Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

Purpose: This study empirically investigates the effect of big data analytics (BDA) on project success (PS). Additionally, in this study, the investigation includes an examination of how intellectual capital (IC) and (KS) act as mediators in the correlation between BDA and KS. Lastly, a connection between entrepreneurial leadership (EL) and BDA is also explored. Design/Methodology- Using a sample of 422 senior-level employees from the IT sector in Peru. The partial least squares structural equation modeling technique tested the hypothesized relationships. Findings- According to the findings, the relationship between BDA and PS is mediated by structural capital (SC) and relational capital (RC), and BDA demonstrates a positive and noteworthy correlation with PS. Furthermore, EL is positively associated with BDA in a significant manner. Practical implications- The finding of this study reinforce the corporate experience of BDA and suggest how senior levels of the IT sector can promote SC, RC, and EL. Originality/Value- This study is one of the first to consider big data analytics as an important antecedent of project success. With little or no research on the interrelationship of big data analytics, intellectual capital and knowledge sharing the study contributes by investigating the mediating role of intellectual capital and knowledge sharing on the relationship between big data analytics and project success.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

Consumers’ interest in green consumption has increased rapidly in recent years with heightening concerns for environmental, social, and health risks. However, increased concerns and interest of consumers may not translate to their behavioral outcome which may be attributed to socio-economic and consumers’ internal stimuli. Furthermore, contextual differences in the marketplace may influence how consumers form their green attitudes and behavior. The purpose of this study is to assess the role of consumers’ intrinsic traits such as consumers’ personal values, their self-motivation for sustainable consumption (i.e., perceived consumer effectiveness), green skepticism, and environmental involvement in their green attitude and behavior, and to see if the country-specific contextual condition may influence consumers’ behavior. In addition, price sensitivity and environmental protection emotions are considered moderating constructs to explain the gap between green attitude and green behavior. Findings from this study provide insights into understanding Chinese and Singaporean consumers’ green behavior which is driven by their intrinsic traits and by extrinsic conditions. This understanding can help companies to develop effective green marketing communication strategies and to enhance consumer engagement in sustainable activities and consumption.

The persistence of coastal ecosystems is jeopardized by deforestation, conversion, and climate change, despite their capacity to store more carbon than terrestrial vegetation. The study’s objectives were to investigate how spatiotemporal changes impacted blue carbon storage and sequestration in the Satkhira coastal region of Bangladesh over the past three decades and, additionally to assess the monetary consequences of changing blue carbon sequestration. For analyzing the landscape change (LSC) patterns of the last three decades, considering 1992, 2007, and 2022, the LSC transformations were evaluated in the research area. Landsat 5 of 1992 and 2007, and Landsat 8 OLI-TIRS multitemporal satellite images of 2022 were acquired and the Geographical Information System (GIS), Remote Sensing (RS) techniques were applied for spatiotemporal analysis, interpreting and mapping the output. The spatiotemporal dynamics of carbon storage and sequestration of 1992, 2007, and 2022 were evaluated by the InVEST carbon model based on the present research years. The significant finding demonstrated that anthropogenic activity diminished vegetation cover, vegetation land decreased by 7.73% over the last three decades, and agriculture land converted to mariculture. 21.74% of mariculture land increased over the last 30 years, and agriculture land decreased by 12.71%. From 1992 to 2022, this constant LSC transformation significantly changed carbon storage, which went from 11,706.12 Mega gram (Mg) to 9168.03 Mg. In the past 30 years, 2538.09 Mg of carbon has been emitted into the atmosphere, with a combined market worth of almost 0.86 million USD. The findings may guide policymakers in establishing a coastal management strategy that will be beneficial for carbon storage and sequestration to balance socioeconomic growth and preserve numerous environmental services.

The young Muslim generation’s embracing digital platforms for Zakat payments represents a dynamic fusion of enduring religious values with the modern digital landscape, heralding a new era in Islamic charitable practices. This trend illustrates a profound transformation within the Islamic world, where the pillars of faith are being reimagined and revitalized through the lens of technological advancement. The present study delved into the factors influencing the young Muslim generation’s preference for digital platforms in Zakat transactions across Indonesia and Malaysia. We examined variables such as Performance Expectancy, Effort Expectancy, Social Influence, Trust, Zakat Literacy, and Digital Infrastructure, aiming to discern their impact on the propensity for digital Zakat contributions with the extension of Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research encompassed a diverse sample of 382 participants and utilized advanced methodologies, specifically Partial Least Squares Structural Equation Modeling (PLS-SEM) and PLS Multi Group Analysis (PLS-MGA), for rigorous data analysis. The results indicated that Effort Expectancy, Social Influence, Digital Infrastructure, and Zakat Literacy notably influenced the use of digital platforms for Zakat. Furthermore, PLS-MGA uncovered significant cross-country differences where Digital Infrastructure showed a more pronounced positive impact in Malaysian context, whereas Social Influence had a greater effect in Indonesia. These findings offer critical insights into the young Muslim community’s digital engagement for religious financial obligations, underscoring the need for tailored digital Zakat solutions that cater to the unique preferences of this demographic. This research not only enriches the understanding of digital adoption in religious practices but also challenges the notion of a universal approach, advocating for context-specific strategies in the realm of digital religious financial services. Future researchers are suggested to consider longitudinal investigations as well as examining cross-regional contexts in this realm of research.