Search Results (377)

The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security. Full article

The perianal skin is a unique “skin–gut” boundary that serves as a critical hotspot for the exchange and evolution of antibiotic resistance genes (ARGs). However, its role in the dissemination of antimicrobial resistance (AMR) has often been underestimated. To characterize the resistance patterns in the perianal skin environment of patients with perianal diseases and to investigate the drivers of AMR in this niche, a total of 51 bacterial isolates were selected from a historical strain bank containing isolates originally collected from patients with perianal diseases. All the isolates originated from the skin site and were subjected to antimicrobial susceptibility testing, whole-genome sequencing, and co-occurrence network analysis. The analysis revealed a highly structured resistance pattern, dominated by two distinct modules: one representing a classic Staphylococcal resistance platform centered around mecA and the bla operon, and a broad-spectrum multidrug resistance module in Gram-negative bacteria centered around tet(A) and predominantly carried by IncFIB and other IncF family plasmids. Further analysis pinpointed IncFIB-type plasmids as potent vehicles driving the efficient dissemination of the latter resistance module. Moreover, numerous unexplained resistance phenotypes were observed in a subset of isolates, indicating the potential presence of emerging and uncharacterized AMR threats. These findings establish the perianal skin as a complex reservoir of multidrug resistance genes and a hub for mobile genetic element exchange, highlighting the necessity of enhanced surveillance and targeted interventions in this clinically important ecological niche. Full article

Turbidity currents pose significant threats to offshore seabed infrastructures, including subsea hydrocarbon production facilities and submarine communication cables. These powerful underwater flows can damage pipelines, potentially causing hydrocarbon spills that endanger local communities, the environment, and negatively impact energy production infrastructures. Therefore, a comprehensive understanding of the spatio-temporal development and destructive force of turbidity currents is essential. While numerical computation of 3D flow, sediment transport, and substrate exchange is possible, field-scale simulations are computationally intensive. In this study, we develop a simplified morphodynamic approach to model the flow properties of channelized turbidity currents and the associated trends of sediment accretion and erosion. This model is applied to the Baco–Malaylay submarine system to investigate the dynamics of a significant turbidity current event that impacted a submarine pipeline offshore the Philippines. The modeling results align with available seabed assessments and observed erosion trends of the protective rock berm. Our simplified modeling approach shows good agreement with simulations from a fully 3D numerical model, demonstrating its effectiveness in providing valuable insights while reducing computational demands. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

Soil acidification, salinization, and heavy metal pollution pose serious threats to global food security and sustainable agricultural development. Biochar, with its high porosity, large surface area, and abundant functional groups, can effectively improve soil properties. However, due to variations in feedstocks and pyrolysis conditions, it may contain potentially harmful substances. Industrial wastes such as fly ash, steel slag, red mud, and phosphogypsum are rich in minerals and show potential for soil improvement, but direct application may pose environmental risks. The co-application of biochar with these wastes can produce composite amendments that enhance pH buffering capacity, nutrient availability, and pollutant immobilization. Therefore, a review of biochar-industrial waste composites as soil amendments is crucial for addressing soil degradation and promoting resource utilization of wastes. In this study, the literature was retrieved from Web of Science, Scopus, and Google Scholar using keywords including biochar, fly ash, steel slag, red mud, phosphogypsum, combined application, and soil amendment. A total of 144 articles from 2000 to 2025 were analyzed. This review summarizes the physicochemical properties of biochar and representative industrial wastes, including pH, electrical conductivity, surface area, and elemental composition. It examines their synergistic mechanisms in reducing heavy metal release through adsorption, complexation, and ion exchange. Furthermore, it evaluates the effects of these composites on soil health and crop productivity, showing improvements in soil structure, nutrient balance, enzyme activity, and metal immobilization. Finally, it identifies knowledge gaps as well as future prospects and recommends long-term field trials and digital agriculture technologies to support the sustainable application of these composites in soil management. Full article

Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I²C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes. Full article

The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) attacks, anomalous network behaviors, and data manipulation, which threaten the security and reliability of IoT ecosystems. New methods based on machine learning have been reported in the literature, addressing topics such as intrusion detection and prevention. This paper proposes an advanced anomaly detection framework for IoT networks expressed in several phases. In the first phase, data preprocessing is conducted using techniques like the Median-KS Test to remove noise, handle missing values, and balance datasets, ensuring a clean and structured input for subsequent phases. The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. This approach identifies the most significant features, reduces dimensionality, and enhances computational efficiency without sacrificing accuracy. In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. This multi-step methodology ensures adaptability and scalability in handling diverse IoT scenarios. The evaluation results demonstrate the superiority of the proposed framework over existing methods. It achieves a 12.5% improvement in accuracy (98%), a 14% increase in detection rate (95%), a 9.3% reduction in false positive rate (10%), and a 10.8% decrease in false negative rate (5%). These results underscore the framework’s effectiveness, reliability, and scalability for securing real-world IoT networks against evolving cyber threats. Full article

We introduce TrustShare, a novel blockchain-based framework designed to enable secure, privacy-preserving, and trust-aware cyber threat intelligence (CTI) sharing across organizational boundaries. Leveraging Hyperledger Fabric, the architecture supports fine-grained access control and immutability through smart contract-enforced trust policies. The system combines Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with temporal, spatial, and controlled revelation constraints to grant data owners precise control over shared intelligence. To ensure scalable decentralized storage, encrypted CTI is distributed via the IPFS, with blockchain-anchored references ensuring verifiability and traceability. Using STIX for structuring and TAXII for exchange, the framework complies with the GDPR requirements, embedding revocation and the right to be forgotten through certificate authorities. The experimental validation demonstrates that TrustShare achieves low-latency retrieval, efficient encryption performance, and robust scalability in containerized deployments. By unifying decentralized technologies with cryptographic enforcement and regulatory compliance, TrustShare sets a foundation for the next generation of sovereign and trustworthy threat intelligence collaboration. Full article

An Identity Management System (IDMS) is responsible for managing and organizing identities and credentials exchanged between users, Identity Providers (IDPs), and Service Providers (SPs). The primary goal of IDMS is to ensure the confidentiality and privacy of users’ personal data. Traditional IDMS relies on a third party to store user information and authenticate the user. However, this approach poses threats to user privacy and increases the risk of single point of failure (SPOF), user tracking, and data unavailability. In contrast, decentralized IDMSs that use blockchain technology offer potential solutions to these issues as they offer powerful features including immutability, transparency, anonymity, and decentralization. Despite its advantages, blockchain technology also suffers from limitations related to performance, third-party control, weak authentication, and data leakages. Furthermore, some blockchain-based IDMSs still exhibit centralization issues, which can compromise user privacy and create SPOF risks. This study proposes a decentralized IDMS that leverages blockchain and smart contract technologies to address the shortcomings of traditional IDMSs. The proposed system also utilizes the Interplanetary file system (IPFS) to enhance the scalability and performance by reducing the on-chain storage load. Additionally, the proposed IDMS employs the Elliptic Curve Integrated Encryption Scheme (ECIES) to provide an extra layer of security to protect users’ sensitive information while improving the performance of the systems’ transactions. Security analysis and experimental results demonstrated that the proposed IDMS offers significant security and performance advantages compared to its counterparts. Full article

This review paper provides a comprehensive review and analysis of the research and practice literature relating to data models and frameworks pertaining to urban and other AI-rich environments, extending to the planetary environment. Elements of focus include the very definition, along with the nature and stability, of the concept of AI itself; consideration of the notion of “open” in an AI context; data sharing, exchange, access, control, and use; and associated challenges and opportunities. Current gaps and problems in the literature on these data models are identified, giving rise to opportunities for research and practice going forward. One of the key gaps associated with AI models and frameworks lies in meeting the needs of the public, with the current top-down approach to AI design, development, and use emerging as a key problem. Such gaps set the stage for a number of recommendations, including human–AI collaboration; extending understanding of human–AI interactions; risk mitigation associated with artificial superintelligence and agentic approaches; and rethinking current AI models and the very definition of AI. This review paper is significant in that it integrates a SWOT (strengths, weaknesses, opportunities, threats) analysis to synthesize challenges, opportunities, gaps, and problems, offering a roadmap for human–AI interactions and collaborations in urban development. Full article

With the increasing demand for drones in diverse tasks, the Internet of Drones (IoD) has recently emerged as a significant technology in academia and industry. The IoD environment enables various services, such as traffic and environmental monitoring, disaster situation management, and military operations. However, IoD communication is vulnerable to security threats due to the exchange of sensitive information over insecure public channels. Moreover, public key-based cryptographic schemes are impractical for communication with resource-constrained drones due to their limited computational capability and resource capacity. Therefore, a secure and lightweight key agreement scheme must be developed while considering the characteristics of the IoD environment. In 2024, Alzahrani proposed a secure key agreement protocol for securing the IoD environment. However, Alzahrani’s protocol suffers from high computational overhead due to its reliance on elliptic curve cryptography and is vulnerable to drone and mobile user impersonation attacks and session key disclosure attacks by eavesdropping on public-channel messages. Therefore, this work proposes a lightweight and security-enhanced key agreement scheme for the IoD environment to address the limitations of Alzahrani’s protocol. The proposed protocol employs a physical unclonable function and simple cryptographic operations (XOR and hash functions) to achieve high security and efficiency. This work demonstrates the security of the proposed protocol using informal security analysis. This work also conducted formal security analysis using the Real-or-Random (RoR) model, Burrows–Abadi–Needham (BAN) logic, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation to verify the proposed protocol’s session key security, mutual authentication ability, and resistance to replay and MITM attacks, respectively. Furthermore, this work demonstrates that the proposed protocol offers better performance and security by comparing the computational and communication costs and security features with those of relevant protocols. Full article

This study examines culture-led urban regeneration as a strategy for revitalizing Meezan Chowk, a historically significant yet deteriorating public space in Quetta, Pakistan. Once a central site of social and commercial exchange, the area suffered from infrastructural decline, overcrowding, and the erosion of its architectural identity. The research proposes a design intervention to restore the site’s heritage value while enhancing its functional and social relevance. A qualitative approach is adopted, incorporating surveys, focus group discussions, and site observations to assess user needs and spatial dynamics. A SWOT analysis serves as the analytical framework to identify the site’s internal strengths and weaknesses, as well as external opportunities and threats. By utilizing the Geographic Information Systems (GIS) and OpenStreetMap data, further information can enhance understanding of the site’s urban morphology. The proposed design integrates vernacular elements, such as arched facades, shaded corridors, and communal courtyards, with contemporary features, including cafes, local artisan shops, and accessible public amenities. Full article

Current improvements in quantum computing present a substantial challenge to classical cryptographic systems, which typically rely on problems that can be solved in polynomial time using quantum algorithms. Consequently, post-quantum cryptography (PQC) has emerged as a promising solution to emerging quantum-based cryptographic challenges. The greatest threat is public-key cryptosystems, which are primarily responsible for key exchanges. In PQC, key encapsulation mechanisms (KEMs) are crucial for securing key exchange protocols, particularly in Internet communication, virtual private networks (VPNs), and secure messaging applications. CRYSTALS-Kyber and NTRU are two well-known PQC KEMs offering robust security in the quantum world. However, even when quantum computers are functional, they are not easily accessible. IoT devices will not be able to utilize them directly, so there will still be a requirement to protect IoT devices from quantum attacks. Concerns such as limited computational power, energy efficiency, and memory constraints in devices such as those used in IoTs, embedded systems, and smart cards limit the use of these techniques in constrained environments. These concerns always arise there. To address this issue, this study conducts a broad comparative analysis of Kyber and NTRU, with special focus on their security, performance, and implementation efficiency in such environments (IOT/constrained environments). In addition, a case study was conducted by applying KEMs to a low-power embedded device to analyze their performance in real-world scenarios. These results offer an important comparison for cyber security engineers and cryptographers who are involved in integrating post-quantum cryptography into resource-constrained devices. Full article

Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture. Full article

Climate change and its adverse effects are now the greatest threats faced by society. As a consequence, it is essential to adopt strategic tools and adaptation measures, especially in the most vulnerable and susceptible areas, such as coastal zones. This study provides an assessment of strategic adaptation instruments in European Union (EU) coastal countries, identifying key gaps and areas for improvement to support more effective and coordinated climate adaptation. To address this, a selection of 19 EU coastal countries was carried out based on inclusion and exclusion criteria. National adaptation strategies and plans were reviewed using scientific, technical, and institutional sources. The analysis focused on the development and evolution of these instruments, particularly in response to major coastal impacts. Identified adaptation measures were classified by typology (structural–physical, social, and institutional) and comparatively assessed to detect implementation trends, regional approaches, and existing gaps across countries. The results of this study indicate that there has been a notable and progressive increase in the adoption of adaptation plans and strategies that address problems faced by coastal areas. Physical–structural adaptation measures are the most widely used, especially ecosystem-based and engineering measures, even in specific projects implemented at different territorial scales of governance. One of the main conclusions is that all coastal EU member states have adopted strategic planning frameworks for adaptation, with increasing emphasis on coastal areas. However, enhanced European solidarity and knowledge exchange are essential to ensure equitable and effective adaptation across all regions. Full article