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Contemporary construction techniques provide benefits of speed and cost savings on a large scale, and is viable in urban regions with exorbitant housing demand. In rural areas, where scale and access to technology are unavailable, locally prevalent vernacular architecture and methods are more suitable. Although vernacular construction techniques have historically proven more sustainable and climate-sensitive, the lack of skilled labour and lack of versatility in material selection limits its application on large-scale projects. This study explores the choice of building design and technology, from the context of embodied energy, carbon and other life cycle impacts for housing construction.

Life cycle assessment (LCA) that evaluates impacts due to the products/processes is used to analyse different construction techniques. Further a detailed estimation of embodied carbon and embodied energy is done for both “vernacular” and “contemporary” choices of construction methodology for a case study project.

The building constructed using vernacular techniques has lower embodied carbon and energy by over 30% compared to the other clusters designed using contemporary confined masonry techniques. However, with a few external interventions the contemporary methods can be implemented with improved sustainability.

The limitation of the study is that it presents a case study-based exploration into comparing construction techniques to provide a practical understanding of making sustainable design choices and, hence, is limited to two construction methods. However, the same method could be extended to compare other construction techniques. Furthermore, it does not present a whole building LCA since the operating phase impacts are assumed to be fairly constant for such housing type, irrespective of the chosen method. Similarly, the demolition phase or the potential of reuse of the waste generated, water consumption and cultural and social heritage are not investigated in comparing the alternatives. Nevertheless, future studies could perform extensive exploratory and modelling studies on the operation phase and demolition phase to understand these impacts further.

In mass housing projects that belong to the so-called “affordable housing” or low-income housing category, sustainability concerns are not yet at the forefront of the decision-making process. Therefore, this study emphasizes the importance of incorporating sustainability into building design and construction and making sustainability accessible to even low-income communities. Adequate planning, social awareness initiatives and imparting skills and knowledge of sustainability to these communities are of utmost importance. The choice of design and materials should be encouraged by keeping in mind lower upfront costs as well as low maintenance and operational costs.

The primary implications of the study are that the vernacular technologies are much superior in terms of sustainability in comparison to conventional construction of RCC framed structures as well as contemporary construction methods such as confined masonry. However, the implementation of such techniques presents significant challenges such as a lack of skilled forces, increased maintenance and lack of flexibility to minor modifications. Hence, although being a sustainable choice its acceptance and execution present practical difficulties. Therefore, this study primarily aims to reinforce the belief in vernacular architecture and techniques to build sustainable and resilient communities while highlighting the challenges of the modern world in implementing them.

Most studies advocate using construction methods based on their ease of implementation, maintenance or cost. However, this study highlights the importance of considering the aspect of sustainability in the context of the choice of methods for housing construction in urban and semi-urban areas. This study also addresses the need not to overlook vernacular construction technologies while selecting technology for housing for low-income communities.

Companies seek to increase the percentage of acquisitions in different parts of the world by expanding operations. Many companies are adopting strategic mergers to expand their influence. However, most strategic change programs fail to achieve their objectives. This study aims to investigate employees’ reactions after strategic mergers through the mediating role of the employees’ psychological context. It was necessary to identify the most prominent postmerger employees’ behaviors. The study addressed this gap by investigating the outcomes of strategic mergers.

Data for this study were collected from 30 family businesses. Accordingly, 341 questionnaires were collected with an overall response rate of 64%. The structural equation modeling (PLS-SEM) approach and the nonlinear relationships approach were adopted by implementing artificial neural network (ANN) analysis.

The results confirm that there is a clear impact of strategic mergers on employees’ postmerger behavior because of the change at the hierarchical level and the process of distributing roles. Employees’ psychological context (individual incentives, anxiety and individual mobbing) mediates the relationship between strategic mergers and postmerger employees’ behavior. In addition, individual incentives are considered the main contributor to retaining or not retaining employees in family businesses after strategic merger.

Policymakers in organizations must pay attention to employees’ possible reactions to the internal and external policies of the organization by increasing individual incentives and reducing individual mobbing toward strategic merger. This study has theoretical implications that are critical guidelines for academics in mitigating the negative consequences for employees’ postmerger behavior. This study captured linear and nonlinear relationships to discover the determinants and antecedents of a strategic merger in family businesses. However, future studies should focus on using more robust statistical methods by adopting decision-making methods to determine the best and worst companies in terms of adopting strategic mergers.

The scarcity of literature on the most important determinants of postmerger employees’ behavior is considered an encouragement to conduct the current study. To this end, this study enriches the ongoing and future literature by examining the most important factors influencing the strategic merger of family businesses. Family businesses have changed the economic landscape of many countries. The investigation of the strategic merger of these companies is considered a worthy matter of study to improve the nation’s economy.

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.

Developed countries control pandemics using smart decisions and processes based on medical standards and modern technologies. Studies on risk-reduction and humantechnology interaction are scarce. This study developed a model to examine the relationship between citizens, pandemic-related technology and official safety practices.

This study investigated the mediating role of new health regulations and moderating role of safety incentives due to COVID-19 case reduction in pandemic severity control. This study included 407 operations managers, nursing staff conducting pandemic testing and reporting, doctors and security personnel in China. An artificial neural network (ANN) was used to check nonlinear regressions and model predictability.

The results demonstrated the impact of the introduction of new technology protocols on the implementation of new health regulations and aided pandemic severity control. The safety incentive of case reductions moderated the relationship between new health regulations and pandemic severity control. New health regulations mediated the relationship between the introduction of new technology protocols and pandemic severity control.

Further research should be conducted on pandemic severity in diversely populated cities, particularly those that require safety measures and controls. Future studies should focus on cloud computing for nurses, busy campuses and communal living spaces.

Authorities should involve citizens in pandemic-related technical advances to reduce local viral transmission and infection. New health regulations improved people's interactions with new technological protocols and understanding of pandemic severity. Pandemic management authorities should work with medical and security employees.

This study is the first to demonstrate that a safety framework with technology-oriented techniques could reduce future pandemics using managerial initiatives.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

In recent years, studies have shown that machine learning significantly improves student performance and retention and reduces the risk of student dropout and withdrawal. However, there is a lack of empirical research reviews focusing on the application of machine learning to predict student performance in terms of learning engagement and self-efficacy and exploring their relationships. Hence, this paper conducts a systematic research review on the application of machine learning in higher education from an empirical research perspective.

This systematic review examines the application of machine learning (ML) in higher education, focusing on predicting student performance, engagement and self-efficacy. The review covers empirical studies from 2016 to 2024, utilizing a PRISMA framework to select 67 relevant articles from major databases.

The findings show that ML applications are widely researched and published in high-impact journals. The primary functions of ML in these studies include performance prediction, engagement analysis and self-efficacy assessment, employing various ML algorithms such as decision trees, random forests, support vector machines and neural networks. Ensemble learning algorithms generally outperform single algorithms regarding accuracy and other evaluation metrics. Common model evaluation metrics include accuracy, F1 score, recall and precision, with newer methods also being explored.

First, empirical research literature was selected from only four renowned electronic journal databases, and the literature was limited to journal articles, with the latest review literature and conference papers published in the form of conference papers also excluded, which led to empirical research not obtaining the latest views of researchers in interdisciplinary fields. Second, this review focused mainly on the analysis of student grade prediction, learning engagement and self-efficacy and did not study students’ risk, dropout rates, retention rates or learning behaviors, which limited the scope of the literature review and the application field of machine learning algorithms. Finally, this article only conducted a systematic review of the application of machine learning algorithms in higher education and did not establish a metadata list or carry out metadata analysis.

The review highlights ML’s potential to enhance personalized education, early intervention and identifying at-risk students. Future research should improve prediction accuracy, explore new algorithms and address current study limitations, particularly the narrow focus on specific outcomes and lack of interdisciplinary perspectives.

This paper focuses on knowledge sharing in health care. The aim of the paper is to further understand how digital applications can facilitate knowledge sharing between different care providers and health-care professionals.

The paper is based on a qualitative action case study, performed as a formative intervention study as a Change Laboratory, where a digital application concerning wound support was used. The Change Laboratory was used for knowledge sharing in the assessment and treatment process of wounds. The collected data was then thematically analyzed.

The findings show how digital applications can facilitate knowledge sharing, but also the need for complementary collaborative sessions. The main contribution is the rich description of how digital applications together with these sessions can facilitate knowledge sharing.

This paper shows that activities as collaborative sessions performed on the organizational level prove to support knowledge sharing and learning when a new digital application has been implemented in the work process. It also shows that these sessions contributed to identifying new knowledge that has potential for being included in the application and hence are important to keeping the application updated and relevant over time.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.