Abstract:Emergency medical responders require high-fidelity training for rare critical scenarios. We compared virtual reality simulation with traditional mannequin-based training for paramedic students in Poland. VR groups demonstrated equivalent procedural skill acquisition with superior decision-making in complex multi-patient scenarios. Training retention at six months showed 20% better performance maintenance for VR-trained cohorts. Motion sickness affected 8% of participants requiring accommodation. Cost-effectiveness analysis favored VR at scales above 50 trainees annually. Our findings support VR integration in emergency medical education curricula.
Abstract:Intensive aquaculture antibiotic use contributes to resistance gene proliferation. We surveyed antimicrobial resistance patterns across Vietnamese shrimp and fish farms examining water, sediment, and animal samples. Multi-drug resistant bacteria prevalence reached 45% with resistance genes detected in downstream water bodies. Farm-level antibiotic use intensity correlated with resistance prevalence. Probiotic alternatives showed promise reducing therapeutic antibiotic requirements. Our surveillance data supports antimicrobial stewardship program development for aquaculture sectors.
Abstract:Heritage building conservation requires accurate documentation of complex architectural features. We integrated terrestrial laser scanning with building information modeling for Byzantine church restoration in Greece. Point cloud processing captured millimeter-accuracy geometric data including deterioration patterns. BIM models enabled structural analysis and intervention planning. Digital twins facilitated remote expert collaboration during pandemic restrictions. Standardized workflows reduced documentation time by 60% compared to traditional methods. Our methodology supports systematic heritage management and disaster preparedness.
Abstract:Microfinance promises economic empowerment for women in developing countries yet evidence remains mixed. We conducted longitudinal tracking of 600 women borrowers across Bangladeshi microfinance institutions over five years. Business survival rates reached 72% with average income increases of 45% for successful enterprises. Group lending mechanisms provided social support beyond financial services. Over-indebtedness affected 15% of borrowers highlighting program design importance. Our findings inform microfinance product development targeting sustainable women entrepreneurship.
Abstract:Timely crop health assessment enables targeted interventions reducing agrochemical inputs. We deployed multispectral drone surveys across Mexican maize and bean farms throughout growing seasons. Vegetation indices identified nitrogen deficiency and water stress weeks before visible symptoms appeared. Variable rate application maps reduced fertilizer use by 28% while maintaining yields. Cloud-based processing enabled farmer access through mobile applications. Our system demonstrates affordable precision agriculture for smallholder farms in developing regions.
Abstract:Traditional noise metrics inadequately capture human perception of urban soundscapes. We developed psychoacoustic assessment frameworks incorporating loudness, sharpness, and fluctuation strength measures. Field studies in Swedish cities revealed sound quality factors predicted annoyance better than decibel levels alone. Green infrastructure integration reduced perceived noise impact even when physical levels remained unchanged. Design guidelines incorporating psychoacoustic principles improved outdoor public space quality. Our methodology enhances noise management strategies for urban planners and architects.
Abstract:Textile industry generates substantial environmental impacts through linear production models. We conducted material flow analysis of Egyptian textile manufacturing tracking fiber inputs through production, consumption, and disposal. Only 12% of textile waste entered recycling streams despite available technologies. Extended producer responsibility schemes in Europe increased collection rates but export to developing countries shifted environmental burdens. Chemical recycling showed promise for blended fabrics unsuitable for mechanical processing. Our analysis informs circular economy policy development for textile sectors.
Abstract:Healthcare AI development requires large datasets while protecting patient privacy. We implemented federated learning across five Chinese hospitals for chest X-ray classification. Decentralized training achieved 94% of centralized model accuracy while keeping data on-premises. Differential privacy mechanisms added computational overhead but prevented model inversion attacks. Non-IID data distributions across hospitals required adaptive aggregation strategies. Our framework enables collaborative medical AI development without centralizing sensitive health records.
Abstract:This study investigates how risk assessment techniques are applied in Build-OperateTransfer (BOT) student housing projects and responds to the growing need for risk assessment frameworks tailored to concessionary educational infrastructure. This is against the background of the knowledge gap in risk assessment techniques in BOT student housing development. A systematic literature review was conducted using the PRISMA protocol. This review contributes to the discourse on PPP infrastructure risk management, showcasing limitations in applying risk assessment techniques to student housing. The review considered 543 peer-reviewed articles published between 2004 and 2024, sourced from Scopus, Web of Science, Google Scholar, and Dimensions. Following screening for relevance and methodological rigour, 45 studies were selected for qualitative synthesis. The analysis shows that while quantitative methods, such as Monte Carlo Simulation, Analytic Hierarchy Process (AHP), and Fuzzy Logic, are widely adopted in BOT risk assessment, contextual variables relevant to student housing projects, such as interruption of academic calendars, student tenant behaviour, student unrest, and shifting policy environments, were overlooked. A significant gap was identified in integrating stakeholder-driven qualitative insights with quantitative modelling approaches. By excluding non-English language sources and incongruous literature, the study may not fully capture region-specific practices, particularly from non-English-speaking developing economies. Future research should consider broader inclusion criteria to strengthen the generalizability of findings.
Abstract:Globally, healthcare systems encounter tremendous obstacles as a result of the progressive neurological disease called Alzheimer's. To effectively intervene and control Alzheimer's disease (AD), the earliest and most accurate diagnosis is necessary. However, traditional diagnostic methods are often expensive, take a long time, and do not provide the accuracy needed for early diagnosis. This study addresses these limitations by proposing a machine learning-based (ML-based) approach for predicting AD using advanced data classification methods and an explainable artificial intelligence (AI) approach. Three distinct methods were utilized to carry out the feature selection procedure: chi-square, mutual information, and analysis of variance (ANOVA). We identified the analysis's most relevant elements by utilizing each technique. We found the best algorithm for predicting the early signs of AD by testing seven different ML methods: logistic regression, AdaBoost, random forest, support vector machine, decision tree, XGBoost, and K-nearest neighbors. We employed the SMOTE method to rectify the data imbalance. To test the proposed method, we employed both a publicly available and a private dataset. We applied multiple cross-validation approaches to provide a strong performance evaluation. The results of the experiments illustrated that, out of all the models tested, the XGBoost classifier performed the best. Using the combined dataset, the XGBoost classifier had 97.32% accuracy, 96.56% precision, 97.00% specificity, 97.68% sensitivity, 98.43% AUC, and 97.12% F1-score. Using the public dataset, XGBoost achieved 97.23% accuracy, 96.14% precision, 96.52% specificity, 98.03% sensitivity, 98.30% AUC, and 97.07% F1-score. Furthermore, XGBoost did exceptionally well on the private dataset with 95.83% accuracy, 93.94% sensitivity, 96.88% precision, 97.44% specificity, 98.52% AUC, and 95.38% F1- score. Understanding the model's findings and decision-making process can be enhanced with the help of an explicable AI framework that was developed using SHAP methods. The proposed approach shows enormous potential as a healthcare solution that reduces healthcare costs and improves efficiency in AD’s diagnosis. Patients benefit from improved diagnostic tools for AD brought about by this study's combination of powerful ML models with explainable AI.