The Paradox of Policing as Protection: A Harm Reduction Approach to Prostitution Using Safe Injection Sites as a Guide

Hälsofrågan har blivit betydelsefull över världen. Med en plattform som exempelvis smartphone kan begreppet gamification förstärka möjligheterna till beteendeförändringar och läkarbehandlingar. Gamification är ett relativt nytt begrepp som använder spelelement och speldesigntekniker som appliceras i annan kontext än spel. Gamification finns i diverse kontexter som; handel, utbildning och lärande, organisationer internt, delning, hållbar konsumtion, arbeten, innovationer, data samlingar och hälsa/träning. I den sist nämnda kontexten hälsa, saknas det tydliga studier inom och i kvalitativ ansats. Den här studien är en kvalitativ studie, och har inriktat sig inom sjukgymnastik/fysioterapeut kontexten. I studien har det undersökts hur gamification kan användas inom sjukgymnaster/fysioterapeuter och i vilket avseende sjukgymnasterna/fysioterapeuterna kan tänka sig att applicera gamification i sitt arbete som ett verktyg i framtiden. I studien har det undersökts om spelelement kan ha koppling till sjukgymnaster/fysioterapeuter. Studien resulterar i att gamification kan stöda sjukgymnaster/fysioterapeuter genom faktorerna motivation, psykologi och beteende. Det finns möjligheter att bygga ett gamification system eller tjänst i kontexten sjukgymnastik/fysioterapeut med hjälp av speldynamikerna. Nyckelord: Gamification, gamification inom hälsa, kritik om gamification, motivation, psykologi, beteende och sjukgymnastik/fysioterapeut.

Comprehensive User Engagement Sites (CUES) in Philadelphia: A Constructive Proposal

This paper is a study about Philadelphia’s comprehensive user engagement sites (CUESs) as the authors address and examine issues related to the upcoming implementation of a CUES while seeking solutions for its disputed questions and plans. Beginning with the federal drug schedules, the authors visit some of the medical and public health issues vis-à-vis safe injection facilities (SIFs). Insite, a successful Canadian SIF, has been thoroughly researched as it represents a paradigm for which a Philadelphia CUES can expand upon. Also, the existing criticisms against SIFs are revisited while critically unpackaged and responded to in favor of the establishment. In the main section, the authors propose the layout and services of the upcoming CUES, much of which would be in congruent to Vancouver’s Insite. On the other hand, the CUES would be distinct from Insite, as the authors emphasize, in that it will offer an information center run by individuals in recovery and place additional emphasis on early education for young healthcare professionals by providing them a platform to work at the site. The paper will also briefly investigate the implementation of a CUES site under an ethical scope of the Harm Reduction Theory. Lastly, the authors recommend some strategic plans that the Philadelphia City government may consider employing at this crucial stage

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Injecting equipment schemes for injecting drug users : qualitative evidence review

This review of the qualitative literature about needle and syringe programmes (NSPs) for injecting drug users (IDUs) complements the review of effectiveness and cost-effectiveness. It aims to provide a more situated narrative perspective on the overall guidance questions

Supervised Injection Facilities: Legal and Policy Reforms

The US Centers for Disease Control and Prevention reported that more than 70 000 deaths from drug overdoses occurred in 2017, including prescription and illicit opioids, representing a 6-fold increase since 1999. Innovative harm-reduction solutions are imperative. Supervised injection facilities (SIFs) create safe places for drug injection, including overdose prevention, counseling, and treatment referral services. Supervised injection facilities neither provide illicit drugs nor do their personnel inject users. Supervised injection facilities are effective in reducing drug-related mortality, morbidity, and needle-borne infections. Yet their lawfulness remains uncertain. The Department of Justice (DOJ) recently threatened criminal prosecution for SIF operators, medical personnel, and patrons

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering