German University in Cairo

Abstract Federated Learning (FL) enables collaborative model training without exposing sensitive data, making it a cornerstone for privacy-aware AI. However, bringing FL from theory to practice in multi-tier architectures that contain hierarchical edge-fog-cloud systems remains difficult, challenged by security vulnerabilities, resource constraints, and system heterogeneity. This paper reviews secure and adaptive optimization techniques that address these challenges in hierarchical environments. We focus on three main areas: (1) security-enhanced FL, covering threat models, adversarial attacks, and defenses tailored to layered architectures; (2) adaptive optimization, including dynamic client selection, resource-aware aggregation, and context-sensitive privacy management; and (3) systems integration, examining architectural designs, communication protocols, and orchestration methods for scalable deployment. By synthesizing recent work, the survey highlights trade-offs between privacy, performance, and scalability, and proposes a taxonomy of current challenges and solutions. It also evaluates approaches to trustworthiness in FL, including fairness, accountability, and robustness, while considering practical issues such as regulation, benchmarking, and deployment constraints. The paper concludes with open research directions, emphasizing the need for secure, adaptive, and production-ready FL systems.

Traditional heritage risk assessments rely on manual surveys and field inspections, which are often time-consuming and may fail to capture evolving risks comprehensively. In contrast, the emergence of geospatial big data presents new opportunities for leveraging artificial intelligence (AI) in heritage conservation. This study introduces an innovative methodology that integrates remote sensing data and machine learning to assess risks to heritage sites. Cairo, Egypt, with its vast and historically significant urban heritage, serves as the focal point of this analysis, as its cultural heritage (CH) faces increasing threats from rapid urbanization and development pressures. The proposed framework utilizes high-resolution satellite imagery and advanced geospatial analytics to systematically evaluate and prioritize vulnerable heritage sites. The analysis encompasses 9 districts, covering 1476 heritage sites, using a novel risk assessment framework that incorporates four key components: (1) urban development pressure measured through building density at multiple radii (50 m, 100 m, 200 m, 500 m); (2) heritage vulnerability assessment based on site age, area, and cultural significance; (3) isolation risk determined by surrounding building counts; and (4) environmental risk factors including material vulnerability, natural hazard exposure, emergency response limitations, and environmental pollution. Five machine learning models were evaluated using rigorous spatial cross-validation, with building density metrics at the 50 m radius emerging as the strongest independent predictors (r = 0.160, p < 0.001). By combining OpenStreetMap data, satellite imagery, and custom algorithms with methodologically rigorous feature selection to prevent data leakage, the study generates risk scores that facilitate data-driven decision-making for heritage preservation and sustainable tourism development. This study demonstrates how geospatial AI (GeoAI) can support CH preservation, and not just disaster prediction or urban analysis. The findings reveal that heritage risk is primarily driven by immediate urban context rather than site-specific characteristics, offering guidance for policymakers, UNESCO, NGOs, and developers in resource allocation.

Traffic management at road intersections remains a challenge, even with the evolving technologies that enable autonomous vehicles to communicate and cooperate. The challenge switched to managing large amounts of data from driving agents and their surrounding environment. In this paper, we adapted the Decentralized Model-Based Policy Optimization (DMPO) [1], developed for large-scale network control, to address this challenge. DMPO, with its great scalability guarantees, is designed for large-scale static network topologies, while the traffic management problem has a large scale of autonomous vehicle nodes; each node or agent’s action decision alters the graph’s architecture, making it a dynamic graph. Our implementation fixes this by rebuilding the κ-hop communication graph at each time step based on the nodes’ proximity. We test the method in a SUMO simulation of a 4-way signalized intersection with 2 lanes per approach, 30 cars, and mixed traffic (60% CAV penetration). The results show that there were no collisions during the evaluation episodes, that the average wait time was only 0.26 seconds, and that fuel use decreased by 27.4% and emissions decreased by 24.8% compared to driving in a city with stop-and-go traffic. These findings show that DMPO is applicable beyond static networks to transportation scenarios defined by time-varying agent populations.

Abstract Providing people with disabilities with the opportunity to practise sports is essential for their physical development, social inclusion and mental health. Therefore, the existence of institutions that promote sports and organise sporting events for people with impairments is crucial. Hence, this chapter serves two purposes. The first one is to shed light on the major organisations that govern disability sports in the world. The second purpose is to highlight the key institutions that govern the field of sports for people with intellectual impairment on the international level, which are Special Olympics (SO) and Virtus. Then, we focus on how the field of disability sports looks like on a national level, using Egypt as a case study.

Abstract This chapter aims at reviewing institutional theory literature that tackled logics, work and fields as well as their underlying concepts in the context of sports, while emphasising studies that tackled societal causes. The authors extend the scoping review of Robertson et al. (2021), who tackled all sport studies relevant to the authors’ constructs until 2019. The authors do this by conducting a Systematic Literature Review (SLR) of sport studies that utilised the constructs of institutional logics, work and fields between 2020 and 2024. The authors analyse the articles from multiple angles, such as common societal themes, geographic allocation and common methodologies. The authors also pinpoint gaps and offer some recommendations for utilising institutional theory constructs in tackling social causes related to sports.

Background: Blockchain technology (BCT) is widely recognised for its potential to transform supply chain (SC) operations by enhancing SC transparency, hence fostering trust among SC partners. However, most empirical research has explored its direct impact through conceptual and causal models, while limited studies have explored how SC professionals themselves perceive the role of BCT in strengthening SC transparency and trust among SC partners, especially within emerging economies.Objectives: This study explores the perceptions of SC professionals regarding BCT technology, SC transparency and trust. The focus is on understanding how practitioners interpret the relational value of BCT in the context of emerging economies, with Egypt serving as a representative case.Method: This study employs an exploratory quantitative research approach through a quantitative method that utilises surveys to collect data from 63 SC professionals working in organisations operating in Egypt.Results: Findings confirm that professionals perceive BCT as a tool that positively influences both SC trust and SC transparency, with SC transparency partially mediating the relationship between BCT and trust. However, SC transparency alone does not fully explain trust-building, highlighting the importance of other BCT features such as immutability, traceability and smart contracts.Conclusion: This study reveals that SC professionals in emerging economies perceive BCT as a multifaceted trust-enabling mechanism, operating through SC transparency and other technological affordances.Contribution: By capturing practitioners’ perceptions, this study offers exploratory observations about the perceived relationship among BCT, SC transparency and trust, offering evidence-based understanding from an emerging economy perspective. The findings inform both academics and managers on how BCT’s perceived benefits can shape future trust-oriented SC practices.

This study proposes a fast image encryption method for color images, integrating an autoencoder to compress the image and a 6D hyperchaotic system to ensure enhanced security. Initially, a hash value is obtained from the original color image. The hash value, which serves as the secret key of the proposed encryption method, is used to initialize the state variables of the hyperchaotic system, which produces six distinct pseudo-random sequences. The input image is then compressed into a latent image (lossy) using a Vision Transformer Autoencoder model. This latent image is scrambled using chaotic sequences and a Random Shuffle technique. Diffusion is achieved through the Trifid Cipher transformation, which utilizes the remaining chaotic sequences to manipulate pixel values, thereby yielding a cipher version of the latent image. The suggested technique is faster and significantly enhances security compared to the state-of-the-art methods. This method achieves an average entropy of 7.9986, a correlation coefficient close to zero ≈ 0.00004, and key sensitivity analysis gives NPCR = 99.6110% and UACI = 33.4637%. Moreover, the key space of [Formula: see text] confirms that the proposed scheme offers strong resistance against brute-force attacks.