Search Results (359)

Lithium is a critical mineral for traction batteries and a cornerstone of the sustainable transition toward low-carbon transportation. Understanding the supply–demand dynamics and resource-saving potential of lithium is essential for advancing circular economy goals and ensuring the long-term stability of the electric vehicle (EV) industry. This study develops an integrated lithium forecast framework by coupling a System Dynamics (SD) model with dynamic Material Flow Analysis (MFA) and multi-scenario pathways. To ensure robust conclusions, the model is validated against historical data, and a multi-level sensitivity analysis is conducted to address the inherent uncertainties of evolving socio-technical assumptions over a ten-year horizon. The simulation results reveal that under the baseline scenario, China’s EV stocks and annual lithium demand will grow by 8.3 and 4.7 times from 2024 to 2035, respectively. This rapid expansion poses a significant sustainability challenge, as cumulative demand will deplete 50–71% of China’s domestic lithium reserves by 2035. Despite a projected supply–demand gap of 110–120 kt/yr, the study identifies critical pathways for resource decoupling and circularity. Technology-driven interventions, such as enhancing energy density and extending battery lifespan, can reduce primary lithium demand by up to 18.9%. Furthermore, optimizing the closed-loop recycling system can contract the supply–demand gap by 31–39%, demonstrating the pivotal role of secondary resource recovery in building a resilient supply chain. Despite this reduction, a persistent reliance on international markets remains inevitable. These findings provide a quantified scientific foundation for policymakers, emphasizing that lithium security requires a synergistic transition from volume-based subsidies to resource efficiency mandates and standardized, formal closed-loop recycling systems. Full article

With the rapid development of cloud-based data sharing technologies, enterprises and organizations tend to outsource their local data to cloud servers. They adopt searchable encryption (SE) techniques to access and search encrypted data. However, most existing SE schemes use static ranking strategies based on query–index similarity. These strategies fail to capture users’ personalized retrieval preferences and often result in suboptimal search performance. In this article, we present a privacy-preserving data sharing framework with personalized encrypted retrieval (PP-PER) that combines SE technology with federated learning. PP-PER trains user interest models locally on user devices by utilizing historical query behavior. Only encrypted model parameters are uploaded for aggregation, which avoids the centralized collection of users’ private data. In addition, we design an attention-based user query update algorithm. The learned personalized features are integrated into the ciphertext query process. This design enables personalized ranking results and improves the user retrieval experience. Furthermore, PP-PER combines matrix factorization with ciphertext-policy attribute-based encryption (CP-ABE). This mechanism ensures secure document key distribution and supports fine-grained access control. Finally, we formalize the security model under a practical threat and leakage setting and provide a theoretical analysis of the proposed scheme. Experimental results on real-world datasets further validated its practicality and effectiveness. Full article

Digital Twins (DTs) are increasingly recognized as a strategic technology for enhancing cybersecurity in industrial environments, particularly in the face of rising threats targeting Operational Technology (OT). After comparatively examining closely related DT–cybersecurity frameworks to position the contribution within the existing research landscape, this paper presents a systematic literature review and comparative analysis of 19 recent DT-based cybersecurity studies, focusing on their relevance to incident detection and response in sectors such as Industrial Internet of Things (IIoT), manufacturing, and energy. The analysis evaluates each study across multiple dimensions, including attack types, detection and response mechanisms, DT integration, and technology stacks. From this review, we derive a consolidated set of requirements, categorized as functional, non-functional, security-specific, and domain-specific. These requirements serve as the foundation for a novel, cybersecurity-focused, ISO 23247-based framework. The proposed architecture formalizes a DT-enabled incident detection and response lifecycle aligned with ISO 23247. It is explicitly mapped to the derived requirements and detailed with practical implementation considerations. This work contributes a structured, evidence-based approach to DT-based security engineering and offers a reference design for researchers and practitioners aiming to build resilient, adaptive cybersecurity solutions in industrial settings. Full article

The Industrial Internet of Things (IIoT) enhances industrial efficiency but also introduces substantial security challenges. Authentication is a key building block for securing IIoT networks. However, many recent IoT authentication schemes rely on offline registration and transmit temporary identity credentials in plaintext during registration, which exposes them to privileged-user attacks and limits their practicality in complex deployment scenarios. To address these issues, this paper presents an efficient three-factor authentication scheme with secure online registration for IIoT. The proposed scheme leverages Intel Software Guard Extensions (SGX) to protect the registration master key and support online registration. In addition, a dynamic credential update mechanism is introduced to mitigate privileged-user attacks. The security of the scheme is validated through ProVerif-based formal verification and informal security analysis, while its performance is evaluated through comparative analysis and NS-3 simulations. The results demonstrate that the proposed scheme provides enhanced security with low overhead, making it suitable for IIoT environments. Full article

The rapid convergence of the Internet of Things (IoT) and decentralized finance (DeFi) is reshaping the digital economy by enabling autonomous, trustless, and value-driven interactions among connected devices. This paper provides a comprehensive survey of the emerging paradigm that combines IoT’s pervasive sensing and communication capabilities with DeFi’s programmable financial infrastructure. We first discuss the motivation behind this convergence and explore key opportunities, including autonomous machine-to-machine (M2M) payments, decentralized data marketplaces, and trustless IoT service provisioning. Despite its potential, IoT–DeFi integration introduces significant security and privacy challenges related to smart contract vulnerabilities, consensus protocol risks, oracle manipulation, and constrained device capabilities. We review existing mitigation approaches such as lightweight cryptography, secure contract design, and decentralized identity management, and critically assess their limitations in heterogeneous, resource-limited environments. Building on this analysis, identify research gaps and propose future directions emphasizing formal verification of IoT-integrated smart contracts, robust oracle design, interoperability frameworks, and privacy-preserving trust models. This survey systematically maps opportunities, threats, and open issues. In doing so, it guides researchers and practitioners toward building secure, scalable, and energy-efficient IoT–DeFi ecosystems for next-generation decentralized applications. Full article

Reliable internal communication and information systems (CISs) constitute a foundational yet often under-examined layer of contemporary digital government and organizational resilience. While existing research has predominantly focused on citizen-facing services and external platforms, comparatively limited attention has been devoted to how internal CIS user support structures function as governance mechanisms that sustain institutional continuity, authority, and operational control. This article reconceptualises CIS user support as a governance mechanism and empirically examines its implementation within a high-security public-sector organization, the Slovenian Armed Forces. The study integrates perspectives from information systems governance and IT service management with an in-depth case study based on legal and organizational framework analysis, structured user interviews (n = 24), and longitudinal operational data on CIS incidents and service performance (2022–2024). The findings demonstrate that a multi-tier CIS user support architecture, informed by IT service management principles and integrated with cybersecurity and cryptographic control functions, contributes to institutional reliability, process accountability, and operational resilience. Building on these results, the article develops a five-dimensional governance model of CIS user support encompassing organizational authority, process formalization, security integration, communication legitimacy, and data-driven oversight. By conceptualizing CIS user support as an embedded governance structure rather than merely an operational IT function, the study extends information systems governance and digital government scholarship and provides analytically transferable insights for high-security public organizations operating under conditions of elevated risk and institutional constraint. Full article

Coastal settlements in Bangladesh are geographically flood-prone areas. This physical nature erodes the size and shape of those settlement boundaries over time. Such changes leave communities vulnerable in terms of securing a living place and livelihoods. However, the research arena rarely addresses the long-term changing aspects of settlement and the local governance responses to vulnerability. To examine this situation, this study explored settlement transformation patterns and governance challenges, using the case study of Chattogram Division in Bangladesh from 2005 to 2025. It applied a mixed-methods approach. The analysis, using the technique of Multi-temporal Landsat imagery with Random Forest classification, revealed complex settlement trajectories. It showed built-up areas expanded significantly between 2005 and 2015 but shrank by 2025, reflecting both hazard exposure and displacement pressures. Union-level analysis identified 62 coastal unions with high to very high settlement change. Conducting field surveys in selected Juidandi and Kalamarchhara unions through focus group discussions with communities and interviews with local officials highlighted recurring inundation, permanent land loss affecting thousands of households, and persistent disruptions to livelihoods. This study also found moderate emergency responses in selected unions; however, strategic planning for relocation, health, and well-being of communities is insufficient. Continuous resource constraints and poor coordination with communities and line organizations made local implementation less effective, which blurs the effectiveness of disaster risk reduction policies. These findings underscore the necessity of union-level governance capacity building, integrating community-based adaptation with formal interventions, and developing spatially differentiated relocation strategies to enhance the resilience of climate-vulnerable coastal settlements. Full article

Sustainable urban development increasingly relies on hyperlocal environmental analytics created by smart city platforms that combine stationary and mobile sensors, Earth observations, meteorology, and land-use data. However, accurate spatio-temporal resolution can provide indirect identification and amplify cybersecurity threats. This article proposes the regulatory and technical mapping that implements the General Data Protection Regulation (GDPR) and the Network and Information Security Directive (NIS2) throughout the lifecycle of environmental data—reception, transport, storage, analytics, sharing, and publication. The methods combine doctrinal legal analysis, a review of the scope of recent research, formalized compliance modeling, modeling with synthetic city-scale datasets, expert identification, and demonstration of integrated analytics. The demonstration links deep evaluation of neural abnormalities (convolutional plus recurrent layers), short-term Fourier transformation of sensor signals, byte-to-image telemetry fingerprints, and protocol event counters, thereby tracking detection to explanatory evidence and to control actions. Deliverables include a matrix aligning lifecycle stages with GDPR principles and rights, as well as with the responsibilities of NIS2; a checklist for assessing the impact on data protection, which takes into account the risks of fairness and stigmatization; a basic set of controls for identification and access, secure design, monitoring, continuity, supplier assurance, and incident reporting; as well as a multi-layered publishing strategy that combines transparency with privacy through aggregation, delayed release, differentiated privacy budgets, and research enclaves. The visualization confirms that technical signals can be included in audit-ready reporting and automated response, while the guidelines legally clarify the relevant bases for common use cases such as air quality assurance networks, noise mapping, citizen sensor applications, and mobility and exposure modeling. The effects of the policy emphasize shared services for small municipalities, supply chain security, and ongoing review to counteract the mosaic effect. Overall, the study shows how cities can maximize environmental and social value based on environmental data, while maintaining privacy, sustainability, and equity by design. Full article

This paper introduces an LLM-driven design-time workflow for Internet of Things (IoT) and network management system development that combines the generative and summarization capabilities of Large Language Models with the formal rigor of Model-Driven Engineering (MDE). The key novelty lies in grounding LLM-assisted topology design and network management, including reasoning about configuration code to formally verifiable models, enabling security- and safety-aware decisions by design with improved trust and explainability compared with black-box runtime solutions. The approach relies on activity-diagram-based models that provide formal semantics for capturing control flows, decision points, and interactions among IoT devices, edge nodes, and network management components, supporting systematic functional safety validation. Design-time security analysis is realized through MDE combined with Object Constraint Language (OCL) rules, allowing explainable detection of misconfigurations, policy violations, and potential vulnerabilities before deployment. The workflow is evaluated using representative IoT and mobile network management scenarios, demonstrating enhanced effectiveness and up to 15 times reduction in detection and corrective action time for critical tasks. Full article

Livestock theft remains a deeply entrenched and economically damaging form of rural crime in South Africa, with significant implications for food security, community trust, and agricultural sustainability. This exploratory study critically evaluates the effectiveness of Livestock Theft Prevention Forums (LTPFs), established in 1995, as a community policing mechanism. Using a mixed-methods approach, the research integrates quantitative survey data from 628 respondents—primarily livestock producers—with qualitative insights from key stakeholders, including members of the South African Police Service (SAPS). The analysis spans multiple dimensions: crime trends, recovery rates, stakeholder engagement, technological adoption, and forum functionality. Findings reveal that while formal reporting of theft has improved, recovery rates remain stagnant, and financial losses continue to rise. Technological interventions, such as Global Positioning System (GPS) tracking and closed-circuit television networks, are increasingly adopted but remain inaccessible to many emerging farmers. Awareness and participation in LTPFs are uneven across provinces, with local forums often inactive and poorly coordinated. The exploratory study concludes that although LTPFs hold promise as participatory governance platforms, their impact is constrained by structural inequalities, limited institutional capacity, and historical mistrust. Strengthening these forums requires inclusive outreach, improved communication, and integration of evidence-based policing within broader rural development strategies. Full article

Wireless sensor networks ($WSNs$) are extensively used in $IoT$ applications. Secure access control and data protection are essential. Nonetheless, the wireless environment has an open nature. The limited resources of sensor devices render $WSNs$ susceptible to a variety of security attacks, causing significant difficulties in the design phase of efficient authentication and key agreement ($AKA$) protocols. This study proposes a physically unclonable function ($PUF$)-based lightweight and secure $AKA$ protocol for $WSNs$ based on elliptic curve cryptography ($ECC$). A secure password update scheme is offered, which would allow legitimate users to reset forgotten passwords without re-registration. According to formal security analysis using $BAN$ logic and $ProVerif$, the proposed protocol is secure against common attacks. Moreover, from an entropy perspective, the use of dynamic pseudonyms and fresh session randomness increase an adversary’s uncertainty about user identities, thereby limiting identity-related information leakage. Performance evaluation shows that the proposed protocol achieves lower computational and communication overhead than the existing ones, making it suitable for $WSNs$ with resource constraints. Full article

Situated within the field of modern Chinese political history, this study investigates the Late Qing New Policies (1901–1911) as a pivotal transition from a traditional tributary empire to a modern multi-ethnic nation-state. A critical limitation in current scholarship is the tendency to reduce these reforms to mere expedients for dynastic preservation, thereby overlooking the complex mechanisms by which they fundamentally reconstructed national identity and interethnic power structures amidst the “triple crisis” of territory, sovereignty, and nationality. To address this, the article employs a comprehensive historical analysis to explore how institutional restructuring in administration, military, and ideology catalyzed the transformation from imperial autocracy toward a “responsible government” framework. The research is distinguished by its innovative application of Anthony D. Smith’s theories of “ethnic” versus “civic” nationalism to deconstruct the “myth-symbol complex” of the Chinese nation, bridging the theoretical divide between the “New Qing History” paradigm and empirical modernization narratives. Findings demonstrate that while the Manchu leadership aimed to secure formal primacy, the practical implementation of reforms engendered a de facto Han-supported power structure, compelling the reconceptualization of the state as a “multi-ethnic constitutional monarchy” and establishing the institutional logic for the “Five Races Under One Union” model. Consequently, this study offers significant academic value by redefining the New Policies as the foundational phase of modern China, providing a crucial theoretical framework for understanding the continuity of China’s multi-ethnic statehood and national identity beyond the dynastic collapse. Full article

Modern TCP/IP networks are increasingly exposed to unpredictable conditions, both from the physical transmission medium and from malicious cyber threats. Traditional stochastic models often fail to capture the non-linear and highly sensitive nature of these disturbances. This work introduces a formal mathematical framework combining classical network modeling with chaos theory to describe perturbations in latency and packet loss, alongside adversarial processes such as denial-of-service, packet injection, or routing attacks. By structuring the problem into four scenarios (quiescent, perturbed, attacked, perturbed-attacked), the model enables a systematic exploration of resilience and emergent dynamics. The integration of artificial intelligence techniques further enhances this approach, allowing automated detection of chaotic patterns, anomaly classification, and predictive analytics. Machine learning models trained on simulation outputs can identify subtle signatures distinguishing chaotic perturbations from cyber attacks, supporting proactive defense and adaptive traffic engineering. This combination of formal modeling, chaos theory, and AI-driven analysis provides network engineers and security specialists with a powerful toolkit to understand, predict, and mitigate complex threats that go beyond conventional probabilistic assumptions. The result is a more robust methodology for safeguarding critical infrastructures in highly dynamic and adversarial environments. Full article

The rapid growth of mobile payment systems has positioned Near Field Communication (NFC) as a core enabling technology. However, conventional NFC protocols primarily emphasize transmission efficiency rather than robust authentication and privacy protection, which exposes users to threats such as eavesdropping, replay, and tracking attacks. In this study, a lightweight and privacy-preserving authentication protocol is proposed for NFC-based mobile payment services. The protocol integrates anonymous authentication, replay resistance, and tracking protection while maintaining low computational overhead suitable for resource-constrained devices. A secure offline session key generation mechanism is incorporated to enhance transaction reliability without increasing system complexity. Formal security verification using the Scyther tool (version 1.1.3) confirms resistance against major attack vectors, including impersonation, man-in-the-middle, and replay attacks. Comparative performance analysis further demonstrates that the proposed scheme achieves superior efficiency and stronger security guarantees compared with existing approaches. These results indicate that the protocol provides a practical and scalable solution for secure and privacy-aware NFC mobile payment environments. Full article

Wind power plants (WPPs), as large-scale cyber–physical systems (CPSs), have become essential to renewable energy generation but are increasingly exposed to cyber threats. Attacks on supervisory control and data acquisition (SCADA) networks can cause cascading physical and economic impacts. The systematic synthesis of cyber risk analysis methods specific to WPPs and cyber–physical energy systems (CPESs) is a need of the hour to identify research gaps and guide the development of resilient protection frameworks. This study employs a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to review the state of the art in this area. Peer-reviewed studies published between January 2010 and January 2025 were taken from four major journals using a structured set of nine search queries. After removing duplicates, applying inclusion and exclusion criteria, and screening titles and abstracts, 62 studies were examined for analysis on the basis of a synthesis framework. The studies were classified along three methodological dimensions, qualitative vs. quantitative, model-based vs. data-driven, and informal vs. formal, giving us a unified taxonomy of cyber risk analysis approaches. Among the included studies, 45% appeared to be qualitative or semi-quantitative frameworks such as STRIDE, DREAD, or MITRE ATT&CK; 35% were classified as quantitative or model-based techniques such as Bayesian networks, Markov decision processes, and Petri nets; and 20% adopted data-driven or hybrid AI/ML methods. Only 28% implemented formal verification, and fewer than 10% explicitly linked cyber vulnerabilities to safety consequences. Key research gaps include limited integration of safety–security interdependencies, scarce operational datasets, and inadequate modelling of environmental factors in WPPs. This systematic review highlights a predominance of qualitative approaches and a shortage of data-driven and formally verified frameworks for WPP cybersecurity. Future research should prioritise hybrid methods that integrate formal modelling, synthetic data generation, and machine learning-based risk prioritisation to enhance resilience and operational safety of renewable-energy infrastructures. Full article