Dahiru Adamu Aliyu

As cities become more digitally governed, centralized infrastructures face growing risks across identity, safety, and data domains. This chapter reframes blockchain as a foundational trust protocol for urban resilience, emphasizing its core features: immutability, decentralization, transparency, and consensus, as tools to address systemic privacy and security challenges. It explores blockchain's role in securing digital identities, logging infrastructure events, and enabling auditable governance. Real-world deployments in land registries, voting, procurement, and incident response, especially in Asia and Europe are analyzed. Privacy-preserving techniques like zero-knowledge proofs and decentralized identity are also examined. The chapter highlights integration hurdles such as interoperability, regulatory gaps, and ethical tensions, while outlining emerging trends like Blockchain 4.0, quantum-safe cryptography, and DAO-led civic participation. It offers a roadmap for decentralized citizen-centric governance in smart cities.

As urban ecosystems evolve into data-intensive infrastructures, the transparency of supply chains has become pivotal to cybersecurity, governance, and trust. This chapter explores how blockchain, initially developed for decentralized finance, is now foundational for traceability in smart urban logistics. It examines its convergence with IoT and governance systems to enable tamper-resistant, auditable, and privacy-aware data flows. The chapter outlines blockchain's core features, immutability, decentralization, smart contracts, and permissioned architectures, and maps these to urban sectors like food safety, pharmaceuticals, procurement, and waste. Real-world deployments across Asia, Europe, and North America demonstrate its role in enhancing collaboration, preventing fraud, and supporting compliance with regulations such as the GDPR. The chapter also identifies adoption challenges and offers ethical governance models and a strategic roadmap to position blockchain as a socio-technical foundation for trust, sovereignty, and equity in the smart city era.

Accurate detection of oil spills in dynamic port environments remains critical for timely response and effective environmental protection. Recent studies have explored the application of deep learning models to high-resolution RGB imagery; however, challenges persist due to the complex visual characteristics of ports, including dynamic water surfaces, infrastructural occlusions, and heterogeneous backgrounds. Traditional detection approaches have demonstrated limited robustness under such conditions, often resulting in elevated rates of false positives and missed detections. In this study, a semantic segmentation model based on the DeepLabV3 architecture was developed and optimized for oil spill detection using drone-acquired RGB imagery. Model performance was enhanced through the integration of convolutional neural networks, transfer learning, and systematic hyperparameter tuning involving batch size, learning rate, and training epochs. Evaluation was conducted through five-fold cross-validation to ensure generalization capability, and the results were benchmarked against established segmentation models. The optimized model achieved a validation accuracy of 96.53%, with an F1-score of 0.9557 and an Intersection over Union (IoU) of 0.8643. Cross-validation yielded a mean accuracy of 89.35% (±0.0103), precision of 88.52% (±0.0081), recall of 85.26% (±0.0138), F1-score of 86.45% (±0.0122), and IoU of 76.97% (±0.0177), indicating consistent performance across all partitions. Per-class performance further supported model reliability, with F1-scores of 0.92 for oil, 0.91 for other objects, and 0.97 for water, and corresponding IoU values of 0.8569, 0.8273, and 0.9469, respectively. When compared to baseline models such as Fully Convolutional Networks (FCN, IoU: 83.48%), U-Net (IoU: 78.80%), and MFSCNet (IoU: 86.24%), the proposed DeepLabV3-based architecture demonstrated superior segmentation accuracy. These findings indicate the model’s potential for real-time deployment in high-risk maritime zones, offering significant contributions to environmental monitoring, rapid incident response, and operational decision-making.

The integration of Blockchain (BC) and the Internet of Things (IoT) has emerged as a transformative solution for addressing traceability challenges in logistics, offering enhanced transparency, data security, and operational efficiency. This study presents a systematic review of the current state of BC-IoT integration for logistics traceability, focusing on its motivations, deployment strategies, technical implementations, and evaluation approaches. A total of 1,619 records were initially retrieved from IEEE Xplore, MDPI, ScienceDirect, Scopus, and Web of Science, from which 61 peer-reviewed studies published between 2015 and 2024 were selected and analyzed. The selection process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous, transparent, and high-quality synthesis. Only studies explicitly addressing the convergence of BC and IoT within logistics traceability contexts were included. Key findings reveal that BC-IoT systems significantly enhance traceability and transparency across logistics networks but face persistent challenges such as scalability, latency, energy efficiency, and security. The review categorizes various deployment architectures including cloud, edge, fog, and hybrid models, and examines their implications for data responsiveness and system reliability. Additionally, it evaluates popular BC platforms (e.g., Hyperledger Fabric, Ethereum, Solana) and consensus mechanisms (e.g., RAFT, PBFT, PoS) based on their suitability for logistics applications. Emerging research directions emphasize the need for cross-chain interoperability, domain-specific frameworks, and decentralized traceability models, particularly in sectors such as humanitarian logistics and regulatory compliance. This review consolidates fragmented knowledge and provides actionable insights for developing scalable, secure, and transparent BC-IoT systems to support next-generation logistics operations.

Asthma exacerbation prediction is critical for preventing severe respiratory complications and improving patient outcomes. Traditional predictive models rely on static machine learning approaches, which lack adaptability to evolving patient conditions and environmental changes. This study proposes a reinforcement learning (RL)-based model using Proximal Policy Optimization (PPO) to enhance personalized early prediction of asthma exacerbation attacks. Unlike conventional models, PPO dynamically refines its policy through iterative learning, allowing for real-time, adaptive risk assessment based on personalized patient data and environmental factors. The PPO model was trained on a comprehensive asthma dataset, integrating demographic, environmental, lifestyle, and Asthma Control Test (ACT) scores. The model achieved 96.60% accuracy, 95.79% precision, 96.65% recall, and 95.92% F1-score, outperforming baseline RL algorithms such as Deep Q-Learning (92.21% accuracy), Advantage Actor-Critic (94.34%), and Trust Region Policy Optimization (95.12%). Comparative evaluation with existing studies further demonstrated PPO’s superior recall and sensitivity to exacerbation cases, making it highly suitable for personalized asthma management. RL performance metrics confirmed the model’s stability and convergence, including cumulative rewards, policy, and value loss. These findings highlight PPO’s potential for real-time clinical integration, enabling continuous patient monitoring and proactive intervention strategies. Future work will focus on training the model on larger, multi-source datasets to improve generalization across diverse populations. Additionally, further research will explore deployment in telemedicine, integration with wearable health technologies, and adaptability to other chronic diseases, ensuring scalability and real-world applicability in personalized healthcare systems.

Software systems are essential in modern life, requiring rigorous testing to ensure reliability. Pairwise testing, a combinatorial testing methodology, optimizes test case generation by minimizing redundancy while maintaining defect detection effectiveness. This review systematically examines the role of metaheuristic algorithms in pairwise test case generation, focusing on their exploration and exploitation mechanisms to address local optima challenges. Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. These methods consistently outperform traditional techniques by reducing test suite sizes and enhancing fault detection capabilities. The findings underscore the growing adoption of the one-test-at-a-time (OTAT) approach over the one-parameter-at-a-time (OPAT) approach and identify key challenges in scalability, hyperparameter tuning, and constraint handling, highlighting the need for hybrid strategies and adaptive optimization techniques. This review offers valuable insights into advancing software testing methodologies and presents recommendations for future research to improve efficiency, scalability, and fault detection in pairwise testing.

The need for sophisticated traceability systems has become essential in increasingly complex and globalized supply chains. The convergence of Blockchain (BC), Internet of Things (IoT), and Artificial Intelligence (AI) technologies offers promising solutions to enhance traceability systems across various sectors, particularly supply chain management (SCM). This paper presents a comprehensive bibliometric and systematic literature review to explore emerging trends, research patterns, and methodologies in integrating BC, IoT, and AI into traceability systems. In the study, 530 documents from the SCOPUS database for bibliometric analysis were examined, alongside a detailed review of 43 selected articles from multiple databases. The findings highlighted a significant increase in research output in recent years, with a dominant focus on agricultural supply chains and SCM. Notably, India and China lead the field in publications and citations. Furthermore, key authors and influential journals significantly contributed to advancing the research. The analysis also revealed a predominance of experimental and hybrid research methodologies, with Ethereum and Hyperledger Fabric emerging as the most widely adopted platforms for system implementations. The study identified critical research trends, including the growing role of AI-driven analytics, the importance of real-time IoT data collection, and the critical need for secure, tamper-proof data provided by BC. However, challenges, such as interoperability, scalability, and standardization, remain hindering the widespread adoption of these technologies. The paper proposes a four-layer conceptual framework for integrating BC, IoT, and AI into future traceability systems, emphasizing their potential to enhance transparency, security, and efficiency across various application areas. The paper concludes by offering directions for future research, highlighting the need for more empirical studies, industry-specific frameworks, and standardization to overcome existing limitations.

Asthma, a chronic respiratory ailment affecting millions worldwide, presents significant challenges due to the unpredictable nature of exacerbation episodes. Existing methodologies struggle to accurately predict exacerbations individually, particularly across diverse patient demographics. This paper introduces an innovative conceptual framework for the early prediction of asthma exacerbations, leveraging advanced reinforcement learning (RL) techniques, specifically proximal policy optimization, along with patient-specific data and environmental factors. The primary goal is to revolutionize asthma management by providing customized predictions and tailored reward mechanisms that enable proactive interventions and optimize resource allocation. The framework comprises critical components such as patient profiling through a mobile application, trigger identification, a RL-based predictive model, an early warning mechanism, and a personalized reward scheme. Data for patient profiling is gathered through a mobile application, which includes medical history, demographics, symptoms, and triggers. Profiling forms the foundation for the prediction model, enabling it to identify subtle patterns and anticipate exacerbation events more accurately and efficiently. The significant contributions of this research include offering a novel approach by incorporating custom reward functions to enhance learning across heterogeneous patient populations, tailoring interventions to unique triggers and symptom presentations, and addressing challenges associated with patient diversity. By addressing the limitations of existing methodologies and offering a comprehensive solution, this research promises significant improvements in asthma care and healthcare delivery, paving the way for future advancements in personalized medicine and predictive healthcare systems.

Asthma exacerbations pose a significant global health concern, necessitating effective predictive models to anticipate and manage these events. This systematic literature review examined the optimization techniques employed in asthma exacerbation prediction models, spanning machine learning algorithms and computational optimization methods. The objective was to synthesize existing evidence, identify trends, and delineate future research directions in predictive modeling for asthma exacerbations to enhance predictive accuracy and clinical utility. A comprehensive search strategy was devised, yielding 27 eligible articles for analysis. The result revealed various optimization techniques, including feature selection, model optimization, and environmental factor integration. The result also revealed that machine learning algorithms’ effectiveness in predicting asthma exacerbations varied depending on various factors (such as dataset quality and model complexity), with various optimization techniques (such as feature selection and ensemble learning) used for improving predictive accuracy. Integrating environmental and spatial factors enhanced prediction models, enabling tailored interventions. In addition, personalized asthma management strategies informed by predictive models led to better control and reduced healthcare utilization. The review also highlighted the implications for personalized asthma management, as well as methodological limitations, and proposed future research directions to improve model reliability and advance personalized healthcare understanding, thereby contributing to the United Nations’ Sustainable Development Goals related to health, innovation, and sustainability. Thus, progress made in asthma exacerbation prediction and the identification of challenges and areas for improvement were covered, providing valuable insights for researchers, clinicians, and policymakers aiming to enhance asthma care through predictive modeling.