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The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al‐Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms‐related products. [ABSTRACT FROM AUTHOR]

Fault analysis (FA) is the process of collecting and analyzing data to determine the cause of a failure. It plays an important role in ensuring the quality in manufacturing process. Traditional FA techniques are time-consuming and labor-intensive, relying heavily on human expertise and the availability of failure inspection equipment. In semiconductor industry, a large amount of FA reports are generated by experts to record the fault descriptions, fault analysis path and fault root causes. With the development of Artificial Intelligence, it is possible to automate the industrial FA process while extracting expert knowledge from the vast FA report data. The goal of this research is to develop a complete expert knowledge extraction pipeline for FA in semiconductor industry based on advanced Natural Language Processing and Machine Learning. Our research aims at automatically predicting the fault root cause based on the fault descriptions. First, the text data from the FA reports are transformed into numerical data using Sentence Transformer embedding. The numerical data are converted into latent spaces using Generalized-Controllable Variational AutoEncoder. Then, the latent spaces are classified by Gaussian Mixture Model. Finally, Association Rules are applied to establish the relationship between the labels in the latent space of the fault descriptions and that of the fault root cause. The proposed algorithm has been evaluated with real data of semiconductor industry collected over three years. The average correctness of the predicted label achieves 97.8%. The method can effectively reduce the time of failure identification and the cost during the inspection stage. [ABSTRACT FROM AUTHOR]

Objective To explore the comorbidity patterns and influencing factors of chronic diseases in middle - aged and elderly people in China, so as to provide new ideas for the development of corresponding chronic disease prevention and control strategies and measures. Methods Using data from the China Health and Retirement Longitudinal Study ( CHARLS) 2018, after excluding samples with missing variables for 14 chronic diseases, a final sample of 19,390 individuals aged 45 and older was included. The Apriori algorithm from association rule analysis was utilized to explore the comorbidity patterns among middle - aged and elderly individuals in China. The occurrence of chronic comorbidity (two or more chronic diseases or three or more chronic diseases) was taken as the dependent variable, and the independent variable was determined according to the five dimensions of social determinants of health. 5 696 samples were included after deletion of the missing values, and the influential factors of chronic comorbidity were analyzed by univariate test and multifile logistic regression. Results The prevalence of comorbid chronic diseases among middle - aged and elderly individuals in our country was 55. 4%. The most common comorbidity patterns were arthritis or rheumatic diseases and hypertension. Association rule analysis identified 24 strong rule combinations, with the most common binary association being gastrointestinal or digestive system diseases with arthritis or rheumatic diseases, and the most common ternary association being gastrointestinal or digestive system diseases, hypertension, and arthritis or rheumatic diseases. The results of the multivariate analysis showed that both two or more chronic diseases and three or more chronic diseases were significantly associated with increased risk factors, including : age 60 - 80 years (two or more chronic diseases : OR = 1. 479, 95 % CI : 1. 244 -- 1. 759; three or more chronic diseases : OR = 1. 526, 95% CI: 1.267 -- 1. 839), age >80 years ( OR = 1.545, 95% CI: 1. 144 --2.087; OR = 1. 591, 95% CI: 1. 175 --2. 154), depressive symptoms ( OR = 1. 435, 95% CI: 1. 267 -- 1. 626 ; OR = 1. 382, 95% CI: 1. 216 -- 1. 570), having a pension ( OR = 1.350, 95% CI$ 1.141 --1.598; OR = 1.332, 95%CI: 1.118 --1.586), and residence in central ( OR = 1. 268, 95%CI: 1.096 --1.470; OR = 1. 269, 95% CI: 1.088 -- 1.479) or western regions ( OR = 1.217, 95% CI: 1.062 -- 1.395; OR = 1. 198, 95% CI: 1. 038 -- 1. 382). Conversely, factors associated with a reduced risk of chronic multimorbidity included alcohol consumption (two or more chronic diseases $ OR = 0.811, 95% CI : 0. 707 -- 0. 930; three or more chronic diseases : OR = 0. 837, 95% CI: 0. 724 -- 0. 968 ), 6 --8 hours of night sleep ( OR = 0. 806, 95% CI: 0. 702 -- 0. 926; OR = 0. 792, 95% CI: 0.688 --0.912), more than 8 hours of night sleep ( OR = 0. 738, 95% CI: 0.635 --0.858; OR=0.745, 95% CI: 0.637 -- 0.872), self -- rated general health ( OR=0.357, 95%CI: 0.307 --0.414; OR=0.343, 95%CI: 0.299 --0.392), and self -- rated good health ( OR = 0. 136, 95% CI: 0. 114 --0. 163; OR = 0. 117, 95% CI: 0.096 --0. 142 ). Conclusion It is suggested that the prevention and intervention of chronic comorbidity should be multidimensional, and the health care personnel should take into account the comorbidity of patients and the problem of multiple drug use in the formulation of comorbidity treatment plan. [ABSTRACT FROM AUTHOR]

Within the evolving field of sentiment analysis, the integration of topic modeling and association rule mining presents a promising yet underexplored method. This approach currently lacks an organized framework for maximizing insights that aid in drawing robust conclusions concerning customer sentiments. Therefore, this study addresses the need and rationale for having comprehensive sentiment analysis systems by integrating topic modeling and association rule mining to analyze online customer reviews of earphones sold on Amazon. It employs Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), a technique that generates coherent topics by effectively capturing contextual information, and Frequent Pattern Growth (FPGrowth), an efficient association rule mining algorithm used for discovering patterns and relationships in a dataset without candidate generation. This analysis of reviews on ten earphone products identified key customer concerns as follows: sound quality, noise cancellation, durability, and battery life. The results indicate an overall positive sentiment towards sound quality and battery life, mixed reviews on noise cancellation, and significant dissatisfaction with product durability. Using integrated topic modeling and association rule mining offers deeper insights into customer preferences and highlights specific areas for product improvement and guiding targeted marketing strategies. Moreover, we focused on algorithm selection to improve the model's performance and efficiency, ensuring effective compatibility with our sentiment analysis framework. This study demonstrates how combining advanced data mining techniques and structuring insights from written customer feedback enhances the depth and clarity of sentiment analysis, furthering its applicability in e-commerce research. [ABSTRACT FROM AUTHOR]

The research aims to investigate the support suppliers' decision making for Textile and Garment Vietnamese Enterprises based on using association rules mining. In order to increase the competitive advantage over other enterprises, Vietnamese enterprises are gradually shifting their production methods from CMT (Cut-Make-Trim) to FOB (Free On Board), ODM (Original Design Manufacturing), and OBM (Original Brand Manufacturing). One of the important steps for this transformation is to be self-sufficient in sourcing raw materials from selected suppliers with sufficient capabilities, meeting the requirements of price, and CSR (corporate social responsibility) to enable Vietnamese enterprises to enjoy tax incentives. The Vietnamese Enterprises participate in the textile and garment production process through importing raw materials from other countries such as China, Korea, etc. Therefore, the selection supplier issue is very important to them to enhance their business products. This study researches on finding positive ARs and negative ARs in Textile and Garment data domain via three scenarios to define the relationship of alternative criteria for selection suppliers. By using alternative method of ARM (Association Rule Mining), the experimental results show that Textile and Garment enterprises in Vietnam appreciate the CSR factor highly. This is completely in line with the previous survey research on the new factor in the supplier selection criteria of Textile and Garment enterprises in Vietnam. In another word, in this field, the CSR plays as an important role to reflect company's image and it is the first of decision choice. The other criteria are considered after including Cost, Quality, and Delivery respectively. The remain factors of Service, Capability, Relationship, and Sourcing Country are not highly evaluated via the experiments. With different scenarios along with the association rules mined, they reflect different sets of criteria that textile and garment enterprises use to select their suppliers. Furthermore, the rare rules show situations that are very unlikely but have high reliability in reality. Based on the findings, the factors will show the essentials for suppliers to improve themselves to meet the requirements. • Select a suitable logistics supplier using the association rules method. • Criteria evaluated highly: CSR, Cost, Quality, and Delivery. [ABSTRACT FROM AUTHOR]

This research paper focuses on enhancing the frequent pattern growth (FP-growth) algorithm, an advanced version of the Apriori algorithm, by employing a parallelization approach using the Apache Spark framework. Association rule mining, particularly in healthcare data for predicting and diagnosing diabetes, necessitates the handling of large datasets which traditional methods may not process efficiently. Our method improves the FP-growth algorithm's scalability and processing efficiency by leveraging the distributed computing capabilities of apache spark. We conducted a comprehensive analysis of diabetes data, focusing on extracting frequent itemsets and association rules to predict diabetes onset. The results demonstrate that our parallelized FP-growth (PFP-growth) algorithm significantly enhances prediction accuracy and processing speed, offering substantial improvements over traditional methods. These findings provide valuable insights into disease progression and management, suggesting a scalable solution for large-scale data environments in healthcare analytics. [ABSTRACT FROM AUTHOR]

As the basic guarantee for people's production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school a priori, k -means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults. [ABSTRACT FROM AUTHOR]

Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies. [ABSTRACT FROM AUTHOR]

Within the evolving field of sentiment analysis, the integration of topic modeling and association rule mining presents a promising yet underexplored method. This approach currently lacks an organized framework for maximizing insights that aid in drawing robust conclusions concerning customer sentiments. Therefore, this study addresses the need and rationale for having comprehensive sentiment analysis systems by integrating topic modeling and association rule mining to analyze online customer reviews of earphones sold on Amazon. It employs Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), a technique that generates coherent topics by effectively capturing contextual information, and Frequent Pattern Growth (FPGrowth), an efficient association rule mining algorithm used for discovering patterns and relationships in a dataset without candidate generation. This analysis of reviews on ten earphone products identified key customer concerns as follows: sound quality, noise cancellation, durability, and battery life. The results indicate an overall positive sentiment towards sound quality and battery life, mixed reviews on noise cancellation, and significant dissatisfaction with product durability. Using integrated topic modeling and association rule mining offers deeper insights into customer preferences and highlights specific areas for product improvement and guiding targeted marketing strategies. Moreover, we focused on algorithm selection to improve the model’s performance and efficiency, ensuring effective compatibility with our sentiment analysis framework. This study demonstrates how combining advanced data mining techniques and structuring insights from written customer feedback enhances the depth and clarity of sentiment analysis, furthering its applicability in e-commerce research.

The research aims to investigate the support suppliers’ decision making for Textile and Garment Vietnamese Enterprises based on using association rules mining. In order to increase the competitive advantage over other enterprises, Vietnamese enterprises are gradually shifting their production methods from CMT (Cut-Make-Trim) to FOB (Free On Board), ODM (Original Design Manufacturing), and OBM (Original Brand Manufacturing). One of the important steps for this transformation is to be self-sufficient in sourcing raw materials from selected suppliers with sufficient capabilities, meeting the requirements of price, and CSR (corporate social responsibility) to enable Vietnamese enterprises to enjoy tax incentives. The Vietnamese Enterprises participate in the textile and garment production process through importing raw materials from other countries such as China, Korea, etc. Therefore, the selection supplier issue is very important to them to enhance their business products. This study researches on finding positive ARs and negative ARs in Textile and Garment data domain via three scenarios to define the relationship of alternative criteria for selection suppliers. By using alternative method of ARM (Association Rule Mining), the experimental results show that Textile and Garment enterprises in Vietnam appreciate the CSR factor highly. This is completely in line with the previous survey research on the new factor in the supplier selection criteria of Textile and Garment enterprises in Vietnam. In another word, in this field, the CSR plays as an important role to reflect company’s image and it is the first of decision choice. The other criteria are considered after including Cost, Quality, and Delivery respectively. The remain factors of Service, Capability, Relationship, and Sourcing Country are not highly evaluated via the experiments. With different scenarios along with the association rules mined, they reflect different sets of criteria that textile and garment enterprises use to select their suppliers. Furthermore, the rare rules show situations that are very unlikely but have high reliability in reality. Based on the findings, the factors will show the essentials for suppliers to improve themselves to meet the requirements.

A coal mine dual prevention construction evaluation model based on association rule mining and combined weighting cloud model is proposed. Firstly, based on association rule mining, the evaluation indicators for the effectiveness evaluation system of the dual prevention mechanism construction in coal mines are determined. Secondly, the analytic hierarchy process and anti entropy weight method are used to obtain the subjective and objective weights of the indicators. Then, the composite normalization method is used to obtain the combined weights of the indicators, and a cloud theory model is introduced to construct an evaluation model for coal mine dual prevention construction based on the cloud model. Finally, by analyzing a coal mine as a sample, the evaluation results of the model are compared with those of the analytic hierarchy process cloud model and the anti entropy weight method cloud model. The results indicate that the evaluation model is more scientific and accurate in the evaluation of dual prevention construction in coal mines, which helps to assist coal mines in establishing a scientific and reasonable “dual prevention” mechanism.

In order to prevent the occurrence of coal mine roof accidents more scientifically, it is crucial to identify the causal factors of coal mine roof accidents and their association rules. First, 56 causal factors affecting the occurrence of roof accidents were identified through text mining and combined with the SIF accident causal model. Second, we constructed a roof accident database and utilized the Apriori algorithm to mine the association rules of roof accidents. Finally, the complex network diagram of the causal factors of roof accidents was drawn, and the core causal factors of roof accidents and the correlation rules between the causal factors were comprehensively analyzed. The results show that there is a high degree of correlation and enhancement between safety training and education, safety supervision and management, low safety awareness and violation of work procedures by operators, inadequate management of the on-duty management and other causes of accidents, and these factors are the core factors causing roof accidents in coal mines.

Abstract The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases.

Cardiovascular diseases (CVDs)continue to be the primary cause of mortality, accounting for approximately one-third of all fatalities globally. This spawned the proposal of models in several studies. Accordingly, this study contributed to diagnosing heart disease through suggesting soft diagnosis paradigm. Various techniques have been volunteered for serving the suggested paradigm toward achieving its objective. Additionally, this study provided set of contributions. For instance, Tree Soft Technique (TrST) is applied for the first time for forming attributes and sub attributes of patients into nodes and sub-nodes of Tree to obtain relations between it. Even, the study support stakeholders to making accurate decision in mysterious circumstances and in problems with incomplete information through Collaborating the utilized techniques of entropy and Technique for order of preference by similarity to ideal solution (TOPSIS) in this study with Single Value Neutrosophic Sets (SVNSs) forked from neutrosophic uncertainty theory. As well, the relationship between sub-attributes which consider antecedent for obtaining consequent of detecting and diagnosing through collaborating TrST with association rules. Accordingly, we applied four transactions (cases) for obtaining findings of the relations in transactions as listed in Table 13 and Table14. [ABSTRACT FROM AUTHOR]

To address the lack of reliable measurement methods for identifying wear mechanisms and predicting the state of mechanical seal tribo-parts, this study proposes a method for characterizing tribological behavior based on measuring face vibration acceleration. It aims to uncover the source mechanism of mechanical seal face vibration acceleration influenced by tribology and dynamic behavior. This research delves into the dynamic behavior characteristics and vibration acceleration of the mechanical seal stationary ring. We explored the variation pattern of face vibration acceleration root mean square (RMS) with rotation speed, sealing medium pressure, and face surface roughness. The results indicate that under constant medium pressure, an increase in rotation speed leads to a decrease in acceleration RMS and an increase in face temperature. Similarly, under constant rotation speed, an increase in medium pressure results in nonlinear changes in acceleration RMS, forming an "M" shape, along with an increase in face temperature. Furthermore, under conditions of constant medium pressure and rotation speed, an increase in the surface roughness of the rotating ring face corresponds to an increase in acceleration RMS and face temperature. Upon starting the mechanical seal, both acceleration RMS and temperature initially increase before decreasing, a trend consistent with the Stribeck curve. [ABSTRACT FROM AUTHOR]

In response to the demand for scientific training of sports athletes, the author combined data mining technology to study an improved sports training mode decision support evaluation system. In this regard, the author analyzed the characteristics of association rule algorithms and elaborated on their functions in data preprocessing, data mining, and pattern evaluation. Based on the software design of decision support systems, the characteristics of system operation were analyzed. At the same time, the author focused on explaining the data fusion processing of association rules in sports evaluation decision support systems, and proposed an improved Apriori algorithm output mode to improve the effectiveness of system evaluation. Compared with other algorithms such as Apriori, DC Apriori and Apriori, this algorithm has higher reliability. When the minimum confidence is increased, the advantage of prior information will gradually disappear, and the final result will be obtained. Experimental results show that this method can effectively provide support for sports training decision-making. [ABSTRACT FROM AUTHOR]

With the development of network technology, online shopping is becoming more and more convenient. But the increasing number of products also makes it difficult for consumers to make the right decision. When there is no apparent market demand, how to recommend products with commercial potential to customers has become an urgent problem for businesses to solve. This paper proposes e-commerce product recommendation based on user preference and association rule algorithm aiming at the problems existing in e-commerce product recommendation. Firstly, this paper constructs a user interest modeling method. Through analyzing users' interests and preferences, to provide users with timely and accurate personalized services. Then, the FP_Growth algorithm is optimized and improved. A more effective CTE-MARM algorithm is designed, and an association rules database based on user benefit items is constructed and analyzed jointly. Analyze products with strong correlations. According to consumers' interest levels, TOP-N is the best product choice. Experiments show that the algorithm has higher prediction accuracy. The research results of this project can not only improve enterprises' ability to analyse data and provide data support for enterprises to carry out effective marketing management. [ABSTRACT FROM AUTHOR]

In complex environments such as computers, networks, and big data, the integrity and security issues of accounting information have become increasingly prominent. This study analyzes existing vulnerabilities in financial systems to identify specific security and cyber threats that challenge data integrity in the financial industry. In response to the above problems, this paper applies a new network security monitoring technology based on association rules. With the advantages of big data analysis, this traditional monitoring method has been further strengthened, allowing it to better realize real-time monitoring of financial data. Through early detection of security incidents, potential risks are reduced and the normal operation of the accounting system is ensured. Experiments have proven that the enhanced monitoring system has made great improvements in the identification and statistics of network security incidents. There is an average accuracy rate of 95%, which shows that the system is reliable and has the ability to enhance safety measurements. Organically integrate big data technology and traditional network security monitoring methods to form a robust network security protection mechanism. [ABSTRACT FROM AUTHOR]

Aimed at mitigating the limitations of the existing document entity relation extraction methods, especially the complex information interaction between different entities in the document and the poor effect of entity relation classification, according to the semi-structured characteristics of patent document data, a patent document ontology model construction method based on hierarchical clustering and association rules was proposed to describe the entities and their relations in the patent document, dubbed as MPreA. Combined with statistical learning and deep learning algorithms, the pre-trained model of the attention mechanism was fused to realize the effective extraction of entity relations. The results of the numerical simulation show that, compared with the traditional methods, our proposed method has achieved significant improvement in solving the problem of insufficient contextual information, and provides a more effective solution for patent document entity relation extraction. [ABSTRACT FROM AUTHOR]

The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases. [ABSTRACT FROM AUTHOR]

IntroductionFalls are the primary cause of unintentional fatalities among individuals aged 65 and older. Enhancing research on fall prevention among older adults is an urgent priority. Consequently, this study aims to investigate the prevalence and influencing factors of falls among community-dwelling older adults in Guangzhou, China, with a particular emphasis on the impact of family functioning.MethodsWe used a multi-stage stratified cluster random sampling technique to successfully survey 2,399 individuals aged 65 and above across 11 districts in Guangzhou City. Data on sociodemographic characteristics, health and lifestyle factors, and fall incidents were collected through telephone interviews. Chi-square tests, Lasso regression, and logistic regression were utilized to pinpoint fall risk factors. Association rule mining uncovered the relationships between falls and associated variables.ResultsA total of 390 participants reported experiencing falls, the prevalence of falls among older adults was 16.3% (95%CI: 14.82% ~ 17.78%). Logistic regression analysis identified several risk factors for falls among older adults: female [OR = 1.511, 95%CI (1.188–1.922)], age 85 years and older [OR = 2.332, 95%CI (1.447–3.758)], stroke [OR = 1.821, 95%CI (1.038–3.192)], hypoglycemia [OR = 1.639, 95%CI (1.228–2.186)], visual impairment [OR = 1.418, 95%CI (1.097–1.833)], need to be cared for [OR = 1.722, 95%CI (1.339–2.215)], chronic pain [OR = 1.663, 95%CI (1.302–2.124)], and anxiety [OR = 1.725, 95%CI (1.243–2.395)]. In addition, it was shown that a well-functioning family was a protective factor against falls [OR = 0.589, 95%CI (0.44–0.789)].ConclusionThe prevalence of falls among community-dwelling older adults in Guangzhou City was high, and the influencing factors were complex. It is recommended to develop and implement comprehensive intervention measures for high-risk groups, including those who are females, older adults, and suffer from chronic diseases while paying special attention to the care of family members for older adults.

Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

ObjectiveTo understand the multiple health service needs of rural residents in China and explore the co-occurrence patterns of these needs throughout the entire life course, providing a basis for the formulation and optimization of health service packaging policies.MethodsThis study utilized a stratified random sampling method, resulting in a final sample size of 15,125 individuals. The R statistical software was employed to apply the Apriori algorithm to mine the co-occurrence relationships among multiple health service needs across the life course and to explore the packaging model of these services.ResultsThe health service needs rate among rural residents in China is 86.76%, with a multiple health service needs rate of 78.72%. The most needed services are health exercise guidance (17.10%), Traditional Chinese Medicine health care (15.53%), and internet health information services (14.40%). The highest combined health service need is for “exercise guidance need + internet health information need + Traditional Chinese Medicine health care need,” followed by “exercise guidance need + internet information need.” There are significant differences in the content and strength of associations in the co-occurrence structure of multiple health service needs across different age groups. During the life preparation stage, the need for multiple health services is high, with modern medical care and child management having the highest support. In the life protection stage, the focus shifts to preventive health needs, with strong associations among co-occurring needs (the highest support rule being Traditional Chinese Medicine health care + exercise guidance, support = 21.12%). The co-occurrence of medical and preventive health service needs among the older adult is diverse, with the strongest association being between chronic disease management services and rehabilitation services (support = 31.24%).ConclusionThe multiple health service needs rate among rural residents in China is high, with the greatest needs being for exercise guidance, Traditional Chinese Medicine health care, and internet health information services. There are significant differences in health service needs across different life stages. It is essential to enhance the integration and packaging of health service resources to promote diversity in health services and meet the multiple health service needs of residents throughout their life courses.

This study examines the access and usage of Information Communication Technologies (ICTs) outside the school environment among upper secondary students in Italy, based on data from the 2021-2022 INVALSI Field Trial. The study investigates the availability of digital devices such as desktops, laptops, and smartphones, and explores usage patterns through a questionnaire addressing the first and second digital divides, socio-demographics, and other relevant factors. The findings provide food for thought for those who need to manage technology and enhance learning. Notably, 96% of students reported having access to a computer at home for both learning and non-learning activities, and 88% had internet connectivity at home. While initial results suggest a reduction in the digital access gap, logistic regression models indicate that the first-level digital divide remains challenging for certain socio-economic groups. Using association rules data mining techniques, therefore, specific activities were identified as the most influential among students. Most of the grade 13 students possessed ICT tools and used them primarily for leisure activities such as social media, online communication platforms, entertainment videos and music, and web browsing.

The advent of the big data era has provided many types of transportation datasets, such as metro smart card data, for studying residents' mobility and understanding how their mobility has been shaped and is shaping the urban space. In this paper, we use metro smart card data from two Chinese metropolises, Shanghai and Shenzhen. Five metro mobility indicators are introduced, and association rules are established to explore the mobility patterns. The proportion of people entering and exiting the station is used to measure the jobs-housing balance. It is found that the average travel distance and duration of Shanghai passengers are higher than those of Shenzhen, and the proportion of metro commuters in Shanghai is higher than that of Shenzhen. The jobs-housing spatial relationship in Shenzhen based on metro travel is more balanced than that in Shanghai. The fundamental reason for the differences between the two cities is the difference in urban morphology. Compared with the monocentric structure of Shanghai, the polycentric structure of Shenzhen results in more scattered travel hotspots and more diverse travel routes, which helps Shenzhen to have a better jobs-housing balance. This paper fills a gap in comparative research among Chinese cities based on transportation big data analysis. The results provide support for planning metro routes, adjusting urban structure and land use to form a more reasonable metro network, and balancing the jobs-housing spatial relationship.

The Sustainable Development Goals (SDGs) in Indonesia are organized into four main pillars, each addressing specific dimensions of development, namely the pillar of social, economic, environmental, and law and governance development, which include 17 goals with 289 indicators to measure achievement. The achievement of one SDG indicator will have an impact on the achievement of other SDG indicators, both within each pillar and with other pillars. Therefore, it is important to understand the relationships among the SDG indicators of the social development pillar and the relationships between the indicators of the social development pillar and the indicators of the other pillars. In this study, the SDG indicators for 2016–2021 were used, which were obtained from BPS-Statistics and several related agencies. The data are then analyzed using fuzzy membership functions and association rules with the Apriori algorithm method. The results show that there is an association among SDG indicators of the social pillar, which are formed from nine indicators. In addition, this study also found that there's an association between the indicators in the pillars of social, economic, environmental, and law and governance development, which are formed from 12 indicators. Of the 12 indicators, there are two indicators of the social development pillar that are related to other indicators of the remaining pillars, which are the extreme poverty level and the number of regencies/cities that have achieved malaria elimination. [ABSTRACT FROM AUTHOR]

Association Rules Mining is the technique of Data Mining. Association Rule Mining finds interesting relationships among large set of data items. Market Basket Analysis is one of the method of ARM. User can use the variables say Dependent Variable and Independent Variable. Here, Association Rules define as Dependent Variable and Support and Confidence are define as Independent Variable. To verify the relationship between Rules, Support and Confidence, apply the Regression Statistics, Regression Coefficients, Multiple Correlation Coefficient and Multi-collinearity. To evaluates the Regression line, to estimate and predict the Rules and other calculation, R-Studio is applying for to evaluate the Regression Statistics. The R-packages arules and arulesviz is apply for the Association Rules and Visulizations. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven framework aimed at investigating how weather factors affect the spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is presented. The framework mainly consists of three core modules, namely, trajectory structure characterization, flow pattern recognition, and association rule mining. To fully characterize trajectory structure, abnormal trajectories and typical operations are sequentially extracted based on a deep autoencoder network with two specially designed loss functions. Then, using these extracted elements as basic components to further construct and cluster per-hour-level descriptions of airspace structure, the spatio-temporal patterns of air traffic flow can be recognized. Finally, the association rule mining technique is applied to find sets of weather factors that often appear together with each flow pattern. Experimental analysis is demonstrated on two months of arrival flight trajectories at Hong Kong International Airport (HKIA). The results clearly show that the proposed framework effectively captures spatial anomalies, fine-grained trajectory structures, and representative flow patterns. More importantly, it also reveals that those flow patterns with non-conforming behaviors result from complex interactions of various weather factors. The findings provide valuable insights into the causal relationships between weather factors and changes in flow patterns, greatly enhancing the situational awareness of TMA. [ABSTRACT FROM AUTHOR]

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes. [ABSTRACT FROM AUTHOR]

Currently, the static configuration data checking of the safety critical system is realized by the existing constraint rules which generated from the professional norms. Most of these constraint rules are for single-category data and relatively simple, and only some basic data errors can be identified with which. Therefore, it is necessary to excavate more complete and comprehensive data constraint rules to further improve the performance of the data verification. Data association rules describe the correlation and dependence between data items and reflect the rules and patterns of some attributes appearing simultaneously. Based on which, the in-depth research on the methods of static data verification in the train control system is carried out. Firstly, clustering and dimension reduction methods are used to divide data items into multiple sub-range intervals, which can solve the problem that association rules of the static data are difficult to extract due to floating-point data measurement. Then, a DSRJ algorithm is proposed to judge the existence of association relations among sub-range data items, and a neural network model will be constructed in which a large number of data samples are trained to obtain the data relation function that is transformed further into an association rule. The new subset of data samples can be conducted safety verification according to this rule. [ABSTRACT FROM AUTHOR]

The way products are placed in a supermarket can be effective in increasing sales and profit. A reasonable approach is to group together items that are likely to be purchased together. Thus, managers with the support of mining methods and techniques, can assist customers locating the products they want to buy in an easy and quick way. Many product placement strategies have been proposed over the years to leverage an effective and efficient way to achieve this goal. In this paper, association rules for product arrangement in supermarkets are studied and an algorithm based on such rules is proposed. The algorithm considers several factors, such as the number of units sold of each product, a hierarchical structure for product classification developed by the United Nations Department of Economic and Social Affairs, a set of association rules generated from sales, and a set of constraints that restrict some products to be placed physically close to each other in a supermarket, even if they are usually purchase together. Real public sales data of a supermarket were used for the experiments, where the proposed algorithm is applied for the generation of supermarket layouts. The results show that some supermarket departments may share the same products or product categories. [ABSTRACT FROM AUTHOR]

The urbanization and industrialization of human society boost the socioeconomic growth but yet inevitably result in unprecedented damages to environment and organisms. One of the threats is the air pollution produced from anthropogenic activities. Moreover, the pollution concentrates longer in certain meteorological phenomena and exacerbates the impact on nature species and human health. This paper presents an association mining approach to identify the influential factors which result in a high volume of air pollution concentration, in particular, the particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5). Since the literature showed that the identified factors are location and spatial-scale dependent, we chose a basin geography, Puli township, Taiwan, and inferred the association relationships with two different-scaled monitoring stations. The government-built supersite at Puli estimates the PM2.5 concentration for the entire township of the area around 150 km2, while the participatory microsites monitor air quality in a smaller region of a hundred thousand square meters. Our research was conducted with relevant data during 2017–2019. The mining result has unique findings as compared to the literature. The relative humidity, precipitation, wind speed and direction, which were identified as major factors in many previous studies, have less impact on air quality of our studied field than temperature and atmospheric pressure. The remarkable distinction is mainly attributed to the special weather patterns of basin geography. We investigated the impact of all national festivals and identified the most significant ones. The probability of observing PM2.5 concentrations greater than 35 μg/m3 in the activity hours of New Year's Eve is 50% which is significantly greater than 11.74%, the probability of observing the same concentration range over all days in the investigated years, while the Tomb Sweeping Day (TSD) has a varying impact on air quality depending on the order of the TSD date within the long holiday. The increase of PM2.5 concentration is remarkably more significant if the TSD is the last day in the long holiday than if it is the middle day. This finding can be taken into consideration when the government agent makes schedules for national festivals. Finally, it was learned in our study that different landmarks and land uses have various significant impacts on micro-scale air quality. The microsites monitor high PM2.5 concentrations at particular landmarks with a greater confidence than the mean confidence over all microsites. These pollution-associated landmarks with the confidence ranked from highest to lowest are night markets, crossroads, paper mills, temples, and highway exits. It is worth noting that the PM2.5 increase contributed by temples is negligible, which may be attributed to the citizen action for promoting reduction in joss paper and incense stick burning. The land uses have diverse impacts on air quality. Anthropogenic activities contribute higher PM2.5 concentrations in business districts and residential areas. The PM2.5 concentration monitored at high lands and agricultural lands is lower than the overall background due to fewer mass gathering and combustion activities in these land uses. [ABSTRACT FROM AUTHOR]

In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit. The primary objective of malware is to disrupt the normal functioning of computers and networks, all the while surreptitiously gathering users' private and sensitive information. The formidable concealment and latency capabilities of malware pose significant challenges to its detection. In light of the operational characteristics of malware, this paper conducts an initial analysis of prevailing malware detection schemes. Subsequently, it extracts fuzzy features based on the distinct characteristics of malware traffic. The approach then integrates traffic detection techniques with Type II fuzzy recognition theory to effectively monitor malware-related traffic. Finally, the paper classifies the identified malware instances according to fuzzy association rules. Experimental results showcase that the proposed method achieves a detection accuracy exceeding 90%, with a remarkably low false alarm rate of approximately 5%. This method adeptly addresses the challenges associated with malware detection, thereby making a meaningful contribution to enhancing our country's cybersecurity. [ABSTRACT FROM AUTHOR]

The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie's layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists' research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie's layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings. [ABSTRACT FROM AUTHOR]

The World Stroke Organization (WSO) survey found 20 million people working age and elderly annually were at risk of stroke. Several factors can affect the risk of stroke, including family history, work stress, lifestyle, and diet. These factors can be intensified by living in an ever-changing society and environment. When the symptoms of a stroke (such as blurred vision, hemiplegia, myasthenia gravis, and Bell's palsy) significantly impact daily life it is crucial to get medical attention. So, it is essential to analyze relevant factors to assist individuals to avoid behaviors that may contribute to the risk of a stroke. This research aims to analyze the risk factors that may yield an opportunity for stroke with association mining rules using the Apriori algorithm. For the experiment, the Apriori algorithm was used to compute and determine the support and confidence values as 0.27 and 0.25, respectively. This research identified the top five factors contributing to stroke: body mass index (BMI), ever-married, work type, heart disease, and age. We then used the gain ratio technique to select features with a gain value of 0.05. The gain ratio algorithm selected the following significant factors: age, body mass index (BMI), ever-married, hypertension, and heart disease, respectively. The experimental results showed that the factors selected using the Gain ratio method were the same as the factors chosen by the Apriori algorithm. Consequently, the five chosen factors significantly impact the cause of stroke. [ABSTRACT FROM AUTHOR]

Purpose: The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions. Design/methodology/approach: Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation. Findings: The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%. Originality/value: The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated. [ABSTRACT FROM AUTHOR]

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems. [ABSTRACT FROM AUTHOR]

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem. [ABSTRACT FROM AUTHOR]

Objective To analyze the medication usage and compatibility patterns of Chinese herbal medicine compound formulas in patents for intervening in colorectal cancer (CRC) over the past 20 years based on data mining, in order to provide a foundation for the development of new clinical medications. Methods Patented Chinese herbal medicine compound formulas for treating CRC were retrieved in the Wanfang database and China National Knowledge Infrastructure (CNKI) from their inception until January 17, 2024. Data analysis was performed using Excel 2019 software to assess drug frequency, properties, flavors, and meridian associations. SPSS Modeler 18.0 and SPSS Statistics 27 software suites were used for deriving drug association rules and performing cluster analysis. A complex network of core drug co-occurrences was constructed using Cytoscape 3.9.0 software to demonstrate associations of potential new formulations and new drugs. Results In total, 158 Chinese herbal medicine compound patents were included for CRC treatment, encompassing 448 Chinese herbal medicines. High-frequency medicines included Astragalus membranaceus (Huangqi), Hedyotis diffusa (Baihua Sheshecao), Poria cocos (Fuling), Atractylodes macrocephala (Baizhu), etc. The predominant type of medicine was deficiency-tonifying drugs, with the nature of the drugs being primarily cold and warm, and the flavors mostly bitter, sweet, and pungent. The meridians were chiefly associated with the liver, spleen, lung, and stomach. Herbs commonly used for pairs included Scutellariae barbatae Herba (Banzhilian), Astragalus membranaceus (Huangqi), Glycyrrhizae Radix et Rhizoma (Gancao), Atractylodes macrocephala (Baizhu), Poria cocos (Fuling), etc., while commonly used triangular medicines included Astragalus membranaceus (Huangqi) and Hedyotis diffusa (Baihua Sheshecao). There were four clusters of drugs, including Scutellaria barbata D. Don (Banzhilian)- Hedyotis diffusa (Baihua Sheshecao), and five groups of potential drugs, including “Phyllanthus emblica (Yuganzi)-Radix hedysari (Hongqi)-Semen punicae granati (Shiliuzi)-Zanthoxylum bungeanum (Chuanjiao)-Entada phaseoloides (Hetengzi)”. The core new formula for treating CRC was selected through topological attribute analysis. Conclusion Chinese herbal medicine patent formulas for treating CRC primarily use tonifying herbs, employing methods such as tonifying Qi and supporting the body, clearing heat and dampness, and resolving blood stasis and detoxification. The clustering of drugs focuses on Hedyotis diffusa (Baihua Sheshecao) and Scutellaria barbata D. Don (Banzhilian), which may offer new formulation ideas and provide reference for clinical treatment and new drug development for CRC.

Abstract Purpose This study aimed to investigate the risk factors for liver disease comorbidity among older adults in eastern, central, and western China, and explored binary, ternary and quaternary co-morbid co-causal patterns of liver disease within a health ecological model. Method Basic information from 9,763 older adults was analyzed using data from the China Health and Retirement Longitudinal Study (CHARLS). LASSO regression was employed to identify significant predictors in eastern, central, and western China. Patterns of liver disease comorbidity were studied using association rules, and spatial distribution was analyzed using a geographic information system. Furthermore, binary, ternary, and quaternary network diagrams were constructed to illustrate the relationships between liver disease comorbidity and co-causes. Results Among the 9,763 elderly adults studied, 536 were found to have liver disease comorbidity, with binary or ternary comorbidity being the most prevalent. Provinces with a high prevalence of liver disease comorbidity were primarily concentrated in Inner Mongolia, Sichuan, and Henan. The most common comorbidity patterns identified were "liver-heart-metabolic", "liver-kidney", "liver-lung", and "liver-stomach-arthritic". In the eastern region, important combination patterns included "liver disease-metabolic disease", "liver disease-stomach disease", and "liver disease-arthritis", with the main influencing factors being sleep duration of less than 6 h, frequent drinking, female, and daily activity capability. In the central region, common combination patterns included "liver disease-heart disease", "liver disease-metabolic disease", and "liver disease-kidney disease", with the main influencing factors being an education level of primary school or below, marriage, having medical insurance, exercise, and no disabilities. In the western region, the main comorbidity patterns were "liver disease-chronic lung disease", "liver disease-stomach disease", "liver disease-heart disease", and "liver disease-arthritis", with the main influencing factors being general or poor health satisfaction, general or poor health condition, severe pain, and no disabilities. Conclusion The comorbidities associated with liver disease exhibit specific clustering patterns at both the overall and local levels. By analyzing the comorbidity patterns of liver diseases in different regions and establishing co-morbid co-causal patterns, this study offers a new perspective and scientific basis for the prevention and treatment of liver diseases.

Sales transaction data contains rich information and can support company competitiveness. However, this transaction data is initially unstructured and needs to be processed into insight for the company's decision-making. Market Basket Analysis (MBA) is a data mining technique that can be used to study consumer purchasing patterns. This paper presents a case study on using an MBA to obtain consumer buying behaviors where the result of the MBA is then used to design a proposed layout. The animal market governed by Yogyakarta Province, known as Pasty Market, was used as a case study. Pasty Market is an animal trading center with around 30,000 square meters area and 255 sellers that sell various kinds of animals such as songbirds, dove birds, rabbits, cats, dogs, iguanas, turtles, ornamental chickens, and ornamental fish as well as animal food and cage. With this enormous area and number of merchants, the layout of Pasty Market becomes crucial in customer satisfaction. Association rules result in four priority levels in proposed layout planning, where these rules are used to determine the proximity among items in the proposed layout. These four levels of priority, ordered by the confidence value, are (1) “songbirds” and “bird food” (confidence value 91%), (2) “ornamental fish” and “turtles” (confidence value 80-90%), (3) “birdcages” and “songbirds” (confidence value 70-80%), and (4) “cats” and “dog” as well as “birdcages” and “birds” (confidence value 50-70%). Association rules were then used as the basis for determining the proximity value between merchants, where the proximity rules were then used for designing a proposed layout.

The existing methods for evaluating voltage sag severity do not sufficiently consider the effect of the multiple line characteristic factors on the line failure probability, which leads to a large error in the evaluation results. Therefore, an evaluation method for voltage sag severity based on multiple line characteristic factors fusion is proposed. Firstly, based on line historical fault data, the influence degree of multiple line characteristic factors on line fault which employ association rules to quantify is researched. Secondly, by improving the D-S evidence theory to fuse multiple line characteristic factors, an accurate line annual failure probability model is established, and the voltage sag severity of nodes by introducing maximum entropy into the method of fault positions are obtained. Finally, a comprehensive voltage sag severity index considering both voltage sag severity of power grid side and tolerance characteristics of sensitive equipment on the user side is proposed to evaluate node voltage sag severity. Based on the actual power quality monitoring data for validation and comparison with the evaluation cases that do not fully consider the line characteristic factors, the results show that the proposed method can effectively improve the accuracy of voltage sag severity evaluation.

Background With the acceleration of population aging, the health problems of middle-aged and elderly people are prominent. Multimorbidity seriously threaten the health and quality of life of middle-aged and elderly people, and hinder the progress of Healthy China Initiative. It is of positive significance to explore the relationship between multimorbidity and social determinants of health. Objective To understand the multimorbidity and social determinants of health among the middle-aged and elderly people in Ningxia, and analyze the relationship between the social determinants of health and multimorbidity, so as to provide reference for the health management and intervention strategies for middle-aged and elderly people. Methods A multi-stage stratified random sampling method was used to investigate the health related data of 1 997 middle-aged and elderly people in 10 districts/counties of Shizuishan City, Yinchuan City, and Guyuan City in Ningxia from June 27, 2022 to August 27, 2022. The Apriori algorithm was used to analyze the comorbidity patterns of middle-aged and elderly people in Ningxia, and unconditional Logistic regression analysis was used to explore the correlation between multimorbidity and social determinants of health among middle-aged and elderly people in Ningxia. Results There were 418 middle-aged and elderly people in Ningxia with a comorbidity rate of 20.9%; the results of association rules showed 14 comorbidity patterns, of which 11 were related to coronary heart disease, 9 to hypertension, and 9 to diabetes; the results of unconditional Logistic regression analysis showed that middle-aged and elderly people aged ≥60 years, with 2-3 children, established family archives, retired or unemployed work status, and resident pension insurance had a higher incidence of multimorbidity (P

Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. In order to tackle this issue, a novel approach called the adaptive Weibull distribution sparrow search algorithm is introduced. This algorithm leverages the adaptive Weibull distribution to improve the traditional sparrow search algorithm's capability to escape local optima and enhance convergence during different iterations. Secondly, an enhancement search strategy and a multidirectional learning strategy are introduced to expand the search range of the population. This paper empirically evaluates the proposed method under real datasets and compares it with other leading methods by using three association rule metrics, namely, support, confidence, and lift, as the fitness function. The experimental results show that the quality of the obtained association rules is significantly improved when dealing with datasets of different sizes. [ABSTRACT FROM AUTHOR]

Objective To understand the current situation and characteristics of chronic disease comorbidities among the elderly living alone in China, to analyze their comorbidity patterns, and to explore the relationship between health-related behaviors and chronic comorbidity in the elderly living alone, so as to provide basis for co-prevention and co-management of multiple diseases in the elderly. Methods Based on the data of the 2018 Chinese longitudinal healthy longevity survey (CLHLS), the network map was used to identify the common binary co-disease patterns of chronic diseases in the elderly living alone, and the Gephi software was used to visualize the chronic disease co-disease network of the elderly. Using Apriorism algorithm, the association rules were used to analyze the correlation between health-related behaviors and co-diseases of the elderly living alone, and multi-factor Logistic regression model was used to analyze the correlation between chronic diseases and health-related behaviors of the elderly living alone. Results A total of 1 905 elderly people living alone over 65 years were included, of whom 766 suffered from two or more chronic diseases, and the co-morbidity of chronic diseases was 40.2%. A total of 45 meaningful binary co-disease patterns with strong links were identified through network map analysis, of which 10 were related to hypertension and 7 were related to heart disease. The strong association rules among the association rules of health-related behaviors and co-diseases of the elderly living alone were poor sleep quality, no physical activity, no exercise, and no outdoor activity. The multivariate Logistic regression model showed that the elderly living alone with smoking (OR =1.791, 95% CI: 1.205-2.664) and alcohol consumption (OR=1.597, 95% CI: 1.084-2.353) had an increased risk of chronic co-disease compared with those without chronic diseases. Elderly people living alone with exercise (OR=0.132, 95% CI: 0.100-0.175), outdoor activities (OR=0.047, 95% CI: 0.035-0.063), good sleep quality (OR=0.469, 95% CI: 0.319-0.688), and adequate sleep (OR=0.648, 95% CI: 0.484-0.867) had a lower risk of chronic comorbidity. Conclusion The comorbidity pattern of the elderly living alone in China is complex and related to health-related behaviors. The risk of comorbidity in the elderly can be reduced by improving health-related behaviors, such as increasing exercise and outdoor activities and adjusting sleep duration and sleep quality. [ABSTRACT FROM AUTHOR]