Search Results (2,649)

Air quality management in greenhouses is critical to safeguarding plant health and occupational safety, yet conventional filtration methods often fall short in performance and sustainability. These enclosed environments are prone to the accumulation of bioaerosols, including fungi, bacteria, pollen, and dust particles, which can compromise crop productivity and pose health risks to workers. This review explores recent advancements in air filtration technologies for controlled environments such as greenhouses, where airborne particulate matter, bioaerosols, and volatile organic compounds (VOCs) present ongoing challenges. Special focus is given to the development of filtration media based on electrospun nanofibers, which offer high surface area, tunable porosity, and low airflow resistance. The use of biodegradable polymers in these systems to support environmental sustainability is examined, along with electrospinning techniques that enable precise control over fiber morphology and functionalization. Antimicrobial enhancements are discussed, including inorganic agents such as metal nanoparticles and bio-based options like essential oils. Essential oils, known for their broad-spectrum antimicrobial properties, are assessed for their potential in long-term, controlled-release applications through nanofiber encapsulation. Overall, this paper highlights the potential of integrating sustainable materials, innovative fiber fabrication techniques, and nature-derived antimicrobials to advance air filtration performance while meeting ecological and health-related standards. Full article

Industrial heritage—particularly large modern cement plants—serves as a crucial witness to the architectural and technological evolution of modern urbanization. In Europe, North America, and East Asia, many decommissioned cement factories have been transformed into cultural venues, creative districts, or urban landmarks, while a greater number of sites still face the risks of functional decline and spatial disappearance. In China, early large-scale cement plants have received limited attention in international industrial heritage research, and their conservation and adaptive reuse practices remain underdeveloped. This study takes the Huaxin Cement Plant, founded in 1907, as the research object. As the birthplace of China’s modern cement industry, it preserves the world’s only complete wet-process rotary kiln production line, representing exceptional rarity and typological significance. Combining social media perception analysis with the Hidalgo-Giralt four-quadrant model, the study aims to clarify the plant’s value positioning and propose a design-oriented pathway for spatial activation. Based on 378 short videos and 75,001 words of textual data collected from five major platforms, the study conducts a value-tag analysis of public perceptions across five dimensions—historical, technological, social, aesthetic, and economic. Two composite indicators, Cultural Representativeness (CR) and Utilization Intensity (UI), are further established to evaluate the relationship between heritage value and spatial performance. The findings indicate that (1) historical and aesthetic values dominate public perception, whereas social and economic values are significantly underrepresented; (2) the Huaxin Cement Plant falls within the “high cultural representativeness/low utilization intensity” quadrant, revealing concentrated heritage value but insufficient spatial activation; (3) the gap between value cognition and spatial transformation primarily arises from limited public accessibility, weak interpretive narratives, and a lack of immersive experience. In response, the study proposes five optimization strategies: expanding public access, building a multi-layered interpretive system, introducing immersive and interactive design, integrating into the Yangtze River Industrial Heritage Corridor, and encouraging community co-participation. As a representative case of modern Chinese industrial heritage distinguished by its integrity and scarcity, the Huaxin Cement Plant not only enriches the understanding of industrial heritage typology in China but also provides a methodological paradigm for the “value positioning–spatial utilization–heritage activation” framework, bearing both international comparability and disciplinary methodological significance. Full article

Falls are one of the most common and devastating effects of neurological diseases, especially in patients with stroke outcomes, Parkinson’s Disease (PD), and Multiple Sclerosis (MS). To prevent negative outcomes and guide tailored rehabilitation, it is necessary to identify risk factors early. The current study aims to assess whether and how the risk of falling is related to spatiotemporal and kinematic parameters in stroke, PD, and MS. It also seeks to determine how these factors can help manage patients and identify more personalized and appropriate rehabilitation treatments. Ninety patients with neurological disorders (stroke, PD, and MS) underwent eight weeks of home-based rehabilitation using the ARC Intellicare device or following a paper-based protocol. At baseline (T0) and at the end of the protocol (T2), they were assessed using the Silver Index of the hunova^® robotic platform to evaluate fall risk, and instrumental gait analysis to record spatiotemporal and kinematic parameters of walking. Statistical analysis showed moderate and significant correlations between the Silver Index and gait spatiotemporal parameters such as stance and swing phase, both in affected (T0, p = 0.007; T2, p = 0.017) and unaffected side (T0, p = 0.022; T2, p = 0.008), double support in affected side (T0, p = 0.002; T2, p = 0.005), cycle length in affected (T0, p = 0.007; T2, p = 0.003) and unaffected side (T0, p = 0.008; T2, p = 0.003), and cadence (T0, p = 0.025; T2, p = 0.003) in stroke patients. No significant results emerged in the PD and MS. No population showed significant correlations between the Silver Index and gait kinematic parameters. The Silver Index may reflect distinct patterns of instability in post-stroke gait, but in PD and MS, multiple factors influence the risk of falling that instrumental gait analysis cannot fully capture, requiring a more extensive and multidimensional approach that includes cognitive aspects. Full article

Falls are a prominent issue in society and the second leading cause of unintentional death globally. Traditional gait analysis is a process that can aid in identifying factors that increase a person’s risk of falling through determining their gait parameters in a controlled environment. Advances in wearable sensor technology and analytical methods such as deep learning can enable remote gait analysis, increasing the quality of the collected data, standardizing the process between centers, and automating aspects of the analysis. Real-world gait analysis requires two problems to be solved: high-accuracy Human Activity Recognition (HAR) and high-accuracy terrain classification. High accuracy HAR has been achieved through the application of powerful novel classification techniques to various HAR datasets; however, terrain classification cannot be approached in this way due to a lack of suitable datasets. In this study, we present the Context-Aware Human Activity Recognition (CAHAR) dataset: the first activity- and terrain-labeled dataset that targets a full range of indoor and outdoor terrains, along with the common gait activities associated with them. Data were captured using Inertial Measurement Units (IMUs), Force-Sensing Resistor (FSR) insoles, color sensors, and LiDARs from 20 healthy participants. With this dataset, researchers can develop new classification models that are capable of both HAR and terrain identification to progress the capabilities of wearable sensors towards remote gait analysis. Full article

The fall armyworm (FAW) Spodoptera frugiperda is a polyphagous pest that causes significant damage to various crops and rapidly develops resistance to insecticides. Broflanilide, a novel meta-diamide insecticide, has shown effectiveness against lepidopteran pests, but the risk of resistance and associated fitness costs in FAW remain unclear. This study evaluated the development of resistance to broflanilide over nine generations of selection using the diet incorporation method at the 70% lethal concentration (LC₇₀) concentration. Following nine generations of selection, the LC₅₀ value increased from 0.134 mg/kg to 0.232 mg/kg, showing a 1.73-fold increase in resistance ratio (RR). The calculated heritability of resistance (h²) was 0.084, which suggested that resistance of FAW against broflanilide is evolving at a slow rate. Based on the projected rate of resistance progression, a 10-fold increase in LC₅₀ would take between 30.1 and 66.4 generations, assuming selection mortality rates of 90% and 50%, respectively. Fitness costs were evaluated using age-stage, two-sex life table analysis, revealing reduced fecundity and pupal weight in the broflanilide-selected (Brof-SEL) strain compared to the wild-type. The relative fitness of the Brof-SEL strain was 0.38, indicating trade-offs in biological traits. These findings suggested a low risk of rapid resistance development against broflanilide. However, effective integrated pest management strategies against FAW require the judicious use of this insecticide in combination with biological control measures, including the deployment of parasitoids and predators, to promote a more environmentally sustainable approach. Full article

Background: Dynapenia refers to the age-related decline in muscle strength that occurs even when muscle mass is preserved. It has become an important issue in older adults because reduced strength is strongly linked to many negative health outcomes. When dynapenia occurs together with obesity—referred to as dynapenic obesity or dynapenic abdominal obesity—the risks, including mortality, falls, and the development of multiple chronic conditions, appear to increase even further. This umbrella review aimed to bring together and summarize existing systematic reviews and meta-analyses that examined how dynapenia and its obesity-related subtypes are associated with mortality, falls, and multimorbidity among community-dwelling older adults. Methods: Following PRISMA 2020 and JBI guidelines, six major databases and search engines (PubMed, Embase, Cochrane Library, Scopus, CINAHL, and Airiti Library) were searched from their inception to October 2025. Systematic reviews and meta-analyses involving adults aged 60 years and older and reporting quantitative results on the relationships between dynapenia-related conditions and adverse health outcomes were included. The methodological quality of each review was evaluated using AMSTAR 2, and the certainty of evidence was assessed with the GRADE approach. This umbrella review followed the PRIOR framework and was reported according to PRISMA 2020. The protocol for this review was registered in PROSPERO (ID: CRD 42023415232). Results: A total of four systematic reviews and meta-analyses were included, covering more than 73,000 community-dwelling older adults. The pooled data showed that dynapenic obesity significantly increased the risk of all-cause mortality, with hazard ratios ranging from 1.50 (95% CI 1.14–1.96) to 1.73 (95% CI 1.38–2.16). Dynapenic abdominal obesity was also strongly linked to falls, with pooled estimates ranging from HR = 1.82 (95% CI 1.04–3.17) to RR = 6.91 (95% CI 5.42–8.80). For multimorbidity, older adults with dynapenia had 1.38 times higher odds of having two or more chronic diseases than those without dynapenia (OR = 1.38, 95% CI 1.10–1.72). Based on the GRADE evaluation, the certainty of evidence was moderate for mortality and falls and low for multimorbidity. Conclusions: Overall, the findings indicate that dynapenia and its obesity-related forms meaningfully increase the risks of mortality, falls, and multimorbidity among community-dwelling older adults. Importantly, these results position dynapenia not merely as a musculoskeletal condition, but as a clinically relevant marker of aging-related vulnerability. This underscores the need for early screening of muscle strength alongside obesity-related indicators, as well as the development of integrated preventive strategies that combine strength-oriented interventions with obesity management in older populations. Full article

Unstable car-following behavior under truck-induced visual occlusion on mountainous two-lane roads significantly increases rear-end crash risk. However, compared with studies focusing on overtaking or curve risk prediction, the car-following-truck (CFT) risk and its influencing factors have received limited attention. Therefore, this study used unmanned aerial vehicles (UAVs) to collect high-resolution trajectory data of CFT scenarios on both straight and curved segments under truck-induced occlusion. First, the CFT risk was quantified based on an anticipated collision time (ACT) indicator, a two-dimensional surrogate safety measure that accounts for vehicle acceleration variations. Then, extreme value theory (EVT) was applied to calibrate alignment-specific risk thresholds. Finally, an XGBoost-based risk identification model was developed using vehicle dynamics-related features, and feature importance analysis combined with partial dependence interpretability was conducted to obtain key influencing factors. The results show that the calibrated ACT thresholds are approximately 3.838 s for straight segments and 4.385 s for curved segments, providing a reliable basis for risk classification. In addition, the XGBoost-based risk identification achieved accuracies of 90.63% and 95.87% for straight and curved segments, respectively. Further analysis indicates that CFT distance was the contributing factor. Moreover, risk increases markedly within a 10–20 m range on straight segments, while it rises rapidly once spacing falls below about 10 m on curved segments. Speed and acceleration differences exhibited stronger amplifying effects under short-spacing conditions. These findings provide a micro-behavioral basis for safety management and intelligent driving applications on mountainous roads with high truck mixing rates, supporting safer and more sustainable traffic operations. Full article

Slag entrainment within the mold is a significant cause of surface defects in continuously cast slabs. As a key component for controlling molten steel flow, the structure of the submerged entry nozzle directly influences the flow field characteristics and slag entrainment behavior within the mold. This paper employs a 1:4-scale water–oil physical model combined with numerical simulation to investigate the effects of elliptical and circular submerged entry nozzles on slag entrainment behavior in a wide slab mold under different casting speeds and immersion depths. High-speed cameras were used to visualize meniscus fluctuations and oil droplet entrainment processes. An alternating control variable method was employed to quantitatively delineate a slag-free “safe zone” and a “slag entrainment zone” where oil droplets fall, determining the critical casting speed and critical immersion depth under different operating conditions. The results show that, given the nozzle immersion depth and slag viscosity, the maximum permissible casting speed range without slag entrainment can be obtained, providing a reference for industrial production parameter control. The root mean square (RMS) of surface fluctuations was introduced to characterize the activity of the meniscus flow. It was found that the RMS value decreases with increasing nozzle immersion depth and increases with increasing casting speed, showing a good correlation with the frequency of slag entrainment. Numerical simulation results show that compared with elliptical nozzles, circular nozzles form a more symmetrical flow field structure in the upper recirculation zone, with a left–right vortex center deviation of less than 5%, resulting in higher flow stability near the meniscus and thus reducing the risk of slag entrainment. Full article

Artificial intelligence (AI) promises large productivity gains, yet growing concern surrounds its implications for social sustainability. This paper develops and empirically evaluates a simple behavioral framework in which unequal access to AI generates mutually reinforcing gaps in economic performance and social visibility, potentially undermining the long-run stability of social systems. Individuals fall into two groups—AI adopters and non-adopters—and differences in productivity and social recognition give rise to two exchange rates: an Economic Exchange Rate (EER), capturing relative economic advantage, and a Social Exchange Rate (SER), capturing relative social visibility and recognition. AI strengthens the feedback between economic success and social standing, and the joint evolution of EER and SER is stable only when the product of two feedback parameters lies below unity. When this threshold is approached, the system enters a regime of systemic disequilibrium, in which economic and social disparities expand endogenously. Using panel data for 30 economies over the period 2012–2025, we provide empirical evidence of strong mutual reinforcement between economic and social advantage, with feedback strength rising as AI diffusion accelerates. The findings suggest that unequal AI access poses risks not only to equality but to social sustainability itself. The paper contributes a diagnostic framework for socially sustainable AI diffusion, highlighting the need for policies that dampen amplification mechanisms and strengthen inclusive pathways from economic performance to social recognition. Full article

Background/Objectives: This study aimed to describe changes in physical performance and nutritional status among frail adults at risk of falls receiving muscle-targeted oral nutritional supplementation (MT-ONS) as part of routine clinical care. Methods: A prospective, open-label, single-centre, uncontrolled, descriptive study was conducted in a real-world clinical setting. Patients ≥ 70 years attending an outpatient fall clinic were consecutively recruited and assessed at baseline and after at least 90 days of MT-ONS (100% whey protein enriched with leucine and vitamin D), provided as part of a comprehensive care plan including exercise recommendations, medication review, and home adaptation advice. Sociodemographic, physical performance [Short Physical Performance Battery (SPPB)], nutritional status [Mini Nutritional Assessment-Short Form, (MNA^®-SF)], walking ability [Functional Ambulation Categories (FACs)], number of falls, muscle strength (dynamometry), body composition (Tanita), health-related quality-of-life (SF-12), functional capacity (Barthel Index), and adherence data were collected. Statistics analyses were descriptive and exploratory. Results: Twenty-six participants were assessed (58% women, age: 82.1 ± 5.4 years). Mean SPPB score increased from 7.3 (±3.6) to 8.0 (±4.0) (p = 0.3). At baseline, 35% were malnourished, 42% at risk of malnutrition, and 23% well-nourished. After ≥90 days of MT-ONS, 4% were malnourished, 54% at risk, and 42% well-nourished. The number of falls decreased from 1.2 falls/month (±0.9) to 0.2 falls/month (±0.3, p < 0.0001). Favourable changes in physical performance were positively correlated with improvements in nutritional status (p = 0.03). Adherence was high (92%), largely attributed to pleasant taste (71%) and smell (58%) and positive health perceptions (58%). Conclusions: In routine clinical practice, frail adults at risk of falls who received MT-ONS, 100% whey protein enriched with leucine and vitamin D for ≥90 days, as part of a comprehensive care plan improved their physical performance and nutritional status and reduced the number of falls. These findings should be interpreted as preliminary. Full article

Forestry logging is among the most hazardous economic activities, so identifying where hazards and risks concentrate supports targeted prevention. This scoping review mapped evidence on logging hazards and risks, their co-occurrence with operations, and preventive measures. PRISMA-ScR was followed. Only peer-reviewed journal articles (2015–2025) in English on occupational hazards/risks, risk-assessment methods or preventive measures in logging were included, found in Scopus, Web of Science, Inspec and Dimensions (last search 15 September 2025). Independent data screening and extraction were performed by two reviewers, with a third reviewer resolving any disagreements. No formal risk-of-bias appraisal was conducted. Forty-two studies were included. Hazards and risks concentrated in three phases—chainsaw/manual cutting, skidding/cable yarding, and loading/short-haul transport—where acute injury mechanisms (struck-by events, slips/trips/falls, rollovers, lacerations) coexisted with chronic exposures (musculoskeletal strain, noise, vibration, diesel exhaust). Preventive measures emphasised engineering and organisational controls, complemented by raining and PPE, but were inconsistently specified and evaluated. Evidence was heterogeneous and geographically concentrated in few countries, limiting generalisability. A small set of tasks consistently concentrates acute and chronic risks; prevention should integrate accident control and health protection, prioritising engineering/organisational measures supported by training and PPE. Future studies should standardise descriptors and outcome metrics to enable comparisons. Full article

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

Despite the extensive application of machine learning (ML) methods to educational datasets, few studies have provided a systematic benchmarking of the available algorithms with respect to both predictive performance and interpretability of the resulting models. In this work, we address this gap by comparing a range of supervised learning methods on a freely available dataset concerning two high schools, where the goal is to predict student performance by modeling it as a binary classification task. Given the high feature-to-sample ratio, the problem falls within the small-data learning regime, which often challenges ML models by diluting informative features among many irrelevant ones. The experimental results show that several algorithms can achieve robust predictive performance, even in this scenario and in the presence of class imbalance. Moreover, we show how the output of ML algorithms can be interpreted and used to identify the most relevant predictors, without any a priori assumption about their impact. Finally, we perform additional experiments by removing the two most dominant features, revealing that ML models can still uncover alternative predictive patterns, thus demonstrating their adaptability and capacity for knowledge extraction under small-data conditions. Future work could benefit from richer datasets, including longitudinal data and psychological features, to better profile students and improve the identification of at-risk individuals. Full article

Falls are a leading cause of bone fractures among the elderly, particularly hip fractures resulting from side falls. This research deals with the feasibility of application of shear-thickening fluids (STFs) to design self-protective wearable devices to rapidly respond to sudden impact due to falls. The device consists of a lightweight, flexible foam structure embedded with STF-filled compartments, which remain soft during normal movements but stiffen upon sudden impact, effectively dissipating energy and reducing force trans-mission to the bones. First, a foam-based sandwich panel filled with STF is fabricated and subjected to several falling scenarios through a ball drop test. The induced strain of the device with and without STF is measured using Fiber Bragg Grating (FBG) sensors. Then, the effect of localized STF is explored by fabricating a soft 3D-printed (TPU) sandwich panel filled with STF at selected cavities. It was observed that the application of STF reduces the induced strain by approximately 50% for the TPU skin device and 30% for the foam-based device. This adaptive response mechanism offers a balance between comfort and protection, ensuring wearability for daily use while significantly lowering fracture risks. The proposed solution aims to enhance fall-related injury prevention for the elderly, improving their quality of life and reducing healthcare burdens associated with fall-related fractures. Full article

Accurately assessing collapse risks of high-elevation, concealed rock mass blocks within the steep cliffs of Bijiashan, Three Gorges Reservoir Area, is challenging. This study employed a multidimensional approach—integrating airborne Light Detection and Ranging (LiDAR), the transient electromagnetic method (TEM), close-range photogrammetry, horizontal drilling, and borehole optical imaging—to characterize the rock mass structure of the WD1 cliff belt and delineate 52 individual blocks. Stability analysis incorporated stereographic projection for macro-scale assessment and employed mechanical models specific to three primary failure modes (toppling, sliding, falling). Finite element strength reduction quantified the stress–strain response of a representative block under natural and rainstorm conditions. Particle Flow Code (PFC) simulated dynamic instability of the exceptionally large block W1-37. Results indicate the WD1 rock mass is highly fractured, with base sections prone to weakness. Toppling failure dominates (90.4%). Under rainstorm conditions, the average Factor of Safety (FOS) decreased by 14.7%, and 73.1% of the blocks that were stable under natural conditions were destabilized—specifically transitioning to marginally stable or substable states—often triggering chain-reaction instability characterized by “crack propagation—base buckling”. W1-37 exhibited staged failure under rainstorm: “strain localization at fissure tips—penetration of basal cracks—overturning of the upper rock mass”. Its frontal rock reached a peak sliding velocity of 15.17 m/s, indicative of base-breaking toppling. The integrated “multi-technology survey—multi-method evaluation—multi-scale simulation” framework provides a quantitative basis for risk assessment of rock mass disasters in the Three Gorges Reservoir Area and offers a technical paradigm for similar high-steep canyon regions. Full article