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Less than 65% of wastepaper can be recycled, and the remaining wastepaper may end up polluting groundwater and generating greenhouse gas emissions if disposed improperly by using traditional approaches. The concept of circular supply chain practices, which integrates circular thinking into supply chain practices that aim to reach zero waste by exploring novel business models and supply chain functions, has been proposed to address this issue. However, previous studies implementing this concept lacked appropriate methods for structuring an influential model to guide resource allocation, which caused difficulty in developing circular supply chain practices. Thus, a hybrid method is proposed to integrate factor analysis with fuzzy synthetic and decision-making and trial evaluation laboratory methods to enable visual analysis and boost understanding. The results reveal that awareness and engagement are the most influential factors in attempts to achieve zero waste, rather than the recycling, reuse and reduction practices emphasised by previous studies. [ABSTRACT FROM AUTHOR]

Purpose;Aiming at the problem of low comprehensive utilization efficiency of coal waste in China,this paper aims to study the influence of reservoir coal pillar dam on the evolution of mine water quality. Method;In this paper,the long-flame coal and different mine water sources in the low-rank coal of Wulanmulun coal mine are taken as an example. The coal-water interaction process is simulated by the indoor static immersion experiment. From the perspective of water chemical composition,correlation analysis,factor analysis,and other statistical methods are used to explore the control factors of different mine water quality evolution under the influence of long-flame coal. Result; After analysis,it was found that the initial hydrochemical types of the four types of water samples were inconsistent, and evolved into HCO3 -SO4-Na-Ca type and SO4-HCO3 -Na-Ca type after reaction. The cumulative variance contribution rates of the main control factors of the four types of water samples were 82. 52%,80. 07%,80. 68%, and 80. 02%, respectively, which all met the principle that the cumulative variance contribution exceeded 80%. Conclusion; Long flame coal has different effects on surface water and ground water. The synergistic effect of coal-water mainly includes the dissolution of various minerals (carbonate rock, silicate rock, sulfate rock salt, etc. ) and cation exchange. The main factors of surface water and groundwater quality evolution are the dissolution of carbonate minerals and cation exchange,respectively. [ABSTRACT FROM AUTHOR]

There is an expectation that health professions schools respond to priority societal health needs. This expectation is largely based on the underlying assumption that schools are aware of the priority needs in their communities. This paper demonstrates how open-access, pan-national health data can be used to create a reliable health index to assist schools in identifying societal needs and advance social accountability in health professions education. Using open-access data, a psychometric evaluation was conducted to examine the reliability and validity of the Canadian Health Indicators Framework (CHIF) conceptual model. A non-linear confirmatory factor analysis (CFA) on 67 health indicators, at the health-region level (n = 97) was used to assess the model fit of the hypothesized 10-factor model. Reliability analysis using McDonald's Omega were conducted, followed by Pearson's correlation coefficients. Findings from the non-linear CFA rejected the original conceptual model structure of the CHIF. Exploratory post hoc analyses were conducted using modification indices and parameter constraints to improve model fit. A final 5-factor multidimensional model demonstrated superior fit, reducing the number of indicators from 67 to 32. The 5-factors included: Health Conditions (8-indicators); Health Functions (6-indicators); Deaths (5-indicators); Non-Medical Health Determinants (7-indicators); and Community & Health System Characteristics (6-indicators). All factor loadings were statistically significant (p < 0.001) and demonstrated excellent internal consistency ([omega]>0.95). Many schools struggle to identify and measure socially accountable outcomes. The process highlighted in this paper and the indices developed serve as starting points to allow schools to leverage open-access data as an initial step in identifying societal needs.

As organic waste is converted to usable amendments via composting, there are large CO2 emissions associated with the decomposition of organic matter via microorganisms. While the active composting phase produces the largest emissions over a short duration, compost can often be stored during and after the maturation phase for much longer periods of time, increasing cumulative emissions. As such, the objectives of this study were to examine the spatial and temporal variability associated with in situ emissions sampling while identifying the environmental and chemical controls on emissions in windrow composting facilities during and after the maturation phase. A total of 665 flux measurements were taken from four windrows representing different ages and compositions between June and November 2020. Factorial analysis of covariance (ANOVA) was used to determine the variability between sampling locations, while multiple linear regression was used to identify those parameters which had the most influence on CO2 flux. Emissions showed significant variability over time that were attributed to ambient temperatures. During the summer, each windrow reached peak emissions between 5.0 and 32.3 g CO2 m-2 hr-1. As temperatures cooled, the windrows saw a 62%-86% decline in emissions, generally falling below 2 g CO2 m-2 hr-1. Significant differences occurred between the top-most sampling location and all others on the windrow, emitting between 33%-100% more CO2. The environmental controls of surface temperature, moisture content, and internal temperature showed the highest influence on emissions (R2 = 0.62). Chemical properties including organic nitrogen, carbon, pH, magnesium, and nitrate also showed significant influence (R2 = 0.43). This research has shown that environmental factors including temperature and moisture show the strongest influence over emission rates in mature compost. A significant negative effect of organic nitrogen on CO2 flux was found, indicating that increased presence of organic nitrogen would aid in the retention of carbon after the maturation phase, acting to lower total emissions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore the key factors through which an optimum pedagogy mix can be determined towards effective teaching practice and enhanced student learning outcomes in business/management education. Design/methodology/approach: An exploratory research design has been used. A sample of 310 was analyzed using exploratory factor analysis. A structured questionnaire was developed to collect data. It was pre-tested, and essential modifications were made before its final implementation. Findings: The study has presented the idea of pedagogy mix, which refers to a set of most obvious teaching methods/tools suitable to deliver marketing education in a context-bound manner. Eight factors have been identified that help to decide and/or maintain an optimum mix of pedagogies for effective teaching. An adequate "pedagogy mix" would help achieve educational objectives and equip students with the essential competencies. Practical implications: The study is particularly significant to educators who are in the initial years of their careers. The identified factors help educators decide and/or maintain an optimum mix of pedagogies by offering an understanding of different pedagogies, their strategic relevance and student needs. Originality/value: An institution's academic philosophy and commitment to the learning outcomes make it excellent or poor. Present institutions have and retain a main focus on preparation for professional careers, and without a perfect blend of pedagogies, it cannot be achieved. An optimum pedagogy mix would facilitate the key learning process and proffer the intricacies of the concerned profession. In this sense, this paper is a significant attempt, particularly in management education and higher education in general, that enables the educators of higher academics to decide and utilize an idyllic blend of pedagogies towards the successful execution of an educational process of higher order and ensuring the holistic student development.

STEAM education with gamification, a method that integrates game elements into the learning process, has proven to be a powerful tool. It not only encompasses various forms of gamified education but also has the potential to captivate and inspire students, breathing life into the course material. This study aims to present a comprehensive summary of the research carried out in the domain of STEAM education, with a particular emphasis on gamification. The focus will be on analyzing studies published in the past seven years. A meticulous bibliometric analysis was conducted to investigate the patterns in the published literature on STEAM education with gamification from 2017 to 2023. The relevant documents were retrieved by using keywords related to steam and gamification in the title, abstract, and keywords of the documents. Thus, 34 documents were acquired from the Scopus database for bibliometric analysis. The review analyzes the rate of publication growth, identifies the papers with the highest number of citations, determines the primary sources of these articles, evaluates the productivity of authors, examines the leading countries contributing to the field, and identifies the prominent subject areas within the research domain. Thailand has the highest output level in terms of publications and citations, as inferred from the results of our analysis. The Ceur Workshop Proceedings are widely acknowledged as the foremost scholarly resource in their subject. We have identified the most important keywords related to gamified STEAM education by conducting keyword analysis. Factorial Analysis provides a visual summary of the complex relationships between various concepts related to educational technology.

The main objective of this study is to revise and validate the assessment of self-presentation skills of middle school students. The assessment is based on existing self-assessment scales and adaptively modified for a more accurate assessment of middle school students' self-presentation skills. Considering the characteristics of middle school students and teachers' assessment, we have reconstructed and adjusted the dimensions of the original assessment. This paper includes background research, scale design and innovation, scale testing, and scale reliability and multiple validity verification. In order to further verify the effectiveness and reliability of the tool, this study added the application of content validity and factor analysis on the basis of previous studies. The results of this study are intended to compensate for the limitations of current teacher assessment for middle school students' self-presentation. At the same time, this study also provides a basis for the development of middle school students' self-presentation skills integrated training programs.

The purpose of this paper is to analyze factors that affect the behavioral intentions of undergraduate students to use blended learning methods. The research uses the three-tier use model (3-TUM) to explore the influencing behavioral factors of students at the undergraduate level to use LMSs. "The research was carried out at a university and consisted of undergraduate students majoring in business-related fields. Students responded to online questionnaires administered at the end of the semester concerning their experience with the LMS." A customized Moodle platform was used to measure the user's perceived usefulness, perceived satisfaction, and perceived ease of use with the LMS. Results show that interactivity in portal and self-efficacy have a direct impact on all second-tier constructs, while multimedia instruction influences only perceived usefulness and perceived satisfaction. In addition, perceived usefulness impacts perceived satisfaction. Perceived usefulness and perceived ease of use significantly impact behavioral intention of students to use learning platforms, while the relation between perceived satisfaction and behavioral intention was unindicative. "The study confirms that the individual experience of users and the quality of the system affect the successful implementation of an LMS and its adoption by users."

Scientometric studies play a crucial role in quantitatively assessing the impact and quantity of scholarly articles, providing a basis for policy-making and informed decision-making processes. Given the ongoing transformation within the Saudi scientific community across various domains, it is essential to establish a baseline for high-quality endodontic research. A comprehensive search in the PubMed database, using the keywords ["Saudi" OR "Saudi Arabia"] within the time frame of 2014 to 2021. The screening process involved evaluating the title and abstract of each article, with only those classified as endodontic articles included for analysis in this study. The analysis included factors such as the type of paper, the authors, the citations, and the year of publication. Featuring one or more authors affiliated with a Saudi dental institution, 70 papers were specifically categorized as endodontic papers. The most prevalent type of paper was case reports, accounting for 27 articles (28%), followed by literature reviews with 13 articles (18%). The number of authors ranged from one to ten, with a general trend of increasing publication rates over the years. In terms of citations, only four papers received more than 20 citations, while the majority of papers garnered between one and ten citations. Most articles in the field of endodontics exhibited low-level evidence. Therefore, it is recommended that the quality of research output and evidence be enhanced. [ABSTRACT FROM AUTHOR]

Background: A practical test that measures the information and communication technology (ICT) skills students need for effectively using ICT in primary education has yet to be developed (Oh et al., 2021). This paper reports on the development, validation, and reliability of a test measuring primary school students' ICT skills required for effectively using ICT (the ECC-ICT test). Objectives: Based on existing literature, three ICT use domains were identified for effectively using ICT--"Effective," "collaborative," and "creative use" of ICT. For these three domains, 24 corresponding teaching objectives were identified from a widely used digital literacy framework. Thirty-four test items cover these teaching objectives in an online test. Methods: A mixed-method approach was used for the ECC-ICT test. Four pilot rounds [superscript (n=25)] implemented qualitative interviews for cognitive validity and refining the test items, followed by a qualitative usability study[superscript (n=6)]. Confirmatory factor analysis and ANOVA provided quantitative insight into the large-scale test administration[superscript (n=575)]. Results and Conclusions: Composite reliability of our conceptual 3-factor confirmatory model showed that the test reliably measured primary school effective use of ICT ([omega] = 0.82), collaborative use of ICT ([omega] = 0.80) and creative use of ICT ([omega] = 0.64). Convergent validity (ranging from 0.41 to 0.46) was acceptable. Internal consistency (ranging from 0.84 to 0.91) and discriminant validity (HTMT values below 0.90) are good. ANOVA results show that mean test scores are higher for students in higher grade levels (p < 0.001). The post hoc Bonferroni results show that most grade-by-grade comparisons are significant (p < 0.001).

The volatile, uncertain, complex and ambiguous future world has highlighted the importance of critical thinking, collaboration, communication, creativity and innovation (4Cs) in today's classrooms. Framed by the Partnership for 21st-Century Learning (P21) Learning and Innovation theme, the study investigates the perceptions of the 4Cs by secondary school students from four schools in Zimbabwe. Two hundred and thirty-six (236) students participated in this quantitative study. Data were collected using online and paper questionnaires. Exploratory and first-order confirmatory analysis revealed a four-factor model with excellent fit indices. The second-order confirmatory factor analysis confirmed that the individual 4Cs were sub-constructs of a higher latent factor, Learning and Innovation (4Cs), where the students strongly endorsed collaboration, followed by communication, critical thinking, creativity, and innovation in decreasing magnitude. Age has a significant influence on critical thinking. The findings highlight the need for teachers to embed critical thinking and creativity activities within collaborative and communication learning activities.

Researchers in second language (L2) and education domain use different statistical methods to assess their constructs of interest. Many L2 constructs emerge from elements/parts, i.e., the elements "define" and "form" the construct and not the other way around. These constructs are referred to as emergent variables (also called components, formative constructs, and composite constructs). Because emergent variables are composed of elements/parts, they should be assessed through confirmatory composite analysis (CCA). Elements of emergent variables represent unique facets of the construct. Thus, such constructs cannot be properly assessed by confirmatory factor analysis (CFA) because CFA and its underlying common factor model regard these elements to be similar and interchangeable. Conversely, the elements of an emergent variable uniquely define and form the construct, i.e., they are not similar or interchangeable. Thus, CCA is the preferred approach to empirically validate emergent variables such as language skills L2 students' behavioral engagement and language learning strategies. CCA is based on the composite model, which captures the characteristics of emergent variables more accurately. Aside from the difference in the underlying model, CCA consists of the same steps as CFA, i.e., model specification, model identification, model estimation, and model assessment. In this paper, we explain these steps. and present an illustrative example using publicly available data. In doing so, we show how CCA can be conducted using graphical software packages such as Amos, and we provide the code necessary to conduct CCA in the R package lavaan.

The most effective way to improve display visibility in ambient light is to suppress the reflectance of the display. After conducting a thorough analysis of the factors contributing to unwanted reflection, specific layers were identified that required improvement. Subsequently, these elements were carefully optimized, resulting in a significant reduction of internal reflection to 0.53% for our 12.3" FFS‐mode prototype, compared to 1.12% in conventional LCDs. This improvement was achieved without the use of circular polarizers and has been demonstrated to provide excellent visibility in bright ambient conditions while allowing for reduced backlight brightness and lower power consumption. [ABSTRACT FROM AUTHOR]

Teacher education quality and effectiveness have been at the centre of policy discussions in the last decades. As part of efforts to improve teacher preparation, law 20.903 requires Chilean universities to design and apply diagnostic tests assessing the construct of their choice to all first-year preservice teachers. Based on these results, universities must design strategies to support their students. However, universities are facing challenges in implementing these tests, primarily due to the complexity, time and cost associated with developing high-quality tests. In collaboration with three universities, we developed three diagnostic tests: Social Thinking Test, Attitudes Towards Diversity Questionnaire, and Mathematics test for Teaching in Primary Education. Unlike many of the tests currently used by universities, these instruments were specifically designed to measure core teaching skills and attitudes defined by the national standards for Chilean teachers. To collect validity and reliability evidence, the tests were piloted with over 850 Chilean preservice teachers. Reliability was analysed using Cronbach's alpha, with results ranging between 0.67 and 0.92. Validity was examined based on content and internal structure evidence. The analysis of content evidence indicated good coverage of the target domains as defined by the assessment frameworks, and internal structure results point towards the presence of multidimensionality in two of the three tests. This paper discusses the results of these pilot studies and how diagnostic tests that are constructed and analysed from the perspective of the standards (American Educational Research Association, American Psychological Association y National Council on Measurement in Education, 2014) can provide valuable information to improve the effectiveness of teaching education.

Background: Teachers' views of nature of science (NOS) play a key-role in how they teach science and can significantly impact their students' understanding of and achievement in science. NOS has been a curriculum goal in science education for several decades. Therefore, there is a need for assessment tools that can examine views of NOS in the language spoken by teachers and used in their teaching. Purpose: This paper reports the development, through translation and validation of a measure for assessing NOS understanding in the Arabic language, specifically, the Reconceptualised Family Resemblance Approach to Nature of Science Questionnaire (RFNQ). The RFNQ has already been validated for use in English and Turkish languages; however, a validated version of the questionnaire in Arabic is lacking. Method: The RFNQ was adapted to Arabic and validated according to widely accepted guidelines for cross-cultural adaptation of questionnaires for different languages. These incorporated forward translation, critical assessment by a panel of Arabic native experts and experienced teachers, and back-translation. Sample: Subsequently, a total of 130 pre-service teachers completed the questionnaire to assess the internal consistency of the constructed Arabic version (RFNQ-AR). Results: The RNFQ-AR was developed through a comprehensive procedure to ensure accurate translation and meaning. Further, the RFNQ-AR questionnaire reliability coefficient for the whole items found to be acceptable ([alpha] = 0.89) and varied per scale between 0.72 and 0.74, which is standard for internal consistency reliability. Conclusions: Current estimates of 422 million Arabic speakers worldwide indicate the need for assessment tools to be available in Arabic. Questionnaires such as the RFNQ-AR will facilitate research into teacher understanding of NOS and enable evidence-based training in this area to ensure optimal student performance.

This study explores the quantitative measurement of Community Cultural Wealth (CCW), an asset-based approach to understanding the experiences of students from systemically marginalized racial/ethnic groups. Grounded in critical race theory, CCW focuses on forms of capital utilized by marginalized populations that are often unrecognized/undervalued by traditional social science research. Most previous studies on CCW have relied on qualitative methods; we argue that quantitative critical race theory, or 'QuantCrit', can complement those studies by statistically specifying assets possessed by students from marginalized populations as a step toward reimagining institutions that elevate their importance. This paper aims to develop a CCW scale to quantitatively explore the concept, while acknowledging the overlaps among and the dynamic nature of the forms of capital emphasized in conceptualization. Findings from exploratory factor analysis are largely consistent with the original CCW framework but suggest some important ways in which the framework can be further developed.

Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct model specification. The current paper presents a simulation study to investigate the performance of omega-unidimensional (based on the parameters of a one-factor model) and omega-hierarchical (based on a bifactor model) under correct and incorrect model misspecification for high and low reliability composites and different scale lengths. Our results show that coefficient omega estimates are unbiased when calculated from the parameter estimates of a properly specified model. However, omega-unidimensional produced positively biased estimates when the population model was characterized by unmodeled error correlations or multidimensionality, whereas omega-hierarchical was only slightly biased when the population model was either a one-factor model with correlated errors or a higher-order model. These biases were higher when population reliability was lower and increased with scale length. Researchers should carefully evaluate the feasibility of a one-factor model before estimating and reporting omega-unidimensional.

The purpose of this paper is to (a) establish whether meaning recall and meaning recognition item formats test psychometrically distinct constructs of vocabulary knowledge which measure separate skills, and, if so, (b) determine whether each construct possesses unique properties predictive of L2 reading proficiency. Factor analyses and hierarchical regression were conducted on results derived from the two vocabulary item formats in order to test this hypothesis. The results indicated that although the two-factor model had better fit and meaning recall and meaning recognition can be considered distinct psychometrically, discriminant validity between the two factors is questionable. In hierarchical regression models, meaning recognition knowledge did not make a statistically significant contribution to explaining reading proficiency over meaning recall knowledge. However, when the roles were reversed, meaning recall did make a significant contribution to the model beyond the variance explained by meaning recognition alone. The results suggest that meaning recognition does not tap into unique aspects of vocabulary knowledge and provide empirical support for meaning recall as a superior predictor of reading proficiency for research purposes.

This paper describes the development and validation of an instrument to assess how students deal with emotionally challenging classroom situations (the DECCS Questionnaire). The questionnaire is based on a vignette with one learning and one performance situation in a classroom, and is intended for students in grades 4 to 7. On a sample of N = 639 students (M[subscript age] = 10.6 years; SD = 1.25, 52.4% girls) in northwest Germany, factor analytic procedures for the subdimensions of students dealing with emotionally challenging classroom situations show that two factors can be distinguished each for students' appraisals, for students' adaptive behavior, for students' maladaptive behavior, and for students' desired teacher behavior. The questionnaire and subscales demonstrate good reliability and validity values. Thus, the DECCS questionnaire shows good psychometric properties and is well suited for determining how students deal with emotionally challenging classroom situations.

With the development of artificial intelligence (AI) applications, it has become critical for scholars, educators and practitioners to understand an individual's perceived self-efficacy regarding the use of AI technologies/products. Understanding users' subsequent behaviors toward the advancement of AI technology is also critical. Despite the growing focus on AI, a suitable scale for measuring AI self-efficacy (AISE) has yet to be developed. Current scales for measuring AISE (i.e., technology self-efficacy scales) are considered inapplicable because they neglect to evaluate perceptions of specific AI characteristics (e.g., AI-based configuration or anthropomorphic design). Given the limitations of existing self-evaluation and diagnostic instruments, the aim of this research is to investigate the construct of AISE, and develop and validate an AISE scale (AISES) for measuring an individual's perceived self-efficacy in regard to the use of AI technologies/products, in accordance with established exploratory and confirmatory scale development procedures. Specifically, a literature review is employed to generate initial items. An exploratory factor analysis is then performed for item purification purposes. At this stage, potential elements of AISE are extracted. Subsequently, factor extraction and confirmatory factor analysis are used to verify the construct structure of AISE. An analysis of 314 responses indicates that the AISE construct contains four factors: assistance, anthropomorphic interaction, comfort with AI, and technological skills. The scale is comprised of 22 items, and is found to have good fit, reliability, convergent validity, discriminant validity, content validity, and criterion-related validity. Moreover, nomological validity is built by the positive correlation between the AISE construct and motivated learning behaviors. This paper is the pioneer in developing and validating a scale to measure AISE. The findings extend existing knowledge of AISE and can help scholars further develop AISE theories. Our findings will also help educators and practitioners assess individuals' AISE and explore related behaviors.

This paper is concerned with the process of selecting between the increasingly popular bi-factor model and the second-order factor model in measurement research. It is indicated that in certain settings widely used in empirical studies, the second-order model is nested in the bi-factor model and obtained from the latter after imposing appropriate parameter constraints. These restrictions can be directly tested within the framework of the latent variable modeling methodology employing widely circulated software. The outlined model selection procedure provides a readily applied means of choosing between the two models of growing interest to measurement scholars, and is illustrated using numerical data.

Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with previously developed methods. In this paper we describe the PSEM framework and illustrate the quality of the method with simulation studies. Maximum-likelihood and weighted least squares PSEM estimation is discussed for SEM models with continuous and categorical variables. We show that traditional EFA, multiple group alignment (MGA), and Bayesian SEM (BSEM) are examples of PSEM. The PSEM framework also extends standard SEM models with the possibility to structurally align various model parameters. Exploratory latent growth models, also referred to as Tuckerized curve models, can also be estimated in the PSEM framework and are illustrated here with simulation studies and an empirical example.

Measurement invariance holds when a latent construct is measured in the same way across different levels of background variables (continuous or categorical) while controlling for the true value of that construct. Using Monte Carlo simulation, this paper compares the multiple indicators, multiple causes (MIMIC) model and MIMIC-interaction to a novel use of alignment optimization (AO) for detecting measurement noninvariance when the violator is a continuous variable. Results showed that MIMIC and MIMIC-interaction in sequential likelihood ratio tests and Wald tests with a Bonferroni correction provided a good balance between identifying invariant and noninvariant (linear violations) items when n=500 in terms of classification accuracy (CA). AO (CA = 0.86) was as competitive as MIMIC and MIMIC-interaction to linear invariance violations but was far better under nonlinear quadratic violations when n= 1,000 (i.e., 100 per group for 10 groups).

Purpose: Under the background of open science, this paper integrates altmetrics data and combines multiple evaluation methods to analyze and evaluate the indicators' characteristics of discourse leading for academic journals, which is of great significance to enrich and improve the evaluation theory and indicator system of academic journals. Design/methodology/approach: This paper obtained 795,631 citations and 10.3 million altmetrics indicators data for 126,424 published papers from 151 medicine, general and internal academic journals. In this paper, descriptive statistical analysis and distribution rules of evaluation indicators are first carried out at the macro level. The distribution characteristics of evaluation indicators under different international collaboration conditions are analyzed at the micro level. Second, according to the characteristics and connotation of the evaluation indicators, the evaluation indicator system is constructed. Third, correlation analysis, factor analysis, entropy weight method and TOPSIS method are adopted to evaluate and analyze the discourse leading in medicine, general and internal academic journals by integrating altmetrics. At the same time, this paper verifies the reliability of the evaluation results. Findings: Six features of discourse leading integrated with altmetrics indicators are obtained. In the era of open science, online academic exchanges are becoming more and more popular. The evaluation activities based on altmetrics have fine-grained and procedural advantages. It is feasible and necessary to integrate altmetrics indicators and combine the advantages of multiple methods to evaluate the academic journals' discourse leading of which are in a diversified academic ecosystem. Originality/value: This paper uses descriptive statistical analysis to analyze the distribution characteristics and distribution rules of discourse leading indicators of academic journals and to explore the availability of altmetrics indicators and the effectiveness of constructing an evaluation system. Then, combining the advantages of multiple evaluation methods, The author integrates altmetrics indicators to comprehensively evaluate the discourse leading of academic journals and verify the reliability of the evaluation results. This paper aims to provide references for enriching and improving the evaluation theory and indicator system of academic journals. [ABSTRACT FROM AUTHOR]

Most existing intelligent editing tools for music and video rely on the cross-modal matching technology of the affective consistency or the similarity of feature representations. However, these methods are not fully applicable to complex audiovisual matching scenarios, resulting in low matching accuracy and suboptimal audience perceptual effects due to ambiguous matching rules and associated factors. To address these limitations, this paper focuses on both the similarity and integration of affective distribution for the artistic audiovisual works of movie and television video and music. Based on the rich emotional perception elements, we propose a hybrid matching model based on feature canonical correlation analysis (CCA) and fine-grained affective similarity. The model refines KCCA fusion features by analyzing both matched and unmatched music–video pairs. Subsequently, the model employs XGBoost to predict relevance and to compute similarity by considering fine-grained affective semantic distance as well as affective factor distance. Ultimately, the matching prediction values are obtained through weight allocation. Experimental results on a self-built dataset demonstrate that the proposed affective matching model balances feature parameters and affective semantic cognitions, yielding relatively high prediction accuracy and better subjective experience of audiovisual association. This paper is crucial for exploring the affective association mechanisms of audiovisual objects from a sensory perspective and improving related intelligent tools, thereby offering a novel technical approach to retrieval and matching in music–video editing. [ABSTRACT FROM AUTHOR]

With the increasing uncertainty of urban security, urban resilience construction with risk awareness and bottom-line thinking has become essential for promoting sustainable urban development. This paper measures China's urban resilience development index (CRDI) based on 284 cities in China (except Tibet) using the entropy method from four dimensions: economic, social, environmental, and infrastructure, and analyzes it by combining coupling coordination degree and barrier factor analysis. We find that: (1) At the national level, CRDI and its sub-dimensions show an increasing trend in time, a decreasing spatial layout from coastal to inland, and a "high-high-low-low" clustering feature in space. (2) At the regional level, the CRDI is from high to low in the east, middle, and west order. The sub-dimensions are from high to low in the order of east, middle, and west for economic, social, and infrastructure resilience and from high to low in the order of east, west, and middle for environmental resilience. (3) To coupling coordination degree, the CRDI index coupling coordination is increasing in time trend but is still on the verge of dissonance. (4) Social resilience is the main obstacle factor. In the indicator layer, human resources, innovation, education, security, living, and environmental protection are the areas where CRDI coordinated development is the key to improvement. Based on the above empirical evidence, this paper proposes countermeasures to optimize urban resilience construction from four perspectives: economic, social, environmental, and infrastructure. [ABSTRACT FROM AUTHOR]

To effectively address climate change, it is necessary to quantify the carbon emissions in high energy-consuming regions, analyze driving factors, and explore effective pathways for achieving green development. Therefore, this paper takes Liaoning Province as research area, using extended Kaya identity and LMDI method to analyze the driving factors of carbon emissions from energy consumption in five major industries and the residential consumption sector from 2011 to 2020 in Liaoning Province. Furthermore, this paper uses the Tapio model to explore the decoupling relationship between carbon emissions and economic development. The results show that: 1) From 2011 to 2020, total carbon emissions from energy consumption in five major industries showed a trend of initially declining and then rising, while carbon emissions from the residential consumption sector exhibited an upward trend. 2) For carbon emissions from the industrial sector, economic output and industrial structure are the primary factors that promote and inhibit carbon emissions respectively. The inhibitory effects of energy structure and energy intensity are not significant. Population scale has a certain promoting effect on carbon emissions. For residential energy consumption carbon emissions, Household consumption expenditure, residential energy structure, and residential population scale are driving factors that promote the growth of carbon emissions, while residential energy intensity restrains the growth of carbon emissions. 3) From 2011 to 2018, carbon emissions from the industrial sector have been decoupled from economic output, and the decoupling state is dominated by weak decoupling. However, carbon emissions are once again correlated with economic development in 2019-2020. Carbon emissions from residential energy consumption have not yet decoupled from consumption expenditure, and its decoupling state is unstable and has no obvious change rule. [ABSTRACT FROM AUTHOR]

In recent years, the custom wardrobe market has been steadily developing. While meeting the functional needs of users, it is gradually shifting towards aesthetic preferences. Rapidly grasping users' preferences for the appearance of custom wardrobes is a key focus of current research. This study collected a large number of decorative surface images of custom wardrobes and objectively analyzed the design features based on color moments and Tamura texture feature data in computer image analysis methods. K-means cluster analysis was performed on the feature data. Collected images of the points closest to the cluster centers were further screened to select representative finish images, and finally a questionnaire survey was conducted at Nanjing Forestry University, with the help of semantic differential method and factor analysis. The characteristics of the samples were comprehensively summarized to infer design elements. The study found that warm-toned, medium-low saturation, and medium brightness surfaces were preferred by the panel. Different colors, contrasts, saturations, brightness, element features, and arrangements have significantly different effects on visual perception. These conclusions can provide a reference for subsequent custom wardrobe design. [ABSTRACT FROM AUTHOR]

Background: Fertility intentions, as a direct driver of fertility behavior, play an important role in the implementation of national fertility policy and population development. This study explored the influencing factors of college teachers' second child fertility intentions in Jinan, China on the basis of TPB. Methodology/Principal findings: Based on the theory of planned behavior, this paper employs basic characteristics analysis, difference analysis, and factor analysis related to the fertility intentions of the participants. Analysis found participants between 31 and 40 years old had the highest second child fertility intentions, and participants with a college-age first child had the lowest second child fertility intentions. Attitude and subjective norms had a positive impact on second child fertility intentions, and policy awareness had a positive impact on attitude, which indirectly affected second child fertility intentions. Subjective norms had the greatest influence on second child fertility intentions, followed by attitude, and policy awareness had the least influence on second child fertility intentions. Significance/Future research: The findings of this paper can enrich the theoretical research on fertility intentions, and also provide more optimal practical references for the formulation and propagation of China's fertility policy as well as the improvement of the division of family roles in China. Future research can further explore the impact of fertility policy on the fertility intentions of other groups. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the intertemporal relationship between the non-performing loan ratio and bank lending and to analyze factors affecting loan growth using data from Mongolian commercial banks. There has been a lack of research on Mongolian banks' lending behavior due to their short history. Thus, this paper investigates the effect of the non-performing loan ratio on total loan growth using an ordinary least squares (OLS) regression model with panel data. We used bank-related variables such as the loan-to-deposit ratio, provision-to-gross loan portfolio ratio, equity-to-asset ratio, and liquidity ratio, and economic variables such as the real gross domestic product (GDP) growth rate, interest rate, and inflation rate. The results of this paper show that non-performing loans have a significant negative impact on total loan growth. The implication of this result is that non-performing loans affect banking efficiency, which, in turn, affects financial stability and the real economy. Moreover, high non-performing loans reduce banks' profits. Also, this paper found that loss reserve and the liquidity ratio have a positive effect on total loan growth, while the effects of the loan-to-deposit ratio and the equity capital ratio were not found to be significant. Additionally, from a macro perspective, the inflation rate has a positive effect on the total loan growth rate, while the interest rate has a positive effect on total loan growth rather than a negative effect. And real gross domestic product (GDP) growth does not affect the total loan growth rate. [ABSTRACT FROM AUTHOR]

In this study, we develop a new paradigm, Circular Lean Six Sigma 4.0 (CLSS4.0) to promote manufacturing sustainability. This paper aims to provide a practical and holistic view of the drivers and barriers that can help companies design an integrated CLSS4.0 model. The paper is based on a qualitative exploratory study using multiple case studies within 12 Moroccan manufacturing firms conducted through semi-structured interviews with top executive managers. The results show that the drivers are related to expected operational and environmental performance, increasing customer requirements, gaining competitive advantage and market growth while barriers are related to insufficient tangible (finance, human and equipment) and intangible (skills and techniques) resources, data privacy, technical issues and management support. The proposed framework identifies the assessment of drivers and barriers and their attributes as a starting point for managers to lead the CLSS4.0 transformation, thereby contributing to its successful implementation. To the best of our knowledge, this study is among the very first studies to discuss the CLSS4.0 drivers and barriers. It could be useful to managers as a diagnostic tool to assess their ability to implement CLSS4.0 before investing in the initiative. [ABSTRACT FROM AUTHOR]

Aim: This study was conducted to evaluate the validity and reliability of the Turkish version of the Clinical Interpersonal Reactivity Index, an instrument to assess nurses' empathy. Background: A practical empathic approach is valuable for planning nursing interventions suitable for the patient's conditions and needs and increasing patient satisfaction. Design: This is a methodological study. Methods: The study was carried out at Muğla Sıtkı Koçman University Training and Research Hospital, recruiting a total of 358 nurses between January and February 2023. Exploratory and confirmatory factor analyses were used to determine and validate the factor structure of the Clinical Interpersonal Reactivity Index. Results: The exploratory factor analysis extracted three factors. According to the confirmatory factor analysis, all goodness of fit measures were within acceptable limits. Cronbach's α values were 0.89 for the total scale with 17 items and between 0.73 and 0.81 for the factors. In test–retest analyses, all intra‐class correlation coefficients were higher than 0.7, and the correlations between the first and second applications of the scale were between 0.61 and 0.73. Conclusion: The Turkish version of the Clinical Interpersonal Reactivity Index can be applied as a valid and reliable measurement tool to evaluate empathy in Turkish nurses. Summary statement: What is already known about this topic? In order for nurses to provide adequate care and help to their patients, their empathy skills should be developed at the desired level, and they should have an empathetic disposition.Numerous empathy scales have been developed to measure empathy, a crucial element in interpersonal relationships, but since these scales have an excessive number of items and explanatory texts, their evaluation takes a long time. The Clinical Interpersonal Reactivity Index, which was originally developed in English, has been designed so that nurses can quickly assess their empathic abilities and perspectives reflecting their respective helping actions. What does this paper add? The factor structure determined as a result of'the confirmatory factor analysis of the Clinical Interpersonal Reactivity Index has three factors, and the factor structure in the original scale has two factors.The Clinical Interpersonal Reactivity Index has 17 items in the present study and 27 in the original scale.The Clinical Interpersonal Reactivity Index is a reliable and valid scale for the Turkish culture. The implications of this paper: The Turkish version of the Clinical Interpersonal Reactivity Index can be used as a self‐assessment tool to evaluate empathic dispositions and practices among Turkish nurses.Nurse educators can also use the Clinical Interpersonal Reactivity Index to evaluate empathy education programmes. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study is to introduce the Copenhagen Multi‐Centre Psychosocial Infertility (COMPI)‐Fertility Problem Stress Scales (COMPI‐FPSS) into China and test its applicability in Chinese infertile population. Background: Infertility‐related stress not only influences patients' psychological well‐being but is also strongly associated with reduced pregnancy rates and poorer assisted conception outcomes, thus warranting focussed attention. Design: The design used in this study is a cross‐sectional survey. Methods: A total of 418 participants were recruited by convenience sampling from March to July 2022. The data were randomly divided into two parts: one for item analysis and exploratory factor analysis and the other for confirmatory factor analysis and reliability test. The critical ratio and homogeneity test were used to verify the differentiation and homogeneity of the COMPI‐FPSS; the construct validity was determined by explanatory and confirmatory factor analyses; Cronbach's α coefficient and Spearman–Brown coefficient were used to assess the reliability; and criterion validity was expressed using correlation coefficients for the Perceived Stress Scale and the Negative Affect Scale as the validity criteria. Results: The revised Chinese version of COMPI‐FPSS has 11 items and 2 dimensions (i.e., personal stress domain and social stress domain). Exploratory factor analysis showed that the cumulative variance contribution rate of the two factors was 68.6%, and confirmatory factor analysis indicated that the model fitted well. The score of the COMPI‐FPSS was significantly and positively associated with perceived stress and negative affect. The Cronbach's α coefficient of the total scale was 0.905, and the Spearman–Brown coefficient was 0.836, explaining excellent reliability. Conclusion: The revised Chinese version of COMPI‐FPSS shows good reliability and validity, and it can be used to evaluate the infertility‐related stress of infertile patients in China. Summary statement: What is already known about this topic? With the number of infertile people on the rise globally, infertility‐related stress needs attention.Copenhagen Multi‐Centre Psychosocial Infertility (COMPI)‐Fertility Problem Stress Scales (COMPI‐FPSS) with its brief and accessible entries is extensively applied abroad.China currently lacks such a tool. What this paper adds? This study translated the COMPI‐FPSS into Chinese and the Chinese version of COMPI‐FPSS contains 11 items and 2 dimensions, including personal stress domain and social stress domain, which has good reliability and validity. The implications of this paper: The Chinese version of COMPI‐FPSS could provide a short and reliable tool for clinical practice and research, which will reduce the burden on researchers and patients. [ABSTRACT FROM AUTHOR]

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies. [ABSTRACT FROM AUTHOR]

To fill a gap in current literature, this paper analyses the impact of human factors on the operational performance of innovation hubs in the SAARC Nations and the European Union. Upon conducting an extensive examination of academic literature, four primary classifications of human factors were determined: organizational factors, individual attributes, the nature of a task and working environment. This research offers a cross-sectional analysis of these factors and their implications on innovation centres in the culturally and economically diverse domain of SAARC Nations and the European Union. The findings suggest that organizations functioning in the context of Innovation hubs in SAARC Nations, where centralized authority and value correctness is mostly emphasised on, face challenges in order to foster employee engagement and creativity. However, the innovation hubs of the European Union receive treatment from a more flexible and tolerant approach which is backed by progressive technology and is incredibly sensitive to concerns touching on ergonomics and safety of the working environment. This research highlights High performing, High committing, and High involvement management, management techniques to manage human factors to enhance operation performance. Adaptive and individual approach to the management of human variables, based on the differences in cultural and economic conditions of each region, is essential for effective development of the innovative substance and competitive advantage, the paper states. The results obtained from this study offer important guidance for the policymakers, managers and practitioners who are involved in managing and creating innovation hubs. It has provided a framework of how the organisation's operation performance may be improved through effective management of human resources. [ABSTRACT FROM AUTHOR]

As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system. [ABSTRACT FROM AUTHOR]

As a significant trade item on the ancient Silk Road, the evolution of mug shapes represents a confluence of Eastern and Western economic history and cultural-artistic exchanges, also reflecting the flourishing export culture of Guangzhou. This paper analyzes the functional and social factors influencing the morphological changes of Lingnan mugs from 1616 to 1949 from the perspective of quantitative typological analysis. The overall design trend of these mugs transitioned from complex to simple, enhancing user comfort, while variations in mug scale reflect the diversity of consumer classes and regional drinking cultures. Among the 30 mugs analyzed, the average capacity was 356ml, with a range of 1588ml. Common shapes included cylindrical bodies and ear-shaped handles. Morphologically, the belly of the mugs transformed from arc-barrel bodies (emphasizing heat retention) to bulbous bodies, and eventually to cylindrical bodies (combining heat retention, practicality, and economy), with handles also showing signs of East-West integration. The analysis of the mug body' s inclination, with handle-side junction angles ranging from 34° to 53° and wall-side junction angles from 50° to 90°, indicates that these features are associated with stability in placement, aesthetic design, and practicality in liquid containment. These morphological evolutions reflect genuine responses to market demands and advancements in production technology, manifesting as products of market orientation and societal needs. By measuring changes in morphology, scale, volume, and external contour curves, this paper addresses how social factors shape material morphology in an academic context. [ABSTRACT FROM AUTHOR]

Aim: This study aims to adapt the "Nursing Practice Readiness Scale" to Turkish culture, and evaluate its psychometric properties. Background: New graduates' nursing practice readiness can impact their work adaptation and performance. Design: The research employed a methodological design. Methods: Data were collected between May and July 2022. The sample consisted of 436 newly graduated nurses. Content validity, construct validity and criterion validity were evaluated. Reliability was examined with adjusted item‐total correlation, Cronbach's a coefficient, composite‐reliability and split‐half reliability. Results: The Turkish version of Nursing Practice Readiness Scale was found to have good content and criterion validity. As a result of confirmatory factor analysis, the original five‐factor structure of the scale was also confirmed for the Turkish version. The scale's overall Cronbach's α coefficient was determined to be 0.96, with subscale coefficients ranging from 0.73 to 0.94. The composite reliability values of the subscales were found between 0.75 and 0.94. In split‐half reliability, the correlation coefficient between half was 0.952, with a Spearman–Brown Coefficient (Unequal Length) of 0.976. Conclusions: The Turkish version of Nursing Practice Readiness Scale is a valid and reliable measurement tool for evaluating the nursing practice readiness of newly graduated nurses. Summary statement: What is already known about this topic? The nursing practice readiness of newly graduated nurses may impact their work adaptation and performance.Work adaptation is an important predictor of intention to leave the profession and productivity. What this paper adds? This study adapted the Nursing Practice Readiness Scale to Turkish culture, and evaluated its psychometric properties.The Turkish version of Nursing Practice Readiness Scale was shown to be a valid and reliable tool that can be used to evaluate newly graduated nurses' nursing practice readiness. The implications of this paper Nurse educators will be able to assess the readiness of graduating students for nursing practice by using the scale.Nurse managers will be able to evaluate the readiness of new graduate nurses for nursing practice using the scale.Nurse managers and educators will be able to identify areas that need to be prioritized to increase the readiness of newly graduated nurses for nursing practice by using the scale. [ABSTRACT FROM AUTHOR]