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

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Test Name : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
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: 120 Real Questions

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IBM Assess: Fundamentals of Applying

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

Apr 08, 2019 (Heraldkeeper via COMTEX) -- HTF MI launched a brand new market study on world Social business utility Market with one hundred+ market facts Tables, Pie Chat, Graphs & Figures unfold through Pages and easy to take 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 Social business application Forecast till 2025*. Some are the important 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.

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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 also range in the last 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 need in the record. closing affirmation to be offered by using analysis crew depending upon the difficulty of survey.** statistics availability can be validated by means of research in case of privately held enterprise. Upto 3 avid gamers may also be delivered at no added can charge.

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

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

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to grasp international Social company utility market dynamics on the earth primarily, the worldwide Social company software market is analyzed across most important global regions. HTF MI also gives custom-made particular regional and nation-level studies for right 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 comment 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 Social business software Product forms In-Depth: , On-premises & Cloud

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

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as a way to get a deeper view of Market size, competitive landscape is equipped i.e. profits (Million USD) by using players (2013-2018), profits Market Share (%) 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 important thing players are incredibly focusing innovation in creation applied sciences to enrich effectivity and shelf existence. The surest long-time period increase opportunities for this sector will also be 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 like criminal name, site, headquarters, its market position, old background and proper 5 closest competitors by means of Market capitalization / income along with contact counsel. every player/ manufacturer earnings figures, increase rate and gross income margin is provided in handy to take into account tabular layout for past 5 years and a separate area on recent construction like mergers, acquisition or any new product/provider launch etc.

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

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

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

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Citrix Certification Guide: Overview and Career Paths | killexams.com real 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 work securely from anywhere on a variety of devices. The company's products fall into five main categories: secure digital workspace, app virtualization and VDI, Enterprise Mobility Management (EMM), file sync and sharing, and networking. Popular Citrix products include XenApp, XenDesktop, XenMobile, NetScaler and ShareFile. More than 400,000 organizations and more than 100 million users run 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 also 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, knowledge 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 proficient 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 support complex  XenApp and XenDesktop 7.15 provisioning environments and solutions. To earn the CCP-V certification, you must have 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 be 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 have 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, disaster recovery, high 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 take the Citrix Certified Professional (CCP-V) exam (1Y0-311). Once you've passed that exam, you can take either Citrix Certified Expert (CCE-V) exam: (1Y0-401) or (1Y0-402).

    These Virtualization certifications are valid 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 have 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, high 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 help you do 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 improve 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 end of each module with a score of 80 percent or higher. The modules are SD-WAN Overview, SD-WAN Architecture, SD-WAN Quality 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)

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    Resilience and efficiency in transportation networks | killexams.com real 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 normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, they represent 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 be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close 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 found 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 normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster 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 include 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 delay in urban areas as the annual delay per auto commuter (11). Other studies define efficiency as delay for the individual driver in terms of time spent moving or stopped (7), or mean travel time between all origin-destination pairs in the network (9). However, as the experience of any motorist in large American cities can attest, conditions beyond the scope of the roadway design, including congestion, accidents, bad 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 normal operating conditions results in little 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 have yet to converge on a shared understanding of resilience suitable to guide 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 ability of a system to prepare for, absorb, recover from, and adapt to disturbances (16). Specific to transportation, resilience has been defined as “the ability 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 describe transportation resilience as simply the ability of a system to minimize operational loss (25) or use the term synonymously with robustness, redundancy, reliability, or vulnerability (26–28).

    Current efforts in transportation resilience research have focused on framework development and quantification methods. These efforts include the specification of resilience indicators, such as total traffic delay (24), economic loss (29), post-disaster maximum flow (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 critical 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 behavior following a disruptive event (34). Moreover, existing resilience quantification approaches lack calibration and testing across a range of transportation systems. Because many disruptive events, and their associated consequences, are difficult to predict, resilient road systems must be characterized and evaluated by the capacity to adapt to a variety of different stress scenarios. Partly because of these obstacles, joint consideration of efficiency and resilience has yet to be 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 delay 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 reveal important considerations for assessing proposals for improvement of roadway infrastructure that maintain efficiency under stress conditions.

    METHODS

    The Methods section appears here to help clarify the subsequent sections. To develop the urban roadway efficiency model, they defined the urban area 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 have been offered and include those based on percolation theory and cascading failures (38–40), human mobility pattern studies (41–43), queueing (44, 45), and the use of historical data to predict traffic. They review these approaches in the Supplementary Materials and note that the main benefit of their model is that it relies solely on readily available public data, rather than on particular data sets that may or may not be practical to obtain for any particular region. The model’s algorithmic simplicity allows us to consider spatial topologies of cities in high 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 area 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 area 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 coarse 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 demand models in the United States (48–51). Other trip distribution models include, for example, destination choice models (52, 53). However, these models are not as widely used in large scale, because the detailed data required by these models are frequently unavailable (48).

    We assumed that (i) the flow of commuters from origin region o to destination region d is proportional to the population at the destination Nd and that (ii) the flow of commuters depends on the distance xod between the origin 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 flow from origin 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 place 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 function (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 speed 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 area 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 area (Eq. 2).

    Because most commuters travel during peak periods, commuter loads Lij can be 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 speed along the ij road segment, lij is its length, l0 is the length correction due to traffic signals, and β is the proportionality coefficient same 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 moving delay in the study of Jiang and Adeli (45), where the authors looked at the delay induced from road repairs.

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

    Embedded Image

    (6)where vmin is the minimum speed 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 average annual delay per peak-period auto commuter. In practice, lower delay 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 range 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 impact of a given physical disruption (and the time for it to unfold) in terms of closing lanes or reducing speed limits on affected roads will not vary much from network to network, although the number and type of these disruptions will. Likewise, the speed of restoring full functionality (through action in the physical domain) is not so much dependent 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 be the full predisruption functionality, that is, eventually all roads can be fully cleared or restored. However, the immediate loss of function for a given traffic flow can very quickly be partially recovered after a disruption by action in the information domain, namely, rerouting of traffic. From the new steady state at that level of functionality, full functionality is gradually restored. Thus, their model proxies for resilience and is calibrated against the data that proxy for efficiency. At the same 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 relevant authorities. Lower additional delay corresponds to higher resilience, but using the same 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 speed vmin, and finite vehicle size correction vveh) and is summarized in Eqs. 5 and 6. Given parameter values of the model, one can estimate the total delay incurred by all commuters in any given suburban area or, equivalently, the average delay per commuter. They take vveh = 9 km/hour and vmin = 5 km/hour and calibrate the model to determine the value of α to match the real data on the annual average delay 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 have found that for the 20 urban areas used for calibration, the R-squared coefficient took values in the range (−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 estimate 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 delay times (11), validating the transportation model. Figure 4 is a Google Maps representation of real and modeled results for Los Angeles and San Francisco. Road conditions under real, average 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 be 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 be caused by snow, ice, or dust storms that affect nearly the entire roadway system. Figure 5 displays the analysis of delay times in six representative urban areas for the full spectrum of adverse event severities, r ⋲ [0; 1]. In addition, fig. S5 shows the results for all urban areas. Some routes within a single urban area experience longer delays than others. The inset of Fig. 5 shows the delay distribution for both Los Angeles, which is narrowly clustered, and Boston, where greater variability between roadways is evident. Traffic delay times grow rapidly as r increases and reach saturation (all routes moving at 1 km/hour) as r approaches 1. They determine the most resilient urban transportation network to be Salt Lake City, UT, whereas the least resilient among the 40 metropolitans is shown to be Washington, DC.

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

    Error bars show mean 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 small number of discrete roadways, and this produces an anomalous impact 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 high efficiency under normal operating conditions (that is, low delays) nevertheless exhibit low resilience (that is, a sharp increase in traffic delays) under stress. Virginia Beach, VA; Providence, RI; and Jacksonville, FL all fall 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 little degradation of efficiency. By contrast, normal traffic delays in San Francisco are comparable to Los Angeles, but mild stress in San Francisco results in large increases in additional delays. These examples indicate that resilience (that is, additional delay response to stress) is independent of normal operating efficiency.

    Fig. 6 Comparison of resilience and efficiency metrics.

    Annual impact of 5% disruption (additional delay) has a low correlation with normal annual delay 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 impact on the structure and function of roadway systems may be unknown (28, 31). These disturbances might be natural and irregular, such as distributed road closures caused by an earthquake or homogeneous vehicle slowing down because of a snowstorm. The disturbances might also be anthropogenic and intentional, such as a street fair or marathon race. Whatever the disturbance, the results of this analysis allow several meaningful inferences to be made that may have important implications for highway transportation policy. The first is that resilience and efficiency represent different aspects related to the nature of transportation systems; they are not correlated and should be 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 fragile 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 area resilience is insensitive to stress levels. That is, cities that exhibit relatively low resilience under mild stress are the same 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 popular 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 be affected, regardless of the availability of plow or sand trucks.

    The third inference follows from Fig. 6, which suggests that urban areas that make capital investments to reduce traffic delays under normal operating conditions may nevertheless be 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 normal operating delays may have the unintended consequence of exacerbating tail risks—that is, the risk of worse catastrophe under unlikely but possible 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 have an important 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 area 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 guide 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 period of decades may have long-lasting consequences that are observable in traffic data today. Consequently, urban areas exhibit different unintentional traffic characteristics, including delays under normal and random stress conditions. Investments motivated exclusively by expected efficiencies under normal 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 speed from the density of vehicles

    Model calibration procedure

    Sensitivity of the model to ramp speeds

    Additional delay as a function 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 speed 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 speed coefficients of 1/3 and 1/2.

    fig. S5. Dependency of the additional delay 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 use is not for commercial advantage and provided the original work is properly cited.

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  • R. Van Haaren, Assessment of Electric Cars’ Range Requirements and Usage Patterns based on Driving Behavior 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 like 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 Development Center 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 grant 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 have 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 be requested from the authors. Map data were copyrighted by OSM contributors and are available at www.openstreetmap.org.

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

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

    Astec Inc.

    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: Quality 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 offer tips to help operators identify and remedy typical paving challenges, including how to control segregation. 

    Instructors: BOMAG Road Building Specialists 

    COURSE: Advanced Paving Techniques to Combat Segregation

    Designed for mainline pavers and contractors often working with mixes prone to segregation, this course covers the origins of material and thermal segregation and how to virtually eliminate segregation using advanced paving techniques with BOMAG Cedarapids Remix Anti-Segregation system pavers and the RoadMix material transfer vehicle.

    Instructors: BOMAG Road Building Specialists 

    COURSE: Asphalt Compaction – Principles & Technology

    COURSE: Soil Compaction – Principles & Technology

    COURSE: Quality Restorations Practices – Milling

    COURSE: Quality Restoration Practices: Recycling & Stabilization

    Brookfield Engineering

    Contact: Vinnie Hebert

    Phone: 800-628-8139 or 508-946-6200

    Email: v_hebert@brookfieldengineering.com

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

    Course: Practical Course on Viscosity Measurements is a one-day course designed to help you get the most out of your Brookfield Viscometer and Rheometer measurements by providing you with information, activities and techniques that you can easily understand — and use. Discussion topics include how to pick a spindle/speed combination as well as calibration and verification checks.

    Course: Lab Day/Advanced Viscosity Test Methods

    This is a one-day, advanced course that goes beyond pass/fail criteria. Using real world sample testing, the class examines how to apply viscometric data as a problem solving/product performance/processing tool. Discussion topics include how Brookfield Rotational Viscometers and Rheometers can be used to provide meaningful analysis of your products. This course is offered by request for companies who have training requirements for seven or more participants. s.

    Caterpillar Inc.

    Contact: Ron Wilson

    Phone: 763.315.5516

    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 have been added to the Customer Course Catalog. The calendar, course descriptions, and registration forms can be found on cat.com. (cat.com > Industries > Paving > Training) [Direct Link: www.cat.com/en_us/by-industry/paving/real-world-training-solution.html]

    Course: F Series Asphalt Paver Service Training, Level II

    Clarence Richard Services

    Contact: Carrie Hudak

    Phone: 952-939-6000

    Email: clarence@clarencerichard.com

    Website: clarencerichard.com 

    COURSE: Classroom Training

    Plant Manager, Operator, Maintenance, Mix Quality, Safety Workshop, Electro-Mechanical Troubleshooting

    COURSE: Web-Based Training

    COURSE: Plant Operation Workshop Self-Based Training 

    COURSE: Quality Control Workshop Self-Based Training  

    COURSE: Electro-Mechanical Systems Maintenance & Troubleshooting Workshop Self-Based Training

    Gencor Industries

    Phone: 407-290-6000, ext. 342

    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 

    HAUCK MFG. CO.  

    Contact: Pat Horn

    Phone: 1-717-389-0455

    Email: Patrick.Horn@honeywell.com

    Website: ThermalSolutions.honeywell.com

    Course: Service Schools

    HCSS

    Contact: Lauren Hill, HCSS Events Coordinator

    Phone: 1-713-270-4000

    E-mail: lauren.hill@hcss.com

    Website: ugm.hcss.com 

    COURSE: 2019 HCSS Users Group Meetings

    For over 25 years, HCSS customers have been gathering annually to learn the latest software features, network with peers, get help from the experts and provide feedback for future releases.

    The HCSS Users Group Meetings are for users of any of the HCSS suite of software including estimating, job management, dispatching, fleet management, telematics, safety, or cloud software. The informative class sessions cover…

    •   HCSS HeavyBid

    •   HCSS HeavyJob

    •   HCSS Dispatcher

    •   HCSS Equipment360

    •   HCSS Telematics

    Dates:

    January 15-17, 2019

    February 5-7, 2019

    Heatec

    Phone: 423-867-4210

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

    COURSE: Terminal Technical School

    Roadtec 

    Phone: 800-272-7100

    Website: Roadtec.com/training 

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

    Course: Paving Professionals Workshop (PPW)

    Students receive comprehensive instruction in asphalt paving from industry leaders. PPW is ideal for those wishing to learn about proper surface preparation, asphalt laydown, preventative maintenance and more. 

    PPW Session 1: Jan 7-8

    PPW Session 2: Jan 9-10

    PPW Session 3: Jan 14-15

    PPW Session 4: Jan 16-17 

    Special instructors for PPW 2019 include John Ball of Top Quality Paving, Brian Horner of E.D. Etnyre, Joey Farrell of Jobsite Technologies, and  Charlie Butler and David Bussard of Roadtec. 

    Course: Technical Service Schools

    Students gain experience in the operation and maintenance of Roadtec equipment through instruction from experienced and skilled factory specialists. 

    Paver Session 1: January 23-25

    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 

    CE units can be earned.

    TransTech Systems Inc.

    Phone: 800-724-6306

    Website: transtechsys.com 

    COURSE: ONLINE TRAINING

    COURSE: DENSITY CLINIC

    Troxler Electronic Laboratories

    Phone: 1-877-TROXLER

    Email: troxtraining@troxlerlabs.com

    Website: troxlerlabs.com 

    ONLINE COURSES:

    Troxler online courses allow to you to earn certifications and gain new skills/refresh old ones when it suits your schedule. For more information or to register for a course, click Online Training - NEW on the Training menu at www.troxlerlabs.com. 

    COURSE: Nuclear Gauge Safety Training plus Hazmat

    Every new nuclear gauge operator must undergo safety training and be certified to use nuclear gauges. (Our online course is accepted for certification by most state agencies.)

    Topics Covered:

    • Radiation safety fundamentals and principles

    • Regulatory requirements

    • Portable gauge theory and operation

    • Gauge maintenance

    • Gauge packaging, storage, and transport 

    COURSE: Radiation Safety Officer Training—NEW!

    This course teaches a radiation safety officer (RSO) the responsibilities of the job.

    Topics Covered:

    • Radiation safety fundamentals and principles

    • License application and renewal

    • Regulatory requirements, record-keeping, and other RSO responsibilities

    • Incidents and accidents

    • Inspections 

    COURSE: Hazmat Certification/Recertification

    This course satisfies US Department of Transportation (DOT) Title 49 CFR, Part 172, Subpart H training requirements for individuals shipping or transporting hazardous materials. (Hazmat recertification is required every three years.) 

    COURSE: Refresher Course for Nuclear Gauge Operators

    If you work in a state that requires gauge operators to renew their radiation safety training periodically, this course will fulfill that requirement. (Please note, however, that it is not a hazmat recertification course.)

    Topics Covered:

    • Radiation safety

    • New regulatory requirements

    • Operating and emergency procedures

    • Maintenance

    • Transportation procedures 

    COURSE: Compaction & Density Testing

    In this course, you will learn the basic principles of compaction and density testing methods (with an emphasis on nuclear testing methods).

    IN-PERSON CLASSES:

    For more information or to view their class schedule, click Classroom Training on the Training menu at www.troxlerlabs.com. (If you are interested in another date and/or location, contact us at training@troxlerlabs.com.)

    In-person, hands-on non-nuclear and nuclear gauge training classes are available at their headquarters in Research Triangle Park, North Carolina, and through their regional sales representatives by request. (To schedule a session, contact us at training@troxlerlabs.com.) 

    CLASS: Nuclear Gauge Safety Training plus Hazmat

    Every new nuclear gauge operator must undergo safety training and be certified to use nuclear gauges. (Our in-person class is accepted for certification by all state agencies.)

    Topics Covered:

    • Radiation safety fundamentals and principles

    • Regulatory requirements

    • Portable gauge theory and operation

    • Gauge maintenance

    • Gauge packaging, storage, and transport 

    CLASS: Radiation Safety Officer Training

    This course teaches an RSO the responsibilities ofthe job.

    Topics Covered:

    • Radiation safety fundamentals and principles

    • License application and renewal

    • Regulatory requirements, record-keeping, and other RSO responsibilities

    • Incidents and accidents

    • Inspections 

    CLASS: Hazmat Certification/Recertification

    This course satisfies DOT Title 49 CFR, Part 172, Subpart H training requirements for individuals shipping or transporting hazardous materials. (Hazmat recertification is required every three years.)

    Volvo Construction Equipment/ Road Institute

    E-mail: roadinstitute@volvo.com

    Website: roadinstitute.com

    COURSE: Paver and Compactor Operation & Maintenance

    Two days of classroom-style learning plus two days of hands-on application in best practices, including an overview of Compact Assist with Density Direct.

    COURSE: Paver Hydraulic and Electrical Systems - P7000/P7000B (Step 1)

    COURSE: Paver Hydraulic and Electrical Systems - P7000/P7000B (Step 2)

    COURSE: Paver Hydraulic and Electrical Systems - Blaw-Knox

    COURSE: Paving and Compaction Application Seminar

    The course objective is to utilize applied training techniques to teach paving and compaction best practices to those people with responsibility for or interest in operation / management of contemporary paving equipment. To use a paver and compactor to their fullest potential while achieving the highest quality work without sacrificing production or increasing costs.

    COURSE: Small Asphalt Compactor Mechanics

    This instructor led course will give you a general product overview of the Shippensburg manufactured Small 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 Small Asphalt Compactors.

    COURSE: Large Asphalt Compactor Mechanics

    Maintenance and technical training for large 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.

    Wirtgen America Inc.

    Phone: 615-501-0600

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

    COURSE: W 200 Series Cold Milling Operation

    COURSE: W 200 Series Technical Service

    COURSE: W 100CFi Cold Milling Operation

    COURSE: W 120CFi /W 150CFi Technical Service

    COURSE: WR 200 Series Cold Recycling Operation

    COURSE: WR 200 Series 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 - Series 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 have been perpetuated throughout the years. Promotes best practices and increases the technical knowledge of those who work in the industry and either design, regulate, evaluate or manage projects.

    Fee: $985

    Certificate COURSE: Paver: Machine Operations & Quality

    Provides equipment owners, operators, user-fixers, paving crews and technicians with efficient, effective and safe methods for getting the most out of paving equipment: i.e, what to do before paving, what to do while paving and what to do when done paving. Increases the level of technical knowledge of those who work in the industry and provides recognition of that achievement.

    Fee: $835



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