Search Results (4,942)

This study investigates the factors influencing Chinese K-12 teachers’ adoption of generative artificial intelligence (GenAI) for instructional purposes by extending the Technology Acceptance Model (TAM) with pedagogical beliefs, perceived intelligence, perceived ethical risks, GenAI anxiety, and demographic moderators. Drawing on a theory-driven framework, survey data were collected from 218 in-service teachers across K-12 schools in China. The respondents were predominantly from urban schools and most had prior GenAI use experience. Eight latent constructs and fourteen hypotheses were tested using structural equation modeling and multi-group analysis. Results show that perceived usefulness and perceived ease of use are the strongest predictors of teachers’ intention to adopt GenAI. Constructivist pedagogical beliefs positively predict both perceived usefulness and intention, whereas transmissive beliefs negatively predict intention. Perceived intelligence exerts strong positive effects on perceived usefulness and ease of use but has no direct effect on intention. Perceived ethical risks significantly heighten GenAI anxiety, yet neither directly reduces adoption intention. Gender, teaching stage, and educational background further moderate key relationships, revealing heterogeneous adoption mechanisms across teacher subgroups. The study extends TAM for the GenAI era and highlights the need for professional development and policy initiatives that simultaneously strengthen perceived usefulness and ease of use, engage with pedagogical beliefs, and address ethical and emotional concerns in context-sensitive ways. Full article

Influenza A viruses exhibit broad host tropism, infecting multiple species including humans, avian species, and swine. Swine influenza virus (SIV), while primarily circulating in porcine populations, demonstrates zoonotic potential with sporadic human infections. In this investigation, we identified two H1N1 subtype swine influenza A virus strains designated A/swine/China/SD6591/2019(H1N1) (abbreviated SD6591) and A/swine/China/SD6592/2019(H1N1) (abbreviated SD6592) in Shandong Province, China. The GenBank accession numbers of the SD6591 viral gene segments are PV464931-PV464938, and the GenBank accession numbers corresponding to each of the eight SD6592 viral gene segments are PV464939-PV464946. Phylogenetic and recombination analyses suggest potential evolutionary differences between the isolates. SD6591 displayed a unique triple-reassortant genotype: comparative nucleotide homology assessments demonstrated that the PB2, PB1, NP, NA, HA, and NEP genes shared the highest similarity with classical swine-origin H1N1 viruses. In contrast, SD6592 maintained genomic conservation with previously characterized H1N1 swine strains, although neither of these two isolates exhibited significant intrasegmental recombination events. Through comprehensive sequence analysis of these H1N1 SIVs, this study provides preliminary insights into their evolutionary history and underscores the persistent risk of cross-species transmission at the human–swine interface. These findings establish an essential foundation for enhancing national SIV surveillance programs and informing evidence-based prevention strategies against emerging influenza threats. Full article

Salmonella is a major foodborne pathogen, and its increasing antimicrobial resistance poses a significant public health challenge. In this study, we conducted a comprehensive genomic epidemiological investigation of Salmonella isolates recovered from meat products across multiple provinces in China. A total of 141 isolates were collected and subjected to antimicrobial susceptibility testing and whole-genome sequencing. Core genome MLST and hierarchical clustering (HierCC) were performed using EnteroBase, while SNP phylogeny and phylodynamic analyses were conducted to characterize the evolutionary dynamics of Salmonella populations. The predominant serovars were Enteritidis and Infantis, with a high proportion of multidrug-resistant isolates. Potentially transferable plasmids carrying ARGs, such as bla_CTX-M, qnrS1, sul2, and mcr-1.1, were frequently detected, indicating a risk of horizontal transfer during transmission. Genomic epidemiological investigation of our sequenced strains and their associated cgMLST HierCC clusters revealed both persistent Salmonella lineages, such as Enteritidis HC50-87 and Agona HC20-419, and emerging China-specific lineages, including Enteritidis HC20-10145 and Typhimurium HC50-2304. The estimated divergence times of these lineages mostly dated to the late mid-20th century, coinciding with the intensification of poultry farming in China. These findings highlight the power of genomic epidemiology in uncovering antimicrobial resistance patterns and transmission dynamics, underscoring the need for strengthened Salmonella surveillance. Full article

This study employs numerical simulations to analyse current densities in modelled human organs originating from extremely low frequency (ELF) electromagnetic fields emanating from a 110 kV single-circuit high-voltage transmission line. Exposure to these ELF fields gives rise to both conduction and displacement currents within the human body, potentially perturbing endogenous bioelectric currents and raising concerns of health risks. Using CST Studio Suite 2018 software, a three-dimensional multipart ellipsoidal anatomical model is developed to analyse these phenomena. Although displacement currents have lower magnitudes than conduction currents, they contribute significantly to the total current density and must therefore be included in rigorous safety assessments. Simulation results indicate that the current density values remain below the basic restrictions of the International Commission on Non-Ionizing Radiation Protection. Full article

The accuracy of dynamics parameters in the transmission system is essential for high-performance motion trajectory planning and stable operation of heavy-duty servo presses. To mitigate the performance degradation and potential overload risks caused by deviations between theoretical and actual parameters, this paper proposes a dynamics model accuracy enhancement method that integrates multi-objective global sensitivity analysis and ant colony optimization-based calibration. First, a nonlinear dynamics model of the eight-bar mechanism was constructed based on Lagrange’s equations, which systematically incorporates generalized external force models consistent with actual production, including gravity, friction, balance force, and stamping process load. Subsequently, six key sensitive parameters were identified from 28 system parameters using Sobol global sensitivity analysis, with response functions defined for torque prediction accuracy, transient overload risk, thermal load, and work done. Based on the sensitivity results, a parameter calibration model was formulated to minimize torque prediction error and transient overload risk, and solved by the ant colony algorithm. Experimental validation showed that, after calibration, the root mean square error between predicted and measured torque decreased significantly from 1366.9 N·m to 277.7 N·m (a reduction of 79.7%), the peak error dropped by 72.7%, and the servo motor’s effective torque prediction error was reduced from 7.6% to 1.4%. In an automotive door panel stamping application on a 25,000 kN heavy-duty servo press, the production rate increased from 11.4 to 11.6 strokes per minute, demonstrating enhanced performance without operational safety. This study provides a theoretical foundation and an effective engineering solution for high-precision modeling and performance optimization of heavy-duty servo presses. Full article

Canine Distemper Virus (CDV) is a highly contagious disease that affects a wide range of wildlife species, posing a serious threat to biodiversity and conservation efforts. Despite its ecological significance, the transmission dynamics of CDV in wildlife remain poorly understood, especially in tropical ecosystems. One of the main challenges in studying CDV transmission is the lack of reliable epidemiological data and the difficulty in capturing and monitoring wild animals for surveillance purposes. Thus, this study aims to develop a model to estimate the potential transmission of CDV in wildlife populations using spatial heat mapping and the basic reproduction number (R₀) as key indicators. A combination of field observation records, environmental data, and reported CDV cases were used to generate predictive heat maps and simulate disease spread across susceptible wildlife hosts. Results showed that certain environmental factors and animal density hotspots significantly contribute to higher transmission potential of CDV. Preliminary results suggest that high-risk zones can be identified based on overlapping wildlife movement corridors and human interface areas. This modeling approach offers a valuable tool to guide targeted monitoring, early detection and conservation strategies against CDV outbreaks in wildlife. Full article

Background/Objectives: Bovine mastitis, a significant global concern in dairy farming, results in substantial economic losses and poses considerable risks to both animal and human health. With the increasing prevalence of antimicrobial resistance (AMR) in mastitis pathogens, the potential for resistant infections to spread from livestock to humans and the environment is becoming a critical public health issue. This narrative review summarizes the current evidence on antimicrobial resistance in pathogens causing bovine mastitis and examines it from a One Health perspective, encompassing animal, human, and environmental interfaces. Results: By examining the complex interplay among animal, human, and environmental health, we highlight key factors that drive resistance, including the overuse of antimicrobials, poor farm management, and environmental contamination. We also discuss innovative strategies, such as enhanced surveillance, pathogen-specific diagnostics, alternatives to antimicrobials, and sustainable farm practices, that can mitigate the emergence of resistance. Key knowledge gaps include a limited understanding of antimicrobial residues, resistant pathogens, and gene transmission pathways and inconsistent implementation of antimicrobial stewardship practices. Conclusions: This review emphasizes the need for a coordinated, multidisciplinary effort to reduce the burden of AMR in bovine mastitis pathogens, ensuring the continued efficacy of antimicrobials and safeguarding public health through responsible management and policy interventions. Full article

Modular multilevel converter (MMC) is widely employed in high-voltage direct current (HVDC) systems for the long-distance renewable energy transmission, where the larger submodule (SM) capacitors significantly increase its size, weight and cost. Conventional capacitor voltage fluctuation suppression methods, such as composite harmonic injection (CHI) strategies, can achieve lightweight MMC. However, these approaches often neglect the dynamic constraints between harmonic injection parameters and their coupled effect on modulation wave, which not only leads to suboptimal global solutions but also increases the risk of system overshoot. Therefore, this paper proposes a comprehensive CHI parameters optimization method to minimize capacitor voltage fluctuations, thereby allowing for a smaller SM capacitor. First, the analytical expression of SM average capacitor voltage is developed, incorporating the injected second-order harmonic circulating current and third-order harmonic voltage. On this basis, an objective function is defined to minimize the sum of the fundamental and second-order harmonic components of the average capacitor voltage, with the harmonic injection parameters and modulation index as optimization variables. Then, these parameters are optimized using a particle swarm optimization (PSO) algorithm, where their constraints are set to prevent modulation wave overshoot and additional power loss. Finally, the optimization method is validated through a ±500 kV, 1500 MW MMC-HVDC system under various power conditions in PSCAD/EMTDC (version 4.6.3). In addition, simulation results demonstrate that the proposed method can achieve a 13.33% greater reduction in SM capacitance value compared to conventional strategies. Full article

This comprehensive review examines pathogens affecting adult anadromous Pacific salmon (Oncorhynchus spp.) during their terminal freshwater migration and spawning across populations from California through Alaska, including Oregon, Washington, and British Columbia. We systematically reviewed selected pathogens based on their significance to adult salmon health or role in epizootiology, categorizing them by their impact on prespawn mortality (PSM), disease severity, and maternal or ‘egg-associated’ transmission risks to progeny. Our analysis encompasses macroparasites, microparasites, bacteria, and viruses affecting anadromous Pink (O. gorbuscha), Chum (O. keta), Coho (O. kisutch), Sockeye (O. nerka), and Chinook Salmon (O. tshawytscha) and Steelhead Trout (O. mykiss), integrating extensive literature analysis with direct field observations and case studies from representative geographic regions. Understanding pathogen impacts during the spawning life stage is crucial for salmon population sustainability, as the unique semelparous nature of Pacific salmon makes this terminal phase critical for reproductive success and the continuation of these ecologically, economically, and culturally vital species. Full article

Accurate volatility forecasting is essential for risk management in increasingly interconnected financial markets. Traditional econometric models capture volatility clustering but struggle to model nonlinear cross-market spillovers. This study proposes a Temporal Graph Attention Network (TemporalGAT) for multi-horizon volatility forecasting, integrating LSTM-based temporal encoding with graph convolutional and attention layers to jointly model volatility persistence and inter-market dependencies. Market linkages are constructed using the Diebold–Yilmaz volatility spillover index, providing an economically interpretable representation of directional shock transmission. Using daily data from major global equity indices, the model is evaluated against econometric, machine learning, and graph-based benchmarks across multiple forecast horizons. Performance is assessed using MSE, $R^2$, MAFE, and MAPE, with statistical significance validated via Diebold–Mariano tests and bootstrap confidence intervals. The study further conducts a strict expanding-window robustness test, comparing fixed and dynamically re-estimated spillover graphs in a fully out-of-sample setting. Sensitivity and scenario analyses confirm robustness across hyperparameter configurations and market regimes, while results show no systematic gains from dynamic graph updating over a fixed spillover network. Full article

Background/Objectives: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to pose a significant global health challenge due to its high transmissibility and potential for severe clinical outcomes. Early identification of patients at risk of hospitalization is essential for effective triage in emergency departments and for the optimal allocation of healthcare resources. Methods: This prospective study included 84 patients aged over 18 years who presented to the emergency department on 23 December 2020, with suspected SARS-CoV-2 infection. Initially, 100 patients were evaluated, and 16 were excluded based on predefined exclusion criteria. The mean age of the participants was 53.65 ± 13.62 years, and 39 (46.4%) were women. Results: At admission, the mean signal peptide, CUB domain, EGF (SCUBE-1) level among SARS-CoV-2 patients was 0.16 ± 0.08 ng/mL. There was no significant difference in SCUBE-1 levels between patient and control groups (n = 59 vs. 25), but levels differed significantly between hospitalized and home-treated patients (n = 37 vs. 22; p = 0.001). Neutrophil count (p = 0.001) and NLR (p = 0.010) were higher in patients than controls and also higher in hospitalized than home-treated patients (p = 0.003 and p = 0.015). ROC analysis revealed that SCUBE-1 predicted hospitalization with 84.6% sensitivity and 88.9% specificity. A positive correlation was observed between SCUBE-1 levels and length of hospital stay (p = 0.007, r = 0.554), with a median stay of 9.0 (5.0–11.0) days. Conclusions: SCUBE-1 levels were significantly associated with disease severity in SARS-CoV-2 patients and may serve as a promising biomarker to support clinical decision-making for hospitalization versus home-based management. Full article

Microgrids are commonly connected through medium-voltage cables in coastal distribution networks and other microgrids. However, a faulted microgrid may increase the collapse risk if the supporting microgrids are disconnected due to voltage sags. Conventional voltage support methods, which primarily rely on the impedance characteristics of the transmission line, typically regulate the active-to-reactive current ratio (hereafter referred to as “current ratio”) to maximize positive sequence voltage while minimizing negative sequence voltage. Nevertheless, the distributed capacitance inherent in cables induces deviations in both the amplitude and phase of the transmitted current, while simultaneously intensifying the coupling between voltage and current. These effects complicate the voltage fluctuation behavior and impair the effectiveness of voltage support, thereby increasing the risk of disconnection and collapse for the faulted microgrid (hereafter referred to as “fault region”). To address this challenge, this study focuses on non-faulted microgrids (hereafter referred to as “microgrids”), proposing a method for active current correction and transient voltage support that considers the influence of cable distributed capacitance. By analyzing the voltage and current characteristics on both ends of the interconnecting cables, the method optimizes the current injection ratio. It mitigates deviation caused by cable capacitance effects, thereby enhancing the voltage support performance of the microgrid. Notably, the proposed method operates independently of real-time voltage and current measurements from the fault region, significantly reducing communication demands. Experimental results based on a practical microgrid validate the effectiveness of the proposed method, demonstrating a 27.9% improvement in voltage support performance compared to conventional methods. Full article

Anthropogenic climate change represents a critical and complex threat to the health and resilience of aquatic ecosystems. This review aims to critically synthesise and evaluate the synergetic and antagonistic mechanisms through which rising water temperature, the most prominent climatic factor, modulates the host–parasite relationship. The systematic literature review was conducted across a high-impact database (Web of Science), focusing on the extraction and qualitative analysis of data concerning infection dynamics and both host and parasite interactions. The findings demonstrate that thermal stress imposes a dual penalty on host–parasite systems: (1) it confers a critical thermal advantage to direct-life cycle parasites, significantly accelerating their virulence, reproduction, and infective capacity; (2) simultaneously, it severely compromises the immunocompetence and physiological resilience of piscine hosts, often through immunometabolic trade-offs and inflammatory dysfunction. This toxic synergy is the root cause of the exponential disease prevalence/intensity of parasites and fish mass mortality events, directly impacting biodiversity and global aquaculture sustainability. In contrast, it may also cause the disruption of the transmission chains to threaten complex life cycle parasites with localised extinction. We conclude that climate mitigation must be urgently recognised and implemented as a primary strategy for biological risk management to secure aquatic health and global food safety. Full article

Many industrial sectors are concerned about microbiological contamination and the associated risk of microbiologically influenced corrosion (MIC). This applies in particular to the transmission and storage of fuels in the refining industry. Exceeding a certain level of these contaminants poses a serious risk to fuel quality and can cause storage and pipeline infrastructure corrosion. This situation requires an urgent evaluation of microorganism levels in the fuel to avert such detrimental consequences. Diesel fuels containing biofuel additives are particularly susceptible to these phenomena. Traditional detection methods are limited by low sensitivity, high costs, and long turnaround times, making them unsuitable for quick, on-site, and real-time detection and monitoring. A novel approach involves the application of microwave dielectric testing to quantify microbial load in diesel fuel. Microwave dielectric spectroscopy offers a non-destructive, label-free solution, providing rapid information on microorganism presence. Combined with chemometric techniques, it effectively estimates total microorganism counts in diesel fuel. Measurement in the X-band range (8.2–12.4 GHz) takes a few seconds. Calibration with known bacterial and fungal concentrations (10³ to 10⁷ CFU/mL) and principal component analysis (PCA) of the spectroscopic data allow for clear differentiation of contamination levels, categorizing them from acceptable to hazardous. The sensitivity limit of the proposed method corresponds to a bacterial concentration of 10³ CFU/mL. Full article

Globally, large quantities of animal waste and human sewage sludge are generated annually. Their application as soil amendments can enhance soil quality and support a circular economy. However, these wastes may harbour pathogenic bacteria, posing contamination risks to soil and water and potential transmission to animals and humans. This study investigated the survival of five bacterial pathogens during six months of storage in five types of organic waste and following their subsequent application to soil. During storage, T₉₀ values ranged as follows: Salmonella Typhimurium (2.3–17.7 days), Campylobacter jejuni (0 to 23.9 days), Escherichia coli O157:H7 (4.3 to 57.8 days), and Listeria monocytogenes (1.9 to 170.4 days). In soil, T₉₀ values were S. Typhimurium (4.2 to 17.4 days), C. jejuni (4.8 to 26.8 days), E. coli O157:H7 (4.3 to 52.9 days), and L. monocytogenes (2 to 83.7 days). Clostridium sporogenes remained stable throughout both experiments, preventing T₉₀ calculation. Contrary to our initial hypothesis that soil microbiota would accelerate pathogen decline, T₉₀ values were higher during storage in 11 cases and higher in soil in nine scenarios. These findings highlight the need for pre-treatment strategies for animal waste and biosolids before land spreading to consistently mitigate risks of pathogen transmission and environmental contamination. Full article