Search Results (9,497)

The nascent quantum computing brings unprecedented threats to the security roots of blockchain technology, potentially compromising cryptographic protocols securing decentralized systems. This review paper discusses the developing quantum threat scenario, focusing on the effect of quantum algorithms on traditional cryptographic systems. We critically examine current blockchain architectures, highlighting their vulnerabilities in a post-quantum future. The paper explores newer quantum-resistant cryptographic and modular architectural techniques to enhance blockchain resilience. This review supports comprehensive comprehension of cutting-edge strategies and research gaps by combining the literature addressing quantum threat modeling and post-quantum cryptography in decentralized systems. Full article

Growing wastewater volumes and intensifying water scarcity are driving the need for affordable, sustainable solutions that enable safe urban water reuse and strengthen climate resilience. Policy frameworks such as SDG6 and EU water reuse requirements highlight that reclaimed water must meet strict environmental and public health standards. In contrast, conventional biological treatment cannot fully remove many emerging contaminants, underscoring the need for advanced treatment approaches that consistently deliver high-quality reclaimed water. In this context, this review examines the role of membrane technologies (MF, UF, NF, RO, FO) and membrane bioreactors (MBRs) in providing safe water in urban environments and in enhancing urban climate resilience, including decentralized systems and advanced reclamation needs. It also discusses the contribution of membrane-based solutions to sustainable cooling systems and heat-stress mitigation, as well as the integration of membrane technologies into green infrastructure and nature-based solutions for climate adaptation. Technical and economic performance is shaped by fouling, cleaning requirements, and energy use, making life-cycle and operational optimization critical for long-term sustainability. Case studies and EU-funded initiatives demonstrate that, with appropriate governance and design, membrane-based approaches can enable reliable reclaimed water supply, enhance water security, and contribute to circular urban water management. The analysis was based on peer-reviewed open-access publications, which may introduce a degree of selection bias. Full article

Against the backdrop of significant climate change, resource constraints, and industrial upgrading, optimizing the coupling and coordination of the Water–Energy–Food (WEF) system in Northeast China is crucial for ensuring regional security and sustainable development. Existing research lacks long-term continuous analysis and inter-provincial comparisons. This article utilizes data from 2005 to 2023 to evaluate the development of the three provinces of Northeast China using a framework of 24 indicators covering safety, coordination, and resilience. Methodologies employed include the entropy weight method, the coupling coordination model, and the constraint model. The results show that: (1) The overall development level fluctuates with an overall upward trend, reaching a medium-coordinated level, and there are notable differences between provinces. (2) The coordination levels among provinces initially diverged but later converged, evolving from near dysfunction to a state of moderate coordination. Additionally, a bidirectional reinforcement mechanism has formed between system security and coupling coordination. (3) The key obstacles are deep-rooted in the system’s structure and have cross-provincial implications due to interconnected infrastructure, among which energy self-sufficiency and water-use efficiency are the primary constraints. (4) Resilience serves as a key mediating variable in regulating the relationship between security and coordination within the WEF system. In order to achieve a high level of coordination between WEF systems, it is necessary to formulate tailor-made subsystem governance policies, enhance the technological empowerment of water and energy conservation and efficiency improvement, and promote the development of resilient infrastructure. This integrated approach could systematically resolve resource competition conflicts, thus enhancing the overall resilience and sustainability of regional development. Full article

The rapid deployment of electric vehicle (EV) charging infrastructure, coupled with the integration of 5G/6G and Internet of Vehicles (IoV) technologies, has transformed charging stations into cyber–physical systems that rely on wireless communication for authentication, control, and grid coordination. While existing security standards such as ISO 15118 provide cryptographic protection at upper layers, they are insufficient to address physical-layer threats inherent to wireless connectivity. In particular, wireless active eavesdropping attacks can corrupt channel estimation during the authentication phase, enabling impersonation, unauthorized charging, and disruption of grid operations. This paper proposes a machine learning-based physical layer security (PLS) framework for detecting active eavesdropping attacks in 5G/6G-enabled EV charging systems. By modeling malicious EVs as pilot-spoofing attackers, three discriminative features, namely mean power, power ratio, and angle-based feature, are extracted from received pilot signals at the charging station. Three classifiers are evaluated: single-class support vector machine (SC-SVM), Random Forest (RF), and DNN. Simulation results demonstrate that the SC-SVM maintains a stable accuracy between 94% and 96% across all attacker power levels, while RF and DNN significantly outperform it under stronger attack conditions. Specifically, under strong attacker conditions, RF achieves an accuracy of 99.9%, and DNN reaches 99.8%, both exceeding 99% detection accuracy. By preventing pilot-spoofing-based impersonation during authentication, the proposed framework enhances charging availability, billing integrity, and grid-aware scheduling in intelligent EV charging infrastructure. Full article

Ransomware represents a critical and escalating threat to public institutions in developing nations, where cybersecurity is often underprioritized. While technical vulnerabilities are significant, this study investigates the under-explored socio-organizational dimensions of cyber resilience within Latin American local governments. Employing a qualitative exploratory approach, the research draws on semi-structured interviews with IT officials from Ecuadorian municipalities. The data were analyzed using Braun and Clarke’s thematic framework, applying a hybrid coding strategy that integrated deductive categories (institutional, human, technological) with inductive themes. The findings identify key vulnerability factors, including low risk perception among personnel, insufficient training, a lack of formal security policies, and weak regulatory enforcement. These human and institutional shortcomings often outweigh purely technological weaknesses, with social engineering serving as a predominant attack vector. Despite these challenges, the study also uncovers emergent resilience practices, including internal security committees, micro-training routines, AI-supported filtering, and informal troubleshooting networks. This research provides empirical evidence from a critically understudied context, underscoring the imperative for human-centric and context-sensitive cybersecurity strategies in the public sector. The conclusions establish a foundational understanding for developing adaptive security models, including future AI-driven solutions, tailored to the operational realities of developing nations. The study offers practical insights for policymakers and institutions aiming to bolster holistic cyber defense capabilities that address both human and technical factors. Full article

The rapid advancement of Industry 5.0 and the concurrent growth of the Industrial Internet of Things (IIoT) present significant cybersecurity challenges necessitating advanced solutions. Digital Twin technology, which enables the creation of near-perfect digital replicas of physical systems, offers a promising approach to enhancing security and safety. This paper presents a literature review of the existing research to identify the challenges and future directions for integrating DT technology into IIoT from a security perspective. We aim to establish a comprehensive understanding of emerging features, including predictive analytics, real-time threat detection, and cybersecurity management. Additionally, this review highlights critical gaps, including complexity, model fidelity, real-time data processing, and scalability, which hinder the successful deployment of DT technology. Our study will assist researchers, cybersecurity practitioners, and policymakers in understanding the potential, limitations, and future advancements of this crucial area. Full article

While the rapid development and widespread application of drone technology have brought about significant advancements, they have also introduced security challenges, making anti-UAV technology a key research focus. However, existing methods still face severe challenges when dealing with UAV tracking in complex scenarios. To address this, this paper proposes an integrated Motion-associated Detection and Tracking Collaboration (MDTC) system for anti-UAV applications. To better handle the perception of target existence states, we designed a motion association module that dynamically senses the presence of targets and responds quickly to target disappearance. Simultaneously, to address the issue of feature degradation in small targets, we optimized the detection branch to enhance robust perception of multi-scale targets. Additionally, the proposed verification matching mechanism can infer the integrity and reliability of targets in occluded scenarios, ensuring stable tracking. Compared to existing methods, our approach achieves superior performance across three benchmark datasets. On Anti-UAV600, it attains IoU, ACC, and SR scores of 0.525, 0.427, and 0.641, respectively—surpassing the second-best method, GlobalTrack, by 6.2%, 6.4%, and 5.9%. These gains highlight the method’s strengths in prompt target response, scale adaptability, and occlusion awareness, underscoring its reliability and practicality for real-world deployment. Full article

With the rapid growth of data transmission and visual encryption technologies, Visual Secret Sharing (VSS) has become an important technique for image-based information protection. However, many existing VSS schemes remain vulnerable to dishonest participants who attempt to recover secret images through unauthorized stacking or manipulation of shares. To address this issue, this paper proposes a dishonest-participant-resistant VSS scheme based on linearly polarized shares and Probability Distribution Trees (PDTs). The proposed method embeds both secret and fake images into polarized shares, such that any unauthorized stacking of ordinary shares produces a visually plausible fake image or random noise, while only stacking that includes the master share under a predefined optical ordering reveals the true secret image. Binary image binarization and probability-guided polarization assignment are employed to improve computational efficiency and increase uncertainty against adaptive attacks. In addition to visual inspection and contrast analysis, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual information fidelity (VIF) are used as complementary metrics to distinguish authorized reconstructions from unauthorized and partial ones. Experimental results show that authorized reconstructions achieve high visual fidelity and perceptual recognizability, whereas unauthorized and partial reconstructions yield significantly degraded or misleading outputs, demonstrating effective suppression of information leakage and strong resistance against dishonest behavior. Consequently, the proposed scheme enhances security and practical usability compared with existing polarization-based VSS approaches. Full article

Saline–alkali soil salinization is a global ecological crisis affecting 932 million hectares of land worldwide, posing a severe threat to food security and ecological sustainability. Traditional improvement methods, such as chemical amendments and hydraulic engineering, are limited by high costs and environmental risks, whereas microbial amendments have emerged as eco-friendly and sustainable alternatives due to their ability to regulate soil microenvironments and enhance plant stress resistance. However, a comprehensive synthesis of their core mechanisms, global application progress, and regional adaptation characteristics is still lacking, hindering the standardization and promotion of related technologies. This review, conducted in accordance with PRISMA guidelines, systematically synthesizes 112 core studies (1990–2025) retrieved from Web of Science, Scopus, and CNKI databases, focusing on three core research objects: salt-tolerant microbial communities in saline–alkali soils (dominant taxa, functional genes, metabolic characteristics), development and optimization of microbial amendments (strain screening, composite formulation, carrier selection), and mechanisms and application effects of microbial remediation (soil–plant–microbe interactions, physicochemical improvement, crop growth promotion). Key findings include the following. (1) Dominant microbial taxa (e.g., Proteobacteria, Actinobacteria) exhibit region-specific adaptation strategies, with salt tolerance thresholds and functional characteristics varying by soil type (coastal vs. inland saline–alkali soils). (2) Composite microbial amendments, especially those combined with biochar or organic fertilizers, achieve synergistic effects in desalination, alkali reduction, and fertility improvement. (3) Core mechanisms involve organic acid-mediated pH regulation, EPS-driven ion adsorption, and plant hormone-induced stress tolerance. (4) Microbial remediation technologies have been successfully applied globally (e.g., China, Africa, Americas), resulting in average crop yield increases of 15–42% and soil salinity reductions of 30–50%. This review provides a standardized technical framework for the development and application of microbial amendments, offers theoretical support for region-specific remediation strategies, identifies key challenges (e.g., strain stability, cost control) and future research directions (e.g., gene-edited strains, smart monitoring integration), and thus facilitates the industrialization and large-scale promotion of microbial remediation technologies to address global saline–alkali soil issues. Full article

The rapid growth of cryptocurrencies and non-fungible tokens (NFTs) has expanded technological opportunities, but it has also increased the exposure surface to cyber threats, creating a need for a more precise understanding of the field’s scientific evolution. This study aims to systematically analyse academic output related to cybersecurity and cyber threats within cryptocurrency and NFT ecosystems, identifying central themes, the most influential authors, and emerging trends. A bibliometric methodology was employed, based on the PRISMA 2020 protocol and scientific mapping tools such as SciMAT (v1.1.06) and VOSviewer (v1.6.20), using a corpus of 337 articles published between 2014 and 2025. The findings indicate sustained growth in the literature, a marked geographical and editorial concentration, and the presence of motor themes such as blockchain, cybersecurity, emerging technologies and illegal mining, alongside emerging areas such as intrusion detection. The results also reveal a progressive integration of artificial intelligence techniques in the detection and prevention of attacks. In conclusion, this study provides a comprehensive overview of the state of the art, identifies critical gaps, and underscores the need for interdisciplinary approaches to strengthen security in decentralised environments. Full article

Soybean (Glycine max (L.) Merr.) plays a pivotal role in global food security as a primary source of vegetable protein and oil. However, its production is increasingly jeopardized by the frequent concurrence of drought and heat stress, a scenario predicted to intensify under ongoing climate change. While the effects of individual stresses have been well documented, the combined occurrence of drought and heat imposes unique physiological challenges, such as the conflict between stomatal closure for water conservation and transpirational cooling, that critically impair yield stability. This review provides a comprehensive synthesis of the physiological and molecular mechanisms governing soybean responses to these combined stresses, with a specific focus on modifications of root system architecture and the sensitivity of biological nitrogen fixation. We critically analyze recent advances in genomic resources, highlighting key quantitative trait loci (QTLs) and candidate genes identified through genome-wide association studies (GWAS) and multi-omics integration. Furthermore, we propose integrated breeding strategies that bridge conventional breeding with cutting-edge technologies, including high-throughput phenotyping, speed breeding, and CRISPR/Cas9-mediated genome editing, underpinned by high-throughput phenotyping and speed breeding. By presenting a roadmap for developing climate-smart soybean cultivars, this review aims to support sustainable agricultural practices that ensure both adaptation and mitigation in a changing climate. Full article

Cassava mosaic disease (CMD) is a major constraint to cassava production in sub-Saharan Africa, particularly in Burkina Faso, where it poses a serious threat to rural food security. This study examined the impact of adopting innovative cassava mosaic disease management practices on cassava yields in the Guiriko and Nando regions of Burkina Faso. To address potential biases arising from differences in characteristics between adopters and non-adopters, an econometric approach based on the instrumental variables (IV) method within a counterfactual framework was employed to estimate the local average treatment effect (LATE). The data were drawn from a survey conducted in September 2023 among 511 cassava producers. The results indicate that the adoption of innovative cassava mosaic disease management practices had a positive and statistically significant effect on agricultural yields. Productivity gains were estimated at 29% in the Guiriko region and 41% in the Nando region, highlighting spatial heterogeneity in impacts. These findings suggest that promoting the diffusion of such practices can substantially improve cassava productivity and reduce the vulnerability of rural households. In addition, the analysis showed that socioeconomic and technical factors, including farmers’ age, membership in cassava producer organizations, household income levels, and the use of chemical fertilizers, also influence productivity outcomes. Overall, the study underscores the importance of strengthening agricultural extension services, supporting producer organizations, and promoting appropriate technologies to maximize the benefits of cassava mosaic disease management practices for food security and rural development. Full article

Measurement, Reporting and Verification (MRV) concepts have emerged as a means for reviewing and ensuring the effectiveness of energy efficiency measures (EEMs) in smart buildings. Nevertheless, high technological and regulatory demands imposed by the Energy Efficiency Directive, Article 8 (EED 8), result in limited adaptation, which makes the transition of the MRV concept into a practically applied framework a challenging endeavor. A significant concern lies in ensuring data integrity, accuracy and transparency throughout the entire adaptation and implementation process of the MRV concept. This study addresses these challenges by developing and evaluating a structured MRV framework tailored to smart building environments. The MRV framework design was tested in a real-world use case in Berlin, demonstrating its applicability for measuring, reporting and verifying energy efficiency data from smart buildings. The results confirmed the applicability of the approach, while also revealing persistent barriers related to data sovereignty, security and interoperability. Ensuring trust, transparency and long-term data accessibility requires robust governance structures and alignment with legal and ethical standards. Future work will focus on scaling the MRV framework to additional sectors and refining mechanisms for secure data sharing and automated verification. Full article

The iron and steel industry is a major contributor to global carbon emissions, necessitating an urgent transition to sustainable production technologies. This study investigates the techno-economic feasibility and environmental impact of a hydrogen-based direct reduced iron (DRI) production process via multi-stage fluidized bed reactors, focusing on the ironmaking stage and excluding downstream steelmaking routes. A comprehensive process model was developed incorporating reduction kinetics and validated against experimental data, achieving high predictive accuracy with an R² exceeding 0.999. The environmental assessment reveals that the renewable energy-based scenario achieves specific CO₂ equivalent (CO₂e) emissions of 36.8 kg per ton of hematite, corresponding to a 98.1% reduction compared to the conventional blast furnace baseline. Although the initial techno-economic analysis indicates a higher unit production cost of 636.83 USD/ton due to renewable energy costs, the sensitivity analysis demonstrates that cost competitiveness against the natural gas-based scenario is secured at a carbon price threshold of 1.44 USD/tCO₂e. Furthermore, under the 2018 average Korea Emission Trading System price of 20.66 USD/tCO₂e, the cost deceases to 589.76 USD/ton, offering a 7.39% economic advantage over the grid-based alternative. These findings indicate that green hydrogen integration and carbon pricing can bridge economic gaps, supporting economically viable low-carbon DRI production. Full article

Artificial Intelligence is increasingly embedded in national security, defense, and critical infrastructure systems, yet the security of these systems remains insufficiently addressed in traditional cybersecurity education. National initiatives led by the National Security Agency and the National Science Foundation have identified the Security of Artificial Intelligence (SecureAI) as a distinct educational priority supported by formal knowledge units and program validation requirements. Concurrently, workforce data and federal reporting reveal persistent shortages of qualified cybersecurity professionals, particularly in defense and government sectors. This paper presents Illinois Institute of Technology as a case study in the design of a SecureAI applied concentration aligned with NSA-style knowledge units and Center of Academic Excellence principles. The paper demonstrates how a four-course SecureAI program, anchored by a shared undergraduate and graduate cybersecurity foundation, addresses emerging AI security risks while strengthening the national cybersecurity workforce pipeline. Full article