Multidimensional Analysis Research Articles

Multidimensional analysis of twin sets during an intensive week-long meditation retreat

Meditation and other alternative practices have long been known to promote various health benefits, presumably by fostering a blood and tissue environment that enhances resilience to stress. Here, we used an integrated, multidisciplinary approach that couples quantitative electroencephalography (qEEG), biometrics, molecular, and biochemical data at multiple time points to investigate the impact of mind-based interventions on the body in a cohort of twins during an intensive week-long meditation retreat. We aim to show the feasibility of a novel design aimed to address individual changes controlling for intersubject trait variation (via twin control) and explore the role of genetic background on multi-omic factors during meditation. Interestingly, similar patterns of change in brain activity, network properties, and complexity were observed in the twin sets. These similarities were not observed in mismatched twin pairs. Heart rate dynamics studies showed alignment among twin pairs that was not present between unmatched pairs. In addition, changes in gene expression, metabolites, and cytokines present in blood plasma and associated with specific meditative states showed patterns of change associated by timepoint. Twin sets were similar in multiple domains before the start of the retreat, showed larger divergence at the mid-point, and looked more similar by the end of the retreat. To our knowledge, this study is novel within the twin research paradigm and is a first step towards exploring the effects of meditation in twins. InnerScience Research Fund. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Investigation into the Methodology and Implementation of Life Cycle Engineering under China's Carbon Reduction Target in the Process Industry

The industrial sector is the primary source of carbon emissions in China. In pursuit of meeting its carbon reduction targets, China aims to promote resource consumption sustainability, reduce energy consumption, and achieve carbon neutrality within its processing industries. An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology. This strategy aims to attain an optimal solution for material performance, resource consumption, and environmental impact. In this study, five types of technologies were considered: raw material replacement, process reengineering, fuel replacement, energy recycling and reutilization, and material recycling and reutilization. The meaning, methodology, and development status of life cycle engineering technology abroad and domestically are discussed in detail. A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry, as an example. The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

The linguistic and cultural script "tea drinking" in Chinese communication

The article explores the cultural significance of tea drinking in Chinese communication by studying the corresponding linguistic and cultural script. The historical context and the current state of Chinese tea culture is considered; the language used to describe the tea drinking process, including individual lexemes, phrases and idiomatic expressions, is analyzed. Also, the article explores the norms of etiquette and traditions associated with tea drinking. A brief overview of tea idioms in the Chinese language reflecting the linguistic and cultural value of tea drinking is provided. The conclusion is made about the high importance of the considered linguistic and cultural script in Chinese culture, as well as the need to introduce this linguistic and cultural component into the process of teaching Chinese to foreign students. The subject of the study is linguistic and cultural scripts relevant within the framework of Chinese linguoculture using the example of the script "tea party". The novelty of the study lies in the fact that it provides a multidimensional analysis of the linguistic and cultural script "Tea Party" in Chinese linguoculture. In particular, the specialized tea vocabulary, phraseological units, as well as etiquette replicas that are related to tea drinking in Chinese linguoculture are considered. In Chinese culture, there is a complex and interesting tea etiquette to study. The linguistic and cultural script "Tea Party" in the framework of Chinese culture consists of three parts: the preamble of the event, the event and the result of the event. The preamble of the event includes a greeting and an order for tea (the latter is relevant if the tea party takes place in a tea room). An event (i.e., a tea party) involves brewing tea and drinking tea. The latter usually goes without words, because in Chinese culture it is customary to turn off all possible stimuli for the sake of enjoying a tea drink. Besides, the important part is gratitude for a good tea. The result of the event is the payment of the bill (if it happens in a tea shop) and farewell at the end of the tea party.

Comparison of ground coffee and coffee puck by IR spectra

Classification of ground coffee and coffee puck samples was carried out using IR spectroscopy and multidimensional analysis. Using the principal component analysis, spectral absorption bands that explain the differences in the composition of ground coffee and coffee puck were identified. It is proposed to use this approach to assess valorization when using coffee waste.

Analyzing the Functional Interdependence of Verbal Behavior with Multiaxial Radar Charts

The functional analysis of complex verbal behavior requires an evaluation of topographically similar responses under multiple sources of control. Traditional graphical displays of behavior were designed to show the manipulation of isolated controlling variables and may not be amenable to displaying the multidimensional properties of complex behavior. Researchers have recently demonstrated the use of multiaxial radar charts for comparing the functional performance of biological systems. Here we extend the use of multidimensional analyses to compare the relative performance distributions of verbal behavior across four potential controlling variables. First, we provide a conceptual analysis of intraverbal and extraverbal control as continua along which stimuli range from formal to thematic and explain how the intersection of these stimulus fields creates a radar chart for multidimensional analysis. Then we demonstrate how data may be gathered through a verbal operant experimental analysis. We employed repeated measures to map the conditioning history of a child with autism spectrum disorder across 2 years of early intensive behavioral intervention and analyzed the results using shape descriptors for quantitative comparisons. We also compared the polygonal language profiles of children with autism against that of a neurotypical peer. Extending a multidimensional analysis to the field of verbal behavior provides the basis for a language growth chart that researchers and clinicians can use to monitor language acquisition over time. We discuss the use of radar charts as a framework for understanding the interdependence of verbal operants and suggest their use for complex analyses of complex verbal behavior.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective: To analyze the technical indexes of students’ online learning behavior analysis based on Kirkman’s evaluation model, sort out the basic indexes of online learning behavior, and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis. Methods: The online learning behavior data of Physiology of nursing students from 2021–2023 and the first semester of 22 nursing classes (3 and 4) were collected and analyzed. The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation. Results: The study found that the demand for online learning of nursing students from 2021–2023 increased and the effect was statistically significant. Compared with the stage assessment results, the online learning effect was statistically significant. Conclusion: The main indicators for evaluating and classifying online learning behaviors were summarized. These two indicators can help teachers predict which part of students need learning intervention, optimize the teaching process, and help students improve their learning behavior and academic performance.

Screening Organic Acid Contents of Tomato Landraces Collected From Aegean-Mediterranean Region of Anatolia

Tomato landraces can serve as valuable sources for breeding new cultivars aimed at enhancing fruit quality in terms of organic acids. Nineteen tomato landraces spread along the Mediterranean coasts of Anatolia were evaluated to determine their basic internal quality parameters and organic acid composition. The parameters assessed included fruit weight, diameter, length, pH, titrable acidity (TA %), soluble solid content (SSC%), SSC/TA ratio, and organic acids, such as oxalic (OA), tartaric (TarA), malic (MA), malonic (MalA), lactic (LA), acetic (AA), citric (CA), and ascorbic acids (AscA). Significant diversity was observed among the different landraces concerning these traits. Citric acid was found to be the most abundant organic acid within the landraces. Notably, the highest and lowest values for CA were recorded in Ege 8 (105.73 mg g-1) and TR62707 (31.10 mg g-1), respectively, making them promising sources for future breeding programs. Ascorbic acid (AscA) exhibited the lowest content among all the organic acids, ranging from 0.06 to 0.12 mg.g-1 (equivalent to 64-116 µg.g-1). Moreover, the landrace TR49646 displayed the highest malic acid content (8.23 mg g-1), making it a potential source for obtaining high malic acid content. Conversely, Ege 6 showed the lowest malic acid content (5.11 mg g-1). For health purposes, the landrace TR63233 was identified as having the lowest oxalic acid content. Multidimensional scale analysis further confirmed the potential candidates identified by the ANOVA and one-way ANOM tests. The results revealed a considerable diversity among the evaluated landraces, and the identified traits could be instrumental in selecting and breeding new cultivars with improved characteristics.

Development of a Decision Support System for University Administration: Integrating Business Process Management and Multidimensional Data Analysis

Development of a Decision Support System for University Administration: Integrating Business Process Management and Multidimensional Data Analysis

개념도 분석을 통해 본 초등학생 학부모의 선행학습에 관한 인식

This study aimed to investigate parent's perceptions of pre-learning among primary school parents using the concept mapping methodology, which is useful for understanding and structuring group perceptions. Sixteen parents participated in individual in-depth interviews. Based on the data obtained, a total of 58 final statements were derived through a statement integration process, with 14 participants contributing to similarity classification. Multidimensional scaling and cluster analysis were conducted based on similarity classification of participants. Subsequently, a survey on the degree of empathy for statements was conducted using a 5-point Likert scale with 76 parents, including statement generators and similarity classification participants, to calculate the degree of empathy for statements and clusters. The results revealed that parent's perceptions were divided into two dimensions: 'children's motivation-social pressure' and 'parent’s conviction- experience and external influence'. They were further classified into four clusters and two sub-clusters. Specifically, the clusters included 'enhancing children's motivation and abilities for pre-learning', 'realities and surrounding circumstances inducing pre-learning', 'value system and conviction towards pre-learning', and 'awareness and coping with the current state of pre-learning'. Among these, the cluster 'enhancing children's motivation and abilities for pre-learning' was further divided into sub-clusters of 'children's motivation for pre-learning' and 'enhancing children's abilities through pre-learning', while the cluster 'realities and surrounding circumstances inducing pre-learning' was divided into sub-clusters of 'experiential realities necessitating pre-learning' and 'surrounding environments prompting pre-learning'. Parents showed the highest empathy towards the cluster 'value system and conviction towards pre-learning'. In conclusion, parents have evolved considering the choice regarding pre-learning to strategizing for the success of their children's pre-learning. Parents have taken an active lead in their children's pre-learning situations, armed with their own educational value system and convictions, considering factors such as their children's motivation, timing, methods, surrounding circumstances, and experiences. The study is significant in suggesting the necessity of observing changes in societal perceptions of pre-learning and improving educational policies and entrance examination systems based on parent's perceptions.

Multi-omics analysis reveals the association between specific solute carrier proteins gene expression patterns and the immune suppressive microenvironment in glioma.

Glioma is the most prevalent malignant brain tumour. Currently, reshaping its tumour microenvironment has emerged as an appealing strategy to enhance therapeutic efficacy. As the largest group of transmembrane transport proteins, solute carrier proteins (SLCs) are responsible for the transmembrane transport of various metabolites and ions. They play a crucial role in regulating the metabolism and functions of malignant cells and immune cells within the tumour microenvironment, making them a promising target in cancer therapy. Through multidimensional data analysis and experimental validation, we investigated the genetic landscape of SLCs in glioma. We established a classification system comprising 7-SLCs to predict the prognosis of glioma patients and their potential responses to immunotherapy and chemotherapy. Our findings unveiled specific SLC expression patterns and their correlation with the immune-suppressive microenvironment and metabolic status. The 7-SLC classification system was validated in distinguishing subgroups within the microenvironment, specifically identifying subsets involving malignant cells and tumour-associated macrophages. Furthermore, the orphan protein SLC43A3, a core member of the 7-SLC classification system, was identified as a key facilitator of tumour cell proliferation and migration, suggesting its potential as a novel target for cancer therapy.

Fostering Sustainability Through Legal Integration: A Multidimensional Analysis of Environmental and Commercial Law Synergies

This paper delves into the intricate relationship between environmental regulations and commercial law, examining how their integration fosters sustainable business practices. It explores the role of environmental regulations in shaping business operations, highlighting the transition from compliance to strategic innovation and competitive advantage. Further, it analyzes how commercial law acts as a catalyst for environmental compliance, emphasizing the influence of contractual obligations and corporate governance standards on sustainable practices. Challenges and opportunities presented by regulatory alignment are scrutinized, alongside the transformative potential of green financing and investment in mobilizing capital for environmental sustainability. Additionally, the paper addresses the integration of environmental responsibility into corporate governance, underscoring the need for legal mechanisms to enhance environmental governance and the balance between commercial objectives and environmental values. Through comprehensive analysis, it argues that the harmonization of environmental and commercial laws is pivotal in advancing sustainable development, providing insights into the legal frameworks and strategies that enable businesses to contribute to a sustainable future.

音乐与舞蹈教育中身体发展的心理学与教育学研究

This article explores the psychological and educational significance of corporeality in the field of music and dance education, emphasizing that the body is not only a medium through which we perceive the world but also a crucial component of an individual’s psychological state. Body psychology, as an emerging branch that delves into the deep connections between the body and the psyche, reveals the distinction between “the body” and “corporeality.” The latter encompasses multiple dimensions including physiological, cultural, psychological, and meaning-making aspects, playing a decisive role in individual growth and personality development. Through a multidimensional analysis of the role of corporeality in artistic creation, performance, and perception, as well as its application in music and dance teaching methods, the article demonstrates how corporeality can enhance the expression of individual emotions and inner experiences, thereby influencing self-awareness and emotional development. Corporeality is a complex characteristic that integrates physical, psychological, biological, and spiritual aspects. It concerns not only physical health but is also a vital resource for an individual’s psychological well-being, having a profound impact on the psychological and emotional development of individuals in the field of music and dance education.

Optimization of a syngas-fueled SOFC-based multigeneration system: Enhanced performance with biomass and gasification agent selection

In light of abundant sources of biomass feedstocks and regarding the higher performance in syngas-fueled-based SOFC systems, this work proposes a novel multigeneration system based on the combination of syngas-fuel SOFC, Kalina cycle, humidification-dehumidification desalination unit, and proton exchange membrane electrolyzer. This system is designed for simultaneous production of power, heating, freshwater, and hydrogen. Comprehensive energy, exergy, exergoeconomic, environmental, and economic analyses have been conducted to evaluate its performance. The optimum input biomasses, gasification agents, and operating conditions were determined using a coupled approach of an artificial neural network, multi-objective Particle Swarm Optimization algorithm, and the Linear Programming Technique for Multidimensional Analysis of Preference decision-making method. Under base input conditions, the energy and exergy efficiencies of the proposed system were found to be 53.66 % and 38.2 %, respectively. The system also demonstrated the capability to produce 0.1633 kg/s of freshwater and generate a net power output of 377.6 kW. The results indicate that using oxygen-enriched air in the gasification process notably enhances efficiency while reducing emissions. Straw and CO₂ were identified as the optimal feedstock and gasification agent, yielding an exergy efficiency of 42.71 % and a total product cost of 6.29 $/GJ at the optimum point. Moreover, with a selling price of $0.20/kWh for electricity and a fuel cost of $6/GJ, the system can achieve total revenue of approximately $1.65 million with a payback period of 4.01 years. These findings underscore the proposed plant's profitability under specific pricing conditions, making it an attractive investment opportunity.

Integration of Unemployed Venezuelan Immigrant Women in Colombia

The integration of immigrants in a host society must consider aspects related to the labour field, as well as other factors including their differences. The existence of 97 unemployed Venezuelan migrant women living in Cúcuta, Los Patios and La Parada, border cities of Norte de Santander, Colombia with the state of Táchira, places them at a disadvantage in terms of integration; hence, this study set out to propose strategies to guide governance officials and actors in managing their integration. By means of a multidimensional analysis, three profiles of these unemployed migrant women were obtained for their diversity, generating strategies for each profile in structural, social and cultural contexts; through this, it became evident that the characteristics of those who settle as immigrants can be considered in order to establish integration strategies in line with these characteristics. Thus, the methodology of the study could be useful in other areas of migration for the design of integration strategies that consider the heterogeneity of immigrants to facilitate their contribution to the society and economy of the country that has hosted them.

Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence

This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques.

A multidimensional analysis of self-esteem and individualism: A deep learning-based model for predicting elementary school students' academic performance

The current research descends into the discrete connection between self-esteem, individuality and academic achievement among primary school learners. It addresses the essential need for predictive algorithms to analyze and forecast academic results. Self-esteem and identity, as diverse psychological variables, have been highlighted as crucial determinants in creating academic success. The fundamental goal is to develop an effective prediction model for elementary school children's academic performance by including self-esteem and individuality in the analytical framework. We suggested an Owl Search Optimized Dynamic Deep Neural Network (OSO-DDNN) based model for making more precise predictions. The sample size for the research was 147,210 elementary school students who took the LegiLexi exam between 2016 and 2021. To standardize the data and improve model performance, the suggested methodology uses a pre-processing strategy based on the Min-Max scalar approach. Furthermore, Independent Component Analysis is used to detect and extract essential factors related to self-esteem, individuality and academic achievement. The architecture and parameters of the suggested approaches are optimized using the OSO algorithm to provide a prediction model that remains accurate and efficient. We quantify the effectiveness of the assessments using the following metrics: precision, recall, accuracy and F1-score to develop a design using conventional methods. The findings demonstrate that the model's resilience and promise for anticipating primary school pupils' academic success are based on psychological aspects. The OSO-DDNN is an essential instrument for educators and policymakers in identifying at-risk pupils and implementing targeted interventions, supporting a comprehensive approach to education that combines psychological well-being with academic performance.

A multidimensional analysis of corporate governance and firm value

A multidimensional analysis of corporate governance and firm value

A new missense mutation c.1240A>G in fumarate hydratase gene leads to uterine leiomyoma associated hereditary leiomyomatosis and renal cell cancer (HLRCC) syndrome in Chinese

ObjectiveThis study presents a detailed analysis of the clinical and genetic characteristics of uterine leiomyoma associated with Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC), combined with exploration of family history, pathology, and management procedures, supported by thorough evidence collection. MethodsBlood samples were collected from the proband, and the pathogenic variant was verified using Sanger sequencing. A comprehensive review of family history, FH deficiency pathology, FH and 2SC immunohistochemistry staining was conducted. Functional evidence was derived from clinical and genetic information, supplemented by a literature collection and mutation was reclassified based on ACMG/AMP guidelines. ResultsThe study successfully identifies a novel missense mutation (c.1240A>G; p.Lys414Glu) in exon 9 of FH, with no prior reports in existing databases. The patient's phenotype and family history, coupled with evidence collected from the literature, contribute to the preliminary determination of the variant as likely pathogenic. We also emphasize that the importance of combining FH-deficient morphology and immunohistochemical staining with 2SC for enhanced sensitivity. ConclusionThis research adds a novel missense mutation to the repertoire of FH gene variants, emphasizing its likely pathogenic nature based on a multidimensional analysis of phenotype, family history, and literature evidence. The findings enhance our understanding of the genetic landscape associated with FH and underscore the importance of thorough characterization for accurate variant classification.

Locating the Intersection of Generative Artificial Intelligence and Human English Writing Skills: A Comparative Study

This paper offers a multi-dimensional comparative analysis of the linguistic characteristics found in the introduction sections of dissertations produced by ChatGPT and those written by Saudi authors. This research paper aims to analyze the linguistic variation in the introduction part of the dissertations written by Saudi writers compared with the introduction produced by ChatGPT. An extensive analysis of more than 150 linguistic features, including lexico-grammatical structures and semantic nuances, is performed using Multidimensional Analysis Tagger (MAT) tagger. A corpus of dissertation introductions written by Saudi students is compiled and analyzed. Furthermore, a corpus is created that comprises introductions produced by ChatGPT. The study investigates the relationship between language produced by AI and human-written content, revealing unique linguistic preferences. The results indicate ChatGPT is more informational, explicit and less non-narrative and non-argumentative than human-written introductions. There is a significant difference in discourse production on dimension five, as human-written introductions produce abstract and ChatGPT produces non-abstract discourse. The study is significant as it highlights the linguistic differences between the writing styles of ChatGPT and Saudi writers, indicating potential areas of improvement for AI-generated texts.

“Allowing Space for Voice…All Our Voices”: Understanding Ho‘ouna Pono Implementation Through Educational Leadership Perspectives in Rural Hawai‘i Schools

Epidemiological research over the past two decades has highlighted substance use disparities that affect Native Hawaiian and Pacific Islander youth, and the lack of effective approaches to address such disparities (Okamoto et al. in Asian American Journal of Psychology 10(3):239–248, 2019). The Ho‘ouna Pono curriculum is a culturally grounded, teacher-implemented, video-enhanced substance use prevention program that has demonstrated efficacy in rural Hawaiʻi in a large-scale trial (Okamoto in Asian American Journal of Psychology 10(3):239–248, 2019). Despite its potential to ameliorate health disparities and address youth substance use, prevention programs such as Ho‘ouna Pono have been poorly disseminated and implemented across Hawaiʻi, raising the question: Why are effective prevention programs not used in communities that most need them? The present study used concept mapping to understand previously identified implementation barriers and develop implementation strategies for Ho‘ouna Pono. Seven Hawaiʻi Department of Education (HIDOE) educational leaders and administrators sorted Ho‘ouna Pono implementation barriers (e.g., “There is a lack of HIDOE funding to support prevention curricula”), named concepts, and rated barriers’ perceived impact and difficulty. Multidimensional scaling and cluster analysis yielded a five-cluster solution: (1) Kumu (Hawaiian word for teacher) Controlled, (2) School Level Buy-in, (3) Curriculum, (4) Student Attitudes + Mindsets (Family + Community), and (5) Policy. Participant ratings identified eight high-impact and low-difficulty barriers. Discussion revealed important intersections among barriers indicating the need for coordinated and cross-level implementation strategies to support Ho‘ouna Pono sustainment. Brainstormed implementation strategies using participants’ own language highlighted a need for participatory methods in school settings to bidirectionally share ways to best sustain substance use prevention programs.