Search Results (1,164)

Background/Objectives: Anastomotic leakage (AL) is a major complication of colorectal surgery. Despite multiple identified risk factors, no single strategy has proven fully effective in preventing AL. This single-arm pilot study aims to evaluate the feasibility, safety, and adherence of a multimodal colorectal anastomosis assessment protocol (CASPI) in patients undergoing surgery for colorectal cancer. Methods: This prospective descriptive interventional single-arm pilot study included patients diagnosed with colorectal cancer who underwent surgical resection. The CASPI protocol consists of five steps: (1) indocyanine green (ICG) perfusion assessment, (2) doughnut integrity checking, (3) air leak testing, (4) intraoperative flexible endoscopy, and (5) postoperative flexible sigmoidoscopy. Results: A total of 34 patients were included. The median age was 63.5 years, and the median BMI was 27.7 kg/m². Twenty-seven patients had rectal tumors, and 66.7% received neoadjuvant therapy. Adherence to the protocol was 100% intraoperatively and 88.2% postoperatively. Adequate perfusion by ICG was confirmed in 94.1% of cases; intact anastomotic doughnuts were obtained in all procedures. Intraoperative endoscopy showed Grade 1 mucosa in 76.5% of patients and Grade 2 in 23.5%. No complications related to the CASPI protocol were observed. Stoma closure was performed in all patients with temporary ileostomy. Conclusions: Implementation of the CASPI protocol in colorectal surgery demonstrated excellent feasibility, high adherence, and strong safety. These findings support its further evaluation in larger, controlled studies designed to assess clinical effectiveness in the incidence of anastomotic complications. Full article

The reliability of high-strength bolted connections is critical to the safety of large-scale engineering structures. This study proposes a non-contact quantitative method for detecting bolt loosening based on the self-magnetic flux leakage (SMFL) effect. Systematic experiments were carried out on M14-12.9 bolts, using nine independent specimens tested under six torque levels, to reveal the intrinsic relationship between bolt preload and the “magnetic valley” feature of the surface leakage field. For quantitative evaluation, the absolute value of the differential peak magnetic field, |ΔPMF|, is defined as the core feature parameter. The results show that, in the reference specimen group, |ΔPMF| exhibits a pronounced linear relationship with the applied torque (R² > 0.96), and the corresponding linear regression parameters display good consistency across the nine specimens (RSD ≈ 4%). Comparative tests on two additional bolt specifications clarify how bolt strength grade and geometric size influence the detection sensitivity and linearity. To address lift-off effects, measurements on a representative specimen at four lift-off heights were used to construct a simplified bivariate linear compensation model, which significantly reduces lift-off-induced bias within the working range h = 10–16 mm. Finally, a hierarchical diagnostic scheme for bolt loosening that incorporates lift-off compensation is established on the basis of |ΔPMF|, providing a feasible approach for rapid assessment of bolt loosening under complex service conditions. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Disposable absorbent hygiene products (AHPs) contain plastics that are challenging to recycle and not biodegradable, making a significant contribution to landfill. Decreasing the nonbiodegradable mass of products could reduce this burden. Despite this, public data on how AHP design and material selection relate to performance is limited. In this work, fifteen commercial AHPs were characterised using dimensional measurement, infrared spectroscopy, and imaging. Simulated urination, air permeability, and moisture management testing were used to assess expected leakage and user comfort. Sustainable materials currently in use were identified, and their performance compared to typical plastics, informing opportunities to replace or reduce nonbiodegradable materials. Polybutylene adipate terephthalate-based leakproof layers replaced polyolefins. Commercial alternatives to polyacrylate superabsorbent polymers (SAPs), with comparable absorption, were not seen. Although absorbency correlated with the mass of absorbants, SAPs reduced surface moisture after absorption and are known for high absorption capacity under pressure, preventing rewetting. Channels and side guards were observed to prevent side leakage and guide fluid distribution, potentially reducing the need for nonbiodegradable nonwoven and absorbant content by promoting efficient use of the full product mass. While synthetic nonwovens typically outperformed cellulosics, apertured and layered nonwovens were associated with improved moisture transport; polylactic acid rivalled typical thermoplastics as a bio-derived, compostable alternative. Although the need for biopolymer-based SAPs and foams remains, it is hoped that these findings will guide AHP design and promote research in sustainable materials. Full article

Water wastage management (WWM) in deep-level mines remains a critical challenge, as wastage increases operational costs and threatens sustainability. This study presents a systematic state-of-the-art review of WWM in deep-level mines. Relevant literature was critically assessed to establish current practices, identify limitations, and explore emerging solutions. Five principal approaches were identified: leak detection and repair, pressure control with fixed schedules, network optimisation, accountability measures, and smart management. While each provides benefits, significant challenges persist. Particularly, current pressure control techniques, essential for limiting leakage, rely on static demand profiles that cannot accommodate the stochastic nature of service water demand, often resulting in over- or under-supply. Smart management systems, which have proven effective for managing stochastic utilities in other industries, present a promising alternative. Enabling technologies such as sensors, automated valves, and tracking systems are already widely deployed in mining, underscoring the technical feasibility of such systems. However, no studies have yet examined their development for WWM in deep-level mines. This study recommends a framework for smart water management tailored to mining conditions and highlights three opportunities: developing real-time demand approximation methods, leveraging occupancy data for demand estimation, and integrating these models with mine water supply control infrastructure for implementation and evaluation. Full article

The lack of clinical data for chronic kidney disease (CKD) prediction frequently results in model overfitting and inadequate generalization to novel samples. This research mitigates this constraint by utilizing a Conditional Tabular Generative Adversarial Network (CTGAN) to enhance a constrained CKD dataset sourced from the University of California, Irvine (UCI) Machine Learning Repository. The CTGAN model was trained to produce realistic synthetic samples that preserve the statistical and feature distributions of the original dataset. Multiple machine learning models, such as AdaBoost, Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN), were assessed on both the original and enhanced datasets with incrementally increasing degrees of synthetic data dilution. AdaBoost attained 100% accuracy on the original dataset, signifying considerable overfitting; however, the model exhibited enhanced generalization and stability with the CTGAN-augmented data. The occurrence of 100% test accuracy in several models should not be interpreted as realistic clinical performance. Instead, it reflects the limited size, clean structure, and highly separable feature distributions of the UCI CKD dataset. Similar behavior has been reported in multiple previous studies using this dataset. Such perfect accuracy is a strong indication of overfitting and limited generalizability, rather than feature or label leakage. This observation directly motivates the need for controlled data augmentation to introduce variability and improve model robustness. The dataset with the greatest dilution, comprising 2000 synthetic cases, attained a test accuracy of 95.27% utilizing a stochastic gradient boosting approach. Ensemble learning techniques, particularly gradient boosting and random forest, regularly surpassed conventional models like KNN in terms of predicted accuracy and resilience. The results demonstrate that CTGAN-based data augmentation introduces critical variability, diminishes model bias, and serves as an effective regularization technique. This method provides a viable alternative for reducing overfitting and improving predictive modeling accuracy in data-deficient medical fields, such as chronic kidney disease diagnosis. Full article

Accurate prediction of ultimate tensile strength (UTS) is central to the design and optimization of heat-treated steels but is traditionally achieved through costly and iterative experimental trials. This study presents a transparent, physics-aware machine learning (ML) framework for predicting UTS using an open-access steel database. A curated dataset of 1255 steel samples was constructed by combining 18 chemical composition variables with 7 processing descriptors extracted from free-text heat-treatment records and filtering them using physically justified consistency criteria. To avoid information leakage arising from repeated measurements, model development and evaluation were conducted under a group-aware validation framework based on thermomechanical states. A Random Forest (RF) regression model achieved robust, conservative test-set performance (R² ≈ 0.90, MAE ≈ 40 MPa), with unbiased residuals and realistic generalization across diverse composition–processing conditions. Performance robustness was further examined using repeated group-aware resampling and strength-stratified error analysis, highlighting increased uncertainty in sparsely populated high-strength regimes. Model interpretability was assessed using SHAP-based feature importance and partial dependence analysis, revealing that UTS is primarily governed by the overall alloying level, carbon content, and processing parameters controlling transformation kinetics, particularly bar diameter and tempering temperature. The results demonstrate that reliable predictions and physically meaningful insights can be obtained from publicly available data using a conservative, reproducible machine-learning workflow. Full article

The rise of multidrug-resistant tuberculosis (TB) necessitates alternative therapeutic sources. This study investigated the polyphenolic content and the antioxidant, antimycobacterial, and anti-virulence activities of selected medicinal plants traditionally used to treat TB and related symptoms. Total phenolics, tannins, and flavonoids were quantified using colorimetric assays. Antioxidant capacity was assessed via DPPH and ferric-reducing power assays. Antimycobacterial activity against Mycobacterium smegmatis was evaluated using broth microdilution, growth kinetics, cell constituent leakage, and respiratory chain dehydrogenase inhibition assays. Anti-virulence effects were examined using crystal violet biofilm and swarming motility assays. Tarchonanthus camphoratus showed the highest polyphenolic levels and, together with Combretum hereroense, strong antioxidant activity. Extracts of Senecio macroglossus, Nerium oleander, and Tetradenia riparia displayed potent antimycobacterial activity (MIC = 0.16 mg/mL), characterized by delayed exponential growth, membrane damage, and metabolic inhibition. Tabernaemontana elegans exhibited the weakest activity (MIC > 2.5 mg/mL). Most extracts also significantly impaired motility (12–100%) and early-stage biofilm formation. Polyphenolic-rich plant extracts demonstrated promising antimycobacterial and anti-virulence properties against M. smegmatis, highlighting their potential as leads for developing novel anti-TB agents. Full article

Background: Differentiating bipolar disorder (BD) from unipolar major depressive disorder (MDD) at first episode is clinically consequential but challenging. Artificial intelligence/machine learning (AI/ML) may improve early diagnostic accuracy across imaging and non-imaging data sources. Methods: Following PRISMA 2020 and a pre-registered protocol on protocols.io, we searched PubMed, Scopus, Europe PMC, Semantic Scholar, OpenAlex, The Lens, medRxiv, ClinicalTrials.gov, and Web of Science (2014–8 October 2025). Eligible studies developed/evaluated supervised ML classifiers for BD vs. MDD at first episode and reported test-set discrimination. AUCs were meta-analyzed on the logit (GEN) scale using random effects (REML) with Hartung–Knapp adjustment and then back-transformed. Subgroup (imaging vs. non-imaging), leave-one-out (LOO), and quality sensitivity (excluding high risk of leakage) analyses were prespecified. Risk of bias used QUADAS-2 with PROBAST/AI considerations. Results: Of 158 records, 39 duplicates were removed and 119 records screened; 17 met qualitative criteria; and 6 had sufficient data for meta-analysis. The pooled random-effects AUC was 0.84 (95% CI 0.75–0.90), indicating above-chance discrimination, with substantial heterogeneity (I² = 86.5%). Results were robust to LOO, exclusion of two high-risk-of-leakage studies (pooled AUC 0.83, 95% CI 0.72–0.90), and restriction to higher-rigor validation (AUC 0.83, 95% CI 0.69–0.92). Non-imaging models showed higher point estimates than imaging models; however, subgroup comparisons were exploratory due to the small number of studies: pooled AUC ≈ 0.90–0.92 with I² = 0% vs. 0.79 with I² = 64%; test for subgroup difference Q = 7.27, df = 1, p = 0.007. Funnel plot inspection and Egger/Begg tests found that we could not reliably assess small-study effects/publication bias due to the small number of studies. Conclusions: AI/ML models provide good and robust discrimination of BD vs. MDD at first episode. Non-imaging approaches are promising due to higher point estimates in the available studies and practical scalability, but prospective evaluation is needed and conclusions about modality superiority remain tentative given the small number of non-imaging studies (k = 2). Full article

The aviation industry, responsible for approximately 2.5–3.5% of global greenhouse gas emissions, faces increasing pressure to adopt sustainable energy solutions. Hydrogen, with its high gravimetric energy density and zero carbon emissions during use, has emerged as a promising alternative fuel to support aviation decarbonization. However, its large-scale implementation remains hindered by cryogenic storage requirements, safety risks, infrastructure adaptation, and economic constraints. This study aims to identify and evaluate the primary technical and operational risks associated with hydrogen utilization in aviation through a comprehensive Monte Carlo Simulation-based risk assessment. The analysis specifically focuses on four key domains—hydrogen leakage, cryogenic storage, explosion hazards, and infrastructure challenges—while excluding economic and lifecycle aspects to maintain a technical scope only. A 10,000-iteration simulation was conducted to quantify the probability and impact of each risk factor. Results indicate that hydrogen leakage and explosion hazards represent the most critical risks, with mean risk scores exceeding 20 on a 25-point scale, whereas investment costs and technical expertise were ranked as comparatively low-level risks. Based on these findings, strategic mitigation measures—including real-time leak detection systems, composite cryotank technologies, and standardized safety protocols—are proposed to enhance system reliability and support the safe integration of hydrogen-powered aviation. This study contributes to a data-driven understanding of hydrogen-related risks and provides a technological roadmap for advancing carbon-neutral air transport. Full article

Self-cleansing intrusion-tolerant systems mitigate attacker intrusions and control through periodic recovery, thereby enhancing both availability and security. However, vulnerabilities in the control link render these systems susceptible to request forgery attacks. Furthermore, existing research on the modeling and performance analysis of such systems remains insufficient. To address these issues, this paper introduces an authentication mechanism to fortify control link security and employs Generalized Stochastic Petri Nets for system evaluation. We constructed Petri net models for three distinct scenarios: a traditional system, a system compromised by forged controller requests, and a system fortified with authentication mechanism. Subsequently, isomorphic Continuous-Time Markov Chains were derived to facilitate theoretical analysis. Quantitative evaluations were performed by deriving steady-state probabilities and conducting simulations on the PIPE platform. To further assess practicality, we conduct scalability analysis under varying system scales and parameter settings, and implement a prototype in a virtualized testbed to experimentally validate the analytical findings. Evaluation results indicate that authentication mechanism ensures the reliable execution of cleansing strategies, thereby improving system availability, enhancing security, and mitigating data leakage risks. Full article

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article

Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system based on fuzzy polymorphic Bayesian networks. This method effectively addresses the shortcomings of traditional assessment approaches in the probabilistic quantification of risk. A Bayesian network with 44 nodes was established from five dimensions: ventilation power, ventilation network, ventilation facilities, human and management factors, and work environment. The risk states were divided into multiple states based on the As Low As Reasonably Practicable (ALARP) metric. The probabilities of evaluation-type root nodes were calculated using fuzzy evaluation, and the subjective bias was corrected by introducing a reliability coefficient. The concept of distance compensation is proposed to flexibly calculate the probabilities of quantitative-type root nodes. Through the verification of the ventilation system of H Coal Mine in Shanxi, China, it is concluded that the high risk of the ventilation system is 18%, and the high-risk probability of the ventilation system caused by the external air leakage of the mine is the largest. The evaluation results are consistent with real-world conditions. The results can provide a reference for improving the safety of the ventilation systems. Full article

Air conditioners are a critical adaptation measure against heat- and cold-related risks under climate change. However, their electricity use and refrigerant leakage increase greenhouse gas (GHG) emissions. This study developed a data-driven life-cycle assessment (LCA) framework for residential room air conditioners in Japan by integrating large-scale field operation data with life-cycle climate performance (LCCP) modeling. We aggregated 1 min records for approximately 4100 wall-mounted split units and evaluated the 10-year LCCP across nine climate regions. Using the annual operating hours and electricity consumption, we classified the units into four behavioral quadrants and quantified the life-cycle GHG emissions and parameter sensitivities for each. The results show that the use-phase electricity dominated the total emissions, and that even under the same climate and capacity class, the 10-year per-unit emissions differed by roughly a factor of two between the high- and low-load quadrants. The sensitivity analysis identified the heating hours and the setpoint–indoor temperature difference as the most influential drivers, whereas the grid CO₂ intensity, equipment lifetime, and refrigerant assumptions were of secondary importance. By replacing a single assumed use scenario with empirical profiles and behavior-based clusters, the proposed framework improves the representativeness of the LCA for air conditioners. This enabled the design of cluster-specific mitigation strategies. Full article

Covered karst collapse is a key geotechnical hazard in infrastructure construction in karst regions of China. In particular, strata consisting of an overlying clay layer and an underlying sand layer are prone to abrupt collapse induced by sand leakage under construction disturbances, which poses serious risks to pile foundation safety. To clarify the disaster-forming mechanism and develop a quantitative analysis method, this study investigates the mechanical behaviour of the entire collapse process by combining theoretical analysis with numerical simulation. A continuous mechanical analysis framework is established that follows the sequence from sand layer leakage to cavity expansion and then clay layer instability. Within this framework, a calculation model for the angle of repose of the sand layer is proposed that considers seepage and confined pressure effects. Simultaneously accounting for the influence of the casing, stability models for overall and localised collapses are developed using limit equilibrium theory. A comprehensive safety factor criterion K_c based on the critical span (or radius) is then proposed, leading to a linked evaluation method that couples the potential span of the sand layer with the ultimate span of the clay layer. The results show that an increase in Δh/h significantly reduces the angle of repose of the sand layer; the mechanical mechanism is confirmed whereby an increase in the roof span leads to shear stress exceeding the soil’s shear strength, thus triggering instability; the proposed safety factor K_c can effectively predict both overall and localised collapse, and case verification demonstrates that the predicted spans match well with actual collapse dimensions. The results provide a theoretical and technical basis for risk prediction, as well as for the prevention and control of pile foundation construction in karst areas. Full article