ]
    Juniper [63 Certification Exam(s) ]
    LEED [1 Certification Exam(s) ]
    Legato [5 Certification Exam(s) ]
    Liferay [1 Certification Exam(s) ]
    Logical-Operations [1 Certification Exam(s) ]
    Lotus [66 Certification Exam(s) ]
    LPI [24 Certification Exam(s) ]
    LSI [3 Certification Exam(s) ]
    Magento [3 Certification Exam(s) ]
    Maintenance [2 Certification Exam(s) ]
    McAfee [8 Certification Exam(s) ]
    McData [3 Certification Exam(s) ]
    Medical [69 Certification Exam(s) ]
    Microsoft [368 Certification Exam(s) ]
    Mile2 [2 Certification Exam(s) ]
    Military [1 Certification Exam(s) ]
    Misc [1 Certification Exam(s) ]
    Motorola [7 Certification Exam(s) ]
    mySQL [4 Certification Exam(s) ]
    NBSTSA [1 Certification Exam(s) ]
    NCEES [2 Certification Exam(s) ]
    NCIDQ [1 Certification Exam(s) ]
    NCLEX [2 Certification Exam(s) ]
    Network-General [12 Certification Exam(s) ]
    NetworkAppliance [36 Certification Exam(s) ]
    NI [1 Certification Exam(s) ]
    NIELIT [1 Certification Exam(s) ]
    Nokia [6 Certification Exam(s) ]
    Nortel [130 Certification Exam(s) ]
    Novell [37 Certification Exam(s) ]
    OMG [10 Certification Exam(s) ]
    Oracle [269 Certification Exam(s) ]
    P&C [2 Certification Exam(s) ]
    Palo-Alto [4 Certification Exam(s) ]
    PARCC [1 Certification Exam(s) ]
    PayPal [1 Certification Exam(s) ]
    Pegasystems [11 Certification Exam(s) ]
    PEOPLECERT [4 Certification Exam(s) ]
    PMI [15 Certification Exam(s) ]
    Polycom [2 Certification Exam(s) ]
    PostgreSQL-CE [1 Certification Exam(s) ]
    Prince2 [6 Certification Exam(s) ]
    PRMIA [1 Certification Exam(s) ]
    PsychCorp [1 Certification Exam(s) ]
    PTCB [2 Certification Exam(s) ]
    QAI [1 Certification Exam(s) ]
    QlikView [1 Certification Exam(s) ]
    Quality-Assurance [7 Certification Exam(s) ]
    RACC [1 Certification Exam(s) ]
    Real-Estate [1 Certification Exam(s) ]
    RedHat [8 Certification Exam(s) ]
    RES [5 Certification Exam(s) ]
    Riverbed [8 Certification Exam(s) ]
    RSA [15 Certification Exam(s) ]
    Sair [8 Certification Exam(s) ]
    Salesforce [5 Certification Exam(s) ]
    SANS [1 Certification Exam(s) ]
    SAP [98 Certification Exam(s) ]
    SASInstitute [15 Certification Exam(s) ]
    SAT [1 Certification Exam(s) ]
    SCO [10 Certification Exam(s) ]
    SCP [6 Certification Exam(s) ]
    SDI [3 Certification Exam(s) ]
    See-Beyond [1 Certification Exam(s) ]
    Siemens [1 Certification Exam(s) ]
    Snia [7 Certification Exam(s) ]
    SOA [15 Certification Exam(s) ]
    Social-Work-Board [4 Certification Exam(s) ]
    SpringSource [1 Certification Exam(s) ]
    SUN [63 Certification Exam(s) ]
    SUSE [1 Certification Exam(s) ]
    Sybase [17 Certification Exam(s) ]
    Symantec [134 Certification Exam(s) ]
    Teacher-Certification [4 Certification Exam(s) ]
    The-Open-Group [8 Certification Exam(s) ]
    TIA [3 Certification Exam(s) ]
    Tibco [18 Certification Exam(s) ]
    Trainers [3 Certification Exam(s) ]
    Trend [1 Certification Exam(s) ]
    TruSecure [1 Certification Exam(s) ]
    USMLE [1 Certification Exam(s) ]
    VCE [6 Certification Exam(s) ]
    Veeam [2 Certification Exam(s) ]
    Veritas [33 Certification Exam(s) ]
    Vmware [58 Certification Exam(s) ]
    Wonderlic [2 Certification Exam(s) ]
    Worldatwork [2 Certification Exam(s) ]
    XML-Master [3 Certification Exam(s) ]
    Zend [6 Certification Exam(s) ]





    References :


    Dropmark : http://killexams.dropmark.com/367904/11740004
    Wordpress : http://wp.me/p7SJ6L-1p8
    Dropmark-Text : http://killexams.dropmark.com/367904/12306763
    Issu : https://issuu.com/trutrainers/docs/000-n04
    Blogspot : http://killexamsbraindump.blogspot.com/2017/11/actual-000-n04-take-look-at-questions-i.html
    RSS Feed : http://feeds.feedburner.com/LookAtThese000-n04RealQuestionAndAnswers
    Box.net : https://app.box.com/s/ku8wklwvkv74u16ironofoix7saenwk1
    zoho.com : https://docs.zoho.com/file/62c50ac24b7cc66ba4c739e95c2efed11f358






    Back to Main Page

    IBM 000-N04 Exam (IBM Commerce Solutions Order Mgmt Technical Mastery Test v1) Detailed Information



    References:


    Pass4sure Certification Exam Questions and Answers - www.founco.com
    Killexams Exam Study Notes | study guides - www.founco.com
    Pass4sure Certification Exam Questions and Answers - st.edu.ge
    Killexams Exam Study Notes | study guides - st.edu.ge
    Pass4sure Certification Exam Questions and Answers - www.jabbat.com
    Killexams Exam Study Notes | study guides - www.jabbat.com
    Pass4sure Certification Exam Questions and Answers - www.jorgefrazao.esy.es
    Killexams Exam Study Notes | study guides - www.jorgefrazao.esy.es
    Pass4sure Certification Exam Questions and Answers and Study Notes - www.makkesoft.com
    Killexams Exam Study Notes | study guides | QA - www.makkesoft.com
    Pass4sure Exam Study Notes - maipu.gob.ar
    Pass4sure Certification Exam Study Notes - idprod.esy.es
    Download Hottest Pass4sure Certification Exams - cscpk.org
    Killexams Study Guides and Exam Simulator - www.simepe.com.br
    Comprehensive Questions and Answers for Certification Exams - www.ynb.no
    Exam Questions and Answers | Brain Dumps - www.4seasonrentacar.com
    Certification Training Questions and Answers - www.interactiveforum.com.mx
    Pass4sure Training Questions and Answers - www.menchinidesign.com
    Real exam Questions and Answers with Exam Simulators - www.pastoriaborgofuro.it
    Real Questions and accurate answers for exam - playmagem.com.br
    Certification Questions and Answers | Exam Simulator | Study Guides - www.rafflesdesignltd.com
    Kill exams certification Training Exams - www.sitespin.co.za
    Latest Certification Exams with Exam Simulator - www.philreeve.com
    Latest and Updated Certification Exams with Exam Simulator - www.tmicon.com.au
    Pass you exam at first attempt with Pass4sure Questions and Answers - tractaricurteadearges.ro
    Latest Certification Exams with Exam Simulator - addscrave.net
    Pass you exam at first attempt with Pass4sure Questions and Answers - alessaconsulting.com
    Get Great Success with Pass4sure Exam Questions/Answers - alchemiawellness.com
    Best Exam Simulator and brain dumps for the exam - andracarmina.com
    Real exam Questions and Answers with Exam Simulators - empoweredbeliefs.com
    Real Questions and accurate answers for exam - www.alexanndre.com
    Certification Questions and Answers | Exam Simulator | Study Guides - allsoulsholidayclub.co.uk