Search Results (1,683)

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for rural older adults. However, existing research rarely links spatiotemporal patterns of outdoor activities to evidence-based thermal environment optimization, leaving a critical knowledge gap for age-friendly and sustainable rural design. This study focuses on the spatiotemporal differentiation patterns of daily outdoor activities among elderly people aged 60 years and above in rural Xi’an, as well as the optimization of spatial variations in thermal environments. Using on-site interviews, thermal environment measurements, thermal comfort questionnaires, continuous thermal environment monitoring, and machine learning based on random forest, this study drew the following conclusions: (1) outdoor activities in winter were concentrated between 9:00–11:00 and 13:00–17:00, while in summer, they shifted to the morning and evening periods, namely 6:00–9:00 and 17:00–21:00. (2) Models for outdoor clothing adjustment, thermal sensation, and thermal acceptability among elderly residents were established. The calculated neutral temperature was 10.19 °C, with a 90% outdoor thermal acceptability range of 9.6–27.2 °C and an 80% outdoor thermal acceptability range of 6.2–30.6 °C. These findings differ from those documented in regions with distinct climate zones and geographical settings. This discrepancy stems from regional climatic features, lifestyle variations between urban and rural older adults, and differences in the thermal environment quality of elderly-oriented outdoor activity spaces. (3) In winter, the acceptable period of the Universal Thermal Climate Index (UTCI) at south-facing entrances (10:30–16:30) was significantly longer than that in the courtyard (13:30–14:00). In summer, the comfortable period in the courtyard (before 10:00 and after 20:00) was longer than that at north-facing entrances (before 09:00). A random forest model for thermal sensation was established, and the relative importance of each parameter influencing thermal sensation was analyzed. On this basis, priority improvement pathways and strategies for the thermal environment, as well as suggestions for the subjective adaptive behaviors of elderly residents, were proposed. The research results of this study can provide technical solutions for age-friendly thermal environment design in rural areas, thereby safeguarding the comfort, health, and social well-being of the elderly population in rural areas. Full article

Existing thermal sensation prediction models typically examine the relationship between skin temperature and thermal sensation during cooling or heating seasons. However, due to significant fluctuations in indoor thermal environments during shoulder seasons and considerable individual variation in clothing preferences, traditional thermal sensation prediction models demonstrate poor predictive performance during shoulder seasons. This study aims to investigate the relationship between facial skin temperature and clothing insulation versus thermal sensation under shoulder seasonal conditions and to establish a predictive model for human thermal sensation influenced by clothing insulation. First, facial temperature data under different clothing conditions are collected online using infrared thermal imaging equipment. Subjective thermal sensations are obtained through questionnaires, enabling analysis of the influence of relationships among clothing insulation, facial temperature, and thermal sensation. Subsequently, correlation analysis is used to identify the facial temperature zones closely related to human thermal sensation. Finally, a random forest algorithm is employed to establish a thermal sensation prediction model. Research findings indicate that during shoulder seasons, the left and right cheeks and lips exhibit a higher correlation with thermal sensation. Due to variations in clothing insulation, thermal sensation models based solely on facial temperature characteristics demonstrate lower predictive accuracy and struggle to overcome interference caused by individual clothing differences. After incorporating clothing insulation as a key input feature parameter, the model’s Root Mean Square Error decreased from 0.869 to 0.533, representing a 38.7% improvement in prediction accuracy. This demonstrates that the clothing insulation parameter plays a crucial role in enhancing the precision of human thermal sensation prediction models during shoulder seasons. Full article

With the rapid expansion of the smart clothing market, designers face increasing pressure to balance functional performance, material suitability, environmental impact, and development efficiency. Conventional design workflows and rule-based assistance methods often struggle to provide adaptive and data-driven support for multi-constraint decision-making. To address this issue, this study proposes an AI-driven decision support system for sustainable smart clothing design based on a multi-scale dynamic graph convolutional network (MDGCN). The proposed system integrates material properties, environmental indicators, and user-oriented design requirements into a unified decision-support framework and further enhances feature extraction through an attention mechanism. Two datasets, the Wearable Technology Material Properties Dataset (WTMPD) and the Environmental Impact Assessment Dataset (EIAD), were used to validate the model and system effectiveness. Experimental results showed that the MDGCN-based model achieved accuracies of 0.964 and 0.943, with recalls of 0.923 and 0.920 on the WTMPD and EIAD datasets, respectively. In system-level evaluation, the proposed decision support system reduced design time from 120 h to 60 h, improved material selection accuracy to 90.2%, and achieved superior operational performance in terms of resource utilization (77.45%), energy consumption (115.25 kWh), and response time (1.56 s). These results demonstrate that the proposed framework can effectively support complex design decision-making while improving efficiency, sustainability, and adaptability in smart clothing development. The study provides a practical AI-enabled system innovation approach for sustainable smart clothing design by linking flexible material selection, environmental impact prediction, and designer-oriented decision support. In addition, the prototype deployment demonstrates the feasibility of applying the proposed system as a design-stage wearable AI tool for mediating human, technological, and environmental considerations in smart clothing development. Full article

Thermal comfort in operating rooms is critical for staff performance and safety, but conflicting requirements among professional groups create complex challenges. In a real operating room with a unidirectional airflow system, air velocity and temperature were measured, and predicted thermal sensation as well as the proportion of dissatisfied staff were calculated according to international standards. Analyses included surgeons, technical assistants, and anesthesiologists, considering clothing insulation, task-specific activity, gender, body mass index, and the use of lead aprons of different weights. Gender, body mass index, and temperature strongly influenced thermal comfort, whereas variation in air velocity had only minor effects. Thermal comfort targets diverged markedly between professional groups. Under identical conditions in our operating room, up to 75% of male surgeons wearing lead aprons experienced pronounced heat stress, whereas approximately 22% of female anesthesiologists experienced predominantly cold discomfort. Female surgeons would require temperatures as low as 16 °C to achieve thermal comfort, while nearly 50% of male surgeons perceived even this temperature as uncomfortably warm. Removing lead aprons reduced heat stress in surgeons but increased cold stress in anesthesiologists. Higher body-mass index improved heat dissipation in surgeons but aggravated cold stress in anesthesiologists. These findings demonstrate that uniform temperature settings cannot ensure thermal comfort for all professional groups. Practical implications include the need for role-specific strategies, such as targeted personal cooling or warming measures and differentiated clothing systems, to improve working conditions and maintain patient safety in operating rooms. Full article

The textile and clothing industry has historically exerted a significant negative impact on the environment. Excessive water consumption, chemical pollution, and soil degradation are just a few of the pressing environmental concerns linked to this sector. Addressing these issues has become a priority not only for regulatory bodies, at the National and European levels, but also for the industry itself. More recently, growing attention has turned to reducing the huge volume of waste generated by consumers’ unbridled purchase of clothing. In this context, the Circular Economy (CE) and the Digital Product Passport (DPP) have emerged as complementary approaches for improving product circularity, transparency, and traceability. However, in the textile and clothing sector, their effective implementation also depends on consumer participation in practices such as prolonged use, repair, reuse, and responsible end-of-life management. This article presents EcoProve, a gamified platform designed to encourage consumer engagement with CE practices through smart wardrobe management. The platform allows users to register garments, track usage, record maintenance and repair actions, and document sharing, donation, remaking, and recycling activities. These functionalities aim both to promote more sustainable clothing-related behaviours and to support the structured recording of use phase data relevant to DPP-oriented lifecycle information. This study reports the development and pilot validation of the platform with end users. The results suggest positive effects on environmental awareness, perceived understanding of sustainable textile-related practices, and initial self-reported changes in habits associated with clothing use and disposal. The findings support the potential of gamified digital platforms to foster consumer participation in CE systems in the textile and clothing sector while also indicating the need for broader and longer-term evaluations. Full article

Accurate detection of drowning victims in open water remains a major challenge for search-and-rescue (SAR) operations due to low illumination, reflections, occlusions, and complex backgrounds that degrade human visual performance. This study proposes a multi-modal AI-assisted UAV system for real-time drowning detection using a multi-rotor platform (<15 kg) equipped with integrated visual, thermal, and distance sensing, along with geolocation capabilities. A deep learning-based detection model was trained on 7103 images collected from real human subjects simulating four drowning scenarios in riverine and coastal environments, with additional stabilization and preprocessing modules to improve data quality. The proposed system achieves 98% detection accuracy, with a mean Average Precision (mAP@0.5) of 0.991 and a peak F1-score of 0.97. Results demonstrate reliable detection performance under challenging conditions, including low light, reflective water surfaces, and complex backgrounds, and show improved identification of low-contrast targets such as dark-clothed victims. These findings indicate that the proposed system provides a robust and scalable solution for real-time aquatic SAR applications and enhances the effectiveness of UAV-assisted rescue operations. Full article

In electrochemical nitrite detection, the strong oxidizing nature of nitrite often leads to high detection potentials, posing a significant challenge. To address this issue, this study successfully fabricated a nickel oxide/carbon cloth (NiO/CC) electrode using a one-step electrodeposition method followed by calcination. Taking advantage of the excellent electrocatalytic reduction properties of nickel oxide—particularly the surface oxygen vacancies that serve as active sites for efficient nitrite ion adsorption and promote the hydrogenation of the key intermediate (*NO)—the reaction energy barrier is substantially reduced. As a result, the NiO/CC electrode enables high-sensitivity nitrite detection at a low potential. Electrochemical evaluations reveal that the NiO/CC sensor performs excellently at −0.15 V (vs. Hg/HgO), featuring a linear detection range of 10–500 μM, a low detection limit of 0.091 μM (S/N = 3), and a high sensitivity of 2910 μA·mM⁻¹·cm⁻². These results highlight the promise of a catalytic reduction-based strategy for lowering detection potentials and provide a crucial foundation for the rational design of high-performance electrochemical sensing interfaces. Full article

Expandable graphite is recognised as an effective flame retardant because of its ability to absorb thermal energy by thermal liquid–gas conversion of the intercalant between the layers in its lamellar structure. Kevlar is widely used in protective clothing due to its excellent mechanical strength and thermal resistance; however, like many materials, it is vulnerable to degradation when exposed to high-energy laser systems, which causes carbonisation and material disintegration. This study demonstrates that coatings of expandable graphite can significantly enhance the thermal protection of Kevlar against 100 W laser radiation, up to 290 J/m², with no detectable thermal damage on the side facing the wearer, using 25 g/m² of expandable graphite. At the same loading (25 g/m²), the material containing expandable graphite provides adequate protection even at higher intensities, with degradation only starting at the highest intensity tested. Coating durability tests showed that the coating, especially when expandable graphite was included, protected the Kevlar substrate from abrasion for at least 10,000 cycles, making it suitable for applications such as laser-protective gloves. Full article

Natural disasters are devastating occurrences that have a major influence on the well-being of numerous individuals on a global scale. The primary goal of this study is to facilitate the rapid, transparent, and safe delivery of various aid such as food and clothing to people in disaster areas. For this purpose, a system has been established using blockchain technology in cooperation with institutions and humanitarian organizations. This system is designed to be accountable and reliable; it will supervise all processes from the source of aid materials to their distribution while protecting the personal information of disaster victims. The assistance process is improved using Smart Contracts in order to provide fast, effective, and coordinated assistance. Unlike existing humanitarian frameworks that rely on permissionless networks such as Bitcoin or Ethereum, this study proposes Hyperledger Fabric to ensure beneficiary privacy and eliminate per-transaction fees for end-users, thereby offering a more sustainable economic model for high-frequency aid distribution compared to public blockchains. The proposed system (Chainguard) addresses the ’efficiency gap’ in the current literature JSON Web Token (JWT)-based authentication layer. The results showed that Chainguard achieves a stable throughput of ~180 TPS with an end-to-end latency of less than 1.5 s, outperforming traditional heavy-cryptography models in terms of scalability and resource efficiency during real-time disaster response. Full article

Antimicrobial resistance (AMR) is emerging as one of the most serious global health problems, necessitating the urgent development of alternative approaches to pathogen control. The present study describes the synthesis and characterization of a novel iodinated BODIPY derivative (BODIPY5), designed as a highly efficient photosensitizer for antimicrobial photodynamic inactivation (aPDI). The molecular design of the compound involves the introduction of two iodine atoms into the BODIPY5 core, which induces a “heavy atom effect”, accelerates the intersystem transition from the singlet to the triplet state, and leads to increased generation of singlet oxygen upon irradiation with visible light. Photophysical measurements show a significant fluorescence quenching of BODIPY5 compared to its unsubstituted counterpart, which is a direct indicator of increased photodynamic activity. The compound’s antimicrobial efficacy was tested in a homogeneous medium and after immobilization on cotton textiles via physical adsorption. In solution, BODIPY5 nearly eliminated the model bacterial strains B. cereus and P. aeruginosa at a low concentration of 10 µg/mL under light, with cell viability below 1%. The functionalized cotton fabric exhibits pronounced self-disinfection properties, retaining high photodynamic activity against the Gram-negative pathogen P. aeruginosa. Scanning electron microscopy results confirm extensive morphological damage and loss of structural integrity in bacterial cells on the treated textile following irradiation. The non-specific mechanism of action, which generates reactive oxygen species (¹O₂) in situ, prevents the development of bacterial resistance and makes the developed material a promising candidate for use in hospital environments, including antibacterial clothing and protective equipment. Full article

Heating, ventilation, and air conditioning (HVAC) systems struggle to maintain optimal thermal comfort because perception is subjective and varies significantly across individuals. Traditional uniform cooling strategies often overlook demographic diversity, leading to inequitable comfort outcomes and inefficient building operations. To address this limitation, this study analyzed a web-based survey of 366 university occupants using a partial proportional odds model with multiple imputation and inverse-frequency weighting. Interaction terms, specifically Age–Activity, Gender–Clothing, and Age–Clothing, were included to assess combined effects that reflect demographic disparities in adaptive capacity. The results show that clothing insulation, activity, age, gender, race/ethnicity, and space type significantly influence thermal responses. Notably, male occupants were more than three times as likely to report feeling too warm (odds ratio [OR] = 3.24), whereas older adults exhibited significantly lower odds of reporting feeling too warm (OR = 0.42). Substantial variation was observed across racial and ethnic groups (ORs ranging from 2.4 to 6.5). These findings highlight the limitations of traditional population-average comfort approaches and provide valuable scientific insights for demand-response-ready HVAC strategies that adjust temperature setpoints dynamically without sacrificing comfort. By offering accurate, real-time estimates across diverse thermal ranges, these occupant-centric models reduce HVAC energy use and associated emissions at the building scale while supporting ancillary services for flexible load shifting and smarter coordination within low-carbon electric grids. Ultimately, incorporating demographic and contextual diversity into building controls reduces unnecessary cooling waste while promoting thermal equity, establishing a human-centric foundation for sustainable built environments. Full article

There is growing concern that antimicrobial use (AMU) in livestock may contribute to antimicrobial resistance (AMR) in humans and lead to the consumption of animal-derived foods contaminated with antimicrobial residues. As stakeholders in the livestock industry, farmers must participate in the joint effort to reduce AMU. This cross-sectional study, based on a survey questionnaire, was conducted to evaluate the biosafety measures implemented on small ruminant farms and to assess the knowledge, attitudes and practices (KAP) of small ruminant farmers regarding AMU, AMR and residues. The mean biosafety score obtained was 8.4 points on a 0–17 scale. Some biosafety measures appeared difficult to implement, namely vehicle disinfection, requiring visitors to change clothing and footwear at the farm entrance, cleaning and disinfecting farm facilities, using high-pressure washing equipment, and requiring employees to change clothing and footwear upon entering the farm. Although farmers self-reported moderate levels of knowledge (4.9 points on a 0–7 scale) and positive attitudes (5.8 points on a 0–7 scale), significant gaps in knowledge about antibiotics and antimicrobial stewardship persisted. Practices received lower scores (4.7 on a 0–7 scale), especially regarding medication recording, leftover antibiotic management, and waste disposal. Cluster analysis identified distinct farmer profiles with different patterns of knowledge and practices. These findings underscore the importance of considering farmer heterogeneity when designing interventions aimed at improving AMU. Full article

Echeveria spp. (Mexican hens and chicks) are among the most popular genera of succulents sold because they are compact and form attractive, symmetrical rosettes with brightly colored, fleshy, broad, iridescent leaves, as well as large, showy inflorescences. However, they are slow-growing, and flower induction protocols are not widely available. Therefore, the objectives of this study were (1) to determine if photoperiod and the photosynthetic daily light integral (DLI) can be manipulated to promote rapid growth and leaf expansion without excessive extension growth of several cultivars of Echeveria and (2) to establish the critical photoperiod for flower induction. Cuttings of E. spp. and hybrids ‘Apus’, ‘Canadian’, ‘Elegans Blue’, ‘Jade Point’, and ‘Topsy Turvy’ were received from a commercial breeder and grown in a greenhouse at 20 °C for 5 weeks. Photoperiods were created using a truncated 9 h short day (SD) or a SD extended to 10, 11, 13, 15, 16 h or a 4 h night-interruption (NI), using light-emitting diode (LED) lamps providing a total photon flux density of ≈2 μmol·m⁻²·s⁻¹ of red (R) + white (W) + far-red (FR) radiation. DLIs of 4.8 and 12.8 mol·m⁻²·d⁻¹ were maintained with and without shade cloth and supplemental lighting. Photoperiod and DLI interacted to influence the final height of E. ‘Canadian’, ‘Elegans Blue’, and ‘Jade Point’; plants were tallest under photoperiods > 13 h and low DLI. Similar trends were observed for growth index and average plant diameter. No clear trend was observed for leaf unfolding or leaf length across DLI or photoperiod treatments. Flower initiation of E. ‘Apus’ and ‘Jade Point’ was highest under a DLI of 12.8 mol·m⁻²·d⁻¹. Additionally, E. ‘Jade Point’ only developed inflorescences under day lengths ≤ 11 h, indicating an obligate SD response. Our results suggest that growers should maintain DLIs > 10 mol·m⁻²·d⁻¹ and SD conditions to promote flower initiation of the Echeveria cultivars tested. Such conditions would prevent excessive stem elongation and encourage flowering, increasing crop quality and marketability. Full article

Self-collision detection has become the dominant computational bottleneck in GPU-accelerated cloth simulation, as modern parallel solvers such as XPBD have drastically reduced the cost of position updates while leaving collision resolution largely unoptimized. Existing spatial partitioning methods treat all cloth regions uniformly, saturating GPU memory bandwidth despite the fact that the vast majority of the mesh surface remains geometrically flat and collision-free at any given frame. We propose a hierarchical self-collision culling framework built upon a resolution-independent discrete curvature metric derived from the $h^2$-normalized Laplace-Beltrami operator, integrated with a discrete Kirchhoff–Love shell model combining distance and dihedral bending constraints within XPBD. Unlike prior cache-dependent acceleration strategies, our method tightly couples curvature-driven geometric pruning with a fused GPU kernel design and shows that this stateless formulation is both faster and physically more reliable. Evaluated on meshes of $512\times 512$ and $1024\times 1024$ particles, our method achieves a $5.5\%$ and $9.7\%$ FPS improvement alongside a $34.9\%$ and $28.4\%$ reduction in active collision pairs, respectively, with qualitative validation via high-fidelity rendering confirming artifact-free self-contact and strict ground-plane non-penetration. Ablation results further reveal that temporal coherence, conventionally regarded as an optimization standard, strictly degrades both performance (FPS decrease of $1.4\%p$ to $1.9\%p$) and physical accuracy (penetration depth increase of $36.1\%$ to $100.0\%$ relative to the curvature-only stage) on RTX 3070 GPU, advocating for stateless per-frame geometric evaluation as the preferred design paradigm. Full article

With the rapid development of electronic devices and wireless communication systems, electromagnetic interference pollution has become a critical concern, driving the urgent demand for high-performance, lightweight, and flexible electromagnetic interference (EMI) shielding materials. To endow fabrics with excellent electromagnetic shielding, a Fe₃O₄/Ag/RGO ternary nanocomposite-coated cotton fabric for electrical conductivity and EMI shielding application was developed. The cotton fabric pretreated with dopamine was coated with graphene oxide (GO), followed by silver nanoparticles (Ag) via a microwave-assisted chemical reduction method, and Ag/reduced graphene oxide (RGO)-coated cotton. Subsequently, nano-ferroferric oxide was deposited on Ag/RGO-coated cotton fabric using a coprecipitation method. The results show that the surface resistance of Fe₃O₄/Ag/RGO-coated cotton fabric arrives at 1.68 Ω/sq, demonstrating excellent electrically conductive performance. Fe₃O₄/Ag/RGO-coated cotton fabric demonstrates outstanding electromagnetic shielding performance, with SE values exceeding 45 dB across the entire 1–18 GHz range. The flexibility and superior electromagnetic shielding performance of Fe₃O₄/Ag/RGO-coated cotton fabric render it a promising candidate for applications in wearable electronics, aerospace, advanced protective systems, and military protective clothing. Full article