Search Results (643)

The construction industry remains one of the most hazardous sectors despite its economic importance, with high-altitude fall accidents being the most prevalent and deadly type of incident. This paper aimed to study and analyze the accident data of the past accident cases in China and find out the key causes and rules of the accidents. This research analyzed 1223 Chinese accident reports (2014–2023) using Latent Dirichlet Allocation topic modeling to identify causal factors, followed by Apriori algorithm correlation analysis to reveal accident causation patterns. This study comprehensively uses topic model, association rules and visualization methods to systematically analyze the causes of high-altitude fall accidents. The research identified 24 distinct accident cause topics across personnel, equipment, management, and environmental dimensions. Key findings revealed that incorrect use of labor protective equipment, inadequate safety inspections, and failure to implement safety management protocols were persistent issues throughout the study period. Notably, the post COVID-19 pandemic introduced new safety challenges, with the intensity of topics related to “subject of responsibility for safety production has not been implemented” showing significant post-pandemic increases. These findings highlight the evolving nature of construction safety challenges and the need for targeted interventions to address persistent and emerging risks. Full article

With the increasing intelligence of modern air-conditioning systems, the difficulty of acquiring data from air-conditioning systems has been significantly reduced. However, analyzing the massive amounts of data collected and obtaining more valuable information still remains challenging, especially considering the internal relationships behind the data. The purpose of this study was to conduct operational experiments on VRF systems under different indoor set temperatures, indoor set air speeds, and terminal load rates. Then, the patterns of various operating parameters and energy consumption of VRF systems during winter operation were analyzed based on unsupervised methods. Three machine learning methods were primarily employed in this study, including correlation analysis, data regression analysis, and association rule analysis. Finally, a regression model was constructed for energy consumption based on eight typical characteristic parameters. The experimental results showed that the system was stable to a certain degree at different wind speeds. Among the characteristic parameters, fixed frequency 1 exhaust temperature, compressor frequency, and other parameters have a significant positive effect on energy consumption, while fixed frequency 1 shell top oil temperature, inlet and outlet pipe temperature difference, and other parameters have a negative effect. The research results provide a reference for air conditioning system data mining and building sustainability. Full article

Background: A large number of substance use disorders are increasingly associated with complex clinical presentations and unknown mental and medical risks, presenting a growing challenge for mental health worldwide. Research exploring the interplay between substance use and psychiatric disorders remains limited in Eastern Europe. Objectives: We investigated the demographic and clinical features of 203 patients admitted to a major Romanian psychiatric hospital, aiming to clarify the patterns of dual diagnosis and symptomatology within this vulnerable population. Results: Cannabis, novel psychoactive substances and unknown substances were the most commonly used drugs. Psychiatric comorbidity was rather the rule than the exception within our group. Cluster analysis revealed three distinct symptom profiles: manic/psychotic, negative affective and disorganized. While individual drug type did not independently predict symptom severity or readmission risk, a significant interaction effect between drug use and psychiatric comorbidity influenced symptom cluster membership. Conclusions: These findings highlight the complexity and heterogeneity of dual diagnoses and underline the importance of an integrated, multidisciplinary approach in addiction medicine. Full article

Introduction: Concussions are a growing concern in professional football due to their potential short- and long-term neurological consequences. Despite increasing global awareness, data on the epidemiology and clinical management of concussions in Portuguese football remain scarce. This preliminary exploratory study aimed to characterize the incidence, mechanisms, symptomatology, and medical response to concussions in Portugal’s Professional Football Leagues during the 2023/2024 season, based on reported cases. Methods: A retrospective observational analysis was conducted on head injuries reported by club medical teams during official matches in Liga Portugal First and Second Leagues. Collected variables included player position, time of injury, mechanism, symptoms, medical interventions and hospital referral. Results: Only six concussions were reported during official matches, with an overall incidence of 0.60 per 1000 player-hours. Most occurred in defenders, primarily due to head-to-head collisions, followed by ball impact, falls, and maxillofacial trauma. Injuries were more frequent during the final third of matches. Common symptoms included loss of consciousness, headache, and amnesia. Half of the players were referred to hospital care and underwent cranial CT scans. Among all variables analyzed, a statistically significant association was found between mechanism of injury and occurrence of amnesia (p = 0.014), with non-head-to-head impacts more frequently associated with amnesia. However, given the extremely limited sample size, this finding should be interpreted with extreme caution and requires replication in larger cohorts. Conclusions: This preliminary study suggests that defenders face a higher risk of head injuries, particularly from head-to-head impacts occurring late in matches. The prevalence of severe symptoms and the potential association between non-head-to-head impacts and amnesia highlight the need for more robust injury surveillance systems and underscore the importance of improved sideline assessment and return-to-play protocols. The findings emphasize the urgent need for comprehensive, standardized reporting mechanisms for concussions. Further research should explore long-term neurological effects and the effectiveness of preventive measures such as rule modifications, protective measures, and enhanced concussion management protocols, supported by more extensive and systematically collected data. Full article

The fatigue constitutive model under cyclic loading is of vital importance for studying the fatigue deformation characteristics of soft rocks. In this paper, based on the classical Bingham model, a modified Bingham fatigue model for describing the fatigue deformation characteristics of soft rocks under cyclic loading was developed. Firstly, the traditional constant-viscosity component was replaced by an improved nonlinear viscoelastic component related to the number of cycles. The elastic component was replaced by an improved nonlinear elastic component that decays as the number of cycle loads increases. Meanwhile, by decomposing the cyclic dynamic loads into static loads and alternating loads, a one-dimensional nonlinear viscoelastic-plastic Bingham fatigue model was developed. Furthermore, a rock fatigue yield criterion was proposed, and by using an associated flow rule compatible with this criterion, the one-dimensional fatigue model was extended to a three-dimensional constitutive formulation under complex stress conditions. Finally, the applicability of the developed Bingham fatigue model was verified through fitting with experimental data, and the parameters of the model were identified. The model fitting results show high consistency with experimental data, with correlation coefficients exceeding 0.978 and 0.989 under low and high dynamic stress conditions, respectively, and root mean square errors (RMSEs) below 0.028. Comparative analysis between theoretical predictions and existing soft rock fatigue test data demonstrates that the developed Bingham fatigue model more effectively captures the complete fatigue deformation process under cyclic loading, including the deceleration, constant velocity, and acceleration phases. With its simplified component configuration and straightforward combination rules, this model provides a valuable reference for studying fatigue deformation characteristics of rock materials under dynamic loading conditions. Full article

Cellular organisms store heritable information in two forms, genetic and epigenetic, the latter being largely dependent on cytosine methylation (5mC). Chargaff’s Second Parity Rule (CSPR) describes the nucleotide composition of cellular genomes in terms of intra-strand DNA symmetry. However, it remains unknown whether DNA methylation patterns display intra-strand DNA symmetry. Computational analysis was conducted of the DNA methylation patterns observed in human cell lines and in tissue samples from healthy donors. Analysis of 5mC marks in mutually reverse-complementary pairs of short oligomers, containing CpG dinucleotide in the middle, revealed deviations from CSPR and methylation asymmetry that can be observed for two non-overlapping mirror groups defined by CpG methylation values. Deviations from CSPR, together with combinatorial probabilities of pattern distributions and computer simulations, highlight the non-random nature of methylation processes and enabled us to identify specific cell types as outliers. Further analysis revealed a compensatory methylation asymmetry that reduces deviations from intra-strand symmetry and implies the existence of strand-specific methylation during cell differentiation. Among six pairs of reverse-complementary tetranucleotides, four pairs with specific sequence motifs display pronounced methylation asymmetry. This mirror asymmetry may be associated with chromosome folding and the formation of a complex three-dimensional landscape. Full article

Oocytes from women of advanced reproductive age exhibit diminished developmental potential, but the underlying mechanisms remain incompletely defined. Oocyte maturation depends on translational control of maternal mRNA synthesized during growth. We performed a computational analysis on human oocytes from women <30 versus ≥40 years and observed that mRNA GC content correlates negatively with half-life in oocytes from young (<30 yr) but positively with oocytes from aged (>40 yr) women. In young oocytes, longer mRNA half-life is associated with lower protein abundance, whereas in aged oocytes GC content correlates positively with protein abundance. During the GV-to-MII transition, codon composition stratifies stability: codons that support rapid translation (optimal) stabilize mRNA, while slow-translating codons (non-optimal) promote decay. With reproductive aging, GC-containing codons become more optimal and align with increased protein abundance. These findings indicate that reproductive aging remodels codon-optimality-linked, translation-coupled mRNA decay, stabilizing a subset of GC-rich maternal mRNA that may be prone to excess translation during maturation. Our analysis is explicitly within human reproductive aging; it does not revisit cross-species stability rules. Instead, it shows that sequence–stability relations are reprogrammed with age within human oocytes, including an inversion of the GC–stability association during GV-to-MII transition. Disruption of the normal mRNA clearance program in aged oocytes may compromise oocyte competence and alter maternal mRNA dosage, with downstream consequences for early embryonic development. Full article

This study investigates the behavioral determinants of tourists’ selection of ESG-certified hotels by applying association rule mining to 895,962 valid records derived from datasets of the Ministry of Transportation and the Ministry of Environment. Tourists were classified into three groups based on length of stay. The results reveal strong associations between ESG hotel choice and factors such as gender, age, port of entry, transport mode, and arrival city, with prominent patterns including “Kaohsiung Port,” “Age 30–39,” and “Airplane.” This study offers both theoretical contributions and actionable policy implications, advocating data-driven strategies to advance sustainable hotel management and effectively engage high-potential market segments. Full article

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts. Full article

This study focuses on the numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation for a two-dimensional cylindrical domain. The tissue undergoes laser irradiation, where internal heat sources are determined based on the Beer–Lambert law. Moreover, key parameters—such as the perfusion rate and effective scattering coefficient—are modeled as functions dependent on tissue damage. In addition, a fuzzy heat source associated with magnetic nanoparticles is also incorporated into the model to account for magnetothermal effects. A novel aspect of this work is the introduction of uncertainty in selected model parameters by representing them as triangular fuzzy numbers. Consequently, the entire Finite Pointset Method (FPM) framework is extended to operate with fuzzy-valued quantities, which—to the best of our knowledge—has not been previously applied in two-dimensional thermal modeling of biological tissues. The numerical computations are carried out using the fuzzy-adapted FPM approach. All calculations are performed due to the fuzzy arithmetic rules with the application of α-cuts. This fuzzy formulation inherently captures the variability of uncertain parameters, effectively replacing the need for a traditional sensitivity analysis. As a result, the need for multiple simulations over a wide range of input values is eliminated. The findings, discussed in the final Section, demonstrate that this extended FPM formulation is a viable and effective tool for analyzing heat transfer processes under uncertainty, with an evaluation of α-cut widths and the influence of the degree of fuzziness on the results also carried out. Full article

Tower crane structural failures remain a major safety concern on construction sites. To improve accident prevention, this study proposes an intelligent framework that combines an improved Transformer model with a Directional Interest Score (DIS) Apriori algorithm and complex-network analysis. A corpus of 535 tower crane accident reports (2002–2024) was compiled and annotated with causal and accident entities according to system–safety theory. Segment embeddings were introduced to the Transformer to reinforce boundary detection, enabling accurate extraction of causative factors and relation triples. The DIS-Apriori algorithm was then used to mine both positive and negative association rules while aggressively pruning irrelevant item sets. Eventually, causative factors were mapped into a weighted, directed complex network where edge weights reflect the absolute frequency difference between positive and negative rules, and edge directions correspond to their signs. Experiments show that the Transformer achieves higher precision and recall than baseline models, and DIS-Apriori substantially reduces unnecessary item-set complexity while preserving critical rules. Network analysis revealed five critical causal links and a closed-loop causal link that warrant priority intervention. The proposed method delivers a data-driven, explainable tool for pinpointing key risk sources and designing targeted mitigation strategies, offering practical value for intelligent safety management of tower cranes. Full article

Background: Peritonitis in peritoneal dialysis and cirrhosis remains common and leads to morbidity. Neutrophil gelatinase-associated lipocalin (NGAL) has been evaluated as a rapid adjunctive biomarker. Methods: Following PRISMA-DTA and PROSPERO registration (CRD420251105563), we searched MEDLINE, Embase, Cochrane Library, LILACS, Scopus, and Web of Science from inception to 31 December 2024, and ran an update on 30 June 2025 (no additional eligible studies). Diagnostic accuracy studies measuring NGAL in peritoneal/ascitic fluid against guideline reference standards were included. When 2 × 2 data were not reported, we reconstructed cell counts from published metrics using a prespecified, tolerance-bounded algorithm (two studies). Accuracy was synthesized with a bivariate random effects (Reitsma) model; 95% prediction intervals (PIs) were used to express heterogeneity; small-study effects were assessed by Deeks’ test. Results: Thirteen studies were included qualitatively and ten were entered into a meta-analysis (573 cases; 833 controls). The pooled sensitivity was 0.95 (95% CI, 0.90–0.97) and specificity was 0.86 (0.70–0.94); likelihood ratios were LR+ ≈7.0 and LR− 0.06. Between-study variability was concentrated on specificity: the PI for a new setting was 0.75–0.98 for sensitivity and 0.23–0.99 for specificity. Deeks’ test showed evidence of small-study effects in the primary analysis; assay/platform and thresholding contributed materially to heterogeneity. Conclusions: NGAL in peritoneal/ascitic fluid demonstrates high pooled sensitivity but variable specificity across settings. Given the wide prediction intervals and the signal for small-study effects, NGAL should be interpreted as an adjunct to guideline-based criteria—not as a stand-alone rule-out test. Standardization of pre-analytics and assay-specific, locally verified thresholds, together with prospective multicenter validations and impact/economic evaluations, are needed to define its clinical role. Full article

Unsafe behavior among air traffic controllers is a significant causal factor in civil aviation safety incidents. To explore the risks and pathways associated with controller-induced aviation accidents, this study develops an analytical model of controller unsafe behavior based on association rules and fault tree Bayesian networks. First, the Human Factors Analysis and Classification System (HFACS) was applied to identify and categorize aviation incident reports attributed to controller errors. Next, association rule algorithms were employed to uncover potential associations between controller unsafe behaviors and related risk factors, and a fault tree Bayesian network (FT-BN) model of controller unsafe behaviors was constructed based on these associations. The results revealed that the most likely unsafe behaviors were: improper allocation of aircraft spacing (30.5%), failure to take necessary intervention measures (28.4%), and improper transfer of control (27.8%). Backward analysis of the FT-BN indicated that improper allocation of aircraft spacing was most likely triggered by failure to provide adequate controller training, failure to take necessary intervention measures was most often caused by forgotten information, and improper transfer of control was most frequently associated with controller fatigue and failure to put risk management efforts in place. This study provides an important framework for the analysis and evaluation of controller behavior management and offers key insights for improving air traffic safety. Full article

Background: Justice-involved adolescents exhibit high rates of mental health disorders with complex comorbidity patterns. Understanding these patterns is crucial for developing targeted interventions in this vulnerable population. Methods: We applied multiple machine-learning techniques to electronic records from 124 justice-involved adolescents (11–21 years; mean = 15.7 ± 1.9). Analyses included association rule mining, K-Means clustering with t-SNE visualization, and topic modeling of clinicians’ recommendation notes. Results: Hyperkinetic disorders (F90.0/F90.1) and family-stress factors (Z63.5) together accounted for approximately 45% of all ICD-10 entries. A four-cluster K-Means solution built on age + F-codes alone showed weak separation (silhouette = 0.044), whereas adding Z-codes markedly improved cohesion (silhouette = 0.468) and isolated a distinct hyperkinetic–family-stress subgroup. Association-rule mining returned one robust rule, F81 → F90.0 (support = 0.048, confidence = 0.46, lift = 1.59), underscoring the frequent co-diagnosis of learning and attention-deficit disorders. Topic modeling of clinicians’ recommendation notes recovered five coherent intervention themes—vocational guidance, parent counseling, psycho-education, family psychotherapy, and psychiatric follow-up—which aligned closely with the data-driven clusters. Conclusions: These findings demonstrate how routine clinical data can reveal actionable comorbidity profiles and guide tailored interventions for complex adolescent mental-health presentations. Full article

In response to the growing urban–rural dichotomy and escalating human–land conflicts in rural China, this study investigates the role of soundscapes as emotional mediators to enhance environmental satisfaction and foster sustainable human–land symbiosis. To address this need, we carried out a series of systematic field surveys at five representative traditional villages in a major provincial capital city in China, and we implemented a comprehensive questionnaire and surveyed 524 residents about their perceptions of sound, land affection, and environment. We employed a mixed-methods approach combining questionnaire surveys, association rule mining (ARM), and structural equation modeling (SEM) to explore the ‘sound–land–environment’ interaction chain. ARM analysis identified strong associations among tour guide narratives, local dialects, natural sounds (e.g., rustling leaves, birdsong), and tourist-generated sounds (support = 50%, confidence = 78%, lift = 1.33). SEM results revealed that soundscapes significantly and positively influence land dependence (β = 0.952, p < 0.001) and land rootedness (β = 1.812, p < 0.001), which in turn jointly affect environmental satisfaction (β = –0.192, p = 0.027) through a chain mediation pathway. These findings suggest that optimizing rural soundscapes can strengthen emotional bonds between people and land, thereby enhancing environmental satisfaction and promoting performance of sustainable human–land symbiosis. The study contributes theoretically by elucidating the emotional mechanisms linking soundscapes to human–land relationships and offers insights for incorporating soundscape considerations into village planning and developing policies to cultivate land attachment, supporting the sustainable development of traditional villages. Full article