Search Results (371)

Meteorological data from automatic weather stations (AWS) in tea plantations is critical for agricultural management, but is often compromised by sensor errors and physical implausibilities that traditional quality control (QC) methods fail to detect. This study proposes a novel, meteorologically informed QC framework that mines association rules from long-term ERA5 reanalysis data (2012–2023) using the Apriori algorithm to establish a knowledge base of normal multivariate atmospheric patterns. A comprehensive feature engineering process generated temporal, physical, and statistical features, which were discretized using meteorological thresholds. The mined rules were filtered, prioritized, and integrated with hard physical constraints. The system employs a fuzzy logic mechanism for violation assessment and a weighted anomaly scoring system for classification. When validated on a synthetic dataset with injected anomalies, the method significantly outperformed traditional QC techniques, achieving an F1-score of 0.878 and demonstrating a superior ability to identify complex physical inconsistencies. Application to an independent historical dataset from a Zhenjiang tea plantation (2008–2016) successfully identified 14.6% anomalous records, confirming the temporal transferability and robustness of the approach. This framework provides an accurate, interpretable, and scalable solution for enhancing the quality of meteorological data, with direct implications for improving the reliability of frost prediction and pest management in precision agriculture. Full article

Ensuring workers’ safety is a critical component of social sustainability in the construction industry. Accidents that occur while workers are walking on construction sites constitute a significant portion of overall accidents, yet they are often overlooked in conventional task-oriented safety risk assessments. This study proposes novel Accident-During-Walk (ADW) risk indices, hierarchical and data-driven metrics designed to quantify workers’ accident risk during walks. The indices are built on Association Rule Mining and utilize structured accident data, accounting for both environmental and work-related attributes. By integrating these indices with project-specific work schedules and worker allocation plans, this study establishes an automated method for daily and weekly look-ahead ADW risk monitoring aligned with construction progress. Case studies on two construction projects validate the discriminative power of the proposed method. The results demonstrate that the indices effectively capture risk fluctuations driven by concurrent multi-trade operations and environmental severity. Notably, the analysis reveals counterintuitive patterns where adverse weather conditions paradoxically reduce risk values by constraining worker mobility, a nuance often missed by static assessments. Ultimately, this framework serves as a data-driven decision-support tool, enabling safety managers to transition from uniform inspections to targeted interventions during high-risk periods, thereby fostering a safer and more socially sustainable construction environment. Full article

This work focuses on finding frequent patterns in continuous flow network traffic Big Data using incremental frequent pattern mining. A newly created Zeek Conn Log MITRE ATT&CK framework labeled dataset, UWF-ZeekData24, generated using the Cyber Range at The University of West Florida, was used for this study. While FP-Growth is effective for static datasets, its standard implementation does not support incremental mining, which poses challenges for applications involving continuously growing data streams, such as network traffic logs. To overcome this limitation, a staged incremental FP-Growth approach is adopted for this work. The novelty of this work is in showing how incremental FP-Growth can be used efficiently on continuous flow network traffic, or streaming network traffic data, where no rebuild is necessary when new transactions are scanned and integrated. Incremental frequent pattern mining also generates feature subsets that are useful for understanding the nature of the individual attack tactics. Hence, a detailed understanding of the features or feature subsets of the seven different MITRE ATT&CK tactics is also presented. For example, the results indicate that core behavioral rules, such as those involving TCP protocols and service associations, emerge early and remain stable throughout later increments. The incremental FP-Growth framework provides a structured lens through which network behaviors can be observed and compared over time, supporting not only classification but also investigative use cases such as anomaly tracking and technique attribution. And finally, the results of this work, the frequent itemsets, will be useful for intrusion detection machine learning/artificial intelligence algorithms. Full article

In smart manufacturing environments, production scheduling is highly susceptible to multi-source disruptions. However, traditional methods often struggle to accurately characterize the complex interdependencies between control points and disruptions, along with their systemic propagation effects, thereby constraining the proactivity and precision of scheduling optimization. This paper proposes a novel data-driven approach that integrates Weighted Association Rule Mining (WARM) with a two-layer directed weighted complex network to achieve precise identification of critical control points in production scheduling. First, a production loss function integrating delay duration and resource idle cost is constructed, and the max-pooling method is applied to map control point weights, thereby quantifying their intrinsic importance. Subsequently, under the constraint that association rule antecedents are restricted to control points, an improved Apriori algorithm is employed to mine directed “Control Point-Disruption” association rules. These rules are then used to construct a two-layer directed weighted complex network. Furthermore, by combining weighted PageRank and edge betweenness centrality analyses, critical control points and high-risk propagation paths are identified from the dual dimensions of node influence and path propagation capability. A case study conducted in a crankshaft production workshop demonstrates that the proposed method effectively identifies low-frequency yet high-impact hidden nodes often overlooked by traditional rules. The resulting scheduling optimization scheme reduces the occurrence rate of high-impact disruptions by 53% and significantly improves key performance indicators such as on-time delivery rate and equipment utilization. This research provides new theoretical support and a technical pathway for manufacturing enterprises to suppress system disturbances through flexible interventions targeting high-betweenness paths. Full article

This study investigates the causes of traffic accidents involving Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS) and their interdependencies. Using a source dataset comprising 3015 ADAS accident records and 1085 ADS accident records from National Highway Traffic Safety Administration (NHTSA), the study categorizes accident severity into four levels and applies association rule mining (ARM) to identify high-frequency risk factor combinations. Key risk factors include environmental, road, vehicle, and accident characteristics. Findings show that ADAS accidents are concentrated in highway straight-driving scenarios, strongly correlated with rainy weather, and often involve rear-end collisions due to delayed driver reactions. ADS accidents predominantly occur in intersection stopping scenarios, favor clear weather, and exhibit better safety performance in non-damage cases with Level 5 (L5) systems, though they still face perception and decision-making challenges in complex scenarios like nighttime wet roads. The study further reveals that vehicle design purpose (ADAS for highways, L5 for urban areas) strongly influences accident severity, with L5 systems reducing fatality risks through advanced perception but still affected by high speeds, extreme lighting, and system aging. Make attributes and technological maturity also significantly impact outcomes. This study provides insights for technological advancement, regulatory improvements, and human–machine collaboration optimization. Full article

Traditional geoportals depend primarily on keyword-based search, which often fails to retrieve relevant datasets when metadata are heterogeneous, incomplete, or inconsistent with user terminology. This limitation reduces the efficiency of data discovery and selection, particularly in domains where metadata quality varies widely. This study aims to address this challenge by developing a semantic collaborative filtering recommendation system designed to enhance dataset discovery in geoportals through the analysis of implicit user interactions. The system captures users’ search queries, viewed datasets, downloads, and applied filters to infer feedback and organize it into a user–item matrix. Because interaction data are typically sparse, semantic user clustering is applied to mitigate this limitation by grouping users with semantically related interests through hierarchical relationships represented in the Simple Knowledge Organization System (SKOS). However, as users often need complementary datasets to complete specific tasks, association rule mining is employed to identify co-occurrence patterns in search histories and enhance task-related result diversity. The final recommendation scores are then computed by factorizing the user–item matrix with Alternating Least Squares (ALS), using cosine similarity on the latent user vectors to identify nearest neighbors, and applying a standard user-based neighborhood prediction model to rank unseen datasets. The system is implemented within an existing ontology-based geoportal as a standalone, configurable component, requiring only access to user interaction logs and dataset identifiers. Evaluation using precision, recall, and Precision@5 demonstrates that increasing user interactions improves recommendation performance by strengthening behavioral evidence used for ranking. The findings indicate that integrating semantic relationships and behavioral patterns can strengthen dataset discovery in geoportals and complement conventional metadata-based search mechanisms. Full article

Background: Teledentistry constitutes a key component of digital health, enabling remote oral healthcare delivery through information and communication technologies (ICT). The COVID-19 pandemic accelerated its global adoption; however, data regarding its implementation within Romanian dental practice remain limited. Understanding usage patterns, perceived benefits, and implementation barriers is essential for effective integration. Objectives: This study examined the adoption of teledentistry among dental practitioners in Sibiu County, Romania, identified its main applications, assessed professional perceptions, and explored barriers and their interrelations using association rule mining (ARM). Methods: A cross-sectional online survey was distributed in 2025 to all 630 registered dentists in Sibiu County. The questionnaire collected demographic data, usage patterns, perceived benefits, and barriers. A total of 197 valid responses were obtained (response rate: 31.2%). Descriptive statistics, Chi-square tests, and ARM were used to identify associations between usage contexts and recorded obstacles. Results: Overall, 44.6% of respondents reported using teledentistry tools, primarily for interdisciplinary consultations (29.4%), postoperative counseling (26.4%), and treatment monitoring (25.3%). The most frequently cited barriers were the inability to perform direct clinical examinations (71.5%), practitioner reluctance (37.1%), insufficient infrastructure (29.9%), and the lack of a clear legislative framework (27.4%). ARM revealed frequent co-occurrence patterns among these barriers. Practitioners with prior experience in teledentistry reported significantly higher perceived utility (58% vs. 22.1%) and greater interest in training (58% vs. 38.5%, p < 0.05). Conclusions: Teledentistry shows moderate but increasing adoption among Romanian dentists. Addressing current barriers, through legislative clarification, infrastructure development, targeted professional training, and public education, is essential for achieving sustainable integration into modern dental practice. Full article

Recent computing technologies and modern information systems require an access control model that provides flexibility, granularity, and dynamism. The Attribute-Based Access Control (ABAC) model was developed to address the new challenges of emerging applications. Designing and implementing an ABAC policy manually is usually a complex and costly task; therefore, many organizations prefer to keep their access control mechanisms in operation rather than incur the costs associated with the migration process. A solution to the above is to automate the process of creating access control policies. This action is known as policy mining. In this paper, we present a novel approach, based on complex network analysis, for mining an ABAC policy from an access control log. The proposed approach is based on the data and the relationships that can be generated from them. The proposed methodology is divided into five phases: (1) data preprocessing, (2) network model, (3) community detection, (4) policy rule extraction, and (5) policy refinement. The results show that it is possible to obtain an ABAC policy using the approach based on complex networks. In addition, our proposed methodology outperforms existing ABAC mining algorithms regarding quality. Finally, we present a novel access decision process that reduces the number of rules to evaluate based on a rule network. Full article

Background/Objectives: Research on the association of adverse social determinants of health (SDOH) with severe pediatric coronavirus disease (COVID-19) is limited. We examined associations between SDOH patterns and COVID-19 severity in children. Methods: We conducted a prospective, observational study of children (<18 years) with symptomatic SARS-CoV-2 infection evaluated in an urban pediatric emergency department (March 2021–April 2022) in Detroit, Michigan. Caregivers completed a 34-item survey based on the Healthy People 2030 framework. Severe disease was defined as the occurrence of respiratory/cardiac failure or death within four weeks of diagnosis. Continuous and categorical variables were described using medians and percentages, respectively. Associations between disease severity and risk factors were determined using chi-square tests. Association rule mining was used for feature selection, followed by multivariate logistic regression. Results: We analyzed data from 354 children [6–12 years: 31.1%, Female: 51.1%, Black: 59%, not Hispanic: 84.7%, public insurance: 77.1%, chronic condition: 27.4%]. Of the total, 113 children had severe disease. Most caregivers were 30–44 years old (53.1%), had less than a college degree (70.4%), and income < USD 50,000 (75.2%). Adverse SDOH reported included food/housing insecurity (24.6%), no support (64.7%), unmet childcare needs (35.9%), and lack of transportation (12.7%). After controlling for age, sex, medical history, income, and obesity, severe disease was associated with caregiver use of drugs/alcohol (OR:5.92, p < 0.001) and social discrimination/lack of support (OR: 1.74, p = 0.030). Conclusions: Two SDOH patterns (caregiver use of drugs/alcohol and social discrimination/lack of support) were associated with severe COVID-19. Further studies are needed to confirm findings and develop interventions. Full article

Construction accidents continue to threaten worker safety despite advances in management systems. Existing research catalogs accident attributes but rarely explains how triggers like human error, equipment failure, or procedural lapses interact with project types and tasks. This limits recognition of high-risk scenarios and hampers targeted prevention. To address this, a two-step framework combining Multiple Correspondence Analysis (MCA) and Association Rule Mining (ARM) is proposed. Using the Korean Construction Safety Management Integrated Information (CSI) database, MCA reduces dimensionality and clusters similar accident cases, while ARM extracts context-specific rules linking accident types, causes, and activities. The analysis reveals the following key patterns: (i) worker negligence during setup or formwork often leads to tool-related cuts; (ii) poor judgment or inadequate waste removal during excavation heightens hit or stuck incidents; and (iii) negligence frequently triggers hit and fall accidents during transportation, dismantling, and finishing. By mapping causes to operational risk factors, the framework supports actionable guidance for daily risk assessments. Safety professionals can align planned tasks with identified risks, enabling proactive interventions such as focused training, stricter supervision, and engineering controls. Thus, the MCA–ARM method establishes a data-driven foundation for improving safety decision-making and reducing construction accidents. Full article

Accurate prediction of gas pipeline incidents through risk factor interdependencies is critical for proactive safety management. This study develops a hybrid SARIMA–association rule mining (ARM) framework integrating time-series forecasting with causal pattern decoding, using 60-month U.S. pipeline incident records (2010–2024) from the Pipeline and Hazardous Materials Safety Administration (PHMSA) database, covering leaks, mechanical punctures, and ruptures. Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling with six-month rolling-window validation achieves precise leak forecasts (MAPE = 14.13%, MASE = 0.27) and reasonable mechanical damage predictions (MAPE = 31.21%, MASE = 1.15), while ruptures exhibit pronounced stochasticity. Crucially, SARIMA incident probabilities feed Apriori-based ARM, revealing three failure-specific mechanisms: (1) ruptures predominantly originate from natural force damage, with underground cases causing economic losses (lift = 3.70) and aboveground class 3 incidents exhibiting winter daytime ignition risks (lift = 2.37); (2) leaks correlate with equipment degradation, where outdoor meter assemblies account for 69.7% of fire-triggering cases (108/155 incidents) and corrosion dominates >50-year-old pipelines; (3) mechanical punctures cluster in pipelines <20 years during spring excavation, predominantly occurring in class 2 zones due to heightened construction activity. These findings necessitate cause-specific maintenance protocols that integrate material degradation laws and dynamic failure patterns, providing a decision framework for pipe replacement prioritization and seasonal monitoring in high-risk zones. Full article

Facing many challenges and the pressure to achieve sustainable development through digitalization, small and medium-sized enterprises (SMEs) should increase their consumption of digital technologies. SMEs are part of the engine of emerging economies, making a significant contribution to economic development. Using Rossmann’s Digital Transformation Maturity Index and a survey-based dataset, the purpose of this paper is to uncover key associations between different dimensions that define digital transformation. Through association rules mining (ARM), our results show that even when resources are constrained, SMEs in central Romania—Transylvania—make efforts to increase human resources competencies to drive digital transformation. Furthermore, we identified that the firms are in a transition stage in terms of digital transformation. Thus, although digital initiatives are considered at the firm level, they are not fully integrated into leadership and human resources. Full article

As a typical high-risk accident in mine safety production, roof accidents occur frequently and cause severe harm, posing a major threat to miners’ lives. Through the causal analysis of the occurrence process of roof accidents, this study creatively constructs an accident causation matrix to realize the characteristic description of accident causes, which serves as the data support for the Bayesian network built based on fault tree modeling. Ultimately, a new analysis framework integrating the accident causation matrix and the Bayesian network model is established. In the process of accident analysis, first, based on the 2–4 causation model theory and combined with the association rule algorithm, the key factors of the accident and their internal correlations are obtained, and the accident causation matrix is constructed. Second, the fault tree is transformed into a Bayesian network model, and the accident causation matrix is used for parameter learning and optimization. Finally, two methods-model comparative analysis and real case verification are adopted to prove the advancement and effectiveness of this study. Researching results indicate that the accident causation matrix can effectively characterize accident causation factors, providing precise input data for Bayesian network models and significantly enhancing their reliability. Through the reverse reasoning function of Bayesian networks, dynamic diagnosis of accident causes and identification of key risk factors are achieved, enabling a more dynamic and detailed analysis of accident causes. This offers a scientific basis for coal mining enterprises to formulate preventive measures. Full article

Background/Objectives: Dynapenia, characterized by an age-related decline in muscle strength, has recently gained attention as a major public health concern. While prior studies identified individual risk factors, little is known about how these factors cluster differently by sex. This study investigated sex-specific risk factors and their combinations associated with dynapenia among Korean middle-aged and older adults. Methods: We analyzed 22,850 participants aged ≥ 40 years from the 2014–2019 Korea National Health and Nutrition Examination Survey. Dynapenia was defined as handgrip strength < 28 kg in men and <18 kg in women. Sex-stratified multivariable logistic regression identified independent predictors, and association rule mining (ARM) detected synergistic risk factor combinations. Results: Dynapenia was more prevalent in women (13.9%) than in men (8.5%). Advancing age, physical inactivity, lack of resistance exercise, and a high incidence of diabetes and stroke were consistent risk factors in both sexes. However, ARM revealed distinct clustering patterns: behavioral factors predominated in men, whereas socioeconomic disadvantage and metabolic comorbidities were more relevant in women with dynapenia. Conclusions: These findings emphasize the need for sex-specific prevention strategies for dynapenia, promoting resistance exercise among men and addressing both inactivity and socioeconomic barriers in women. Full article

Keyword data analysis aims to extract and interpret meaningful relationships from large collections of text documents. A major challenge in this process arises from the extreme sparsity of document–keyword matrices, where the majority of elements are zeros due to zero inflation. To address this issue, this study proposes a probabilistic framework called Bayesian Pattern Mining (BPM), which integrates Bayesian inference into association rule mining (ARM). The proposed method estimates both the expected values and credible intervals of interestingness measures such as confidence and lift, providing a probabilistic evaluation of keyword associations. Experiments conducted on 9436 quantum computing patent documents, from which 175 representative keywords were extracted, demonstrate that BPM yields more stable and interpretable associations than conventional ARM. By incorporating credible intervals, BPM reduces the risk of biased decisions under sparsity and enhances the reliability of keyword-based technology analysis, offering a rigorous approach for knowledge discovery in zero-inflated text data. Full article