Search Results (958)

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

To address the widespread issue of abnormally high pump pressure during hydraulic fracturing of deep-to-ultra-deep reservoirs (burial depth > 4500 m) in the Junggar Basin, this study systematically reveals the mechanical mechanism underlying this phenomenon by integrating well logging curve analysis and elastoplastic mechanics theory. Statistical results demonstrate that the actual fracture initiation pressure of 60% of wells in the target block is significantly higher than the values predicted by traditional elastic theory, primarily attributed to plastic yielding and stress concentration effects around perforations induced by high in situ stress. An elastoplastic rock fracture initiation pressure model is established based on the Mohr–Coulomb criterion and the plastic zone radius criterion, which is applied to predict the fracture initiation pressure of selected wells in the target block. The relative error between the model predictions and field measurements is less than 2%, significantly improving the prediction accuracy of fracture initiation pressure in deep-to-ultra-deep formations. This provides precise guidance for subsequent optimization of operational parameters and selection of pressure ratings for wellhead equipment. The study further clarifies that in situ stress difference, rock yield stress, and the power-law hardening exponent are the key factors controlling the transition of fracture initiation modes. To mitigate the high pump pressure challenge in deep-to-ultra-deep reservoir fracturing, the field application of weighted fracturing fluid effectively increases the wellbore hydrostatic column pressure, reduces wellhead operational pressure, and ensures construction safety. The findings of this study provide critical theoretical and technical support for achieving the goal of “successful fracture initiation and effective fracture control” in deep-to-ultra-deep reservoir fracturing. Full article

Background/Objectives: Hysteroscopy is the gold standard for visualization and treatment of intrauterine pathology. Because hysteroscopic interpretation remains operator-dependent, artificial intelligence (AI) has been evaluated as a tool to improve consistency, lesion recognition, and decision support. We aimed to systematically review AI, machine learning (ML), deep learning (DL), or computer-aided diagnosis (CAD) applications in hysteroscopy. Methods: A systematic search of PubMed/MEDLINE and EBSCOhost was performed from database inception to 8 March 2026, supplemented by targeted searches. Risk of bias was assessed using QUADAS-2 (diagnostic), PROBAST (prognostic), RoB2, and structured technical quality domains. Results: Nineteen primary studies were included, covering five areas: diagnostic classification and object detection (n = 8), real-time lesion detection and localization (n = 4), segmentation and visual-field support (n = 3), operative guidance (n = 1), and prognostic or decision-support applications (n = 3). Performance was highest in narrowly defined binary tasks and in large multicenter systems (e.g., ECCADx: AUC 0.979 internal, 0.975 external) and in prognostic fertility-prediction models after hysteroscopic adhesiolysis (AUC up to 0.992). Broader multiclass classification of heterogeneous lesions showed uneven and lower performance. Most studies were single-center, retrospective, and lacked external validation. Only one randomized study linked AI support to measurable procedural outcomes. Conclusions: The available studies indicate good technical performance in selected hysteroscopic tasks, particularly binary classification, focal lesion detection, and postoperative fertility stratification. Current evidence, however, remains limited by retrospective design, operator-dependent image acquisition, inconsistent validation, and scarce outcome-based clinical testing. In the short term, the most likely role of these systems is to support image interpretation, improve visual quality control, highlight suspicious lesions, and integrate hysteroscopic findings with complementary clinical data. Full article

Ammonia is a promising zero-carbon energy carrier for maritime decarbonisation, but its deployment is limited by safety risks that are not adequately addressed by conventional marine fuel safety frameworks. This study critically reviews safety assessment, risk management and control strategies for ammonia storage and handling in maritime applications using a PRISMA-informed literature synthesis. Evidence is analysed across hazard characterisation, storage configurations, transfer operations, risk assessment methods, mitigation barriers and regulatory frameworks. The review shows that ammonia safety is governed by coupled release–exposure–barrier interactions shaped by storage condition, tank configuration, pressure–temperature behaviour, material compatibility, transfer mode, ventilation, ship geometry and human intervention. Existing methods, including HAZID, HAZOP, risk matrices and QRA, support hazard screening and prioritisation, but remain limited in representing flashing two-phase releases, dense gas dispersion, confined-space accumulation, exposure duration, ventilation effectiveness and safeguard timing under maritime conditions. CFD, FTA, Bayesian approaches and Monte Carlo analysis offer higher analytical resolution, but their reliability is constrained by limited validation data, uncertain leak-frequency inputs and simplified assumptions for human exposure and emergency response. Effective risk control therefore requires a toxicity-centred barrier strategy linking containment integrity, ammonia-compatible materials, gas and process monitoring, emergency shutdown, ventilation, water-based mitigation, PPE, competency-based training and emergency planning. Current regulatory and classification guidance provides an essential foundation but remains fragmented and insufficiently aligned with ammonia-specific requirements for exposure modelling, safety-zone definition and approval pathways. This review contributes a maritime-specific synthesis of ammonia storage and handling safety by connecting hazard behaviour, storage design, transfer operations, risk assessment limitations, control-barrier logic and regulatory approval needs. The findings highlight the need for validated source-term models, full-scale release and dispersion data, exposure-based safety criteria and harmonised regulatory pathways to support the safe and scalable use of ammonia in maritime decarbonisation. Full article

The dynamic reliability of steam-turbine governing systems is essential for the safe operation of nuclear power units. As a key regulating and protection component, the reheat valve must complete rapid closure under abnormal operating conditions. This study addresses the closing timeout problem observed in a nuclear reheat-valve oil-motor actuator after domestic substitution, with particular attention to sluggish motion and discontinuous closing at small openings. A coupled hydraulic–mechanical model was then established by integrating the coned-disc spring assembly, hydraulic circuit, cartridge valve, gear–rack transmission, and load resistance based on the mathematical model. The model was used to identify the dominant parameters controlling the fast-closing process, and the optimization strategy was subsequently verified by experiments on an actual actuator platform. The results show that coned-disc spring degradation is a critical source of closing timeout risk. When the equivalent elastic modulus decreases to approximately 195 GPa, the fast-closing time approaches the critical limit of 0.8 s, while further degradation results in evident timeout. The C0 throttling orifice has the strongest influence on the effective closing time by governing the pressure-relief capacity of the working chamber. A coordinated correction strategy, involving coned-disc spring force compensation and throttling parameter adjustment, restores the closing margin, shortens the fast-closing time to 0.78 s, and improves closing smoothness. This work provides the practical guidance for design verification, field commissioning, and domestic improvement of nuclear reheat-valve oil-motor actuator systems. Full article

This study examined the instructional effects of integrating a game-based learning system into a programming course, focusing on how tool-supported practice influences students’ learning outcomes and learning experiences. A quasi-experimental design was employed, involving an experimental group that used a game-based learning system for programming practice and a control group that completed traditional programming assignments. Both groups were taught by the same instructor using identical instructional content over an eight-week period. Pre-tests and post-tests were administered to assess learning performance. Baseline-adjusted and conditional effect analyses were conducted to examine whether the instructional effect varied according to students’ prior programming knowledge. The results showed that students in the experimental group achieved higher post-test performance than those in the control group, and the Group × Pre-test interaction indicated that the learning effect was conditional on learners’ baseline programming competence. In addition, students in the experimental group completed questionnaires on system use perceptions and flow experience. The findings indicated generally positive perceptions of the game-based learning system and a significantly positive level of flow during programming practice. The findings suggest that the GBPLS can support programming practice when it is embedded within a coherent instructional design. However, the observed benefits should be interpreted as conditional rather than universal. The educational value of the system appears to depend on the alignment among programming tasks, feedback, game-based engagement, generative AI-supported question generation, and teacher guidance. Full article

Metal-organic framework (MOF)-based materials have become promising platforms for tumor hyperthermia by integrating energy conversion, tumor microenvironment regulation, and multimodal therapy within programmable porous structures. This review summarizes recent advances in intrinsic MOFs, MOF composites, and MOF-derived materials for photothermal therapy, microwave hyperthermia, and magnetic hyperthermia. The reviewed studies show that high-valence metal MOFs mainly provide stable and modifiable frameworks, whereas transition-metal, magnetic, and multimetallic MOFs contribute to redox regulation, ROS generation, magnetic response, and microwave energy dissipation. Beyond localized heat generation, MOF-based platforms enhance therapeutic efficacy by combining hyperthermia with chemotherapy, chemodynamic therapy, metabolic intervention, immunotherapy, and imaging guidance. These integrated strategies help overcome incomplete ablation, thermotolerance, oxidative stress resistance, and tumor recurrence. However, clinical translation is still limited by insufficient standardization, uncertain degradation behavior, metal-ion safety, and inadequate thermal dose control. Future development should emphasize mechanism-oriented design, controllable composition, long-term biosafety, and image-guided thermal regulation to advance MOF-based hyperthermia toward precise and clinically relevant cancer therapy. Full article

Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment. Full article

AI-enabled quality control (AI-QC) tools are increasingly available to manufacturing SMEs in emerging economies, yet the firm-level conditions associated with their adoption remain underexamined. Building on the Technology–Organization–Environment (TOE) framework of Tornatzky and Fleischer, integrated with the perceived usefulness and perceived ease-of-use constructs of the Technology Acceptance Model (TAM), this study examines the determinants of AI-QC adoption intention, and its association with operational performance improvement, in 284 manufacturing SMEs from Turkey, Malaysia, and Egypt. The focal dependent construct is adoption intention rather than realized adoption. The AI-QC technologies considered are machine learning defect detection, computer vision inspection, predictive maintenance, and digital twin integration. Three complementary analytical procedures are applied to the same data: partial least squares structural equation modeling (PLS-SEM) to estimate the strength of the modeled associations, importance–performance map analysis (IPMA) to identify high-importance but low-performance predictors, and fuzzy-set qualitative comparative analysis (fsQCA) to identify combinations of conditions jointly sufficient for high adoption intention. The PLS-SEM estimates indicate positive associations for the technological, organizational, and environmental predictors, with top management support, perceived usefulness, and organizational readiness showing the largest coefficients and data security concern showing a negative association; effect magnitudes varied considerably, and several were small. The IPMA results indicate that the two most important predictors exhibit comparatively low performance scores in the sample. The fsQCA results identify three configurations associated with high adoption intention. Because the design is cross-sectional and based on self-reported, single-respondent data, the findings are interpreted as associations rather than causal effects. The paper concludes with guidance for SME managers, AI technology vendors, and industrial policymakers. Full article

Gantry-type dual-drive feed systems are widely used in high-precision CNC machine tools, and their synchronization performance directly affects machining accuracy and operational stability. To reduce synchronization errors caused by load-position variation, nonuniform stiffness distribution, and inertia mismatch, this study proposes a structural parameter optimization method for a gantry-type dual-drive feed system. The novelty of this work lies in integrating position-dependent dynamic modeling, critical-position identification, sensitive structural-parameter selection, and response-surface-based optimization into a unified framework for synchronization-error reduction. First, a position-dependent dynamic model is established using modal reduction, spline interpolation, and substructure synthesis. The dynamic model is then coupled with a servo control model to construct an electromechanical coupling model, which is validated experimentally on a gantry-type dual-drive feed system. Next, the synchronization-error distribution over the entire workspace is evaluated, and the critical position with the poorest synchronization performance is identified. Based on sensitivity analysis, the key structural parameters affecting synchronization error are selected as design variables. A response surface surrogate model is then constructed, and particle swarm optimization is used to obtain the optimal structural-parameter combination. The results show that the synchronization error at the critical position is reduced by 20.5%, while the average synchronization error at the validation positions is reduced by 17.3%. These results demonstrate that the proposed method can effectively improve the synchronization accuracy of gantry-type dual-drive feed systems and provide practical guidance for the structural design of high-precision dual-drive machine tools. Full article

In view of the frequent occurrence of trapped annular pressure and the increasingly prominent risk of wellbore integrity under the periodic high-intensity injection and production conditions of gas storage wells, a trapped annular pressure prediction model suitable for deep gas storage wells is established based on the comprehensive heat transfer characteristics of the tubing string-cement sheath-formation. The calculation results of the model are in good agreement with field-measured pressure data, with a coincidence degree of about 95%. Based on the established model, the influence laws of four major factors, including tubing specification and dimension, thermophysical properties of annular fluid, casing material characteristics and daily gas production rate, on trapped annular pressure are systematically analyzed. Meanwhile, the pressure control effects of three measures, namely Annulus A pressure relief, application of insulated tubing and nitrogen injection into Annulus B, are quantitatively compared for the case well. The research results show that adopting tubing with larger outer diameter and thinner wall thickness, injecting fluid with lower thermal expansion coefficient or higher isothermal compressibility coefficient into the annulus and appropriately reducing daily gas production can effectively decrease trapped annular pressure. Among them, the influence of fluid properties on trapped annular pressure is far greater than that of pipe material parameters. Among the three pressure control measures, nitrogen injection into Annulus B presents the optimal pressure control effect; when the nitrogen volume accounts for approximately 3% of the total annular fluid volume, the trapped annular pressure is reduced by about 82%. The research findings provide a theoretical basis and technical guidance for the prediction and control of trapped annular pressure in gas storage wells. It is recommended to prioritize the nitrogen injection technology for Annulus B in the well construction stage, and realize pressure management for producing wells by combining Annulus A pressure relief and production regulation. Full article

Web Application Programming Interfaces (Web APIs) have become fundamental components of modern software ecosystems. At the same time, they have emerged as major attack surfaces in web applications and distributed services. Although many web API vulnerabilities are well documented, a critical gap remains in understanding how insecure development practices, usability limitations, and developer-related issues contribute to recurring API security problems. To address this gap, this study presents a systematic review of web API security research using a PRISMA-guided methodology and a taxonomy-driven analytical approach. The review synthesizes findings from 50 selected studies covering web API architectural styles, usability concerns, authentication and access-control weaknesses, and common vulnerabilities. These vulnerabilities include SQL Injection (SQLi), Cross-Site Scripting (XSS), Broken Authentication, and Denial-of-Service (DoS) attacks within the context of the OWASP API Security Top 10 framework. The findings indicate that recurring web API vulnerabilities are associated not only with technical weaknesses but also with API usability issues, insecure development practices, inconsistent security guidance, and increasing implementation complexity. The review also identifies persistent research gaps involving usability-security integration, API evolution, secure-by-design development practices, and empirical validation of security tools and frameworks. By synthesizing these dimensions into a unified conceptual perspective, this study provides researchers and practitioners with a clearer understanding of the factors contributing to web API insecurity. The study also highlights directions for developing more resilient and developer-aware API security practices. Full article

Demand-responsive transit (DRT) is increasingly promoted as a means to enhance the resilience and inclusiveness of sustainable urban mobility. However, how users form early-stage adoption intentions toward such interface-mediated services remains insufficiently understood. While prior research has focused on conventional transit or mature mobility-on-demand platforms, the role of fine-grained human–computer interaction (HCI) characteristics in shaping initial adoption intentions toward DRT received limited empirical attention. This study proposes an integrated framework linking five HCI characteristics—interaction responsiveness, real-time interaction, controllability of interactivity, personalization of interactivity, and playfulness—to behavioral intention through the mediating mechanisms of perceived service quality and platform trust. The framework was tested by applying partial least-squares structural equation modeling to cross-sectional survey data (N = 147) collected from existing early users of an early-stage DRT pilot in Wuxi, China. Platform trust emerged as the strongest direct predictor of behavioral intention, while real-time interaction and interaction responsiveness contributed mainly through trust- and service-quality-based pathways. Controllability and personalization showed no statistically significant association with the mediators in this early-stage sample, and playfulness exhibited a significant but modest effect on platform trust. By integrating HCI design, service-quality perceptions, and platform trust into a single nomological framework, this study offers context-sensitive guidance for designing interface-mediated shared mobility services that may support more resilient and sustainable urban transport. Full article

Background/Objectives: Antimicrobial resistance (AMR), driven by high-risk bacterial pathogens, is a major healthcare threat. This scoping review mapped artificial intelligence/machine learning (AI/ML) and computational approaches integrated with whole-genome sequencing (WGS), genomic surveillance, rapid typing, epidemiological data, or clinical metadata for early warning of AMR outbreak clones. Methods: Following PRISMA-ScR guidance and the Population–Concept–Context (PCC) framework, PubMed/MEDLINE, Scopus, and Web of Science were searched for English-language studies published between 2010 and 2026. Eligible studies addressed AI/ML or computational approaches for AMR outbreak detection, clone surveillance, transmission analysis, or infection prevention and control (IPC). Results: Thirty-eight studies were grouped into five domains: genomic surveillance; rapid typing; resistance, risk-factor, and lineage prediction; transmission reconstruction; and IPC-oriented genomic epidemiology. AI/ML supported automation, isolate prioritization, typing triage, prediction, transmission modelling, and electronic health record (EHR)-linked route identification. Conclusions: AI/ML may enhance WGS-based AMR surveillance, but validation, dataset dependence, heterogeneity, and limited IPC outcome reporting remain key gaps. Full article

Cervical cancer continues to require more precise and clinically adaptable local treatment strategies, particularly in the face of insufficient drug accumulation at the lesion site, systemic toxicity of conventional chemotherapy, limited development of postoperative tissue-interfacing platforms, and the anatomical constraints of standard radiotherapy devices. In this mini-review, we summarize the current landscape of polymer-based biomaterials for local therapy in cervical cancer from both materials and clinical perspectives. Specifically, we discuss three interconnected application domains: local drug delivery systems, polymeric scaffolds and tissue-interfacing platforms, and 3D-printed radiotherapy devices. Recent studies indicate that polymer-based local delivery systems, including nanofiber- and hydrogel-based formulations, can improve cervicovaginal retention, controlled release, and local therapeutic exposure. Scaffold-based systems further extend the role of biomaterials by combining sustained local delivery with defect-specific support and tissue interaction, whereas 3D-printed radiotherapy devices contribute primarily through precision enablement, individualized implantation guidance, and improved conformity in anatomically challenging cases. Despite these advances, most available studies remain preclinical or early translational, and important barriers persist in long-term safety, standardization, clinically relevant validation, and workflow integration. Future progress will depend on application-specific design, stronger translational rigor, and closer integration of biomaterials, imaging, and personalized clinical practice. Full article