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In view of the frequent occurrence of marine accidents and the complex interaction of various risk-influencing factors (RIFs), a data-driven method to risk analysis that combines association rule mining (ARM) and complex network (CN) analysis is proposed in this study. The efficient FP-Growth algorithm is applied to facilitate ARM to examine risk patterns that frequently occur in marine accidents. Subsequently, CN theory is employed to scrutinise the multifaceted role of various RIFs and their interactions in the complex marine accident system, which involves the basic characteristics of the network, the identification of key RIFs through the application of the weighted LeaderRank (WLR) algorithm, and a robustness analysis. The results of the study indicate that compared with random networks, marine accident networks exhibit a higher level of complexity, which brings challenges to safety prevention and control. Inadequate regulation, violations, and deficiencies in safety management systems are identified as key RIFs, stressing the urgency of improving supervision, strengthening law enforcement and strengthening the safety management system. This study may facilitate maritime safety management of maritime traffic and the development of risk analysis methods. [ABSTRACT FROM AUTHOR]

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn't been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training. Results: The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. Conclusions: The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others. [ABSTRACT FROM AUTHOR]

• Significant stressors which affect miners' health conditions are screened out. • Associations of stressors and subsequent health outcomes are updated by meta-analysis. • Core influencing factors and paths of miners' psychosocial stressors are identified. Introduction : The workplace's health hazard remains a significant concern to workers in the mining industry, where miners are continually exposed to various kinds of exposure sources. Method : First, the determinants of miners' health were systematically extracted from 259 publications, comprising chemical, physical, ergonomic, and psychosocial stressors, vulnerability factors, and common health outcomes. Then, 16 meta-analyses were performed to ascertain the epidemiological evidence for associations between four stressors and three health outcomes. The seven top contributing factors affecting miners' health were identified through 166 available prospective studies. Finally, based on the classic and domestic measurement scales, a cross-sectional survey of 559 Chinese miners was conducted to determine the core psychosocial predictors. In addition to the traditional mechanisms, complex interactive networks among the antecedents and consequences and the reversed effects of consequences were also obtained, where 379 strong association rules were yielded via the Apriori algorithm. Results : The results showed that occupational dust, NO 2 , heavy metals, heat, vibration, awkward posture, and job stress are significant risk factors associated with individuals' health conditions. Psychological capital, coping style, job demand, social support, organizational support, justice, and culture are core psychosocial predictors of miners. Conclusions : This study presents a case for identifying the most significant occupational risk factors related to individuals' health, which could be extended and applied to other industries, as working populations around the world are suffering from various chemical, physical, ergonomic, and psychosocial stressors. Practical Applications : Identifying the significant occupational risk factors affecting workers' health conditions is essential for comprehensive occupational health risk assessment and management. Therefore, this study could be important for health management in mines and other industries. [ABSTRACT FROM AUTHOR]

With the progress of the Internet and information technology, emotion analysis has been applied to analyse the emotional orientation and evolution trend of online public opinion of online tweets. At present, most of the existing methods use econometric model and machine learning algorithm to predict the trend of online public opinion. Although these methods have achieved good prediction results, they do not take into account the influence of internal factors on network public opinion prediction, such as mutual migration among emotion classes. The emotion may change dynamically because different events trigger it in the evolution process. In this view, this article proposes a novel method, called Deviation Rule Markov Model (DRMM), to predict the emotional change trend of Internet users in online public opinion by analysing the correlation between Internet users' emotional categories. Structurally, the proposed DRMM involves various processes such as pre-processing, emotion classification, data mining and transfer prediction. For the processing of network comment data, the proposed model initially undergoes pre-processing to delete unnecessary data. Then, the extended fuzzy emotion ontology is used to annotate the emotion class of the comment data. Besides, an extended association rule mining algorithm is used in the emotion association analysis process to obtain the transfer probability between emotion classes. Moreover, Markov chain is used to construct an emotional state transition matrix to predict the transition probability of positive or negative emotions. According to the predicted single emotion transfer probability results, the analytic hierarchy process is used to assign values to different emotion classes, and finally, the transfer probability of the overall emotion in a certain period is obtained. Compared with the actual case, the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model are 2.7119 and 3.7254, respectively, which has good prediction performance. [ABSTRACT FROM AUTHOR]

Predicting air pollution is complex due to intertwined factors among local climate, built environment, and development stages. This study leverages K‐means clustering and an improved Apriori algorithm to investigate the combined effects of local meteorological, morphological, and socioeconomic factors on air quality in 244 prefectural‐level Chinese cities. Results reveal that the secondary industry in GDP and saturation vapor pressure strongly relate to air quality. Severe air pollution occurs when urban development is coupled with reduced green areas and high temperatures, confirming that a single factor cannot predict air quality well. For example, we find that combining low population, low regional GDP, high maximum temperatures, and longer roads worsens air quality in small urban built‐up areas. Additionally, temperature and altitude differences associate with highway passenger volume, regional GDP, and population differently. Given our rules mining methods have broader applications in diversified urban environments, this study provides new insights for improving air quality and local Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Work zones are crucial for maintaining and enhancing road infrastructure, but they also pose a significant risk to traffic safety. Between 2016 and 2020, work zone-related crashes in the United States increased by 13%, highlighting the pressing need for effective safety measures. This study examines the factors that influence work zone crashes, including traffic control devices, geometric configurations, traffic operations, and human factors. The study also investigates how these factors vary by work zone type, day of the week, and time of day. To explore these temporal and spatial impacts, the study utilized five years of fatal crash data from the Fatality Analysis Reporting System (FARS) and applied association rules mining. The findings demonstrate that rear-end crashes and collisions with other vehicles are the primary contributing factors. Although some common patterns emerged in the association rules, the study revealed temporal instability, highlighting the importance of developing work zone-specific safety countermeasures. These findings will inform the development of targeted safety interventions and ultimately reduce the risk of work zone crashes. [ABSTRACT FROM AUTHOR]

Associative rule mining is a technique for discovering common patterns and correlations in data sets from different databases, including relational, transactional and other types of data repositories, such as relational databases. Different types of patterns exist in data mining such as frequent patterns, extended patterns, regular patterns etc. Many searches have focused on finding the frequent patterns and very little work has been carried out on negative or rare patterns. It has also been observed that only those items which are positively correlated(frequent) are been executed by various algorithms but very less attention is been given to negatively correlated items. Negatively correlated items also called infrequent items are the items which negate with each other. The items which do not satisfy the minimum threshold value generally are always been ignored by many researchers. Mining of Negative association helps in business such as for customer segmentation, in risk management as well as in medical field. So the main aim of writing this paper is to provide a short overview of various research issues involved in finding out positive and negative associations. [ABSTRACT FROM AUTHOR]

MiRNA (microRNA)‐disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time‐consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA‐disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention‐based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA‐disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high‐precision feature mining and association scoring prediction. We conducted a five‐fold cross‐validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1‐score, which were.8986,.8869,.9115, and.8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases. [ABSTRACT FROM AUTHOR]

Objective: Whether coal mine dust exposure increases cardiovascular diseases (CVDs) risk was rarely explored. Our objective was to examine the association between coal mine dust exposure and cardiovascular risk. Methods: We estimated cumulative coal mine dust exposure (CDE) for 1327 coal miners by combining data on workplace dust concentrations and work history. We used brachial-ankle pulse wave velocity (baPWV, a representative indicator of arterial stiffness) and ten-year atherosclerotic cardiovascular disease (ASCVD) risk to assess potential CVD risk, exploring their associations with CDE. Results: Positive dose-response relationships of CDE with baPWV and ten-year ASCVD risk were observed after adjusting for covariates. Specifically, each 1 standard deviation (SD) increase in CDE was related to a 0.27 m/s (95% CI: 0.21, 0.34) increase in baPWV and a 1.29 (95% CI: 1.14, 1.46) elevation in OR (odds ratio) of risk of abnormal baPWV. Moreover, each 1 SD increase in CDE was associated with a 0.74% (95% CI: 0.63%, 0.85%) increase in scores of ten-year ASCVD and a 1.91 (95% CI: 1.62, 2.26) increase in OR of risk of ten-year ASCVD. When compared with groups unexposed to coal mine dust, significant increase in the risk of arterial stiffness and ten-year ASCVD in the highest CDE groups were detected. Conclusion: The study suggested that cumulative exposure to coal mine dust was associated with elevated arterial stiffness and ten-year ASCVD risk in a dose-response manner. These findings contribute valuable insights for cardiovascular risk associated with coal mine dust. [ABSTRACT FROM AUTHOR]

Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models. [ABSTRACT FROM AUTHOR]

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task's performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests. [ABSTRACT FROM AUTHOR]

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls. [ABSTRACT FROM AUTHOR]

Dentistry processes include prevention, examination of symptoms, and treatment of oral diseases. Since there are various dental services, exploring the combination of services can help both dentists and patients for planning accurately to follow the treatment process in an appropriate order. This study aims to extract different dental services' frequent rules. So, an integrated LRFM, K-means, and APRIORI approach is proposed and implemented in Python programming language. Furthermore, patients' characteristics and the services provided to patients by an Iranian dental center as a case study are collected. Customers are first divided into five categories via LRFM (i.e., Length, Recency, Frequency, and Monetary) analysis considering the number of referrals, duration of referrals, duration of the last visit, and the total service fee. Subsequently, they are clustered based on features including age, type of insurance, referrer, and group of services received. Finally, the sequential rules for dental services are extracted and several scenarios are proposed to dental center managers for each cluster separately. Results indicate that the rules of dental services can lead to finding some treatment procedures for each cluster of patients to remind them of their subsequent referral. The proposed approach provides a better patient treatment process and may result in more profits for service providers. [ABSTRACT FROM AUTHOR]

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. [ABSTRACT FROM AUTHOR]

The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations. [ABSTRACT FROM AUTHOR]

The qualitative nature of co-deployed or "associated" metaphorical source domains in discourse has been extensively researched, often in terms of whether they share common conceptual roots. There are however few empirical studies on the strength of these associations and their implications. In mental healthcare activities like psychological interviews and counseling, for example, strongly associated sources may suggest unique "conceptual assemblages" that highlight underexplored (dis)similarities between clients or client groups, beyond the typical focus on isolated sources and frequencies. Our case study compares source domain associations produced by two interviewee groups – one meeting the diagnostic threshold for acute stress disorder (ASD), the other not – relating their first-hand experiences of traumatic events during the 2019 Hong Kong social unrest. The machine learning method of association rule mining is used to extract and quantify the top association rules in both groups. Results show i) a shared rule suggesting a highly schematic construal of trauma that nevertheless varies in instantiating details, and ii) several distinct rules that corroborates and lends further insight into the "agentive vs. non-agentive" characteristics exhibited by ASD vs. non-ASD individuals. Implications and future directions, including potential extensions to other discourse contexts, are discussed. [ABSTRACT FROM AUTHOR]

This study aims to explore the association between distracted driving (including cognitive, visual, operational, and auditory distractions) and multi-source features of ride-hailing drivers, and to discover frequent patterns of distracted driving. To achieve this, a natural driving experiment was conducted, and an association rule mining ('Apriori' algorithm) was used to uncover hidden rules between distracted driving and the multi-source features (including emotion, valence, arousal, driving tasks, cumulative driving hours, velocity, longitudinal acceleration, lateral acceleration, heading rate, presence of intersections, traffic status, and driving time of day). Results indicate that distracted driving is prevalent among ride-hailing drivers in specific scenarios, including trips without passengers, non-intersection sections, driving for over 4 h, and congested traffic conditions. The emotional state of drivers has also been found to have an interesting association with distracted driving. For instance, cognitive and operational distraction were highly associated with positive driving emotions, but auditory distraction caused by passenger interference was highly associated with negative driving emotions. Moreover, there are variations in distracted driving patterns across different categories. These findings can help ride-hailing platforms develop more scientific and effective distracted driving monitoring and prevention strategies. [ABSTRACT FROM AUTHOR]

Understanding visitors' spatial choice behavior is important in developing effective policies to counteract overcrowdedness in attractive urban heritage areas. This research presents a comprehensive analysis of visitor location choice behavior, aiming to address two primary objectives. First, this paper investigates the relationship between visitor segments and the choice of particular Points of Interest (POIs). Second, this paper explores the impacts of visitors' experiences and visitor segments on their revisit intentions. We used a sample of 320 visitors who had been to Amsterdam within the last five years to collect data about their location choice behavior and intention to revisit after a recent visit to the city. Combining the revealed choices and intentions of pre-defined visitor segments obtained from a stated choice experiment, association rules are extracted to reveal differences in the patterns of behaviors related to the segment. The findings identify associations between various POIs, including museums such as the Rijksmuseum and Madame Tussauds, and visitor classes, which include "cultural attraction seekers", "selective sightseers", and "city-life lovers". Furthermore, binary logistic regression analysis reveals that affective experiences, such as feelings of comfort, happiness, and annoyance, have a significant influence on visitors' intentions to revisit the destination in the future. This research found that "cultural attraction seekers" and "selective sightseers" display a higher likelihood of considering a return visit to the city. [ABSTRACT FROM AUTHOR]

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare. [ABSTRACT FROM AUTHOR]

Despite the increasing recognition of the potential of Circular Economy (CE) strategies, their effective application to decarbonize the construction value chain faces multiple challenges. Ambiguity surrounding CE, stemming from a lack of clarity on actionable strategies, is a common concern. Existing studies lack a holistic approach for systematically integrating CE principles throughout the construction value chain, resulting in a scarcity of information on how to practically adopt CE within projects. This research aims to accelerate CE adoption by creating a value chain-structured portfolio of CE strategies and analyzing the implementation of such strategies in actual projects. To this end, a portfolio of CE strategies was compiled in a value chain-based-structure, which was evaluated against 71 US-based projects of exemplary sustainability criteria to scrutinize their successful implementation. Additionally, the study examined the interconnections and network dynamics among these strategies through association rule mining, network, and statistical analysis techniques. Results revealed a pronounced focus on strategies within the design, construction, and end-of-life value chain phases, with significant gaps existing in areas related to project financing, planning, logistics, property market, and operations. "Requiring sustainability certifications, standards, or labels" emerged as the most pivotal strategy in terms of frequency and number of associations, highlighting owners' significant role in CE transition. This study contributes to the body of knowledge by providing practical guidance and insights for industry practitioners in terms of actionable CE strategies and their state of implementation, ultimately fostering a more sustainably built environment. [ABSTRACT FROM AUTHOR]

States often use market access as a bargaining chip in international politics. A state that requires simultaneous compliance in multiple issue areas before granting market access maximizes incentives to comply but also makes them brittle – any targeted states that cannot comply in one issue area have no incentive to comply in any. More generally, programs of economic coercion can achieve at most two of the following three objectives: 1) secure a broad coalition of domestic political support, 2) the association of meaningful trade value with each policy issue, and 3) assurance that enforcing one political issue will not reduce the target's incentives to comply with conditionality on others. Characteristics of the program's domestic constituency, of the issues themselves, and of the international economy are key determinants of how the state prioritizes the three objectives. The trilemma explains the number and types of issues that can be linked to economic value. [ABSTRACT FROM AUTHOR]

Sustainable agriculture is becoming increasingly important in the face of growing environmental challenges. One key aspect of sustainable agriculture is managing soil resources effectively. In this context, digital soil mapping (DSM) has emerged as a powerful tool to understand soil variability better and inform land management decisions. This paper proposes a comprehensive data warehouse for DSM that supports climate change analysis. Our architecture integrates frequent itemset mining (FMI) and association rules mining (ARM) to extract insights from large‐scale soil data. We review related studies in soil data warehousing and ARM, identify gaps, and propose a data warehouse architecture leveraging the galaxy multidimensional model for DSM based on the SCORPAN model, which incorporates all relevant soil forming factors. We employ and compare A‐priori, FP‐growth, and ECLAT algorithms to efficiently mine frequent itemsets and generate association rules. Our intensive experiments evaluation demonstrates that FP‐growth outperforms the other algorithms in accuracy, scalability, and speed and requires less memory. Additionally, we utilized correlation metrics for ARM, such as lift, cosine, kulc, and Imbalance ratio, to obtain the most significant and relevant association rules. These rules provide valuable insights into the complex relationships between soil properties and environmental factors, which can inform land management decisions and improve sustainable agriculture practices. This work contributes to the growing body of research on DSM and data‐driven approaches to sustainable agriculture. [ABSTRACT FROM AUTHOR]

Aiming at the problem that substation robots cannot automatically find and analyze the fault equipment and carry out patrol inspection, a method of autonomous patrol inspection for ultra-high voltage (UHV) substation robots based on deep learning in cloud computing environment is proposed. Firstly, based on the cloud computing environment, an autonomous patrol system is designed to upload the data obtained by robots to the cloud platform for processing, so as to complete data analysis quickly and with high quality. Then, the deep learning algorithm (DL) is used for fault diagnosis, and the FP-growth algorithm is combined to realize the association mining of fault data, so as to clarify the patrol order of fault-related nodes. Finally, the improved ant colony algorithm (IAC) is used to optimize the path of the robot to complete the reliable inspection of the substation in the shortest time. Based on the selected UHV substation, the experimental analysis of the proposed method shows that the fault diagnosis error rate and time are about 4.3% and 14.2 s, respectively, and the patrol path is only 180.351 m, and the patrol time is 19.708 s, which can realize the optimal patrol of the robot. [ABSTRACT FROM AUTHOR]

Background: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. Results: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. Conclusions: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods. [ABSTRACT FROM AUTHOR]

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators. [ABSTRACT FROM AUTHOR]

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence. [ABSTRACT FROM AUTHOR]

Chinese Language learning grows ever more essential to develop the students' personalities and values as the curriculum, thereby improving teaching strategies based on students' learning preferences are more crucial. Students' participation in learning the Chinese Language is generally minimal and typically operates in a passive learning mode. The development of the Chinese Language instruction in these higher educational settings will be impacted by the absence of an organized strategy for teaching the Chinese Language. An algorithm is called the Fuzzy Pattern-driven Personalized Teaching (FPPT) has been proposed to identify the association between the students learning patterns and interests in the Chinese Language in the higher education for providing the personalized teaching to solve these challenges. Fuzzy sets are incorporated into FP-Growth for personalized the Chinese Language learning to improve the suggestions by considering the ambiguous preferences and the proficiency levels. The fuzzy pattern is unrevealed by implementing the Frequent Pattern (FP) growth algorithm to find patterns in the students learning activity and preferences so that personalized the teaching methods can be developed to meet the needs of each student and maximize their motivation for the language learning. Using the support and the confidence measures, these identified Fuzzy association relationships of student learning interest results in personalized the Chinese Language teaching in the higher education. The experimental results showed that the proposed FPPT system significantly improved each student's learning outcome, communication effectiveness, learning motivation, and the Language proficiency level. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is the most common cancer among women worldwide. Early detection of breast cancer reduces mortality and morbidity. Acquiring or identifying valuable information in the form of rules is the key to an accurate diagnosis and differentiation between benign and malignant breast cancers. Our goal is to find the hidden but beneficial knowledge in the form of rules from datasets. In this paper, we use association rule mining algorithms to obtain information in the form of data rules for differential diagnosis between benign and malignant breast masses based on radiomic features, extracted from mammography images. Material and Methods: In this study, 703 patients with both benign and malignant tumors were selected from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBISDDSM) database. The embedded method was employed to select the radiomic features of the image and uncover the hidden patterns of data through the Apriori algorithm. Results: The association rules were generated from separated rules for benign and malignancy classes. The important features of the benign class include mass margins, horizontal long-run emphasis, 135drfraction, WavEnHLs3, vertical short-run emphasis, Teta1, 45dgr run-length with no uniformity, Teta2, and differential entropy2. However, the important features of the malignant class include assessment, correlation4, Teta1, WavEnLHs3, contrast5, vertical short-run emphasis, and differential entropy2. Conclusion: It can be concluded that the proposed method has been successful in determining specific features for tumor prediction. [ABSTRACT FROM AUTHOR]

Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM's decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT's dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT's reliability emphasize the importance of iterative approaches for improving prediction accuracy. [ABSTRACT FROM AUTHOR]

Increasing outsourcing of operations in process industries is leading to contractors facing higher exposure to complex processes and machinery. This necessitates development of safety management systems by performing fault detection and diagnosis, accident/failure modelling and risk assessment of contractors' operations and associated hazards. Unfortunately, limited work has been done in this area. Moreover, in broad process safety domain, using text mining to perform accident modelling and risk assessment has not been fully exploited. We address these research gaps by proposing an unsupervised text mining framework to generate accident/failure models like chain of events and fault trees from investigation reports of contractor incidents. A case study is used to demonstrate the framework where incident investigation reports of contract personnel working at a steel plant in India are analysed using a combination of topic modelling and association rule mining. Twenty sequences of basic events are identified as major chains of events. These chains of events are then combined using logic rules to generate fault trees depicting all possible propagation paths of contractor safety failures leading to top events. Our framework enables generation of chain of events and fault trees from unstructured incident reports with minimal human supervision with accuracy of 81%. [ABSTRACT FROM AUTHOR]

Numerical association rule mining (NARM) is a popular method under the umbrella of data mining, focused on finding relationships between attributes in transaction databases. Numerical association rules for time series are a new paradigm that extends the applicability of NARM to the domain of time series. Association rule mining algorithms result in numerous rules, the interpretation of which is sometimes not easy for human experts. Therefore, various visualization methods have been developed to improve the explanation results of the rule mining process. This article is a novel contribution to the development of a new visualization method capable of presenting the association rules for time series developed according to the principles of explainable artificial intelligence. The experiments are conducted in the context of smart agriculture (i.e., agricultural time series data), and show the great potential of the proposed visualization method for the future. [ABSTRACT FROM AUTHOR]

Purpose: Data mining has been used to help discover Frequent patterns in health data. it is widely used to diagnose and prevent various diseases and to obtain the causes and factors affecting diseases. Therefore, the aim of the present study is to discover frequent patterns in the data of the Kashan Trauma Registry based on a new method. Methods: We utilized real data from the Kashan Trauma Registry. After pre-processing, frequent patterns and rules were extracted based on the classical Apriori algorithm and the new method. The new method based on the weight of variables and the harmonic mean was presented for the automatic calculation of minimum support with the Python. Results: The results showed that the minimum support generation based on the weighting features is done dynamically and level by level, while in the classic Apriori algorithm considering that only one value is considered for the minimum support manually by the user. Also, the performance of the new method was better compared to the classical Apriori method based on the amount of memory consumption, execution time, the number of frequent patterns found and the generated rules. Conclusions: This study found that manually determining the minimal support increases execution time and memory usage, which is not cost-effective, especially when the user does not know the dataset's content. In trauma registries and massive healthcare datasets, its ability to uncover common item groups and association rules provides valuable insights. Also, based on the patterns produced in the trauma data, the care of the elderly by their families, education to the general public about encountering patients who have an accident and how to transport them to the hospital, education to motorcyclists to observe safety points in Recommended when using a motorcycle. [ABSTRACT FROM AUTHOR]

Background: Many U.S. colleges and universities offer access to a healthcare center that provides sexual and reproductive health (SRH) resources, services, and products. The importance of health centers in college and university settings in reducing sexual health disparities in student populations cannot be stressed enough. This article evaluates a student-led, mutual-aid, grassroots health promotion strategy for students with limited access to healthcare services, supplies, and tools via an anonymous and discrete distribution of SRH resources without charge. Methods: In partnership with faculty, undergraduate students worked to address their school's unmet SRH needs by increasing on-campus access to comprehensive, evidence-based, and sex-positive resources. Referred to as Just in Case, this student-led, grassroots health promotion program provided students with supply kits containing contraceptives, sexual health wellness products, basic hygiene supplies, and education materials. Students were surveyed in a pre- (n = 95) post- (n = 73) pilot study to identify contraception acquisition barriers, discern perceptions of on-campus SRH resources, and elucidate trends in this program's use and impact. Chi-square tests of independence were used to compare survey group responses, and association rule mining was employed in tandem to identify SRH items that students requested. Results: Students identified cost and privacy as significant barriers to acquiring sexual health products on campus. Of the 182 Just in Case supply kits requested by students during the 2022–2023 academic year, condoms were requested most frequently in 75% of fulfilled kits, while emergency contraception and pregnancy tests were asked most often in 61% of kits. 50% of students reported access to contraceptives on campus before this program's implementation, growing to 75% (p < 0.001) 1 year later post-implementation. Similar jumps were observed for reported access to sexual health education (30 to 73%, p < 0.001) and services (36 to 73%, p < 0.001). Conclusion: A student-led SRH supply and resource delivery strategy may immediately reduce SRH inequities and decrease barriers to contraceptive use for students with limited access to on-site SRH product availability. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for regional tourism integration and sustainable development by identifying cooperation opportunities among tourist attractions within a region. We introduce a novel methodology that combines association rule mining with complex network analysis and utilizes search index data as a dynamic and contemporary data source to reveal cooperative patterns among tourist attractions. Our approach delineates a potential cooperative network within the destination ecosystem, categorizing tourist attractions into three distinct communities: core, intermediary, and periphery. These communities correspond to high, medium, and low tourist demand scales, respectively. The study uncovers a self-organizing network structure, driven by congruences in internal tourist demand and variances in external tourist experiences. Functionally, there is a directed continuum of cooperation prospects among these communities. The core community, characterized by significant tourist demand, acts as a catalyst, boosting demand for other attractions. The intermediary community, central in the network, links the core and periphery, enhancing cooperative ties and influence. Peripheral attractions, representing latent growth areas within the destination matrix, benefit from associations with the core and intermediary communities. Our findings provide vital insights into the dynamics, systemic characteristics, and fundamental mechanisms of potential cooperation networks among tourist attractions. They enable tourism management organizations to employ our analytical framework for real-time monitoring of tourism demand and flow trends. Additionally, the study guides the macro-control of tourism flows based on the tourism network, thereby improving the tourist experience and promoting coordinated development among inter-regional tourist attractions. [ABSTRACT FROM AUTHOR]

Objective: Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis of AKI. In addition, organ crosstalk in AKI was systematically explored based on comorbidities to obtain clinically reliable results. Methods: We collected data from the Medical Information Mart for Intensive Care-IV database on patients aged ≥ 18 years in intensive care units (ICU) who were diagnosed with AKI using the criteria proposed by Kidney Disease: Improving Global Outcomes. The Apriori algorithm was used to mine association rules on the diagnoses of 55,486 AKI and non-AKI patients in the ICU. The comorbidities of AKI mined were validated through the Electronic Intensive Care Unit database, the Colombian Open Health Database, and medical literature, after which comorbidity results were visualized using a disease network. Finally, organ diseases were identified and classified from comorbidities to investigate renal crosstalk with other distant organs in AKI. Results: We found 579 AKI comorbidities, and the main ones were disorders of lipoprotein metabolism, essential hypertension, and disorders of fluid, electrolyte, and acid-base balance. Of the 579 comorbidities, 554 were verifiable and 25 were new and not previously reported. In addition, crosstalk between the kidneys and distant non-renal organs including the liver, heart, brain, lungs, and gut was observed in AKI with the strongest heart-kidney crosstalk, followed by lung-kidney crosstalk. Conclusion: The comorbidities mined in this study using association rules are scientific and may be used for the early diagnosis of AKI and the construction of AKI predictive models. Furthermore, the organ crosstalk results obtained through comorbidities may provide supporting information for the management of short- and long-term treatment practices for organ dysfunction. [ABSTRACT FROM AUTHOR]

The spatial structure of tourist flow represents the tourists' flow pattern, shows the difference of tourism resources, and provides the basis for the balance and sustainable development of tourism. Based on the digital footprint data obtained from the online travel notes on Qunar.com, this article describes and analyzes the spatial structure of tourism flow in Sanya City using the research methods of flow statistics, association rule mining, and social network analysis under the framework of anchor theory; further, it condenses the characteristics of different types of tourists at different time scales. The key findings include: (1) The spatial structure of Sanya's tourism flow is affected by the popularity of scenic spots, showing a flow pattern of "one core and two cores" and several core paths; the core area is located along the coastline, showing the characteristics of coastal tourism city. (2) In the spatial structure of Sanya's tourist flow, the distribution of core nodes is uneven, and the phenomenon of structural holes is obvious, showing the spatial pattern of uneven distribution of scenic spots and fierce competition among scenic spots. (3) In the structure network of Sanya's tourist flow, most tourists are medium-scale travelers, followed by small-scale travelers and large-scale travelers. (4) The tourist attraction system of Sanya's tourist flow is hierarchically distributed and has multiple anchor attractions. Different anchor attractions have different attractions to tourists of different types and time, except for beach tourist destinations and island areas. Therefore, targeted marketing for different types of tourists is a challenge for the future development of Sanya's tourism. In this article, the anchor point theory is introduced into the study of tourism flow. Based on the flow direction statistics, association rule mining and field investigation, the rules of the anchor point selection of tourism flow are established, verified, and supplemented by the social network analysis method. Theoretical support and quantitative data support complement each other. The application of anchor theory and association rule mining not only enriches the theoretical framework in the field of tourism flow, but it also scientifically determines the anchor points of the tourism flow's structure network. This is helpful to understand tourist behavior and mode, and it can provide targeted suggestions for the precision marketing of tropical coastal tourist cities. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is an important research issue in the field of data mining that aims to find relations among different items in binary databases. The conventional ARM algorithms consider the frequency of the items in binary databases, which is not sufficient for real time applications. In this paper, a novel hash table based Type-2 fuzzy mining algorithm (T2FM) with an efficient pruning strategy is presented for discovering multiple fuzzy frequent itemsets from quantitative databases. The algorithm employs a hash table based structure for efficient storage and retrieval of item/itemset which reduces the search efficiency to O(1) or constant time. Previously, type-2 based Apriori and FP-growth based fuzzy frequent itemsets mining were proposed, which required large amounts of computation and a greater number of candidate generation and processing. Meanwhile, the proposed approach reduces a huge amount of computation by finding the common keys before the actual intersection operation takes place. An efficient pruning strategy is proposed to avoid unpromising candidates in order to speed up the computations. Several experiments are carried out to verify the efficiency of the approach in terms of runtime and memory for different minimum support threshold and the results show that the designed approach provides better performance compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Intelligent virtual model assistance is a key challenge in cultivating model-driven engineering proliferation and growth. Such assistance will help improve the quality of software models, support education for students learning modeling, and lower the entry barriers to new modelers. We present SimIMA, an intelligent modeling assistant for Simulink, which is an extremely popular modeling language in both industry and academia. SimIMA provides modelers with two different forms of data-driven guidance using a knowledge base of configurable repositories and sources. The first form of guidance, SimGESTION, suggests to modelers single-step operations they can perform on their models as they edit them in their modeling environment. These suggestions are based on the machine learning technique of ensemble learning through association rule mining and frequency classification. The second form of guidance, SimXAMPLE, presents modelers with similar/related Simulink systems for modelers to either insert directly into their environments or to view for inspiration. SimXAMPLE accomplishes this through model clone detection. To validate SimIMA, we conduct experiments using an established, open, and curated large set of Simulink models coming from a variety of application domains. Our results show that both of SimIMA's forms of guidance are inferring the appropriate model and element suggestions given SimIMA's knowledge base and that SimIMA is both scalable and efficient. Through our evaluation, SimIMA demonstrates a prediction accuracy of 78.86% for block-level suggestions and 82.04% for full system suggestions. [ABSTRACT FROM AUTHOR]

Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings. [ABSTRACT FROM AUTHOR]

This paper presents a comparative effectiveness analysis of fuzzy and non-fuzzy association rule mining (ARM). The corresponding motivation is the lack of relevant papers devoted to the effectiveness comparison between fuzzy and non-fuzzy ARM. The current research applies the results of fuzzy/non-fuzzy ARM to associative classification and uses the classification accuracy of the corresponding classifiers as the effectiveness measure. The research demonstrates that basic effectiveness comparison between fuzzy and non-fuzzy ARM does not necessarily speak in favour of fuzzy ARM. However, then the research demonstrates that fuzzy ARM has a distinctive ability to handle data inconsistencies, which results in the ability of corresponding fuzzy classifiers to not only provide class predictions but also indicate their certainty in the provided output. The research reveals some distinct correlation between classification accuracy and the degree of certainty in fuzzy associative classification: the greater certainty nearly always results in the better classification accuracy. This ability of fuzzy associative classifiers to indicate their certainty in the performed classification and the corresponding ability of fuzzy ARM to handle data inconsistencies are not applicable in non-fuzzy associative classifiers and ARM. Therefore, these abilities are used in the paper to substantiate the relevance of applying fuzzy logic in ARM. [ABSTRACT FROM AUTHOR]

In recent years, the escalating global concerns surrounding climate change have placed a growing emphasis on achieving dual objectives: reducing carbon emissions and achieving carbon neutrality. Businesses and organizations are under mounting pressure to align their operations with these crucial environmental goals. This paper introduces the concept of an enterprise carbon performance evaluation index system (ECPIS) to strike a balance between economic development and environmental protection and enhance overall enterprise management and development strategies. The ECPIS framework is constructed using machine learning and advanced data mining techniques, particularly support vector machines (SVM). Its core purpose is to provide enterprises with a systematic tool to gauge, analyze, and enhance their carbon performance, addressing dual carbon objectives. ECPIS development hinges on data mining techniques, extracting insights from diverse data sources to construct a comprehensive system that accommodates these dual carbon goals' intricacies. Its methodology includes data collection, preprocessing, feature selection, and data mining algorithms to unveil vital patterns and relationships within data. It conforms to international standards, establishing a tailored carbon performance index system aligned with China's national conditions. It validates carbon-related enterprise data and employs data mining's association rules to uncover pertinent carbon performance information. The results obtained from ECPIS are auspicious, boasting an experiential accuracy rate of 97.5%. This level of accuracy surpasses that achieved by other algorithms like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Naïve Bayes (NB), and Logistic Regression (LR). ECPIS stands out by considering various factors, including carbon emissions reduction, energy consumption, supply chain efficiency, and financial performance indicators. This multifaceted approach enables enterprises to gain a comprehensive understanding of their carbon performance and identify areas for improvement. [ABSTRACT FROM AUTHOR]

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform. [ABSTRACT FROM AUTHOR]

As bike-share systems proliferate across the US, a deeper understanding of current bike-share users could enable an expansion of these services and their benefits to a larger population. To grow bike-share demand sustainably and equitably, cities must understand how different segments of the population use this service. Understanding the different segments within a market enable operators to develop strategies that are tailored toward the needs of each segment. This study uses data from bike-share user surveys in the Sacramento region to perform market segmentation based on perceptions of the bike-share service, mode use patterns, and bike-share use. We focus on the market segments with more limited means of transportation: low-income individuals, zero-car households, students, and transit and car-share users. The results show that individuals with low incomes, those without cars, and students use the service frequently for commuting and a variety of non-commuting purposes. Bike-share is generally adopted by all mode user groups but is used at a higher rate by the super multimodal and active multimodal groups. The occasional users of the bike-share service are mainly those with higher incomes and individuals who have access to a personal car. The segments consisting of non- and infrequent-personal bike users use the bike-share service at a greater rate for different purposes than regular bicyclists, suggesting that bike-share may act as a lever for increasing bike travel for some users. Results from data mining suggest that bike-share operators should target low-income and zero-car owners for new recruitment as these groups are more likely to use bike-share frequently. The results provide insights that may be helpful to cities as they consider strategies to increase bike-share demand in a way that enhances social equity. • We perfom market segmentation based on the perception of the bike-share service, mode use patterns, and bike-share use. • Bike-share is generally adopted by all types of modality groups. • Individuals with low incomes, students, and non-auto owners use the service frequently for variety of travel purposes. • Bike-share may act as a lever for increasing bike travel for some users. [ABSTRACT FROM AUTHOR]

Work zones are crucial for infrastructure maintenance and improvement. However, ongoing projects within work zones sometimes can place workers and drivers in dangerous situations. Despite safety regulations, work zone accidents persist with notable severity and frequency. Previous research has explored work zone accident causation, but it has not provided a comprehensive understanding of the factors that impact work zone safety. This paper addresses this research gap by following a multistep methodology. First, a systematic literature review (SLR) was conducted to identify a comprehensive list of 37 factors impacting work zone safety. Second, social network analysis (SNA) was employed to analyze the connectivity between these identified factors, based on the theoretical, mathematical, and computational approaches found in the literature. Third, the factors were grouped into clusters using the k -means clustering technique, considering the overall discussions present in the literature. Last, association rules were mined from the clustered factors to determine combinations of factors that have not been extensively examined together, thereby identifying gaps and deficiencies in the present body of knowledge. The findings of the SNA indicate that design-related factors have received more extensive attention in the literature, whereas certain driver- and state-related factors have received comparatively less attention. The clustering analysis shows that a significant number of theoretically addressed safety factors in the literature frequently are examined together, which indicates a necessity for additional computational and mathematical investigations to gain a more comprehensive understanding of these factors. Moreover, the association rules highlight the groups of underexamined relationships of factors that affect work zone safety. Ultimately, this research contributes to the body of knowledge by consolidating the studies concerning work zone safety and using them to provide a robust roadmap for future research, thereby enhancing the advancement of this field and ultimately leading to improved work zone safety practices. [ABSTRACT FROM AUTHOR]

Objective: In order to apply association rule mining to data sets with continuous variables, it is necessary to convert the variables into categorical structure. Therefore, we aim to compare the results obtained by categorizing continuous variables using different methods and analyzing them with association rule mining. Material and Methods: In this study, ChiMerge, clustering, Minimum Description Length Principle, equal interval, equal frequency methods were used to transform continuous variables into categorical structure by discretizing them. Various datasets were generated in the R with sample sizes of 100, 200, 500 and one binary dependent variable was created in each dataset. Additionally, 2, 3, 4, 5, 6, 7 continuous variables with a standard normal distribution were generated and various methods for transforming variables into categorical format were applied. A support of 10%, a confidence level of 80% were used. The number of rules varies based on the number of variables and the number of categories. Results: The study's results compared the descriptive statistics of the number of rules and the lift values. It can be said that high lift values are observed in scenarios with high levels of correlation and a higher number of variables and increasing the sample size can reduce the lift values of rules. Conclusion: In terms of the number of rules, the ChiMerge is the most affected by increasing the sample size. Furthermore, the ChiMerge yields stricter and higher lift values compared to other methods. While using association analysis, data type and multi-level associations should be considered. [ABSTRACT FROM AUTHOR]

In this decade, the internet becomes indispensable in companies and people life. Therefore, a huge quantity of data, which can be a source of hidden information such as association rules that help in decision-making, is stored. Association rule mining (ARM) becomes an attractive data mining task to mine hidden correlations between items in sizeable databases. However, this task is a combinatorial hard problem and, in many cases, the classical algorithms generate extremely large number of rules, that are useless and hard to be validated by the final user. In this paper, we proposed a binary version of grey wolf optimizer that is based on sigmoid function and mutation technique to deal with ARM issue, called BGWOARM. It aims to generate a minimal number of useful and reduced number of rules. It is noted from the several carried out experimentations on well-known benchmarks in the field of ARM, that results are promising, and the proposed approach outperforms other nature-inspired algorithms in terms of quality, number of rules, and runtime consumption. [ABSTRACT FROM AUTHOR]

In the decades following the end of the Civil War, Black southerners asserted their right to enter the legal, medical, and educational professions, understanding that higher education would vault them out of poverty and farm work and into a small but growing Black middle class. Mining underutilized primary sources such as the Southern Workman magazine, this essay argues that emerging middle-class Black professionals solidified their personal financial and social advancement by joining with others across the region, forming medical associations, dental associations, business-minded alliances, and ministerial conferences. In educational associations at state and local levels, Black teachers facilitated professionalization by elevating standards for teaching, supporting licensure requirements, and backing continued training even after licensure. Despite persistent hardships and ever-present racism, southern Black teachers felt that their profession was akin to a religious mission, one that provided them with a modicum of status and salary above sharecroppers, tenant farmers, or day laborers. [ABSTRACT FROM AUTHOR]

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between injury types and player recovery durations, and their socioeconomic impacts. Our methodology involved data collection, engineering, and mining; the application of techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), isolation forest, and the Z score for anomaly detection; and the application of the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining indicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from "Rest" injuries, affirming the NBA's treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to a financial influence on recovery decisions. This study offers critical insights into sports injuries and recovery, providing valuable information for sports professionals and league administrators. This study will impact player health management and team tactics, laying the groundwork for future research on long-term injury effects and technology integration in player health monitoring. [ABSTRACT FROM AUTHOR]