Search Results (1,091)

Lignin, an abundant and renewable biopolymer, holds significant potential for asphalt modification owing to its unique aromatic structure and reactive functional groups. This review summarizes the main lignin preparation routes and key physicochemical attributes and assesses its applicability for enhancing asphalt performance. The physical incorporation of lignin strengthens the asphalt matrix, improving its viscoelastic properties and resistance to oxidative degradation. These enhancements are mainly attributed to the cross-linking effect of lignin’s polymer chains and the antioxidant capacity of its phenolic hydroxyl groups, which act as free-radical scavengers. At the mixture level, lignin-modified asphalt (LMA) exhibits improved aggregate bonding, leading to enhanced dynamic stability, fatigue resistance, and moisture resilience. Nevertheless, excessive lignin content can have a negative impact on low-temperature ductility and fatigue resistance at intermediate temperatures. This necessitates careful dosage optimization or composite modification with softeners or flexible fibers. Mechanistically, lignin disperses within the asphalt, where its polar groups adsorb onto lighter components to boost high-temperature performance, while its strong interaction with asphaltenes alleviates water-induced damage. Furthermore, life cycle assessment (LCA) studies indicate that lignin integration can substantially reduce or even offset greenhouse gas emissions through bio-based carbon storage. However, the magnitude of the benefit is highly sensitive to lignin production routes, allocation rules, and recycling scenarios. Although the laboratory research results are encouraging, there is a lack of large-scale road tests on LMA. There is also a lack of systematic research on the specific mechanism of how it interacts with asphalt components and changes the asphalt structure at the molecular level. In the future, long-term service-road engineering tests can be designed and implemented to verify the comprehensive performance of LMA under different climates and traffic grades. By using molecular dynamics simulation technology, a complex molecular model containing the four major components of asphalt and lignin can be constructed to study their interaction mechanism at the microscopic level. Full article

The sustainable development of smart regions directly depends on the dependability and resilience of critical energy grids (CEGs) and their key components—electrotechnical systems (ETSs). Accurate and reliable assessment of ETS dependability is a critical factor in ensuring the sustainability of CEGs. The article presents a triplet-based decomposition model for complex electrotechnical systems, in which the system is represented as three-component blocks (triplets). The aim of the study is to reduce the dimensionality of the state space and simplify the Markov analysis of system sustainability and dependability under destructive impacts. Based on this model, the TRICAM method for triplet clustering and aggregation has been developed, combining structural decomposition and functional aggregation within a single stage of analysis. Unlike quasi-lumping methods, TRICAM implements a formalized three-state aggregation with clearly defined rules and preservation of the Markov structure, which simultaneously performs aggregation and structural decomposition and enables hierarchical model construction. The method maintains uniformity in the number of aggregated states and a fixed model dimensionality, eliminating exponential growth of the state space and simplifying implementation. TRICAM is particularly suitable for flat or hierarchically structured symmetric systems, making it an effective tool for the engineering analysis of electrotechnical systems. The results of the study demonstrate the potential contribution of the proposed method for ETS-CEG to regional sustainable development by reducing the risks associated with inaccurate and non-verifiable assessment of ETS dependability. Full article

Remote sensing image semantic segmentation faces substantial challenges in training and transferring models across images with varying resolutions. This issue can be effectively mitigated by continuously learning knowledge derived from new resolutions; however, this learning process is severely plagued by catastrophic forgetting. To address this problem, this paper proposes a novel continual learning framework termed Language-Guided Resolution-Continual Learning (i.e., LaGu-RCL), which alleviates catastrophic forgetting through two complementary strategies. On the one hand, a multi-resolution image augmentation pipeline is introduced to synthesize higher- and lower-resolution variants for each training batch, allowing the model to learn from images of diverse resolutions at every training step. On the other hand, a language-guided learning strategy is proposed to aggregate features of the same resolution while separating those of different resolutions. This ensures that the knowledge acquired from previously learned resolutions is not disrupted by that from unseen resolutions, thereby mitigating catastrophic forgetting. To validate the effectiveness of the proposed approach, we construct MR-ExcavSeg, a multi-resolution dataset covering several counties in Chongqing, and conduct comparative experiments between LaGu-RCL and several state-of-the-art continual learning baselines. Experimental results demonstrate that LaGu-RCL achieves significantly superior segmentation performance and continual learning capability, verifying its advantages. Full article

Accurate forecasting of citywide electric vehicle (EV) charging load is critical for alleviating station-level congestion, improving energy dispatching, and supporting the stability of intelligent transportation systems. However, large-scale EV charging networks exhibit complex and heterogeneous spatio-temporal dependencies, and existing approaches often struggle to scale with increasing station density or long forecasting horizons. To address these challenges, we develop a modular spatio-temporal prediction framework that decouples temporal sequence modeling from spatial dependency learning under an encoder–decoder paradigm. For temporal representation, we introduce a global aggregation mechanism that compresses multi-station time-series signals into a shared latent context, enabling efficient modeling of long-range interactions while mitigating the computational burden of cross-channel correlation learning. For spatial representation, we design a dynamic multi-scale attention module that integrates graph topology with data-driven neighbor selection, allowing the model to adaptively capture both localized charging dynamics and broader regional propagation patterns. In addition, a cross-step transition bridge and a gated fusion unit are incorporated to improve stability in multi-horizon forecasting. The cross-step transition bridge maps historical information to future time steps, reducing error propagation. The gated fusion unit adaptively merges the temporal and spatial features, dynamically adjusting their contributions based on the forecast horizon, ensuring effective balance between the two and enhancing prediction accuracy across multiple time steps. Extensive experiments on a real-world dataset of 18,061 charging piles in Shenzhen demonstrate that the proposed framework achieves superior performance over state-of-the-art baselines in terms of MAE, RMSE, and MAPE. Ablation and sensitivity analyses verify the effectiveness of each module, while efficiency evaluations indicate significantly reduced computational overhead compared with existing attention-based spatio-temporal models. Full article

With the wide application of cloud computing in multi-tenant, heterogeneous nodes and high-concurrency environments, model parameters frequently interact during distributed training, which easily leads to privacy leakage, communication redundancy, and decreased aggregation efficiency. To realize the collaborative optimization of privacy protection and computing performance, this study proposes the Heterogeneous Federated Homomorphic Encryption Cloud (HFHE-Cloud) model, which integrates federated learning (FL) and homomorphic encryption and constructs a secure and efficient collaborative learning framework for cloud platforms. Under the condition of not exposing the original data, the model effectively reduces the performance bottleneck caused by encryption calculation and communication delay through hierarchical key mapping and dynamic scheduling mechanism of heterogeneous nodes. The experimental results show that HFHE-Cloud is significantly superior to Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Personalization (FedPer) and Federated Normalized Averaging (FedNova) in comprehensive performance, Homomorphically Encrypted Federated Averaging (HE-FedAvg) and other five baseline models. In the dimension of privacy protection, the global accuracy is up to 94.25%, and the Loss is stable within 0.09. In terms of computing performance, the encryption and decryption time is shortened by about one third, and the encryption overhead is controlled at 13%. In terms of distributed training efficiency, the number of communication rounds is reduced by about one fifth, and the node participation rate is stable at over 90%. The results verify the model’s ability to achieve high security and high scalability in multi-tenant environment. This study aims to provide cloud service providers and enterprise data holders with a technical solution of high-intensity privacy protection and efficient collaborative training that can be deployed in real cloud platforms. Full article

This study focuses on fabrication and comprehensive characterization of gold nanoparticles (AuNPs) stabilized with polyvinylpyrrolidone (PVP) and polyethylene glycol (PEG), correlating polymer degradation with colloidal stability and localized surface plasmon resonance (LSPR) behavior under controlled gamma doses from 5 to 125 Gy. AuNPs were synthesized via laser-assisted synthesis (LAS) in aqueous medium containing PVP or PEG as a stabilizing and capping agent. Morphology, size distribution, and surface functionalization of the resulting AuNPs@polymer-stabilized were verified through UV-Vis spectroscopy, FTIR, XRD, DLS, zeta potential, and TEM. Results show that the polymer shell effectively preserved the nanoparticles’ integrity by minimizing aggregation and maintaining LSPR features even after exposure to high gamma doses (>75 Gy). PVP demonstrated superior protection compared to PEG, due to the robustness of the solvation layer and carbonyl groups of PVP coating around the AuNPs. These findings highlight the potential of polymer-stabilized AuNPS for applications in radiation-rich environments, while demonstrating LAS as an environmentally friendly and efficient synthesis route. Full article

For the first time, this paper introduces Nabla fractional calculus into the distributed Nash equilibrium (NE) seeking problem of aggregative games (AGs) with partial decision information in undirected communication networks, and proposes two novel fractional-order distributed algorithms. In the considered setting, each agent can access to only local information and collaboratively estimates the global aggregate through communication with its neighbors. Both algorithms adopt a backward-difference scheme followed by an implicit fractional-order gradient descent step. One updates local aggregate estimates via fractional-order dynamic tracking and the other uses fractional-order average dynamic consensus protocols. Under standard assumptions, convergence of both algorithms to the NE is rigorously proved using nabla fractional-order Lyapunov stability theory, achieving a Mittag-Leffler convergence rate. The feasibility of the developed schemes is verified via numerical experiments applied to a Nash-Cournot game and the coordination control of flexible robotic arms. Full article

Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems. Full article

Linkable ring signatures are a type of ring signature scheme that can protect the anonymity of signers while allowing the public to verify whether the same signer has signed the same message multiple times. This functionality makes linkable ring signatures suitable for applications such as cryptocurrencies and anonymous voting systems, achieving the dual goals of identity privacy protection and misuse prevention. However, existing post-quantum linkable ring signature schemes often suffer from issues such as excessive linear data growth the adoption of post-quantum signature algorithms, and high circuit complexity resulting from the use of post-quantum zero-knowledge proof protocols. To address these issues, a logarithmic-size post-quantum linkable ring signature scheme based on aggregation operations is proposed. The scheme constructs a Merkle tree from ring members’ public keys via a hash algorithm to achieve logarithmic-scale signing and verification operations. Moreover, it introduces, for the first time, a post-quantum aggregate signature scheme to replace post-quantum zero-knowledge proof protocols, thereby effectively avoiding the construction of complex circuits. Scheme analysis confirms that the proposed scheme meets the correctness requirements of linkable ring signatures. In terms of security, the scheme satisfies the anonymity, unforgeability, and linkability requirements of linkable ring signatures. Moreover, the aggregation process does not leak information about the signing members, ensuring strong privacy protection. Experimental results demonstrate that, when the ring size scales to 1024 members, our scheme outperforms the existing Dilithium-based logarithmic post-quantum ring signature scheme, with nearly 98.25% lower signing time, 98.90% lower verification time, and 99.81% smaller signature size. Full article

Given global efforts to promote sustainable energy transitions, this study investigates how the deployment of renewable energy technologies (RETs) relates to multidimensional societal welfare and provides empirical evidence on these linkages in Lithuania. The purpose of the study is to provide an integrated, Lithuania-specific assessment of how economic, social, and environmental determinants associated with RET deployment are related to multiple dimensions of societal welfare. Drawing on scientific literature, an integrated indicator framework is developed that links the economic, social, and environmental determinants of renewable energy technology (RET) deployment to six societal welfare dimensions, as defined by the Lithuanian Quality of Life Index. Using official Lithuanian statistics for 2020–2024, a standardized panel dataset is constructed and Pearson correlation analysis and multiple linear regression are applied using aggregated determinant categories, with model assumptions verified using the Breusch–Pagan and Durbin–Watson tests. Correlation results show very strong positive links between RET intensity indicators and key economic welfare measures (for example, wages, GDP per capita, foreign direct investment, disposable income), with absolute correlation coefficients typically between 0.90 and 0.99 (p < 0.05), and strong negative correlations between air-pollution indicators and GDP, income, FDI, and education (correlation coefficients between −0.96 and −0.90; p < 0.05). The results indicate that RET-related economic determinants have a statistically significant positive effect on the societal welfare dimensions of material living conditions; entrepreneurship/business competitiveness; and public infrastructure, living-environment quality/safety. Social factors also significantly support the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. In the retained regression models, explanatory power is very high (R² between 0.91 and 0.999), with positive and statistically significant coefficients for the economic determinant (regression coefficients between 0.43 and 0.96; p < 0.05) and negative, statistically significant coefficients for the environmental determinant in the entrepreneurship and public-infrastructure dimensions (regression coefficients between −1.13 and −1.51; p < 0.05). Environmental determinants are associated with lower air pollution but show negative effects on the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. Overall, the findings suggest that RET deployment is an important correlate of the economic aspects of societal welfare, while environmental and social dimensions display more complex, domain-specific impacts. Full article

With the rapid development of the Internet of Things (IOT), 5G, and modern power systems, demand-side loads are becoming increasingly observable and remotely controllable, which enables demand-side flexibility to participate more actively in grid dispatch and emergency support. Under extreme rainstorm conditions, however, component failure risk rises and the availability and dispatchability of demand-side flexibility can change rapidly. This paper proposes a risk-aware emergency regulation framework that translates rainstorm information into actionable multi-load aggregation decisions for urban power systems. First, demand-side resources are quantified using four response attributes, including response speed, response capacity, maximum response duration, and response reliability, to enable a consistent characterization of heterogeneous flexibility. Second, a backpropagation (BP) neural network is trained on long-term real-world meteorological observations and corresponding reliability outcomes to estimate regional- or line-level fault probabilities from four rainstorm drivers: wind speed, rainfall intensity, lightning warning level, and ambient temperature. The inferred probabilities are mapped onto the IEEE 30-bus benchmark to identify high-risk areas or lines and define spatial priorities for emergency response. Third, guided by these risk signals, a two-level coordination model is formulated for a load aggregator (LA) to schedule building air conditioning loads, distributed photovoltaics, and electric vehicles through incentive-based participation, and the resulting optimization problem is solved using an adaptive genetic algorithm. Case studies verify that the proposed strategy can coordinate heterogeneous resources to meet emergency regulation requirements and improve the aggregator–user economic trade-off compared with single-resource participation. The proposed method provides a practical pathway for risk-informed emergency regulation under rainstorm conditions. Full article

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive energy consumption, high communication cost, and compromised convergence that hinder practical deployment. To address these issues in mobile/UAV networks, this paper proposes an energy-efficient optimization scheme for HFL under non-convex loss, integrating a dynamically adjustable partial-dimension model upload mechanism. By screening key update dimensions, the scheme reduces uploaded data volume. We construct a total energy minimization model that incorporates communication/computation energy formulas related to upload dimensions and introduces an attendance rate constraint to guarantee learning performance. Using Lyapunov optimization, the long-term optimization problem is transformed into single-round solvable subproblems, with a step-by-step strategy balancing minimal energy consumption and model accuracy. Simulation results show that compared with the original HFL algorithm, our proposed scheme achieves significant energy reduction while maintaining high test accuracy, verifying the positive impact of mobility on system performance. Full article

Aiming at the core issues of the traditional YOLO11n model in rail surface defect detection—fine-grained feature loss of small defects, insufficient micro-target recognition accuracy, and the mismatch of existing downsampling/fusion methods for micro-defect feature extraction—this paper proposes an improved YOLO11n algorithm with two-dimensional network structure innovations. First, the Adaptive Downsampling (ADown) module is introduced into the backbone network for the first time, retaining global features via 2D average pooling and extracting local details through channel-split multi-path convolution/max pooling to avoid fine texture loss. Second, the original SOEP-RFPN-MFM neck network is designed, integrating SNI, GSConvE and MFM modules to achieve dynamic weighted fusion of multi-scale features and break the bottleneck of inefficient small-target feature aggregation. Trained and verified on a 4020-image rail dataset covering four defect types (Spalling, Squat, Wheel Burns, Corrugation), the improved algorithm achieves 93.7% detection accuracy, 92.4% recall and 95.6% mAP, realizing incremental improvements of 1.2, 2.6 and 0.8 percentage points, respectively, compared with the original YOLO11n, which is particularly optimized for rail micro-defect detection scenarios. This study provides a new deep learning method for rail transit micro-defect detection and a reference for scenario-specific improvement of lightweight YOLO11n models. Full article

As distributed generators (DGs) and flexible adjustable loads (FALs) further penetrate distribution networks (DNs), to reduce regulation complexity compared with traditional centralized control frameworks, DGs and FALs in DNs should be packed in several clusters to enable their dispatch to become standard in the industry. To mitigate the negative influence of DGs’ and FALs’ spatiotemporal distribution and uncertain output characteristics on dispatch, this paper proposes an intelligent dynamic cluster partitioning strategy for DNs, from which the DN’s resources and loads can be intelligently aggregated, organized, and regulated in a dynamic and optimal way with relatively high implementation efficiency. An environmental model based on the Markov decision process (MDP) technique is first developed for DN cluster partitioning, in which a continuous state space, a discrete action space, and a dispatching performance-oriented reward are designed. Then, a novel random forest Q-learning network (RF-QN) is developed to implement dynamic cluster partitioning by interacting with the proposed environmental model, from which the generalization and robust capability to estimate the Q-function can be improved by taking advantage of combining deep learning and decision trees. Finally, a modified IEEE-33-node system is adopted to verify the effectiveness of the proposed intelligent dynamic cluster partitioning and regulation strategy; the results also indicate that the proposed RF-QN is superior to the traditional deep Q-learning (DQN) model in terms of renewable energy accommodation rate, training efficiency, and portioning and regulation performance. Full article

The growing environmental effect of asphalt pavements has fueled interest in sustainable alternatives including the application of recycled materials and self-healing systems. This research investigates the synergistic possibilities of steel slag aggregates and steel wool fibers in hot-mix asphalt compositions to increase sustainability and let crack healing via electromagnetic induction heating. Using either recycled steel slag or natural porphyritic aggregates, two kinds of AC16 Surf S mixtures with 35/50 bitumen were created incorporating two levels of steel fiber content (2% and 4%). Based on repeated semi-circular bending (SCB) testing following regulated induction heating and confinement, a committed self-healing evaluation plan was developed. The results verified that combinations including recycled steel slag met or outperformed traditional mixes in terms of mechanical behavior. Induction heating successfully set off partial recovery of fracture toughness, with more fiber content and repeated heating cycles producing better healing values. Recovery levels ran from 14.6% to 40%, therefore proving the practicality of this approach. These results encourage the creation of asphalt mixtures with improved endurance and environmental advantages. The research offers both an approved approach for assessing healing and real-world recommendations for the construction of low-maintenance, round pavements utilizing induction-based techniques. Full article