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Measurement of SARS-CoV-2 in air and on surfaces in Scottish hospitals
Get PDFBackgroundThere are still uncertainties in our knowledge of the amount of SARS-CoV-2 virus present in the environment; where it can be found, and potential exposure determinants, limiting our ability to effectively model and compare interventions for risk management.AimThis study measured SARS-CoV-2 in three hospitals in Scotland on surfaces and air, alongside ventilation and patient care activities.MethodsAir sampling at 200 L/min for 20 minutes and surface sampling were performed in two wards designated to treat COVID-19 -positive patients and two non-COVID-19 wards across three hospitals in November and December 2020. FindingsDetectable samples of SARS-CoV-2 were found in COVID-19 treatment wards but not in non-COVID-19 wards. Most samples were below assay detection limits, but maximum concentrations reached 1.7x10 3 genomic copies/m3 in air and 1.9x10 4 copies per surface swab (3.2x10 2 copies/cm2 for surface loading). The estimated geometric mean air concentration (geometric standard deviation) across all hospitals was 0.41 (71) genomic copies/m3 and the corresponding values for surface contamination were 2.9 (29) copies/swab. SARS-CoV-2 RNA was found in non-patient areas (patient/visitor waiting rooms and personal protective equipment (PPE) changing areas) associated with COVID-19 treatment wards.ConclusionsNon-patient areas of the hospital may pose risks for infection transmission and further attention should be paid to these areas. Standardization of sampling methods will improve understanding of levels of environmental contamination. The pandemic has demonstrated a need to review and act upon the challenges of older hospital buildings meeting current ventilation guidance
Factor Structure of the International Trauma Questionnaire in Trauma Exposed LGBTQ+ Adults: Role of Cumulative Traumatic Events and Minority Stress Heterosexist Experiences
Get PDFExposure to prolonged and/or multiple types of psychological trauma and stressors has shown to be more strongly associated with ICD-11 complex posttraumatic stress disorder (CPTSD) than posttraumatic stress disorder (PTSD). Lesbian, gay, bisexual, trans- and queer adults (LGBTQ+) are at a heightened risk of exposure to traumatic events, and minority stressors including harassment, discrimination, rejection by family, and isolation. Objective. To examine the factor structure of the international trauma questionnaire (Cloitre et al., 2018), a self-report measure of PTSD and CPTSD, and the associations of cumulative lifetime trauma exposure assessed via the life events checklist (Gray et al., 2004) and minority stress assessed via the daily heterosexist experiences scale (Balsam et al., 2010), with CPTSD (3 PTSD symptom clusters, 3 clusters reflecting disturbances in self-organization [DSO]) among LGBTQ+ adults. Method. Participants comprised 225 LGBTQ+ adults (including 74 transgender and gender diverse individuals; age-range: 18-60 years; M/SD = 31.35/9.48) residing in Spain. Results. Confirmatory factor analyses indicated that both a first-order six-factor model and a hierarchical two-factor model, comprising PTSD and DSO as second-order factors, fit the data best. Cumulative traumatic events score was associated with PTSD, and cumulative minority stress was associated with PTSD and DSO. Among the minority stress subscales, harassment based on gender expression was positively associated with all symptom clusters of PTSD and DSO. Conclusions. This is the first study to examine the role of minority stressors alongside exposure to psychological traumas in ICD-11 PTSD and CPTSD and emphasizes the inclusion of minority stressors in trauma-related assessments
An adaptive large neighbourhood search metaheuristic for hourly learning activity planning in personalised learning
Get PDFPersonalised learning offers an alternative method to one-size-fits-all education in schools, and has seen increasing adoption over the past several years. Personalised learning’s focus on learner-driven education requires novel scheduling methods. In this paper we introduce the hourly, learner-driven activity planning problem of personalised learning, and formulate scheduling methods to solve it. We present an integer linear programming model of the problem, but this model does not generate schedules sufficiently quickly for use in practice. To overcome this, we propose an adaptive large neighbourhood search metaheuristic to solve the problem instead. The metaheuristic’s performance is compared against optimal solutions in a large numerical study of 14,400 instances. These instances are representative of secondary education in the Netherlands, and were developed from expert opinions. Solutions on average deviate only 1.6% from optimal results. Further, our experiments numerically demonstrate the mitigating effects changes to the structure and staffing of secondary education have on the challenges of satisfying learner instruction demands in personalised learning
Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach
Get PDFThe tap-on smart-card data provides a valuable source to learn passengers’ boarding behaviour and predict future travel demand. However, when examining the smart-card records (or instances) by the time of day and by boarding stops, the positive instances (i.e. boarding at a specific bus stop at a specific time) are rare compared to negative instances (not boarding at that bus stop at that time). Imbalanced data has been demonstrated to significantly reduce the accuracy of machine-learning models deployed for predicting hourly boarding numbers from a particular location. This paper addresses this data imbalance issue in the smart-card data before applying it to predict bus boarding demand. We propose the deep generative adversarial nets (Deep-GAN) to generate dummy travelling instances to add to a synthetic training dataset with more balanced travelling and non-travelling instances. The synthetic dataset is then used to train a deep neural network (DNN) for predicting the travelling and non-travelling instances from a particular stop in a given time window. The results show that addressing the data imbalance issue can significantly improve the predictive model’s performance and better fit ridership’s actual profile. Comparing the performance of the Deep-GAN with other traditional resampling methods shows that the proposed method can produce a synthetic training dataset with a higher similarity and diversity and, thus, a stronger prediction power. The paper highlights the significance and provides practical guidance in improving the data quality and model performance on travel behaviour prediction and individual travel behaviour analysis
Radial piezoelectric magnetic fans (RPMF) mathematical model development and design optimization for electronics cooling
Get PDFNowadays, almost everything was run by electronic-based devices regardless of its size and applications, functioning for kids to adults and operating throughout the days and nights. It is critical to manage the electronic thermal management to sustain the electronic device for longer period. This paper enhances the design of multiple piezoelectric magnetic fans (MPMF) to achieve maximum thermal efficiency. Some geometric parameters were investigated such as the magnet location, x, distance between magnets, d, and the orientation, of the fans. Response Surface Method (RSM) was used as optimization tool. Therefore, this paper presents a mathematical model of MPMF to predict the maximum fan deflection by optimizing the value of x, d, and . The experimental results showed that the optimal value of x was 44 mm from the origin, the range of d value was in the range of 14.5 mm to 15.6 mm and in overall, fan deflection of radial piezoelectric magnetic fans (RPMF) was better than array piezoelectric magnetic fans (APMF). The most consistent average fan deflection was 11.6 mm at d=14.5 mm and resonant frequency, =42.66 Hz. The Reynolds number, Re for RPMF has increased from 437 to 577 (improved by 32%) compared to APMF. The heat convection coefficient, h, for RPMF has improved 8.07% from 32.96 to 35.62 and the thermal resistance reduced by 7.6% from 1.58 to 1.46 which led to 5% increment of overall thermal efficiency, 63%. This clearly shows that the thermal efficiency has been improved by optimizing the x, d and values of the MPMF
Narrative modelling: A comparison of high and low mass dwelling solutions in Afghanistan and Peru
No full textDisplaced populations are housed in various constructions, including lightweight predesigned structures. Theoretically, self-built heavyweight structures should ensure better temperatures and be closer to cultural norms. To examine this directly for the first time, lightweight pre-designed solutions are compared with high-mass self-built alternatives in Afghanistan and Peru, via monitoring, dynamic simulation, occupant surveys, the Shelter Assessment Matrix (SAM) and ShelTherm. Lightweight solutions increase peak summer temperatures, but only by 2°C, but reduce minimum temperatures by up to 5°C. Simulations only provided a qualitatively similar time series to the monitoring, because identical homes showed a large variance in temperatures. This questions the benefit of simulation compared to approaches which concentrate on whether shelters exacerbate or ameliorate external temperatures. In addition, a dwelling provides more than comfort, it supports family life, which is best addressed by tools like SAM, not thermal simulation. Hence it might be ideal to recommend high-mass self-build if possible, and to focus modelling efforts on qualitative aspects of simulation time-series, such as whether the building suppresses or exacerbates external conditions, and equally on psycho-cultural aspects. The term narrative modelling is introduced to describe this new approach which will be of direct benefit to the humanitarian community
A Multi-robot Distributed Collaborative Region Coverage Search Algorithm Based on Glasius Bio-inspired Neural Network
No full textThere are many constraints for a multi-robot system to perform a region coverage search task in an unknown environment. To address this, we propose a novel multi-robot distributed collaborative region coverage search algorithm based on Glasius bio-inspired neural network (GBNN). Firstly, we develop an environmental information updating model to represent the dynamic search environment. This model converts the environmental information detected by the robot into dynamic neural activity landscape of GBNN. Secondly, we introduce the distributed model predictive control method in search path planning to improve search efficiency. In addition, we propose a distributed collaborative decision-making mechanism among the robots to produce several dynamic search sub-teams. Within each sub-team, collaborative decisions are made among the robot members to optimize the solution and obtain the next movement path of each robot. Finally, we conduct experiments in three aspects to verify the effectiveness of the proposed method. Compared with three algorithms in this field, the experimental results demonstrate that the proposed algorithm exhibits good performance in a multi-robot region coverage search task
Cost-Effective Heating Control Approaches by Demand Response and Peak Demand Limiting in an Educational Office Building with District Heating
Get PDFThis study examined three different approaches to reduce the heating cost while maintaining indoor thermal comfort at acceptable levels in an educational office building, including decentralized (DDRC) and centralized demand response control (CDRR) and limiting peak demand. The results showed that although all these approaches did not affect the indoor air temperature significantly, the DDRC method could adjust the heating set point to between 20–24.5 °C. The DDRC approach reached heating cost savings of up to 5% while controlling space heating temperature without sacrificing the thermal comfort. The CDRC of space heating had limited potential in heating cost savings (1.5%), while the indoor air temperature was in the acceptable range. Both the DDRC and CDRC alternatives can keep the thermal comfort at good levels during the occupied time. Depending on the district heating provider, applying peak demand limiting of 35% can not only achieve 13.6% maximum total annual district heating cost saving but also maintain the thermal comfort level, while applying that of 43% can further save 16.9% of the cost, but with sacrificing a little thermal comfort. This study shows that demand response on heating energy only benefited from the decentralized control alternative, and the district heating-based peak demand limiting has significant potential for saving heating costs
Information overload: a concept analysis
No full textPurpose: With the shift to an information-based society and to the de-centralisation of information, information overload has attracted a growing interest in the computer and information science research communities. However, there is no clear understanding of the meaning of the term, and while there have been many proposed definitions, there is no consensus. The goal of this work was to define the concept of “information overload”. In order to do so, a concept analysis using Rodgers' approach was performed. Design/methodology/approach: A concept analysis using Rodgers' approach based on a corpus of documents published between 2010 and September 2020 was conducted. One surrogate for “information overload”, which is “cognitive overload” was identified. The corpus of documents consisted of 151 documents for information overload and ten for cognitive overload. All documents were from the fields of computer science and information science, and were retrieved from three databases: Association for Computing Machinery (ACM) Digital Library, SCOPUS and Library and Information Science Abstracts (LISA). Findings: The themes identified from the authors’ concept analysis allowed us to extract the triggers, manifestations and consequences of information overload. They found triggers related to information characteristics, information need, the working environment, the cognitive abilities of individuals and the information environment. In terms of manifestations, they found that information overload manifests itself both emotionally and cognitively. The consequences of information overload were both internal and external. These findings allowed them to provide a definition of information overload. Originality/value: Through the authors’ concept analysis, they were able to clarify the components of information overload and provide a definition of the concept
Human Trafficking Situation in Ukraine and Stakeholder Mapping 2023: A Report Produced for the European Institute for Crime Prevention and Control (HEUNI)
No full textThis report was commissioned within the context of the ELECT THB project, which aims to enhance the identification and investigation of trafficking in human beings (THB) for sexual and labour exploitation and increase collaboration between law enforcement authorities and other key actors – in particular practitioners - to combat it. This report reviews the current (as of February 2023) situation in Ukraine and reflects on specific challenges posed by the ongoing Russian invasion and war; it looks into the modus operandi (MO) of perpetrators of trafficking for labour exploitation, sexual exploitation and possible other forms of human trafficking
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