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The study aims to identify the areas of flood susceptibility and to categorize the Gangarampur sub-division into various flood susceptibility zones. It also aspires to evaluate the efficacy of integrating Geographic Information Systems (GIS) with Artificial Neural Networks (ANN) for flood susceptibility analysis.

The factors contributing to floods such as rainfall, geomorphology, geo-hazard, elevation, stream density, land use and land cover, slope, distance from roads, Normalized Difference Water Index (NDWI) and distance from rivers were analyzed for flood susceptibility analysis. The use of the ANN model helps to construct the flood susceptibility map of the study area. For validating the outcome, the Receiver Operating Characteristic (ROC) is employed.

The results indicated that proximity to rivers, rainfall deviation, land use and land cover are the most significant factors influencing flood occurrence in the study area. The ANN model demonstrated a prediction accuracy of 85%, validating its effectiveness for flood susceptibility analysis.

The research offers a novel approach by integrating Geographic Information Systems (GIS) with Artificial Neural Networks (ANN) for flood susceptibility analysis in the Gangarampur sub-division. By identifying key factors such as proximity to rivers, rainfall deviation and land use, the study achieves 85% prediction accuracy, showing the effectiveness of ANN in flood risk mapping. These findings provide critical insights for planners to devise targeted flood mitigation strategies.

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.

Purpose: In Greece, large companies have started to focus more and more on corporate social responsibility (CSR) and ESG (environmental, social, and governance) activities, realising the importance of sustainability and social responsibility beyond traditional profits. Using machine-learning (ML) methods and artificial neural networks (ANNs) can enhance the process of measuring performance in these areas in several ways, including data analytics. This paper investigates and explores the correlation between CSR and ESG actions with financial and non-financial factors for the 100 largest companies operating in Greece.

Methodology: The study runs from January 2019 until December 2021, and ANNs and ML techniques are employed. The comparison concerns both the control variables and the predictability of the methods.

Findings: The main findings that emerged are the confirmation of the correlation between CSR and ESG actions and the financial performance and determinants of corporate responsibility of the companies in the sample. Moreover, good results were obtained for almost all of the techniques examined, but the superiority of deep learning models and gradient-boosted trees (GBTs) was found for the selected variables.

Significance/Implications/Conclusions: The findings suggest that using ML techniques and neural networks to measure CSR actions can help companies evaluate their performance and make effective decisions to improve their sustainability. It can also be a valuable tool for institutional investors, banks, and regulators.

Future Research: We believe that future research should focus on improving these models, exploring hybrid approaches that combine the strengths of different techniques, and expanding the range of variables considered.

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.

The Metaverse holds vast amounts of user data, making it essential to address threats to its confidentiality, integrity and availability. These threats are not purely technological, as user actions and perceptions, shaped by psychological factors, can influence cybersecurity challenges. Thus, a holistic approach incorporating technological and psychological dimensions is crucial for safeguarding data security and privacy. This research explores users’ cybersecurity behavior in the Metaverse by integrating the technology threat avoidance theory (TTAT) and the theory of planned behavior (TPB).

The model was assessed using data collected from 746 Metaverse users. The empirical data were analyzed using a dual structural equation modeling-artificial neural network (SEM-ANN) approach.

The main PLS-SEM findings indicated that cybersecurity behavior is significantly affected by attitude, perceived behavioral control, subjective norms, perceived threat and avoidance motivation. The ANN results showed that perceived threat with a normalized importance of 100% is the most significant factor influencing cybersecurity behavior. The ANN results also showed that perceived severity with a normalized importance of 98.79% significantly impacts perceived threat.

The novelty of this research stems from developing a unified model grounded in TTAT and TPB to understand cybersecurity behaviors in the Metaverse. Unlike previous Metaverse studies that solely focused on measuring behavioral intentions or user behaviors, this study takes a step further by evaluating users’ cybersecurity behaviors. Alongside its theoretical insights, the study offers practical recommendations for software developers, decision-makers and service providers.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

The article serves to both introduce the special edition of Qualitative Research Journal (QRJ) and explain the purpose of the Foundation of Learning and Literacy (FFLL) Touchstones as principles that should inform language and literacy policy development, leadership in the field and classroom literacy practices. It particularly focuses on Touchstone 1 and the importance of fairness and equity for all literacy learners. It draws on a range of research that articulates ways that inequality of opportunity can be addressed.

This article both introduces the FFLL for the Special Edition of QRJ and examines the first Touchstone or guiding, overarching principle that led to the establishment of the FFLL and the 11 Touchstones that are discussed in subsequent articles. In essence, this article addresses the importance of fairness and equity for all children and young people as they develop deep literacy. The article begins with a brief contextual background explaining how and why FFLL was formed. It then highlights the first Touchstone.

The article demonstrates the need to support all learners as they strive to be deeply literate so they can become active and compassionate members of their communities. Based on a range of research evidence, it suggests ways to make sure that all children and young people can become successful literacy learners. These include homes with books that learners can self-select, sharing stories, substantive conversations, the provision of quality literary texts and rich pre-school experiences.

Practical classroom implications arising from the research are discussed.

The article is a brief introduction to the FFLL and a synthesis of some of the research that underpins the first Touchstone.

The study aims to investigate factors impacting consumers’ intention to adopt collaborative consumption (CC, hereafter) services. It extends the existing knowledge of CC services by testing and validating the moderating role of coercive power and the mediating role of attitude.

Applying random sampling, the data was collected through the mall intercept method across four cities in India and was analyzed using partial least square-structural equation modeling and artificial neural network to test the proposed hypotheses. A follow-up qualitative study consisting of 15 in-depth structured interviews helped validate the results of the quantitative research.

Results of the conducted survey revealed that a sense of community, reason-based trust and enjoyment positively impact consumers’ attitudes toward CC services. Attitude positively impacts intention and shows a partial mediating influence on the relationship between motivations and intentions. Furthermore, the results revealed the moderation of the perceived coercive power of the service provider. The results of the follow-up qualitative study validated the results of the quantitative research.

Managers of CC firms must understand the relative importance of intrinsic and extrinsic motivations in formulating the attitudes of consumers and intentions toward CC services. CC managers should acknowledge the influence of the consumer’s perceived coercive power of service providers and consumer’s reason-based trust.

To the best of the authors knowledge, the current research is the first of its kind. It has justifiably and logically applied self-determination theory and a slippery slope framework in a single context. By testing the moderating impact of coercive power, the research extends the existing literature on CC and the applicability of coercive power in CC. The present study extends the knowledge regarding the consumer’s perception of reason-based trust and the perceived coercive power of service providers in CC.