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The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

The main aim of this paper is to establish a reasonable and scientific evaluation index system to assess the high quality and full employment (HQaFE).

This paper uses a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria framework to evaluate the quality and quantity of employment, wherein the integrated weights of attributes are determined by the combined the Criteria Importance Through Inter-criteria Correlation (CRITIC) and entropy approaches.

Firstly, the gap in the Yangtze River Delta in employment quality is narrowing year by year; secondly, employment skills as well as employment supply and demand are the primary indicators that determine the HQaFE; finally, the evaluation scores are clearly hierarchical, in the order of Shanghai, Jiangsu, Zhejiang and Anhui.

A scientific and reasonable evaluation index system is constructed. A novel CRITIC-entropy-TOPSIS evaluation is proposed to make the results more objective. Some policy recommendations that can promote the achievement of HQaFE are proposed.

This paper aims to explore the interplay between industrial diversity and sustainable economic development in US counties.

Among other popularly used measures, this study uses an underused measure, Hachman index, to gauge the degree of industrial diversity in the models. To capture the impact of industrial diversity on the local community, this study estimates the relationship of two diversity measures to four traditional socioeconomic indicators: per capita personal income growth, gross domestic product per worker, income inequality ratio and poverty rate.

Statistical results suggest that industrial diversity, which is measured by Hachman index, is significantly related to the four socio-economic indicators. Industrial diversity can positively contribute to regional per capita personal income growth and mitigate income inequality and poverty stress; however, it is negatively related to the gross domestic product (GDP) per worker, which means industrial specialization may contribute to GDP per worker growth.

The findings of this study show that there is a nonlinear relationship between industrial diversity and all socioeconomic indicators. Most of the control variables, human capital variables and business and industry profile variables also display significant and positive impacts on economic development.

This study aims to empirically analyze the impact of technological innovation on the quantity and quality of employment in the hospitality industry.

Using the data of 30 provinces in China from 2010 to 2020, this paper makes an empirical analysis through the fixed effect model.

The results show that process innovation has a significant positive impact on employment quantity, while product innovation has a significant negative impact on employment quantity. The creative effect of process innovation and the substitution effect of product innovation offset each other, so in the long run, the impact of technological innovation on employment quantity is not significant. However, technological innovation has significantly improved the employment quality of the hospitality industry.

Because technological innovation has replaced part of the labor force, hospitality could guide the labor force in a positive direction. To promote innovation and retain talents, hotels should train employees’ digital thinking and attract high-skilled talents.

This research is unique in using process innovation and product innovation as the main measurement indicators of technological innovation, unlike previous studies that often relied on technological progress to conclude.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Building information modeling (BIM) is a growing technology and methodology for project design in the construction industry. However, when the project design team designs with BIM in a free-form manner (without a qualified instructor), it is not clear how behavior or trust might develop among project team members, nor if there are variables that might influence the improvement of such collaboration.

A sample of 92 responses was obtained from managers of project design firms in the architecture, engineering and construction (AEC) sector. The questionnaire data were analyzed using partial least square structural equation modeling (PLS-SEM).

This paper provides an explanation, from a happiness management perspective, to reflect on the importance of establishing policies to enhance effective communication between project team members in BIM design, as it improves trust between team members and their collaborators, developing the overall satisfaction of all the agents involved in the project.

The researchers suggest that there is a gap in the literature on how effective communication influences the implementation of BIM methodology.

This research investigates Airbnb’s financial implications in emerging economies and their potential to influence stock market profitability.

Employing a multifaceted approach, the study combines parametric and nonparametric tests, robustness checks, and regression analysis to assess the impact of Airbnb’s announcements on emerging economy stock markets.

Airbnb’s announcements affect emerging economies' stock markets with a distinct pattern of cumulative abnormal returns (CAR): negative before the announcement and positive afterward. Informed investors strategically leverage this opportunity through short selling before the announcement and acquiring positions following it. Regression analysis validates these trends, revealing that stock index returns and inbound tourism affect CAR before announcements, while GDP growth influences CAR afterward. Announcements pertaining to emerging economies exert a more pronounced impact on stock indices compared to city-specific announcements, with COVID-19 period announcements demonstrating greater significance in abnormal returns than non-COVID-19 period announcements.

This study advances existing literature through a comprehensive range of statistical tests, differentiation between emerging countries and cities, introduction of five macroeconomic variables, and reliance on credible primary Airbnb data. It highlights the potential for investors to leverage Airbnb announcements in emerging markets for stock market profits, emphasizing the need for adaptive investment strategies considering broader macroeconomic factors.

Safety professionals' primary job is to execute safety control measures towards frontline personnel, and previous studies focus on the effectiveness of such controls. Rare research efforts, however, have been devoted to the effectiveness of management control measures towards safety professionals themselves. This study aimed to fill up this knowledge gap by examining whether safety professionals under differing management control configurations differ in their work attitudes, including affective commitment, job satisfaction, career commitment and intention to quit.

Drawing on a holistic view of control, five forms of management control, i.e. outcome control, process control, capability control, professional control and reinforcement, were investigated. A cross-sectional questionnaire survey targeting at construction safety professionals was conducted. The latent profile analysis approach was employed to identify how the five forms of management control are configured, i.e. identifying the distinctive patterns of control profiles. The Bolck–Croon–Hagenaars method was then used to examine whether safety professionals' work attitudes were different across the identified control profiles.

Seven distinct control profiles were extracted from the sample of 475 construction safety professionals. The overall test of outcome means showed that mean levels of affective commitment, job satisfaction and intentions to quit were significantly different across the seven profiles. The largest that was also the most desirable subgroup was the high control profile (n = 161, 33.9%). The least desirable subgroups included the low control profile (n = 75, 15.8%) and the low capability and professional control profile (n = 12, 2.5%). Pairwise comparison suggested that capability, professional and process controls were more effective than outcome control and reinforcement.

In theory, this study contributes to the burgeoning literature on how to improve the effectiveness of control measures targeted at safety professionals. The results suggested that effective management controls involve a fine combination of formal, informal, process and output controls. In practice, this study uncovers the ways in which managers leverage the efforts of safety professionals in achieving safety goals. Particularly, it informs managers that the control configurations, instead of isolated controls, should be executed to motivate safety professionals.

This aim of this study is to identify the ways of helping public authorities bring about change to environmentally sustainable household food practices.

The authors identified the practices involved in this concept from the consumer perspective and measured their diffusion among French households. The analyses were conducted following two successive data collection campaigns comprising 571 and 501 respondents in France. The methodology involved two complementary scaling techniques: factor analysis and item response theory.

The results show that consumers understand sustainable food through five food practices: buying and cooking products with sustainable attributes, anti-waste storage, self-production, plant protein consumption and anti-waste cooking.

The findings suggest that while at the individual level people appear to have incorporated anti-waste practices into their daily lives, at the household level, there is still work to be done for improving diets and stimulating the production of home-grown food. It is also worth noting that the emerging vision typically involves sustainable foods that are organic, locally grown, seasonal, based on fair trade and packaging-free.