Heart Failure Anticoagulation Teach-Back Education and Readmissions

abstract: Heart failure affects millions of Americans each year. Treatment of advanced heart failure with reduced ejection fraction and left ventricular failure is sometimes treated with implantation of a left-ventricular assist device. While living with this life-sustaining machine, anticoagulation with Coumadin is necessary. Many of these patients are readmitted within 30-days of being discharged for pump clots, gastro-intestinal bleeds and even strokes. Patients are often discharged without adequate education on Coumadin management, which promotes inadequate self-care and medication non-adherence. In current practice, healthcare providers lecture information in a quick manner without the evaluation of patients’ comprehension. Research suggests implementing the teach-back method during education sessions to assess for comprehension of material to improve medication adherence. Healthcare providers should implement Coumadin teach-back education to heart failure patients with left-ventricular assist devices to improve quality of life, increase medication adherence and decrease 30-day hospital readmission rates

Finding the way forward for forensic science in the US:a commentary on the PCAST report

A recent report by the US President’s Council of Advisors on Science and Technology (PCAST) [1] has made a number of recommendations for the future development of forensic science. Whereas we all agree that there is much need for change, we find that the PCAST report recommendations are founded on serious misunderstandings. We explain the traditional forensic paradigms of match and identification and the more recent foundation of the logical approach to evidence evaluation. This forms the groundwork for exposing many sources of confusion in the PCAST report. We explain how the notion of treating the scientist as a black box and the assignment of evidential weight through error rates is overly restrictive and misconceived. Our own view sees inferential logic, the development of calibrated knowledge and understanding of scientists as the core of the advance of the profession

Investigating impacts of environmental factors on the cycling behavior of bicycle-sharing users

As it is widely accepted, cycling tends to produce health benefits and reduce air pollution. Policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems (BSS). It is increasingly interesting to investigate how environmental factors influence the cycling behavior of users of bicycle-sharing systems, as users of bicycle-sharing systems tend to be different from regular cyclists. Although earlier studies have examined effects of safety and convenience on the cycling behavior of regular riders, they rarely explored effects of safety and convenience on the cycling behavior of BSS riders. Therefore, in this study, we aimed to investigate how road safety, convenience, and public safety affect the cycling behavior of BSS riders by controlling for other environmental factors. Specifically, in this study, we investigated the impacts of environmental characteristics, including population density, employment density, land use mix, accessibility to point-of-interests (schools, shops, parks and gyms), road infrastructure, public transit accessibility, road safety, convenience, and public safety on the usage of BSS. Additionally, for a more accurate measure of public transit accessibility, road safety, convenience, and public safety, we used spatiotemporally varying measurements instead of spatially varying measurements, which have been widely used in earlier studies. We conducted an empirical investigation in Chicago with cycling data from a BSS called Divvy. In this study, we particularly attempted to answer the following questions: (1) how traffic accidents and congestion influence the usage of BSS; (2) how violent crime influences the usage of BSS; and (3) how public transit accessibility influences the usage of BSS. Moreover, we tried to offer implications for policies aiming to increase the usage of BSS or for the site selection of new docking stations. Empirical results demonstrate that density of bicycle lanes, public transit accessibility, and public safety influence the usage of BSS, which provides answers for our research questions. Empirical results also suggest policy implications that improving bicycle facilities and reducing the rate of violent crime rates tend to increase the usage of BSS. Moreover, some environmental factors could be considered in selecting a site for a new docking station

A context aware recommender system for tourism with ambient intelligence

Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais

Studienlandschaft Schwingbachtal: an out-door full-scale learning tool newly equipped with augmented reality

This paper addresses education and communication in hydrology and geosciences. Many approaches can be used, such as the well-known seminars, modelling exercises and practical field work but out-door learning in our discipline is a must, and this paper focuses on the recent development of a new out-door learning tool at the landscape scale. To facilitate improved teaching and hands-on experience, we designed the Studienlandschaft Schwingbachtal. Equipped with field instrumentation, education trails, and geocache, we now implemented an augmented reality App, adding virtual teaching objects on the real landscape. The App development is detailed, to serve as methodology for people wishing to implement such a tool. The resulting application, namely the Schwingbachtal App, is described as an example. We conclude that such an App is useful for communication and education purposes, making learning pleasant, and offering personalized options

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

AbstractRecommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user's ratings or choices. But, when apreciseRS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user's observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called and follow a classical Nearest Neighbour approach, while the other two, and are based on Inverse Reinforcement Learning. and optimise precision, tries to identify the characteristics of POIs that make them relevant, and , a novel RS here introduced, is similar to but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by and optimise precision essentially by recommending quite popular POIs. can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of and are liked more than those produced by . The rationale of that was found in the large percentage of novel recommendations produced by , which are difficult to appreciate. However, excels in recommending items that are both novel and liked by the users

ANALYSIS OF US ACCIDENTS AND SOLUTIONS

Reducing traffic accidents is an essential public safety challenge all over the world; therefore, accident analysis has been a subject of much research in recent decades. The objective of the project is to analyze the US accident data from 50 states to inform the US government agencies and the general public on trends and possible causes of traffic accidents and what could be done to reduce them. The analysis include number of accidents by year, number of accidents by state, best time to travel by month, day and hour, accident-prone area in each state, factors responsible of the accidents like weather, wind flow, temperature, location, etc., deaths in each state, age group of fatalities, drivers involved in accident, drivers age group, vehicles involved in accident, driver with alcohol consumption. The analysis platform is built using Tableau. Government agencies and the general public can leverage these insights and take a preventive measure which can reduce US accidents. The significant findings from the analysis are: (a) most of the accidents are happening in October, November, December; (b) nighttime is safe to travel, (c) a more substantial number of deaths are from drivers in the 20-35 age group; (d) weather, temperature, and location are the factors responsible for 9% of accidents, and (e) about 60% accidents are attributed to drunk driving. The main recommendations from the project focus on Infrastructure, Policy, Administrative, and Human behavior-related changes that can be implemented by the state and the federal government