Exploring Peer Instruction: Should Cohort Clicker Responses Appear During or After Polling?

During problem-based learning sessions, undergraduate students were tasked with answering chemistry-related questions using clicker-handset technology in which the last response made by each handset would override any previous vote. The benefits, if any, of showing cohort responses from clicker questions during versus after polling were explored. Preliminary work suggested that cohort responses shown live during polling created greater unprompted peer instruction, which was inferred from a noticeably louder level of classroom debate. To test if subtle polling changes can promote greater peer instruction, this study monitored cohort performance, clicker response times, and voting-behavior patterns throughout the polling process. Profiling individual and team-based clicker activity in this manner highlighted contrasting performance data. No significant differences were seen when clicker handsets were used individually by students; however, certain trends were seen in the team-based model, which depended on how cohort responses were displayed and were also influenced by question type, with multiple-choice questions (MCQs) performing differently from true−false style questions. The results highlight improved performance in the team-based clicker model with peer instruction taking place during MCQ polls in which cohort responses were displayed live during voting. These findings highlight a clicker strategy embedded with peer instruction that bypasses the need for the standard three-phase process of polling, discussing, and then repolling. Displaying polling responses live enables multiple polling and discussion opportunities to occur in a single interchangeable phase, thus providing a time-efficient voting and peer-instruction method that may attract more instructors to adopt clicker technology within their teaching

Evolutionary game of coalition building under external pressure

We study the fragmentation-coagulation (or merging and splitting) evolutionary control model as introduced recently by one of the authors, where $N$ small players can form coalitions to resist to the pressure exerted by the principal. It is a Markov chain in continuous time and the players have a common reward to optimize. We study the behavior as $N$ grows and show that the problem converges to a (one player) deterministic optimization problem in continuous time, in the infinite dimensional state space

Allocating HIV Prevention Funds in the United States: Recommendations from an Optimization Model

The Centers for Disease Control and Prevention (CDC) had an annual budget of approximately $327 million to fund health departments and community-based organizations for core HIV testing and prevention programs domestically between 2001 and 2006. Annual HIV incidence has been relatively stable since the year 2000 [1] and was estimated at 48,600 cases in 2006 and 48,100 in 2009 [2]. Using estimates on HIV incidence, prevalence, prevention program costs and benefits, and current spending, we created an HIV resource allocation model that can generate a mathematically optimal allocation of the Division of HIV/AIDS Prevention’s extramural budget for HIV testing, and counseling and education programs. The model’s data inputs and methods were reviewed by subject matter experts internal and external to the CDC via an extensive validation process. The model projects the HIV epidemic for the United States under different allocation strategies under a fixed budget. Our objective is to support national HIV prevention planning efforts and inform the decision-making process for HIV resource allocation. Model results can be summarized into three main recommendations. First, more funds should be allocated to testing and these should further target men who have sex with men and injecting drug users. Second, counseling and education interventions ought to provide a greater focus on HIV positive persons who are aware of their status. And lastly, interventions should target those at high risk for transmitting or acquiring HIV, rather than lower-risk members of the general population. The main conclusions of the HIV resource allocation model have played a role in the introduction of new programs and provide valuable guidance to target resources and improve the impact of HIV prevention efforts in the United States

A Semi-Lagrangian scheme for a modified version of the Hughes model for pedestrian flow

In this paper we present a Semi-Lagrangian scheme for a regularized version of the Hughes model for pedestrian flow. Hughes originally proposed a coupled nonlinear PDE system describing the evolution of a large pedestrian group trying to exit a domain as fast as possible. The original model corresponds to a system of a conservation law for the pedestrian density and an Eikonal equation to determine the weighted distance to the exit. We consider this model in presence of small diffusion and discuss the numerical analysis of the proposed Semi-Lagrangian scheme. Furthermore we illustrate the effect of small diffusion on the exit time with various numerical experiments

Neuropathic Pain and Psychological Morbidity in Patients with Treated Leprosy: A Cross-Sectional Prevalence Study in Mumbai

Peer reviewe

Quantum Smoluchowski equation: Escape from a metastable state

We develop a quantum Smoluchowski equation in terms of a true probability distribution function to describe quantum Brownian motion in configuration space in large friction limit at arbitrary temperature and derive the rate of barrier crossing and tunneling within an unified scheme. The present treatment is independent of path integral formalism and is based on canonical quantization procedure.Comment: 10 pages, To appear in the Proceedings of Statphys - Kolkata I

Mean-Field Limits Beyond Ordinary Differential Equations

16th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2016, Bertinoro, Italy, June 20-24, 2016, Advanced LecturesInternational audienceWe study the limiting behaviour of stochastic models of populations of interacting agents, as the number of agents goes to infinity. Classical mean-field results have established that this limiting behaviour is described by an ordinary differential equation (ODE) under two conditions: (1) that the dynamics is smooth; and (2) that the population is composed of a finite number of homogeneous sub-populations, each containing a large number of agents. This paper reviews recent work showing what happens if these conditions do not hold. In these cases, it is still possible to exhibit a limiting regime at the price of replacing the ODE by a more complex dynamical system. In the case of non-smooth or uncertain dynamics, the limiting regime is given by a differential inclusion. In the case of multiple population scales, the ODE is replaced by a stochastic hybrid automaton

Prevalence of asymptomatic leishmania infection in people living with HIV and progression to symptomatic visceral leishmaniasis in Bihar, India

Intro People living with HIV (PLHIV) have an increased risk of developing visceral leishmaniasis (VL) and poor outcomes compared to HIV-negative individuals. Here, we aim to establish the prevalence and determinants of asymptomatic Leishmania infection (ALI) and the rate and risk factors for progression of ALI to VL in a cohort of PLHIV in Bihar, India. Methods We conducted a cross-sectional survey of PLHIV ≥18 years of age with no history or current diagnosis of VL or PKDL at anti-retroviral therapy centres within VL endemic districts of Bihar. ALI was defined as a positive rK39 ELISA, rK39 RDT, and/or qPCR. Additionally, the urinary Leishmania antigen ELISA was evaluated. The ALI and non-ALI cohorts were followed up every three months for 18 months in person and by telephone, respectively. Determinants for ALI were established using logistic regression model. Findings A total of 1,296 PLHIV enrolled in HIV care, 694 (53.6%) of whom were female and a median age of 39 years (IQR 33–46), were included in the analysis. The baseline prevalence of ALI was 7.4% (n=96). All 96 individuals were positive by rK39 ELISA, while 0.5% (n=6) and 0.4% (n=5) were positive by qPCR and rK39 RDT, respectively. Risk factors for ALI were CD4 counts <100 (OR 3.1; 95%CI 1.2–7.6) and CD4 counts 100-199 (OR=2.1; 95% CI: 1.1-4.0) compared to CD4 counts ≥300, and a household size ≥5 (OR=1.9; 95%CI: 1.1-3.1).Within the ALI cohort, four (3.7%) participants developed VL, compared to no progression in the non-ALI cohort. Mortality rates were higher in ALI compared to non-ALI (OR =2.7; 95% CI: 1.1-6.1). Conclusion The prevalence of ALI in PLHIV in VL endemic villages in Bihar was relatively high. However, the progression rate from ALI to VL in PLHIV was low. Patients with low CD4 counts and larger household size were at higher risk of ALI

Recommendations for increasing the use of HIV/AIDS resource allocation models

The article of record as published may be found at: http://dx.doi.org/10.1186/1471-2458-9-S1-S8Background: Resource allocation models have not had a substantial impact on HIV/AIDS resource allocation decisions in spite of the important, additional insights they may provide. In this paper, we highlight six difficulties often encountered in attempts to implement such models in policy settings; these are: model complexity, data requirements, multiple stakeholders, funding issues, and political and ethical considerations. We then make recommendations as to how each of these difficulties may be overcome. Results: To ensure that models can inform the actual decision, modellers should understand the environment in which decision-makers operate, including full knowledge of the stakeholders' key issues and requirements. HIV/AIDS resource allocation model formulations should be contextualized and sensitive to societal concerns and decision-makers' realities. Modellers should provide the required education and training materials in order for decision-makers to be reasonably well versed in understanding the capabilities, power and limitations of the model. Conclusion: This paper addresses the issue of knowledge translation from the established resource allocation modelling expertise in the academic realm to that of policymaking