Search Results (558)

Dengue virus (DENV), an important mosquito-borne orthoflavivirus, represents a growing global threat due to its geographic expansion and recent outbreaks worldwide. In resource-limited endemic settings, the development of affordable diagnostic assays is needed. In this study, we developed and validated a colorimetric reverse transcription loop-mediated isothermal amplification assay (RT-LAMP) for the detection of DENV type 3 (DENV-3) using 95 previously diagnosed clinical samples from Southeastern Mexico. Primers targeting the 3′ untranslated region (3′ UTR) of DENV-3 were designed, and assay conditions were standardized. The colorimetric RT-LAMP DENV-3 system achieved a preliminary limit of detection of 1 × 10³ copies per reaction, with 90.7% sensitivity and 100% specificity. The colorimetric format enabled visual readout without specialized equipment, supporting its potential applicability in point-of-care and resource-limited settings. The developed colorimetric RT-LAMP detection for DENV-3 is intended as a rapid screening/triage tool that can trigger confirmatory testing or public-health actions. Full article

As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality. Full article

Abandoned mining areas provide valuable opportunities to investigate ore-forming processes, supergene mineral transformations, and the geochemical behaviour of metals. In this sense, the old Preguiça mine (Beja, Portugal), exploited for Fe–Zn–Pb, was studied providing new mineralogical and geochemical data aimed at improving the understanding of the secondary mineral assemblages of this deposit. A total of 70 samples collected from three accessible underground levels (first, second and third) and mine waste, complemented by 16 samples from a deeper level (fourth) previously collected, were analysed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and a portable X-ray fluorescence (pXRF) equipment. Mineralogical phases are dominated by a wide range of secondary oxides, carbonates, arsenates, vanadates, silicates, phosphates and sulphates, but remnants of primary sulphides were also found. The following minerals can be emphasised: goethite, hematite, calcite, dolomite, descloizite, willemite, mimetite, cerussite, smithsonite and fraipontite. The presence of massicot in the Preguiça mine, is described for the first time. Bulk geochemical analyses show high concentrations of Fe, Ca, Zn and Pb, consistent with the observed mineralogy. The presence of vanadium- and arsenic-bearing minerals highlights the occurrence of critical raw materials, supporting the importance of reassessing other abandoned mining areas in the context of sustainable resource management and strategic raw-material planning. Full article

In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. Experimental studies involve physical testing and measurements but are often costly and time-consuming. Numerical simulations are more flexible and cost-effective, though they can require significant computational resources for large or complex systems. To address these challenges, researchers are increasingly employing various machine learning techniques, which offer strong potential for data analysis and predictive modeling. In this study, CFD-based sorption simulations are integrated with machine learning models to predict the spatiotemporal evolution of water uptake. Several ML techniques including support vector regression (SVR), Random Forest, XGBoost, CatBoost (gradient boosting decision trees), and multilayer perceptron neural networks (MLPs) are evaluated and compared. A fixed-bed reactor equipped with fins and tubes is considered within a closed adsorption thermal storage system. Numerical simulations are conducted for three different fin lengths (10 mm, 25 mm, and 35 mm) to generate a comprehensive dataset for training the ML models and capturing the complex temporal evolution of water uptake, thereby enabling predictions for unseen fin geometries. The results indicate that neural network-based models achieve superior predictive performance compared to the other methods. For water uptake training, the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination $\left(R^2\right)$ are approximately 2.83, 4.37, and 0.91, respectively. The predicted water uptake shows close agreement with the numerical simulation results. For the prediction cases, the MAE, MSE, and $R^2$ values are approximately 1.13, 1.2, and 0.8, respectively. Overall, the study demonstrates that machine learning models can accurately predict water uptake beyond the training dataset, indicating strong generalization capability and significant potential for improving thermal management system design. Additionally, the proposed approach reduces simulation time and computational cost while providing an efficient and reliable framework for modeling complex sorption processes in thermal energy storage systems. Full article

Background: Medical emergencies in dental practice are uncommon but may have serious consequences if not promptly recognized and managed. Dentists are expected to identify and initiate appropriate interventions during such events; however, the extent to which theoretical knowledge translates into clinical confidence and preparedness remains unclear. Methods: This nationwide cross-sectional survey evaluated dentists’ knowledge, attitudes, and preparedness regarding medical emergencies encountered in routine dental practice. A total of 300 dentists practicing in Türkiye completed two structured questionnaires: a scenario-based single-best-answer multiple-choice questionnaire assessing knowledge of medical emergencies and a Likert-scale questionnaire evaluating attitudes and clinical preparedness. Of the 450 dentists invited to participate, 300 completed the survey (response rate: 66.6%). Overall knowledge scores were calculated from 16 emergency scenarios, and participants were categorized into knowledge-level groups. Associations between knowledge, attitudes, and availability of emergency resources were analyzed using chi-square tests with effect size estimation. Results: The median overall knowledge score was 11 (IQR: 9–13). While high correct response rates were observed for commonly encountered emergencies such as syncope and intraoral bleeding, lower accuracy was noted for high-risk conditions including hypertensive crisis, anaphylaxis, and epileptic seizures. Only 40% of dentists reported feeling sufficiently competent to manage medical emergencies, and avoidance of treating high-risk patients was common. Higher knowledge levels and availability of emergency equipment and medications were significantly associated with greater self-perceived competence and reduced avoidance behavior. Conclusions: Although dentists demonstrate adequate theoretical knowledge of medical emergencies, significant gaps persist in clinical confidence, preparedness, and management of high-risk scenarios. Strengthening emergency preparedness in dental practice requires structured, hands-on training and improved access to essential emergency resources to ensure patient safety and support effective clinical decision-making. Full article

Introduction: Healthcare institutions and care providers, including medical professionals, were at the forefront of the rapid response to the challenges of the pandemic. As medical professionals across the country actively fought the COVID-19 pandemic, many ethical, social, and legal challenges arose that had not been previously encountered. This study was conducted to determine the ethical challenges and dilemmas faced by medical professionals during the COVID-19 pandemic. Materials and Methods: A descriptive cross-sectional questionnaire-based survey was conducted among the registered medical practitioners in the year 2022. The study setting included health care institutions in India where COVID patients were treated. Results: 558 participants took part in this study. The availability of personal protective equipment was sufficient in 519 (93%) of cases, while 39 (7%) of respondents reported insufficient quantities of personal protective equipment. Overall, 318 (56.99%) respondents were comfortable with their duty patterns, and 435 (77.96%) medical professionals received clear-cut and updated guidelines for practicing safely; 534 (95.70%) completed both doses of vaccines available at that time in India. Conclusions: Indian medical professionals experienced substantial ethical, occupational, and psychological challenges during the COVID-19 pandemic, despite the high availability of protective equipment and vaccination coverage. Resource allocation dilemmas, demanding duty patterns, and psychological distress during quarantine underscore the need for stronger institutional support, clear guidelines, and mental health interventions during future public health crises. Full article

In large-scale dam construction, the efficiency of concrete transport operations is fundamentally governed by the coordination between horizontal hauling and vertical hoisting capacities. Traditional experience-based scheduling approaches often fail to capture the stochastic, cyclic, and resource-coupled nature of these transport systems. This study developed a closed queuing network-based stochastic simulation framework to model dam concrete transportation as a finite-population cyclic service system. The process was abstracted into sequential service stages with stochastic service times, and a structured state-space representation combined with time-step simulation was constructed to describe dynamic resource occupation and task transitions under varying truck and cable crane configurations. Application to a real large-scale dam project revealed a characteristic multi-stage performance evolution pattern governed by capacity matching mechanisms. As the truck fleet size increased, system performance transitioned from a transport-limited regime to a capacity-coordination regime and ultimately to a hoisting-saturated regime in which further fleet expansion yielded diminishing returns. Sensitivity analysis demonstrated that hoisting capacity imposed an upper bound on system throughput, while adaptive fleet reconfiguration could restore operational equilibrium under constrained equipment availability. The results indicated that dam concrete transport should be treated as a dynamic capacity regulation problem rather than a static allocation task. The proposed framework provides an interpretable and quantitative decision-support tool for equipment configuration, bottleneck identification, and adaptive scheduling in large-scale hydraulic infrastructure projects. Full article

The South African Government prioritises Small, Micro-, and Medium Enterprises (SMMEs) as catalysts for employment creation, in alignment with Sustainable Development Goal 8 (SDG 8), Decent Work and Economic Growth, which advocates for sustained, inclusive, and sustainable economic growth. However, the extent to which agro-processing SMMEs translate this policy ambition into measurable socio-economic gains remains contested due to persistent structural, financial, and operational constraints. This study develops a comprehensive, data-driven business support framework tailored to agro-processing SMMEs in the Free State province of South Africa. Employing a mixed-methods approach, survey data from 88 agro-processing SMMEs were analysed across 18 business performance dimensions. Average agreement scores and performance gaps were utilised to diagnose strengths and vulnerabilities within the sector. While overall performance was relatively strong (average agreement score: 86.7%), a critical weakness emerged in operational cost management (76.1%), revealing a 14.2% gap relative to the highest-performing dimension, equipment selection (90.3%). Based on these empirical insights, the study proposes a three-tiered business support architecture: (i) maintaining and leveraging high-performing dimensions (≥85% agreement), (ii) targeted enhancement for moderate-performing areas (80–84.9%), and (iii) crisis intervention for critical weaknesses (<80%). The framework integrates cross-cutting support services, including financing, regulatory guidance, and technology access, delivered through a phased implementation strategy comprising crisis intervention, system establishment, and optimisation and scaling. A multi-channel delivery mechanism, combining a hub-and-spoke model, mobile support units, and a digital platform, ensures provincial accessibility. By translating performance diagnostics into differentiated policy action, the framework promotes efficient resource allocation, supports both high-potential and vulnerable agro-processing SMMEs, and embeds a robust monitoring and evaluation system to track key performance indicators. The study contributes to the SMME development literature by demonstrating how structured, tiered, and context-specific support models can strengthen resilience, competitiveness, and sustainable agro-industrial growth in developing-country settings. Full article

Graduate employability has become a central concern for higher education institutions as labour markets undergo rapid transformation driven by digitalisation, technological change, and evolving organisational practices. Universities are increasingly expected to equip graduates with a broad range of employability skills and to collaborate with industry to enhance labour market readiness. However, existing research on employability skills development and university-industry collaboration remains fragmented across disciplines, contexts, and stakeholder perspectives. This systematic review synthesises evidence on how universities prepare their graduates for the labour market through employability skills development and university-industry collaboration. Following PRISMA guidelines, 84 journal articles and conference papers published between 2015 and 2025 were identified through a systematic search of the Scopus database and analysed thematically. The findings reveal that graduate employability is conceptualised as a multidimensional and context-dependent construct encompassing discipline-specific, transversal, digital, career management, and professional disposition-related skills. Employability skills development is most strongly supported through pedagogical approaches that emphasise authentic engagement with professional contexts, including work-integrated learning, project- and challenge-based learning, and technology-mediated collaboration. Reported outcomes extend beyond immediate employment metrics to include enhanced confidence, skills acquisition, employability awareness, curriculum relevance, and organisational learning. However, the effectiveness and sustainability of these initiatives are shaped by structural and institutional conditions, including policy frameworks, resourcing, partnership coordination, and equity of access. The review contributes an integrative synthesis that connects employability skills, pedagogical design, and university-industry collaboration, and outlines implications for policy, educational practice, and future research. Full article

As climate-driven disasters intensify globally, this study investigates how environmental volatility influences the pro-environmental initiatives of micro-entrepreneurs in Chile. While Chile possesses world-class seismic resilience, the 2020–2025 period marked a dramatic shift toward hydro-climatological extremes, including mega-fires and catastrophic flooding. Integrating construal level theory, protection motivation theory, and the concept of focusing events, this research examines the psychological and structural drivers of business adaptation. Results indicate that residing in disaster-prone regions is insufficient to trigger proactivity; instead, a stark distinction exists between abstract geographic proximity and the behavior triggered by personal exposure. Furthermore, mediation analysis provides mixed support for the role of business profit; while profit loss negatively mediated equipment efficiency and recycling, the magnitude was marginal. This coping gap suggests that resource-constrained actors favor low-cost survivalist tactics over systemic shifts due to depleted organizational slack. Ultimately, the study highlights that disasters are powerful but inefficient teachers; without addressing technical and financial barriers to mitigation, global supply chains remain fragile despite localized disaster experiences. Full article

In the context of high penetration of distributed energy resources and new load integration, existing research primarily focuses on capacity optimization under pre-established interconnection structures, addressing issues such as uneven spatiotemporal distribution of loads and low equipment utilization in distribution transformer areas. However, these studies lack a planning-stage interconnection object selection mechanism. To address this, this paper proposes a planning-oriented flexible interconnection potential assessment and optimization configuration method for distribution transformer areas. First, a quantitative interconnection potential assessment model is developed, integrating load rate improvement after interconnection and geographical connection costs, enabling the ranking and selection of candidate transformer area combinations. On this basis, a flexible interconnection system optimization configuration model is established, aiming to minimize the overall system cost, and collaboratively optimizing converter and energy storage capacities. A case study of 20 distribution transformer areas in a certain city shows that the optimal transformer area combination increases the load factor from 64.6% to 79.4%, an improvement of 22.9%; when considering energy storage configuration, the total economic cost of the interconnection system is reduced by approximately 20.2% compared to the independent operation mode. The results validate the effectiveness of the proposed method in improving equipment utilization and reducing the system’s total lifecycle cost, providing decision support for flexible planning of urban distribution networks. Full article

Vehicular edge computing (VEC) enables vehicles to offload computation-intensive tasks to roadside units (RSUs) equipped with deep learning (DL) models, thereby supporting low-latency and accuracy-sensitive intelligent vehicular tasks. To adapt DL models to evolving task requirements and time-varying vehicular environments, the RSUs must consume limited computing and memory resources to retrieve optimized parameters from the cloud to update local models. During these updates, the DL models cannot provide services to tasks, and vice versa. However, the limited computational and memory resources of RSUs make it challenging to determine which tasks to offload and which DL models to update, in order to maximize task acceptance rates and quality of service. In this paper, we investigate the joint optimization of accuracy-sensitive task offloading and DL model updating in VEC systems. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem that aims to maximize a weighted utility function of task acceptance rate (AR) and quality of service (QoS), subject to latency, accuracy, and resource constraints. The formulated problem is shown to be NP-hard. To enable efficient decision making, we propose a heuristic algorithm termed the Load-Accuracy-Sensitive Joint Task Offloading and Model Update algorithm. The proposed algorithm leverages real-time system state information and jointly considers transmission feasibility, RSU workload, model accuracy matching, and queue-aware load balancing when making task offloading and model update decisions. Extensive simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms. Full article

By expanding the Technology–Organization–Environment (TOE) framework to match the Complex Adaptive System (CAS) characteristics of high-speed rail (HSR) enterprises, this study adopts fuzzy-set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) to investigate the driving mechanisms and configuration paths of high-quality development (HQD). Using data from 137 listed Chinese HSR concept companies during 2018–2023, the results reveal that HSR enterprises operate as CAS, where HQD emerges from the synergistic interaction of technology, organization, and environment subsystems rather than isolated factor contributions. Four equivalent configuration paths to HQD are identified, categorized into three models: Technology-Dominant, Dual-Driven Technology + Environment, and Multi-Collaborative Technology + Organization + Environment. Policy support is a necessary condition for system evolution, digital intelligence empowerment serves as the core “order parameter” driving subsystem adaptation, and high-quality human resources act as the key coordinating element for inter-subsystem coupling. The degree of subsystem synergy has a significant positive correlation with HQD levels. This study enriches the application of CAS theory in the transportation equipment manufacturing industry, expands the TOE framework’s analytical boundary from linear dimension division to systematic synergy, and provides theoretical insights for understanding the nonlinear, emergent mechanisms of HSR enterprise HQD. It also offers practical references for governments to optimize policy supply and for enterprises to enhance adaptive capacity. Full article

Buildings account for a substantial share of global energy demand, and decisions made during conceptual design strongly influence long-term operational consumption. This study presents an open, simulation-derived dataset to support early-stage estimation of residential energy use in a hot–arid context (New Cairo, Egypt). A parametric Rhino/Grasshopper workflow coupled with EnergyPlus was used to generate 12,000 annual simulations. The simulations were produced by systematically sampling key geometric, envelope, glazing, and operational variables, including building dimensions, orientation, window-to-wall ratio, envelope construction options, glazing properties, internal loads (lighting and equipment), and thermostat setpoints. For each case, annual end-use outputs (heating, cooling, lighting, and equipment energy) are reported alongside the corresponding input features, enabling design-space exploration, sensitivity analysis, and the development of surrogate and machine-learning models for rapid decision support. Verification checks and plausibility screening were applied to confirm successful simulation execution and consistent data extraction. In addition, dataset-level sampling diagnostics (marginal balance and correlation screening) are reported to support robust reuse in surrogate and machine-learning studies. The resulting dataset and documentation provide a reusable resource for researchers and practitioners investigating energy-informed residential design under hot-climate boundary conditions. Full article

Controlled drainage technology in farmland not only raises groundwater levels to provide additional water directly to crops but also improves rainfall utilization efficiency, thereby helping to alleviate agricultural water scarcity. In 2002, a large-scale field research area covering 80 km² was established and equipped with controlled sluices, a weather station, groundwater observation wells, and monitoring gauges. An analysis of the impacts of controlled projects on water levels in the main drainage ditches, groundwater dynamics, drainage processes, and overall water resource regulation showed that the average water storage capacity in the main ditches increased to 1.14 m, the groundwater level rose by 0.64 m, and the total volume of water regulated through the sluice control project ac-counted for approximately 10% of the annual precipitation. Therefore, implementing controlled drainage in main ditches for agricultural water resource regulation represents an effective strategy suited to the characteristics of the Huaibei Plain. This approach not only mitigates water scarcity but also enhances the farmland ecological environment and supports the rational spatiotemporal allocation of regional water resources. Moreover, it provides valuable insights for other regions facing similar challenges. Full article