Search Results (149,762)

Distributed Energy Resource (DER)-integrated power grids are vulnerable to cascading effects under concurrent cyber–physical attacks, where even minor disruptions in system states accumulate and amplify over time, leading to significant system failures. Traditional static risk assessment methods are insufficient for modeling these time-varying, dynamic scenarios, particularly in the context of concurrent attacks. This paper presents a dynamic risk assessment framework leveraging time-synchronized co-simulation, which integrates power system and communication network simulations within a unified time framework. Cyber-attack actions in the communication layer are mapped to corresponding physical disturbances in the distribution network, including voltage, frequency, and power variations. Using the resulting system state evolution trajectories, a Markov Decision Process (MDP)-based state transition tree captures the progression of system risk under concurrent attacks. This framework accounts for cumulative risk across different attack paths and identifies critical nodes and high-risk propagation paths within the network. By incorporating a concurrent event detector into the MDP model, the method quantifies evolving risk dynamics, overcoming the limitations of traditional static methods. Case studies on the IEEE 13-node test feeder and IEEE 14-bus system demonstrate that concurrent attacks result in a security risk metric 2.3 times higher than single-point attacks, validating the effectiveness of the proposed approach in identifying vulnerable nodes whose compromise could lead to cascading failures, supporting the risk-aware prioritization of defensive resources. Full article

Given the growing concern over the operational safety and long-term reliability of lithium-ion batteries, the accurate assessment of battery state of health (SOH) is of paramount importance. With the aim of elevating the SOH estimation exactitude and remedying the model degradation induced by data paucity, this paper proposes an SOH estimation method that integrates a data-augmentation strategy with a Long Short-Term Memory (LSTM)-iTransformer model. Specifically, multiple health characteristic factors characterizing the aging behavior are first extracted from the battery charge–discharge curves and incremental capacity (IC) curves, and the features that are highly correlated with the SOH are screened by a Pearson correlation coefficient analysis. Subsequently, the data augmentation technique is used to extend the degradation sample set. The LSTM-iTransformer model is trained based on the extended samples and evaluated on multiple performance metrics. A comparative analysis reveals a marked enhancement in predictive accuracy achieved by this method over the baseline model trained with the initial data, which validates the effectiveness of the data augmentation strategy in improving the performance of SOH estimation models. Additionally, in scenarios characterized by abundant data availability, the direct application of this model facilitates enhanced predictive precision. Full article

Voids behind the lining that develop during long-term operation can seriously compromise the seismic safety performance of metro shield tunnels. To investigate the influence of such void defects on large-diameter shield tunnels, this study systematically analyzed the causes and distribution patterns of voids. A three-dimensional discontinuous finite element model was developed to simulate the interaction among lining segments, connecting bolts, and surrounding rock. The seismic responses, including circumferential stress, interface slip, interface opening, and bolt tensile stress, were analyzed considering coupled factors such as the void circumferential angle, radial depth, distribution location, and geological conditions. Single-factor and multi-factor sensitivity analyses were conducted to evaluate the significance of the above coupled factors on the overall seismic response. The results show that lining circumferential stress, displacement, interface opening, and bolt stress increase with void enlargement, a shift in void location from the crown to the haunch, and deterioration of geological conditions. A void located at the right haunch leads to a peak circumferential stress of 3.27 MPa, causing local segment damage. Sensitivity analysis reveals that void location is the most influential factor affecting the seismic response, while geological conditions exhibit lower sensitivity. A predictive model for the peak circumferential stress around the void was established using multiple linear regression, incorporating void position, circumferential angle, and radial depth. Within the parameter range considered in this study, this model provides a theoretical basis and practical reference for rapid seismic risk assessment and safety management of shield tunnels with void defects. Full article

Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study ($N=25$, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is $\beta =0.0036$ ($p=0.495$) and the Alphabet Rank coefficient is $\beta =-0.0027$ ($p=0.642$), with the results robust to all tested parameterizations ($\lambda \in [0.05,0.20]$; $p>0.50$ throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower. Full article

Cross-site transfer learning is a promising approach to address data scarcity at newly deployed photovoltaic (PV) stations by leveraging knowledge from data-rich source sites. However, existing domain adaptation methods align feature representations without distinguishing physically meaningful causal relationships from site-specific spurious correlations, leading to negative transfer when local environmental conditions differ substantially between stations. This paper proposes CausalTransPV, a causal invariant representation learning framework that integrates explicit temporal causal discovery with selective domain alignment for cross-site PV power forecasting. The framework comprises three synergistic modules: (i) a multi-station temporal causal discovery module that jointly learns shared and station-specific causal graphs through differentiable acyclicity-constrained optimization with a cross-station invariance regularizer; (ii) a causal-guided disentangled encoder that decomposes representations into causal-invariant and site-specific subspaces using the discovered causal graph as a structural prior; and (iii) a causal-subspace transfer and prediction module that performs maximum mean discrepancy (MMD)-based domain alignment exclusively on the causal subspace. Experiments on the Desert Knowledge Australia Solar Centre (DKASC) multi-station dataset under varying target label ratios (0–50%) demonstrate that CausalTransPV achieves relative mean absolute error (MAE) reductions of 6.9–9.9% over the strongest baseline. Ablation studies, causal graph analysis, feature space visualization, and weather-conditioned case studies further validate the contribution of each component. These results suggest that causal-guided selective transfer offers an effective paradigm for reliable PV forecasting under data-scarce cross-site scenarios. Full article

This study investigates the ongoing electrical activity of local neural networks—referred to as neurodynamics—across 37 anatomically defined brain regions. We analyzed stereotactic intracranial EEG (sEEG) recordings from 106 subjects during wakeful rest, focusing on scale-free (power-law) properties to determine whether distinct brain regions exhibit unique neurodynamic signatures. Results revealed a power-law regime in two frequency ranges (approximately 0.5–4 Hz and 33–80 Hz). Notably, the power-law exponent (slope) in the high-frequency band differed significantly between cortical and subcortical areas (p < 0.01). These findings suggest that local neurodynamics, as reflected in scale-free characteristics, may serve as a functional “fingerprint” for brain region classification. This approach may contribute to functional brain parcellation efforts and offer new insights into the intrinsic organization of neuronal networks as revealed by resting-state activity analysis. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

This paper investigates the feasibility of an interior permanent magnet (IPM) rotor for 1 MW-class high-speed permanent magnet synchronous machines (PMSMs) in a hybrid propulsion system of electrified aviation. A double-layer IPM machine and a surface-mounted PM (SPM) benchmark machine with Halbach-array PMs, which are typically employed in aviation applications; are designed using the same design specifications, the same stator, double-three-phase winding layout, physical air-gap length, outer and inner diameters of rotor; and the same materials. The rotor robustness of the IPM machine using high-strength iron material has been verified through mechanical strength analysis with an outstanding safety factor margin. The electromagnetic performances of IPM and SPM benchmark machines are compared. It is found that the IPM design can achieve similar high torque/power density and high efficiency to the SPM benchmark machine, using 48% less rare-earth PM materials and a simpler rotor structure without a carbon fiber sleeve for easy manufacturing. The investigation confirms the feasibility of IPM topology for MW-class high-speed aviation propulsion machines for lower cost and more sustainable purposes. Full article

Traffic incident impact prediction remains challenging for intelligent transportation systems due to complex spatiotemporal dependencies. This study analyzes 38,430 real-world traffic incidents from Istanbul (2022–2024) to predict normalized traffic deviation $\Delta Traffic(\%)$ using machine learning with rigorous temporal validation. Three models—Random Forest (RF), XGBoost, and LightGBM—were evaluated using rolling-origin cross-validation (2022 training, 2023 testing; 2022–2023 training, 2024 testing) to prevent temporal leakage, employing a strictly operational 13-feature set that excludes information unavailable at incident onset (t₀). LightGBM achieved MAE = 26.81 ± 1.94% and R² = 0.506 ± 0.042 (mean ± std across folds) with 95% bootstrap confidence intervals of [27.54%, 28.81%] for MAE on the 2024 test set, significantly outperforming historical baselines (R² = 0.100 ± 0.054, p < 0.001, Bonferroni-corrected). Feature ablation studies revealed that temporal features contribute 65.2% of predictive power, while incident type contributes only 1.3%. Distributional robustness analysis confirms conclusions are stable across distributional treatments (log, winsorised, quantile), with feature importance rank correlations ρ = 1.000 between all treatment pairs. This work provides empirical evidence for context-aware traffic management systems and demonstrates the importance of proper temporal validation in transportation forecasting. Full article

In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on the XGBoost algorithm and LSTM neural networks, applied to predict PV energy production based on meteorological data and historical generation records from four medium-sized PV installations (30–50 kWp) located in Poland. Meteorological data were retrieved from open sources and combined with actual production measurements to build the training dataset. This paper discusses the challenges posed by these data at the given latitude, as well as issues related to processing data from newly launched installations. The performance of both approaches was evaluated in short- and medium-term forecasting, with particular attention to prediction accuracy, robustness to noisy data, and the ability to capture nonlinear relationships. Full article

This paper develops a functional operator-theoretic framework for nonlinear Erdelyi–Kober (EK) fractional dynamical systems formulated in Banach spaces endowed with the compact-open topology. Within this setting, sufficient conditions for existence, uniqueness, and Ulam–Hyers stability of solutions are established using the Banach and Schaefer fixed-point theorems. The continuity, boundedness, and Lipschitz properties of the associated nonlinear operators are analyzed to ensure well-posedness of the fractional system. As a constructive complement to the theoretical results, a power series iterative method (PSIM) is employed to obtain an explicit fractional series representation of the solution in the case $0<\alpha <1$. The applicability of the theoretical framework is illustrated through a nonlinear fractional dynamical Belousov–Zhabotinsky system (DBZS), where the assumptions of the main theorems are verified and the solution is constructed via the proposed series scheme. The results provide a coherent link between abstract fixed-point analysis and a constructive semi-analytical representation of solutions for EK fractional systems. Full article

Accurate prediction of the energy yield of bifacial photovoltaic (PV) modules requires a proper evaluation of albedo irradiance and the associated mismatch losses. In this work, an advanced tool for the assessment of the power production of bifacial modules is presented. The tool benefits from a refined numerical evaluation of ground-reflected irradiance performed through a view-factor-based cell-level approach within a realistic three-dimensional (3D) Sun-module-shadow geometry. This allows capturing both vertical and lateral nonuniformities in the irradiance distributions over the module surfaces, which are neglected in conventional module-level models. The irradiances incident on the cells are subsequently supplied to a circuit-based block, operating with a cell-level granularity as well, which computes the I–V characteristics and the maximum power point (MPP) at selected time instants. Simulations performed on a simplified tool variant assuming uniform albedo irradiance show that this approximation leads to a non-negligible overestimation of power output. An extensive comparison against state-of-the-art tools, including the previous version of our framework, allows us to conclude that the proposed method is especially advantageous for standalone modules or short-row configurations under medium-to-high albedo conditions. Moreover—like its previous version—the tool can handle a large variety of detrimental effects, namely, partial architectural shading, localized snow coverage, bird droppings, and faulty cells. Additionally, a non-zero elevation from the ground can be effectively described. It is also found that south-oriented 30°-tilted bifacial modules suffer from appreciable albedo-induced mismatch losses on the rear surface during summer under medium-albedo conditions, whereas vertically-mounted West- and East-oriented configurations are less affected by such losses. Experimental validation confirms the accuracy of the proposed framework. Full article

This paper investigates the deployment of four electric vehicle (EV) fast-charging stations (FCSs) in a commercial facility’s parking area, where multiple service centers operate on varying schedules. The commercial load demand is modeled using Monte Carlo Simulation (MCS), introducing realistic stochastic variability and overlapping power patterns with FCS operations. A single-point sensing strategy at the point of common coupling (PCC) is adopted for load disaggregation. Continuous Wavelet Transform (CWT) is employed for feature extraction, and multiclass classification is performed using Error-Correcting Output Codes (ECOC). Under commercial load interference, conventional machine-learning classifiers achieve a macro classification accuracy of 89.53%, with the lowest class accuracy dropping to 76.74%. To address this limitation, a deep learning (DL)-based framework is implemented. Simulation results demonstrate that the proposed DL approach improves overall classification accuracy from 89.53% to 100%, corresponding to a 10.47 percentage-point absolute improvement, an 11.7% relative gain, and complete elimination of misclassification errors. Notably, the most affected charging station class (FCS₂) accuracy increases from 76.74% to 100%. These results demonstrate that the proposed deep learning framework reliably detects FCS activations even under overlapping, variable, and high-power commercial load conditions, enabling more efficient energy management and optimal utilization of electrical resources, reduced energy waste, and enhanced sustainability of EV charging infrastructure within commercial facilities. Full article

Arid salt marsh ecosystems endure chronic water scarcity and high salinity stress, with the stability of their functions inextricably linked to the pivotal role of the rhizosphere microenvironment of halophytes. This study focused on three typical halophytes (Kalidium cuspidatum, Nitraria tangutorum, Reaumuria soongarica) in the Jiantan wetland, and deeply explore how these halophytes differently regulate the soil microenvironment through the rhizosphere effect. The results showed that the rhizosphere soil of Kalidium cuspidatum had higher pH, Na⁺, and K⁺ contents, while the rhizosphere soil of R. soongarica had higher total carbon, soil organic carbon, alkali-hydrolyzable nitrogen, and microbial biomass. Microbial community analysis revealed that rhizosphere soil of fungal diversity was significantly higher in K. cuspidatum than in R. soongarica, with distinct differences in bacterial and fungal community structures. These differences were closely associated with factors such as Na⁺, Olsen phosphorus, microbial biomass carbon and alkali-hydrolyzable nitrogen. Among the dominant phyla, Proteobacteria and Ascomycota predominate, with Desulfobacterota and Mortierellomycota exhibiting the highest explanatory power (>48%) for physicochemical property variations. The microbial network of rhizosphere soil of R. soongarica has the highest complexity (with 633 nodes and 3300 edges), but the proportion of positive correlation edges was the lowest (21.58%). Structural equation modeling indicates that soil physical properties indirectly influence network complexity by negatively regulating chemical properties and microbial biomass, while microbial diversity had a direct positive effect on dominant phylum composition and network complexity. This study elucidated the differentiated adaptive strategies of rhizosphere microenvironment-microbe interactions in halophytes, providing a theoretical basis for wetland ecological restoration. Full article

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS. Full article