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PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home
No full textIndoor electrical systems are aimed to provide comfort to the occupants. However, their operation is contingent on the presence or needs of the residents. Hence, to optimize energy consumption and guarantee the desired comfort level of residents, any indoor energy control system should consider the occupancy dynamism within houses and the occupants’ behavior patterns.
Moreover, there is a growing demand for localized and personalized comfort controls in residential buildings to improve the occupants’ satisfaction. This paper presents a Personalized Energy and Comfort Management System (PECMS) that optimizes building energy consumption and meanwhile maintains residents’ intended comfort levels by predicting their trajectories.
Considering home thermal characterization, PECMS coordinates the building system devices and residents by anticipating
residents’ behavior using an activity mining and tracking method. With this capability, efficient scheduling of the electrical devices would be achieved. PECMS is simulated and tested on a dataset from a real-world smart home project, including the home’s actual thermal zones, temperatures, and residents’ preferences. Comparative analysis is conducted to evaluate PECMS against existing methods and systems, showcasing its effectiveness in achieving the desired trade-off between energy consumption and occupant comfort levels
Sick Britain: A call for mandatory Health Impact Assessments across government with the support of a dedicated ‘Health in All Policies’ support unit
Get PDFHealth is affected by a wide range of factors, from income, employment, and education, to pollution, access to green space and social networks. Health gaps arise through the unequal distribution of the wider determinants of health. Many of these factors are outside the direct control of the Department of Health and Social Care (DHSC). Working cross-government for health improvement is likely to lead to better designed and implemented policies which improve every aspect of society.
A Health in All Policies (HiAP) approach describes how health and health equity can be improved through embedding consideration of health in multi-sector decision making. Health Impact Assessments (HIAs) are a mechanism for delivering an HiAP approach across national and local governments, creating the conditions for healthy lives
Dynamic Multi-time Scale User Admission and Resource Allocation for Semantic Extraction in MEC Systems
Get PDFThis paper investigates the semantic extraction task- oriented dynamic multi-time scale user admission and resource allocation in mobile edge computing (MEC) systems. Amid prevalence artificial intelligence applications in various industries, the offloading of semantic extraction tasks which are mainly composed of convolutional neural networks of computer vision is a great challenge for communication bandwidth and computing capacity allocation in MEC systems. Considering the stochastic nature of the semantic extraction tasks, we formulate a stochastic optimization problem by modeling it as the dynamic arrival of tasks in the temporal domain. We jointly optimize the system revenue and cost which are represented as user admission in the long term and resource allocation in the short term respectively. To handle the proposed stochastic optimization problem, we decompose it into short-time-scale subproblems and a long-time-scale subproblem by using the Lyapunov optimization technique. After that, the short-time-scale optimization variables of resource allocation, including user association, bandwidth allocation, and computing capacity allocation are obtained in closed form. The user admission optimization on long-time scales is solved by a heuristic iteration method. Then, the multi-time scale user admission and resource allocation algorithm is proposed for dynamic semantic extraction task computing in MEC systems. Simulation results demonstrate that, compared with the benchmarks, the proposed algorithm improves the performance of user admission and resource allocation efficiently and achieves a flexible trade-off between system revenue and cost at multi-time scales and considering semantic extraction tasks
Black Hole Illusion in Aviation – A Simulator Experiment to Examine Predominant Criteria in a Real-Life Environment
Get PDFContinuous reports of aviation accidents related to Human Error and manual flying skills indicate the necessity for research in that area. Black Hole Illusion (BHI), an optical illusion that occurs during night-time visual approaches overhead featureless terrain and ultimately leads to fatal low flight paths, combines those factors. To increase flight safety in this phase of a flight, optical illusion theory and all factors that lead to this illusion were examined in an exploratory simulator study in real-life conditions with active airline pilots, representing a complete cockpit crew. The brightness levels of the approach lights as a conducive factor were examined for the first time in these conditions. A mixed-methods approach was used to quantitatively analyse the flown altitude errors in relation to the optimum flight path. Qualitative data was obtained via observations from the monitoring pilot. BHI and the optical illusion theory could be confirmed in the near approach sector before the runway. Both pilots seem to have not experienced BHI to the same extent. Findings can be used to recommend improvements in operational and pilot training policies
Advancements in Using Deep Learning Methods for GPR Detection of Tree Roots
Get PDFIn recent years, the effects of emerging diseases have caused significant worries among environmentalists and communities, requiring putting efforts into the monitoring and management of natural resources. In this regard, tree roots are one of the most vital and fragile organs of the tree, as well as one of the most complex to investigate. In this way, non-destructive testing (NDT) methods have become one of the most popular techniques for assessing and monitoring tree roots, as opposed to conventional destructive techniques. In this context, ground penetrating radar (GPR) applications have proved to be precise and effective for investigating and mapping tree roots. The inhomogeneity of the soil, however, is a significant obstacle towards the GPR identification of tree roots, and a deep learning (DL)-based method has been recently proposed to tackle this issue. This research, therefore, aims to improve upon the above-mentioned approach, by customising two convolutional neural networks (CNN) methods for the analysis of GPR spectrograms. In this study, the GPR signal is first processed in both the temporal and frequency domains to filter out noise-related information, and subsequently spectrograms are generated. Afterwards, two specifically modified CNN classifiers are implemented and then compared to other DL methods, already validated for tree roots detection. The findings of this study further support the viability of the suggested methodology and open the way for the application of new approaches for evaluating tree root systems
Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey
Get PDFMobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network’s generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications
New frontiers in healthcare environmental hygiene: thoughts from the 2022 healthcare cleaning forum
Get PDFHealthcare environmental hygiene (HEH) has become recognized as being increasingly important for patient safety and the prevention of healthcare-associated infections. At the 2022 Healthcare Cleaning Forum at Interclean in Amsterdam, the academic lectures focused on a series of main areas of interest. These areas are indicative of some of the main trends and avenues for research in the coming years. Both industry and academia need to take steps to continue the momentum of HEH as we transition out of the acute phase of the Covid-19 pandemic. There is a need for new ways to facilitate collaboration between the academic and private sectors. The Clean Hospitals® network was presented in the context of the need for both cross-disciplinarity and evidence-based interventions in HEH. Governmental bodies have also become more involved in the field, and both the German DIN 13603 standard and the UK NHS Cleaning Standards were analyzed and compared. The challenge of environmental pathogens was explored through the example of how P. aeruginosa persists in the healthcare environment. New innovations in HEH were presented, from digitalization to tracking, and automated disinfection to antimicrobial surfaces. The need for sustainability in HEH was also explored, focusing on the burden of waste, the need for a circular economy, and trends towards increasingly local provision of goods and services. The continued focus on and expansion of these areas of HEH will result in safer patient care and contribute to better health systems
Protocol for the feasibility and implementation study of a model of best practice in primary care led postdiagnostic dementia care: PriDem
Get PDFIntroduction: Care is often inadequate and poorly integrated after a dementia diagnosis. Research and policy highlight the unaffordability and unsustainability of specialist-led support, and instead suggest a task-shared model, led by primary care. This study is part of the PriDem primary care led postdiagnostic dementia care research programme and will assess delivery of an evidence-informed, primary care based, person-centred intervention. The intervention involves Clinical Dementia Leads (CDLs) working in primary care to develop effective dementia care systems that build workforce capacity and support teams to deliver tailored support to people living with dementia and their carers.
Methods and analysis: This is a 15-month mixed-methods feasibility and implementation study, situated in four National Health Service (NHS) primary care networks in England. The primary outcome is adoption of personalised care planning by participating general practices, assessed through a patient records audit. Feasibility outcomes include recruitment and retention; appropriateness and acceptability of outcome measures; acceptability, feasibility and fidelity of intervention components. People living with dementia (n=80) and carers (n=66) will be recruited through participating general practices and will complete standardised measures of health and well-being. Participant service use data will be extracted from electronic medical records. A process evaluation will explore implementation barriers and facilitators through methods including semistructured interviews with people living with dementia, carers and professionals; observation of CDL engagement with practice staff; and a practice fidelity log. Process evaluation data will be analysed qualitatively using codebook thematic analysis, and quantitatively using descriptive statistics. Economic analysis will determine intervention cost-effectiveness.
Ethics and dissemination:The study has received favourable ethical opinion from Wales REC4. NHS Confidentiality Advisory Group support allows researchers preconsent access to patient data. Results will inform intervention adaptations and a future large-scale evaluation. Dissemination through peer-review journals, engagement with policy-makers and conferences will inform recommendations for dementia services commissioning.
Trial registration number: ISRCTN11677384
Application of Decision-Making Techniques for Prioritizing Water Treatment Technology in Flood Events: A Preventive Crisis Management in the Czech Republic
No full textFlood is one of the phenomena that threaten people's life and property, which occurs every year in developed and developing countries [1]. Meanwhile, rapid response to water quality problems during this natural disaster is one of the most critical factors of an Early-Warning System (EWS). Due to the change in the river network and the washing of urban and rural environments, the quality of water in flood is significantly reduced, and the residents face the problem of water supply during this period [2]. This paper presents a fast response framework for selecting the best water treatment techniques in unusual pollution loads of urban floods based on water qualitative analysis and methods of Game Theory (GT) as decision-making techniques. The main goal of this study is to provide a framework for improving drinking water supply services during flood risk management in the Czech Republic. To achieve the fast water treatment technologies, Ordered Weighted Averaging (OWA), mulTi-noRmalization mUlti-distance aSsessmenT (TRUST) and
VIekriterijumsko KOmpromisno Rangiranje (VIKOR) computations as Multi Criteria Decision Making (MCDM) were applied. In fact, based on this structure, an operational model for the Czech Republic as per the Preventive Crisis Management (PCM) approach has been expressed as the primary outcome of this investigation. The results demonstrated that mobile membrane technologies could have higher efficiency than other methods. However, from the economic aspect, many options can be utilized in different scenarios according to the managerial opinions
A study of the required sustainability‑driven institutional and behavioural mechanisms to tackle the anticipated implications of agricultural water price shocks: a system dynamics approach
No full textEconomic policies for managing agricultural water use are often complicated by the challenge of using water prices as an efficient economic tool when other non-economic concerns are involved in the decision-making process. This study aims to analyse the impact of water pricing policies on preserving agricultural water resources in Iran. This study applies a system dynamics approach to simulate the system performance and behaviour of stakeholders and the economic implications.
Our finding shows that water pricing policies will likely fail due to low water price elasticity and if there are lack of institutional and physical infrastructure, alternative professions, manufacturing technology, education, and training opportunities. The results also illustrate how agricultural water price increase (AWPI) fails to reduce water consumption in the absence of an adequate institutional arrangement. Also, it shows how the lack of advanced institutional infrastructure in the presence of
physical infrastructure enhances pervasive overuse and destructive competition among stakeholders by increasing the area under cultivation. In the discussion, the paper portrays a way out of the decision-making body by following AWPI effects on water conservation in the agricultural sector as the most significant water consumer. It investigates the absence and subsequent presence of specific institutional conditions and evaluates training and enhancing farmers’ skills and alternative career source with higher income and technology as the architecture of good environmental governance.
Finally, it concludes that a series of inclusive measures must be considered to increase the elasticity of the water price. These measures must stimulate farmers towards pursuing the goals of global sustainable development and enhancing social welfare