Search Results (6,791)

Physical activity (PA) integration in special education has gained recognition as a neuroeducational intervention supporting emotional and social development in students with special educational needs and disabilities (SEND), yet teacher perceptions remain underexplored. This cross-sectional study examined how Israeli special education teachers perceive physical activity’s benefits and how teaching experience and educational setting influence these perceptions. A structured questionnaire was administered to 45 female special education teachers from northern Israel. The instrument assessed perceptions of physical activity’s emotional benefits, social outcomes, and implementation practices using Likert-type scales. Teachers strongly endorsed PA as a means to foster emotional resilience and coping, with most preferring group-based activities. Mixed activities were the most preferred approach, followed by movement games. Experienced teachers reported significantly stronger perceptions of emotional benefits compared to less experienced colleagues. Secondary teachers demonstrated higher extracurricular promotion and perceived greater social benefits than elementary teachers. Despite positive attitudes, implementation barriers, including infrastructure limitations and training gaps, were evident. These findings highlight physical activity’s potential as a neuroeducational tool for fostering regulation and inclusion while revealing the need for differentiated professional development, infrastructure investment, and policy integration. Full article

The floral choices of honey bees (Apis mellifera) were studied using artificial flower patches to understand how foragers manage changing floral landscapes. Bees were observed under conditions where reward quality changed over time in blue and white flowers. We evaluated initial learning and reversal learning, varying the magnitude of reward quality-difference and color distinctness in the honey bee’s color vision space (being either similar or more distinct). Flower color fidelity was higher when flower colors were more distinct, but it also made it more difficult for bees to abandon the flower color in the reversal learning phase. Smaller differences in reward quality reduced flower color fidelity and promoted reversal learning. When reward difference between flower colors was created (initial learning), a decrease in one of the flower color rewards elicited a stronger behavioral response from foragers than an increase in reward. Our work highlights that bees used and integrated information from different axes of information: distinctiveness of color cues, magnitude of reward difference, and directionality (being stronger for losses than gains). Thus, flower distinctiveness, opportunity cost, and loss aversion drive honey bee foraging decisions. Higher accuracy at initial learning has stronger costs in behavioral adaptations at changing floral landscapes. Full article

A novel dual-band dual-mode wearable button antenna for wireless body area network (WBAN) applications is proposed in this paper. The antenna ingeniously integrates a monopole structure and an optimized planar inverted-F antenna (PIFA) configuration in a shared radiator, enabling dual-mode operation with a compact size. In the low-frequency band, the monopole structure generates an omnidirectional radiation pattern, facilitating efficient on-body communication. Meanwhile, the PIFA structure in the high-frequency band exhibits directed radiation, optimizing off-body communication. To enhance bandwidth, a parasitic structure is incorporated into the design. Both numerical simulations and experimental measurements are conducted to evaluate the antenna’s bandwidth and radiation performance in free space and on-body environments, with results showing excellent agreement. The measured bandwidth of the antenna on the human tissue is 300 MHz (2.3–2.6 GHz) in the low-frequency band and 4.5 GHz (5.5–10 GHz) in the high-frequency band. The maximum radiation efficiency reaches 76% in the low band (2.4–2.4835 GHz) and 93% in the upper band (5.725–5.875 GHz). Additionally, the peak gain on the human body can achieve 2.5 dB and 6.9 dB for the low and upper bands, respectively. The results confirm that the antenna meets the design requirements for Industrial, Scientific, and Medical (ISM) band applications, making it a promising candidate for WBAN systems. Full article

This study investigates the electro-thermal transient response of a photovoltaic–thermal (PV/T)–heat pump system under dynamic disturbances to optimize operational stability. A dynamic model integrating a PV/T collector and a heat pump was developed by the transient heat current method, enabling high-fidelity simulations of step perturbations: solar irradiance reduction, compressor operation, condenser water flow rate variations, and thermal storage tank volume changes. This study highlights the thermal storage tank’s critical role. For V_tank = 2 m³, water tank volume significantly suppresses the water tank and PV/T collector temperature fluctuations caused by solar irradiance reduction. PV/T collector temperature fluctuation suppression improved by 46.7%. For the PV/T heat pump system in this study, the water tank volume was selected between 1 and 1.5 m³ to optimize the balance of thermal inertia and cost. Despite PV cell electrical efficiency gains from PV cell temperature reductions caused by solar irradiance reduction, power recovery remains limited. Compressor dynamic performance exhibits asymmetry: the hot water temperature drop caused by speed reduction exceeds the rise from speed increase. Load fluctuations reveal heightened risk: load reduction triggers a hot water 7.6 °C decline versus a 2.2 °C gain under equivalent load increases. Meanwhile, water flow rate variation in condenser identifies electro-thermal time lags (100 s thermal and 50 s electrical stabilization), necessitating predictive compressor control to prevent temperature and compressor operation oscillations caused by system condition changes. These findings advance hybrid renewable systems by resolving transient coupling mechanisms and enhancing operational resilience, offering actionable strategies for PV/T–heat pump deployment in building energy applications. Full article

Privacy risk mining, a crucial domain in data privacy protection, endeavors to uncover potential information among datasets that could be linked to individuals’ sensitive data. Existing anonymization and privacy assessment techniques either lack quantitative granularity or fail to adapt to dynamic, heterogeneous data environments. In this work, we propose a unified two-phase linkability quantification framework that systematically measures privacy risks at both the inter-dataset and intra-dataset levels. Our approach integrates unsupervised clustering on attribute distributions with record-level matching to compute interpretable, fine-grained risk scores. By aligning risk measurement with regulatory standards such as the GDPR, our framework provides a practical, scalable solution for safeguarding user privacy in evolving data-sharing ecosystems. Extensive experiments on real-world and synthetic datasets show that our method achieves up to 96.7% precision in identifying true linkage risks, outperforming the compared baseline by 13 percentage points under identical experimental settings. Ablation studies further demonstrate that the hierarchical risk fusion strategy improves sensitivity to latent vulnerabilities, providing more actionable insights than previous privacy gain-based metrics. Full article

Magnaporthe oryzae-induced rice blast remains a critical threat to sustainable rice farming, causing extensive losses in many rice-producing regions worldwide. Due to increasing concerns about pesticide overuse and its impact on the environment and human health, alternative control methods are being actively explored. Nanotechnology has recently gained attention as a potential tool for sustainable disease management. This review summarises current progress in the use of nanomaterials—including metal and biopolymer nanoparticles, nanoemulsions, targeted delivery systems, and biosensors—for the detection and control of rice blast. Studies have reported that nanomaterials can reduce disease severity by up to 70% and improve rice yield by 10–20% under field or greenhouse conditions. The mode of action, effectiveness under field conditions, and possible integration into integrated pest management (IPM) programs are discussed. The selection of literature followed the PRISMA-P framework to ensure a systematic and transparent review process. Challenges such as biosafety, environmental risks, and regulatory issues are also addressed, with emphasis on green synthesis methods and the need for field validation before practical application. Full article

This study evaluates the eco-efficiency and eco-productivity of 141 countries using data-driven analytical frameworks over the period 2018–2023, covering the pre-COVID, COVID, and post-COVID phases. We employ an input-oriented Slack-Based Measure Data Envelopment Analysis (SBM-DEA) under variable returns to scale (VRS), combined with the Malmquist Productivity Index (MPI), to assess both static and dynamic performance. The analysis incorporates three inputs—labor force, gross fixed capital formation, and energy consumption—one desirable output (gross domestic product, GDP), and one undesirable output (CO₂ emissions). Eco-efficiency (the joint performance of energy and carbon efficiency) and eco-productivity (labor and capital efficiency) are evaluated to capture complementary dimensions of sustainable performance. The results reveal significant but temporary gains in eco-efficiency during the peak pandemic years (2020–2021), followed by widespread post-crisis reversals, particularly in labor productivity, energy efficiency, and CO₂ emission efficiency. These reversals were often linked to institutional and structural barriers, such as rigid labor markets and outdated infrastructure, which limited the translation of technological progress into operational efficiency. The MPI decomposition indicates that, while technological change improved in many countries, efficiency change declined, leading to overall stagnation or regression in eco-productivity for most economies. Regression analysis shows that targeted policy stringency in 2022 was positively associated with eco-productivity, whereas broader restrictions in 2020–2021 were less effective. We conclude with differentiated policy recommendations, emphasizing green technology transfer and institutional capacity building for lower-income countries, and the integration of carbon pricing and innovation incentives for high-income economies. Full article

This study investigated the effects of integrating biofloc with microalgae on growth performance and immune gene expression in red tilapia (Oreochromis sp.). The experiment consisted of four treatments: C (Biofloc), T1 (Chlorella vulgaris and Nannochloropsis sp.; 1:1), T2 (Biofloc + Chlorella vulgaris and Nannochloropsis sp.; 1:1), T3 (Biofloc + Chlorella vulgaris and Nannochloropsis sp.; 2:1) in 500 L plastic tanks for 60 days. T2 and T3 exhibited the lowest ammonia and nitrite levels, respectively. T3 exhibited the highest chlorophyll a and chlorophyll b levels, while T2 showed the highest carotenoid content. T2 showed the highest weight gain (142 ± 0.7 g) and SGR (1.61 ± 0.02) and the lowest FCR (1.79 ± 0.009). T2 exhibited the highest gene expression levels in the intestine, with 7.8-fold upregulation of the cathepsin L (ctsl) gene, 3-fold upregulation of toll-like receptor 7 (tlr7), 6.7-fold upregulation of interleukin-1 b (il-1b), 4.7-fold upregulation of tumor necrosis factor-alpha (tnf-a), and 2.8-fold upregulation of metallothionein (mt). In the head kidney, the mt upregulation was highest in T3 (7.2-fold), while tnf-a and tlr7 upregulations were highest in T2 (5.9-fold and 5-fold, respectively). In the liver, the gene expressions were highest in T3, with 6.4-fold upregulation of mt, 5-fold upregulation of ctsl, 2.7-fold upregulation of tlr7, 3-fold upregulation of il-1b, and 5.4-fold upregulation of tnf-a. These results suggest a synergistic effect of algae and bacteria on immune and antioxidative capacity in red tilapia. Full article

Background: Assistance dogs can support children with autism by improving emotional regulation and social functioning, but access is limited. The Family Dog Service was developed to help families of autistic children gain similar benefits through companion dogs. It offers tailored support for selecting, training, and integrating a dog into the home. This study explored parent perspectives on the service and perceived impacts of companion dogs. Methods: A cross-sectional online survey was conducted among UK residents who attended Family Dog Service workshops. The survey included demographic data, mental health and wellbeing measures, and questions about human–animal interactions. Quantitative data were analysed descriptively; qualitative responses underwent thematic analysis. Results: Of 118 participants, 101 (85.6%) owned a dog, while 17 (14.4%) were considering acquisition. Most owners reported improvements in their child’s mood (75.2%; n = 76) and reduced anxiety-related behaviours (70.3%, n = 71) following dog acquisition. Nearly half (49.5%, n = 50) rated the child–dog relationship as ‘very’ or ‘extremely’ positive within the first month, increasing to 86.1% (n = 87) by the time of data collection. Families also reported enhanced dynamics and reduced caregiver stress. Despite some challenges, the service was valued for its autism-specific guidance and ongoing support. Conclusions: The Family Dog Service may offer a practical, accessible alternative to assistance dogs, supporting autistic children’s wellbeing and strengthening family relationships through positive interactions between children and their dogs. Full article

This study addresses the critical need to enhance productivity in industrial automatic systems by optimizing the mass of moving components. The primary challenge is determining the complex, dynamic loads on structural elements, a prerequisite for effective redesign, without access to physical prototypes for experimental measurement. This paper presents a solution through a case study of a load-bearing pylon in a fine blanking plant, which is subject to inertial loads and shocks from pneumatic actuators and shock absorbers. To overcome this challenge, a high-fidelity multibody simulation model is developed to accurately estimate the dynamic loads on the pylon. This data is given as input to the topology optimization (TO) process, following the Design for Additive Manufacturing (DfAM) framework, to redesign the pylon for mass reduction using a Powder Bed Fusion-Laser Beam (PBF-LB). Two materials, EOS Aluminum Al2139 AM and EOS Maraging Steel MS1, are evaluated. The findings demonstrate that the integrated simulation and redesign approach is highly effective. The redesigned pylon’s performance is verified within the same simulation environment, confirming the productivity gains before manufacturing. A cost analysis revealed that the additively manufactured solution is more expensive than traditional methods, and the final choice depends on the overall productivity increase. This research validates a powerful methodology that integrates dynamic multibody analysis with topology optimization for AM. This approach is recommended in the design phase of complex industrial machinery to evaluate and quantify performance improvements and make informed decisions on the cost-effectiveness of introducing AM components without the need for physical prototyping. Full article

Feeding a growing global population requires sustainable, innovative, and cost-effective solutions, especially in light of the environmental damage and nutrient imbalances caused by excessive chemical fertilizer use. Microalgae have gained prominence due to their phylogenetic diversity, physiological adaptability, eco-compatible characteristics, and potential to support regenerative agriculture and mitigate climate change. Functioning as biofertilizers, biostimulants, and bioremediators, microalgae accelerate nutrient cycling, improve soil aggregation through extracellular polymeric substances (EPSs), and stimulate rhizospheric microbial diversity. Empirical studies demonstrate their ability to increase crop yields by 5–25%, reduce chemical nitrogen inputs by up to 50%, and boost both organic carbon content and enzymatic activity in soils. Their application in saline and degraded lands further promotes resilience and ecological regeneration. Microalgal cultivation platforms offer scalable in situ carbon sequestration, converting atmospheric carbon dioxide (CO₂) into biomass with potential downstream vaporization into biofuels, bioplastics, and biochar, aligning with circular economy principles. While the commercial viability of microalgae is challenged by high production costs, technical complexities, and regulatory gaps, recent breakthroughs in cultivation systems, biorefinery integration, and strain optimization highlight promising pathways forward. This review highlights the strategic importance of microalgae in enhancing climate resilience, promoting agricultural sustainability, restoring soil health, and driving global bioeconomic transformation. Full article

Background/Objectives: The growing threat of antibiotic resistance necessitates the development of novel antimicrobial agents that effectively target pathogenic microorganisms while minimizing toxicity. Methods: Two series DABCO-based cationic homopolymers (D-subs 1kDa, D-subs 5kDa, D-subs 15kDa) and DABCO–pyridinium-based copolymers (PyH-subs 5kDa_Dsubs 5kDa, PyH-subs 7kDa_Dsubs 3kDa, PyH-subs 3kDa_Dsubs 7kDa) were synthesized to mimic to host-defense cationic peptides via ring-opening metathesis polymerization (ROMP). The antimicrobial activities of these polymers were determined by their minimum inhibitory concentrations (MICs) against E. coli (Gram-negative bacteria), P. aeruginosa (Gram-negative bacteria), S. aureus (Gram-positive bacteria), and C. albicans (fungus). In vitro cytotoxicity assays revealed selective toxicity towards bacterial cells, with high selectivity indices for several copolymers. To gain insight into the mechanism of action, morphological changes in S. aureus upon exposure to D-subs 1kDa were examined using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results: The D-subs 15kDa homopolymer demonstrated the highest overall antimicrobial activity, particularly against S. aureus (MIC: 8 µg/mL), with all polymers exhibiting minimal hemolytic activity (HC₅₀ ≥ 1024 µg/mL). SEM and TEM results revealed membrane disruption indicative of polymer–bacteria interactions. Additionally, stability studies confirmed polymer integrity under physiological conditions for at least 28 days. Conclusions: These results support the potential of DABCO-based cationic polymers as a promising platform for next-generation antimicrobial therapeutics. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterised by cognitive decline, synaptic loss, and multifaceted pathology involving amyloid-β (Aβ) aggregation, tau hyperphosphorylation, neuroinflammation, and impaired proteostasis. In recent years, biologic therapies, such as monoclonal antibodies, vaccines, antisense oligonucleotides (ASOs), and gene therapies, have gained prominence as promising disease-modifying strategies. In this review, we provide a comprehensive synthesis of current biologic approaches under clinical evaluation for AD. Drawing on data curated from ClinicalTrials.gov (as of 2025), we systematically summarise the molecular targets, therapeutic modalities, mechanisms of action, trial phases, and sponsors of over 60 biologic agents. These include Aβ-directed antibodies targeting distinct conformers such as protofibrils, pyroglutamate-modified species, and soluble oligomers; tau-targeted immunotherapies and RNA-based interventions; and emerging platforms focused on neuroimmune modulation, peptide hormones, and microbiota-based strategies. Gene and RNA therapeutics, particularly ASOs and small interfering RNAs (siRNAs) delivered intrathecally or via lipid nanoparticles, are also reviewed for their potential to modulate intracellular targets with high specificity. We also analyse the historical landscape of biologic candidates that failed to reach approval, discussing key reasons for trial discontinuation, including lack of clinical efficacy, safety concerns (e.g., amyloid-related imaging abnormalities), or inadequate biomarker responses. These cases offer crucial insights for refining future drug design. Looking ahead, we highlight major challenges and evolving perspectives in AD biologic therapy: expanding therapeutic targets beyond Aβ and tau, overcoming delivery barriers to the brain, designing prevention-oriented and genetically stratified trials, and navigating regulatory and ethical considerations. Together, these efforts signal a paradigm shift in AD drug development, from symptomatic treatment to mechanism-based precision biologics. By integrating real-time clinical trial data with mechanistic insight, this review aims to inform both translational research and therapeutic innovation in AD. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article