Search Results (5,148)

Zearalenone (ZEN), a stable mycotoxin with estrogenic activity produced by various Fusarium species, poses a serious food safety risk. To facilitate the rapid, sensitive, on-site detection of ZEN in maize and ensure consumer dietary safety, a colloidal gold immunochromatographic assay (CG-ICA) based on a monoclonal antibody was established. ZEN was converted via oxime derivatization into hapten ZAN-O, which was conjugated to a carrier protein to prepare an immunogen for producing a highly specific and sensitive monoclonal antibody. Then, the antibody was conjugated into colloidal gold nanoparticles (AuNPs) and used as capture bioprobes of the CG-ICA test strip. The highly sensitive and specific detection platform was established through systematic optimization of pH value, coating antigen concentration, antibody-labeling dosage, incubation time, and strip assembly conditions. Under optimized conditions, the strip exhibited a detection limit of 11.79 pg/mL and an IC₅₀ of 99.06 pg/mL, with a linear detection range of 13.40–732.48 pg/mL. In addition, the anti-interference capability assay demonstrated that the developed test strip possessed excellent specificity. In spiked maize samples, the CG-ICA test strip demonstrated recoveries ranging from 85.36% to 98.86%, with relative standard deviations (RSDs) below 10%. Thus, the CG-ICA strip provides a rapid, sensitive, and robust on-site tool for ZEN screening in maize, and can be adapted to other hazards by simply switching the antibody. Full article

Photoacoustic spectroscopy is a promising method for detecting dissolved acetylene (C₂H₂) in transformer oil, facilitating early fault diagnosis in power transformers. However, temperature variations significantly influence the resonance frequency of the photoacoustic cell, potentially reducing detection accuracy. This study investigates the temperature effects on the first-order longitudinal acoustic mode of a resonant photoacoustic cell using finite element simulations with thermo-viscous acoustics. The results show that as the temperature increases, the resonant frequency increases linearly and the sound pressure amplitude decreases, consistent with analytical models. To enhance system robustness, a perturbation observation method is proposed, treating operating frequency as the independent variable and acoustic pressure as the dependent variable. Time-domain simulations validate its effectiveness in tracking resonance frequency shifts under varying temperatures, ensuring reliable detection. Future work should focus on improving frequency resolution, noise filtering, and adaptive step-size optimization for practical applications. Full article

Background/Objectives: Secondary stroke prevention is a cornerstone of long-term recovery and healthy aging among older adults, yet adherence to preventive strategies remains suboptimal. This global systematic review aimed to synthesize evidence from randomized controlled trials evaluating interventions that support sustained secondary prevention in older adults after stroke. Methods: A systematic search of PubMed and Scopus databases was conducted up to April 2025, following PRISMA 2020 guidelines and registered in PROSPERO (CRD420251177501). Eligible studies included randomized controlled trials targeting adults aged 60 years or older and assessing pharmacological, behavioral, educational, rehabilitative, or technology-assisted interventions for stroke recurrence prevention. Data were narratively synthesized due to study heterogeneity, and methodological quality was appraised using the Cochrane RoB 2 tool. Results: Seventeen randomized trials involving approximately 17,000 participants met the inclusion criteria. Multicomponent programs integrating medication management, behavioral education, exercise, cognitive rehabilitation, and digital support consistently improved adherence, vascular risk control, and quality of life. Pharmacological strategies alone showed limited or transient benefits, underscoring the importance of patient education and sustained follow-up. Common barriers included low motivation, cognitive decline, and technological challenges, while key facilitators were personalized education, multidisciplinary coordination, and culturally adapted implementation. Conclusions: Effective secondary stroke prevention in older adults depends on integrated, person-centered models that combine education, behavioral reinforcement, and technology-assisted monitoring. Structured, continuous educational programs, embedded within rehabilitation and primary care, emerge as the most promising pathway to improve adherence, reduce recurrence, and promote active, autonomous aging. Full article

This exploratory study examines how discursive design—using provocative, speculative artifacts to spark reflection and discussion—might expand public health experts’ problematization of approaches to tailoring and targeting interventions. Cultural tailoring and targeting (CTT) refers to adapting interventions for specific sociocultural populations. Because LGBTQ+ communities experience disproportionately high rates of tobacco use, this study applies discursive intervention concepts within this context to explore how they might help experts critically engage with CTT strategies for reaching LGBTQ+ populations more effectively. To investigate this, two pairs of discursive intervention concepts were designed and presented to three focus groups of public health experts. Each pair juxtaposed a conventional intervention approach with a more provocative, unfamiliar one—for example, deepfake-driven behavior disruption. The goal was to document the type of conversation discursive design could stimulate around CTT considerations and generate insights relevant to the value of design methodologies to foster new ways to problematize public health matters. Findings indicate that the concepts prompted critical conversations about CTT, although the depth and focus of engagement varied. Those with greater expertise in LGBTQ+ issues engaged more with CTT mechanisms and implications, while others focused on implementation and feasibility concerns—essential to intervention development but outside the study’s focus. These patterns highlight who should be included in such efforts and how they should be engaged from a facilitation perspective, raising important considerations for methodological refinements and future research. Overall, this initial exploration aims to uncover the potential of discursive design to deepen understanding of CTT interventions and inform more responsive, innovative approaches to addressing tobacco use among priority populations. Full article

Heat extremes or heatwave events have significantly impacted socioeconomic activities and ecological systems, causing serious health issues and increased mortality rates in Pakistan over the past few decades. This study investigates the relationship between heat extremes in the northern Indian Ocean’s sea surface temperature (SST) and atmospheric temperature over Land (ATL) in Pakistan, and their connection to the Niño 3.4 Index, for monthly (March–August) and seasonal (spring and summer) basis from 1979 to 2015. Results show that SST has a higher frequency of heat extreme anomalies over different stretches of days than ATL. On a seasonal scale, heat extremes in ATL showed a significant correlation with SST, while the relationship was insignificant on a monthly basis. Both ATL and SST exhibited strong associations with the Niño 3.4 Index for land and ocean. These findings suggest that large-scale ocean-atmosphere interactions, particularly El Niño Southern Oscillation (ENSO), play a key role in modulating heat extremes in the region. The results of this study support SDGs by improving adaptive capacity and resilience on health, hunger, and climate by guiding policymakers in mitigating heat extremes. Integrating the findings of this study into national and provincial heat extreme plans may facilitate timely resource allocation and adaptation strategies in one of the world’s most climate-vulnerable regions. Full article

Aquaporins (AQPs) facilitate transmembrane transport of water and small solutes, critically influencing plant growth, development, and stress adaptation. However, tea plant AQPs (CsAQPs) remain incompletely characterized genome-wide. In this study, 61 CsAQPs were identified from the tea plant genome and could be classified into five subfamilies. The bioinformatics characteristics, including phylogenetic relationships, gene structures, chromosomal locations, conserved motifs, promoter cis-acting elements, and three-dimensional protein structure, were systematically investigated. Additionally, the expression patterns of CsAQPs in tea plants in response to abiotic and biotic stresses were comprehensively explored based on transcriptome data and qRT-PCR, suggesting that CsAQPs were closely associated with the tea plant responding to environmental adaptation. Notably, the functions of CsPIPs in response to drought and salt, as well as potential H₂O₂ transporters and their subcellular localization, were investigated in yeast. Collectively, our study delivers a complete genomic and evolutionary dissection of the CsAQPs gene family in the tea plant, providing valuable insights into their diverse functions for further investigation. Full article

Federated learning enables collaborative model training across decentralized devices, preserving data privacy by keeping sensitive information localized. Federated learning facilitates the development of robust machine learning models by leveraging diverse datasets distributed across numerous clients, without necessitating data centralization. Federated learning frameworks utilize a suite of aggregation algorithms, such as weighted averaging (FedAvg), proximal optimization (FedProx), and adaptive optimization (FedOpt), to perform a convergent synthesis of locally computed model parameter vectors, thus enabling distributed model refinement while maintaining data locality. Fuzzy logic provides a computational framework for approximate reasoning, enabling the modeling of imprecise and uncertain information through the utilization of fuzzy sets and linguistic variables. This study introduces the development of a novel fuzzy-logic-driven aggregation mechanism, FedFZY, for a federated learning approach. This implemented methodology eliminates the requirement for complex mathematical operations and derivative applications. To validate the efficacy and observe the operational behavior of the proposed methodology, the FedFZY method has been deployed on a photovoltaic solar energy generation system, comprising 14 clients. The obtained results have been subjected to comparative analysis with alternative aggregation methodologies, yielding demonstrably successful outcomes. Full article

Inclusive sports and recreational programs are essential for enhancing the physical, social, and psychological well-being of learners with disabilities. In South Africa (SA), and particularly in rural provinces such as Limpopo, the development of such programs remains limited due to infrastructural, socio-economic, and attitudinal barriers. This study explored the barriers and facilitators influencing inclusive sports and recreational opportunities, as well as their impact on the well-being of learners with disabilities in rural Limpopo. A qualitative study design was employed in selected rural special schools. Data were collected through semi-structured interviews with educators (n = 5) and focus group discussions with leaners with disabilities (n = 25) of ages ranging from 10 to 18 years using purposive sampling. Thematic analysis was guided by the Social Model of Disability, Self-Determination Theory, and Ecological Systems Theory. Findings revealed key barriers, including inaccessible infrastructure, a lack of adaptive equipment, and social exclusion. Educators further highlighted inadequate training, limited resources, and inconsistent policy implementation. Facilitators included teacher support, family involvement, and community initiatives. Participation in inclusive sports was associated with improved confidence, happiness, social skills, and belonging among learners. The study concludes that inclusive sports programs hold transformative potential in rural contexts. Addressing infrastructural gaps, teacher capacity community engagement policy implementation is critical for sustainable inclusion. Full article

Background: Patient safety through root cause analyses and action planning (RCA2) is often taught in healthcare system-specific formats, in ways that are not applicable to interprofessional teams. The purpose of this article is to describe and evaluate an interprofessional RCA2 simulation conducted via videoconferencing, where attendees used cause mapping to identify root causes. Methods: Educators from medicine, nursing, and pharmacy schools developed the curriculum. Sessions included residents from graduate medical education programs, senior nursing students, and pharmacy residents. Facilitators provided brief didactics, and the simulation reviewed a safety event, root cause analysis, development of action plans, and a formal debrief session. Google Suite tools were used to create a cause map and action plan. Participants completed the Interprofessional Collaborative Competencies Attainment Survey (ICCAS), facilitators conducted after-action reviews, and survey responses were used for quality improvement. Results: Most participants found this simulation helpful. There were significant improvements in self-perception of skills in the six ICCAS domains. Participants also noted that learning RCA would be helpful in their future practice. Conclusions: This patient safety simulation in an interprofessional team environment helped participants develop teamwork and an understanding of the RCA2 process. Participants learned to ask clarifying questions and voice concerns, which is essential when identifying root causes. This process serves as a guide for teaching these skills. Future iterations can implement this simulation-based RCA and adapt it for other, diverse populations. Full article

Purpose: High-intensity interval training (HIIT) has emerged as a time-efficient alternative to traditional endurance training. This study investigated the molecular, systemic, and physiological adaptations induced by an 8-week HIIT program in national-level flatwater kayak athletes. Methods: Six trained male kayakers completed an 8-week HIIT intervention. Skeletal muscle biopsies and venous blood samples were collected before and after training to assess markers related to endocrine function, growth and remodeling, angiogenesis, and inflammation. Physiological and performance measures were evaluated using a maximal oxygen uptake (VO₂max) test and kayak ergometer trials. Results: HIIT elicited significant improvements across molecular, systemic, and performance parameters. Post-training analyses showed increased expression of IGF-1R, MMP-4, MMP-9, and TNF-α (p < 0.05), along with elevated serum testosterone concentrations (p < 0.05). Notable performance gains were observed in paddling speed at the second ventilatory threshold (PSVT2; p < 0.05) and in 1000 m (p < 0.01) and 200 m (p < 0.001) time-trial performances. Conclusions: An 8-week HIIT program effectively enhanced molecular signaling, systemic adaptation, and sport-specific performance in elite flatwater kayak athletes. The concurrent upregulation of anabolic, remodeling, and inflammatory pathways suggests that HIIT facilitates coordinated muscular and systemic adaptations beneficial for kayak performance. Full article

This study explores the dynamics of engaged spiritualities within contemporary Buddhist communities in Italy. By employing an ethnographic approach, the research examines how physical space fosters spiritual experiences, facilitates social interactions, and serves as a site for personal and collective transformation. The study integrates insights from religious studies, anthropology, and neuroscience to analyse the cognitive and emotional effects of meditation while also engaging with Foucault’s theories on power and space to understand Buddhist centres as structured environments that shape individual and collective subjectivities. The research highlights how engaged Buddhism in Italy adapts traditional practices to contemporary challenges, particularly in response to mental health concerns among university students. Through participant observation and interviews conducted during mindfulness and contemplative education programmes, the study demonstrates how meditation contributes to psychological well-being, emotional regulation, and social connection. This analysis aligns with theoretical discussions on the conceptualisation of spirituality in modern societies, illustrating how engaged spiritualities manifest in secular and pluralistic contexts. The findings suggest that Buddhist spaces in Italy function not only as sites of religious practice but also as transformative environments where power relations are renegotiated, identity is reconstructed, and alternative ways of living emerge. The study further explores how scientific advancements in neuroscience inform spiritual practices, shedding light on the reciprocal relationship between spiritual yearning and scientific inquiry. Finally, the research contributes to the debate on the future of engaged spiritualities in the face of global crises. It argues that while Buddhist communities in Italy preserve traditional wisdom, they also actively shape new forms of spiritual engagement that respond to contemporary social, political, and environmental challenges. This work situates engaged Buddhism as a key player in fostering alternative models of coexistence, well-being, and ethical responsibility in the modern world. Full article

In order to address challenges in small object recognition for remote sensing imagery—including high model complexity, overfitting with small samples, and insufficient cross-scenario generalization—this study proposes CAGM-Seg, a lightweight recognition model integrating multi-attention mechanisms. The model systematically enhances the U-Net architecture: First, the encoder adopts a pre-trained MobileNetV3-Large as the backbone network, incorporating a coordinate attention mechanism to strengthen spatial localization of min targets. Second, an attention gating module is introduced in skip connections to achieve adaptive fusion of cross-level features. Finally, the decoder fully employs depthwise separable convolutions to significantly reduce model parameters. This design embodies a symmetry-aware philosophy, which is reflected in two aspects: the structural symmetry between the encoder and decoder facilitates multi-scale feature fusion, while the coordinate attention mechanism performs symmetric decomposition of spatial context (i.e., along height and width directions) to enhance the perception of geometrically regular small targets. Regarding training strategy, a hybrid loss function combining Dice Loss and Focal Loss, coupled with the AdamW optimizer, effectively enhances the model’s sensitivity to small objects while suppressing overfitting. Experimental results on the Xingtai black and odorous water body identification task demonstrate that CAGM-Seg outperforms comparison models in key metrics including precision (97.85%), recall (98.08%), and intersection-over-union (96.01%). Specifically, its intersection-over-union surpassed SegNeXt by 11.24 percentage points and PIDNet by 8.55 percentage points; its F1 score exceeded SegFormer by 2.51 percentage points. Regarding model efficiency, CAGM-Seg features a total of 3.489 million parameters, with 517,000 trainable parameters—approximately 80% fewer than the baseline U-Net—achieving a favorable balance between recognition accuracy and computational efficiency. Further cross-task validation demonstrates the model’s robust cross-scenario adaptability: it achieves 82.77% intersection-over-union and 90.57% F1 score in landslide detection, while maintaining 87.72% precision and 86.48% F1 score in cloud detection. The main contribution of this work is the effective resolution of key challenges in few-shot remote sensing small-object recognition—notably inadequate feature extraction and limited model generalization—via the strategic integration of multi-level attention mechanisms within a lightweight architecture. The resulting model, CAGM-Seg, establishes an innovative technical framework for real-time image interpretation under edge-computing constraints, demonstrating strong potential for practical deployment in environmental monitoring and disaster early warning systems. Full article

In the realm of medical image processing, the segmentation of dermatological lesions is a pivotal technique for the early detection of skin cancer. However, existing methods for segmenting images of skin lesions often encounter limitations when dealing with intricate boundaries and diverse lesion shapes. To address these challenges, we propose VMPANet, designed to accurately localize critical targets and capture edge structures. VMPANet employs an inverted pyramid convolution to extract multi-scale features while utilizing the visual Mamba module to capture long-range dependencies among image features. Additionally, we leverage previously extracted masks as cues to facilitate efficient feature propagation. Furthermore, VMPANet integrates parallel depthwise separable convolutions to enhance feature extraction and introduces innovative mechanisms for edge enhancement, spatial attention, and channel attention to adaptively extract edge information and complex spatial relationships. Notably, VMPANet refines a novel cross-attention mechanism, which effectively facilitates the interaction between deep semantic cues and shallow texture details, thereby generating comprehensive feature representations while reducing computational load and redundancy. We conducted comparative and ablation experiments on two public skin lesion datasets (ISIC2017 and ISIC2018). The results demonstrate that VMPANet outperforms existing mainstream methods. On the ISIC2017 dataset, its mIoU and DSC metrics are 1.38% and 0.83% higher than those of VM-Unet respectively; on the ISIC2018 dataset, these metrics are 1.10% and 0.67% higher than those of EMCAD, respectively. Moreover, VMPANet boasts a parameter count of only 0.383 M and a computational load of 1.159 GFLOPs. Full article

To address the issues of high cost and poor low-temperature adaptability in cement-based backfill materials, this study developed a high-volume fly ash-based solid waste cementitious backfill material (FAPB) along with a specialized low-temperature admixture. Investigated the fundamental properties and microscopic curing mechanisms of the FAPB at different temperatures. The results indicate that the yield stress and plastic viscosity of FAPB slurry increase with higher contents of the curing agent and admixture, and rise as the temperature decreases. The variation in slump flow aligns with the rheological parameters, with the minimum slump flow being 14.5 cm (>10 cm). Bleeding rate increases with decreasing amounts of curing agent and admixture content, as well as lower temperatures, reaching a maximum bleeding rate of 9.26% (T5C10). Setting time decreases with increased amounts of curing agent and admixtures, and significantly increases with decreasing temperature. Strength increases with curing time and curing agent content, but decreases significantly as temperature drops. Adding admixtures can compensate for strength deterioration caused by low temperatures, with an optimal dosage of 3%. Microstructural analysis showed that the main hydration products of hardened backfill include AFt, C-S(A)-H, and Ca(OH)₂. Low temperature (5 °C) restricts hydration product formation, and the admixture facilitates continuous polycondensation of C-S(A)-H gel, resulting in sustained strength gain. This study provides a theoretical basis for the preparation and application of low-temperature-resistant Fly ash-based backfill materials, holding significant importance for advancing green mining practices in cold regions. Full article

The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article