Control of Networked Multiagent Systems with Uncertain Graph Topologies

Multiagent systems consist of agents that locally exchange information through a physical network subject to a graph topology. Current control methods for networked multiagent systems assume the knowledge of graph topologies in order to design distributed control laws for achieving desired global system behaviors. However, this assumption may not be valid for situations where graph topologies are subject to uncertainties either due to changes in the physical network or the presence of modeling errors especially for multiagent systems involving a large number of interacting agents. Motivating from this standpoint, this paper studies distributed control of networked multiagent systems with uncertain graph topologies. The proposed framework involves a controller architecture that has an ability to adapt its feed- back gains in response to system variations. Specifically, we analytically show that the proposed controller drives the trajectories of a networked multiagent system subject to a graph topology with time-varying uncertainties to a close neighborhood of the trajectories of a given reference model having a desired graph topology. As a special case, we also show that a networked multi-agent system subject to a graph topology with constant uncertainties asymptotically converges to the trajectories of a given reference model. Although the main result of this paper is presented in the context of average consensus problem, the proposed framework can be used for many other problems related to networked multiagent systems with uncertain graph topologies.Comment: 14 pages, 2 figure

Cooperative Reinforcement Learning Using an Expert-Measuring Weighted Strategy with WoLF

Gradient descent learning algorithms have proven effective in solving mixed strategy games. The policy hill climbing (PHC) variants of WoLF (Win or Learn Fast) and PDWoLF (Policy Dynamics based WoLF) have both shown rapid convergence to equilibrium solutions by increasing the accuracy of their gradient parameters over standard Q-learning. Likewise, cooperative learning techniques using weighted strategy sharing (WSS) and expertness measurements improve agent performance when multiple agents are solving a common goal. By combining these cooperative techniques with fast gradient descent learning, an agent’s performance converges to a solution at an even faster rate. This statement is verified in a stochastic grid world environment using a limited visibility hunter-prey model with random and intelligent prey. Among five different expertness measurements, cooperative learning using each PHC algorithm converges faster than independent learning when agents strictly learn from better performing agents

WoLF Ant

Ant colony optimization (ACO) algorithms can generate quality solutions to combinatorial optimization problems. However, like many stochastic algorithms, the quality of solutions worsen as problem sizes grow. In an effort to increase performance, we added the variable step size off-policy hill-climbing algorithm called PDWoLF (Policy Dynamics Win or Learn Fast) to several ant colony algorithms: Ant System, Ant Colony System, Elitist-Ant System, Rank-based Ant System, and Max-Min Ant System. Easily integrated into each ACO algorithm, the PDWoLF component maintains a set of policies separate from the ant colony\u27s pheromone. Similar to pheromone but with different update rules, the PDWoLF policies provide a second estimation of solution quality and guide the construction of solutions. Experiments on large traveling salesman problems (TSPs) show that incorporating PDWoLF with the aforementioned ACO algorithms that do not make use of local optimizations produces shorter tours than the ACO algorithms alone

Antiretroviral Therapy for HIV-2 Infection: Recommendations for Management in Low-Resource Settings

HIV-2 contributes approximately a third to the prevalence of HIV in West Africa and is present in significant amounts in several low-income countries outside of West Africa with historical ties to Portugal. It complicates HIV diagnosis, requiring more expensive and technically demanding testing algorithms. Natural polymorphisms and patterns in the development of resistance to antiretrovirals are reviewed, along with their implications for antiretroviral therapy. Nonnucleoside reverse transcriptase inhibitors, crucial in standard first-line regimens for HIV-1 in many low-income settings, have no effect on HIV-2. Nucleoside analogues alone are not sufficiently potent enough to achieve durable virologic control. Some protease inhibitors, in particular those without ritonavir boosting, are not sufficiently effective against HIV-2. Following review of the available evidence and taking the structure and challenges of antiretroviral care in West Africa into consideration, the authors make recommendations and highlight the needs of special populations

Time-Series Classification of High-Temporal Resolution AVHRR NDVI Imagery of Mexico

Time-series data from wide-field sensors, acquired for the period of a growing season or longer, capitalize on phenological changes in vegetation and make it possible to identify vegetated land cover types in greater detail. Our objective was to examine the utility of time-series data to rapidly update maps of vegetation condition and land cover change in Mexico as an input to biodiversity modeling. We downloaded AVHRR NDVI 10-day composites from the USGS EROS Data Center for 1992-1993 and adjusted for cloud contamination by further aggregating the data. In the first phase of our analysis, we selected training sites for various land cover types using a land cover map created by the Mexican National Institute of Statistics, Geography, and Informatics (INEGI) as a guide. Since there is a high degree of spectral variability within many of the vegetated land cover types, we subjected the spectral response patterns to cluster analysis. We then used the statistics of the clusters as training data in a supervised classification. We also compared unsupervised and univariate decision tree approaches, but these provided unsatisfactory results. Best results were achieved with a 19-class map of land use/land cover employing a supervised approach

Cycle Training Modulates Satellite Cell and Transcriptional Responses to a Bout of Resistance Exercise

This investigation evaluated whether moderate‐intensity cycle ergometer training affects satellite cell and molecular responses to acute maximal concentric/eccentric resistance exercise in middle‐aged women. Baseline and 72 h postresistance exercise vastus lateralis biopsies were obtained from seven healthy middle‐aged women (56 ± 5 years, BMI 26 ± 1, VO2max 27 ± 4) before and after 12 weeks of cycle training. Myosin heavy chain (MyHC) I‐ and II‐associated satellite cell density and cross‐sectional area was determined via immunohistochemistry. Expression of 93 genes representative of the muscle‐remodeling environment was also measured via NanoString. Overall fiber size increased ~20% with cycle training (P = 0.052). MyHC I satellite cell density increased 29% in response to acute resistance exercise before endurance training and 50% with endurance training (P \u3c 0.05). Following endurance training, MyHC I satellite cell density decreased by 13% in response to acute resistance exercise (acute resistance × training interaction, P \u3c 0.05). Genes with an interaction effect tracked with satellite cell behavior, increasing in the untrained state and decreasing in the endurance trained state in response to resistance exercise. Similar satellite cell and gene expression response patterns indicate coordinated regulation of the muscle environment to promote adaptation. Moderate‐intensity endurance cycle training modulates the response to acute resistance exercise, potentially conditioning the muscle for more intense concentric/eccentric activity. These results suggest that cycle training is an effective endurance exercise modality for promoting growth in middle‐aged women, who are susceptible to muscle mass loss with progressing age

Evaluation of a community pharmacy-based intervention for improving patient adherence to antihypertensives: a randomised controlled trial

BackgroundThe majority of patients using antihypertensive medications fail to achieve their recommended target blood pressure. Poor daily adherence with medication regimens and a lack of persistence with medication use are two of the major reasons for failure to reach target blood pressure. There is no single intervention to improve adherence with antihypertensives that is consistently effective. Community pharmacists are in an ideal position to promote adherence to chronic medications. This study aims to test a specific intervention package that could be integrated into the community pharmacy workflow to enable pharmacists to improve patient adherence and/or persistence with antihypertensive medications - Hypertension Adherence Program in Pharmacy (HAPPY).Methods/DesignThe HAPPY trial is a multi-centre prospective randomised controlled trial. Fifty-six pharmacies have been recruited from three Australian states. To identify potential patients, a software application (MedeMine CVD) extracted data from a community pharmacy dispensing software system (FRED Dispense&reg;). The pharmacies have been randomised to either \u27Pharmacist Care Group\u27 (PCG) or \u27Usual Care Group\u27 (UCG). To check for \u27Hawthorne effect\u27 in the UCG, a third group of patients \u27Hidden Control Group\u27 (HCG) will be identified in the UCG pharmacies, which will be made known to the pharmacists at the end of six months. Each study group requires 182 patients. Data will be collected at baseline, three and six months in the PCG and at baseline and six months in the UCG. Changes in patient adherence and persistence at the end of six months will be measured using the self-reported Morisky score, the Tool for Adherence Behaviour Screening and medication refill data.DiscussionTo our knowledge, this is the first research testing a comprehensive package of evidence-based interventions that could be integrated into the community pharmacy workflow to enable pharmacists to improve patient adherence and/or persistence with antihypertensive medications. The unique features of the HAPPY trial include the use of MedeMine CVD to identify patients who could potentially benefit from the service, control for the \u27Hawthorne effect\u27 in the UCG and the offer of the intervention package at the end of six months to patients in the UCG, a strategy that is expected to improve retention.Trial RegistrationAustralian New Zealand Clinical Trial Registry ACTRN12609000705280<br /

Assessment of the quality of existing patient educational tools focused on sudden cardiac arrest: a systematic evaluation by the Sudden Cardiac Arrest Thought Leadership Alliance

Background Conveying contemporary treatment options for those at risk of sudden cardiac arrest (SCA) is challenging. The purpose of the present research was to evaluate the quality and usability of available patient educational tools relevant to SCA and its treatment options, such as implantable cardioverter defibrillators (ICDs). We hypothesized that this review would identify gaps in areas of information for the enhancement of patient education and decision-making materials. Methods We used a formal instrument to assess specific domains of content, development, and effectiveness of 18 available SCA and ICD educational tools. The multidisciplinary review panel included two electrophysiologists, two general cardiologists, a cardiac psychologist, a health services researcher, and a patient advocate. Results Of the 18 education tools, four were rated as “good, may need revisions, but sufficient for use�, 12 were rated as “marginal, needs revision prior to use�, and two were rated as “poor, inadequate for use�. None of the tools were rated as being of “very good� or “excellent� quality. Conclusion There appear to be opportunities to improve the quality and completeness of existing educational tools for patients with SCA and ICD. While many tools have been developed, they fall below current standards for supporting informed medical decision-making