Search Results (27,061)

This study focuses on the influence of chemically pure nanodiamonds produced by cavitation synthesis on structure formation and physical and mechanical properties of self-compacting concrete (SCC) using an analytical approach. The objective of this research was to develop a high-performance, self-compacting concrete with enhanced properties suitable for the construction sector. Chemically pure nanodiamonds produced by cavitation synthesis (KHA-HC) were added to SCC at weight percentages of 0%, 0.15%, 0.3%, 0.45%, 0.6%, and 0.75% of the binder mass. Fresh SCC modified with nanodiamonds was evaluated for density and cone spread diameter. Hardened SCC with KHA-HC was tested for density, water absorption, compressive strength, and axial compressive strength. The most effective KHA-HC content was established at 0.3%, correlating with 13.5% greater compressive strength, 15.1% higher axial compressive strength, and 19.6% lower water absorption. Incorporating KHA-HC into SCC resulted in a wider spread cone as the additive content was raised. The microstructure of SCC containing KHA-HC was found to be more homogeneous with a decrease in void content within the cement matrix. Polynomial relationships for changes in SCC properties as a function of nanodiamond content were proposed. The effectiveness of nanodiamond additives for modifying SCC was demonstrated. Highly effective self-compacting concretes with improved performance properties have been developed, which are suitable for construction. Full article

Extrusion processing can modify dietary fibre structure and enhance feed quality, but its temperature-dependent effects on Cyperus esculentus meal (CEM) remain unclear. This study evaluated the effects of varying extrusion temperatures (110–125 °C) on fibre properties, pellet quality, and growth performance in nursery pigs. A total of 527 pigs were randomly assigned to six dietary treatments for 21 days: a corn–soybean meal control, a diet containing 20% unextruded CEM, and four diets containing 20% extruded CEM (ECEM) processed at 110, 115, 120 or 125 °C. Extrusion at 110–120 °C optimally improved fibre functionality, reducing neutral detergent fibre by approximately 12%, while enhancing water- and oil-holding capacities. Pellet quality was substantially improved, with hardness increasing from 29.5 N to 52.7 N and fines content dropping from 5.4% to 1.9% at optimal temperatures. Growth performance and diarrhoea incidence were unaffected by dietary treatments. Notably, ECEM increased faecal microbial diversity, enriching beneficial bacteria while suppressing potential pathogens. In conclusion, extrusion at 110–120 °C effectively transforms CEM into a functional ingredient that enhances feed quality and gut health without compromising growth, offering a practical strategy to reduce reliance on conventional grains in nursery pig diets. Full article

Plant factories with artificial light (PFAL) enable precise environmental control for vertical indoor agricultural production systems. However, their multi-layer configuration often creates stagnant air zones with significant temperature and humidity gradients. While ventilation systems are essential for mitigating these issues, their effective design requires accurate and distributed climate monitoring. The deployment of distributed microclimate sensors in PFAL environments remains challenging when multiple identical I2C sensors are required, particularly in low-cost monitoring architectures. Because of their fixed I2C addresses, these devices cannot be connected directly to the same bus without conflicts, prompting the need for multiple controllers and thereby increasing system costs. This study evaluates the implementation of a low-cost, multiplexed IoT sensor network for PFAL microclimate monitoring based on an ESP32 microcontroller and an I2C multiplexer (TCA9548A). This network enables simultaneous operation of five SHT20 temperature and humidity sensors at different levels within a PFAL structure. The multiplexing architecture generated coherent multipoint measurements, demonstrating its practical suitability for microclimate monitoring in PFAL environments. However, the system’s performance was compromised by its reliance on the local Wi-Fi network, resulting in intermittent connectivity failures and significant data loss during internet outages. The presented findings and observed limitations underscore the need for complementary strategies, such as local data buffering or a dedicated private network, to ensure reliable long-term monitoring in smart agricultural environments. Full article

Digital transformation in international physical distribution is often constrained by fragmented customer, documentary, operational, fleet, cost, and financial information in transport firms operating in emerging economies. Although ERP and CRM systems have been widely studied, their integration is rarely operationalized as a measurable decision-support capability for Lean–Agile logistics. This article addresses this gap by proposing a KPI-based ERP–CRM framework structured around process-fragmentation diagnosis, ERP–CRM functional architecture, KPI operationalization, scenario-based comparison of digital integration configurations, and sensitivity analysis. The framework is evaluated through a case-based design science research approach using aggregated and anonymized company records from January to December 2025. KPIs cover commercial, documentary, operational, fleet, cost, financial, and documentation-related dimensions, and scenarios range from S0 baseline to S5 ERP–CRM with alerts. Under the defined scenario assumptions, the global performance index increases from 100.0 to 132.9, quotation cycle time decreases from 18.50 to 12.76 h, and operating cost decreases from USD 3.05 to 2.35 million. ERP-focused configurations improve documentary control, fleet coordination, and cost visibility, while CRM-focused configurations strengthen quotation management and customer responsiveness. Overall, integrated ERP–CRM coordination produces a more balanced KPI performance pattern and strengthens logistics–financial visibility, while green implications remain limited to documentation-related process outcomes. Full article

Generative artificial intelligence (GenAI) has expanded opportunities for English learning beyond formal classroom settings. From an intelligence perspective, speaking in English as a foreign language (EFL) represents an applied domain in which learners must adaptively retrieve, integrate, monitor, and deploy linguistic knowledge under communicative demands. The present study examined the effects of a pedagogically guided form of AI-mediated informal digital learning of English (AI-IDLE) on Chinese university EFL learners’ speaking ability, speaking anxiety, and speaking enjoyment. To this end, 89 Chinese university EFL learners participated in a 12-week intervention and were assigned to an experimental group (EG) or a control group (CG). Pre- and post-intervention evaluations were administered to assess the results, including standardized speaking skills and valid questionnaires regarding enjoyment and speaking anxiety. The English-speaking skills test and the two questionnaires were administered at post-test and again 10 weeks after the intervention as the delayed post-test. Mixed-effects modeling (MEM) was used, and the findings showed that the EG demonstrated significantly greater improvements in speaking ability and enjoyment and a greater reduction in speaking anxiety than the CG, with these between-group advantages evident at the post-test. Overall, the findings suggest that structured AI-IDLE can provide a digitally mediated context for intelligence-relevant adaptive learning by supporting communicative performance while fostering affective conditions conducive to its development. These findings have implications for understanding how digitally mediated informal learning environments may support adaptive intelligence in applied language-learning contexts. Full article

Background: The Extended Fetal Cardiovascular System (EFCS) encompasses the cardiac, infracardiac, and supracardiac vascular territories that contribute to fetal cardiovascular development and function. The CASSEAL 3 × 3 framework was developed to provide a structured anatomical assessment of the EFCS. This study aimed to evaluate its feasibility, reproducibility, and diagnostic performance during first-trimester screening. Methods: In this prospective single-center study, singleton pregnancies undergoing ultrasound examination between 11 + 0 and 13 + 6 weeks of gestation were assessed using the CASSEAL 3 × 3 framework. Feasibility, examination time, use of complementary techniques, interobserver reproducibility, and diagnostic performance were evaluated. Prenatal follow-up and postnatal findings served as the reference standard. Results: A total of 288 pregnancies were included in the final analysis. Complete visualization of all nine axial views was achieved in 86.5% of examinations. Mean examination time was 12.5 ± 3.3 min. Higher gestational age increased the likelihood of complete visualization, whereas higher maternal body mass index reduced feasibility. Interobserver agreement was high, with no significant systematic differences between operators. Six suspected EFCS abnormalities were identified during first-trimester examination. Diagnostic performance demonstrated high specificity (99.64%) and negative predictive value (98.58%), whereas sensitivity was moderate and should be interpreted cautiously due to the limited number of confirmed abnormalities. Conclusions: The CASSEAL 3 × 3 framework is a feasible and reproducible approach for structured first-trimester assessment of the EFCS. Diagnostic performance estimates should be interpreted cautiously given the limited number and spectrum of confirmed abnormalities and the use of a composite reference standard without systematic postnatal echocardiography. The framework may be better suited to specialized fetal medicine settings or as an adjunct to routine first-trimester assessment rather than as a universal screening tool. Full article

In order to investigate the soliton solutions of the third-order nonlinear modified Benjamin–Bona–Mahony equation, this research presents a hybrid analytical and machine-learning methodology. A novel combination of symbolic computation and data-driven modeling is introduced to strengthen the theoretical analysis and improve the simulation capabilities. Next, we propose a Riccati sub-equation neural network (RSENN) framework in which the traveling-wave transformation and Riccati sub-equation structure are incorporated into the neural network model. The proposed RSENN framework accurately approximates the soliton solutions and predicts their spatiotemporal evolution governed by the modified Benjamin–Bona–Mahony equation. The data-driven discovery of the model equation is also performed by estimating the unknown parameters under different noise intensities. The RSENN model is trained using the Levenberg–Marquardt algorithm, and its accuracy and predictive capability are evaluated by comparing its numerical outputs with the exact analytical solutions. The results demonstrate the robustness of the proposed RSENN approach under different levels of noise contamination. Full article

Background: Medication non-adherence remains a major challenge in psychiatric care, yet its prediction often relies on clinical and sociodemographic characteristics, while giving comparatively limited attention to modifiable psychological–cognitive factors. Integrating mental health literacy and medication-related beliefs with explainable machine learning approaches may provide a more informative framework for understanding and predicting adherence. Objective: This study examined the association of mental health literacy and medication-related beliefs with psychotropic medication adherence and evaluated their explanatory and predictive values using conventional statistical modeling, machine learning, explainable artificial intelligence, and structural path analysis. Methods: A cross-sectional study was conducted with 225 psychiatric patients. Medication adherence; mental health literacy; and beliefs about medication necessity, concerns, harm, and overuse were assessed using self-report measures, including a MARS-derived eight-item adherence measure. Associations were examined using Spearman correlations, hierarchical regression with robust inference, and a secondary observed-variable path representation of multivariable associations with 5000 bootstrap resamples. The Elastic Net, Random Forest, and Gradient Boosting models were evaluated using repeated five-fold cross-validation. Shapley Additive exPlanations (SHAP) were used to interpret the best-performing model. Results: Greater adherence was associated with higher mental health literacy (ρ = 0.361) and stronger necessity beliefs (ρ = 0.353), whereas concern (ρ = −0.474), perceived harm (ρ = −0.528), and overuse beliefs (ρ = −0.284) were negatively associated with adherence (all p < 0.001). The psychological–cognitive model explained 40.0% of the variance in the primary eight-item adherence composite. The integrated Random Forest achieved the best out-of-sample performance (R² = 0.402, MAE = 0.958, RMSE = 1.299; n = 208), although the improvement over the psychological–cognitive Random Forest (R² = 0.375) was modest. SHAP identified perceived harm and medication concerns as leading predictors. Conclusions: Medication adherence in psychiatric patients was more strongly characterized by psycho-cognitive factors than by sociodemographic and clinical characteristics alone. Mental health literacy, necessity beliefs, medication concerns, and perceived harm may represent particularly informative targets for individualized adherence assessments and future intervention development. Prospective external validation is required before clinical implementation. Full article

This paper proposes a post-training structured channel pruning scheme leveraging Gradient-Weighted Class Activation Mapping (Grad-CAM) for few-class fire detection under limited computational resources. After standard training, category-specific gradient signals extracted from the detection heads are used to generate channel-wise class activation maps over multi-scale feature layers, and fire and smoke responses are fused to rank channel importance. A layer-level retention quota is further applied to implement structured channel pruning, followed by lightweight fine-tuning. The pipeline does not require additional sparsity-inducing training. We validate the method on a self-established fire and smoke dataset containing 9041 images, using YOLOv5s, YOLOv5m, and YOLOv5l as baseline detectors. In workflow-level comparisons, the proposed method achieved higher mAP@0.5 than the implemented L1-based workflow at 40% and 60% pruning, but not at 80%. At 60% pruning, the pruned YOLOv5s model contained 2.158 M parameters and required 2.901 GFLOPs. These results indicate that Grad-CAM provides a useful class-aware criterion for channel importance and offers a promising model-compression strategy for resource-constrained fire detection, although physical edge-device performance remains to be evaluated. Full article

Antimicrobial nanomaterials have attracted increasing attention as potential alternatives for controlling pathogenic microorganisms. In this study, pure ZnO and Ag-modified ZnO nanoparticles prepared using nominal Ag contents of 1 and 5 wt.% were synthesized using a reflux-assisted polyol method and evaluated to determine the influence of Ag content on their structural characteristics and antibacterial activity. The synthesized materials were characterized by UV-Vis spectroscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and high-resolution transmission electron microscopy (HRTEM). UV-Vis and FTIR analyses confirmed the characteristic optical response and chemical features of ZnO-based materials. XRD patterns revealed that all samples retained the hexagonal wurtzite structure of ZnO, while additional reflections corresponding to face-centered cubic (FCC) Ag were observed in the Ag-containing samples and increased in intensity with Ag content. Crystallite sizes estimated by the Scherrer equation were 16.9 ± 2.6 nm for ZnO, 12.9 ± 1.6 nm for ZnO-Ag 1%, and 32.6 ± 10.1 nm for ZnO-Ag 5%. HRTEM confirmed the formation of crystalline nanoparticles with average particle sizes of approximately 16 nm and 12 nm for ZnO and ZnO-Ag 1%, respectively. Antimicrobial activity was evaluated against the reference strains Escherichia coli ATCC 25922 and Salmonella typhimurium ATCC 14020. ZnO–Ag 5% exhibited the greatest antibacterial activity, with minimum inhibitory concentration (MIC) values of 250 ppm against E. coli and 750 ppm against S. typhimurium, and minimum bactericidal concentration (MBC) values of 750 and 1500 ppm, respectively. These findings demonstrate that increasing Ag content influences the structural properties of ZnO nanoparticles and enhances their antibacterial performance, highlighting the potential of ZnO-Ag nanomaterials for antimicrobial applications. Full article

Rock bolt reinforcement governs underground excavation stability through the combined effects of embedment depth, installation pattern, and interaction with the evolving stress and structural environment. Despite substantial advances across mechanistic, empirical, numerical, discontinuum, dynamic, and field-based research traditions, no unified framework currently integrates these approaches across geological and stress regimes. Current practice, therefore, relies on design methods calibrated within specific contexts, producing optimisation outcomes that are model-dependent, metric-sensitive, and not reliably transferable across site conditions. This review critically synthesises evidence from 30 peer-reviewed studies organised into six analytical categories: mechanistic confinement frameworks, empirical classification systems, numerical parametric investigations, discontinuum- and discrete fracture network (DFN)-based optimisation studies, high-stress and dynamic performance analyses, and field-based performance evaluations. The synthesis establishes three principal findings. First, optimal bolt embedment is stress-regime-dependent; plastic-radius-based design logic is appropriate under moderate static conditions but becomes insufficient under high stress or dynamic loading, where energy absorption capacity and controlled yielding govern performance. Second, in discontinuous rock masses, joint geometry and spacing dominate reinforcement effectiveness, shifting optimisation from uniform length selection toward pattern-specific alignment and multi-length configurations that outperform equal-length grids under DFN-controlled conditions. Third, numerical optimisation outcomes are sensitive to the choice of objective metric and modelling paradigm, such that bolt length and spacing recommendations cannot be transferred across analytical frameworks without explicit mechanism comparison. To integrate these findings, a unified conceptual framework is proposed based on regime classification using three dimensionless indicators: the bolt penetration ratio (Π₁ = L/r_p), which relates embedment to plastic zone radius; the structural interception ratio (Π₂ = S/S_j), which relates bolt spacing to dominant joint spacing; and the stress intensity ratio (Π₃ = σ_in situ/σ_cm), which relates in situ stress to rock mass compressive strength. These indicators identify whether confinement-dominated, structure-dominated, or stress-dominated behaviour governs stability, and direct design logic accordingly. The framework does not prescribe universal geometric thresholds; rather, it provides a structured classification pathway that integrates mechanistic and empirical evidence into a coherent and transferable design logic. Probabilistic validation incorporating geological variability, stochastic fracture network modelling, and iterative field calibration is identified as the necessary development path toward a statistically robust optimisation methodology. Full article

Blast-induced flyrock is one of the most critical hazards in surface mining and quarrying, posing significant risks to occupational safety, nearby structures, and the environment. Accurate prediction of flyrock distance is therefore essential for safe blast design and effective risk management. In this study, Random Forest (RF), Extra Trees (ET), and Support Vector Regression (SVR) models were developed to predict flyrock distance, and their hyperparameters were optimized using the Secretary Bird Optimization Algorithm (SBOA) and the Spider-Tailed Horned Viper Optimizer (STHVO). Prior to optimization, six cross-validation strategies were compared using GridSearchCV to identify the most appropriate strategy for each model. The final models were evaluated on an independent test dataset using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), variance accounted for (VAF), and Nash–Sutcliffe efficiency (NSE). Model interpretability was investigated using SHapley Additive exPlanations (SHAP). The results showed that appropriate cross-validation and metaheuristic hyperparameter optimization improved predictive performance, with the ET–STHVO model achieving the best overall results. SHAP analysis identified B/D, PF, H/B, RBS, and U/B as the most influential predictors. The proposed framework provides an accurate, robust, and interpretable decision-support tool for safer blasting operations. Full article

Background/Objectives: Emergency department (ED) overcrowding contributes to delayed care, prolonged length of stay (LOS), resource strain, and adverse patient outcomes. This systematic review aimed to examine how artificial intelligence (AI) and machine learning (ML) have been used to address ED crowding and patient flow, with emphasis on modeling approaches, validation practices, and real-world implementation. Methods: Following PRISMA 2020 guidelines, Scopus, Embase, Ovid MEDLINE, and CENTRAL were searched for relevant studies published from 2020 onward. After deduplication, 1888 records underwent title and abstract screening using two locally deployed LLaMA models with human adjudication. Screening performance was assessed against 150 manually annotated records. Full-text eligibility assessment and structured data extraction were conducted independently by multiple reviewers, with disagreements resolved by consensus. Results: Thirty-two studies were included. Most were retrospective, single-site investigations using electronic health record, administrative, or operational data. Common outcomes included ED LOS, waiting time, occupancy, boarding, disposition, and crowding indices. Tree-based and boosting models frequently performed well, although no approach was consistently superior across tasks and settings. Most studies relied on same-site validation, while external and temporal validation were uncommon. Prospective implementation, workflow integration, model maintenance, and direct operational, clinical, economic, or equity impacts were rarely evaluated. For LLM-assisted screening, LLaMA 4 Scout achieved 84.0% accuracy, 80.0% recall, 88.9% precision, and an F1 score of 84.2%, compared with 78.0%, 67.5%, 88.5%, and 76.6%, respectively, for LLaMA 3.3 on 150 randomly sampled papers. Conclusions: AI and ML show promise for addressing ED overcrowding, but the literature remains concentrated at the model-development stage. Future research should prioritize standardized outcomes, multicenter validation, prospective implementation, and direct evaluation of operational and patient-care outcomes. Full article

Countermovement jump (CMJ) assessment is widely used to evaluate lower-body neuromuscular performance, but its potential relationship with bone health remains insufficiently understood. This study aimed to determine whether eccentric-phase force–time characteristics during the CMJ are associated with proximal femur bone density, mass, and structural properties in postmenopausal women. One hundred and ten physically inactive women within 10 years after menopause underwent bone and jump assessments. Areal bone mineral density and bone mineral content were measured using dual-energy X-ray absorptiometry, and three-dimensional cortical and trabecular proximal femur parameters were estimated from dual-energy X-ray absorptiometry images. Participants performed maximal countermovement jumps on a force platform. Seventeen eccentric-phase force–time variables were extracted and reduced using principal component analysis. Multivariable linear regression models adjusted for age, body mass, height, waist circumference, and hip circumference examined associations between eccentric mechanical profiles and bone outcomes. Four principal components explained 90.5% of the variance in eccentric jump mechanics. Regression models explained between 11% and 41% of the variance across bone outcomes, with the strongest models observed for trabecular bone mineral content at the femoral neck (R² = 0.41), bone mineral content at the femoral shaft (R² = 0.40), and total trabecular volume (R² = 0.39). The component reflecting greater countermovement depth and longer eccentric duration showed the most consistent contribution across models, whereas the force–power component contributed to selected trabecular outcomes. These findings suggest that the combined eccentric mechanical profile of the CMJ is associated with proximal femur bone properties in physically inactive women in early postmenopause and may help inform the biomechanical assessment of bone health and the development of bone-targeted exercise interventions in this population. Full article

Background/Objectives: Self-management for adults living with intestinal stomas secondary to colorectal cancer encompasses technical ostomy care, physical symptom control, psychological adjustment, and social participation, creating demand for standardised assessment instruments to identify unmet rehabilitation needs. Definitions of generic self-care and stoma-specific self-management remain inconsistent across published literature, and psychometric indicators are incompletely reported across available measurement instruments. This review includes both stoma-specific instruments and generic self-care scales that have been validated or widely applied specifically in colorectal cancer ostomy cohorts. Following JBI scoping review methodology, this work systematically maps all published self-management assessment instruments developed for adults with intestinal stomas secondary to colorectal cancer, summarises their core measurement domains and documented psychometric properties, and identifies prevailing gaps in scale development and validation research. Methods: This scoping review was conducted in accordance with Joanna Briggs Institute methodological guidance and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting checklist. Ten electronic databases were searched from inception to 31 December 2025, with supplementary manual screening of reference lists. Eligible sources were peer-reviewed English or Chinese articles focusing exclusively on adults with colorectal cancer–related intestinal stomas. Two independent researchers completed title/abstract and full-text screening, followed by data extraction using a piloted standardised form. Consistent with descriptive scoping review design, no formal methodological quality evaluation or risk-of-bias assessment was performed. Results: Thirty-two studies involving 12 self-management assessment instruments were finally included. All tools documented Cronbach’s α coefficients ranging from 0.805 to 0.977. Content validity indicators were documented for eight instruments, whereas construct validity verified via factor analysis or structural equation modelling was only available for three tools. Criterion-related validity, cross-cultural validity and responsiveness were scarcely reported across most instruments. Only three instruments were fully developed based on complete and clearly articulated theoretical frameworks. Four consistent core measurement domains were identified across scales: stoma technical care, symptom and complication management, psychological adaptation, and family-social interaction. Conclusions: Although internal consistency metrics are universally reported, comprehensive multi-faceted validity testing and responsiveness evaluation are largely absent from existing published evidence. Cross-cultural measurement invariance between original and locally adapted versions (such as the ESCA and its Chinese revised forms) has rarely been examined. Future research priorities include rigorous cross-cultural adaptation following International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and Consensus-based Standards for the selection of health Measurement INstruments (COSMIN) protocols, full psychometric verification of currently available instruments, and development of brief validated short forms for clinical screening. New instrument development should only be initiated if existing tools fail to capture core rehabilitation priorities identified by patients, caregivers, and clinical staff. No responsiveness metrics were identified across the 32 included colorectal cancer-specific clinical studies. The absence of relevant evidence in this literature pool does not prove that these instruments cannot detect longitudinal changes; formal responsiveness testing is required before applying these tools for long-term outcome monitoring. Full article