Achieving a New Standard in Primary Care for Low-Income Populations -- Case Studies of Redesign and Change Through a Learning Collaborative

Describes four case studies that focus on improving patient care delivery systems through learning collaboratives that were undertaken by New York City's nonprofit Primary Care Development Corporation

A survey of performance enhancement of transmission control protocol (TCP) in wireless ad hoc networks

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2011 Springer OpenTransmission control protocol (TCP), which provides reliable end-to-end data delivery, performs well in traditional wired network environments, while in wireless ad hoc networks, it does not perform well. Compared to wired networks, wireless ad hoc networks have some specific characteristics such as node mobility and a shared medium. Owing to these specific characteristics of wireless ad hoc networks, TCP faces particular problems with, for example, route failure, channel contention and high bit error rates. These factors are responsible for the performance degradation of TCP in wireless ad hoc networks. The research community has produced a wide range of proposals to improve the performance of TCP in wireless ad hoc networks. This article presents a survey of these proposals (approaches). A classification of TCP improvement proposals for wireless ad hoc networks is presented, which makes it easy to compare the proposals falling under the same category. Tables which summarize the approaches for quick overview are provided. Possible directions for further improvements in this area are suggested in the conclusions. The aim of the article is to enable the reader to quickly acquire an overview of the state of TCP in wireless ad hoc networks.This study is partly funded by Kohat University of Science & Technology (KUST), Pakistan, and the Higher Education Commission, Pakistan

Optimization of vehicle routing and scheduling with travel time variability - application in winter road maintenance

This study developed a mathematical model for optimizing vehicle routing and scheduling, which can be used to collect travel time information, and also to perform winter road maintenance operations (e.g., salting, plowing). The objective of this research was to minimize the total vehicle travel time to complete a given set of service tasks, subject to resource constraints (e.g., truck capacity, fleet size) and operational constraints (e.g., service time windows, service time limit). The nature of the problem is to design vehicle routes and schedules to perform the required service on predetermined road segments, which can be interpreted as an arc routing problem (ARP). By using a network transformation technique, an ARP can be transformed into a well-studied node routing problem (NRP). A set-partitioning (SP) approach was introduced to formulate the problem into an integer programming problem (I PP). To solve this problem, firstly, a number of feasible routes were generated, subject to resources and operational constraints. A genetic algorithm based heuristic was developed to improve the efficiency of generating feasible routes. Secondly, the corresponding travel time of each route was computed. Finally, the feasible routes were entered into the linear programming solver (CPL EX) to obtain final optimized results. The impact of travel time variability on vehicle routing and scheduling for transportation planning was also considered in this study. Usually in the concern of vehicle and pedestrian\u27s safety, federal, state governments and local agencies are more leaning towards using a conservative approach with constant travel time for the planning of winter roadway maintenance than an aggressive approach, which means that they would rather have a redundancy of plow trucks than a shortage. The proposed model and solution algorithm were validated with an empirical case study of 41 snow sections in the northwest area of New Jersey. Comprehensive analysis based on a deterministic travel time setting and a time-dependent travel time setting were both performed. The results show that a model that includes time dependent travel time produces better results than travel time being underestimated and being overestimated in transportation planning. In addition, a scenario-based analysis suggests that the current NJDOT operation based on given snow sector design, service routes and fleet size can be improved by the proposed model that considers time dependent travel time and the geometry of the road network to optimize vehicle routing and scheduling. In general, the benefit of better routing and scheduling design for snow plowing could be reflected in smaller minimum required fleet size and shorter total vehicle travel time. The depot location and number of service routes also have an impact on the final optimized results. This suggests that managers should consider the depot location, vehicle fleet sizing and the routing design problem simultaneously at the planning stage to minimize the total cost for snow plowing operations

Active and Reactive Power Control of Flexible Loads for Distribution-Level Grid Services

Electric vehicle (EV) charging/discharging can take place in any P-Q quadrants, which means EVs could provide reactive power at any state-of-charge (SOC). This dissertation shows four-quadrant operation of EVs and aggregation of EVs for support of grid operations. First, this work develops hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed EVs incorporating distribution grid level constraints. This work demonstrates benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5,000). Case studies demonstrate that, in constrained distribution grids, coordinated charging reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to the unity power factor. Next, this work utilizes detailed EV battery model that could be leveraged for its four-quadrant operations. Then, the developed work coordinates the operations of EVs and distribution feeder to support voltage profile on the grid in real time. The grid level problem is devised as a distribution optimal power flow model to compute voltage regulation signal to dispatch active/reactive power set points of individual EVs. The efficacy of the developed models are demonstrated by using a LV secondary feeder, where EVs\u27 operating in all four quadrants are shown to compensate the feeder voltage fluctuations caused by daily time varying residential loads, while honoring other operational constraints of the feeder. Furthermore, a novel grid application, called virtual power plant (VPP), is developed. Traditional nonlinear power flow problems are nonconvex, hence, time consuming to solve. In order to be used in real time simulation in VPP, an efficient linearized optimal power flow model is developed. This linearization method is used to solve a 534-bus power system with 3 VPPs in real-time. This work also implements VPP scheduling in real-time using OPAL-RT\u27s simulator in hardware-in-the-loop (HIL), where the loads are emulated using micro-controller devices

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques