Multidimensional Association Research Articles

Assessing the Association of Pain Intensity Scales on Quality of Life in Elderly Patients with Chronic Pain: A Nursing Approach.

Chronic pain is prevalent among the elderly and significantly affects their quality of life (QoL). Pain intensity scales are crucial tools in evaluating the severity of pain and tailoring management strategies. This study investigates the relationship between various pain intensity scales and QoL among elderly patients with chronic pain, highlighting the implications for nursing practice. A cross-sectional study was conducted with 150 elderly patients (aged 65 and above) in Riyadh, Saudi Arabia. Participants were assessed using the Numeric Rating Scale (NRS), Visual Analog Scale (VAS), and McGill Pain Questionnaire (MPQ) alongside the 36-Item Short-Form Health Survey (SF-36) to evaluate QoL. Data analysis involved Pearson correlation and multiple regression to explore the association of pain intensity on QoL. All pain scales showed significant negative correlations with QoL. The MPQ exhibited a significant association, suggesting its comprehensive nature captures the multidimensional association of pain more effectively. Regression analysis identified pain intensity, age, and duration of chronic pain as significant predictors of reduced QoL. The findings emphasize the importance of selecting appropriate pain assessment tools that reflect the complex nature of pain in elderly patients. Implementing comprehensive pain assessments like the MPQ can enhance individualized care strategies and potentially improve the QoL in this population. This study underscores the role of nurses in optimizing pain management approaches tailored to the elderly.

Exploring the pathways of urban green space exposure on respiratory health: An empirical study in Nanjing, China

Pathways of association between green space (GS) exposure and respiratory health continue stimulating scholars' research interest. However, the mechanisms underlying the multidimensional association between GS exposure and respiratory health remain to be clarified. Therefore, this study aimed to construct a research framework for exploring the mechanisms affecting the hypothesized pathways (physical activity, air pollution) based on the dynamic quantification of multidimensional exposure indicators (spatial and visual exposure). This study used urban green spaces in Nanjing, China, as sample sites for a pilot experiment. The parallel mediating effect of GS exposure on respiratory health through physical activity and air pollution was explored by partial least squares (PLS) modeling, and the effect of differences in GS exposure levels on this parallel mediating model was explored. The results showed that a dynamic monitoring system of multiple indicators is more conducive to characterizing the actual GS exposure of the population. Promoting physical activity and reducing air quality were both mediating pathways affecting GS exposure and respiratory health, with physical activity being the most critical mediating variable, accounting for 43.06% and 51.02% of the total indirect effects, respectively. And variables such as age, BMI, and gender do not affect the mechanism. The respiratory health-promoting benefits of performing short-term exposure in urban green spaces had a lagged effect of at least 4hours. The results of this study will help environmental epidemiologists to elucidate the pathways and mechanisms by which the green space system affects respiratory health and provide evidence for local governments to implement efficient and respiratory-healthy green space planning and design.

Creating Personalized Higher Education Teaching System Using Fuzzy Association Rule Mining

Universities and colleges aim to provide students with a solid academic foundation. Quality instruction is one strategy for achieving the highest possible standard in the higher education system. Personalized teaching caters to each student by adapting the learning pace and method to their specific requirements. However, the present state of customized education in higher education resources prevents proper resources from being extracted due to a lack of multi-dimensional association analysis between students, circumstances, and materials. A hybrid personalized teaching system utilizing fuzzy association rules mining is the goal of this research to improve learning in higher education. Effective multi-dimensional association analysis among students, settings, and instructional materials is facilitated by the fuzzy association rules mining-based hybrid personalized teaching system (FARM-HPT). The proposed study conforms to AI standards, is based on fuzzy logic theories, and guarantees precise university-level resource discovery. The study builds on earlier work in data mining by presenting a new, learner-specific recommendation model for personalized teaching that uses FARM to ensure accurate resource recognition and efficient mining of instructional assets at the higher education level. This new approach generates fewer set comparisons and does them faster than the current standard. Focusing on experimental validation, the study shows that the FARM-HPT system can generate individualized lessons while overcoming the constraints of traditional information mining methods. These findings align with AI standards, which shows how important it is to validate new AI approaches using robust empirical evidence. The system ensures effective accuracy on various datasets: LFW (89.76%), JAOLAD (94.43%), OECD (95.43%) and OULAD (97.45%).

Probiotics and Vegetable Oil Association: A Review

Abstract Probiotics are live microorganisms that confer a health benefit on the host when administered in adequate amounts. Currently, seven genera are used for their probiotic’s potential. Strains having the desired characteristics are associated with plenty of health benefits. Recent literature shows that different plant and vegetable oils are associated with probiotics. Other reports indicated their various aspects. For instance, the effect of probiotics on plant-based oil, edible oil, probiotics, the use of oils for probiotics encapsulation, etc., are documented. It is also reported that probiotics from vegetable sources and plant oil have some advancements, such as vegetarian and lactose-intolerant people using them. The multidimensional association between probiotics and vegetable oils attracts researchers to explore it. This research area is growing fast, but more limited research data is needed. Hence, this literature study was performed to identify the relationship, pros, and cons and provide recent insight into the literature for the researchers. Materials were collected by searching the related terminologies on different websites, finding the downloaded literature, and analyzing it critically. The results were presented as described by the authors, and a correlation was found that helps to enhance the field. Collectively, it was found that probiotics and plant and vegetable oils have a multifactorial relationship, and both can increase the effects of one another.

A Comparative Study of the Effectiveness of Exercise Training in Promoting Physical Fitness and Health in College Students

Abstract As we all know, a healthy body is a guarantee that students to do better in their studies, so students’ health should not be neglected in the process of students’ development. This study examines the impact of exercise training on the physical health of college students by combining college physical education teaching and exercise training. Sixty students from a college in Pudong New Area, Shanghai, were selected as experimental subjects. The effect of exercise training on college students’ physical fitness and health promotion was studied using pre- and post-test analysis of data, correlation analysis, and multiple regression, and an independent t-test was performed on the study subjects. There were significant differences (P<0.05) in the physical fitness of the students in the experimental group. The only indicators that did not show significant differences were the seated forward bend, the 800-meter run, and the 1000-meter run, where the P values were >0.05. There is no significant difference between the indicators of physical function. It indicates that sports training has a significant effect on promoting physical fitness improvement in students. The students’ body mechanics and physical fitness improved significantly after 12 weeks of sports training. It shows that exercise training helps to improve students’ body mechanics and physical fitness. There was a multidimensional association between students’ physical fitness and athletic training skills, and there was a significant positive correlation between students’ functional movements, physical fitness, and body mechanics on total athletic training, recovery training, and sports fitness training.

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

Calibrating subjective data biases and model predictive uncertainties in machine learning-based thermal perception predictions

Heating, Ventilation, and Air Conditioning (HVAC) systems in large-scale buildings often struggle to ensure satisfactory thermal comfort for diverse occupants while minimizing energy waste. Achieving this goal requires developing reliable prediction models that capture the changing and varied occupant thermal perceptions in different spaces. Despite their widespread use, many machine learning (ML) based prediction models suffer from subjective data biases and model predictive uncertainties, causing inaccurate estimation for occupant needs and leading to suboptimal building controls. The authors propose a data-model integration method that identifies and calibrates the inherent uncertainties of existing ML models in both data and model dimensions, ensuring reliable thermal perception predictions. This method introduces the Multidimensional Association Rule Mining (M-ARM) algorithm to identify biased human responses by exploring interrelationships among four perception metrics: thermal sensation, comfort, acceptability, and preference. Our method reveals significant performance enhancements in seven ML models, enhancing the F1-score by up to 5.53%. By leveraging reliability diagrams and Expected Calibration Error (ECE) scores, we also expose the models’ vulnerability to miscalibration and the need for calibrated predictions. We further evaluate six calibration techniques (e.g., Platt Scaling and Isotonic Calibration) on these models and uncover their potential to enhance prediction reliability performance, highlighting a reduction of up to 80.66% in ECE scores. The authors also investigated the impacts of dataset sizes, classifiers, and calibration methods on the proposed method. Our research offers insight into creating robust data-driven strategies for thermal perception predictions, ultimately contributing to optimized occupant comfort and energy efficiency in buildings.

Exploring the effect of carbon cap-and-trade policy in China based on the evidence from multi-dimensional association

ABSTRACT The integration or segmentation of regional carbon prices implies that barriers may hinder prices from adjusting efficiently and impede linkages between regional carbon markets. Past studies have found that cross-regional carbon prices in China tend to diverge, indicating unbalanced regional markets and that carbon cap-and-trade policies may be insufficiently effective. China’s carbon trading pilots opened in 2013. Is the integration efficient? This is the research question addressed in this study. This paper uses the carbon prices of four carbon-trading pilot regions in China (i.e. Beijing, Shanghai, Tianjin, and Guangdong) over the period of January 1, 2014, to March 23, 2021, to explore whether prices are correlated across three dimensions: long-term convergence, short-term correlation, and volatility transmission by adopting a vector error correction model with heterogeneous variances. And by using longer-term daily data and empirical tests that can account for the multiple-faceted correlations, this paper provides evidence showing that long-term convergence and risk transfer between the four regional carbon prices exist, which indicates significant regional integration. The results indicate that the price behavior of the Beijing pilot is efficient and that the Shanghai pilot has the lowest volatility. The paper also documents an information transfer pattern. For example, the Beijing pilot is shown to be the leading risk transfer market, whereas the Shanghai pilot is shown to be the market where most risk is transferred. Contrary to findings elsewhere, our main results demonstrate that China’s carbon markets exhibit integration efficiency and no imbalance between regions. This may be because the efficiency of China’s carbon markets has improved over time. The evidence also implies that if a multi-dimensional correlation has not been considered, it may underestimate the efficiency of regional carbon prices’ integration.

Dynamic gene regulatory networks improving spike fertility through regulation of floret primordia fate in wheat.

The developmental process of spike is critical for spike fertility through affecting floret primordia fate in wheat; however, the genetic regulation of this dynamic and complex developmental process remains unclear. Here, we conducted a high temporal-resolution analysis of spike transcriptomes and monitored the number and morphology of floret primordia within spike. The development of all floret primordia in a spike was clearly separated into three distinct phases: differentiation, pre-dimorphism and dimorphism. Notably, we identified that floret primordia with meiosis ability at the pre-dimorphism phase usually develop into fertile floret primordia in the next dimorphism phase. Compared to control, increasing plant space treatment achieved the maximum increasement range (i.e., 50%) in number of fertile florets by accelerating spike development. The process of spike fertility improvement was directed by a continuous and dynamic regulatory network involved in transcription factor and genes interaction. This was based on the coordination of genes related to heat shock protein and jasmonic acid biosynthesis during differentiation phase, and genes related to lignin, anthocyanin and chlorophyll biosynthesis during dimorphism phase. The multi-dimensional association with high temporal-resolution approach reported here allows rapid identification of genetic resource for future breeding studies to realise the maximum spike fertility potential in more cereal crops.

Fogging to combat dengue: factors influencing stakeholders' attitudes in Malaysia

BackgroundFogging is a conventional technique used to combat Aedes mosquitoes and prevent dengue disease. It is often implemented in outbreak areas or areas harbouring a high density of Aedes mosquitoes. Currently, studies on stakeholders' attitudes towards fogging are still limited in number. Therefore, this study aims to assess Malaysian attitudes, and identify the predicting factors influencing such attitudes.MethodsA validated instrument was used to interview 399 randomly selected respondents from the public (n = 202, 50.6%) and scientists (n = 197, 49.4%) in the Klang Valley region of Malaysia. The data were analysed using PLS-SEM involving Smart-PLS software.ResultsThe results confirmed that stakeholder attitudes toward fogging should be viewed in terms of a multi-dimensional association. The stakeholders surveyed were highly positive with regard to the application of fogging to control dengue but professed moderate concerns as to associated risks. The PLS-SEM analyses demonstrated that the perceived benefit was the most important factor influencing attitudes, followed by trust in key players.ConclusionsThis result provides a good insight from the perspective of education and unravels the underlying fundamentals of stakeholders' attitudes toward the fogging technique. The findings also provide a positive indicator to the responsible parties involved to continue the usage of this technique in conjunction with improvements with regard to its safety aspects, and possibly in combination with other environmental-friendly alternatives in order to achieve a healthy environment without dengue in Malaysia.

A Bibliometric Analysis of Current Knowledge Structure and Research Progress Related to Urban Community Garden Systems

Community gardens offer broad research opportunities and analytical resources encompassing urban planning to environmental sustainability, food systems, and social capital. However, little is known about the knowledge structure and research development related to community gardens. This study presents an in-depth bibliometric performance analysis and visual scientific mapping analysis of the literature on community gardens by examining 487 published papers selected from the Web of Science database. The results indicated a considerable rise in research papers in this subject area from 2012 onwards, with most contributions from the United States. Studies from high-income countries accounted for 93.22%, and 38 countries have cooperated 167 times in this field. “Health” is the most frequent keyword, and the terms “ecosystem services” has been gaining popularity over the last five years. A combination of co-citation clustering and keyword co-occurrence clustering analysis identified three major research themes in the field of community gardens: “ecosystem services and disservices”, “multidimensional association”, and “sustainable garden systems”. The development of ecosystem value assessment frameworks, the establishment of region-wide soil monitoring databases, accounting for the cost-effectiveness of nature-based solutions, the integration of garden systems into smart cities, and the integration of water management into regulation will be important future research directions regarding community gardens. Overall, this study provides scholars with a systematic and quantitative understanding of community gardens.

Representation, mining and analysis of unsafe behaviour based on pan-scene data.

To describe the safety rules of various industrial process data and explore the characteristics of unsafe behaviour, the association rules of unsafe behaviour based on pan-scene were proposed in this study. First, based on the scene data theory, unsafe behaviour was described by eight dimensions (time, location, behavioural individual, unsafe action, behavioural attribute, behavioural trace, professional category and risk level) to achieve scene data description and structural transformation. Second, the Apriori algorithm was used to explore the distribution rules of unsafe behaviour dimensions and the interaction between different dimensions from two perspectives: single-dimensional statistical analysis and multidimensional association rule mining. Finally, through SPSS Modeler software, an empirical analysis of pan-scene data for subway construction was conducted, and the association rules between type of work, construction stage, working time and unsafe action were identified. Some strong association rules were produced by the association analysis. For example, during the 13:00–17:00 of the excavation floor stage, the most frequent unsafe action of machine operators is the irregular binding of lifting objects. This result could explain why some unsafe actions are prone to occur in different construction stages and working times for workers of different types, which can be controlled and managed in a targeted manner, thus reducing the possibility of accidents.

Automatic Integration Algorithm of Vocal Performance Learning Materials Based on Multidimensional Association Rules

As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability.

Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network.

This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies.

Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

Evaluation Model of Product Shape Design Scheme Based on Fuzzy Genetic Algorithm Mining Spatial Association Rules

Put forward a kind of association rules mining method based on fuzzy genetic algorithm, this approach by building a mining model, the association rules and fuzzy genetic algorithm fuses in together, and then given to the fitness function of the mining space, and uses threshold to limit the fuzzy genetic algorithm will cross distribution and compile the fitness function, the improved method excavation stability is strong, the mining accuracy is high. The clustering analysis method of multidimensional fuzzy genetic algorithm mapping association network is studied, and the multidimensional module layout target is analyzed by using fuzzy hierarchical analysis technology and improved genetic algorithm combined with the clustering target of each angle, and the module division of each angle is realized. The main structure of the product is constructed with process model as the integration framework, style as the organization form, and feature list as the expression mechanism. The product characteristics based on fuzzy genetic algorithm are studied, the main structure configuration design process model mapping relation analysis, combined with the main structure of the joint model, together to achieve the fuzzy genetic algorithm (GA) variant design of fine-grained axiomatic mode, based on the associated network building and integration of new product design process, product structure of multidimensional optimization problem is solved.

Multidimensional distribution data association algorithm based on DNAzyme

Multidimensional distribution data association algorithm based on DNAzyme

Multidimensional distribution data association algorithm based on DNAzyme

Multidimensional distribution data association algorithm based on DNAzyme

Condensed Representations of Association Rules in n-ary Relations

The problem of association rules mining has given rise to a rich literature, especially in classic binary bidimensional data. In particular, the representation of the set of rules without loss of information is well understood. This is not the case in multidimensional binary data. This paper shows that the knowledge of n-1 components of every closed n-sets of a multidimensional Boolean tensor, as well as the cardinality of the remaining dimension, is enough to allow for the derivation of the confidence of every multidimensional association rule. This generalises well-known results in the bidimensional case. This paper provides experimental comparisons between the numbers of closed n-sets and frequent associations.