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This study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.

This study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.

Based on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.

To the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

The purpose of this study is to explore linguistic stylometric patterns encompassing lexical, syntactic, structural, sentiment and politeness features that are found in librarians’ responses to user queries.

A total of 462 online texts/transcripts comprising answers of librarians to users’ questions drawn from the Internet Public Library were examined. A Principal Component Analysis, which is a data reduction technique, was conducted on the texts and transcripts. Data analysis illustrates the three principal components that predominantly occur in librarians’ answers: stylometric richness, stylometric brevity and interpersonal support.

The results of the study have important implications in digital information services because stylometric features such as lexical richness, structural clarity and interpersonal support may interplay with the degree of complexity of user queries, the (a)synchronous communication mode, application of information service guideline and manuals and overall characteristics and quality of a given digital information service. Such interplay may bring forth a direct impact on user perceptions and satisfaction regarding interaction with librarians and the information service received through the computer-mediated communication channel.

To the best of the authors’ knowledge, the stylometric features encompassing lexical, syntactic, structural, sentiment and politeness using Principal Component Analysis have not been explored in digital information/reference services. Thus, there is an emergent need to explore more fully how linguistic stylometric features interplay with the types of user queries, the asynchronous online communication mode, application of information service guidelines and the quality of a particular digital information service.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.

The main aim of this study is to investigate the role of fathers and mothers in the intergenerational educational persistence for sons and daughters under two dimensions that characterize the clusters of countries: redistributive policy and governance.

Data from the Global Database of Intergenerational Mobility (GDIM), hierarchical cluster analysis on principal components and panel regression are used in this study to estimate intergenerational educational correlation and to investigate its determinants related to the parents’ and descendants’ education variables in 93 countries grouped in four clusters. The empirical analysis is differentiated by gender combinations of parents and descendants.

In the clusters of countries characterized by high inequalities and poor governance, our findings show that the role of the fathers is stronger than that of the mothers in educational transmission; fathers and mothers are more influential for the daughters rather than for the sons; parental educational privilege is the main driver of intergenerational educational persistence; there is an inverse U-curve in the association between educational inequality of the parents and educational correlation for the sons. Differently, in the countries characterized by high income, low redistributive conflict and better governance, the role of the mothers is stronger and education mobility for the daughters is higher than that for the sons.

The authors’ results remark on the importance of social welfare policies aimed to expand a meritocratic public education system including schooling transfers for lower social class students and narrowing the gender gap in educational mobility between daughters and sons. Social welfare policies should also be oriented to spread high quality child care systems that help to foster greater women equality in the labor market, because the strength of educational persistence depends on the position of the mother in the economic hierarchy.

The distinctiveness of the paper can be found in the fact that this study investigates the parental role differentiating by gender and coupling hierarchical cluster analysis on principal components with panel regression models. This allows us to have a sample of 93 countries aggregated in four groups defined in two dimensions: redistributive policy and governance. Amongst the determinants of educational transmission, we consider not only education’s years of the parents but also other determinants, such as educational inequality and privilege of the parents. We also identify the effects of investment in human capital and educational inequalities for the descendants on education mobility.

The paper assessed the required drivers of mentoring practices in construction-related firms: a perspective of the quantity surveying (QS) firms in Nigeria with a view to providing possible drivers for effective mentoring relationships in the respective construction firms.

Survey design was used to assess the level of knowledge, mentoring concept adoption and the required drivers of mentoring relationships in construction-related firms. QS firms in Abuja, Nigeria were sampled through the use of semi-structured questionnaires. A total of 149 questionnaires were administered to principal partners, partners, senior quantity surveyors and quantity surveyors in QS firms in Abuja through personal delivery. A total of 142 of them were returned and found suitable for analysis, representing 95.30% effective response rate. Percentage, mean, Kruskal–Wallis and factor analyses were utilized for the analysis of the data.

The study revealed an appreciable knowledge of mentoring concept but not so much with the adoption in the respective firms. This connotes that there is a problem with the adoption of mentoring concept in construction-related firms despite knowledge of the concept. Also revealed by the study, are the required drivers of mentoring practices in construction-related firms.

The findings show the required drivers of mentoring practices in construction-related firms and the recommendations to further the adoption and implementation of these drivers to ensure mentoring practices thrive in construction-related firms in the country.

This paper reveals the required drivers of mentoring practices in Nigerian construction-related firms and provides ways through which the drivers can be implemented in the construction firms.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

Spatial olfactory design in the library appears to be a practical approach to enhance the coordination between architectural spaces and user behaviors, shape immersive activity experiences and shape immersive activity experiences. Therefore, this study aims to explore the association between the olfactory elements of library space and users’ olfactory perception, providing a foundation for the practical design of olfactory space in libraries.

Using the olfactory perception semantic differential experiment method, this study collected feedback on the emotional experience of olfactory stimuli from 56 participants in an academic library. From the perspective of environmental psychology, the dimensions of pleasure, control and arousal of users’ olfactory perception in the academic library environment were semantically and emotionally described. In addition, the impact of fatigue state on users’ olfactory perception was analyzed through statistical methods to explore the impact path of individual physical differences on olfactory perception.

It was found that users’ olfactory perception in the academic library environment is likely semantically described from the dimensions of pleasure, arousal and control. These dimensions mutually influence users’ satisfaction with olfactory elements. Moreover, there is a close correlation between pleasure and satisfaction. In addition, fatigue states may impact users’ olfactory perception. Furthermore, users in a high-fatigue state may be more sensitive to the arousal of olfactory perception.

This article is an empirical exploration of users’ perception of the environmental odors in libraries. The experimental results of this paper may have practical implications for the construction of olfactory space in academic libraries.

The aim of this paper is to better understand European consumers' behaviour in relation to Short Food Supply Chains (SFSCs), so as to provide insights to support their development as part of a sustainable food system. Specifically, it aims to analyse consumer purchase patterns, motivations and perceived barriers and to identify patterns of behaviour amongst different consumer groups.

An online consumer survey was conducted in 12 European countries (n = 2,419). Quantitative data analysis, including principal component analysis (PCA) and cluster analysis, was undertaken using SPSS.

Four consumer clusters are named according to their behavioural stage in terms of SFSC engagement: Unaware Unengaged, Aware Unengaged, Motivationally Engaged and Executively Engaged. Unaware Unengaged and Aware Unengaged are in the non-engagement phase of behaviour. Motivationally Engaged are motivationally activated to engage in the behaviour but fail to do so consistently. Executively Engaged is the fully engaged cluster, being motivated to act and purchasing local food on a frequent basis. The results show an interesting interplay between motivations and barriers, i.e. higher scores for motivations and lower scores for barriers do not necessarily translate into higher purchase frequency.

The research gleans insights into the contextual factors that may inhibit SFSC purchases in different consumer segments. It offers practical implications for policymakers and others seeking to develop SFSCs as part of a sustainable food system.