Search Results (12,121)

Foot-and-mouth disease (FMD) is a highly transmissible viral infection of livestock that threatens food security and causes substantial economic losses in endemic regions. Despite its economic impact, the role of environmental viral load and wildlife reservoirs in sustaining FMD transmission remains poorly quantified. The aim of this study is to assess the extent to which community viral load sustains FMD persistence and to identify key transmission drivers in a coupled livestock–wildlife–environment system. A Susceptible–Exposed–Infected (SEI) model with a free-living virus compartment was analyzed via the basic reproduction number ($R_0$) and solved numerically using a Nonstandard Finite Difference Method. Sensitivity analysis identified wild host population size, transmission rates, host recruitment, environmental viral decay, and viral load thresholds as major determinants of $R_0$. Results indicate that higher transmission rates accelerate susceptible depletion and increase exposed and infected classes, with wildlife dominating environmental viral contributions. Community viral load is central to sustaining outbreaks and informs targeted control strategies. Full article

Brain-on-a-chip (BOC) refers to a miniaturized in vitro platform that integrates living neuronal networks on a micro-engineered chip, enabling the simulation of brain functions, neural activities and physiological responses. BOC technology is an advanced evolution of microphysiological systems (MPS) and Lab-on-a-Chip platforms, providing novel paradigms for in vitro modeling and exploring early-stage biocomputing by interfacing living neural networks with engineered electronics. Microelectrode arrays (MEAs) serve as the critical physical interface for bidirectional communication in these systems. In this review, we systematically examine the technological landscape and engineering requirements of MEAs tailored for BOC applications, evaluating them across electrical characteristics, structural properties, and biocompatibility. Two primary classes of current MEA technologies, including planar arrays for 2D neural cultures and 3D flexible arrays for brain organoids, are discussed in detail. We highlight the transition from passive planar electrodes to high-density active CMOS and TFT-based arrays, and detail how 3D flexible MEAs utilize endogenous integration and exogenous wrapping strategies to overcome tissue-mechanics mismatches. Furthermore, the integration of MEAs with microfluidics, optoelectronics, and electrochemical sensors to enable multimodal monitoring is explored. With the advantages of the various MEAs, the application of MEAs for BOC, particularly in biological computing and network plasticity research, is discussed. Finally, future technological developments in scalability bottlenecks, chronic stability, and the incorporation of artificial intelligence for MEAs of BOC are prospected. Full article

Using our terahertz sensor, we addressed the agricultural challenge of nondestructively and cost-effectively detecting internal plant moisture. For plant health assessment, we developed a low-cost nanobolometer imaging sensor array. The proposed terahertz imaging system can detect changes in leaf moisture content under stress, even at low moisture levels. The system enables terahertz imaging of living plant tissues to assess moisture and nutrient distribution in leaves. Because terahertz radiation is non-ionizing and strongly interacts with water molecules, it can reveal internal plant processes. Plant development can also be monitored using time-series imaging. In addition, specialized software was used to enhance the quality of terahertz images and to fuse them with conventional images. This feature enables a more comprehensive assessment of plant health. Such an approach may support future applications, such as disease detection and evaluation of fertilizer effects. Full article

The rapidly aging global population is placing immense pressure on healthcare systems, which are struggling to meet the needs of older adults. Information and communication technologies (ICTs) are considered a key driver in supporting the development of age-friendly healthcare models. This scoping review aims to map and structure the multifaceted applications of ICTs in age-friendly healthcare, focusing on their design, benefits, challenges, and implementation in different contexts. We followed the PRISMA-ScR guidelines and conducted a systematic search of five major databases (Web of Science, Scopus, PubMed, ScienceDirect, and IEEE Xplore), supplemented with backward citation chaining to improve the robustness of literature identification. The results show that ICTs can help older adults by improving their access to healthcare information, enhancing their care coordination, supporting their independent living, and personalizing their health management. Key challenges include user experience issues for older adults, data privacy and security concerns, and implementation barriers related to resources and professional support. Effective implementation of ICTs requires greater emphasis on age-centered design, robust data governance, and scalable integration with existing healthcare systems. We further propose a Technology Design–Scenario Application–Effect Evaluation (TD-SA-EE) analytical framework for ICT application in age-friendly healthcare; the framework is grounded in sociotechnical systems theory to provide explanatory insights beyond descriptive classification. This research provides insights into optimizing age-friendly healthcare through ICTs and contributes to fully leveraging ICTs in building sustainable and equitable age-friendly healthcare systems. Full article

Recurrent intrauterine adhesions (IUA) and refractory thin endometrium are associated with impaired endometrial regeneration, reduced implantation, and poor live birth outcomes. Regenerative therapy using mesenchymal stromal cells (MSCs) has shown promising results; however, factors associated with reproductive success remain unclear. In this prospective, single-centre, single-arm uncontrolled observational study, 35 women with recurrent IUA and thin endometrium (<7 mm) unresponsive to standard surgical and hormonal therapy received combined subendometrial and systemic administration of placenta-derived MSCs. The primary endpoint was live birth. Secondary endpoints included clinical pregnancy rate, time to pregnancy, endometrial thickness changes, uterine blood flow (resistance index, RI), and anti-Müllerian hormone (AMH) levels. Univariable logistic regression was performed to identify factors associated with live birth. Clinical pregnancy occurred in 13/35 patients (37.1%), and live birth was achieved in 11/35 (31.4%). Median time to pregnancy was 7 (5–8) months. Shorter duration of infertility or prior pregnancy loss (OR 1.55 per year; 95% CI 1.10–2.57), AFS stage I adhesions (OR 6.8; 95% CI 1.1–42; p = 0.04), lower baseline RI in uterine, arcuate and radial arteries, and higher baseline AMH (OR 2.59 per doubling; 95% CI 1.15–6.89) were significantly associated with live birth. Endometrial thickness increased after therapy but was not significantly associated with live birth. No severe adverse events were observed. Placenta-derived MSC therapy was followed by live birth in 31.4% of women with recurrent IUA and refractory thin endometrium. A shorter duration of reproductive disorders, less severe adhesions, lower baseline RI in uterine, arcuate and radial arteries, and higher AMH levels were associated with live birth after treatment and may help identify patients with a more favourable reproductive prognosis in future controlled studies. Full article

Using surface observations, ADTD lightning data, and radar reflectivity from April-September 2022–2024 in the Xi’an terminal area, this study classified severe convective events into four categories: ordinary thunderstorms, short-duration heavy precipitation, convective wind gust, and hail events. Their temporal variability, spatial distribution, life cycle characteristics, and propagation pathways were systematically analyzed. The results reveal significant differences among convective event types across multiple temporal and spatial scales. Convective wind gust events exhibited the strongest interannual variability, with a decrease of 44% from 2023 to 2024. Hail events occurred relatively infrequently, totaling only 16 cases from 2022 to 2024. Seasonally, convective wind gusts were concentrated in April-May, while ordinary thunderstorms and short-duration heavy precipitation events mainly occurred in July–August. Most events initiated during the afternoon and intensified toward evening, with short-duration heavy precipitation events showing a bimodal diurnal variation. Ordinary thunderstorms were dominated by short-lived events lasting 30–60 min, whereas heavy precipitation, convective wind gust, and hail events were primarily associated with long-lived convective systems exceeding 180 min. Spatially, severe convective weather generally initiated in the western part of the terminal area and propagated eastward. Lightning activity was more concentrated in the southeastern sector, indicating greater impacts on the SHX waypoint. Propagation paths were predominantly oriented toward the east-northeast. Full article

Nanotextured thin film metallic glasses (TFMGs) have emerged as promising antimicrobial coatings for biomedical applications; however, systematic comparisons across compositionally distinct Ti- and Zr-based systems, as well as their early-stage bactericidal mechanisms, remain limited. Here, we show, for the first time, a comparative, compositionally resolved correlation linking alloy chemistry, nanotexture, and bactericidal mechanisms across polymorphic TFMGs. Three co-sputtered biocompatible coatings (Ti₄₇Fe₄₁Cu₁₂, Zr₇₁Fe₃Al₂₆, and Zr₅₈W₃₁Cu₁₁) were deposited on medical-grade titanium and stainless steel (SS316L) via magnetron co-sputtering, producing uniform amorphous films (190–298 nm) with nanoscale roughness of 1.6 ± 0.05 to 8.1 ± 0.05 nm. Surface wettability spanned hydrophilic (71.1 ± 5.6°) to hydrophobic (106.5 ± 3.5°), modulating bacterial interactions. Antimicrobial performance against Pseudomonas aeruginosa was evaluated using live/dead fluorescence imaging, quantitative image analysis, and electron microscopy after 2–4 h incubation. All coatings reduced bacterial adhesion and viability relative to bare substrates, with Zr₅₈W₃₁Cu₁₁ achieving >60% reduction in surface-associated bacterial coverage. Time-resolved analysis revealed a rapid transition to predominantly non-viable populations on coated surfaces, in contrast to sustained viability on controls. Mechanistically, bactericidal activity arises from the synergistic coupling of nanotopography-induced membrane stress, wettability-governed adhesion energetics, and in situ formation of CuO, Fe₂O₃, WO₃, and ZrO₂ oxides that promote electrostatic interactions and proposed reactive oxygen species generation, driving oxidative membrane damage. These results establish a scalable design framework for TFMGs, while highlighting the need for long-term biofilm and electrochemical validation. Full article

Artificial intelligence (AI) systems increasingly depend on complex, multi-stage supply chains that incorporate pre-trained models, third-party datasets, open-source libraries, and automated training pipelines. This dependency creates a rapidly expanding attack surface in which model poisoning, dependency compromise, and provenance manipulation can undermine system integrity long before deployment. Existing AI governance frameworks—including the NIST AI Risk Management Framework and NIST’s Secure Software Development Framework—acknowledge supply chain risks but do not define a verifiable model provenance structure or cryptographically durable integrity guarantees. Simultaneously, the transition to post-quantum cryptography (PQC) introduces new requirements for long-lived AI artifacts: classical digital signatures used to verify model lineage, dataset integrity, and pipeline attestation will become vulnerable to quantum-enabled forgery within the expected operational lifetime of many AI systems. This paper synthesizes evidence from policy, standards, and benchmark sources to characterize the emerging AI supply chain threat landscape and identify cryptographic dependencies that the PQC transition disrupts. We propose a formal Model Bill of Materials with PQC-safe extensions (MBOM-PQC), a unified signing and attestation pipeline integrating ML-DSA and hybrid signature modes, and a five-level Supply Chain Assurance Maturity Model (SCAMM) supporting repeatable organizational evaluation. Together, these contributions aim to provide a structured foundation for AI supply chain integrity, supporting verifiable model lineage, authenticity, and trustworthiness through the PQC transition and beyond. The framework is presented as a design-science contribution comprising three integrated artifacts and is extended with operational guidance for continuous-learning pipelines (§6.5), a formal scoring methodology for organizational assessment (§7.3.5), and a hardware-root-of-trust migration cost matrix (§8.3.6). Full article

Inclusion has become a central concept in disability policy, education, and welfare state reform, yet its practical implementation remains ambivalent. While inclusion is promoted as a rights-based ideal grounded in equality, it can also function as an administrative label that obscures persistent exclusion. Drawing on critical disability studies, this article analyses inclusion as a contested, power-laden concept and develops a three-stage framework—access, participation, and agency—to distinguish formal inclusion from substantive belonging and influence. The framework is applied to key domains of disabled people’s lives—education, housing, service systems, working life, crises, and digitalised everyday life—showing how ableist norms, managerial governance, and institutional logics can reproduce exclusion within ‘inclusive’ reforms, including forms of transformed institutionalisation. The article argues that meaningful inclusion requires dismantling ableist norms, addressing structural power relations, resourcing supports, and strengthening disabled people’s agency in decision-making. Full article

The non-invasive determination of live weight and body composition of ewes is an important element in ensuring precision livestock management and animal well-being. Traditional practices tend to be subjective, labor-intensive, or rely on expensive medical imaging such as Computed Tomography (CT). This paper proposes a new hybrid deep learning method to predict live weight and carcass traits in Coopworth ewes. The dataset of 1184 images taken from 156 ewes was analyzed and compared using a hybrid model (ResNet18 with Multi-Layer Perceptron through simple concatenation) and two more advanced models: Attention-Guided Feature Fusion Network (AGFF-Net) based on cross-modal attention and a Vision Transformer-based Hybrid Regressor (ViT-HR). Auxiliary tabular variables are the Body Condition Score (BCS) and size category. The Transformer architecture predicts (R² = 0.93) the live weight of ewes by dynamically ranking each visual patch and asking it to query the self-attention sequence. This technique treats the BCS as a distinct token in the self-attention sequence. Data partitioning at the animal level was stringent, thereby giving strong generalization. Findings indicate that the best advanced fusion systems are far better than baseline concatenation, with a high accuracy confirmed with gold standards obtained by CT. Grad-CAM visual explainability makes sure that models are able to localize biologically relevant anatomical locations successfully. The study closes the gap between complex deep learning models and real-world agriculture implementation to provide a correct, interpretable and scalable solution to real-time livestock measurements. Full article

The integration of artificial intelligence (AI) in education has made significant advancements in personalized learning and adaptive instruction. However, current systems remain limited by three critical gaps: (a) fragmented architectures that decouple technical performance from ethical governance, (b) the treatment of fairness and accountability as external constraints rather than embedded design principles, and (c) reliance on single-modality data that inadequately represents complex learning environments. These restrictions hinder scalability and limit the capacity of AI systems to deliver equitable, transparent, and context-aware educational experiences. This study aims to address these challenges by designing and validating an ethics-aware, multi-agent conceptual framework for adaptive education in which personalization and responsible AI are co-developed as integrated system properties. The proposed architecture uses five coordinated agents: perception, pedagogy, assessment, feedback, and ethics monitoring. These five agents share one knowledge layer containing learner profiles, domain models, competency structures, interaction histories, and machine-readable policy rules. A four-stage feedback loop comprises: (a) outcome aggregation, (b) system evaluation and validation, (c) teacher review and intervention, and (d) agent update and policy refinement. It enables real-time adaptation, teacher oversight, and iterative system improvement. Adopting a design science research (DSR) methodology and mixed-methods evaluation across functional, pedagogical, ethical, and system-level dimensions, the proposed framework is expected to demonstrate improved learner modeling accuracy, enhanced knowledge tracing, and more robust multimodal engagement analysis compared to centralized and single-modality approaches. Based on design science evaluation against established benchmarks and component-level validation in a simulated learning management system (LMS), this theoretical framework is projected to improve learner modeling accuracy, enhance knowledge tracing, and enable more robust multimodal engagement analysis compared with centralized and single-modality approaches. These projections constitute theoretically derived hypothesis and remain subject to empirical validation in live deployment studies. This study’s theoretical contribution lies in demonstrating that ethics-by-design and adaptive personalization are architecturally compatible and mutually reinforcing design principles. Full article

Background: Live-attenuated Listeria monocytogenes (Lm) vectors are a clinically validated cancer immunotherapy platform, but translation requires reproducible, clinically realistic workflows for dose preparation and infusion. For live bacterial products, in-use stability and device compatibility can drive dose variability through adsorption, settling, and device losses. Methods: We developed and GMP-manufactured an attenuated Lm vaccine expressing human GUCY2C (Lm-GUCY2C) and performed translational characterization, including construct verification and immunogenicity readouts, and defined the administration-focused in-use stability and device compatibility. Post-thaw stability was assessed in primary cryovials and during preparation and delivery from 250 mL saline infusion bags using standard clinical devices (syringes/needles, filter-free IV tubing) and OnGuard2 closed-system components. Samples were collected over 24 h at room temperature, and viable Lm-GUCY2C were quantified by CFU recovery. Results: Lm-GUCY2C remained stable in thawed cryovials for 24 h with no significant CFU loss. High-dose infusion bags (3 × 10⁹ CFU/bag) maintained CFU recovery through 6 h, whereas low-dose bags (3 × 10⁸ CFU/bag) exhibited significant losses beginning at 3 h, supporting a practical in-use window of up to 2 h for low-dose preparations. OnGuard2 intravenous (i.v.) connectors did not measurably affect CFU recovery, while OnGuard2 vial adapters reduced recovery. Conclusions: This work provides an end-to-end, translationally focused characterization of a GMP-manufactured Lm cancer vaccine, including clinically actionable in-use handling constraints and device compatibility. These data define preparation and administration guardrails (notably, time-to-infusion limits for low-dose bag preparations) that can improve dose accuracy and reproducibility in clinical testing. Full article

Diabetes Mellitus is a chronic metabolic disorder characterized by elevated blood glucose due to defects in insulin secretion or action, or both, leading to serious short- and long-term complications if not effectively managed. However, there is limited qualitative evidence exploring how youths diagnosed with Type 1 Diabetes Mellitus (T1DM) experience disease onset, management, complications, emotional adaptation, and education within the South African public healthcare system. The study aims to investigate the lived experiences of youths living with T1DM in a selected public hospital in Limpopo province, South Africa. The objectives were to explore and describe the lived experiences of youths living with T1DM. A qualitative, explorative, descriptive, and contextual design was used to gain a thorough understanding of the experiences of youths living with T1DM. A non-probability sampling technique was used to select 12 participants using a pre-determined criterion. Data were collected through individual semi-structured interviews using an interview guide. The data were analyzed using Colaizzi’s method, where themes and sub-themes were developed with the inclusion of an independent coder. Measures to ensure trustworthiness and ethical considerations were adhered to throughout the study. The findings revealed that, despite the participants sharing the same diagnosis, they experience multiple interrelated barriers that significantly hindered effective self-care management, such as limited access to diabetic diet, glucometers and supplies, treatment and informational-related barriers, school-related challenges, transportation constraints and inadequate social support. Furthermore, the findings highlighted gaps in early recognition of symptoms, standardized diabetes education, psychosocial support, and continuity of care. The study recommends the need for holistic, patient-centred, and contextualized interventions that do not only address medical management but the socioeconomic, educational, and psychological needs of youths. Full article

This study evaluated the carcass characteristics and nutritional composition of duck meat from farmed (organic and conventional) and wild sources. Duck carcasses were analyzed to determine carcass traits (weight and yield of individual carcass portions) and meat quality parameters, including chemical composition and fatty acid profile. Results showed that farmed ducks (conventional and organic) had significantly higher live weight, carcass weight, and dressing percentage compared with wild ducks (p < 0.05). Conventional and organic groups exhibited comparable carcass yields; however, organic ducks demonstrated greater deposition of skin and subcutaneous fat, which may be partly attributed to their longer production period (~7 months) compared with conventional ducks (~45 days). Wild ducks had markedly lower carcass yield but a higher proportion of muscle protein in breast and thigh meat. Fatty acid analysis revealed that conventionally farmed ducks had significantly higher levels of polyunsaturated fatty acids (PUFA), particularly PUFA n-3, compared with organic and wild ducks (p < 0.05), which could be attributed to dietary basis (rapeseed-rich compound feed). Organic and wild ducks had higher levels of monounsaturated fatty acids than conventionally famed ducks. The study demonstrates that the rearing system significantly influences duck carcass traits and meat quality. Farmed ducks showed higher carcass yields, whereas wild ducks provided leaner meat with a higher protein content. These findings enhance understanding of the nutritional and technological properties of duck meat from different production systems. Full article

As a multi-functional systemic carrier, rural tourism integrates diverse rural resources and serves as a key endogenous driver for sustainable rural development and the enhancement of rural residents’ livelihoods. However, excessive tourism development may lead to environmental pressures and exacerbate inequities in benefit distribution, rendering well-being gains uncertain. This study aims to explore the multidimensional mechanisms through which rural tourism influences rural residents’ well-being by utilizing national data from the 2020 China Rural Revitalization Survey (CRRS). The results indicate that village-level tourism development exerts a positive effect on material and psychological well-being. Effects are particularly strong in eastern and hilly regions and in villages where the party secretary also serves as committee director. Further analysis identifies four channels through which rural tourism enhances well-being: fostering digital financial inclusion, advancing empowerment reforms, reallocating resources, and optimizing governance frameworks. Additionally, tourism development leads to improvements in indicators such as road quality, living environment, and satisfaction with village committee performance—while highlighting policy attention to social security, housing, and income satisfaction. Full article