Search Results (1,121)

In the context of the deep restructuring of the global industrial chain and the concurrent pursuit of green and sustainable development, enterprises need to secure long-term, reliable supply chain competitiveness. The burgeoning wave of digitalization is simultaneously reshaping industry landscapes. Based on a sample of the Chinese manufacturing sector, this study explores how supply chain digital transformation enhances commercial credit financing performance by improving corporate adjustment capability. The research finds that supply chain digital transformation strengthens a firm’s commercial credit financing capacity through a dual-core mediating mechanism of corporate adjustment capability: (1) enhancing the adjustment capability of operational management, which mitigates the negative impact of cost stickiness on financing; (2) enhancing the adjustment capability of organizational management, which amplifies the positive effect of organizational resilience on financing. The study further reveals key moderating effects: (1) External Governance: Strong ESG performance strengthens the financing effect of digitalization by building reputational capital. High industry competition strengthens the financing effect by prompting firms to optimize operational efficiency. (2) Internal Endowments: Environmental risk aversion stemming from a firm’s polluting nature significantly weakens the credit supply effect of digitalization. The market-oriented foundation underpinning private ownership effectively activates the credit supply effect of digitalization. This study constructs an integrated pathway model of “Digital Transformation–Corporate Adjustment Capability–Supply Chain Credit Access.” It provides a research perspective for understanding how digitalization reshapes the logic of supply chain finance and offers empirical evidence for pathways empowering enterprises through digital transformation. Full article

Energy security remains a central challenge in the context of global sustainability, as geopolitical dynamics, technological advancements, and environmental imperatives converge to reshape energy systems worldwide. This comprehensive review explores the intricate relationship between geopolitics and energy policy, with a particular focus on Türkiye’s evolving energy landscape. It examines how diversification of energy sources, investments in renewable technologies, resilient infrastructure development, and international collaborations collectively contribute to securing national and global energy futures. The study highlights the geopolitical significance of strategic energy corridors, resource competition, and the emergent role of renewable energy in redefining power structures. Drawing on comparative case studies, including Germany’s Energiewende, Norway’s resource management, Japan’s post-Fukushima challenges, and the United States’ shale revolution, the review distills lessons applicable to Türkiye and other emerging economies navigating energy transitions. Future trends discussed include the rising importance of energy storage, smart grids, digitalization, cybersecurity, and equitable energy access. Policy recommendations emphasize integrated approaches that balance economic growth, environmental stewardship, and national security, advocating for proactive diversification, innovation, and multilateral cooperation. The article concludes that achieving a resilient, low-carbon, and geopolitically stable energy system requires coordinated global efforts anchored in adaptive governance and inclusive stakeholder engagement. This work provides a valuable framework for policymakers, researchers, and practitioners committed to advancing sustainable and secure energy pathways in Türkiye and beyond. Full article

Water is a vital resource for human survival, economic development, and environmental sustainability. It is essential to agriculture, energy production, public health, and biodiversity preservation. Efficient water management is even more important in areas that are prone to scarcity. This paper presents a systematic review of the management of water resources in South Africa, a country characterized by significant water scarcity challenges compounded by its socio-economic and ecological needs. South Africa’s limited freshwater resources are under extreme stress due to its semi-arid climate, unequal rainfall distribution, expanding population, and industrial needs. The nation’s water security has also been made more difficult by historical injustices, climatic fluctuations, and decaying infrastructure. Through a systematic review of 60 scholarly articles published between 2011 and 2025 in the Web of Science database, this study discusses the historical context of water management in South Africa, including the legacy of apartheid-era policies and their impact on access to water. It also examines current management practices, governance structures involving national and local authorities, the role of key institutions such as the Department of Water and Sanitation (DWS), climate change impact on water availability, population growth and urbanization, inequality and access, and challenges in South Africa’s water resources management (WRM). In particular, this review highlights the integration of scientific water quality and biostability assessment into the Integrated Water Resources Management (IWRM) framework in order to produce actionable insights that enhance resilience, sustainability, and equity in WRM. Furthermore, it explores future strategies for sustainable WRM, emphasizing the importance of IWRM, community participation, technological innovation, and climate change adaptation. Through this comprehensive analysis, the paper aims to contribute to a deeper understanding of the complexities and opportunities in ensuring water security for all South Africans. Full article

Background: Healthcare data interoperability faces significant barriers, including regulatory compliance complexities, institutional trust deficits, and technical integration challenges. Current centralized architectures demonstrate inadequate mechanisms for balancing data accessibility requirements with patient privacy protection, as mandated by HIPAA and GDPR frameworks. Traditional compliance approaches rely on manual policy implementation and periodic auditing, which are insufficient for dynamic, multi-organizational healthcare data-sharing scenarios. Objective: This study develops and proposes a blockchain-based healthcare data management framework that leverages Hyperledger Fabric, IPFS, and the HL7 FHIR standard and incorporates automated regulatory compliance mechanisms via smart contract implementation to meet HIPAA and GDPR requirements. It assesses the theoretical system architecture, security characteristics, and scalability considerations. Methods: We developed a permissioned blockchain architecture that employs smart contracts for privacy policy enforcement and for patient consent management. The proposed system incorporates multiple certification authorities for patients, hospitals, and research facilities. Architectural evaluation uses theoretical modeling and system design analysis to assess a system’s security, compliance, and scalability. Results: The proposed framework demonstrated enhanced security through decentralized control mechanisms and cryptographic protection protocols. Smart contract-based compliance verification can automate routine regulatory tasks while maintaining human oversight in complex scenarios. The architecture supports multi-organizational collaboration with attribute-based access control and comprehensive audit-trail capabilities. Conclusions: Blockchain-based healthcare data-sharing systems provide enhanced security and decentralized control compared with traditional architectures. The proposed framework offers a promising solution for automating regulatory compliance. However, implementation considerations—including organizational readiness, technical complexity, and scalability requirements—must be addressed for practical deployment in healthcare settings. Full article

Education is pivotal in shaping a future geared toward climate neutrality and cultivating agents of change capable of addressing sustainability challenges. Educators’ understanding of food sustainability influences how effectively it is integrated into educational programs. This study examined the knowledge and understanding of food sustainability among nutrition and agri-food educators in Europe as part of a joint European-funded project (2023-1-IE01-KA220-VET-00156916: Train to Sustain). After ethical approval, a survey was distributed to educators in Ireland, Slovenia, Poland, and Italy. Data were analysed using qualitative and quantitative methods. Of the 123 educators who participated, 81% agreed they fully understood “food sustainability,” and 97% believed it is important to include it in education, yet only 62% reported that it was currently taught in their programs. This reveals a gap between perceived importance and implementation. The data also offered insights into how educators understand food sustainability across environmental, economic, social, cultural, and food security dimensions and how these are incorporated into teaching and awareness raising. Three themes were identified: (1) understanding sustainable food systems, (2) sustainable resource management, and (3) educating for food security and resilience. Participants emphasised environmentally friendly food practices, often linked to zero waste goals. The qualitative findings underscored the importance of societal access to healthy food, focusing on food security. However, references to economic and cultural aspects of sustainability were limited, suggesting a potential gap in educators’ understanding. This study highlights the need for educators to possess comprehensive knowledge of food sustainability to advance education and address climate and sustainability challenges. Full article

The forest food industry, as a typical low-carbon green ecological industry, holds strategic significance in addressing global food security challenges. This review takes forest protein resources as an example to analyze the current development status, opportunities, and challenges from a global industrial perspective. Research indicates that forests, as a vital food treasure for humanity, can provide diverse protein sources such as insects, plants, microorganisms, and bio-manufactured proteins. Currently, numerous technological innovations and market practices have emerged in fields such as insect protein (e.g., there are over 3000 edible insect species globally, with a market size of approximately USD 3.2 billion in 2023, projected to reach USD 7.6 billion by 2028), plant-based alternative protein (e.g., plant-based chicken nuggets by Impossible Foods in the United States), microbial fermentation protein (e.g., the production capacity of Solar Foods’ production base in Finland is 160 tons per year), and cell-cultured meat (e.g., cell-cultured chicken is sold in Singapore), demonstrating significant potential in alleviating food supply pressures and reducing environmental burdens. However, industrial development still faces practical challenges including insufficient resource exploration, incomplete nutritional and safety evaluation systems, low consumer acceptance, high costs of core technologies (e.g., the first cell-cultured meat burger in 2013 cost over 1 million USD/lb, and current costs need to be reduced to 17–65 USD/kg to achieve market competitiveness), and imperfect regulatory mechanisms (e.g., varying national standards lead to high compliance costs for enterprises). In the future, it is necessary to achieve efficient development and sustainable utilization of forest protein resources by strengthening resource exploration, clarifying the basis of nutrients, promoting multi-technology integration and innovation, and establishing a sound market access system, thereby providing solutions for global food security and high-quality development of the food industry. Full article

We present a quantum-assisted data authentication protocol that integrates classical information-theoretic security with quantum communication techniques. We assume only that the participants have access to open classical and quantum channels, and share a random static key material. Building on the Wegman–Carter paradigm, our scheme employs universal hashing for message authentication and leverages quantum channels to securely transmit random nonces, eliminating the need for key recycling. The protocol utilizes polar codes within Wyner’s wiretap channel model to ensure confidentiality and reliability, even in the presence of an all-powerful adversary. Security analysis demonstrates that the protocol inherits strong guarantees from both classical and quantum frameworks, provided the quantum channel maintains low loss and noise. Full article

Security is paramount in space–air–ground integrated networks (SAGINs) due to their inherent openness and the broadcast characteristics of wireless transmission. In this paper, we propose a secure downlink transmission scheme with NOMA-based mixed FSO/RF communications for SAGINs. Specifically, the satellite communicates with two ground users through an unmanned aerial vehicle (UAV) relay, where FSO and RF transmissions are adopted for the satellite–relay and relay–user links, respectively. Furthermore, the NOMA technique is integrated to further enhance secrecy performance. Subsequently, exact closed-form expressions for the secrecy outage probability of the downlink transmission link in SAGINs are derived. Finally, Monte Carlo simulations are performed to validate the effectiveness of the proposed secure downlink transmission scheme and the accuracy of the analytical expressions. Full article

Numerous industries have begun using cloud computing. Among other things, this presents a plethora of novel security and dependability concerns. Thoroughly verifying cloud solutions to guarantee their correctness is beneficial, just like with any other computer system that is security- and correctness-sensitive. While there has been much research on distributed system validation and verification, nobody has looked at whether verification methods used for distributed systems can be directly applied to cloud computing. To prove that cloud computing necessitates a unique verification model/architecture, this research compares and contrasts the verification needs of distributed and cloud computing. Distinct commercial, architectural, programming, and security models necessitate distinct approaches to verification in cloud and distributed systems. The importance of cloud-based Service Level Agreements (SLAs) in testing is growing. In order to ensure service integrity, users must upload their selected services and registered services to the cloud. Not only does the user fail to update the data when they should, but external issues, such as the cloud service provider’s data becoming corrupted, lost, or destroyed, also contribute to the data not becoming updated quickly enough. The data saved by the user on the cloud server must be complete and undamaged for integrity checking to be effective. Damaged data can be recovered if incomplete data is discovered after verification. A shared resource pool with network access and elastic extension is realized by optimizing resource allocation, which provides computer resources to consumers as services. The development and implementation of the cloud platform would be greatly facilitated by a verification mechanism that checks the data integrity in the cloud. This mechanism should be independent of storage services and compatible with the current basic service architecture. The user can easily see any discrepancies in the necessary data. While cloud storage does make data outsourcing easier, the security and integrity of the outsourced data are often at risk when using an untrusted cloud server. Consequently, there is a critical need to develop security measures that enable users to verify data integrity while maintaining reasonable computational and transmission overheads. A cryptography-based public data integrity verification technique is proposed in this research. In addition to protecting users’ data from harmful attacks like replay, replacement, and forgery, this approach enables third-party authorities to stand in for users while checking the integrity of outsourced data. This research proposes a Cloud Integrity Verification and Validation Model using the Double Token Key Distribution (CIVV-DTKD) model for enhancing cloud quality of service levels. The proposed model, when compared with the traditional methods, performs better in verification and validation accuracy levels. Full article

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article

The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patient’s heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems. Full article

This study evaluates potential routes for electric bicycles (E-Bikes) in Valparaíso, Chile, using street audits performed by both humans and artificial intelligence (AI). Audit methods were compared to identify routes connecting the Puerto metro station with Avenida Alemania (a strategic city avenue), prioritizing criteria such as street infrastructure, habitability, and street coexistence. The results show that the human audit gives higher scores in subjective variables, such as the perception of security and urban dynamism, while AI penalizes infrastructure deficiencies more severely, especially in areas with steep slopes and low tree cover. Despite these differences, both methods highlight the inadequacy of current infrastructure to promote the use of E-Bikes in the city. This work provides a novel perspective by evaluating human and AI-assisted methodologies, suggesting that an integration between the two could improve accuracy and reduce subjectivity in urban audits. In addition, the results underline the need for public policies that prioritize accessibility, safety, and equity in urban mobility, especially in vulnerable areas. Future research should explore training AI algorithms with human audit data to strengthen AI’s ability to interpret contextual variables and dynamics in complex urban environments. Full article

This paper provides a perspective on the use of RGB-D cameras and non-invasive brain–computer interfaces (BCIs) for human activity recognition (HAR). Then, it explores the potential of integrating both the technologies for active and assisted living. RGB-D cameras can offer monitoring of users in their living environments, preserving their privacy in human activity recognition through depth images and skeleton tracking. Concurrently, non-invasive BCIs can provide access to intent and control of users by decoding neural signals. The synergy between these technologies may allow holistic understanding of both physical context and cognitive state of users, to enhance personalized assistance inside smart homes. The successful deployment in integrating the two technologies needs addressing critical technical hurdles, including computational demands for real-time multi-modal data processing, and user acceptance challenges related to data privacy, security, and BCI illiteracy. Continued interdisciplinary research is essential to realize the full potential of RGB-D cameras and BCIs as AAL solutions, in order to improve the quality of life for independent or impaired people. Full article

Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs. Full article

With the rapid growth of Artificial Intelligence of Things (AIoT), identity management and trusted communication have become critical for system security and reliability. Continuous AI learning and large-scale device connectivity introduce challenges such as permission drift, cross-domain access, and fine-grained API calls. Traditional identity management often fails to balance privacy protection with efficiency, leading to risks of data leakage and misuse. To address these issues, this paper proposes a blockchain-based self-sovereign identity (SSI) management mechanism for AIoT. By integrating SSI with a zero-trust framework, it achieves decentralized identity storage and continuous verification, effectively preventing unauthorized access and misuse of identity data. The mechanism employs selective disclosure (SD) technology, allowing users to submit only necessary attributes, thereby ensuring user control over self-sovereign identity information and guaranteeing the privacy and integrity of undisclosed attributes. This significantly reduces verification overhead. Additionally, this paper designs a context-aware dynamic permission management that generates minimal permission sets in real time based on device requirements and environmental changes. Combined with the zero-trust principles of continuous verification and least privilege, it enhances secure interactions while maintaining flexibility. Performance experiments demonstrate that, compared with conventional approaches, the proposed zero-trust architecture-based SSI management mechanism better mitigates the risk of sensitive attribute leakage, improves identity verification efficiency under SD, and enhances the responsiveness of dynamic permission management, providing robust support for secure and efficient AIoT operations. Full article