Search Results (12,498)

The rapid development of intelligent learning guidance systems has created a favorable environment for personalized learning. By accurately predicting students’ future performance, education can be tailored and teaching strategies optimized. However, traditional prediction algorithms seldom account for highly imbalanced datasets in basic education, overlook temporal factors, and lack further interpretability of the prediction results. To address these shortcomings, we propose Temporal Variational Autoencoder-Generative Adversarial Network (TVAE-GAN), a temporal variational autoencoder-generative adversarial network model aimed at providing early warnings for high-risk students in basic education, with in-depth interpretability analysis of the prediction results to suit the unique context of basic education. TVAE-GAN extracts features from real samples and introduces a Long Short-Term Memory (LSTM) network to capture dynamic features in time series, helping the model better understand temporal dependencies in the data, remember the sequential causal information of students’ online learning, and achieve better data generation performance. Using these features, the generative model generates new samples, and the discriminator model evaluates their quality, producing outputs that closely resemble real samples through training. The effectiveness of the TVAE-GAN model is validated on a collected online basic education dataset while also advancing the timing of interventions in predictions. The performance differences between the proposed method and classic resampling methods, as well as their impact in the educational field, are analyzed, highlighting that misclassification increases teacher workload and affects students’ emotions. Key influencing factors are identified using a decision-tree surrogate model, providing teachers with multidimensional references for academic assessment. Full article

The performance of biological wastewater treatment processes directly impacts water resource recycling and ecological safety. This year-long study compared full-scale wastewater treatment plants (WWTPs) using either the anaerobic/anoxic/aerobic (AAO) or modified Bardenpho process. By integrating water quality analysis with 16S rRNA sequencing, we examined how process type, influent quality, and seasonal factors affect microbial communities and treatment performance. Systems with high chemical oxygen demand (COD) and biochemical oxygen demand (BOD)/COD influent exhibited the best pollutant removal performance, with average nitrogen and phosphorus concentrations in the effluent as low as 7.0 mg/L and 0.1 mg/L, respectively. Optimizing a 1:9 influent distribution ratio between the pre-anoxic and first anoxic zones in the modified Bardenpho process increased total nitrogen (TN) removal efficiency by an average of 14 percentage points compared to the AAO process. Additionally, the modified Bardenpho process identified 1100 bacterial genera, indicating a more complex and stable community. Influent water quality had the most significant impact on microbial communities and treatment efficiency, followed by seasonal factors and process type. This study provides theoretical and data support for the optimization of wastewater treatment processes and seasonal regulations. Full article

Injection molding process optimization has predominantly relied on simulation-generated data, which cannot capture machine-specific variability and stochastic process noise inherent in real manufacturing environments. This paper presents an experiment-driven machine learning framework for multi-objective optimization of injection molding process parameters targeting volumetric shrinkage, warpage, cycle time, and part weight. Physical experiments were conducted on an industrial injection molding machine using high-density polyethylene with a face-centered central composite design. Systematic benchmarking of four machine learning algorithms under identical cross-validation protocols identified Gaussian process regression as the best-performing surrogate model for the majority of quality metrics, while warpage prediction remained challenging across all algorithms due to its complex thermo-mechanical origins. Permutation-based feature importance analysis established a clear parameter hierarchy, identifying holding time as the dominant factor governing multiple quality responses. Constrained Bayesian optimization with progressive constraint tightening was employed to identify optimal parameter sets and fundamental process capability boundaries. The resulting parameter configurations were validated against a held-out test set. This work demonstrates that rigorous, data-driven optimization using exclusively experimental data provides a viable and practically achievable alternative to simulation-based approaches, contributing to experiment-centric smart manufacturing in polymer processing. Full article

Methodological guidelines for real-world evidence (RWE) in European Union (EU) joint clinical assessments (JCA) are lacking. This manuscript explores RWE potential in EU health technology assessment (HTA) and offers recommendations for generating high-quality RWE. An environmental scan of peer-reviewed and gray literature was conducted to review RWE frameworks and documents in EU regulatory and HTA decision-making. Extraction elements were standardized across key RWE themes: data quality, methodological rigor, stakeholder engagement, and applications. In JCA, RWE has multiple uses, including informing PICO simulation exercises, understanding disease landscape, identifying prognostic factors and effect modifiers, and directly or indirectly informing comparative clinical assessments. Methodological guidance from the HTA Coordination Group is limited to cases in which evidence from non-randomized studies is used as direct inputs in comparative assessments. Individual HTA bodies provide more detailed guidance, missing an opportunity to leverage RWE within JCAs that can offer insight for local Member State submissions. Generating high-quality RWE that is credible, actionable, and acceptable for JCA submissions and local HTA bodies requires careful attention to methodological considerations and early planning. Broader RWE integration that reflects patient journeys is needed. Expanding the HTA Coordination Group guidance can unlock RWE’s full potential in supporting EU JCA submissions. Full article

Continuous cropping obstacles in watermelon are closely linked to rhizosphere microbial imbalance, posing a major threat to the sustainability of the industry in Xinjiang. Exogenous additives are widely used to regulate soil health, yet comprehensive comparisons of their mechanisms and effects remain limited. In this study, a field experiment was conducted under continuous watermelon cropping conditions in Xinjiang to evaluate the impact of eight treatments, including chemical fertilizer (NPK) alone and its combination with organic fertilizer (NPKM), glucose (NPKG), oxalic acid (NPKOA), amino acids (NPKGA), citric acid (NPKCA), and acetic acid (NPKAA), with unfertilized soil as the control (CK). Treatment effects were assessed through soil physicochemical analysis, fruit quality evaluation, and high-throughput sequencing (16S rRNA and ITS). Among all treatments, NPKM showed the greatest improvement in soil fertility, increasing soil organic matter by 13.91%, total nitrogen by 23.08%, and single fruit weight by 35.75% compared to CK. NPKGA also enhanced fruit weight (+33.06% vs. CK) and increased catalase activity, while oxalic acid exhibited the strongest activation of alkaline phosphatase. Microbiome analysis revealed that NPKM and NPKAA significantly reshaped both bacterial and fungal community structures. NPKM enriched beneficial taxa such as unclassified Chitinophagaceae and Lophotrichus, whereas NPKCA enriched the biocontrol bacterium Pseudomonas chlororaphis. Soil organic matter and total nitrogen were identified as key environmental drivers, showing significant positive correlations with core bacterial genera (Dokdonella) and negative correlations with the pathogenic fungus Alternaria. Collectively, this study elucidates the distinct mechanisms of various additives by linking treatment-specific microbial shifts to key soil factors and crop performance, providing a theoretical and technical framework for mitigating watermelon continuous cropping obstacles through rhizosphere environmental regulation. Full article

Mesenchymal stem cells (MSCs) are multipotent cells that have the ability to mediate cellular repair through a combination of soluble paracrine factors, as well as bioactive cargo packaged within extracellular vesicles (EVs). Although MSC-derived EVs have been widely investigated for their regenerative potential, progress toward translational evaluation has been limited in part by challenges in scalable and reproducible manufacturing. We recently reported that human telomerase reverse transcriptase (hTERT)-immortalized MSCs reproducibly produce EVs that retain key characteristics of EVs derived from primary MSCs. Building on this work, three-dimensional (3D) culture systems have emerged as promising platforms for large-scale manufacturing. In this study, we compared the yield, molecular composition, and functional activity of EVs produced from hTERT-immortalized MSCs cultured in either a fixed-bed bioreactor or conventional two-dimensional (2D) flasks. Our data demonstrate that bioreactor culture results in increased EV yield as compared to an equivalent production from 2D cultures. Molecular analyses indicated that bioreactor-derived EVs were associated with a broader spectrum of cargo and were enriched with molecules that may contribute to enhanced reparative function. Importantly, bioreactor-derived EVs also exerted a more pronounced effect in cellular repair assays in vitro. Collectively, these results highlight the potential of fixed-bed bioreactors as scalable platforms for EV production, offering higher yields while preserving molecular composition and functional activity. This approach represents an important step toward achieving the reproducible, high-quality EV production required for research and future translational applications. Full article

Background/Objectives: Patients with breast cancer show substantial heterogeneity in terms of psychological adjustment following diagnosis. We aimed to characterize longitudinal trajectories of quality of life (QoL) and depressive symptoms during the first 18 months post-diagnosis and to identify robust clinical, psychosocial, and behavioral predictors associated with distinct adjustment pathways. Methods: Women (N = 538; mean age 55.4 years; range 40–70) with operable breast cancer (stages I–III) were drawn from the multicenter BOUNCE cohort. QoL (Global Health Status/QoL scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) and depressive symptoms (depression subscale of the Hospital Anxiety and Depression Scale) were assessed at baseline and months 3, 6, 9, 12, 15 and 18. Latent class growth analysis and growth mixture modeling identified distinct trajectory classes. Associations between early predictors and trajectory membership were examined using logistic regression combined with elastic net regularization. Results: Depression trajectories demonstrated heterogeneity, with groups characterized by persistent resilience (59.7%), stable moderate/high (25.3%), delayed onset (5.0%), and recovery (10.0%). QoL trajectories ranged from stable excellent (13.2%) and stable high (40.7%) to moderate (31.4%) and persistent low/deteriorating (6.9%), as well as a distinct recovering trajectory (7.8%). Trajectory differentiation was primarily driven by psychological resources, symptom burden, functional status, and coping processes, alongside specific contributions from clinical factors. Conclusions: Distinct subgroups of women with breast cancer follow divergent adjustment pathways. These findings highlight the multidimensional nature of resilience and support the need for tailored interventions that promote long-term well-being beyond simple risk reduction. Full article

High quality factor (Q factor) resonant metasurfaces enable efficient mid-infrared (MIR) upconversion, yet their narrow operating bandwidths severely limit practical broadband detection and imaging applications. Although high Q magnetic toroidal dipole (MTD) modes exhibit outstanding momentum space ($k$-space) stability in linear optics, their application in nonlinear processes has primarily been confined to degenerate second-harmonic generation (SHG), leaving complex non-degenerate processes such as sum-frequency generation (SFG) largely unexplored. Here, we propose a tunable MIR upconversion platform based on an all-dielectric gallium phosphide (GaP) dimer metasurface. Breaking the in-plane symmetry to trigger Brillouin zone folding excites robust MTD quasi-guided modes (MTD-QGM), tightly confining the locally enhanced optical fields within the highly nonlinear GaP nanostructure. Synchronizing this high Q resonance with a spatially overlapping pump mode yields an exceptional SFG conversion efficiency of $7.9\times {10}^{-4}$, successfully translating a 3101.8 nm MIR signal to the 903 nm near-infrared band. Crucially, the intrinsic $k$-space stability of the MTD-QGM enables continuous, broadband upconversion through simple angle tuning. This mechanism effectively overcomes the narrow-band limitations characteristic of typical symmetry-protected resonators, establishing a robust paradigm for room-temperature MIR detection. Full article

This paper addresses a dynamic multi-period pricing problem that incorporates time-varying contextual information and inventory constraints. Sales are modeled as a function of both price and a multidimensional context vector, which may include factors such as customer location, income, loyalty, competitor prices, and promotional activity. This formulation captures complex market dynamics over a finite selling horizon. The problem is formulated as a quadratic programming model, and two alternative solution approaches are proposed. The first uses a multivariate regression model to approximate the sales function linearly, allowing an exact quadratic programming solution that serves as a benchmark. The second is a ‘learnheuristic’ algorithm that combines a nonlinear sales learning model with metaheuristic optimization to generate high-quality pricing strategies under realistic operational constraints. Computational experiments demonstrate the effectiveness of the proposed learnheuristic approach. Full article

Background: Occupational exposure to cytotoxic drugs remains a major concern for pharmacy personnel, due to their well-established, carcinogenic, mutagenic and organ-specific effects. Despite the existence of robust international guidelines, evidence suggests substantial variability in compliance, training quality and operational conditions across healthcare settings. Objective: This study aimed to characterise current handling practices, assess working conditions, training, safety procedures, exposure patterns, and perceived risk factors among pharmacy technicians involved in the preparation of cytotoxic drugs in Portugal. Methods: A cross-sectional descriptive study was conducted using a structured questionnaire grounded in international standards (ISOPP, NIOSH, ASHP, USP <800>). The instrument was developed through literature review, expert panel validation (n = 42), and pre-testing. Data were collected electronically between April and May 2025 from pharmacy technicians actively handling cytotoxic drugs in Portugal. Results: A total of 124 valid responses were analysed. Most participants were female (78%) and under 50 years, with nearly one-third having less than one year of experience. Prolonged daily exposure (31.5% participants worked ≥ 5 h/day) extended uninterrupted handling periods (28.2% worked > 120 min), and high preparation workloads were common. While adherence to core protective measures—such as reinforced gowns, double gloves, and Class II B2 biological safety cabinets—was high, important gaps were identified, including incomplete use of closed system transfer devices, inconsistent respiratory and foot protection, limited automation, and insufficient environmental monitoring. Structured competency assessment, periodic training, and formal documentation were frequently absent. Institutional policies on reproductive risk showed strong protection for women but less clarity for male workers. Conclusions: Cytotoxic drug handling practices in Portugal demonstrate satisfactory adherence to fundamental protective measures but reveal significant structural and organisational gaps related to workload management, environmental monitoring, and continuous training. The absence of unified national guidance contributes to variability across institutions. These findings highlight the need for greater standardisation of occupational safety practices. Full article

A structured optimization approach was applied to ultrasonic surface rolling process (USRP) parameters, aiming to enhance the material surface characteristics of TC4 titanium alloy. To overcome the premature convergence and limited exploration capability of the standard Whale Optimization Algorithm (WOA), three enhancement strategies were introduced, including population initialization based on an optimal point set, a sinusoidal nonlinear convergence factor, and an adaptive inertia-based position update strategy. By optimizing the structural parameters of the RBF neural network with the improved WOA, an IWOA–RBF predictive model for surface performance evaluation was developed and rigorously validated in terms of prediction accuracy. Using the developed IWOA–RBF model, a multi-criteria decision-making framework integrating the CRITIC weighting method and the TOPSIS ranking approach was constructed to evaluate surface quality. This framework was further combined with a multi-objective Grey Wolf Optimization (MOGWO) algorithm to perform Pareto-based optimization and determine the optimal USRP parameter set. Experimental validation showed that the optimized parameters resulted in a significant reduction in surface roughness, while enhancing both surface hardness and residual compressive stress. The results confirm the robustness and effectiveness of the proposed IWOA–RBF and MOGWO optimization framework, providing a reliable strategy for high-precision parameter optimization and coordinated enhancement of surface properties in the TC4 titanium alloy USRP. Full article

The lignin in chestnut rose waste restricts composting efficiency. This study aimed to optimize lignin degradation in feedstock pretreatment using response surface methodology (RSM) and evaluate the effects on composting efficiency and greenhouse gas emissions. A Box–Behnken design with three factors (temperature, time and biochar) was used to determine optimal conditions. The RSM-optimized pretreatment was then compared against biochar pretreatment, high-temperature pretreatment without biochar, and no pretreatment to evaluate their effects on composting efficiency. The results showed that the RSM-supported optimal pretreatment (79.2 °C, 5.2 h, 10.3% biochar, R² = 0.9970, p < 0.0001) degraded 59.31% of lignin in chestnut rose waste. The optimized pretreatment condition increased the lignin degradation rate by 31.5% during composting compared to a lack of pretreatment. The quality of the compost was significantly improved, with the total N and NO₃⁻ contents increasing by 22.0% and 65.2%, respectively. Furthermore, the optimized pretreatment reduced cumulative CH₄ and N₂O emissions by 37.5% and 36.5%, respectively. These findings suggest that RSM-optimized pretreatment effectively enhances composting efficiency and mitigates the environmental impacts of chestnut rose waste composting. However, this study was limited to laboratory-scale conditions, and further field-scale validation is needed. Full article

Background: Obesity remains a major public health challenge, with central and visceral fat distribution conferring particularly high cardiometabolic risk. Lifestyle factors, including diet, physical activity, and sleep quality, have been implicated in adiposity, yet their combined and interactive effects in working populations remain insufficiently characterized. Methods: We conducted a cross-sectional study in 88,343 Spanish employees (53,122 men, 35,221 women) attending occupational health examinations between 2021 and 2024. Obesity was assessed using four complementary indices: body mass index (BMI), waist-to-height ratio (WHtR), Clínica Universidad de Navarra–Body Adiposity Estimator (CUN-BAE), and Metabolic Score for Visceral Fat (METS-VF). Lifestyle factors included sleep quality (Pittsburgh Sleep Quality Index), Mediterranean diet adherence (MEDAS), and physical activity (IPAQ). Multivariable logistic regression models were adjusted for sociodemographic and lifestyle variables, with interaction, stratified, joint exposure, and dose–response analyses. Results: Obesity prevalence varied widely by index, ranging from 18.9% (BMI) to 55.6% (CUN-BAE). Poor sleep quality was independently associated with higher odds of obesity across all indices, particularly central obesity (WHtR OR 1.58, 95% CI 1.48–1.69), with stronger associations observed in women. Physical inactivity and non-adherence to the Mediterranean diet were robust predictors, with inactivity showing the largest effect sizes (METS-VF OR 9.92, 95% CI 8.70–11.15). Interaction analyses indicated that both Mediterranean diet adherence and regular physical activity attenuated the adverse association between poor sleep and obesity outcomes. Restricted cubic spline models revealed a progressive dose–response relationship between increasing PSQI score and central obesity. Joint exposure analyses showed nearly five-fold higher odds of central obesity among workers with concurrent poor sleep, physical inactivity, and low Mediterranean diet adherence. A graded inverse association was observed between a composite healthy lifestyle score (0–3) and obesity, with a score of 3 associated with 72–75% lower odds of BMI-obesity and WHtR-high. Conclusions: In this large occupational cohort, poor sleep quality, physical inactivity, and low Mediterranean diet adherence emerged as independent and combined determinants of general, central, and visceral obesity. Integrated workplace strategies promoting sleep hygiene, physical activity, and dietary quality—particularly among women and lower socioeconomic groups—may represent an effective approach to reducing obesity risk in working populations. Full article

Community social capital (CSC) and the community-built environment (CBE) are key resources associated with residents’ health, yet their combined associations with multidimensional health and well-being remain insufficiently understood. Drawing on large-scale data from the 2018 China Labor-force Dynamics Survey, this study analyzed a filtered sample of 14,127 respondents nested within 326 communities and employed multilevel models to examine the direct, interactive, and combined associations of CSC and CBE on four outcomes: residents’ self-rated health, subjective well-being, loneliness, and mental health. The results identified two CSC indicators: neighborhood mutual support and resident relationship quality, and two CBE indicators: community type and residential density, as key factors associated with multidimensional health, each showing significant associations with multiple health outcomes, with CSC indicators demonstrating relatively stronger and more consistent effect sizes than CBE indicators. The study further identified a dual mechanism involving key indicators of CSC (relationship quality and neighborhood mutual support) and CBE (community type and residential density). When one dimension was constrained, the other was associated with compensatory patterns in residents’ health, whereas high levels of both social and physical resources were associated with synergistic advantages. Stratified analyses revealed that individuals with low external social engagement appeared more sensitive to these environmental associations. This study emphasizes that sustainable community planning and governance should consider key social and physical indicators to support both social sustainability and built-environment sustainability. Depending on the level of community resources, it may be beneficial to leverage the compensatory and synergistic patterns among different community attributes to support improvements in residents’ health benefits. Particular attention may be warranted for individuals with low social engagement to build healthier and more sustainable communities. This research advances an integrated resource configuration framework that contributes to a shift in urban health governance from isolated environmental interventions toward an adaptive, balanced approach. Full article

This study investigated consumer expectations and perceived quality of strawberries across different geographical contexts to identify the main drivers of purchasing behavior within a cross-country framework. An online survey was conducted among consumers in Italy, Germany, and Turkey to explore consumption habits, purchasing channels, sensory expectations, product perceptions, and willingness to pay (WTP) for specific product attributes. Results confirmed a high level of consumer appreciation for strawberries across all countries, primarily driven by their sensory characteristics. However, purchasing behavior and consumption patterns were strongly influenced by cultural and market-related factors. Visual attributes were confirmed to be key cues guiding product choice; however, label indications related to sensory traits and functional properties exerted a greater influence. Flavor, firmness, and overall taste balance represented critical determinants of consumer satisfaction. Differences across demographic groups were also observed, with younger and male consumers reporting lower levels of satisfaction with key sensory attributes, including juiciness, aroma, and freshness. Cross-country comparisons revealed heterogeneous WTP patterns, with Turkish consumers showing a greater propensity to pay premium prices for quality-related, local, organic, and environmentally friendly attributes compared with German and Italian consumers. Overall, the findings highlight the combined influence of sensory quality, cultural context, and sociodemographic characteristics in shaping strawberry perception and purchasing behavior. These insights may support breeders, producers, and retailers in developing targeted product strategies and market positioning across different geographical areas and consumer segments. Full article