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This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

The purpose of this paper is to explore the integration of business intelligence (BI) systems in the digital transformation context and its impact on management control (MC) and organizational performance by delving into the implementation of a BI pilot project in a multinational Engineering and Construction (E&C) group.

This research adopts a qualitative insider action research approach. The initial stage of a BI system (BIS) is implemented by leveraging the critical success factors (CSFs) framework, based on the organizational, process and technological dimensions.

The results show that the BI solution leverages the capabilities of the reporting process with impacts on MC and overall organizational performance. The study uncovers the intricate dynamics of integrating BI and reports gains that span across streamlined and standardized processes, optimized use of resources, improved data flexibility and nurturing a data-driven culture.

This research adds to the current literature on MC and accounting information systems by providing an in-depth analysis of the early stages involved in implementing a BIS. The approach uses a well-established CSFs framework, which enhances the comprehension of how these factors impact the success of the implementation process. Focusing on the E&C sector, where the adoption of digital transformation is still gaining traction, this study provides practical knowledge and extends the theoretical understanding of BI technology applications with empirical evidence from the accounting field.

This study investigates the factors that lead to the adoption of blockchain technology through payment transactions and how this not only affects real estate (RE) and blockchain transparency but also RE performance.

Data gathered across RE firms in the United Arab Emirates (UAE) were employed to test the model. The measurement model and structural equation modeling (SEM) were used to test the items and the hypotheses illustrated in the proposed model.

Perceived financial benefits, competitive pressure and top manager support were demonstrated to successfully influence blockchain adoption (BA). Despite blockchain’s early stages of development, its impact on RE operations cannot be ignored and should be more objectively examined in order to gain a better understanding of it. UAE blockchain-based companies could be seen as having a competitive advantage that maximizes resource consumption.

This study introduces the positive influence of blockchain technology on RE payment transactions and may advance information on how blockchain technology has the potential to change the RE sector. The paper finds its significance in exploring how RE payment systems must change to remain competitive in the market amid emerging digitalization trends.

As the global community increasingly directs its attention towards sustainable urban development, integrating artificial intelligence (AI) into circular economy (CE) management within smart cities has become a potent strategy. This study aims to examine the potential influence of AI-based technologies on optimizing resources and minimizing waste, which constitute critical components of the principles underpinning the CE. The focus is mainly on applying these technologies within smart city environments. Artificial Intelligence can significantly enhance the processes of gathering, analyzing and decision-making by integrating internet of things (IoT) sensors, data analytics, machine learning algorithms and predictive analytics. This chapter explores the potential of AI in predicting trends, optimizing circular supply chains, improving waste management and recycling practices, facilitating sustainable product design, fostering citizen engagement and aiding policy development. The current research presents a comprehensive examination of the interrelated connection between the principles of CE and the advanced technology of AI. Doing so contributes significantly to our holistic comprehension of how these advancements might collectively influence the development of a more sustainable and resilient future for urban populations.

Introduction: Investments in environmentally friendly initiatives can bolster infrastructure projects, agricultural methods, and water management systems that improve the ability to withstand climate-related difficulties. Green investments encompass endorsing carbon markets and financial instruments that incentivise reducing emissions. This research helps attain the climate objectives described in sustainable development goal 13 (SDG 13).

Purpose: This chapter aims to investigate the relationship between greenhouse gases (GHG) and gross domestic product (GDP), with the underlying objective of understanding the relevance of green investment for sustainable development.

Methodology: For the analysis, the top five countries: the USA, China, Germany, Japan, and India, were chosen based on the world’s largest economies in 2023, as per their GDP data. For testing the hypothesis, data from the World Bank database during the period 2002-2022 was retrieved and GDP is used as a dependent variable and GHG as an independent variable. For the study, panel data are used, and the Johansen cointegration test and ordinary least squares (OLS) regression are applied.

Findings: In the case of China and India, the null hypothesis has been rejected, which is depicted by the significant and high degree of relation between GHGs and the GDP of these two countries. The null hypothesis is also rejected for the USA and Germany, but it shows a significant and moderate degree of relationship between GHG and GDP. For Japan, the null hypothesis is accepted and reflects a negative relationship between GHG and GDP.

Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics.

The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management.

The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes.

The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

Soft actuators using pneumatic-chamber (PneuNet)-based designs have been of interest in the area of soft robotics with scope of application in the area of biomedical assistance and smart agriculture. Researchers have attempted to investigate multiple chambers in parallel to examine their deformation characteristics. However, there is a lacuna for investigation of the deformation characteristics of four parallel chambered soft actuators. The purpose of this study is to comprehensively investigate the different possible actuation scenarios and the resulting bending/deformation behaviours.

Therefore, in this study, a four-chambered PneuNet actuator is numerically investigated to evaluate the effects of pressurization scenarios and pressure levels on its performance, operating reaching and working volume.

The results of this study revealed that two-adjacent chamber equal pressurization and three-chamber pressurizations result in increased bending. However, two-opposite chamber pressurization reduces the bending angle with pressure levels in the lower pressure chamber. The maximum bending angle of 97° was achieved for single-chamber pressurization of 300 kPa. The two-adjacent chamber unequal pressurization can achieve a sweeping motion in the actuator along with bending. The working volume and reaching capability analysis revealed that the actuator can reach around 71% of the dimensional operating space.

The results provide fundamental guidance on the output nature of motion which can be obtained under different pressurization scenarios using the four-chambered design soft actuator, thereby making it a practical guide for implementation for useful applications.

The comprehensive pressurization scenarios and pressure level variations reported in this study will serve as fundamental operating guidelines for any practical implementation of the four-chambered PneuNet actuator.

Cybersecurity has received growing attention from academic researchers and industry practitioners as a strategy to accelerate performance gains and social sustainability. Meanwhile, firms are usually prone to cyber-risks that emanate from their supply chain partners especially third-party logistics providers (3PLs). Thus, it is crucial to implement cyber-risks management in 3PLs to achieve social sustainability in supply chains. However, these 3PLs are faced with critical difficulties which tend to hamper the consistent growth of cybersecurity. This paper aims to analyze these critical difficulties.

Data were sourced from 40 managers in Nigerian 3PLs with the aid of questionnaires. A novel quantitative methodology based on the synergetic combination of interval-valued neutrosophic analytic hierarchy process (IVN-AHP) and multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA) is applied. Sensitivity analysis and comparative analysis with other decision models were conducted.

Barriers were identified from published literature, finalized using experts’ inputs and classified under organizational, institutional and human (cultural values) dimensions. The results highlight the most critical dimension as human followed by organizational and institutional. Also, the results pinpointed indigenous beliefs (e.g. cyber-crime spiritualism), poor humane orientation, unavailable specific tools for managing cyber-risks and skilled workforce shortage as the most critical barriers that show the highest potential to elicit other barriers.

By illustrating the most significant barriers, this study will assist policy makers and industry practitioners in developing strategies in a coordinated and sequential manner to overcome these barriers and thus, achieve socially sustainable supply chains.

This research pioneers the use of IVN-AHP-MULTIMOORA to analyze cyber-risks management barriers in 3PLs for supply chain social sustainability in a developing nation.

The aim of the present study was to identify the pattern of intergenerational relations in interdependent society, examine the role of family functioning on it and the role of demographic variables (location, economic class, and familial role) on intergenerational relations, and study the processes of different types of intergenerational relations (solidarity, ambivalence, and conflict). India being an interdependent society emphasizes the family bonds where more than one generation cohabits together. Thus, exploring the intergenerational ties and family dynamics becomes an important factor to examine. The present study anchored in Kagitcibasi’s Family Change Theory investigates intergenerational relationships in India, examining the shift from joint to modified extended families. With 720 participants in urban and rural areas, the research explores cultural attitudes, socioeconomic variables, and coping strategies and intergenerational dynamics within families. Findings reveal prevalent solidarity, ambivalence, and affection, with rural–urban distinctions. The study underscores the importance of understanding intergenerational dynamics, providing insights for scholars, policymakers, and social workers to enhance support networks. The research contributes valuable knowledge to address evolving family needs in the context of changing societal norms and economic factors.