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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.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

Despite the vast potential of mobile phone use, grape smallholder farmers’ satisfaction with mobile phone use has attracted insufficient attention among scholars in Tanzania. The study examined factors influencing satisfaction with mobile phone use for accessing agricultural marketing information.

The study used a cross-sectional research design and a mixed research method. Structured questionnaire and focus group discussions were used to collect primary data from 400 sampled grape smallholder farmers. Data were analysed inferentially involving two-way analysis of variance, ordinal logistic regression and thematic analysis.

The findings indicate a statistically significant disparity in grape smallholder farmers’ satisfaction across different types of agricultural marketing information. Grape smallholder farmers exhibited higher satisfaction levels concerning information on selling time compared to all other types of agricultural marketing information (price, buyers, quality and quantity). Factors influencing grape smallholder farmers’ satisfaction with mobile phone use were related to perceived usefulness, ease of use, experience and cost.

This study contributes to scientific knowledge by providing actionable insights for formulating unique strategies for smallholder farmers’ satisfaction with agricultural marketing information.

The purpose of this paper is to evaluate the insights of founding entrepreneurs to understand what they consider as motivating factors in their decision to act upon entrepreneurial intentions. Using this information, the entrepreneurial trigger event influence was conceptualized, and a scale developed for use in subsequent testable models.

Qualitative and quantitative techniques were used to construct an instrument that measures the presence and influence of entrepreneurial behavior triggers. The concept of triggering events was explored with 14 founding entrepreneurs. Themes emerged from this enquiry process which informed the development of four primary entrepreneurial triggering events. Over 600 entrepreneurs participated in the study. Exploratory factor analysis was used to identify dimensions of entrepreneurial triggers and was tested using confirmatory factor analysis.

Entrepreneurs perceive that personal fulfillment and job dissatisfaction serve as two significant trigger events which will lead individuals to engage in entrepreneurial behaviors. This research supports theorizing that suggests entrepreneurial trigger events have influence in motivating individuals to act upon entrepreneurial intentions and some trigger events may have more influence toward behavior than others.

This research is subject to multiple limitations. Trigger events were limited to those identified in literature and the interviews. Most entrepreneurs participating in this study were from a limited geographic region. The entrepreneurs in this study reported their triggering event based on their memory which could have been affected by inaccurate recall or memory bias. No attempt has been made to model the comparative effects of the different variables on entrepreneurial outcomes. Finally, the entrepreneurial trigger event instrument did not measure the participant's demographics or psychographics which could have played a role in the influence of reported trigger event.

This study extends previous research that trigger events serve as catalysts for entrepreneurial behavior. Findings support the premise that different types of triggers have different levels of influence as antecedents of entrepreneurial behavior. Specifically, positive, negative, internal and external entrepreneurial triggering events were explicated. The Entrepreneurial Trigger Event Scale created to facilitate this study enables researchers to explore the effects of types and perceived influences of precipitating trigger events on the intentions of the individual that result in entrepreneurial behavior. The optimized instrument further expanded Shapero's (1975) proposed theory of the origins of entrepreneurial behavior.

The development of a scale provides researchers with the opportunity to include the influence of entrepreneurial trigger events, as perceived by entrepreneurs, in future testable models. Entrepreneurial development organizations can use the knowledge to assist in understanding when potential entrepreneurs may act upon entrepreneurial intentions. Information gained can have significant implications for understanding the initiation of entrepreneurial behavior, entity establishment and business growth.

This research responds to a call for investigation into the influence of entrepreneurial trigger events on a person's decision to act upon entrepreneurial intentions. It is an early attempt to conceptualize a relevant construct of entrepreneurial trigger event influence and to develop a scale for use in empirical testing. It is distinguished by using planned behaviors, push and pull, motivation and drive reduction theories. These theories are applied to the perceptions of successful entrepreneurs to develop a construct and validate it.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

This study delves into the aspirations of young individuals to assume leadership roles in their family businesses. It assesses the impact of family embeddedness and the perception of positive family business performance on succession intentions and investigates potential gender differences in this context.

Hierarchical multiple regression was determined for utilizing a sample of university students in seven countries from the Middle East–North African (MENA) region (N = 3,908).

The present study’s findings suggest that embeddedness in the family business has a much stronger role in shaping the succession intentions than previously envisioned. Females are more inclined to take over the family business when they perceive that the family business is not performing well.

This study provides important insights into the dynamic of family business succession intentions and family embeddedness. By providing a better understanding of some of the key drivers of family business succession intentions, it enables families in the MENA region to develop better family plans to engage with their successors effectively.

This paper highlights the need for studies that explore the experiences of entrepreneurs based in a family business setting who are considering retirement.

This paper is based on a concise review of the literature.

Retirement planning and subsequent succession management is a significant event in a family business and is an issue that requires research that captures and interprets the perspectives of the different stakeholders.

This paper synthesizes existing research on retirement in an entrepreneurial context, with an emphasis on family business and proposes three key areas of research.

This paper aims to explore the factors affecting cybersecurity implementation in organizations in various countries and develop a cybersecurity framework to improve cybersecurity practices within organizations for cybersecurity risk management through a systematic literature review (SLR) approach.

This SLR adhered to RepOrting Standards for Systematics Evidence Syntheses (ROSES) publication standards and used various research approaches. The study’s article selection process involved using Scopus, one of the most important scientific databases, to review articles published between 2014 and 2023.

This review identified the four main themes: individual factors, organizational factors, technological factors and governmental role. In addition, nine subthemes that relate to these primary topics were established.

This research sheds light on the multifaceted nature of cybersecurity by exploring factors influencing implementation and developing an improvement framework, offering valuable insights for researchers to advance theoretical developments, assisting industry practitioners in tailoring cybersecurity strategies to their needs and providing policymakers with a basis for creating more effective cybersecurity regulations and standards.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

In this work, the authors analyze the dynamic interdependencies between military expenditures and the real economy for the period 1970–2018, and the authors' approach allows for the existence of dominant economies in the system.

In this study, the authors employ a Network General Equilibrium GVAR (global vector autoregressive) model.

By accounting for the interconnection among the top twelve military spenders, the authors' findings show that China acts as a leader in the global military scene based on the respective centrality measures. Meanwhile, statistically significant deviations from equilibrium are observed in most of the economies' military expenses, when subjected to an unanticipated unit shock of other countries. Nonetheless, in the medium run, the shocks tend to die out and economies converge to an equilibrium position.

With the authors' methodology the authors are able to capture not only the effect of nearness on a country's military spending, as the past literature has documented, but also a country's defense and economic dependencies with other countries and how a unit's military expenses could shape the spending of the rest. Using state-to-the-art quantitative and econometric techniques, the authors provide robust and comprehensive analysis.