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

Test Code : 000-N04
Test denomination : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
Vendor denomination : IBM
: 30 actual Questions

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IBM IBM Commerce Solutions Order

Cloud Computing: IBM Acquires Sterling Commerce | killexams.com actual 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 befriend shoppers precipitate 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 shrewd networks of company partners, purchasers and suppliers to subsist 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 ample sweep of industries. The amalgam of IBM and Sterling Commerce permits the mixing of key traffic 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 advocate companies automate, control and accelerate core traffic 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 traffic methods, resulting in superior claim technology, customer adventure and achievement. using the combined applied sciences of IBM and Sterling Commerce, valued clientele Enjoy the flexibleness to exploit 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 augment Hostess' give chain performance. In June, Cengage researching went reside with the newest edition of Sterling Multi-Channel selling to capture odds 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 amenable timeframe. I foretell the fabulous solutions we've got from Sterling Commerce will handiest subsist 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 relish Toshiba and desirable retailers equivalent to Auchan and John Lewis.

The acquisition builds on IBM's growing to subsist portfolio of traffic application options designed to assist organizations automate, exploit 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 augment the company's capacity to aid customers' wants in this becoming market.

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

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

A key component of overseas enterprise Machines' (NYSE: IBM) turnaround trouble 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 sensible of it searching at the business's third-quarter effects. The cognitive solutions segment suffered a 5% revenue decline, even after adjusting for a odd money-linked headwind. That seems relish terrible news for a company making a ante its future on AI.

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

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

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

CFO James Kavanaugh went into some aspect entire through the revenue convoke 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 relish Watson, and blockchain. On excellent of entire that, "horizontal domains" relish collaboration and commerce are additionally protected.

Transaction processing software comprises "utility that runs mission-crucial workloads leveraging their hardware platform," in accordance with Kavanaugh. here is ordinarily on-premises application used by passage of industries relish 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 passage 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 Enjoy an consequence on sales. Kavanaugh expects a recur to boom, in accordance with a tenacious pipeline of deals.

The options utility component of the side 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 moreover 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 trouble to apply AI to the healthcare trade, loved vast-based augment entire 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 Enjoy confidence, a original blockchain-primarily based platform that allows for meals to subsist 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 Enjoy the erudition to develop into meaningful organizations for the company.

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

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IBM's options power Bulgaria-primarily based Praktiker's revenue | killexams.com actual 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 original 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 concomitant instances.

We are expecting that the growing to subsist 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 traffic won best 3.9% entire through the period, the inventory liked 5.1%

Is IBM Poised to improvement?

We solemnize 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 style 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 traffic Machines employer earnings (TTM)

View pictures

international traffic 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 tall quality).

Zacks Rank

At present IBM has a Zacks Rank #3 (grasp). that you would subsist able to discern the complete checklist of nowadays’s Zacks #1 Rank (strong buy) stocks birthright 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 subsist a pivotal 12 months to acquire in on one more emerging technology anticipated to rock the market. claim may start from well-nigh nothing to $forty two billion by passage of 2025. experiences insinuate it may store 10 million lives per decade which might in flip reserve $200 billion in U.S. healthcare prices.

Story Continues




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

Test Code : 000-N04
Test denomination : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
Vendor denomination : IBM
: 30 actual Questions

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

Introduction

Knowledge of population connectivity is necessary for effectual 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 captious 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 Enjoy a bi-phasic life history in which adults array limited geographic sweep and tall 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 Enjoy been shown to Enjoy a home sweep 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). erudition of larval dispersal patterns can subsist used to inform effectual 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 import 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 institute in low densities across large areas of the open ocean (Clarke, 1991; Wren & Kobayashi, 2016). A variety of genetic and chemistry techniques Enjoy therefore been developed to appraise larval connectivity (Gillanders, 2005; Leis, Siebeck & Dixson, 2011; Toonen et al., 2011; Johnson et al., 2018). Computer models informed by bailiwick and laboratory data Enjoy moreover become a valuable instrument 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 subsist successfully transported by ocean currents, and so is expected to directly impress 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 entire play a role in shaping connectivity patterns. Physical factors such as temperature, bathymetry, and current direction can moreover substantially influence connectivity (Cowen & Sponaugle, 2009). In this study, they incorporated both biotic and abiotic components in an IBM coupled with an oceanographic model to foretell 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 shun east to west and are driven by the current 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 knotty and varies seasonally (Friedlander et al., 2005; Vaz et al., 2013).

Hawaiian marine communities puss 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 squabble for implementation of a more holistic approach than traditional single-species maximum sustainable relent techniques, which Enjoy proven ineffective (Goodyear, 1996; Hilborn, 2011). There is a generic movement toward the use of ecosystem-based management, which requires erudition 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 locality (MPA) located on the north shore of Moloka‘i, an island in the Maui Nui knotty 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 Enjoy 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 locality 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, they used a Lagrangian particle-tracking IBM (Wong-Ala et al., 2018) to simulate larval dispersal around Moloka‘i and to appraise the larval exchange among sites at the scale of an individual island. They Enjoy parameterized their 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. Their goals were to examine patterns of species-specific connectivity, characterize the location and relative magnitude of connections around Moloka‘i, relate 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 shaded gray. Spawn season and timing from traditional ecological erudition 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 relate 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 locality that includes the islands of Moloka‘i, Maui, Lana‘i, Kaho‘olawe, O‘ahu, and Hawai‘i. While Ni‘ihau and southern Kaua’i moreover topple within the domain, they discarded connectivity to these islands because they prevaricate within the 0.5° boundary zone of the current model. boundary conditions are enforced over 20 grid points on entire sides of the model domain. Vertically, the model is divided into 50 layers that augment in thickness with depth, from five m at the surface (0.0–5.0 m) to 510 m at the foundation (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 generic 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). They conclude not embrace tides in the model due to temporal resolution. Their model epoch represents a neutral ocean state; no El Niño or La Niña events occurred during this time period. To ground-truth the circulation model, they compared surface current output to real-time trajectories of surface drifters from the GDP Drifter Data Assembly headquarters (Fig. S2) (Elipot et al., 2016), as well as other current models of the locality (Wren et al., 2016; Storlazzi et al., 2017).

Biological model

To simulate larval dispersal, they 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 replete resolution NOAA Global Self-consistent Hierarchical High-resolution Geography database, v2.3.0 (Wessel & Smith, 1996). Their 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, they 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 divide 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. They chose this implementation over a more computationally intensive interpolation method (i.e., fourth-order Runge–Kutta) because they did not solemnize a contrast at this timestep length. Biological processes modeled embrace 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 linger 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 limn 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 epoch were available for their study species, so this parameter was not included. Mortality rates were calculated as larval half-lives; e.g., one-half of entire larvae were assumed to Enjoy survived at one-half of the maximum PLD for that species (following Holstein, Paris & Mumby, 2014). Since their focus was on potential connectivity pathways, reproductive rates were calibrated to allow for saturation of practicable 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 Enjoy demonstrated that fish larvae Enjoy a tall degree of control over their vertical position in the water column (Irisson et al., 2010; Huebert, Cowen & Sponaugle, 2011). Therefore, they modeled broadcast-spawning fish species with a 24-hour passive buoyant side to simulate eggs pre-hatch, followed by a pelagic larval side with a species-specific depth distribution. For C. ignoblis, C. melampygus, P. porphyreus, C. perspicillatus, and S. rubroviolaceus, they 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.) conclude not Enjoy 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, they used congener-level estimates for PLD (see Table 1). For example, there is no appraise 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 endemic 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). They 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 locality not covered by sand or mud, for a total of 87 sites. Number of adults was assumed equal at entire sites. For regional analysis, they 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 very benthic habitat classification and current wave direction, and/or were Part of the very 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 duty 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 compute dispersal kernels to examine and compare species-specific distributions. They moreover 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 appraise the role of specific sites around Moloka‘i, they moreover 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 contrast between number of successfully exported and imported larvae by the sum of entire 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 they measured successful dispersal to adjacent islands, they did not spawn larvae from them, and therefore these islands limn exogenous sinks. For this reason, settlement to other islands was not included in source–sink index calculations.

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

Graph-theoretic analysis

To quantify connections between sites, they applied graph theory to population connectivity (Treml et al., 2008; Holstein, Paris & Mumby, 2014). Graph theoretic analysis is highly scalable and can subsist 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 moreover 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 moreover provides a powerful instrument for spatial visualization, allowing for rapid, intuitive communication of connectivity results to researchers, managers, and the public alike. This sort of analysis can subsist used to model pairwise relationships between spatial data points by breaking down individual-based output into a succession of nodes (habitat sites) and edges (directed connections between habitat sites). They then used these nodes and edges to examine the relative import 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). They used the R package ‘igraph’ to examine several measures of within-island connectivity (Csardi & Nepusz, 2006). Edge density, or the harmony of realized edges out of entire practicable edges, is a multi-site measure of connectivity. Areas with a higher edge density Enjoy more direct connections between habitat sites, and thus are more strongly connected. They measured edge density along and between the north, south, east, and west coasts of Moloka‘i to examine practicable population structure and degree of exchange among the marine resources of local communities.

The distribution of shortest path length is moreover 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 obtain a connection. tolerable shortest path, therefore, is a descriptive statistic to appraise connectivity of a network. If two sites are unconnected, it is practicable to Enjoy infinite-length shortest paths; here, these sempiternal values were notable but not included in final analyses.

Networks can moreover subsist broken in connected components (Csardi & Nepusz, 2006). A weakly connected component (WCC) is a subgraph in which entire nodes are not reachable by other nodes. A network split into multiple WCCs indicates divide populations that conclude 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 entire nodes are directly connected and indicates a tall degree of connectivity. A region with many miniature SCCs can bespeak tall local connectivity but low island-wide connectivity. Furthermore, component analysis can identify crop nodes, or nodes that, if removed, crash a network into multiple WCCs. Pinpointing these crop nodes can identify potential necessary 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 necessary to note which sites are exporting larvae to, or importing larvae from, other sites. To this end, they 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 tall out-degree seed a large number of other sites, and bespeak potentially necessary larval sources, while habitat sites with a low in-degree rely on a limited number of larval sources and may therefore subsist 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 bespeak connectivity pathways or ‘chokepoints’ that are necessary to overall connectivity on a multigenerational timescale. BC was weighted with the harmony of dispersal as described in the preceding section. They calculated in-degree, out-degree, and weighted betweenness centrality for each region in the network for each species.

As with the source–sink index, they did not embrace sites on islands other than Moloka‘i in their calculations of edge density, shortest paths, connected components, crop 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 harmony 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 side 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, they moreover examined tolerable 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 harmony of larvae settled out of the total number of larvae spawned. Local retention is measured as the harmony of larvae spawned from a site that settle at the very site. Shortest path is measured as the minimum number of steps needed to connect two sites. Strongly connected sites refers to the harmony of sites in a network that belong to a strongly connected component. insinuate 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, significance that on average, it would capture fewer generations for these species to demographically bridge any given pair of sites. Using maximum shortest path, it could capture these species three generations at most to connect sites. Cellana spp. and P. meandrina, by comparison, could capture as many as five generations. Other medium- and long-dispersing species showed relatively equivalent shortest-path distributions, with trevally species showing the highest insinuate path length and therefore the lowest island-scale connectivity.

The number and size of weakly-connected and strongly-connected components in a network is moreover an informative measure of connectivity (Fig. 2). No species in their 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 entire 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 demonstrate fragmentation with seven and six SCCs respectively, ranging in size from two to 22 sites. This SCC pattern suggests low global connectivity but tall 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 moreover necessary 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 demonstrate 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 nearby to home, while few long dispersers are retained at or near their spawning sites.

Minimum PLD was positively correlated with insinuate 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 insinuate distance of 24.06 ± 31.33 km, medium dispersers travelled a insinuate distance of 52.71 ± 40.37 km, and long dispersers travelled the farthest, at a insinuate of 89.41 ± 41.43 km. However, regardless of PLD, there were essentially two peaks of insinuate 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 insinuate at 8.85 km, indicating that most of these larvae settled relatively nearby 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 harmony 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 tolerable of 74% of larvae from short-dispersing species settled back to Moloka‘i, as compared to an tolerable of 41% of medium dispersers and 9% of long dispersers. A large harmony of larvae moreover 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 significantly lower. Larvae of every species settled to Lana‘i, and settlement to this island made up less than 5% of settled larvae across entire 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 entire 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 moreover examined coast-specific patterns of rearward settlement harmony to other islands, discarding connections with a very low harmony 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 entire larvae settling to O‘ahu from Moloka‘i. The east and south shores of Moloka‘i had the highest tolerable 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 tolerable 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 bespeak that multiple coasts of Moloka‘i Enjoy the potential to export larvae to neighboring islands.

Temporal settlement profiles moreover 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 epoch of time. Some species moreover showed distinctive patterns of settlement to other islands; their 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.) materialize as narrow spikes, while broad distributions generally bespeak more common settlement pathways. Regional patterns of connectivity in Moloka‘i coastal waters

Within Moloka‘i, their model predicts that coast-specific population structure is likely; averaged across entire species, 84% of individuals settled back to the very coast on which they were spawned rather than a different coast on Moloka‘i. Excluding connections with a very low harmony of larvae (<0.1% of total larvae of that species that settled to Moloka‘i), they institute that the harmony 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 tall degree of local retention in every species except for the long-dispersing Panulirus spp., and the east coast moreover had tall local retention overall. Between coasts, a tall harmony 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) tolerable rearward settlement harmony by species per pair of coastlines, calculated by the number of larvae settling at site s from site o divided by entire settled larvae at site s. Directional coastline pairs (Spawn > Settlement) are ordered from left to birthright by increasing median settlement proportion. (B) Heatmap of edge density for coast-specific networks by species. Density is calculated by the number of entire realized paths out of total practicable paths, disregarding directionality.

We moreover calculated edge density, including entire connections between coasts on Moloka‘i regardless of settlement harmony (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 tall edge density for short and medium dispersers (0.16–0.39) but low for long dispersers (<0.005). The north shore moreover showed relatively tall 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 entire 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 entire species (<0.05), a divide that was especially divide 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, their model predicts very little, if any, demographic connectivity.

To explore patterns of connectivity on a finer scale, they pooled sites into regions (as defined in Fig. 1) in order to anatomize relationships between these regions. Arranging model output into node-edge networks clarified pathways and regions of note, and revealed several patterns which did not ensue 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 entire 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 Enjoy a low out-degree, red nodes Enjoy a medium out-degree, and black nodes Enjoy a tall 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 limn substantial connections). Edge thickness represents log-transformed harmony 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 tall 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 tall BC scores. P. meandrina and C. strigosus showed several regions along the south shore with tall 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 entire species. Betweenness centrality is a measure of the number of paths that pass through a certain region; a tall score suggests potentially necessary 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 Enjoy entire been normalized to values between 0 to 1 per species. Local retention is measured as the harmony of larvae that settled back to their spawn site out of entire larvae spawned at that site. Source-sink index is a measure of net export or import; negative values (blue) bespeak a net larval sink, while positive values (red) bespeak a net larval source. White indicates that a site is neither a tenacious source nor sink. Gray values for Cellana spp. denote a want 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 moreover 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, entire 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 entire species. Puko‘o, Pauwalu Harbor, and Cape Halawa were either frail net sources or neither sources nor sinks, which corresponds to the tall levels of local retention observed at these sites. Pala‘au and Mo‘omomi acted as either frail sinks or sources for short dispersers and as sources for long dispersers.

Only four networks showed regional cut-nodes, or nodes that, if removed, crash 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 miniature Bay and Lono Harbor, respectively. C. perspicillatus, and S. rubroviolaceus showed a similar pattern in regards to Mokio Point; removal of this node isolated miniature 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 limn rearward proportion, calculated by the number of larvae settling at site s from site o divided by entire settled larvae at site s. White squares bespeak 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 entire regions on Moloka‘i, East Kalaupapa Peninsula was the single largest exporter of larvae to Hawai‘i Island, accounting for 19% of entire larvae transported from Moloka‘i to this island; West Kalaupapa Peninsula accounted for another 10%. The park moreover contributed 22% of entire 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, significance that replete population breaks are not predicted in the case of habitat or population loss in this area. Nevertheless, in their model Kalaupapa exported larvae to multiple regions along the north shore in entire 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 necessary role for long-dispersing species; settlement from Kalaupapa made up 18%–29% of entire 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 tall out-degree, or number of outgoing connections to other regions, and West Kalaupapa was moreover one of the few regions on Moloka‘i that acted as a net larval source across entire species (Fig. 8). Their study has moreover demonstrated that different regions of a marine protected locality can potentially fulfill different roles, even in a miniature 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 harmony of dispersal along that edge; red indicates higher harmony 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 their model to augment biological realism, and assess how such traits impact predictions of larval dispersal. The Wong-Ala et al. (2018) IBM provides a highly resilient model framework that can easily subsist modified to incorporate either additional species-specific data or entirely original biological traits. In this study, they included specific spawning seasons for entire species, as well as spawning by moon side 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 subsist easily added for these species and others. Some potential additions to future iterations of the model might embrace 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, they Enjoy 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 demonstrate greater connectivity along the northern coast, while the goatfish P. porphyreus shows higher connectivity along the eastern half of the island. These species Enjoy 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 side (Fig. 2). While maximum PLD did materialize play a role in the probability of rare long-distance dispersal, minimum PLD appears to subsist the main driver of tolerable dispersal distance (Fig. 2). Overall, species with a shorter minimum PLD had higher settlement success, shorter insinuate 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 moreover influenced connectivity patterns. Because mesoscale current patterns can vary substantially over the course of the year, the timing of spawning for certain species may subsist captious 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 harmony of larvae settled to O‘ahu, which is significantly 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 their model long-distance dispersal to Hawai‘i Island was practicable 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). prefatory analysis moreover 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 Enjoy several caveats. Because fine-scale density estimates are not available for their species of interest around Moloka’i, they assumed that fecundity is equivalent at entire sites. This simplification may lead us to under- or over-estimate the energy of connections between sites. want of adequate data moreover 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 plane without genetic analysis, they used genus-level larval distribution data (Boehlert & Mundy, 1996), or lacking that, an appraise of 50–100 m as a depth layer that is generally more enriched with larvae (Boehlert, Watson & Sun, 1992; Wren & Kobayashi, 2016). They moreover estimated PLD in several cases using congener-level data (see Table 1). While specificity is model 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 their MITgcm current model shows annual consistency, it only spans two and a half years chosen as neutral condition ‘average’ ocean conditions. It does not span any El Niño or La Niña (ENSO) events, which reason wide-scale sea-surface temperature anomalies and may therefore impress patterns of connectivity during these years. El Niño can Enjoy a particularly tenacious impact on coral reproduction, since the warm currents associated with these events can lead to austere temperature stress (Glynn & D’Croz, 1990; Wood et al., 2016). While there has been exiguous study to date on the effects of ENSO on fine-scale connectivity, previous travail has demonstrated increased variability during these events. For example, Wood et al. (2016) showed a subside in eastward Pacific dispersal during El Niño years, but an augment in westward dispersal, and Treml et al. (2008) showed unique connections in the West Pacific as well as an augment in connectivity during El Niño. While these effects are difficult to predict, especially at such a miniature scale, additional model years would augment aplomb in long-term connectivity estimations. Additionally, with a temporal resolution of 24 h, they could not adequately address the role of tides on dispersal, and therefore did not embrace them in the MITgcm. Storlazzi et al. (2017) showed that tidal forces did impress larval dispersal in Maui Nui, underlining the import of including both fine-scale, short-duration models and coarser-scale, long-duration models in final management decisions.

We moreover confine their model’s scope geographically. Their goal was to determine whether they could resolve predictive patterns at this scale germane to management. Interpretation of connectivity output can subsist biased by spatial resolution of the ocean model, since knotty coastal processes can subsist smoothed and therefore impact larval trajectories. To confine this bias, they focused mainly on coastal and regional connectivity on scales greater than the current resolution. They moreover used the finest-scale current products available for their study area, and their results demonstrate generic 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 erudition of island-scale connectivity is necessary for local management, it does disregard potential connections from other islands. In their calculations of edge density, betweenness centrality and source-sink index, they included only settlement to Moloka‘i, discarding exogenous sinks that would bias their analysis. Likewise, they cannot foretell the harmony of larvae settling to other islands that originated from Moloka‘i, or the harmony of larvae on Moloka‘i that originated from other islands.

It is moreover necessary to note scale in relation to measures of connectivity; they anticipate that long-dispersing species such as Caranx spp. and Panulirus spp. will demonstrate much higher measures of connectivity when measured across the total archipelago as opposed to a single island. The cut-nodes observed in these species may not actually crash 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 label necessary habitat locations, especially in terms of providing links between two otherwise disconnected coasts. It may subsist that for certain species or certain regions, stock replenishment relies on larval import from other islands, underscoring the import 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 their model stresses the requisite for multi-scale management, specifically tailored to local and regional connectivity patterns and the suite of target species. Even on such a miniature 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 subsist more at risk of isolation. Seasonal variation should moreover subsist taken into account, as mesoscale current patterns (and resulting connectivity patterns) vary over the course of a year. Their 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 demonstrate similar network dynamics may capitalize from similar management strategies. Areas that act as larval sources either by harmony of larvae (high source–sink index) or number of sites (high out-degree) should receive management consideration. On Moloka‘i, across entire species in their study, these sources fell mostly on the northern and eastern coasts. Maintenance of these areas is especially necessary 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 necessary intergenerational pathways, particularly as a means of connecting north-coast and south-coast populations, which showed a want of connectivity both in total number of connections (edge density) and harmony of larvae. Both of these connectivity measures were included because edge density includes entire connections, even those with a very miniature harmony of larvae, and may therefore embrace rare dispersal events that are of exiguous relevance to managers. Additionally, edge density comparisons between networks should subsist viewed with the caveat that these networks conclude not necessarily Enjoy the very number of nodes. Nevertheless, both edge density and harmony demonstrate very similar patterns, and embrace 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 moreover reckon 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 Enjoy more direct connections and therefore may subsist more resilient to local population loss. care should subsist 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 rehearse may subsist especially necessary for species for which they appraise multiple miniature SCCs, such as Cellana spp. or P. meandrina.

Kalaupapa Peninsula emerged as an necessary 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 entire species, and moreover exported larvae to sites along the east and west shores in entire 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, moreover on the north shore) that acted as a net larval source across entire species. Eastern Kalaupapa Peninsula was particularly highly connected, and was Part of a strongly connected component in every species. The Park moreover emerged as a potential point of connection to adjacent islands, particularly to O‘ahu and Hawai‘i. Expanding the spatial scale of their model will further elucidate Kalaupapa’s role in the greater pattern of inter-island connectivity.

In addition to biophysical modeling, genetic analyses can subsist used to identify persistent population structure of relevance to managers (Cowen et al., 2000; Casey, Jardim & Martinsohn, 2016). Their 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, they foretell some plane of shoreline-specific population structure for most species included in the study (Fig. 6). Unfortunately, genetic analyses to date Enjoy 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 warrant such miniature scale genetic analyses because there are species, such as the coral P. meandrina, for which the model predicts lucid separation of north-shore and south-shore populations which should subsist 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 necessary for conservation. In this study, they contribute to the growing cadaver of travail in biophysical connectivity modeling, focusing on a region and suite of species that are of relevance to resource managers. Furthermore, they demonstrate the value of quantifying fine-scale relationships between habitat sites via graph-theoretic methods. Multispecies network analysis revealed persistent patterns that can befriend define region-wide practices, as well as species-specific connectivity that merits more individual consideration. They demonstrate that connectivity is influenced not only by PLD, but moreover by other life-history traits such as spawning season, moon-phase spawning, and ontogenetic changes in larval depth. tall local retention of larvae with a short- or medium-length PLD is consistent with population genetic studies of the area. They moreover 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 necessary multigenerational pathways. Connectivity is only one piece of the puzzle of MPA effectiveness, which must moreover account for reproductive population size, long-term persistence, and post-settlement survival (Burgess et al., 2014). That being said, their study provides a quantitative roadmap of potential demographic connectivity, and thus presents an effectual instrument 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, tall 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. entire drifter information was extracted from the GDP Drifter Data Assembly headquarters (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 revise day where they entered the model domain, at the uppermost depth layer of their 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 entire 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 bespeak settlement earlier in the year (February–March), while black tracks bespeak 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 harmony are transported through Maui Nui.

Eddy differences by depth layer.

Differences in eddy pattern and energy in surface layers (A, 2.5 m) vs. profound 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, tall velocity). While large gyres remain consistent at different depths, smaller features vary along this gradient. For example, the currents around Kaho‘olawe, the miniature gyre off the eastern coast of O‘ahu, and currents to the north of Maui entire vary in direction and/or velocity.


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

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

Note: simulated intelligence (AI) endows applications with the ability to automatically learn and reconcile from sustain via interacting with the surroundings / environment. discern the blog “Artificial Intelligence is not Fake Intelligence” for a more circumstantial explanation on simulated intelligence and machine learning.

The fleet Company article “How to discontinue Worrying and worship the remarkable AI War of 2018,” projected that the AI battle would ultimately simmer down between the “AI ample 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 subsist the ultimate winners in this remarkable 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 Enjoy the following characteristics:

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

    #1 Users, Not Purveyors, of AI TechnologyThe Market Capitalization Leaderboard shown in pattern 1 offers necessary clues as to which organizations will likely subsist the AI winners. What will set these organizations apart will subsist 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 assume 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 sensible of any AI or data technologies that Facebook sells to the generic 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 original sources of customer, business, operational, and financial value that matters. Ultimately, the AI war is about “value capture.”

    The companies listed in pattern 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 convivial media, and advertising
  • Each of these AI leaders seeks to extend their value capture capabilities into original markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

    Other market leaders are moreover poignant aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on edifice 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 ante with their Predix platform (see “GE’S ample ante 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 convivial 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 traffic 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 animate 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 simulated 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 subsist 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 original 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 necessary is that these AI leaders are seeking to avoid technology and architecture lock-in. They Enjoy watched outmoded school organizations struggle with proprietary software packages that took months if not years for upgrades and bug fixes, while paying a oppressive 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 remarkable AI War.

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

    However, organizations Enjoy struggled to monetize this wealth of data. Enter simulated intelligence.

    Figure 2: Fueling the Insatiable Appetite for Data

    More Data = Better AI. simulated intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional traffic 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 profound Learning application by virtue of owning the world’s largest repository of photos.

    To illustrate the symbiotic relationship between ample data and AI, let’s recognize at autonomous vehicles (AV). AV require colossal quantities of data to feed the AV machine learning algorithms. It would capture tens of thousands of hours of real-world driving data across a variety of driving scenarios to educate cars how to navigate on their own. To address this data volume problem, AV companies are using the video game “Grand Theft Auto” to befriend generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the actual 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 edifice the machine learning and simulated intelligence models (see “Data Lake traffic Model Maturity Index” for more details on data lake traffic 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 subsist used simultaneously at near-zero marginal cost across an sempiternal traffic and operational use cases. Understanding the proper economic value of the organization’s data can befriend to prioritize technology and traffic 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 traffic Strategies with Data Science,” AI winners will ultimately subsist those organizations that are the most effectual at leveraging data and analytics to power their traffic models (see pattern 3).

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

    Ultimately, AI winners will master three key characteristics:

  • Focus on Value Capture by identifying, validating and prioritizing the organization’s key traffic 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 ample Data and the Data Lake to exploit the unique economic value of the data and analytic digital assets (see “Data Lake traffic Model Maturity Index”).
  • So in conclusion, let’s Enjoy some fun with this blog and assume outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

  • What would subsist the traffic model ramifications to GE if they were to open source Predix and offer Predix training to universities and third party developers?
  • What would subsist the traffic model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?
  • What would subsist the traffic model ramifications if IBM moved out of the technology purveyor traffic 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 remarkable AI war, now is not the time for organizations to subsist diffident or to cling to old, outdated traffic 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 remarkable simulated Intelligence (AI) War! appeared first on InFocus Blog | Dell EMC Services.

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    Publisher : McGraw-Hill (Jan 2018)
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    ISBN13 : 9780078021787
    Our ISBN10 : 1260084493
    Our ISBN13 : 9781260084498
    Subject : Business & Economics
    Price : $65.00
    Understanding BusinessUnderstanding Business
    By William G Nickels, James McHugh, Susan McHugh
    Publisher : McGraw-Hill (Feb 2018)
    ISBN10 : 126021110X
    ISBN13 : 9781260211108
    Our ISBN10 : 126009233X
    Our ISBN13 : 9781260092332
    Subject : Business & Economics
    Price : $75.00
    Understanding BusinessUnderstanding Business
    By William Nickels, James McHugh, Susan McHugh
    Publisher : McGraw-Hill (May 2018)
    ISBN10 : 1260682137
    ISBN13 : 9781260682137
    Our ISBN10 : 126009233X
    Our ISBN13 : 9781260092332
    Subject : Business & Economics
    Price : $80.00
    Understanding BusinessUnderstanding Business
    By William Nickels, James McHugh, Susan McHugh
    Publisher : McGraw-Hill (Jan 2018)
    ISBN10 : 1260277143
    ISBN13 : 9781260277142
    Our ISBN10 : 126009233X
    Our ISBN13 : 9781260092332
    Subject : Business & Economics
    Price : $77.00
    Understanding BusinessUnderstanding Business
    By William Nickels, James McHugh, Susan McHugh
    Publisher : McGraw-Hill (Jan 2018)
    ISBN10 : 1259929434
    ISBN13 : 9781259929434
    Our ISBN10 : 126009233X
    Our ISBN13 : 9781260092332
    Subject : Business & Economics
    Price : $76.00
    000-N04000-N04
    By Peter W. Cardon
    Publisher : McGraw-Hill (Jan 2017)
    ISBN10 : 1260128474
    ISBN13 : 9781260128475
    Our ISBN10 : 1259921883
    Our ISBN13 : 9781259921889
    Subject : Business & Economics, Communication & Media
    Price : $39.00
    000-N04000-N04
    By Peter Cardon
    Publisher : McGraw-Hill (Feb 2017)
    ISBN10 : 1260147150
    ISBN13 : 9781260147155
    Our ISBN10 : 1259921883
    Our ISBN13 : 9781259921889
    Subject : Business & Economics, Communication & Media
    Price : $64.00
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