Search Results (329)

Energy storage systems (ESSs) play a crucial role in mitigating the intermittency and variability of renewable energy sources (RESs) and enhancing the stability and reliability of modern power systems. However, the inherent limitations of individual storage technologies, particularly the trade-off between energy density and power density, restrict their ability to satisfy diverse operational requirements. In this context, hybrid energy storage systems (HESSs), which combine complementary storage technologies, such as batteries, supercapacitors, and flywheels, have emerged as an effective solution capable of simultaneously delivering high-energy and high-power performance. This paper presents a comprehensive review of HESS architectures, converter topologies, energy management strategies (EMSs), and applications. The EMS taxonomy is organized into classical and intelligent control. Classical EMS approaches are categorized into filtration-based, rule-based, deadbeat, droop, sliding mode, and fuzzy logic control, whereas intelligent EMS approaches encompass optimization-based methods, including model predictive control, as well as learning-based techniques such as supervised and reinforcement learning. Moreover, HESS applications are examined across grid-scale systems, microgrids, renewable energy systems, transportation, power quality improvement, frequency regulation, peak shaving, and uninterruptible power supply systems. Representative implementations are also reviewed to identify current technological trends, operational challenges, and performance trade-offs. Finally, future research directions are outlined, with emphasis on digital twins, privacy-preserving and explainable learning frameworks, cyber–physical security, and adaptive and scalable EMSs. Full article

Agricultural subsidy policies have increasingly shifted from production-oriented support toward incentives that reward environmental stewardship and the provision of ecosystem services. Despite their rapid expansion, evidence regarding the environmental effectiveness, economic efficiency, market implications, and food-security consequences of these eco-centric agricultural subsidies remains fragmented across policy frameworks and regions. This review synthesizes current evidence on eco-centric agricultural subsidies by comparatively evaluating their environmental, economic, and policy outcomes across developed and developing economies. The review was conducted using a structured literature search following PRISMA-informed review procedures, drawing upon peer-reviewed articles, systematic reviews, policy evaluations, and international institutional reports retrieved from major scientific databases and policy sources. The evidence indicates that eco-centric subsidies generally improve biodiversity conservation, soil health, carbon sequestration, water quality, and reductions in chemical inputs when payments are appropriately targeted and supported by effective monitoring and institutional capacity. Performance-based and results-oriented payment schemes frequently demonstrate greater environmental additionality and cost-effectiveness than conventional practice-based payments; however, their broader implementation remains constrained by monitoring costs, verification requirements, administrative complexity, and regional institutional capacity. Economic outcomes are more heterogeneous, with benefits depending on program design, agroecological conditions, market structures, and farm characteristics. While these subsidies can enhance environmental returns on public investment, challenges including land-value capitalization, unequal benefit distribution, transaction costs, market distortions, and potential short-term productivity trade-offs remain important policy concerns. Evidence regarding food-security impacts is similarly context-dependent and varies across production systems and geographical regions. Overall, the review demonstrates that no single subsidy instrument is universally effective. Instead, the greatest environmental and economic benefits are achieved through integrated policy portfolios combining targeted incentives, outcome-based payments, robust monitoring systems, digital technologies, carbon-market integration, and equitable program design. The review also identifies important evidence gaps concerning developing-country experiences, long-term cost-effectiveness, and standardized evaluation frameworks, providing priorities for future research and policy development. Full article

Image steganography is an information-hiding technique, aiming to achieve confidentiality and data privacy while transmitting the data in a digital environment. Over the last two decades, steganography has evolved from classical spatial domain embedding to intelligent and adaptive steganographic systems capable of balancing imperceptibility, embedding capacity, robustness and security. This paper presents a review by categorizing the existing techniques into spatial domain-based, transform domain-based, hybrid and adaptive intelligent techniques. The review follows the PRISMA approach to make the selection process transparent for the inclusion and exclusion of papers in the study. Initially, the reviews include the spatial domain-based methods focusing on higher embedding capacity and simple embedding strategy, followed by transform-domain based techniques, including discrete cosine transform, discrete wavelet transform, and other multi-resolution wavelet transforms aiming to enhance robustness and imperceptibility by embedding the data into frequency coefficients. This paper further explores the methods that combine these techniques with other recent trends to develop adaptive and hybrid techniques. These techniques mainly integrate chaotic theory to enhance the security of secret data before embedding and optimization algorithms such as genetic algorithm, particle swarm optimization, Firefly, etc., for adaptive embedding to achieve an improved tradeoff. Finally, intelligent and adaptive techniques based on deep learning models such as convolutional neural networks, autoencoders, and generative adversarial networks are examined, highlighting their ability to learn intelligent embedding strategies and resist modern steganalysis. A comparative analysis is presented, including the technique, strengths, and limitations, together with the discussion of performance evaluation metrics and vulnerability analysis under image processing attacks. The review highlights the current trends and outlines the future direction to develop next-generation secure image steganographic systems. Full article

The security of a signature scheme given in the standard model (SM) will be more stable and reliable than that given in the random oracle model (ROM). Fuzzy identity-based signature (FIBS) enables a user to generate a signature for a set of descriptive attributes, defined as $\omega ={\left\{{\omega }_j\right\}}_{j=1}^n$. Any attributes set ${\omega }^{\prime }={\left\{{\omega }_j^{\prime }\right\}}_{j=1}^n$ can validate the signature provided that the distance between $\omega$ and ${\omega }^{\prime }$ is below a predefined threshold. Most of the existing FIBS schemes are based on the ROM. It is of great significance to design a FIBS scheme based on the SM. In this work, we adopt fingerprint minutiae as the biometric modality and present a feature extraction algorithm $\mathcal{E}$ that transforms raw minutiae into quantized, privacy-preserving attribute sets, and we present a False Rejection Rate (FRR)–False Acceptance Rate (FAR) trade-off framework to calibrate matching threshold t, with adjustable n for qualified error performance. Subsequently, we present a novel and efficient FIBS scheme, which is proven to be unforgeable in SM for any polynomially bounded adversary under selective identity attack model. Compared to the existing FIBS schemes based on the ROM, our new FIBS scheme has a strong security model. Compared to the existing FIBS scheme based on the SM, our new FIBS scheme reduces total computation consumption by approximately $33.55\%$ and achieves a significant reduction in communication consumption, saving approximately $68.07\%$ of the message and signature size, which is suitable for biometric authentication. Full article

Subterranean metro stations require substantial structural material inputs and can generate prolonged disruption in dense urban environments. Rather than assessing the environmental performance of an entire metro system, this study compares the embodied environmental impacts and surface-occupation effects of two representative underground station typologies from the Istanbul M7 Metro Line: a deep Top-Down station and a shallower Cut-and-Cover station. The proposed SECURE framework integrates Building Information Modelling (BIM)-based Life Cycle Assessment (LCA) with a Spatiotemporal Disruption Index (SDI), which is used as a physical proxy for cumulative surface occupation during construction. The assessment covers material production, transport, end-of-life processes, and recovery benefits in accordance with ISO 14040/14044, while excluding operational energy and on-site construction machinery from the comparative LCA boundary. The results show that the deep Top-Down typology requires 3.11 m³/m² of structural concrete, compared with 2.22 m³/m² for the Cut-and-Cover typology. Within the analysed cases, this higher structural material intensity is associated with a 54% increase in Global Warming Potential, from 9953.62 to 15,343.14 kg CO₂ eq/m². The difference primarily reflects the combined influence of excavation depth, station geometry, reinforced concrete volume, and permanent retaining elements, rather than the construction sequence alone. In contrast, the Top-Down typology substantially reduces modelled surface occupation, with cumulative SDI decreasing from 115,800 to 45,675 m²·month. These findings indicate a trade-off between embodied environmental burden and potential socio-spatial disruption. The study therefore suggests that early-stage metro station planning should evaluate excavation depth, structural mass, construction sequence, and surface continuity together. For deep urban stations where Top-Down construction is required, low-carbon cement substitution and localised material sourcing may help reduce material-related environmental impacts. Full article

Large Language Model (LLM)-based agents automate complex tasks by integrating external tools such as web browsers, e-mail clients, file readers, and APIs, but this same integration exposes them to indirect prompt injection (IPI) attacks, in which malicious instructions hidden in tool content hijack the agent. A central but often overlooked question is how defending against such attacks affects the LLM and its own task performance and computational efficiency. In this study, we design a comprehensive testbed and a layered defense, Spotlight-Guard, that combines spotlighting-based input isolation, an LLM detection-and-quarantine pipeline, and instruction integrity based on a Hash-based Message Authentication Code (HMAC) into a single framework, and we evaluate it jointly along two axes: security and LLM performance. Experiments on locally hosted 7B-class open-weight models (Qwen-2.5-7B, Mistral-7B, and DeepSeek-Coder) use Attack Success Rate (ASR) for security and benign-task success rate together with confusion-matrix-based metrics (precision, recall, and F1) for task performance, all with bootstrap 95% confidence intervals. Across a stratified, fixed-seed benchmark of 250 adversarial and 250 benign cases per configuration, the full system reduces the ASR from 36.0% to 17.2% while preserving a 97.2% benign-task success rate and raising the detection F1 from 0.749 to 0.892, demonstrating that strong protection need not degrade the model’s task performance. A component ablation isolates each layer’s contribution, an adaptive-attack evaluation confirms a low ASR (6.7%) under attacks crafted to target the pipeline, and an analysis of computational cost (model invocations per request) quantifies the efficiency overhead, characterizing the security–performance trade-off of layered defenses on open-weight LLMs. Full article

Renewable hydrogen storage can absorb surplus wind and solar generation, reduce curtailment, and provide a flexible energy carrier. Its value depends not only on the electrolyzer, storage tank, and fuel cell, but also on the operational strategy used to coordinate these components. This study develops a sensor-informed model predictive control (MPC) framework for renewable hydrogen storage and evaluates its performance against a no-hydrogen configuration and a reactive hydrogen storage controller under identical operating conditions, component parameters, and evaluation metrics. The MPC uses receding horizon optimization with measured hydrogen storage and safety states, together with short-term forecasts of renewable generation, load, electricity price, and hydrogen service demand. Using synthetic but physically motivated time-series profiles, the results show that hydrogen hardware under reactive rule-based operation eliminates renewable curtailment and reduces operating cost; however, it frequently depletes the tank to the reserve boundary and fails to reliably meet subsequent hydrogen demand. In contrast, the sensor-informed MPC achieves full hydrogen accessibility, eliminates curtailment, reduces total operating cost to 71.2% of the no-hydrogen baseline, and lowers cost by 46.3% relative to the rule-based controller. The MPC imports more grid electricity than the rule-based case, showing a clear trade-off between hydrogen service reliability and grid dependence. These results indicate that reliable renewable-hydrogen operation depends not only on storage hardware, but also on predictive supervisory control that coordinates renewable use, hydrogen service, reserve security, and grid interaction. Full article

Vehicular ad hoc networks (VANETs), as a key communication component of the Internet of Vehicles (IoV), enable vehicles and roadside infrastructure to exchange information efficiently, thereby supporting road safety and traffic management. However, because these communications take place over open wireless channels, VANETs are exposed to message forgery, replay, identity disclosure, and unauthorised access by revoked vehicles. To address these issues, this paper proposes EPAF, an efficient privacy-preserving batch authentication scheme with revocation support for fog-enabled VANETs. EPAF uses roadside fog nodes to distribute update keys and report information related to misbehaving vehicles, thereby reducing reliance on remote centralised processing. Rather than assuming ideal tamper-proof devices that store system-wide secrets, EPAF requires protected storage only for vehicle-local certificates, limiting the impact of compromising an individual vehicle device. The scheme employs batch verification to authenticate multiple messages from different vehicles in a single procedure, reducing verification overhead in message-intensive traffic conditions. It further introduces an update-key mechanism through which legitimate vehicles obtain current authentication keys, whereas revoked vehicles are prevented from generating valid authentication messages in subsequent revocation periods. Under the honest-authority model, the security analysis establishes the EUF-CMA security of an authentication packet in the random-oracle model and separately addresses conditional identity privacy, traceability, and unlinkability across different pseudonym periods. Performance evaluation examines the trade-off among authentication efficiency, communication overhead, and revocation performance, showing that EPAF is a practical solution for fog-enabled vehicular communication. Full article

In this paper, a multi-image encryption and hiding scheme based on controllable compressive sensing and adaptive matching embedding is proposed. The highlights of this scheme include the use of adaptive data segmentation, controllable integer compressive sensing, and adaptive matching embedding. Firstly, a 512-bit master key, together with an HMAC-SHA512-based session key derivation mechanism incorporating plaintext hashes and random noise, is used to initialize the proposed Fractal–Chaos Hybrid Map—a novel chaotic system combining the Lorenz system with Mandelbrot fractal perturbations. This produces high-quality random sequences with superior randomness and a large key space. Secondly, the scheme applies DCT and adaptive data segmentation to process the plain images. The sparse data obtained from segmentation is compressed using a sensing matrix generated from random sequences, and then encoded and encrypted via random flipping. Finally, the adaptive matching embedding technique is used to embed the encrypted data into the carrier image. The proposed scheme performs excellently in both the compression encryption of plain images and the embedding of encrypted data into the carrier image. It supports the encryption and hiding of up to six plain images with an acceptable trade-off between embedding capacity and carrier image quality. A comprehensive security analysis, including statistical and differential attack evaluations, confirms the scheme’s strong resistance to various cryptographic threats while maintaining high efficiency. Full article

Intensified paddy production plays a crucial role in sustaining rice productivity and food security; however, long-term high-frequency puddling, heavy machinery operations under wet soil conditions, simplified cropping systems, and insufficient organic matter inputs have progressively degraded the physical structure of paddy soils. Such structural degradation not only weakens soil water movement, nutrient supply, and aeration but also restricts rice root penetration, alters rhizosphere processes, and disrupts plant–soil feedbacks. Previous studies have largely focused on individual aspects such as soil compaction, amendment-based improvement, water management, or root responses, whereas an integrated understanding of the multi-source drivers, functional consequences, and restoration pathways of soil structural degradation in intensified paddy fields remains limited. Following the overarching theme of soil degradation and regeneration, this review systematically synthesizes the indicator framework, formation mechanisms, degradation typology, ecological consequences, and regulation strategies of paddy soil structural degradation. We further clarify the transition of degraded paddy soils from single physical constraints to the coupled decline of physical, chemical, and biological functions, and compare the agronomic performance, environmental implications, implementation feasibility, and trade-offs of different restoration pathways. Existing evidence indicates that soil structural degradation in paddy fields can impair root-zone pore connectivity, rhizosphere oxygen supply, nutrient acquisition, microbial-mediated carbon and nitrogen cycling, and greenhouse gas regulation, thereby affecting rice growth, yield stability, and the ecological sustainability of paddy systems. Accordingly, the restoration of degraded paddy soils should move beyond short-term loosening or single-factor amendment toward integrated regeneration strategies that maintain soil structural health, reconstruct plough-layer functions, enhance root–soil interactions, and promote the synergistic recovery of pore networks, aggregates, organic carbon, and microbial processes. This review provides a theoretical basis and research reference for the precise restoration of soil structural constraints and the sustainable management of plant–soil systems in intensified paddy fields. Full article

Traditional techno-economic assessments of zero-emission mobility frequently rely on static Total Cost of Ownership (TCO) models that fail to capture the concurrent evolution of economic and environmental parameters. To address this research gap, this study develops a novel dynamic multi-objective optimization framework that jointly assesses TCO and Well-to-Wheel (WTW) emissions across the period 2026–2060, capturing the non-linear trade-offs between cost minimization and lifecycle decarbonization. The model developed compares light-duty hydrogen Fuel Cell Electric Vehicles (FCEVs) with diesel, petrol, and battery-electric vehicles (EVs), incorporating time-varying Capital Expenditure (CAPEX) learning curves, fuel price trajectories, carbon pricing effects, and emissions-decay pathways. Hydrogen break-even prices are computed annually against each competing technology to identify the market conditions under which FCEVs become cost competitive. The results show that light-duty hydrogen FCEVs face a substantial entry barrier in 2026, with a TCO of approximately €275,000, far above diesel, petrol and EV alternatives. However, their relative competitiveness improves over time as hydrogen production costs decline and fossil-fuel vehicle costs increase due to the EU ETS2 and Eurovignette CO₂ surcharges. The analysis identifies two key inflection points, that is, light-duty hydrogen FCEVs become more cost effective than petrol vehicles in 2037 and reach parity with EVs in 2046. In emissions terms, light-duty FCEVs occupy a strong position on the low-WTW frontier, while EVs combine the lowest TCO with similarly favorable emissions performance. To bridge the intermediate cost-parity gap and mitigate infrastructure lock-in risks, targeted policy measures, such as carbon-weighted road toll exemptions, upstream fuel-tax subsidies under the EU ETS2 framework, and capital grants for localized commercial fleet refueling units, are essential to accelerate early-stage market industrialization and secure the economic viability of hydrogen mobility. Full article

Pumped storage hydropower (PSH), with its advantages of fast response and large-scale energy storage, has become a key means of enhancing power system flexibility. However, the time-varying nature of transmission line capacity may constrain the effective utilization of its regulating capability. Most existing studies treat transmission capacity as a fixed boundary, failing to adequately account for the impact of its dynamic variations on scheduling strategies. To address this gap, this paper constructs three typical transmission capacity scenarios: fixed high (no congestion), fixed low (persistent severe congestion), and time varying (capacity reduced during daytime and restored at night). A power system dispatch optimization model incorporating wind power, solar power, and pumped storage is established, with the objective of minimizing total system operating cost. Under different capacity scenarios, the pumping/generating behavior of PSH, unit output structure, controlled line operation status, and economic indicators are compared and analyzed. Furthermore, sensitivity analyses are conducted from three dimensions—congestion severity, congestion time window, and PSH installed power capacity—to comprehensively evaluate the marginal impacts of key factors on system performance. Results based on a modified IEEE 14-bus system indicate that under the fixed high scenario, PSH can achieve free arbitrage with the best economic performance, but does not account for capacity fluctuation risks. Under the fixed low scenario, persistent congestion leads to substantial wind and solar curtailment, significantly increased operating costs, and prolonged full loading of lines, posing the highest security risk. Under the time-varying scenario, PSH is forced to pump at full power during nighttime and generate continuously during daytime, effectively alleviating line flow pressure. Although the operating cost is slightly higher than that of the fixed high scenario, line overload is avoided, renewable energy accommodation is significantly improved, and substantial security gains are achieved at a moderate economic cost. This paper reveals the forcing mechanism of time-varying transmission capacity on PSH scheduling strategies, verifies the feasibility of the time-varying capacity strategy as an effective trade-off between economics and security, and provides a theoretical basis for optimal PSH operation and transmission capacity management in power systems with high shares of renewable energy. Full article

Private information retrieval (PIR) enables a client to retrieve records from a server-hosted database without revealing the requested item. Most high-performance single-server PIR systems are optimized for exact index or keyword lookup, whereas controlled-vocabulary and structured-identifier applications may require a partially specified root-aligned prefix with per-position wildcards. This paper presents a stateless protocol for that restricted but practically relevant retrieval model. The construction combines a compact trie with leveled Brakerski–Gentry–Vaikuntanathan (BGV) homomorphic encryption, ciphertext–plaintext equality testing, single-instruction multiple-data (SIMD) packing, compressed-edge batching, and power-of-two slot rotations. The client stores no database-dependent hint, and the server stores no persistent client-specific evaluation material; all the query ciphertexts and required evaluation keys are uploaded in the online phase. We explicitly position the construction as a protocol-level integration for richer private retrieval semantics rather than as a new foundational PIR or homomorphic-encryption primitive. Security is formulated for an honest-but-curious single-query adversary under an explicit public leakage function covering trie topology, compressed-edge lengths, payload layout, query-upload shape, and the fixed response schedule. We further analyze broad wildcard queries: the match cardinality can reach the number of indexed keys, while the number of response ciphertexts is determined by public output capacity rather than by the private query. Experiments on three controlled synthetic datasets and one anonymized enterprise dataset show that compact trie compression and packed edge evaluation reduce server-side online latency relative to uncompressed and serial homomorphic baselines, with the largest gains on high-prefix-sharing workloads. The implementation achieves exact set-level agreement with a plaintext oracle at two wildcard densities while exposing an explicit trade-off among richer query semantics, stateless deployment, public structural leakage, and communication overhead. Full article

The large-scale integration of wind and solar power has significantly intensified peak-shaving pressure and operational risk in provincial power grids. Effectively leveraging the flexible regulation capability of hydropower to mitigate the uncertainty of wind and solar output is a promising approach to enhancing grid security and stability. To simultaneously improve the peak-shaving performance and risk resilience of hydro-wind-solar systems for a provincial power grid, this paper proposes a multi-objective short-term scheduling model that jointly minimizes the peak value of net load and the Conditional Value-at-Risk (CVaR) of flexibility shortage. Specifically, the residual peak load is used to quantify the system’s peak-shaving burden, while the average CVaR of upward/downward ramping deficits across all time periods characterizes the tail risk associated with insufficient flexibility. Historical wind and solar forecast error data are employed to generate representative uncertainty scenarios via Gaussian mixture model, and the Rockafellar–Uryasev formulation is adopted to accurately embed CVaR into a mixed-integer linear programming (MILP) framework. Furthermore, the normalized normal constraint (NNC) method is introduced to compute a well-distributed Pareto front. Numerical simulations based on a real-world hydro-wind-solar system in a provincial grid in Southwest China demonstrate that the proposed model can significantly reduce the peak load while effectively mitigating flexibility shortfall risk. The resulting Pareto front clearly reveals the trade-off between peak-shaving effectiveness and risk control, providing a scientific basis for day-ahead generation scheduling and coordinated dispatch of flexible resources. Full article

There is a need to ensure that the application of artificial intelligence (AI) in increasingly automated operations is safe, human-centric, and trustworthy, and respects ethical principles. To this end, this paper presents an innovative holistic assessment framework to support certification-aware design of AI-based sociotechnical systems with a range of levels of automation along multiple design stages from low to high technology and human readiness levels (TRLs/HRLs). The holistic scope considers a range of relevant key performance areas (KPAs): safety, resilience, security, Human Factors, accountability, responsibility, liability, efficiency, societal sustainability, and environmental sustainability. The core of the framework is a seven-step cycle that assesses the KPAs for critical scenarios and evaluates the combined performance, including uncertainty and trade-offs. This provides feedback to either adapt the design at the same TRL/HRL or refine it at higher TRLs/HRLs. The framework enacted by a toolbox of assessment methods for the KPAs. The framework has been developed in the aviation domain, but it is formulated in a generic manner, enabling application to various AI techniques and operational domains. Its application is illustrated in detail for an air traffic management use case that employs an AI-based system to support air traffic controllers in sequencing aircraft. It is concluded that the framework provides a viable approach for holistic assessment of AI-based sociotechnical systems. Full article