Search Results (2,048)

Timely classification of cardiovascular diseases is crucial to improve medical outcomes. Emerging remote patient monitoring systems help achieve this by enabling continuous monitoring of electrocardiogram signals in home environments. However, these systems struggle with unique challenges like missing genuine medical emergencies, rising energy demands, scalability challenges, handling vast medical databases, data processing delays, and safeguarding patient records. To overcome these challenges, we propose a single framework with three main phases: (a) an embedded hardware-driven K-Nearest Neighbor (KNN)-assisted real-time ECG monitoring and classification method; (b) a differentiated communication strategy (DCS) formed with a priority-based ECG data packaging framework and multi-layered security protocols; and (c) a multi-level blockchain network (MLBN) architecture armed with adaptive security mechanisms and real-time cross-chain medical data communication bridges. Simulations are conducted using the ECG signals (1000 fragments) dataset and the Ganache Ethereum development framework. The classification accuracies obtained for patient urgent categories U1 to U5 are 91.43%, 95.71%, 94.23%, 90.00%, and 91.43%, respectively. The performance evaluation results of the KNN-guided classification method, along with DCS and MLBN simulation results obtained from average gas consumption analysis, confirms reliability and viability of our framework, while also revolutionizing remote patient monitoring technology and addressing critical challenges in existing systems. Full article

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

Maintenance is crucial for the durability of the existing building stock and should be perceived as an opportunity to improve the built environment. The implementation of thermal retrofitting measures to the building’s envelope enhances global energy performance, which is economically and environmentally beneficial. Building-related energy consumption during the operation phase is key to tackling carbon neutrality and climate change. Introducing thermal retrofitting within the context of maintenance planning can be cost-optimizing, as it reveals the technical–economic synergy between building pathology and energy efficiency. Maintenance activities and energy demand throughout the building’s service life influence life-cycle costs (LCCs). Decision-making based on LCC awareness is an advantage for owners. This study discusses the impact of implementing an optimal retrofitting solution (ORS), according to different maintenance strategies, on the LCC of an existing single-family home. The ORS comprises the following measures: adding an external thermal insulation composite system (ETICS) to external walls, extruded polystyrene (XPS) panels to the roof, and replacing the existing windows with others with improved thermal performance. The three maintenance strategies involve different complexity levels, concerning the type, number and timing of activities. Moving beyond isolated assessments, this study develops an integrated framework that bridges based on two existing background methodologies, involving optimal thermal retrofitting and condition-based maintenance planning, which, combined with new research, enable the assessment of maintenance, energy and global LCC for a time horizon of 100 years. The evaluation of energy-related LCC is based on simulations. The results indicate that these costs represent the majority of the global LCC. The ORS has a considerable positive impact on energy and global LCC. Adopting a maintenance strategy characterized by fewer planned activities and an earlier schedule of replacement interventions, which determines the implementation of the retrofitting measures, is better in terms of LCC savings. Full article

Background/Objectives: Cognitive dysfunction, or “brain fog”, following COVID-19 viral infection is strongly associated with diminished work capacity which disproportionality affects working-age adults. This study examined an existing method of cognitive rehabilitation training applied to adults struggling with workplace functioning and self-efficacy due to post-COVID-19 brain fog. Methods: Nine adults with post-COVID-19 cognitive dysfunction participated in this single arm pilot trial of a severity-adaptive cognitive training program. The participants completed 45–90 h of clinician-delivered cognitive training exercises delivered remotely in 60- to 90-min sessions, two or three times per week. The primary outcome measure was overall workplace self-efficacy with subskills of perceived workplace functioning, perception of cognitive functioning, and perception of home functioning assessed through pre and post surveys and qualitative interviews. The secondary outcome was cognitive function operationalized by an IQ score administered before and after the intervention. Results: The participants achieved significant improvements in workplace self-efficacy and cognition following cognitive training. The main qualitative themes of self-reported improvements were in executive function, health and energy, daily living activities, productivity, and socioemotional functioning. A cross-case synthesis of pre-intervention struggles, and post-intervention improvements revealed subthemes at work or school in cognitive processing and comprehension, memory, executive function, fatigue, emotional distress, confidence in work or academics, and work/academic performance impairment. As a group, the mean gain in IQ score was 10.5 points. Conclusions: This study adds to the growing body of literature examining the possibility of using cognitive rehabilitation for post-COVID-19 cognitive dysfunction impacting workplace self-efficacy and work functioning. Full article

Sleep quality is closely associated with cardiovascular, metabolic, and mental health outcomes, yet the clinical gold standard, polysomnography (PSG), is costly and intrusive for long-term home monitoring. Ballistocardiography (BCG) enables unobtrusive in-bed sensing and is therefore attractive for low-burden sleep assessment in natural environments. However, most existing BCG studies are PSG-referenced and mainly focus on sleep staging, while movement and out-of-bed episodes are often treated as artifacts rather than modeled jointly. In this study, we propose an interpretable unsupervised proxy-state modeling framework for three-state in-bed monitoring from BCG signals under an unlabeled setting. BCG recordings were segmented into 30 s windows with 50% overlap, and multi-domain features were extracted from waveform morphology, spectral power, heart rate-related dynamics, and wavelet energy distribution. K-means clustering (K = 3) was used to construct cluster-derived proxy labels, TreeSHAP-based feature ranking together with inner-CV-guided Top-N subset selection was used for training-only feature screening, and multiple classifiers were compared under a strict leave-one-subject-out protocol, with an ROA-optimized RBF-SVM achieving the best overall performance. Using data from 32 volunteers, the framework achieved an accuracy of 0.9932 ± 0.0047 (mean ± SD), together with consistently strong Macro-F1 and MCC scores. Overall, it outperformed the alternative methods compared in this study. Full article

In response to the redundant spatio-temporal modeling and insufficient adaptation to dynamic decision-making in eye-tracking interaction of smart home interfaces, a smart home interface eye-tracking response optimization model based on spatio-temporal Transformer and gate control cross-attention is proposed. It adapts the physiological characteristics of eye-tracking jumps through dynamic sparse attention gating to compress computational redundancy and combines multi-objective reinforcement learning attention modulation to construct a closed-loop decision-making mechanism, optimizing interface parameters in real-time. Experiments showed that the model reduced eye-tracking trajectory prediction error by 23.7% compared to advanced benchmarks, increased the success rate of adapting to dynamic mutation scenarios to 89.2%, and controlled performance fluctuations within 2.3% under noise interference. In high-fidelity user testing, the accuracy of cross-task gaze transfer reached 93.4%, the failure rate of glare interference was optimized to 2.4%, and the user cognitive load index was reduced by 27.9%. Its resource consumption and energy consumption were reduced by 26.7% and 44.9%, respectively, while its posture deviation tolerance remained at 3.5°. The sparse spatio-temporal modeling of the spatio-temporal adaptive Transformer module and the enhanced gating mechanism of the hierarchical gated cross-attention module work together to break through the limitations of traditional methods in computational efficiency and dynamic feedback, providing high-precision and low-latency eye-tracking interaction solutions for smart home interface systems, and promoting the practical evolution of personalized human–machine collaborative control. Full article

This study analyzes the feasibility of increasing the energy and economic efficiency of a residential heating and domestic hot water (DHW) preparation system with a solar-assisted air-to-water heat pump (AWHP), implemented in southeast Romania. The following options are evaluated from the sustainability point of view (energy, economic and CO₂ emissions): renovation of the building and modernization of the system by integrating an electric accumulator, increasing the capacity of photovoltaic panels (PV) and solar thermal collectors (STCs), and the option of replacing the AWHP with a ground-source heat pump (GSHP) with a vertical loop (GSHP-VL) and a GSHP with a horizontal loop (GSHP-HL). The energy performance of heating systems was simulated using GeoT*SOL software. The results show that by renovating a home, the energy requirement for heating decreased by about 58%; therefore, following the current financial rules applied to prosumers, the GSHP-VL system has the best energy performance (electricity consumption and solar coverage rate of this consumption), economic performance (investment recovery period and annual operating cost) and environmental performance (lowest CO₂ emissions) and that through a government program that promotes energy efficiency and the use of renewable energy sources in homes, capital costs can be reduced by (43–57)% in the case of systems with HP, PV and electric storage. This study shows that a 5 kW PV system combined with 5 kWh battery cannot cover the full heat demand of a medium-to-large house during the winter, and for full energy independence, a larger PV array paired with a higher-capacity battery is necessary. Generous government subsidies amounting to 50% can reduce the payback period for such investments from (11.26–14.68) years to (5.86–7.26) years. Full article

This paper aims to define the optimal microgrid topology for rural electrification based on the lowest total cost by comparing LVAC and LVDC microgrids across three different scenarios. An LVAC radial topology is first designed using mixed-integer linear programming for phase balancing and the shortest path for connections, then implemented with a genetic algorithm to allocate and size solar home systems, forming an LVAC microgrid. Next, an LVDC topology is then derived from the LVAC structure and integrated with solar home systems under three scenarios: (1) using the same solar home system sizes, locations, and quantities as the LVAC microgrid; (2) using a genetic algorithm to re-determine solar home system sizes and locations, forming an LVDC microgrid; and (3) clustering the LVDC topology into nano-grids, each defined by genetic algorithm for solar home system sizing and placement and connected to the main feeder via bi-directional converters. Finally, all LVAC and LVDC scenarios are simulated over a 30-year planning horizon for analysis. A non-electrified village located in Cambodia has been selected for a case study to validate the proposed methods. The results have been obtained and provide a comparison of performance indicators (i.e., costs, energy production, losses, CO₂ emissions, and autonomous energy) among the microgrids (LVAC and LVDC). The LVAC microgrid produced lower total energy losses than the LVDC microgrid in all scenarios. However, when considering environmental impact, LVDC Scenario 2 is preferable. Based on the total cost results, the LVAC microgrid is considered more economical than the LVDC microgrid in each scenario in this study. Full article

Stadiums are large buildings that attract attention due to their high energy consumption and environmental impact. Considering the effects of climate change, the integration of sustainable energy solutions and energy efficiency is of great importance in the design and planning of these buildings. This study focuses on pitch lighting, which accounts for a significant and fluctuating share of energy consumption in stadiums, and aims to reduce its carbon footprint through the integration of renewable energy. This study aims to analyze the feasibility of achieving a net-zero annual energy balance for different levels of field lighting of a football stadium in accordance with FIFA lighting standards with solar energy systems in different climate zones and under future climate change scenarios. In addition, it is aimed at revealing the effect of climate change scenarios and climate zone differences on the azimuth angle, tilt angle, and area of the solar panel. In the study, a stadium model was created using parametric design—Grasshopper—and optimization software; lighting systems were designed according to FIFA standards, and lighting performance on the field was optimized with simulations through ClimateStudio and Galapagos. Based on Liverpool FC’s home match data, the annual illumination time is calculated, and the azimuth angle, tilt angle, and area of the solar panel systems are optimized for different climate scenarios. The most useful result of this study is that it demonstrates that the solar panel area required to meet stadium lighting needs varies depending on climate scenarios and geographical conditions and that the same energy production can be achieved with less panel area in low-emission scenarios. For instance, simulation results for Liverpool under the RCP 2.6 scenario show a decrease in the required panel area from 86.09 m² in 2050 to 84.27 m² by 2100. Similarly, in Moscow for the year 2050, the medium-emission scenario (RCP 4.5) requires a larger panel area (92.22 m²) compared to the low-emission RCP 2.6 scenario (88.12 m²) to achieve the same energy output. Full article

Electrification is vital for economic growth, poverty reduction, and improved quality of life. Over 80% of Afghanistan’s rural population lacks electricity. Despite increasing interest in decentralized energy systems, there remains a lack of site-specific studies that jointly assess the technical, economic, and policy feasibility of decentralized solar PV for rural electrification in Afghanistan. This study addresses that gap through a mixed-method case study of Syahgel, Ghazni, combining a household survey of 30 households, PVsyst-based system sizing, economic evaluation, and policy analysis. The study compares multi-tier Solar Home Systems (SHSs) with a community microgrid under local demand and affordability conditions. The results show that SHSs, with entry-level costs starting from USD 95, are more suitable for small, dispersed settlements, while microgrids remain relevant for larger or more concentrated communities. Financing mechanisms, including subsidies and interest-free loans, can improve affordability by up to 75%, while electrification can reduce annual fuelwood expenditure by approximately USD 51.5 per household and generate broader health, educational, and livelihood benefits. The findings highlight the need for integrated policy reform, targeted financial support, and context-sensitive system design to support sustainable and inclusive rural electrification in Afghanistan. Full article

The wide voltage range of energy storage batteries, as currently required in the electric vehicle industry, presents significant challenges for the optimal design of the dual active bridge (DAB) converters used in bidirectional DC–DC (BCD) plug-in electric vehicle (PEV) chargers and home energy management systems (HEMS) applications. This article proposes a DAB converter with an enhanced single-phase-shift (ESPS) modulation that extends the operating voltage range while maintaining zero-voltage-switching (ZVS) conditions by including a DC-blocking capacitor and modifying the trigger sequence of the bridge converter on the secondary side. The operational modes of this modulation scheme are presented, and a control strategy is developed to extend the ZVS range. To validate the concept, a 3.7 kW, 100 kHz prototype is designed and tested, interfacing a 400 V DC bus with a 400–800 V battery. Using 1200 V silicon carbide (SiC) devices, the prototype achieves a peak efficiency of 95.5%. Full article

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

Understanding the socio-demographic drivers of residential energy-saving behavior is critical for designing effective energy policies and technologies. This study applies a Multiple Indicators, Multiple Causes (MIMIC) model to examine how individual characteristics are associated with two latent constructs: energy concern and smart-home interest and usage. Using survey data collected from urban residents in Guangdong Province (N = 261), structural equation modeling was employed to assess both measurement and structural components of the model. The results show that income is positively associated with both energy concern and smart-home interest and usage, whereas being a bill payer is negatively related to both. Gender also plays a role, with females reporting higher energy concern. Other factors, such as age, education, and time spent at home, did not show significant effects. The model showed acceptable global fit indices; however, the reliability and convergent validity of the latent constructs were limited. Accordingly, the findings should be interpreted as exploratory associations observed within this sample and may serve as a basis for future research on segmentation in similar urban contexts. Full article

Residential photovoltaic/thermal (PV/T) systems can reduce electricity consumption by supplying both electricity and heat; however, their performance depends on household composition and lifestyle-driven demand profiles. This study simulates a PV/T system for a detached house in Tokyo while accounting for occupant-behavior variability using Japanese time-use statistics from 2015 and 2020, which capture the pandemic-related increase in time spent at home in 2020. Both a PV/T system and a conventional PV system were evaluated for four representative household scenarios, reflecting changes in domestic hot water (DHW), space conditioning, and appliance electricity demand. In the 2020 dataset, the large-household case (Case C) showed the largest improvement in net electricity balance relative to the PV system, with an improvement of 1.8 GJ, while the elderly-couple case (Case D) achieved the highest overall thermal efficiency, with a DHW COP of 6.26 and a space-heating COP of 5.75. In the young-couple case (Case A), the CO₂ reduction increased from 169 kg in the 2015 dataset to 239 kg in the 2020 dataset, showing that lifestyle changes affected the energy-saving benefit. These findings indicate that lifestyle-dependent behavioral changes should be considered in PV/T performance assessment and system sizing. Full article

Small-scale prosumer installations are playing an increasingly important role in the Polish electricity sector. These primarily include photovoltaic systems and heat pumps installed for internal use. Noticeable losses for individual investors, generated by the power flow mechanism during peak production hours (connection to the grid) and peak demand (drawback from the grid), as well as the issue of fluctuating grid capacity and the observed redispatch procedures for photovoltaic installations, are driving increased interest in equipping home energy installations with energy storage systems, strengthening the aspect of sustainable energy development in this dimension. The impact of energy storage on investment motivation and the actual effects of incorporating it into home energy installations have not yet been sufficiently researched, particularly in Poland. Therefore, the aim of the study was to assess the use of energy storage in home installations as a socio-technical direction of power development at the micro level, in light of the constantly increasing energy demand observed worldwide in line with the challenges of sustainable development. The results of a survey of 206 individual users of power installations equipped with energy storage systems in Poland were used for this study. The research was qualitative and quantitative in nature, with descriptive statistics and a logistic regression model used in the in-depth section, and the findings were supported by PQStat software. The research revealed that the selection of energy storage systems in home power grids is related to the potential for prosumer optimization. On the other hand, they are seen as a path towards increasing energy security at the household level. Supporting this direction of installation development at the micro level is a justified concept for the development of green energy in Poland, socially and environmentally beneficial as well as economically justified, i.e., in line with the trend of sustainable development. The information campaign, combined with financial support for this type of investment, should be continued and strengthened in Poland. Full article