Search Results (583)

This work develops an analytical framework for nonlinear fractional partial differential equations that combine Kirchhoff-type terms, double-phase operators, and $\psi$-Hilfer fractional derivatives. This paper investigates two classes of problems involving variable-exponent growth conditions. The first problem analyzes general nonlinear sources and formulates the solution as a fixed point of a nonlinear operator. Precisely, by proving that the functional energy is coercive, hemicontinuous, and strictly monotone, we establish the existence and the uniqueness of weak solutions via monotone operator theory. The second problem incorporates a convection-type nonlinearity, which breaks variational structure and requires the more robust theory of pseudomonotone operators. Under suitable growth and mixed-order assumptions on the nonlinearity, we prove the existence of at least one weak solution. The main tools are grounded in variable-exponent Lebesgue and Musielak–Orlicz–Sobolev spaces, with compact embeddings, modular estimates, and fractional integral identities playing a key role in the proofs. We note that the results contribute to the mathematical modeling of phenomena involving nonlocal elasticity, viscoelastic materials, phase-transition media, and fractional dynamical systems where the stiffness of the medium depends on the total deformation (Kirchhoff effect) and the energy density alternates between distinct growth regimes (double-phase). The $\psi$-Hilfer derivative enhances the scope by enabling models with tunable memory and hereditary effects. Full article

Accurate identification of key nodes in complex networks is vital for optimizing system robustness and controlling information spread. Existing centrality metrics struggle to balance the continuous extraction of global topological features with the fine-grained perception of local structures, while traditional heuristic algorithms also face severe resolution limitations. To address these issues, this paper proposes a node importance evaluation method based on fractional-order topological propagation and local information entropy (FSEC). This method overcomes the limitations of discrete integer-order propagation inherent in traditional graph walks. It constructs a continuous fractional-order topological propagation operator within the spectral graph theory framework. This enables the smooth projection of node degree features into the global topological space, thereby yielding high-order global impact factors. Furthermore, an information theory mechanism is introduced to quantify the probability distribution of a node’s information contribution within its local neighborhood. The local structural information entropy is then calculated to reflect the node’s asymmetric control over micro-level information flow. Deliberate attack simulations were conducted on nine real-world networks and three types of artificial network models. The results show that the proposed FSEC algorithm significantly outperforms baseline algorithms like Autoencoder and Graph Neural Network (AGNN), Degree Centrality, k-shell, PageRank, and Mixed Degree Decomposition (MDD) in degrading the largest connected component (LCC) and global network efficiency (NE). The proposed method also achieves the minimum Area Under the Curve (AUC) values globally. Its monotonicity is slightly lower than that of AGNN but superior to all other baseline algorithms. In addition, SIR simulations further confirm the effectiveness of the FSEC method. This approach successfully resolves the ranking tie problem among nodes in the same topological layer. Full article

New-energy plants and battery energy-storage stations increasingly depend on software-defined supervision, remote maintenance, and event-driven control, which makes cyber protection inseparable from operational responsiveness. This study presents a calibrated context-aware Security-as-a-Service orchestration framework, denoted SECaaS-CARO, for station-oriented adaptive risk control. The framework separates field assets, control services, security services, and an adaptive decision layer, and it uses a monotone nine-indicator risk score whose weights are calibrated from the training split rather than fixed heuristically. A validation-based threshold search maps that score to low-, medium-, and high-intensity service chains so that protection strength changes with session context instead of remaining static. A reproducible semi-synthetic dataset containing 17,000 station sessions was used to emulate operator login, remote maintenance, gateway misuse, and malicious command scenarios. Across 10 independently resampled 5000-session test streams, SECaaS-CARO achieved an F1 score of 0.973, a blocking success of 0.965, and the highest deployment utility of 1.173 while reducing mean latency to 21.28 ms compared with 27.06 ms for Logistic-Fixed and 28.15 ms for RandomForest-Fixed. The results indicate that an interpretable calibrated service-orchestration policy can preserve near-supervised detection quality while materially improving deployment-oriented efficiency for new-energy and energy-storage stations. Full article

The growing complexity of modern decision-making environments, characterized by multi-dimensional data, uncertainty, and dynamic behavior, demands advanced mathematical frameworks for effective information aggregation. Although fractional fuzzy tensor (FFT) models provide a powerful tool for representing such complex systems by integrating fuzzy logic, tensor structures, and fractional dynamics, the lack of suitable aggregation mechanisms significantly limits their practical applicability. To address this challenge, this paper proposes a novel family of Bonferroni mean-based aggregation operators within the fractional fuzzy tensor environment. The proposed framework extends the classical Bonferroni mean to multi-dimensional fractional fuzzy settings, enabling the effective modeling of interrelationships among criteria while preserving the structural and dynamic properties of FFTs. Specifically, four aggregation operators—namely, the fractional fuzzy tensor Bonferroni mean (FFT-BM), weighted Bonferroni mean (FFT-WBM), ordered Bonferroni mean (FFT-OBM), and hybrid Bonferroni mean (FFT-HBM)—are systematically developed. A comprehensive theoretical analysis is conducted to investigate fundamental properties such as idempotency, monotonicity, boundedness, commutativity, and stability, thereby establishing the mathematical consistency and reliability of the proposed operators. Furthermore, a structured multi-criteria decision-making (MCDM) algorithm is formulated, incorporating tensor construction, aggregation, evaluation, and sensitivity analysis phases to handle complex uncertain information effectively. To demonstrate the practical applicability of the proposed framework, a real-world case study related to disaster management decision-making is presented. The results are further validated through quantitative comparative analysis with classical and recent aggregation operators, revealing improved discrimination power, robustness, and ranking consistency. Additionally, sensitivity analysis confirms the stability of the proposed approach under varying parameters. The findings indicate that the proposed Bonferroni mean-based aggregation framework significantly enhances the capability of FFT models in handling high-dimensional, uncertain, and dynamic decision-making problems. This study not only strengthens the theoretical foundation of aggregation in tensor-based fuzzy environments but also provides a flexible and reliable decision-support tool for complex real-world applications. Full article

Addressing the challenges of fluctuating renewable energy integration and stable green ammonia production, this study develops and optimizes a deeply integrated system comprising Solid Oxide Electrolysis Cells (SOEC), Liquid Air Energy Storage (LAES), Air Separation Units (ASU), and Haber–Bosch (HB) synthesis. We constructed a simulation model in Aspen Plus incorporating Ru/C catalyst kinetic parameters to analyze key subsystem parameters and optimize operating conditions based on maximized economy and efficiency. At the integrated system level, a parametric analysis of ammonia condensation temperature was further conducted to investigate the coupling characteristics. Using real power output data from Inner Mongolia, we formulated a dynamic energy scheduling strategy satisfying 24-h self-balancing constraints. Results indicate that a system producing 1415 tons of ammonia per day achieves a maximum hourly integrated profit of 69,838 CNY under optimal conditions: a hydrogen-to-nitrogen ratio of 2.98:1, operating pressure of 169 bar, reactor inlet temperature of 380 °C, and ammonia condensation temperature of −9 °C. Increasing the LAES throttle valve outlet pressure from 1 bar to 9 bar improved round-trip efficiency from 52.65% to 72.18%. The integrated-level parametric analysis reveals that the specific electricity consumption per unit mass of ammonia exhibits a non-monotonic trend with a minimum of 8.67 kWh/kg at −10 °C, reflecting the trade-off between refrigeration power consumption and cold energy recovery. In dynamic scheduling scenarios, the system maintains a maximum constant load of 45.78 MW with a steady-state liquid ammonia output of 6543 kg/h. This work optimizes both economic performance and system stability, providing a significant reference for the large-scale development of green ammonia systems. Full article

Accurate prediction of superheated steam temperature (SST) is critical for the safe and efficient operation of large-scale thermal power units, particularly under large load variations and high thermal inertia. This study proposes an iTransformer-based SST prediction framework (iTransformer-SST) to address limitations of conventional proportional–integral–derivative (PID) control and existing data-driven models in capturing multivariable coupling, time-delay effects, and physical consistency. Using the A-side subsystem of a 1000 MW thermal power unit, 19-dimensional process data were collected continuously over two months with a sampling interval of 2.4 s. After data preprocessing, time-lagged cross-correlation (TLCC) analysis combined with expert knowledge was employed for feature screening, resulting in ten highly relevant input variables. To enhance predictive robustness, the baseline iTransformer was augmented with a Local Temporal Convolution (LTC) module for local disturbance modeling and a physics-guided regularization term to enforce delayed monotonicity and smoothness constraints. In 240 min rolling forecasts of the final-stage superheater outlet temperature, the proposed model achieved a mean squared error (MSE) of 0.0887, a mean absolute error (MAE) of 0.2312, and a coefficient of determination ($R^2$) of 0.9650, significantly outperforming long short-term memory (LSTM), Informer, and the baseline iTransformer. The combined LTC and physics-guided design reduced MSE by 13.5%, demonstrating strong potential for feedforward-assisted SST control in industrial thermal power applications. Full article

The output voltage harmonics of electric vehicle (EV) charging converters directly affect grid power quality. This paper proposes a harmonic analysis method based on geometric algebra (GA), which employs a multivector representation of signals and least squares estimation to accurately extract fundamental, integer-order, and inter-harmonics. A coupling coefficient is defined to quantify the phase correlation between frequency components. Based on measured data, harmonic characteristics under four typical operating conditions are analyzed, and an adaptive PID controller is designed to dynamically adjust the virtual resistance for harmonic suppression. The results show that the GA method significantly reduces spectral leakage under non-integer-period sampling conditions, with amplitude estimation errors below ±2%. The total harmonic distortion (THD) decreases with increasing active power and increases with reactive power injection. The droop coefficient exhibits a non-monotonic effect, while reducing the proportional gain raises the THD. Adaptive control reduces the average THD by 14.0–28.5% with a total response time of less than 0.05 s. The coupling coefficient C₁₃ is strongly positively correlated with THD and negatively correlated with the maximum Lyapunov exponent computed using the Rosenstein small-data method (correlation coefficient −0.85), confirming the intrinsic relationship between coupling and stability. Compared with fast Fourier transform (FFT) and other methods, GA achieves higher accuracy under short data records and non-integer-period sampling. This paper provides a complete theoretical framework and engineering solution for harmonic suppression in charging converters. Full article

Spiking neural networks can exhibit complex emergent dynamics, but the credibility of spatially explicit agent-based implementations depends on systematic verification and validation (V&V). This study introduces LANA (Local Adaptive Neural Agents), an agent-based spiking neural network in which neurons, propagating signals, directed synapses, and a diffusive environmental field are represented as distinct interacting components. We present a five-level V&V framework spanning operator-level tests, single-neuron mechanisms, propagation behavior, network-level dynamics, and sensitivity/robustness analysis. Across 13 predefined tests and approximately 2000 simulation runs, the model satisfied all prespecified pass criteria: synaptic delays reproduced the expected propagation law exactly, environmental decay and diffusion matched analytical expectations, threshold and refractory mechanisms behaved as predicted, inhibition suppressed firing monotonically, and environmental coupling induced a transition toward higher variability and oscillatory-like activity. Matched-seed comparisons further showed that explicit signal transport and environmental feedback substantially amplify activity relative to a neuron-only baseline while leaving synaptic delay propagation unchanged. Additional regime and lesion experiments demonstrated distinct resting, hyperexcitable, and focal-lesion states, with the lesion condition producing an acute decline followed by only partial recovery. Together, these results provide a transparent V&V baseline for LANA and illustrate how agent-based spiking models can be tested and interpreted across multiple scales. Full article

In winter, ice readily accretes on the HDPE sheath of stay cables, creating shedding hazards and exacerbating wind-induced vibrations, thereby threatening bridge and traffic safety. Cable-climbing de-icing devices have been proposed to replace manual operations, yet their performance is often limited by climbing instability caused by abrupt changes in cable-surface friction. This study develops a quadrotor-driven stay-cable de-icing device that integrates an arc-shaped milling wheel with an embedded heating module to realize thermo-mechanically coupled de-icing. The device climbs via rotor-generated aerodynamic lift and performs continuous top-down de-icing using gravity-assisted motion together with rotor thrust. Laboratory tests and ANSYS LS-DYNA explicit dynamic simulations are conducted to quantify the effects of clamping force and axial thrust on the ice removal ratio in a purely mechanical mode. In addition, a three-stage experimental campaign—temperature-rise, thermo-mechanical de-icing, and thermal-balance tests—is carried out to verify heating feasibility and to examine the roles of heating power and initial wheel temperature. The results indicate that, under purely mechanical de-icing, the ice removal ratio increases monotonically with clamping force and thrust but gradually approaches saturation. Under thermo-mechanical de-icing, higher heating power and initial temperature improve removal performance. Notably, thermo-mechanical de-icing under low thrust achieves a higher removal level than purely mechanical de-icing under high loads, demonstrating improved effectiveness and engineering practicality. An initial equivalence relationship between mechanical parameters and temperature is established to support further optimization. Full article

Lithium iron phosphate batteries have seen a recent rise in usage in electric vehicles and battery energy storage systems. For these applications, reliability is of paramount importance, influences long-term adoption and high return on investment, especially regarding battery replacement. Remaining Useful Life (RUL) prediction is at the core of avoiding unexpected failure and enabling proactive battery maintenance. Physics-based and data-driven methods have been explored by researchers, whilst Physics-Informed Neural Networks (PINNs) can combine their strengths in estimating battery RUL. This paper investigates the integration of the Gompertz function, an inherently interpretable white-box model, into Long Short-Term Memory (LSTM) networks to follow the physical laws of degradation, capture downward monotonic behavior and long-term dependencies from data resulting in Gompertz-Informed LSTMs (GILSTMs). Pure LSTMs are regarded as black box systems and critical infrastructure operators such as battery energy storage system (BESS) operators may refrain from using such systems. Gray-box models such as GILSTMs may get over this hurdle by increasing model interpretability and helping industry adopters know when they will benefit from data-driven modeling. This study explores two GILSTM architectures. The first uses an LSTM to predict Gompertz parameters, which are then converted into RUL via the inverse Gompertz equation. The second uses the inverse Gompertz equation as a verification step to cross-check the RUL values generated by the LSTM. The first type of GILSTM was constrained by both a physics loss and an inverse Gompertz layer to predict RUL while the second verified the results of an LSTM, despite that the GILSTMs failed to generalize. The first type of GILSTM achieved an average RMSE of 22.97%, while the second type achieved an average RMSE of 26.99%. The models in this paper are also benchmarked on the first 100 cycles, a current state of art for battery degradation testing. The best overall implementation was an LSTM that predicted RUL by recursively predicting SoH achieving an average RMSE per cycle of 9.18% and a 100th cycle RMSE of 17.02%. This study evaluates the trade-off between the predictive accuracy of black-box LSTMs and physical interpretability of Gompertz models. While pure LSTMs provide superior accuracy, the Gompertz parameters stabilize by 85% SoH. This 85% threshold serves as an interpretable confidence trigger, informing BESS operators when to rely on LSTM RUL forecasts. Full article

Spar-type floating offshore wind turbines (FOWTs) operating in deep-sea environments are subjected to coupled wind and wave excitations spanning a wide frequency range, rendering single-frequency passive damping solutions inadequate. A folded-beam nonlinear energy sink (FB-NES) is proposed for broadband vibration suppression of spar-type FOWTs. The device employs pre-buckled elastic beam arms integrated with constrained layer damping patches, and a closed-form analytical relationship between the beam geometric parameters and the nonlinear stiffness coefficients is derived, enabling direct parameter design without iterative calibration. The pre-buckled geometry introduces a negative-stiffness mechanism that substantially lowers the targeted energy transfer (TET) threshold, ensuring device engagement under all normal operational sea states. A 14-degree-of-freedom aero-hydro-elastic model of the NREL 5 MW OC3-Hywind FOWT with the FB-NES is established via the Euler–Lagrange formulation and validated against OpenFAST. Based on the numerical results under operational and extreme parked load cases, the FB-NES achieves substantial broadband vibration reductions that grow monotonically with wave severity, consistently and substantially surpassing both the optimally tuned mass damper (TMD) and a conventional cubic nonlinear energy sink of equal mass. Wavelet analysis confirms that targeted energy transfer, rather than direct viscous damping, is the dominant energy dissipation mechanism. The FB-NES also maintains effective control over a wide frequency detuning range, demonstrating superior robustness compared to the TMD. Full article

This survey presents an overview of recent developments in the analysis and numerical treatment of spectral fractional diffusion equations. Particular attention is devoted to efficient strategies for solving spectral fractional diffusion problems, including approaches based on rational approximation that enable efficient numerical realization of fractional powers of elliptic operators. Building on these approximations, we discuss adaptive finite element discretization techniques for polygonal domains, where singularities and geometric irregularities require carefully designed mesh refinement strategies. The survey also highlights the role of fractional diffusion operators in the preconditioning of coupled and multiphysics problems, where they can significantly improve the robustness and convergence of iterative solvers. Furthermore, we review recent results on maximum principles and monotonicity preservation for spectral fractional diffusion–reaction equations, which are essential for ensuring physically meaningful numerical solutions. Finally, we discuss current efforts aimed at improving robustness and computational efficiency through reduced and multilevel iteration methods. These approaches provide scalable algorithms for large-scale problems while maintaining accuracy and stability. The survey concludes by outlining several open problems and promising directions for future research in the numerical analysis of fractional diffusion models. Full article

Modern enterprise breaches are no longer isolated events but coordinated, multi-stage campaigns whose success depends on the defender’s inability to translate detection into timely containment. While existing frameworks—such as attack-lifecycle models, Zero Trust architectures, and detection-driven systems—provide valuable capabilities, they lack a formal mechanism for coupling inferred adversarial state with coordinated, cross-layer enforcement. This paper presents AMNDA, an Adaptive Multi-layer, stage-aware Network Defense Architecture that operationalizes lifecycle-aware defense through explicit state-to-control mapping and executable orchestration. Adversarial progression is modeled as a probabilistic state-transition process, and inferred states are systematically mapped to synchronized controls across edge protection, identity governance, internal segmentation, and behavioral detection. A formally defined orchestration function transforms detection outputs into stage-conditioned policy updates, enforcing monotonic tightening of containment as adversarial capability escalates. AMNDA is implemented and validated in a reproducible Microsoft Azure environment. Empirical results show that stage-aligned enforcement actions execute within 1.0–3.1 s, while detection latency remains the dominant constraint, with a median of 1034 s across the validation corpus. This separation reveals a critical operational insight: in modern cloud environments, the limiting factor in lifecycle defense is not enforcement capability but detection timing. The contribution of AMNDA is therefore not a new detection technique but a formal, deployable architecture that converts attack-stage inference into coordinated, low-latency containment. By bridging lifecycle modeling, Zero Trust principles, and automated orchestration, the proposed approach establishes a practical foundation for state-aware, adaptive cyber defense. Full article

This paper introduces a novel inertial forward–backward–forward algorithm driven by a newly conceptualized moving point projection technique for solving monotone inclusion problems in real Hilbert spaces. By leveraging the properties of a Lipschitz continuous, monotone operator and a maximally monotone operator alongside this innovative projection strategy, we dynamically construct a sequence of nonempty, closed, and convex sets that contain the zeros of the sum of the two operators. This geometric construction ensures that the resulting sequence is well defined and guarantees its weak convergence to a solution. Furthermore, to validate the practical efficacy of the proposed theoretical framework, we evaluate our method on image restoration problems. Numerical experiments measuring the improvement in signal-to-noise ratio (ISNR) and the structural similarity index measure (SSIM) confirm that the proposed algorithm is highly efficient and significantly outperforms existing state-of-the-art methods. Full article

In 2003 W. Takahashi and M. Toyodaestablished the weak convergence of an iteration process to solve a variational inequality problem induced by an inverse strongly-monotone mapping. Recently we proved that for the same iterative process, most of its exact iterates are approximate solutions of the variational inequality. It was also shown that the iteration process for solving a variational inequality problem for an inverse strongly-monotone mapping generates approximate solutions in the presence of computational errors. In this work we employ the Cimmino algorithm in order to generalize these results for common approximate solutions of a finite family of variational inequalities with inverse strongly-monotone mappings and of a finite family of fixed point problems in the presence of computational errors. Full article