Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the e_ects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model _ne-grained interactions among people at speci_c locations in a community; (2) an RL- based methodology for optimizing _ne-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts

The Use of Software Agents for Autonomous Control of a DC Space Power System

In order to enable manned deep-space missions, the spacecraft must be controlled autonomously using on-board algorithms. A control architecture is proposed to enable this autonomous operation for an spacecraft electric power system and then implemented using a highly distributed network of software agents. These agents collaborate and compete with each other in order to implement each of the control functions. A subset of this control architecture is tested against a steadystate power system simulation and found to be able to solve a constrained optimization problem with competing objectives using only local information

Etiological Profile and Treatment Outcome of Epistaxis at a Tertiary Care Hospital in Northwestern Tanzania: A Prospective Review of 104 Cases.

Epistaxis is the commonest otolaryngological emergency affecting up to 60% of the population in their lifetime, with 6% requiring medical attention. There is paucity of published data regarding the management of epistaxis in Tanzania, especially the study area. This study was conducted to describe the etiological profile and treatment outcome of epistaxis at Bugando Medical Centre, a tertiary care hospital in Northwestern Tanzania. This was a prospective descriptive study of the cases of epistaxis managed at Bugando Medical Centre from January 2008 to December 2010. Data collected were analyzed using SPSS computer software version 15. A total of 104 patients with epistaxis were studied. Males were affected twice more than the females (2.7:1). Their mean age was 32.24 ± 12.54 years (range 4 to 82 years). The modal age group was 31-40 years. The commonest cause of epistaxis was trauma (30.8%) followed by idiopathic (26.9%) and hypertension (17.3%). Anterior nasal bleeding was noted in majority of the patients (88.7%). Non surgical measures such as observation alone (40.4%) and anterior nasal packing (38.5%) were the main intervention methods in 98.1% of cases. Surgical measures mainly intranasal tumor resection was carried out in 1.9% of cases. Arterial ligation and endovascular embolization were not performed. Complication rate was 3.8%. The overall mean of hospital stay was 7.2 ± 1.6 days (range 1 to 24 days). Five patients died giving a mortality rate of 4.8%. Trauma resulting from road traffic crush (RTC) remains the most common etiological factor for epistaxis in our setting. Most cases were successfully managed with conservative (non-surgical) treatment alone and surgical intervention with its potential complications may not be necessary in most cases and should be the last resort. Reducing the incidence of trauma from RTC will reduce the incidence of emergency epistaxis in our centre

Designing a fashion driving forces website as an educational resource

Electronic educational resources support search activities and manipulate information effectively in learning environments, thus enhancing education. This paper discusses the development of an electronic timeline database that classifies design and fashion details; technological developments; socio-economical influences; availability and popularity within fashion trends; marketing and distribution; and influential people including designers, in a manner that facilitates ease of cross referencing events at the same point in time for a rich analysis of fashion. The study focuses on the driving forces of fashion during the 1920s as a starting point for a much larger database. The data is presented in the form of a website allowing students to better understand fashion trends with macro-environmental and marketing strategies. The electronic resource is a useful tool for fashion, textile and marketing students as an educational interface providing design, production and marketing data for fashion-related products particularly useful for the analysis of fashion trends

Interfaces for science: Conceptualizing an interactive graphical interface

6,849.32 new research journal articles are published every day. The exponential growth of Scientific Knowledge Objects (SKOs) on the Web, makes searches time-consuming. Access to the right and relevant SKOs is vital for research, which calls for several topics, including the visualization of science dynamics. We present an interface model aimed to represent of the relations that emerge in the science social space dynamics, namely through the visualization and navigation of the relational structures between researchers, SKOs, knowledge domains, subdomains, and topics. This interface considers the relationship between the researcher who reads and shares the relevant articles and the researcher who wants to find the most relevant SKOs within a subject matter. This article presents the first iteration of the conceptualization process of the interface layout, its interactivity and visualization structures. It is essential to consider the hierarchical and relational structures/algorithms to represent the science social space dynamics. These structures are not being used as analysis tools, because it is not objective to show the linkage properties of these relationships. Instead, they are used as a means of representing, navigating and exploring these relationships. To sum up, this article provides a framework and fundamental guidelines for an interface layout that explores the social science space dynamics between the researcher who seeks relevant SKOs and the researchers who read and share them.This work has been supported by COMPETE: POCI-01-0145-FEDER- 007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: (UID/CEC/00319/2013) and the Project IViSSEM: ref: POCI-010145-FEDER-28284

The 2015 Plains Elevated Convection at Night Field Project

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night. To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings