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A2010-578 Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

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A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Test Code : A2010-578
Test appellation : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Vendor appellation : IBM
: 120 true Questions

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IBM Assess: Fundamentals of Applying

IBM: Incessant Buyback machine | killexams.com true Questions and Pass4sure dumps

No result found, try new key phrase!We investigate the dividend safeguard and give ... We, besides the fact that children, deem those aren't the basics to focal point on. They do their case beneath. IBM's revenues believe rigor staying flat, and now believe posted ...

listed here are the biggest analyst calls of the day: Apple, Chipotle, IBM, Xerox, U.S. steel & extra | killexams.com true Questions and Pass4sure dumps

credit Suisse stated Apple's iPhone revenue are in a, "intricate," spot and that it'll grasp time to peer how its provider enterprise pans out.

"As Apple's iPhone matures, the company is asking to seriously change itself birthright into a extra routine, bigger-growth, and finally higher-cost traffic because it pushes to increasingly monetize its expansive 900mn iPhone build in base. They admire the edge in the shift to services, which they prognosticate will attain $65bn in salary by passage of FY21, however deem it's going to grasp time for that view to play out. close-term upside from here likely requires the varied to re-expense larger; investor perception of Apple as a hardware-centric traffic can exist tough to quiver towards a backdrop of double-digit iPhone revenue declines (CSe -eleven% y/y in CY19), in their view. With the top off 40% from its Jan low and nearby a top distinctive (15x CY20 EPS), they remain on the sidelines expecting a much better entry point and/or line-of-sight to expansive capabilities-led upside to assemble away of the historic valuation latitude."

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Social company application Market Is Booming global | IBM, Oracle, SAP, SAS Institute, Adobe programs | killexams.com true Questions and Pass4sure dumps

Apr 08, 2019 (Heraldkeeper via COMTEX) -- HTF MI launched a brand new market study on world companionable traffic utility Market with one hundred+ market facts Tables, Pie Chat, Graphs & Figures unfold through Pages and light to grasp note exact evaluation. At present, the market is establishing its presence. The analysis report items an entire assessment of the Market and contains a future style, existing growth factors, attentive opinions, statistics, and industry validated market information. The analysis study provides estimates for international companionable traffic application Forecast till 2025*. Some are the notable thing players taken below insurance for this examine are IBM, Oracle, SAP, SAS Institute, Adobe systems, Attensity neighborhood, Beevolve, Clarabridge, Crimson Hexagon, Evolve24, Google, HP, Kapow utility/ Kofax, Lithium applied sciences, NetBase solutions, Radian6/Salesforce, Sysomos & Cision.

click on to assemble global companionable enterprise utility Market analysis sample PDF replica birthright here @: https://www.htfmarketreport.com/pattern-file/1316059-international-social-company-application-market-1

vital facets which are under providing & key highlights of the record :

1) What All agencies are currently profiled within the document?Following are list of avid gamers which are currently profiled within the the record “IBM, Oracle, SAP, SAS Institute, Adobe methods, Attensity community, Beevolve, Clarabridge, Crimson Hexagon, Evolve24, Google, HP, Kapow utility/ Kofax, Lithium technologies, NetBase options, Radian6/Salesforce, Sysomos & Cision”

** record of companies outlined can likewise scope in the ultimate record discipline to identify exchange / Merger and so forth.

2) can they add or profiled new company as per their need?yes, they are able to add or profile new company as per client necessity in the record. closing affirmation to exist offered by using analysis crew depending upon the rigor of survey.** statistics availability can exist validated by means of research in case of privately held enterprise. Upto 3 avid gamers may likewise exist delivered at no added can charge.

three) What All regional segmentation coated? Can certain nation of activity exist introduced?at present, research record gives particular consideration and headquarters of attention on following regions:u.s., Europe, China, Japan, Southeast Asia, India & valuable & South the usa** One nation of selected interest may likewise exist blanketed at no introduced can charge. For inclusion of greater regional segment quote might likewise fluctuate.

four) Can inclusion of additional Segmentation / Market breakdown is viable?sure, inclusion of additional segmentation / Market breakdown is viable topic to records availability and rigor of survey. despite the fact a minute requirement has to exist shared with their analysis earlier than giving closing affirmation to customer.

** based upon the requirement the deliverable time and quote will range.

Enquire for customization in document @ https://www.htfmarketreport.com/enquiry-earlier than-purchase/1316059-world-social-company-software-market-1

to grasp international companionable company utility market dynamics on the earth primarily, the worldwide companionable company software market is analyzed across most notable global regions. HTF MI likewise gives custom-made particular regional and nation-level studies for birthright here areas.

? North america: united states, Canada, and Mexico.? South & crucial the us: Argentina, Chile, and Brazil.? core East & Africa: Saudi Arabia, UAE, Turkey, Egypt and South Africa.? Europe: UK, France, Italy, Germany, Spain, and Russia.? Asia-Pacific: India, China, Japan, South Korea, Indonesia, Singapore, and Australia.

2-page profiles for 10+ main manufacturers and 10+ leading agents is included, along with three years fiscal heritage as an instance the recent performance of the market. Revised and up-to-date discussion for 2018 of key macro and micro market influences impacting the sphere are provided with a notion-upsetting qualitative observation on future alternatives and threats. This report combines the better of each statistically principal quantitative statistics from the trade, coupled with imperative and insightful qualitative remark and analysis.

world companionable traffic software Product forms In-Depth: , On-premises & Cloud

global companionable enterprise utility foremost applications/end users: SMEs, gigantic companies & executive organizations

Geographical analysis: u.s., Europe, China, Japan, Southeast Asia, India & germane & South the usa

as a passage to assemble a deeper view of Market size, competitive landscape is equipped i.e. profits (Million USD) by using players (2013-2018), profits Market partake (%) by means of avid gamers (2013-2018) and extra a qualitative analysis is made towards market attention cost, product/service adjustments, new entrants and the technological tendencies in future.

aggressive evaluation:the notable thing players are incredibly focusing innovation in creation applied sciences to enrich effectivity and shelf existence. The surest long-time era multiply opportunities for this sector will likewise exist captured by ensuring ongoing procedure advancements and fiscal flexibility to invest in the choicest suggestions. enterprise profile component to players equivalent to IBM, Oracle, SAP, SAS Institute, Adobe systems, Attensity neighborhood, Beevolve, Clarabridge, Crimson Hexagon, Evolve24, Google, HP, Kapow application/ Kofax, Lithium technologies, NetBase solutions, Radian6/Salesforce, Sysomos & Cision contains its simple guidance fancy criminal name, site, headquarters, its market position, dilapidated background and proper 5 closest competitors by means of Market capitalization / income along with contact counsel. every player/ manufacturer earnings figures, multiply rate and shameful income margin is provided in handy to grasp into account tabular layout for past 5 years and a divide zone on recent construction fancy mergers, acquisition or any new product/provider launch etc.

purchase plenary reproduction international companionable traffic utility report 2018 @ https://www.htfmarketreport.com/buy-now?structure=1&document=1316059

in this examine, the years considered to assay the market dimension of world companionable traffic utility are as follows:history yr: 2013-2017Base yr: 2017Estimated 12 months: 2018Forecast yr 2018 to 2025

Key Stakeholders/international experiences:Social traffic utility ManufacturersSocial traffic application Distributors/traders/WholesalersSocial company utility Subcomponent ManufacturersIndustry AssociationDownstream vendors

Browse for plenary file at @: https://www.htfmarketreport.com/reviews/1316059-global-social-enterprise-application-market-1

precise Numbers & In-Depth evaluation, company opportunities, Market dimension Estimation accessible in plenary record.

Thanks for reading this article, that you could additionally assemble individual chapter shrewd partake or region smart document version fancy North the united states, Europe or Asia.


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Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

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Citrix Certification Guide: Overview and Career Paths | killexams.com true questions and Pass4sure dumps

Citrix is a cloud-computing company that offers solutions for mobility, desktop virtualization, cloud networking, cloud platforms, collaboration and data sharing that enable users to travail securely from anywhere on a variety of devices. The company's products descend into five main categories: secure digital workspace, app virtualization and VDI, Enterprise Mobility Management (EMM), file sync and sharing, and networking. favorite Citrix products comprehend XenApp, XenDesktop, XenMobile, NetScaler and ShareFile. More than 400,000 organizations and more than 100 million users sprint Citrix XenApp and XenDesktop products globally.

Citrix Systems started in 1989 and is headquartered in Fort Lauderdale, Florida. Today, the company offers a number of professional certifications that are recognized globally.

Citrix Certification Program Overview

Citrix certifications are solutions-based to reflect the needs of organizations and IT professionals. The three main Citrix certification topic areas are Desktop Virtualization, Networking and Enterprise Mobility Management. Citrix likewise offers credentials for Enterprise File Synchronization and Sharing. Last, but not least, Citrix Cloud certifications were introduced in 2018. 

Within the virtualization, networking and mobility certification topic areas, Citrix credentials are available at associate, professional and expert levels. These levels reflect the skills, erudition and experience required of professionals who earn such Citrix certifications. Associate-level exams typically cost $200, whereas professional- and expert-level exams cost $300.

Here’s a summary of current certifications available from Citrix:

  • Citrix Certified Associate – Virtualization (CCA-V)
  • Citrix Certified Professional – Virtualization (CCP-V)
  • Citrix Certified Expert – Virtualization (CCE-V)
  • Citrix Certified Associate – Networking (CCA-N)
  • Citrix Certified Professional – Networking (CCP-N)
  • Citrix NetScaler SD-WAN Certified (CC-SDWAN)
  • Citrix Certified Professional – Mobility (CCP-M)
  • Citrix ShareFile Certified (CC-SHAREFILE)
  • XenApp and XenDesktop Service on Citrix Cloud Certified (CC-XAD-CC)
  • XenApp and XenDesktop Service Integration with Microsoft Azure Certified (CC-XAD-MA)
  • Citrix Virtualization Certifications CCA-V: Citrix Certified Associate – Virtualization

    The Citrix Certified Associate – Virtualization (CCA-V) certification is for operators and administrators expert in installing, configuring, managing, maintaining, monitoring and troubleshooting XenApp and XenDesktop 7.15 solutions. There are no prereqs, but you must pass exam 1Y0-203 to earn this certification.

    The Citrix XenApp and XenDesktop 7.15 Administration exam (1Y0-203) covers 14 topics including an architectural overview, installation, provisioning, access, configuring, securing, monitoring, troubleshooting and more in a XenApp and XenDesktop 7.15 environment.

    CCP-V: Citrix Certified Professional – Virtualization

    The Citrix Certified Professional – Virtualization (CCP-V) certification is for engineers and consultants who manage and champion tangled  XenApp and XenDesktop 7.15 provisioning environments and solutions. To earn the CCP-V certification, you must believe attained CCA-V certification and pass exam 1Y0-311.

    The Citrix XenApp and XenDesktop 7.15 LTSR Advanced Administrator exam (1Y0-311) covers 14 topics including advanced architecture, Virtual Desktop Agents (VDA), HDX and multimedia, provisioning (infrastructure, services, integrating and supporting), layering, vDisk streaming and more.

    CCE-V: Citrix Certified Expert – Virtualization

    The Citrix Certified Expert – Virtualization (CCE-V) certification is geared toward solution designers who may exist architects, engineers or consultants, and who assess and design comprehensive XenApp and XenDesktop 7.15 or XenDesktop 7.6 solutions. To earn the CCE-V certification you must believe already earned the CCA-V and CCP-V certifications and pass either of the 1Y0-402 or 1Y0-401 exams.

    The Designing Citrix XenDesktop 7.6 Solutions Exam (1Y0-401) is divided into six sections, which cover designing and documenting infrastructure and security infrastructure, designing integrated virtualization solutions, analyzing existing environments, conducting planning and risk assessments, and assessing existing environments. The Citrix XenApp and XenDesktop 7.15 Assessment, Design and Advanced Configurations exam (1Y0-402) covers 11 topics including design methodology, catastrophe recovery, tall availability, access and security, store and resource design and more. 

    Required Exams

    Citrix Certified Associate – Virtualization (CCA-V)

    Citrix XenApp and XenDesktop 7.15 Administration (1Y0-203)

    Citrix Certified Professional – Virtualization (CCP-V)

    Citrix XenApp and XenDesktop 7.15 LTSR Advanced Administrator (1Y0-311)

    Citrix Certified Expert – Virtualization (CCE-V)

    Designing Citrix XenDesktop 7.6 Solutions (1Y0-401)

    OR

    Citrix XenApp and XenDesktop 7.15 Assessment, Design and Advanced Configurations  (1Y0-402)

    Citrix Virtualization Certification Ladder

    Citrix's Virtualization certifications build upon one another. Once you earn the Citrix Certified Associate (CCA-V) certification (exam 1Y0-203), you can grasp the Citrix Certified Professional (CCP-V) exam (1Y0-311). Once you've passed that exam, you can grasp either Citrix Certified Expert (CCE-V) exam: (1Y0-401) or (1Y0-402).

    These Virtualization certifications are sound for three years from the date you obtain them. If a new exam is released during this time, you must pass the latest exam for your level before the previous exam’s expiration date to maintain certification currency.

    Citrix Networking Certifications CCA-N: Citrix Certified Associate – Networking

    If you're a network or systems administrator who is an expert in app and desktop virtualization, and you want to expand your expertise to include NetScaler Gateway 12 for data, app and desktop solutions, the Citrix Certified Associate - Networking (CCA-N) certification is for you. Training is for administrators who believe limited or no previous NetScaler experience.

    You must pass either the Citrix NetScaler 12 Essentials and Unified Gateway (1Y0-230) or Citrix NetScaler 12 Essentials and Traffic Management (1Y0-240) exam to earn the credential. Exam 1Y0-230 targets basic networking, Unified Gateway (including configuration), integration, managing client connections, load balancing, NetScaler platforms, AppExpert Expressions and policies, SSL offload, authentication and security. Exam 1Y0-240 covers basic networking, NetScaler platforms, tall availability, load balancing, SSL offing, security, troubleshooting, default policies, URL transformation, switching, optimization and load balancing for global servers.   

    CCP-N: Citrix Certified Professional – Networking

    The Citrix Certified Professional - Networking (CCP-N) certification is aimed toward network engineers and people with some networking experience who implement, manage and optimize Citrix NetScaler Application Firewall in Citrix NetScalrer 12 environments, manage and optimize NetScaler application traffic, and manage NetScaler environments using NetScaler Management and Analytics System (NMAS).

    The five-day training Citrix NetScaler 12.x Advanced Topics – Security, Management and Optimization  (CNS-320) will capitalize you result just that. It's for individuals with some understanding of NetScaler Essentials, traffic management and Unified Gateway.

    In addition to earning the CCA-N, you must pass the Citrix NetScaler Advanced Topics – Security, Management and Optimization (1Y0-304) exam to achieve the CCP-N certification. The exam targets firewalls, expressions, attacks, optimization (tuning and front-end), caching, monitoring and troubleshooting, monitoring and managing NetScaler configurations, instances and weblogging.  

    Citrix NetScaler SD-WAN Certified (CC-SDWAN)

    The newest addition to the Citrix cert portfolio is the Citrix NetScaler SD-WAN (CC-SDWAN), which is for professionals who configure the technology and ameliorate its performance and reliability.

    To prepare for the credential, Citrix offers a four-module NetScaler SD-WAN hands-on workshop training course. Candidates must pass an exam at the conclude of each module with a score of 80 percent or higher. The modules are SD-WAN Overview, SD-WAN Architecture, SD-WAN trait of Service, and SD-WAN Deployment Modes and Configuration. Additional modules that focus on SD-WAN 9.0, 9.1 and 9.2 features are included in the training. Citrix awards the certification only after passing All modules.

     

    Required Exams

    Citrix Certified Associate – Networking (CCA-N)

    Citrix NetScaler 12 Essentials and Unified Gateway (1Y0-230)

    OR

    Citrix NetScaler 12 Essentials and Traffic Management (1Y0-240)

    Citrix Certified Professional – Networking (CCP-N)

    Citrix NetScaler Advanced Topics – Security, Management and Optimization (1Y0-304)

    Citrix NetScaler SD-WAN Certified (CC-SDWAN)

    Four exams at the conclude of each NetScaler SD-WAN training module

    Citrix Networking Certification Ladder

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    XenApp and XenDesktop Service on Citrix Cloud Certified (CC-XAD-CC)

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    Attend one of the following training courses:

    Moving to the XenApp and XenDesktop Service on Citrix Cloud and Microsoft Azure (CXD-252)

    OR

    Citrix XenApp and XenDesktop Service on Microsoft Azure (CXD-251)

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    Resilience and efficiency in transportation networks | killexams.com true questions and Pass4sure dumps

    Abstract

    Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under usual conditions to ameliorate the efficiency of urban road systems, analytic champion for investments that ameliorate resilience (defined as system recovery from additional disruptions) is silent scarce. In this effort, they limn paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. They built road networks for 40 of the urban areas defined by the U.S. Census Bureau. They developed and calibrated a model to evaluate traffic delays using link loads. The loads may exist regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the tolerable annual dilatory per peak-period auto commuter, and modeled results were establish to exist nearby to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was establish to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under usual conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should exist considered explicitly in roadway project selection and warrant investment opportunities related to catastrophe and other disruptions.

    INTRODUCTION

    Existing roadway design standards emphasize the efficient movement of vehicles through a transportation network (1–4). Efficiency in this context may comprehend identification of the shortest or fastest route (1, 5–7), or the route that minimizes congestion (8). It is the primary criterion on which road networks are modeled and design alternatives are considered (6, 7, 9, 10). The Texas A&M Transportation Institute defines and reports traffic dilatory in urban areas as the annual dilatory per auto commuter (11). Other studies define efficiency as dilatory for the individual driver in terms of time spent poignant or stopped (7), or connote travel time between All origin-destination pairs in the network (9). However, as the experience of any motorist in great American cities can attest, conditions beyond the scope of the roadway design, including congestion, accidents, sinister weather, construction, and special events (for example, a marathon race), can cause costly delays and frustrating inefficiencies that result in fuel waste, infrastructure deterioration, and increased pollution (12, 13). Evaluating road networks based only on efficiency under usual operating conditions results in diminutive to no information about how the system performs under suboptimal or disrupted conditions.

    Infrastructure systems that exhibit adaptive response to stress are typically characterized as resilient (14–21). Given the essential role of transportation in emergency response, provision of essential services, and economic well-being, the resilience of roadway networks has received increasing policy attention. Nonetheless, scholars believe yet to converge on a shared understanding of resilience suitable to pilot design, operation, and reconstruction of roadway networks. Although resilience in infrastructure systems is characterized as a multidimensional concept (22, 23), in many engineering and civil infrastructure implementations, resilience is defined as the talent of a system to prepare for, absorb, recover from, and conform to disturbances (16). Specific to transportation, resilience has been defined as “the talent of the system to maintain its demonstrated level of service or to restore itself to that level of service in a specified timeframe” (24). Others narrate transportation resilience as simply the talent of a system to minimize operational loss (25) or expend the term synonymously with robustness, redundancy, reliability, or vulnerability (26–28).

    Current efforts in transportation resilience research believe focused on framework evolution and quantification methods. These efforts comprehend the specification of resilience indicators, such as total traffic dilatory (24), economic loss (29), post-disaster maximum current (30), and autonomous system components (31). Practical concerns with this type of resilience evaluation are that it relies on uncertain performance data and often omits indicators that are unquantifiable (19). Other resilience approaches apply traffic network modeling to identify locations for censorious buildings (for example, hospitals and fire stations) (32), minimize trip distance for individual passengers (33), and minimize travel time across the system (12). One drawback of existing network resilience methods is that they are data-intensive, often requiring limited information about resources for unusual road system repair (26, 28) or network conduct following a disruptive event (34). Moreover, existing resilience quantification approaches lack calibration and testing across a scope of transportation systems. Because many disruptive events, and their associated consequences, are difficult to predict, resilient road systems must exist characterized and evaluated by the capacity to conform to a variety of different stress scenarios. Partly because of these obstacles, joint consideration of efficiency and resilience has yet to exist implemented for transportation networks.

    Here, they study the interconnections between resilience and efficiency (20) among road transportation networks in 40 major U.S. cities. They develop an urban roadway efficiency model, calibrate it on the basis of the observed data (11) of annual dilatory per peak-period auto commuter, and apply the model to calculate efficiency in 40 cities. Then, they model traffic response to random roadway disruptions and recalculate expected delays to determine the sensitivity of each city to loss of roadway linkages. The results may betray notable considerations for assessing proposals for improvement of roadway infrastructure that maintain efficiency under stress conditions.

    METHODS

    The Methods section appears here to capitalize clarify the subsequent sections. To develop the urban roadway efficiency model, they defined the urban zone boundaries, constructed the road networks, and evaluated the population density within cities using the Census Bureau data sets (35, 36) and OpenStreetMap (OSM) data sets (37). They relied on these data to assess commuter patterns, which they used to measure efficiency and resilience of road networks.

    Alternative approaches to transportation believe been offered and comprehend those based on percolation theory and cascading failures (38–40), human mobility pattern studies (41–43), queueing (44, 45), and the expend of historical data to prognosticate traffic. They review these approaches in the Supplementary Materials and note that the main capitalize of their model is that it relies solely on readily available public data, rather than on particular data sets that may or may not exist practical to obtain for any particular region. The model’s algorithmic simplicity allows us to deem spatial topologies of cities in tall resolution including tens of thousands of nodes and links. They did not create a more accurate transportation model than the existing ones, but they were able to obtain measurable characteristics of transportation systems (average delays) using their model.

    Geospatial boundaries and population density

    To define geospatial boundaries for the transportation infrastructure networks, they used the U.S. Census Bureau geospatial data set (35) for urban areas—densely developed residential, commercial, and other nonresidential areas (46). They approximated the exact urban zone polygon with a simplified manually drawn one (Fig. 1A) and included All roadways within 40 km (25 miles) of it in the network. For each of the links, they calculated its length on the basis of the polyline defining the link and assigned a number of lanes m and the FFSs (see the Supplementary Materials).

    Fig. 1 Definition of urban areas and assignment of nodes’ population.

    (A) Boston, MA-NH-RI urban zone as defined by the U.S. Census Bureau shapefiles (gray background). To simplify the model and the algorithms calculating the distance from network nodes to the city boundary, they approximate each of the urban areas shapefiles with a obscene manually drawn polygon (pink outline). (B) Assignment of the number of people departing from each of the network nodes. Population distribution (color polygons; red corresponds to higher population density), Voronoi polygons (black outline), and network nodes (dots) in Downtown Boston.

    We next estimated population in vicinity of each intersection i using the Census Tract data (36). To this end, they split the map into Voronoi cells centered at intersections and then evaluated the population of each cell Ni asEmbedded Image

    Embedded Image

    (1)

    Above, Nt is the population of Census Tract t, and Pi and Pt are the polygons of the cell and the tract, respectively (Fig. 1B and table S2).

    Transportation model

    We built on the gravity model to generate commuting patterns. The gravity model (47) is a classical model for trip distribution assignment and is extensively adopted in most metropolitan planning and statewide travel exact models in the United States (48–51). Other trip distribution models include, for example, destination altenative models (52, 53). However, these models are not as widely used in great scale, because the minute data required by these models are frequently unavailable (48).

    We assumed that (i) the current of commuters from inception region o to destination region d is proportional to the population at the destination Nd and that (ii) the current of commuters depends on the distance xod between the inception and destination and is given by a distance factor, P(xod). Using these assumptions, they assessed the fraction of individuals commuting from region o to destination region d, fod, asEmbedded Image

    Embedded Image

    (2)

    Then, the commuter current from inception region o to destination region d isEmbedded Image

    Embedded Image

    (3)

    Although individual driving habits may vary (54), they assumed that All drivers tended to optimize their commute paths such that their travel time was minimized. This assumption allowed us to calculate commute paths for every origin-destination pair using inferred FFSs. To calculate commuter flows between All pairs of intersections, they estimated distances xod as the distance of the shortest time path from o to d. Furthermore, in situation of the distance factor P(xod), they used the distribution of trip lengths from the U.S. Federal Highway Administration National Household Travel Survey (55, 56), which they approximated with the exponential office (Fig. 2A and table S3).

    Fig. 2 Model details.

    (A) Distance factor P(xod) (Eq. 2) of trips given the distance between nodes (solid line) and the statistical data (bars). (B) Dependency of accelerate on density for V = 100 km/hour.

    Next, they defined the commuter load on each road segment asEmbedded Image

    Embedded Image

    (4)where θod(ij) is a binary variable equal to 0 when the link ij is not on the shortest path connecting nodes o and d, and 1 otherwise. Note that in Eq. 4, they only considered origins that were not farther than 30 km from the urban zone boundary polygon. The nodes farther than 30 km from the boundary were only used as destinations to evaluate the fraction of commuters not going toward the urban zone (Eq. 2).

    Because most commuters travel during peak periods, commuter loads Lij can exist regarded as traffic-based centrality measures estimating the number of individuals using corresponding road segments. Then, the cumulative time lost by All commuters isEmbedded Image

    Embedded Image

    (5)where Vij and vij are, respectively, the FFS and the actual traffic accelerate along the ij road segment, lij is its length, l0 is the length correction due to traffic signals, and β is the proportionality coefficient identical for All urban areas. The summation in Eq. 5 includes only links, whose origins and destinations are within the boundary polygon. A similar equation was obtained for the poignant dilatory in the study of Jiang and Adeli (45), where the authors looked at the dilatory induced from road repairs.

    The actual traffic accelerate vij depends on many factors including the accelerate limit, the number of drivers on the road, and road conditions. Although there exist a number of approaches to assay actual traffic accelerate (57, 58), they chose to expend the Daganzo model (59) to derive the traffic speed, as shown in the Supplementary MaterialsEmbedded Image

    Embedded Image

    (6)where vmin is the minimum accelerate in the traffic, vveh is the correction for the finite size of the car, and α is the proportionality coefficient (Fig. 2B).

    Efficiency and resilience metrics

    We measured efficiency as the tolerable annual dilatory per peak-period auto commuter. In practice, lower dilatory means higher efficiency. There are multiple ways to map from delays to efficiency, such as taking the inverse values of delays, taking negative values of delays, etc. To avoid ambiguity and facilitate the interpretation of results, they used the delays themselves to quantify the transportation efficiency of urban areas.

    We operationalized resilience through the change in traffic delays relative to stress, which is modeled as loss or impairment of roadway linkages. Looking at resilience from the network science perspective, they focused on topological features of cities, rather than on recovery resources available. Sterbenz et al. (60) evaluated a network’s resilience as a scope of operational conditions for which it stays in the acceptable service region and highlighted that remediation mechanisms drive the operational state toward improvement. They are studying how availability of alternate routes helps remediate the consequences of the initial disruption to the network. In the traffic context, the immediate repercussion of a given physical disruption (and the time for it to unfold) in terms of closing lanes or reducing accelerate limits on affected roads will not vary much from network to network, although the number and type of these disruptions will. Likewise, the accelerate of restoring plenary functionality (through action in the physical domain) is not so much contingent on the road network as it is on the nature of the disruption (snow versus earthquake versus flood) and the resources that the city allocates to such repair. The level of functionality that these repairs achieve ought to exist the plenary predisruption functionality, that is, eventually All roads can exist fully cleared or restored. However, the immediate loss of office for a given traffic current can very quickly exist partially recovered after a disruption by action in the information domain, namely, rerouting of traffic. From the new constant state at that level of functionality, plenary functionality is gradually restored. Thus, their model proxies for resilience and is calibrated against the data that proxy for efficiency. At the identical time, they note that to fully capture resilience characteristics of a transportation system, it is required to analyze recovery resources available and the effectiveness of coordination between the germane authorities. Lower additional dilatory corresponds to higher resilience, but using the identical reasoning that they had for efficiency, they quantified resilience through additional delays.

    RESULTS Efficiency

    Together, their traffic model has three parameters (proportionality coefficient α, minimum accelerate vmin, and finite vehicle size correction vveh) and is summarized in Eqs. 5 and 6. Given parameter values of the model, one can assay the total dilatory incurred by All commuters in any given suburban zone or, equivalently, the tolerable dilatory per commuter. They grasp vveh = 9 km/hour and vmin = 5 km/hour and calibrate the model to determine the value of α to match the true data on the annual tolerable dilatory per peak-period auto commuter provided by the Urban Mobility Scorecard (11).

    We divide the 40 urban areas into two equally sized groups for model calibration and validation, respectively. They believe establish that for the 20 urban areas used for calibration, the R-squared coefficient took values in the scope (−0.01 to 0.83) (Fig. 3 and Supplementary Materials). This allows us to set model parameters α and β (see Methods) as follows: α = 4.30 × 104 hour−1 and β = 10.59. These values correspond to the Pearson coefficient of 0.91 (P = 2.17 × 10−8).

    Fig. 3 Modeled and observed delays in 40 urban areas.

    Pearson correlation coefficients and P values between observed and modeled delays are (0.91, 2.17 × 10−8) for the 20 cities used to calibrate the model and (0.63, 3.00 × 10−3) for the 20 cities used to validate the model. Observed delays were taken from the Texas A&M Transportation Institute Urban Mobility Scorecard (11).

    To validate the model, they assay travel delays in 20 different urban areas. As seen from Fig. 3, the estimated travel delays are significantly correlated (R = 0.63, P = 3.00 × 10−3) with actual dilatory times (11), validating the transportation model. pattern 4 is a Google Maps representation of true and modeled results for Los Angeles and San Francisco. Road conditions under real, tolerable traffic patterns at 8 a.m. provided by Google Maps are in Fig. 4 (A and D). Modeled conditions are given for comparison in Fig. 4 (B and E). Finally, Fig. 4 (C and F) shows the new, modeled traffic patterns that result from redistribution of travel in response to a disruption of 5% of the links.

    Fig. 4 Traffic distributions.

    Typical congestion at 8 a.m. for Los Angeles (top) and San Francisco (bottom) as given by Google Maps (A and D), modeled with no disruptions (B and E), and modeled with a 5% link disruption (C and F). Notably, in Los Angeles, the disruption results in traffic redistribution to smaller roads, whereas in San Francisco, it results in increased congestion along the major highways.

    Resilience

    Our approach to model stress is inspired by percolation theory. For every independent simulation of stress, they select a finite fraction of affected road segments r at random, with the probability of failure proportional to segment length. They collect statistics for 20 realizations of the percolation. On failed segments, free-flow speeds (FFSs) are reduced to 1 km/hour (representing near-total loss), and loads L and traffic delays are then recalculated using the updated FFSs. Low-stress scenarios (r < 0.1) might exist caused by accidents or construction. Larger disruptions might occur during power failures that disrupt traffic signals or severe flooding that makes many roadways nearly impassable. Finally, widespread stress might exist caused by snow, ice, or dust storms that move nearly the entire roadway system. pattern 5 displays the analysis of dilatory times in six representative urban areas for the plenary spectrum of adverse event severities, r ⋲ [0; 1]. In addition, fig. S5 shows the results for All urban areas. Some routes within a unique urban zone experience longer delays than others. The inset of Fig. 5 shows the dilatory distribution for both Los Angeles, which is narrowly clustered, and Boston, where greater variability between roadways is evident. Traffic dilatory times grow rapidly as r increases and achieve saturation (all routes poignant at 1 km/hour) as r approaches 1. They determine the most resilient urban transportation network to exist Salt Lake City, UT, whereas the least resilient among the 40 metropolitans is shown to exist Washington, DC.

    Fig. 5 Dependency of the additional dilatory on the severity of the links disruption for six representative urban areas.

    Error bars point to connote values ± SD. The inset shows distribution densities for two selected urban areas for 1000 realizations of 5% disruption. Note that San Francisco’s unique topology makes it susceptible to failures of a tiny number of discrete roadways, and this produces an anomalous repercussion at 5 to 15% disruption.

    Figure 6 shows both the efficiency (in blue) and resilience response (additional delays due to 5% link disruption, in orange) for the 40 urban areas modeled. Some cities with tall efficiency under usual operating conditions (that is, low delays) nevertheless exhibit low resilience (that is, a keen multiply in traffic delays) under stress. Virginia Beach, VA; Providence, RI; and Jacksonville, FL All descend into this category of urban areas in which traffic operates well under ordinary circumstances but rapidly become snarled under mild stress. On the other hand, Los Angeles is notorious for traffic delays under All conditions—yet minor stress levels result in diminutive degradation of efficiency. By contrast, usual traffic delays in San Francisco are comparable to Los Angeles, but mild stress in San Francisco results in great increases in additional delays. These examples witness that resilience (that is, additional dilatory response to stress) is independent of usual operating efficiency.

    Fig. 6 Comparison of resilience and efficiency metrics.

    Annual repercussion of 5% disruption (additional delay) has a low correlation with usual annual dilatory per peak-period auto commuter (delay). Pearson R = 0.49, P = 1.18 × 10−3.

    DISCUSSION

    The disturbances affecting the road infrastructure are often complex, and their repercussion on the structure and office of roadway systems may exist unknown (28, 31). These disturbances might exist natural and irregular, such as distributed road closures caused by an earthquake or homogeneous vehicle slowing down because of a snowstorm. The disturbances might likewise exist anthropogenic and intentional, such as a street impartial or marathon race. Whatever the disturbance, the results of this analysis allow several meaningful inferences to exist made that may believe notable implications for highway transportation policy. The first is that resilience and efficiency limn different aspects related to the nature of transportation systems; they are not correlated and should exist considered jointly as complementary characteristics of roadway networks.

    Second, there are characteristic differences in the resilience of different urban areas, and these differences are persistent at mild, medium, or widespread levels of stress (Fig. 5). Except for San Francisco, CA, which is the most breakable of All cities represented in Fig. 5 at stress levels r < 20% but then surpassed by Boston, MA and Washington, DC, the rank ordering of urban zone resilience is insensitive to stress levels. That is, cities that exhibit relatively low resilience under mild stress are the identical cities that exhibit low levels of resilience (relative to peers) under widespread roadway impairment. This suggests that the characteristics that impart resilience (such as availability or alternate routes through redundancy of links) are protective against both the intermittent outages caused by occasional car crashes and those caused by snow and ice storms. For cities without resilience, a widespread hazard such as snow may lead to a cascade of conditions (for example, crashes) that rapidly deteriorate into gridlock. This was exactly the case for Washington, DC 20 January 2016 under only 2.5 × 10−2 m or 2.5 cm of snow (61), and for Atlanta, GA 2 years earlier, which experienced 5.1 × 10−2 m or 5.1 cm of snow in the middle of the day that resulted in traffic jams that took days to disentangle (62). Whereas favorite explanations of these traffic catastrophes focus on the failure of roadway managers to prepare plows and emergency response equipment, Fig. 5 suggests that cities with similar climates (Memphis, TN and Richmond, VA) are less likely to exist affected, regardless of the availability of plow or sand trucks.

    The third inference follows from Fig. 6, which suggests that urban areas that do capital investments to reduce traffic delays under usual operating conditions may nevertheless exist vulnerable to traffic delays under mild stress conditions. Because these stressors are inevitable, whether from crashes, construction, special events, extreme weather, equipment malfunctions, or even deliberate attack, investment strategies that prioritize reduction of usual operating delays may believe the unintended consequence of exacerbating tail risks—that is, the risk of worse catastrophe under unlikely but workable conditions.

    Finally, the exceptional position of New York City in Fig. 3 calls attention to the fact that substitutes for roadway transportation are available in many cities and believe an notable role to play in relieving traffic congestion. According to the Texas A&M Institute (63, 64), public transit reduces delays per peak-period auto commuter in the New York urban zone by 63 hours, in Chicago by 23 hours, and by less than 20 hours in other urban areas. Because their model considers only roadway transit, and New York City contains a myriad of nonroad-based options to avoid roadway congestion, it is unlikely that their model can provide informative results for the New York urban area.

    Although interest has increased in policies that enhance roadway resilience, few analytic tools are available to pilot new investments in achieving resilience goals. It is widely understood that roadway infrastructure is expensive, both in acquiring land for rights-of-way and in construction of improvements, and thus, decisions regarding alignment, crossing, and access made over a era of decades may believe long-lasting consequences that are observable in traffic data today. Consequently, urban areas exhibit different unintentional traffic characteristics, including delays under usual and random stress conditions. Investments motivated exclusively by expected efficiencies under usual operating conditions are unreliable safeguards against loss of efficiency under stress conditions. Therefore, new analytic tools are required that allow designers to assess the adaptive capacity of roadway infrastructure and assess the potential of new investments to provide enhanced resilience. The adaptive network-based model described herein is one such approach.

    SUPPLEMENTARY MATERIALS

    Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1701079/DC1

    Alternative approaches to model transportation

    Mapping from OSM Foundation shapefiles to network nodes and links

    Population assignment algorithm

    Distance factor of the likelihood of travel between nodes

    Estimation of the traffic accelerate from the density of vehicles

    Model calibration procedure

    Sensitivity of the model to ramp speeds

    Additional dilatory as a office of the severity of link disruption

    table S1. Mapping original OSM types to network link types and assignment of the number of lanes.

    table S2. The algorithm of the node population assignment.

    table S3. Distance factor P(xod) of the likelihood of travel between nodes.

    table S4. Model sensitivity to ramp accelerate coefficient.

    fig. S1. Effects of the removal of nodes of degree 2.

    fig. S2. Density-flow relationship in the Daganzo traffic model.

    fig. S3. Model calibration.

    fig. S4. Modeled delays for ramp accelerate coefficients of 1/3 and 1/2.

    fig. S5. Dependency of the additional dilatory on the severity of the link disruption for All 40 urban areas.

    This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant expend is not for commercial edge and provided the original travail is properly cited.

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  • National Research Council (U.S.), Metropolitan Travel Forecasting: Current rehearse and Future Direction (Transportation Research Board, 2007).

  • R. Van Haaren, Assessment of Electric Cars’ scope Requirements and Usage Patterns based on Driving conduct recorded in the National Household Travel Survey of 2009 (Solar Journey, 2012), p. 25.

  • B. D. Greenshields, J. R. Bibbins, W. S. Channing, H. H. Miller, R. W. Crum, A study of traffic capacity, in Proceedings of the 14th Annual Meeting of the Highway Research Board, 6 to 7 December 1934, vol. 1.

  • Acknowledgments: They would fancy to thank S. Buldyrev (Yeshiva University) and J. Palma-Oliveira (University of Lisbon) for their insightful comments. Funding: This study was supported by the U.S. Army Engineer Research and evolution headquarters and by the Defense Threat Reduction Agency, Basic Research Program (P. Tandy, program manager). A.A.G. was additionally supported by the Virginia Transportation Research Council and Virginia Department of Transportation. T.S. was supported by the NSF under accord no. 1441352. Author contributions: A.A.G., M.K., and I.L. conceived the model and designed the simulations. A.A.G. developed software and performed data retrieval and simulations. A.A.G. and M.K. analyzed results. I.L. provided senior guidance. A.A.G., M.K., J.M.K., T.S., and I.L. wrote the paper and contributed to the interpretation of the results. Competing interests: The authors declare that they believe no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may exist requested from the authors. Map data were copyrighted by OSM contributors and are available at www.openstreetmap.org.

    Asphalt Contractor's 2018-2019 Training pilot | killexams.com true questions and Pass4sure dumps

    Training keeps your employees engaged and invested in the business. deem sending them to some of the courses offered by manufacturers in the asphalt industry for the 2018-2019 off-season.

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    Contact: Margaret Parker

    Phone: 423-827-1899

    Email: mparker@astecinc.com

    Website: astecinc.com/schools

    COURSE: Advanced Customer Schools

    BOMAG

    Contact: Parker Campbell Weaver

    Phone: 803-337-0700

    Email: parker.weaver@bomag.com

    Website: bomag.com/us

    COURSE: trait Paving Principles

    From proper paver and screed set-up to best practices for improving mat quality, BOMAG specialists provide thorough instruction on paving basics, proper applications and machine controls for laying smooth mats. Instructors review routine maintenance needs and common troubleshooting techniques to extend machine longevity and proffer tips to capitalize operators identify and remedy typical paving challenges, including how to control segregation. 

    Instructors: BOMAG Road edifice Specialists 

    COURSE: Advanced Paving Techniques to Combat Segregation

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    Contact: Vinnie Hebert

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    Email: v_hebert@brookfieldengineering.com

    Website: www.brookfieldengineering.com/services/educational-programs 

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    Contact: Ron Wilson

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    Email: cat_mpls_training@cat.com

    Website: www.cat.com/en_us/by-industry/paving/real-world-training-solution.html

    Caterpillar announces that the following customer training courses believe been added to the Customer Course Catalog. The calendar, course descriptions, and registration forms can exist establish on cat.com. (cat.com > Industries > Paving > Training) [Direct Link: www.cat.com/en_us/by-industry/paving/real-world-training-solution.html]

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    Website: gencor.com/training 

    Course: Gencor Advanced Training 2018

    Week 1: Jan. 14-18, 2019

    Week 2: Jan. 21-25, 2019

    Week 3: Feb. 18-22, 2019

    Week 4: Feb. 25 - March 1, 2019 

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    Contact: Pat Horn

    Phone: 1-717-389-0455

    Email: Patrick.Horn@honeywell.com

    Website: ThermalSolutions.honeywell.com

    Course: Service Schools

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    E-mail: lauren.hill@hcss.com

    Website: ugm.hcss.com 

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    February 5-7, 2019

    Heatec

    Phone: 423-867-4210

    Website: http://www.heatec.com/customer-schools/terminal-technical-school.html 

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    Roadtec 

    Phone: 800-272-7100

    Website: Roadtec.com/training 

    Roadtec, Inc. offers Paving Professionals Workshops (PPW) and Technical Service Schools in Chattanooga, TN.  Students believe direct access to experienced instructors and skilled professionals who provide minute classroom instruction and hands on demonstrations. 

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    PPW Session 3: Jan 14-15

    PPW Session 4: Jan 16-17 

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    Paver Session 2: January 28-30 

    Mill Session 1: February 6-8

    Mill Session 2: February 11-13 

    Roadtec Shuttle Buggy Session 1: February 18-19

    Roadtec Shuttle Buggy Session 2: February 20-21 

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    Website: transtechsys.com 

    COURSE: ONLINE TRAINING

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    Phone: 1-877-TROXLER

    Email: troxtraining@troxlerlabs.com

    Website: troxlerlabs.com 

    ONLINE COURSES:

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    COURSE: Nuclear Gauge Safety Training plus Hazmat

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    • Gauge maintenance

    • Gauge packaging, storage, and transport 

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    • Regulatory requirements

    • Portable gauge theory and operation

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    This instructor led course will give you a general product overview of the Shippensburg manufactured tiny Asphalt Compactors (less than 7 ton). general service course for personnel with basic asphalt compactor experience. Models covered: DD15, DD25B, DD30B, DD35B, CR30B and CR35B tiny Asphalt Compactors.

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    Maintenance and technical training for great asphalt compactors, including system functions, troubleshooting and adjustments of hydraulic and electrical systems.

    COURSE: Soil Compactor Mechanics

    This course provides students with general service instruction for soil compactors.

    COURSE: Paver Screed Operation and Adjustment

    COURSE: Automatic Grade and Slope Systems

    Lessons on how to safely set up, operate and troubleshoot common control issues.

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    Phone: 615-501-0600

    Website:  https://www.wirtgen-group.com/america/en-us/customer-support/know-how/training-program/

    COURSE: W 200 succession Cold Milling Operation

    COURSE: W 200 succession Technical Service

    COURSE: W 100CFi chilly Milling Operation

    COURSE: W 120CFi /W 150CFi Technical Service

    COURSE: WR 200 succession chilly Recycling Operation

    COURSE: WR 200 succession Technical Service

    COURSE: SP 25i Operations & Application

    COURSE: SP Basic Auto Pilot

    COURSE: SP 60i / 90i Inset Paving Fundamentals

    COURSE: SP Advanced Autopilot

    COURSE: Vogele 10ft-3i Super Technical Service

    COURSE: Vogele Super 700 -3 Technical Service

    COURSE: Vogele Paver Operations/Application

    COURSE: Vogele Super 1800-3iSJ Operations & Technical Service

    COURSE: Hamm Introduction & Fundamentals

    COURSE: Hamm Compact, Asphalt and Dirt Rollers

    COURSE: MC EVO - succession Crusher Operation & Maintenance

    COURSE: WITOS FleetView Basics

    Florida International University Paving Academy hosted at Wirtgen America

    Certificate COURSE: Principles of Mainline Asphalt Paving

    Gives attendees an insight into the paving business: materials, road-owner specifications, equipment used, etc. If offers evidence-based practices to dispel some of the myths and misconceptions that believe been perpetuated throughout the years. Promotes best practices and increases the technical erudition of those who travail in the industry and either design, regulate, evaluate or manage projects.

    Fee: $985

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