Search Results (3,849)

Industrial Internet of Things (IIoT) and SCADA-connected networks are increasingly vulnerable to Distributed Denial of Service (DDoS) attacks, which can disrupt time-sensitive industrial processes and compromise operational continuity. Effective mitigation requires accurate and low-latency attack detection at the network edge, where industrial gateways operate under strict constraints in computation, memory, and energy. This study investigates Deep Reinforcement Learning (DRL) for real-time binary DDoS detection and proposes a detector based on Proximal Policy Optimisation (PPO) for deployment in resource-constrained IIoT environments. Four DRL agents, namely Deep Q-Network (DQN), Double DQN, Dueling DQN, and PPO, are trained and evaluated within a unified experimental pipeline incorporating automatic label mapping, numerical feature selection, robust scaling, and class balancing. Experiments are conducted on three representative benchmark datasets: CIC-DDoS2019, Edge-IIoTset, and CICIoT23. Performance is assessed using accuracy, precision, recall, F1-score, false positive rate, false negative rate, and CPU inference latency. The reward function is asymmetric: +1 for correct classification, −1 for false positive, and −2 for false negative, penalising missed attacks more heavily for IIoT safety. The results show that PPO provides a competitive accuracy–latency tradeoff across all three datasets, achieving the highest mean accuracy of 97.65% and ranking first on CIC-DDoS2019 with a score of 95.92%, while remaining competitive on Edge-IIoTset (99.11%) and CICIoT23 (97.92%). PPO also converges faster than the value-based baselines. Inference latency is below 0.8 ms per sample on a standard CPU (Intel i7-11800H), confirming real-time feasibility. To support practical deployment, the trained PPO policies are exported to ONNX format (≈9 KB per model), enabling lightweight and PyTorch-independent inference on industrial edge gateways. Full article

Financial time series in turbulent markets exhibit complex long-memory dynamics and multifractal features that traditional deep learning models fail to capture due to inherent exponential forgetting mechanisms. To address this, we propose Frac-Attn-GL, a novel Fractional-order Spatiotemporal Attention-based GRU-LSTM framework. Grounded in the Fractal Market Hypothesis, the model embeds Grünwald–Letnikov fractional-order operators into a dual-channel architecture (FracLSTM and FracGRU) to characterize long-range memory with rigorous power-law decay priors. Furthermore, an extreme-aware asymmetric loss function is designed to drive a dynamic spatiotemporal routing mechanism, enabling adaptive shifts between long-term macro trends and short-term micro shocks. Empirical tests on major U.S. stock indices reveal three significant findings. First, the Frac-Attn-GL framework substantially reduces prediction errors, achieving up to a 93.1% RMSE reduction on the highly volatile NASDAQ index compared to standard baselines. Second, the adaptively learned fractional-order parameters exhibit a consistent quantitative alignment with the market’s empirical multifractal singularity spectrum, supporting the physical interpretability of the model’s endogenous memory mechanism. Finally, hybrid residual multifractal diagnostics indicate that the framework effectively captures deep long-range correlations, reducing the Hurst exponent of the prediction residuals from ~0.83 to approximately 0.50, a level consistent with the absence of significant long-range dependence. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

The development of high-performance electrode materials with controlled morphology remains a key challenge for advancing supercapacitor technologies. In this study, polyethylene glycol (PEG)-assisted hydrothermal synthesis was employed to engineer NiCo₂S₄ nanostructures with controlled morphology for enhanced supercapacitor performance. The influence of PEG concentration on nucleation behavior, structural evolution, and electrochemical characteristics was systematically investigated. The optimized NiCo₂S₄ electrode synthesized with 0.2% PEG (NiCoS-P2) exhibited a hierarchical flower-like nanosheet architecture with reduced agglomeration and improved electrochemically accessible surface area. As a result, the electrode delivered a high areal capacitance of 13.689 F/cm² (specific capacitance of 6845 F/g) at 5 mA/cm², along with excellent rate capability and superior cycling stability, retaining 84.16% capacitance after 12,000 cycles. Electrochemical analysis revealed that the charge storage process is predominantly diffusion-controlled with enhanced ion transport kinetics. Furthermore, an asymmetric supercapacitor device assembled using NiCoS-P2 as the positive electrode and activated carbon as the negative electrode demonstrated a wide operating voltage of 1.5 V, delivering an areal capacitance of 0.409 F/cm² (specific capacitance of 204.5 F/g), an energy density of 0.128 mWh/cm², and a power density of 2.99 mW/cm². The device also exhibited excellent long-term stability with 85.3% capacitance retention after 7000 cycles. This work highlights the effectiveness of polymer-assisted structural engineering in optimizing transition metal sulfide electrodes for advanced energy storage applications.: Full article

Rapid urbanization has intensified jobs–housing separation and increased commuting distances in megacities, posing challenges for sustainable urban development. Existing studies often examine commuting behavior at a single spatial scale or focus on either residential or employment locations. Using mobile phone signaling data, this study derives network-based commuting distances within the suburban ring of Shanghai and integrates multiple built environment indicators. A multiscale framework is developed using six spatial units, ranging from 2 to 4 km grids to street-level zones, to assess spatial scale effects and support the selection of an appropriate analytical unit. The 3.5 km grid was selected for subsequent analysis as a balance between spatial detail and statistical stability. Within this framework, Multiscale Geographically Weighted Regression (MGWR) examines the spatial heterogeneity and scale effects of built environment factors from both residential and employment perspectives. The results show: (1) The choice of spatial unit significantly affects model performance, with the 3.5 km grid providing a suitable balance between spatial detail and statistical stability. (2) Built environment indicators exhibit clear multiscale effects, with different variables operating at global and local spatial scales. (3) Residential and employment locations show significant asymmetric effects, as enterprise density is associated with shorter commuting distances at residential locations but longer distances at employment centers. These findings indicate the joint role of multiscale spatial structure and dual-end built environments, supporting spatially differentiated planning and transport policies. Full article

Carbon fiber-reinforced polyimide composites are critical for aerospace applications in high-temperature environments of 300–500 °C. However, conventional PMR-15- and PEPA-terminated polyimides are limited by their insufficient glass transition temperatures (T_g) and low crosslinking densities. This study proposes a reactive backbone construction strategy by employing 4,4′-(ethyne-1,2-diyl)diphthalic anhydride (EBPA) as a difunctional monomer copolymerized with asymmetric 2,3,3′,4′-biphenyl tetracarboxylic dianhydride (α-BPDA) and 4,4′-oxydianiline to synthesize polyimide resins containing both backbone ethynyl and terminal phenylethynyl groups. The effects of EBPA content on the curing behavior, thermomechanical properties, and elevated temperature mechanical performance were systematically investigated. The incorporation of EBPA significantly elevated T_g from 378 °C to 486 °C. Compared to the EBPA-0 control, the optimized EBPA-2 composite exhibited 7.3% and 3.6% improvements in room temperature flexural strength and modulus, respectively. Notably, at 400 °C, EBPA-2 demonstrated retention rates of 69.9%, 93.7%, and 61.6% for flexural strength, flexural modulus, and interlaminar shear strength, exceeding EBPA-0 by 16.9, 8.9, and 18.6 percentage points. SEM analysis confirmed the effective suppression of interfacial debonding at elevated temperatures. These findings elucidate the structure–property relationships between molecular structure, T_g, and short-term high-temperature mechanical retention, providing a promising resin matrix for advanced aerospace carbon fiber composites. Full article

Fully decoupled radio access networks (FD-RANs) achieve spectral efficiency and coverage flexibility for 6G via independent uplink (UL) and downlink (DL) base station operation, yet dynamic user mobility brings critical challenges to joint user association and resource allocation. Asymmetric interference and heterogeneous base station capacities cause persistent network unfairness, while uncoordinated mobility management triggers ping-pong handovers and heavy handover overheads. To resolve these intertwined problems, we propose a fully decoupled, mobility-resilient and fairness-guaranteed framework, which integrates short-term congestion pricing with the long-term Jain fairness index for equitable resource distribution and introduces a composite handover penalty with a strict physical hysteresis margin to block invalid handovers. We formulate the optimization problem as a novel Sliding-Window Hysteresis-Integrated Fairness Two-Layer Multi-Armed Bandit (SHIFT-MAB) model, embedding an exponentially weighted moving average (EWMA) sliding-window mechanism to track real-time channel fluctuations efficiently. Theoretical analysis confirms the model’s decoupling optimality, sublinear regret bound and fairness convergence. Extensive simulations show that SHIFT-MAB effectively suppresses invalid handovers, ensures high network fairness, optimizes system utility and achieves a superior handover–throughput trade-off. Full article

DC-bias may accumulate in bidirectional full-bridge LLC converters during reverse power transfer because the magnetizing branch lacks an inherent DC-blocking mechanism. This bias may cause asymmetric flux excitation in the transformer core, thereby increasing magnetic stress and even leading to core saturation. To address this issue, a confidence-scheduled hybrid DC-bias estimation and suppression method is proposed. An integration-based indicator is constructed for sensitive weak-bias detection, while a reduced-order extended Kalman filter (EKF) is introduced to improve noise immunity and dynamic tracking under strong-bias conditions. Moreover, a confidence-scheduling mechanism is developed to adaptively fuse the two estimates according to bias severity. Based on the fused estimate, a two-level suppression strategy is implemented for severe- and weak-bias conditions. Simulations and experiments on a 2 kW prototype verify that the proposed strategy achieves fast detection, highly accurate robust estimation with a steady-state error of less than 2%, and effective suppression over a wide operating range without additional bulky DC-blocking hardware. Full article

As the primary load-bearing structure of deployable mesh antenna reflectors, the surface accuracy of cable-net structures directly determines the performance of cable-net antennas. To meet surface accuracy requirements, installed cable-net antennas must undergo surface adjustments, making the measurement of cable tension very important. However, constrained by measurement capabilities and conditions, large-scale cable tension measurement is highly challenging. To address this issue, this paper proposes a piezoelectric-integrated cable-net structure. By embedding piezoelectric patches at the nodes of the cable-net structure, the deformation of crimp terminals is converted into voltage signals via the direct piezoelectric effect. Furthermore, a cable force prediction method based on a BP neural network is introduced for piezoelectric-integrated cable-net structures. This method uses piezoelectric voltage values as the input layer and self-stress equilibrium factors of the cable-net as the output layer, thereby reducing the complexity of cable force prediction. Building on this, the influence of the quantity and placement of piezoelectric patches on the accuracy of the cable force prediction model is investigated. The study demonstrates that accurate prediction can be achieved when the number of piezoelectric patches is greater than or equal to the number of self-stress equilibrium factors. Additional piezoelectric patches and asymmetric placement can further enhance the prediction model’s accuracy. Finally, the predictive model was validated in triangular, quadrilateral, and tensegrity cable-net structures, demonstrating the validity of the cable force prediction method based on the backpropagation neural network. This work leverages neural networks to provide a new approach and solution for predicting cable forces in piezoelectric-integrated cable-net structures. Full article

Central auditory processing efficiency is considered a predictor of how well children can learn to read, with the Frequency Following Response (FFR) serving as a sensitive biomarker of neural speech encoding ability. However, data regarding the 170 ms [da] stimulus in children who are native speakers of Brazilian Portuguese (BP) remain limited. This study investigated FFR results in 37 typically developing, normal-hearing children aged 8 to 10 years. Participants underwent audiological, behavioral, and academic performance screenings, followed by monaural FFR recording (using a 170 ms [da] stimulus at 80 dBnHL). Linear mixed models (LMM) were used to examine the effects of age, sex, and ear on the latencies of waves V, A, D, E, F, and O. The analysis revealed a medium effect size for waves D, E, and F, regarding the Ear factor, though statistical significance was specifically observed for wave E. For this wave, sex was also associated with a medium effect size, characterized by longer latencies in female participants. While the results for age did not reach broad statistical significance, the presence of medium effect sizes in wave E may suggest ongoing refinement of neural synchrony and asymmetric maturation during this developmental period. This study contributes to the characterization of neural speech encoding in the Brazilian Portuguese-speaking children and may support future investigation involving auditory processing disorders and learning difficulties. Full article

This study examines the time–frequency dynamics between CO₂ emissions and their determinants—oil prices, renewable energy deployment, and climate policy uncertainty—in Türkiye from 1987Q2 to 2024Q1. We integrate a rolling-window Nonlinear Autoregressive Distributed Lag (NARDL) model with wavelet coherence analysis to capture evolving asymmetric effects and multi-scale transmission mechanisms. Our findings reveal pronounced, persistent asymmetries. Oil price decreases stimulate CO₂ emissions substantially more than equivalent price increases reduce them, yielding a negative asymmetry effect. Renewable energy demonstrates a stable, negative long-run relationship with emissions, with wavelet analysis indicating this effect concentrates over medium-to-long-term horizons, underscoring its structural decarbonization role. Climate policy uncertainty exerts fragmented, episodic influences, disrupting short-to-medium-term emission trajectories. Rolling-window estimates confirm these asymmetric relationships shift markedly around structural breaks, including the 2001 domestic crisis and the 2008 global financial crisis. The study concludes that effective decarbonization requires temporally calibrated policies: counter-cyclical carbon pricing to offset oil price asymmetries, and credible long-term frameworks to sustain renewable energy investments. Methodologically, the results demonstrate the value of combining time-domain and frequency-domain techniques to diagnose complex, evolving interactions in the energy–environment nexus. Full article

This study focuses on the of key engineering parameters for the repair of shipboard carbon fiber reinforced polymer composite structures using a scarf patch repair configuration. A three-dimensional finite element model was developed to systematically analyze the effects of repair location (center-symmetric, diagonal-asymmetric, and edge-unidirectional) and cut-out depth (2.0 mm, 3.0 mm, and 4.0 mm) on the mechanical response of the repair structure. The results indicate that although the local stress level of the center-symmetric repair is slightly higher, it provides a continuous load transfer path with more balanced stress distribution, demonstrating the best overall mechanical performance. When the cut-out depth is 3.0 mm, the repair structure achieves an optimal balance between stress uniformity and displacement coordination, effectively reducing the risk of early adhesive layer failure and local buckling. This study identifies the optimal parameter combination for scarf patch repairs, providing important theoretical foundations and references for the design of repair processes and the standardization of engineering practices in shipboard composite structures. Full article

The security of financial messaging systems is critical to maintaining trust in digital financial platforms. Despite advances in cryptography, many contemporary systems remain vulnerable to channel-based and cryptographic threats, including eavesdropping, interception, tampering, and unauthorized access. Hybrid cryptographic models that combine asymmetric encryption for secure key exchange with symmetric encryption for efficient data protection have emerged as effective approaches for strengthening confidentiality, integrity, and authenticity in financial message communications. This study presents a scoping review of literature published between 2015 and 2025, mapping research on user vulnerabilities in financial messaging systems and examining the role of hybrid cryptographic models in mitigating these risks. Guided by the PRISMA-ScR reporting standards, 615 articles were identified across nine scholarly databases. Forty-four studies met the inclusion criteria after systematic screening. The findings reveal a growing emphasis on hybrid encryption strategies, particularly RSA–AES and ECC–AES combinations, due to their balance of security strength and computational efficiency. However, significant gaps persist in empirical validation, real-world deployment, and user-centred security design, especially in mobile-first and resource-constrained environments. Existing research largely prioritizes theoretical performance and algorithmic efficiency, with limited attention to practical integration, usability, and operational constraints. This review highlights the need for holistic security frameworks that integrate cryptographic robustness with usability, regulatory compliance, and contextual deployment considerations. It provides a structured foundation for future research focused on developing scalable, user-centric, and resilient security solutions for financial messaging systems. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article

This study reveals the internal mechanism of the synergetic evolution of financial support, technological innovation, and industrial upgrading from the perspective of system synergy. It aims to provide a theoretical basis and reference for promoting benign interactions among these elements, thereby driving high-quality economic development. During the research process, an evaluation indicator system was constructed based on China’s industrial development data, utilizing the entropy method to determine indicator weights and the Haken model to analyze synergy effects. In a methodological innovation, this study identifies the system’s order parameters to derive the potential function. Through this approach, it systematically analyzes the dynamic evolution characteristics and synergetic mechanisms of the composite system. The research results indicate that the three systems have formed a mutually promoting and closely coupled compound synergetic mechanism, rather than following a single linear transmission path. The overall synergy level presents a medium-to-low development trend, following an asymmetric U-shaped evolution trajectory that first decreases and then slowly recovers. Furthermore, the degree of synergy exhibits an inverse relationship with the volatility of the subsystems, suggesting that the stability of synergy is highly susceptible to external forces and remains in a state of dynamic flux. Full article