Effat University

Abstract This chapter investigates the intersection of artificial intelligence (AI) and neuroarchitecture, focusing on how AI can enhance architectural design by integrating neuroscientific insights. This chapter highlights, utilizing a theoretical research approach, the importance of understanding how the human brain and nervous system process and interact with the built environment, impacting each other. Additionally, it discusses the significance and challenges facing the field of neuroarchitecture and presents AI applications that can advance and streamline various aspects of this field. This chapter’s main objective is to develop a conceptual framework for a knowledge-based system (KBS) that incorporates AI to facilitate the application of neuroscience principles in design. The AI tool as a research and decision-support system aims to help architects and students make informed decisions that optimize user experience and well-being. The KBS consists of three main components: inputs, system functions, and outputs, while considering critical criteria within its scope, including the use of the architectural space, the occupants’ age groups, and their functional abilities. The system inference engine uses designers’ inputs to derive conclusions, generating design recommendations based on neuroscience principles as outputs, categorized into space distribution and context, light and shadows, shapes and forms, colors and materiality, height, width, and enclosure. In conclusion, this chapter acknowledges and outlines the framework’s limitations and suggests future improvements, such as integrating user-friendly interfaces and expanding the framework’s scope to include additional factors like gender within the input categories to yield more detailed results. Future research could focus on designing the tool and testing its effectiveness.

Abstract The COVID-19 pandemic has altered a number of facets of our daily life, including how we utilize and interact with our dwellings. The epidemic has created new difficulties and opportunities for reconsidering residential space design in Egypt, where a sizable section of the population lives in urban areas with little access to outside public spaces. This chapter explores how the epidemic has affected Egypt’s residential areas with the goal of identifying design approaches that might improve inhabitants’ quality of life and well-being. This chapter adopts a quantitative and qualitative approach, a literature review, and an online survey of locals to examine the pandemic’s effects. According to this chapter, the pandemic has changed how people use their homes, placing a greater emphasis on leisure time, online learning, and remote work. This chapter also highlights the importance of having access to outdoor spaces, natural light, and ventilation in order to raise the standard of living spaces. In projecting future trends, this chapter anticipates a paradigm shift toward eco-conscious and technologically integrated architecture. Green roofs, sustainable materials, and energy-efficient designs emerge as essential elements, promoting environmental sustainability and reducing the ecological footprint of residential spaces. Moreover, integrating smart home technologies, augmented reality interfaces, and advanced security systems envisions a futuristic living experience, where architecture seamlessly interfaces with digital innovations to enhance comfort, convenience, and safety. Based on these findings, this chapter proposes design strategies that can be applied to new and existing residential spaces in Egypt to enhance their functionality and improve residents’ quality of life.

The Bullwhip Effect (BWE) occurs when orders made to suppliers have a bigger variation than sales to the buyer. This is a major concern that businesses are working on eliminating, as it has numerous side effects, such as excessive inventory, stock-outs, insufficient production, increased costs, etc. The new study adds to the literature by demonstrating how to quantify and assess the bullwhip impact on any supply chain. The results show that when consumer demand is unstable, the BWE is magnified. This was achieved by using the proposed formula which was based on an explanation and graph of the traditional BWE. A stochastic simulation based on a case study that replicates the behavior of a generic supply chain in a real-world market was used to evaluate the formula.

The present study has examined the use of an AI-based supply chain management system (AISCM) and its positive impact on international business performance (BP). Herein, a sequential mediation analysis has been performed using SmartPLS-SEM 4.0, with the constructs of Unique Competitive Advantage (UCA) and Supply Chain Performance serving as mediating variables in the study framework. The research has been conducted on a population sample of operations managers and supervisors working in international firms in Pakistan who oversee supply chain operations or strategic planning of international business. A non-probability, purposive sampling technique was employed to recruit study respondents, and a sample size of 205 was selected for statistical analysis using SmartPLS. The study's outcomes are significant for both theory and practice. The study has extended resource-based theory by employing AISCM in the study framework. Asia-based global firms can gain substantially from the findings of this study. For future research endeavors, it is recommended that moderating variables be identified among the relationships between study variables. The research framework may also be checked in GCC countries for a regional perspective and generalizability.

Purpose This study uncovers three critical relationships in banking governance: how board-specific skills (BSS) influence board member compensation (BMC), how BMC affects nonperforming loans (NPLs) and whether BSS moderates the relationship between BMC and NPLs. Design/methodology/approach Examining 483 banks across Asia (259) and Europe (224) from 2011 to 2022, we reveal intriguing regional differences by conducting a two-step system generalized method of moment (SGMM) as an econometric approach. Findings Empirical findings provide strong evidence that the relationship between BSS, BMC and NPLs is not straightforward; it varies significantly by region and the skill level of board members. First, results reveal that an increase in BSS is associated with a decrease in BMC for the overall sample and, more specifically, Asian banks. In contrast, BSS is associated with increased board remuneration for European banks. Second, we found that BMC significantly raises nonperforming loans (NPLs). Finally, we confirm that BSS moderates the relationship between BMC and NPLs. Originality/value To the authors’ knowledge, only a few studies have explored the combined effects of board-specific skills, board remuneration and nonperforming loans. Additionally, we examine how BSS moderates the relationship between board remuneration and NPLs at different levels of specific skills.

Solar photovoltaic (PV) systems are installed worldwide to promote green energy and mitigate climate change. In cold and snowy regions, however, snow accumulation on PV surfaces blocks sunlight and halts power generation until the snow melts or is manually removed. This phenomenon not only decreases energy yield but also complicates grid stability during winter seasons. Therefore, timely and accurate detection of snow-covered PV panels is crucial to ensure continuous and reliable energy production. This study presents a simple yet novel approach using an isolated Generative Adversarial Network (GAN) discriminator to classify PV panels as fully exposed, partially covered, or completely covered by snow. GAN-based discriminators are advantageous because they can learn high-level spatial and textural features directly from images without extensive preprocessing or handcrafted filters. The proposed model was validated on a publicly available University of Waterloo dataset and trained using the Adam optimizer with a learning rate of 0.001 and momentum of 0.9. It achieved 100% accuracy in both training and testing, with a 0% error rate. These results demonstrate a robust framework for automated PV health monitoring, real-time snow detection, and large-scale renewable energy optimization.

Family-owned SMEs play a vital role in emerging economies but often struggle to internationalize due to limited resources, emotional attachment to legacy, and governance constraints. While socioemotional wealth (SEW) significantly influences strategic decision-making, its role in shaping international performance (IP) remains underexplored. This study examines how SEW impacts IP through entrepreneurial bricolage (EB), with generational stage serving as a moderating factor, in the context of Pakistani family-owned Small and Medium Enterprises (SMEs). This focus is important because EB provides a mechanism for transforming family-centred emotional priorities into international competitiveness, addressing the strategic tensions family SMEs face in global growth. Drawing on the SEW theory, this research proposes and tests a conceptual model that examines the partially mediating role of EB and the moderating influence of generational stage (founder, second, and third generations). Using data collected from 312 manufacturing family firms registered with the Pakistan Manufacturing & Export Association (PMEA), analysed through Smart PLS and multi-group analysis (MGA), the findings reveal that earlier-generation founders tend to prioritise ownership and identity over profitability, while later generations leverage established social and business capital to enhance IP. The results offer valuable insights not only in strengthening governance structures in family firms, but also in seeking to enhance them. By highlighting generational differences in entrepreneurial behavior and performance orientation, this study contributes to the broader discourse on innovation and sustainability in family business ecosystems worldwide.

The review examines the impact of structural literacy on learning the discipline of architecture at the undergraduate level, as well as its influence on design processes. To bridge the knowledge application gap between structural literacy and design application in architecture, this review synthesizes published work on learning through the application of structural literacy in design processes. It also considers learning outcome assessment, design effectiveness measurement, and new learning approaches. The review aimed to compare hands-on learning with traditional learning methods, measure teaching methods, identify new learning methods, measure the impact of structural literacy on design and learning outcomes, and observe collaborative practices across diverse disciplines. The review conducted a comprehensive survey of international research on undergraduate architecture education using mixed, qualitative, and quantitative methods. The results indicate that active, hands-on collaborative learning approaches are more effective than continuous lecture-based approaches in enhancing structural comprehension and design integration. Although financial and access constraints restrict large-scale applications, digital and physical modeling software enhances conceptual understanding and design exploration. Although institutional and curricular barriers limit its application, interdisciplinary working enhances communication skills and facilitates structural integration. Curriculum revisions that initiate structured subjects early, with design studio linkages, strengthen student motivation and design efficiency. These findings indicate the extent to which collaborative frameworks and integrated teaching impact the development of structural literacy. The review emphasizes the need for curriculum revisions and interdisciplinary instruction to equip architecture students with the skills necessary for practical, creative, and contextually aware design work.