Search Results (441)

With the accelerated progress of computational aesthetics and digital interior design, the demand for real-time and precise detection of aesthetic objects on edge devices has become increasingly pressing in applications such as intelligent design assistance, domestic aesthetic assessment, and augmented reality-based interior staging. As a core task in digital home aesthetics governance, virtual interior furnishing, household cultural archive development, and automated aesthetic evaluation, multi-view aesthetic object detection plays an essential role. However, this task still faces substantial difficulties arising from pronounced viewpoint variation, scale inconsistency, reflective materials, intricate decorative patterns, and cluttered indoor scenes. To address these issues, this study presents GhostNetV2-YOLO, a lightweight yet robust detection framework designed for accurate localization of aesthetic objects under unconstrained multi-view acquisition settings. The task is formally defined as closed-set detection of 10 pre-selected home aesthetic decorative items, including both planar decorative pieces and three-dimensional ornamental objects, and all performance claims are bounded within the horizontal bounding box detection paradigm. The framework incorporates three complementary components tailored to the target task. First, a task-adapted GhostNetV2 backbone is employed to enable efficient multi-scale feature extraction and long-range dependency modeling, with optimization specifically oriented toward structured aesthetic objects with stable global contours under viewpoint variation. Second, an improved Attention-based Intra-scale Feature Interaction (AIFI) module is introduced, integrating compressed QKV projection, linear attention, depthwise spatial refinement, and channel gating so that reflection-induced noise and background disturbance can be effectively reduced. Third, an enhanced Distance-IoU regression loss is adopted, in which explicit edge alignment and dynamic sample weighting are incorporated to improve boundary regression accuracy for rectangular and regularly contoured aesthetic objects. These designs jointly enhance contextual representation, boundary localization, and computational efficiency. Extensive experiments on two newly constructed multi-view aesthetic object datasets (AestheticHome-12K and AestheticHome-2K) demonstrate that the proposed detector achieves 94.80 ± 0.32%/94.20 ± 0.37% mAP@0.5, 96.30 ± 0.28%/95.60 ± 0.31% precision, and 94.70 ± 0.35%/93.80 ± 0.39% recall across two datasets (reported as mean ± standard deviation of 5 independent training runs with distinct random seeds), with only 2.89 M parameters and 6.0 GFLOPs. Statistical significance is verified via paired two-tailed t-tests with Bonferroni correction (adjusted p < 0.05) for all performance comparisons against baseline models. Compared with the YOLOv11n baseline, the method improves mAP@0.5 by 1.87–2.09 percentage points and recall by 3.27–3.48 percentage points while reducing computational cost. Notably, it also achieves 79.2–80.5% mAP@0.5:0.95, outperforming the baseline by 4.7–4.9 percentage points, indicating significantly superior localization accuracy under stricter criteria. The proposed model achieves a remarkable balance between accuracy and efficiency, making it highly suitable for deployment on resource-constrained edge devices commonly used in digital design and home aesthetic monitoring systems. The results indicate that combining lightweight long-range feature extraction optimized for rigid aesthetic objects, compact attention-based feature interaction for interference suppression, and geometry-aware regression tailored for aesthetic targets provides an effective and efficient solution for robust aesthetic object detection in real-world computational aesthetics and digital interior design applications. Full article

Rural houses in cold regions of China usually have high energy demands, particularly for space heating and domestic hot water. Passive design can reduce building energy demand, but additional renewable energy systems are still needed to improve on-site energy supply. This study evaluates the performance and limitations of photovoltaic (PV) and solar water heating (SWH) systems in a passive-designed rural house in Xi’an, China. Hourly simulations were conducted for PV-only and PV–battery configurations with different south-facing roof coverage ratios and battery capacities, together with an evacuated-tube SWH system. The results show that PV electricity supply was limited by the mismatch between household electricity demand and PV generation. Household demand mainly occurred in the morning and evening, whereas PV generation was concentrated around noon. The 13 m² PV case achieved approximately 11% electricity supply capacity with a utilization ratio of 62%, while increasing the PV area to 50 m² raised the supply capacity to only 15% and reduced the utilization ratio to 23%. With battery storage, the largest PV–battery configuration supplied 48% of annual household electricity demand, while the overall electricity utilization ratio was 73%, indicating a trade-off between household electricity self-supply and system utilization. The SWH system showed better applicability for domestic hot water supply, with an annual average hot water supply capacity of 60.2% and an average device efficiency of 43.5%, but its winter performance remained weak. These results indicate that PV and SWH are useful but insufficient solar energy strategies for passive-designed rural houses. PV is mainly constrained by daily time mismatch, while SWH is mainly constrained by seasonal climate variation. Full article

With heatwaves becoming increasingly frequent and intense in recent years, summer comfort is now a major issue for the design of low-energy buildings. Currently, most French homes are not equipped with air conditioning. To prevent the widespread adoption of this energy-intensive equipment while limiting indoor thermal discomfort during heatwaves, it is crucial to accurately design and assess climate adaptation measures. Occupants actively employ various strategies to limit overheating inside their dwellings, adapting their actions on the specific constraints they face. However, they are still frequently treated as passive entities in building performance simulations. This paper introduces an agent-based model, MOBAPY, developed to simulate the summer adaptive behaviour of households in urban dwellings. Grounded in qualitative data derived from semi-structured interviews, the model integrates commonly reported actions for one occupant profile, including the operation of windows, solar shading devices, air conditioning, and fans, alongside clothing adjustments. MOBAPY is applied to one case study: an urban dwelling, simulated under multiple climate scenarios and varying behavioural constraints within a building energy modelling framework. The results highlight the significant impact of occupant behaviour on summer comfort results for this occupant profile in a well-insulated dwelling. Notably, under the most severe future climate scenario, the percentage of uncomfortable occupied time drops to 20% when behaviour is unconstrained, whereas it surges to 68% under highly constrained conditions. Full article

Biomimetic wearable devices provide non-invasive physical support for elderly patients with dysphagia. However, most existing assistive products adopt fixed layouts designed through empirical methods and cannot meet differentiated needs in household, social, and medical scenarios. To fill this gap, this study constructed an integrated design framework combining the KANO-EWM-TOPSIS hybrid model and extended FCBS mapping. The KANO model was used to classify functional attributes and exclude mandatory safety indicators from weighting calculations to prevent data dilution. The entropy weight method (EWM) and TOPSIS were then adopted to calculate scenario weights and prioritize core functions. To resolve structural incompatibility in traditional rigid braces, a morphological biomimetic design scheme was proposed. Referring to the anatomical outline of the thyroid cartilage and movement rules of the infrahyoid muscle groups, a high-fidelity 3D digital model of the wearable collar was established. The biomimetic structure replicates the soft tissue compliance of the human neck. It offers adequate physical support without limiting vertical laryngeal displacement during swallowing. Simulated usability tests were conducted with 16 participants, and the fuzzy comprehensive evaluation (FCE) returned an overall score of 77.91. The digital design balances physiological safety and users’ psychological feelings while reducing obvious medical styling. This study offers a repeatable quantitative process and practical reference for developing human larynx anatomy-based biomimetic wearable rehabilitation devices at the preliminary design stage. Full article

Residential radon is an invisible radioactive gas produced during the uranium-238 decay series and is an established cause of lung cancer. In Canada, recent national surveillance suggests that about one in five residential buildings may exceed the national radon guidelines, yet household testing remains uncommon. This narrative clinical review translates international and Canadian evidence into practical primary-care and hospital-to-community actions. Evidence was prioritized from carcinogen classifications, World Health Organization guidance, pooled residential case–control analyses, Canadian surveillance and guidance, and the peer-reviewed literature on mechanisms, histology, and risk communication. Radon-222 can enter buildings from soil gas; its short-lived progeny deposits in the respiratory tract and emits high-linear-energy-transfer alpha particles that can damage bronchial epithelial DNA. Because elevated levels cannot be reliably predicted from symptoms, smoking status, house age, or community maps, clinicians should use a simple workflow: ask about prior testing and lower-level occupancy; test with a long-term, approved device in the lowest occupied level; act by interpreting the result, advising mitigation, and directing patients to certified radon professionals when needed; document the exposure assessment, result, advice, referral, and retesting plan; and follow up after mitigation or major building changes. This review clarifies Canadian residential and workplace guidance, contrasts global reference-level approaches, and distinguishes radon prevention from lung cancer screening. No individual patient information or human-subject data are reported. Full article

The increasing diffusion of Renewable Energy Communities offers new opportunities to support vulnerable households through locally generated renewable energy. However, current Home Energy Management Systems mainly optimize energy efficiency and cost reduction, while providing limited support for protecting critical household loads under constrained energy availability. This paper proposes an AI-based Energy Guardianship framework that combines a commissioning phase, in which a Local Appliance Atlas is created from the electrical signatures of the appliances actually installed in a specific dwelling, with an online phase that identifies operating appliances from aggregated measurements and dynamically allocates available energy according to appliance priority. Appliance identification is performed using rich electrical signatures including transient behavior, dynamic V-I trajectories, harmonic information, power profiles, and conventional electrical features extracted from aggregate voltage and current measurements. Unlike conventional home energy management systems, where appliance identification is mainly used to optimize energy consumption, the proposed framework exploits NILM information to support socially aware decisions that preserve critical services while delaying or limiting non-essential loads. A low-cost monitoring architecture is developed to recognize household appliances through electrical signatures and classify loads according to their criticality. When power thresholds are approached, the system recommends demand-side actions, postpones non-essential consumption, and protects critical devices. Preliminary simulation scenarios demonstrate the feasibility of the proposed framework in protecting vulnerable users under limited energy availability while simultaneously improving photovoltaic self-consumption and reducing dependence on grid energy. Although optimization is not the primary objective, the framework naturally supports renewable-aware energy scheduling and future interaction with energy service providers. Full article

Understanding indoor and outdoor airborne organic mixtures, including semi-volatile organic compounds (sVOCs), remains challenging as quantitative monitoring is often costly and difficult to scale across buildings and individuals. Here we present a low-cost, miniaturised passive sampler-based methodology for static and wearable deployment to generate time-integrated chemical fingerprints and source prioritisation. New sampler devices containing replicate 9 mm sorbent discs (Tenax^® TA and/or polydimethylsiloxane) were deployed for 28 days in indoor (kitchen, bedroom) and outdoor (roadside) environments and worn by five participants; extracts were analysed by liquid extraction and gas chromatography–mass spectrometry (GC-MS) using conservative, transparent criteria for tentative compound identification. Across the household deployments, 52 compounds met inclusion criteria and distinct room-specific and outdoor chemical signatures were observed. Wearable deployments also produced differentiable chemical profiles, with greater similarity among co-inhabitants, but still could differentiate co-habitant activities based on exposure. These results demonstrate the feasibility of using miniature passive samplers to obtain reproducible, information-rich profiles that can help discriminate environments and exposure scenarios. Full article

Background: Equitable engagement with technology-mediated learning, including artificial intelligence (AI)-supported pedagogies envisioned in current education policy, depends on material preconditions—school and home connectivity, device access—that are unevenly distributed. Empirical baselines for these preconditions in Central European secondary education remain sparse. This study examines digital infrastructure conditions among Slovak secondary-school students and tests whether connectivity and device access predict curriculum exposure to the smart-city concept, used here as one observable indicator of access to technology-mediated curricular content rather than as a direct measure of AI literacy. Methods: A cross-sectional survey collected data from N = 419 Slovak secondary-school students recruited through the Ministry of Education of the Slovak Republic, regional school authorities, and cooperating secondary schools. Self-rated school internet quality, home internet quality, and total household connected devices were analysed individually and combined into a standardised composite digital readiness index. Curriculum exposure to the smart-city concept (binary: any exposure vs. none/unsure) served as the outcome. Logistic regression was applied in unadjusted and gender-adjusted models (valid n = 383); component-level models tested which infrastructure dimension carried the association. Results: School internet quality was rated low (1–2 on a five-point scale) by 47.6% of students (mean = 2.59), whereas home internet quality was rated high (4–5) by 69.3% (mean = 3.89), indicating a substantial school–home connectivity gap. Only 35.8% of students (148/413) reported any curriculum exposure to the smart-city concept. School internet quality was the principal predictor: each one-standard-deviation (SD) increase corresponded to an odds ratio of 1.564 (95% CI: 1.261–1.939, p < 0.001), and in component-level models, neither home internet (OR = 1.038, p = 0.74) nor household device count (OR = 0.977, p = 0.83) carried independent predictive value. The composite index (OR = 1.337, 95% CI: 1.081–1.654, p = 0.0075 unadjusted; OR = 1.328, p = 0.0095 gender-adjusted) was essentially a noisier reflection of the school-connectivity signal. Male students were significantly more likely to report exposure than female students (OR = 1.794, 95% CI: 1.166–2.762, p = 0.0079). A readiness × gender interaction approached significance (OR = 0.641, p = 0.064), tentatively suggesting a steeper connectivity gradient for female students—an exploratory finding warranting replication. Model discrimination was modest (area under the curve, AUC = 0.621); the association, not predictive performance, is the quantity of interest. Conclusions: School-level connectivity—not home infrastructure or device count—is the infrastructure dimension associated with curriculum exposure to technology-mediated content. These findings indicate that school connectivity should be treated as a precondition for equitable participation in technology-mediated and AI-supported learning, and that pedagogical designs should function under infrastructural constraints. Limitations include reliance on self-rated measures, school-mediated convenience sampling, an observational cross-sectional design, and the indirect mapping between smart-city curriculum exposure and AI literacy proper. Full article

Smart-city advanced metering infrastructure enables utility-scale remote analytics, but some forms of under-registration closely resemble lawful changes in demand and are hard to model as anomalies. We study a narrow, physically motivated event family at the single-meter level, namely multiplicative under-registration with unknown onset (a shunt-like attack), in which recorded active energy is approximately scaled by a factor $\alpha <1$ after a change-point while the daily-profile structure and spectral shape remain invariant. We formalize the problem and develop a physics-guided detector family based on weighted daily-profile regression (GLS) and its robust variant (RGLS), with quality-control filters, spectral-consistency checks, and an optional reactive-channel gate, designed to stay selective under confounders such as rooftop photovoltaics, electric-vehicle charging, and heat-pump onsets. On a device-disjoint Low Carbon London benchmark (487 households) the preferred GLS detector attains precision $0.915$, recall $0.978$, and $F_1=0.945$ at $\alpha =0.10$ while keeping the non-theft suspected rate near $1\%$; a cross-dataset check on Open Power System Data with real EV/PV/heat-pump overlays yields zero false alarms on all 72 cases, and Mendeley and WPuQ benchmarks add a second large family and a reactive-channel test. We compare against external baselines (classical change-point detection, Isolation Forest, autoencoder, LSTM, gradient boosting, and a supervised statistical pipeline) on the same protocol: generic anomaly detectors fail on this shape-preserving attack, and supervised models match the detector only in-distribution while, unlike it, failing to transfer to real lawful confounders. All metrics carry bootstrap confidence intervals, and a full reproducibility bundle accompanies the submission. Full article

The increasing penetration of distributed renewable energy sources and electric vehicles (EVs) introduces significant operational challenges for residential energy management systems (HEMS), including stochastic renewable generation, uncertain load demand, device coupling, and physical safety constraints. This paper proposes a digital-twin-aided safe multi-agent reinforcement learning framework for coordinated energy management in renewable-integrated residential systems. The proposed approach models the battery energy storage system and the EV as independent agents and employs a multi-agent soft actor–critic (MASAC) algorithm with a centralised critic to capture the interactions among distributed energy resources. To improve decision quality under uncertainty, a digital twin module is developed to maintain a virtual representation of the residential energy system, synchronise operational states, update degradation-sensitive parameters, and generate short-term predictive information on photovoltaic (PV) generation and household load. The updated digital twin states and forecasts are incorporated into the observations of the reinforcement learning agents. In addition, a safety projection layer is incorporated to improve operational feasibility during both training and deployment. The environment considers realistic residential characteristics, including time-of-use electricity prices, battery degradation, EV mobility patterns, and grid energy trading. Simulation results show that the proposed framework reduces daily energy costs compared with rule-based baselines while maintaining EV charging reliability and operational feasibility. These results highlight the potential of combining predictive information, safety-constrained action execution, and multi-agent reinforcement learning for intelligent residential energy management. Full article

Wearable fitness trackers are increasingly popular for monitoring health-related metrics, yet their ownership patterns across socioeconomic, demographic, and gender-diverse populations remain underexplored at a population level. This study utilized data from the NIH All of Us Research Program to investigate how area-level socioeconomic status, race, and gender identity influence wearable device ownership. Methods. Data were analyzed from 633,547 participants from the All of Us Dataset. Fitbit ownership was modeled with four binary logistic regression models: a demographics-only model, a ZIP3-level socioeconomic indicators model, and a combined model incorporating four demographic × median household income interactions (race, gender, age, and Hispanic/Latino ethnicity), and an intersectional model adding a race x gender interaction. Continuous socioeconomic predictors were rescaled for interpretability (median income per USD 10,000; area-level fractions per 10 percentage points). Socioeconomic-adjusted models were restricted to 606,414 participants with available ZIP3-linked data. Fitbit ownership was defined as having a Fitbit record in the database. Results. Fitbit ownership was observed in 8.34% of the study population. Logistic regression analyses revealed significant demographic disparities: female participants and gender-diverse identities had significantly higher odds of ownership than males (OR = 1.25–2.2). Black or African American (OR = 0.38) and NHPI/MENA (OR = 0.82) participants had lower odds compared to White participants, while Asian (OR = 1.13), more than one race (OR = 1.25), and Hispanic or Latino (OR = 1.25) participants had higher odds. Each USD 10,000 increase in ZIP3 median household income was associated with 12.5% lower odds of ownership overall (OR = 0.875), but this gradient varied significantly by race. For Black or African American participants, the relationship reversed direction (OR = 1.08 per $10,000). A race x gender interaction further showed that female ownership was not uniform across race, being the largest among Black or African American participants (OR = 2.27) and reversed among Asian participants (OR = 0.87). ZIP3 socioeconomic data were structurally unavailable for all American Indian or Alaska Native participants due to the All of Us program’s small-population ZIP3 aggregation policy, precluding their inclusion in socioeconomic-adjusted models. Conclusions. This analysis demonstrates significant gender, racial, and socioeconomic disparities in wearable fitness tracker ownership, showing significantly higher device usage among females and gender-diverse individuals, but lower usage among certain racial groups and a seemingly contradictory negative ownership rates among higher socioeconomic levels. Ownership patterns nonetheless appear more equitable than in consumer cohorts, likely reflecting the device-provision programs undertaken by the NIH. Full article

This study examines motivational and capability barriers to internet non-adoption in Thailand’s near-saturated digital economy. Using the 2025 Q4 ICT Household Survey conducted by Thailand’s National Statistical Office, the analysis focuses on 20,633 adult non-adopters who report either motivational or capability-related barriers. The dependent variable distinguishes capability non-adoption, defined as lack of skill or awareness, from motivational non-adoption, defined as lack of perceived need or privacy/security concerns. Weighted logistic regression with normalised population weights, PSU-clustered robust standard errors, and average marginal effects is used to estimate associations between household ICT access, age, education, employment, smartphone access, and barrier type. Motivational barriers account for 56.2% of the two-category non-adopter population and capability barriers for 43.8%. Although motivational reasons are the more common, household ICT access is positively—if modestly—associated with capability rather than motivational barriers (average marginal effect +1.7 percentage points): capability-constrained non-adopters are concentrated in connected households, the compositional signature predicted by the second-level digital divide. Age does not significantly moderate this association. Among older non-adopters, education, employment, and smartphone access are negatively associated with capability barriers, while household ICT access is not. The findings suggest that in post-access digital economies, household connectivity is insufficient for digital market inclusion; individual-level skills and device access become central to expanding effective digital market reach. Full article

Background/Objectives: Advanced heart failure is associated with reduced functional capacity and impaired quality of life. Left ventricular assist devices (LVADs) are increasingly used as a long-term treatment option in patients with end-stage heart failure. Despite improvements in hemodynamic function after LVAD implantation, many patients continue to experience limitations in daily functioning. The aim of this study was to evaluate the relationship between physical activity and functional status in patients with LVAD support. Methods: This study included 262 adult participants divided into four groups according to LVAD support and declared physical activity. Functional status and quality of life were assessed using the Short Form-36 Health Survey (SF-36) and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Results: Significant differences were observed between the analyzed groups in both SF-36 and MLHFQ scores. Physically active patients with LVAD achieved the most favorable results, indicating a better functional status and lower symptom burden, whereas inactive individuals demonstrated poorer outcomes. Significant correlations were found between physical activity and selected aspects of daily functioning, including walking, climbing stairs, performing household activities, and carrying groceries. Higher levels of physical activity were associated with better quality of life and fewer functional limitations. Conclusions: Physical activity may positively influence functional status and quality of life in patients with LVAD support. The findings suggest that regular physical activity should be considered an important component of rehabilitation and long-term management in patients with advanced heart failure treated with LVAD therapy. Full article

Decoupled maximum power point tracking control and output voltage control can be accomplished simultaneously using dual-duty cycle control. However, developed triple switch triple mode (TSTM) exhibits absence of the common ground between the solar panel and output load therefore causing the leakage current to flow which creates safety concern especially for household electrification. In addition to having a negative effect on the solar panel, leakage current increases power losses. Thus, this work proposes a unique TSTM dc-dc converter. The suggested converter has the following advantages: (1) The presence of a common ground between the output load and the solar panel eliminates the leakage current. (2) Reduced electromagnetic interference issues present due to leakage current. (3) Enhanced voltage gain over wider duty cycle. (4) Enables simultaneous decoupled control of MPPT and output voltage. (5) Absence of voltage oscillation across the switches. The proposed TSTM converter is an unique combination of switched inductor and switched capacitor. Both inductor and capacitors are connected in order to boost the level of voltage at the output terminal. The operating principle, design equations and device stress are analyzed in detail for the proposed TSTM. The comparison over existing converter in terms of voltage gain and switch stresses are highlighted in details. Lastly, a laboratory prototype (40/400 V) for 400 W is created and thoroughly tested in order to validate mathematical calculations. Full article

This study develops a mobile-supported system that municipalities can use in their irregular waste collection services within the scope of smart cities. Irregular waste refers to waste that individuals or organizations produce non-periodically, which arises unexpectedly or in an unusual manner. Unlike small-volume household waste collected at routine times, irregular waste is generally large-volume waste such as construction rubble, vegetable oil, mineral oil, and garden waste. In the irregular waste collection system developed in this study, waste locations are marked on the map of an application running on mobile devices, and notifications are sent to the municipality. The Google Distance Matrix API was used for processing and visualizing the notification locations on the map. Daily or 4 h planning is carried out using this data. In this study, a genetic algorithm and a differential evolution algorithm were used for vehicle routing and vehicle type optimization. To compare the efficiency of both methods, four different scenarios were designed with different numbers of waste locations and different types and amounts of waste, and the successes of the methods were compared. Differential evolution is found to be on average 0.8% better. Optimizations performed with actual road distances were found to be 8.0% more successful than optimizations performed with Euclidean distances. Full article