Search Results (1,206)

With the increasing penetration of wind power, enhancing the renewable energy accommodation rate and reducing the carbon footprint of the IES, this study proposes a comprehensive evaluation method to assess the impact of a novel dynamic Green Certificate Trading (GCT) and Green Hydrogen Certificate Trading (GHCT) joint mechanism. First, considering the integration of the IES into the carbon trading market, a coupled dynamic GCT-GHCT framework is established. This framework links dynamic green electricity certificate revenues with green hydrogen certificate revenues, leveraging cross-subsidization to incentivize renewable energy consumption. Subsequently, an optimal operation model for the IES is formulated with the objective of minimizing comprehensive costs, which encompass energy procurement, green certificates, carbon trading, and wind curtailment penalties. A piecewise linearization approach is applied to transform the optimization model into a Mixed-Integer Linear Programming problem for efficient solving. Furthermore, based on the dispatch results, a multidimensional evaluation index system is constructed, extracting key indicators from economic, technical, and environmental perspectives. To ensure the rationality of the evaluation, a dynamic reward–penalty asymmetric cloud matter-element (ACME) comprehensive evaluation method based on game theory combinatorial weighting is introduced to calculate the index weights and the final comprehensive evaluation value. Finally, multi-scenario simulations are conducted to verify the superiority of the integrated GCT-GHCT trading framework. The results reveal that the proposed approach not only maximizes renewable energy integration but also provides a robust decision-making tool for the low-carbon transition of multi-energy systems. Full article

This paper addresses the problem of constructing adaptive stream encryption transformations based on dynamically generated Boolean mappings. A formal framework for modeling and modifying asymmetric two-operand Conditional Elementary Transformations (CETs) is proposed, where new operations are obtained through permutation-driven modification of operands and transformation results. The main contribution of the study is the development of a method for generating groups of CET-based transformations and corresponding generator models that enable the construction of pseudorandom sequences of dynamically varying substitution rules. The proposed approach ensures preservation of bijectivity and establishes formal relationships between direct and inverse operations under transformation modifications. Experimental evaluation demonstrates that the generated CET-based transformations produce output sequences with entropy close to the theoretical maximum (H ≈ 1) while providing enhanced diffusion properties. In particular, the CET_base configuration achieves an avalanche effect of approximately 0.79 compared to ≈0.5 for the classical XOR baseline. At the same time, permutation-based variants introduce additional structural diversity, enabling flexible trade-offs between diffusion strength and variability of transformation behavior. The obtained results confirm that the proposed framework enables systematic construction of large families of Boolean mappings, including up to 16 and 64 S-box transformations for 2Ci- and 3Ci-quanta operations, respectively, exceeding the capabilities of fixed XOR-based schemes. The proposed approach is intended as a flexible design paradigm for adaptive and lightweight cryptographic systems. However, the current study is limited to structural and statistical analysis, and a formal evaluation of resistance to established cryptanalytic attacks remains a subject of future research. Full article

This paper proposes a voltage-sensitivity estimation and droop-allocation framework for virtual power plant (VPP)-based frequency regulation in partially observable distribution feeders. In practical distribution systems, active-power adjustments by distributed energy resources (DERs) for frequency regulation may cause voltage excursions, while full real-time feeder information is often unavailable. To address this issue, an anchor-least-squares (Anchor-LS)-aided sensitivity-estimation method is developed using only point-of-common-coupling (PCC) voltage measurements and feeder-network information. Unlike state-estimation-based, data-driven, or optimization-heavy approaches that typically require wider measurement coverage, large training datasets, or repeated centralized computation, the proposed framework is designed for fast VPP-based frequency regulation under partial observability using only limited PCC measurements and feeder information. The proposed method reconstructs an approximate operating point and derives an operating-point-sensitive PCC voltage-magnitude-sensitivity matrix based on a coupled Z-bus formulation. Based on the estimated sensitivity, a voltage-constrained asymmetric droop-allocation framework is developed for under-frequency and over-frequency events, together with a practical iterative droop-adjustment method that mitigates PCC voltage violations without relying on a full optimization-based dispatch model. The proposed framework is validated through two case studies. In Monte Carlo simulations on the IEEE 33-bus feeder, the proposed sensitivity model reduced the mean RMSE by about 117 times compared with the common-path resistance method and by about 30 times compared with the conventional Z-bus method. In simulations on a practical 115-bus Korean distribution feeder, the proposed method achieved acceptable droop capacities comparable to those of a centralized LP baseline while reducing the mean computation time by about 3.2 times for both under-frequency and over-frequency events. These results confirm the practical usefulness of the proposed framework for fast VPP-based frequency regulation in real distribution networks under partial observability. 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

The rational design of electrode architectures is essential for advancing high-performance supercapacitors. In this study, Nd₂O₃ electrodes with controlled structural features were developed via a polyacrylic acid (PAA)-assisted hydrothermal approach. By systematically tuning PAA concentration, the growth mechanism of Nd₂O₃ was effectively regulated, leading to a distinct morphological transition from compact agglomerates to well-defined hierarchical structures. The optimized Nd₂O₃-P2 electrode exhibits a porous and interconnected architecture, providing enhanced electrolyte accessibility and shortened ion diffusion pathways. This structural optimization significantly improves electrochemical performance, delivering a high areal capacitance of 26.889 F/cm² at 10 mA/cm², along with excellent rate capability and reduced internal resistance. Kinetic analysis reveals that charge storage is predominantly governed by diffusion-controlled Faradaic processes, with the optimized structure facilitating rapid ion transport and efficient redox activity. Additionally, the electrode demonstrates excellent cycling durability, retaining 87.08% capacitance over 12,000 cycles. An asymmetric supercapacitor assembled using Nd₂O₃-P2 and activated carbon achieves stable operation up to 1.5 V, delivering good capacitance retention (81.2%) after 7000 cycles. This work highlights the effectiveness of PAA-induced structural tuning and provides a practical strategy for developing advanced rare earth oxide-based electrodes for energy storage applications. Full article

The widespread adoption of photovoltaic systems in residential electrical installations has increased the importance of Automatic Transfer Switches (ATSs) for ensuring power continuity during grid outages. However, many low-cost ATS solutions available on the market prioritize economic efficiency over operational safety, leading to significant risks under fault conditions. This paper investigates a real overvoltage incident in a residential three-phase installation equipped with a photovoltaic inverter and an ATS, which resulted in the failure of multiple electronic loads. The study reconstructs the event and demonstrates that the loss of the neutral conductor during backup operation caused severe phase voltage imbalance, generating overvoltage conditions across lightly loaded phases. A simplified electrical model is used to explain current paths and voltage redistribution under asymmetric loads, highlighting the critical role of correct neutral switching in ATS design. Two commercially available ATS architectures, one based on a changeover-contact mechanism and one employing four-pole miniature circuit breakers, are experimentally evaluated. The evaluation reveals major design deficiencies, including the absence of protective elements for control circuits, reliance on mechanical end-position limiters, and the use of switching devices not intended for frequent source transfer. These shortcomings introduce risks such as uncontrolled actuator operation, overheating, mechanical damage, and potential fire hazards. To overcome these limitations, a new ATS architecture was developed using a phase-monitoring relay, interlocked ABB contactors, and dedicated fuse protection for all control circuits. Detailed laboratory measurements were conducted to characterize contactor switching times and internal relay command delays. By optimizing the command sequence, the proposed ATS achieves predictable, fault-tolerant operation with competitive transfer times, representing a meaningful safety improvement over the evaluated commercial alternatives. The proposed solution is scoped to three-phase residential installations equipped with a hybrid photovoltaic inverter providing a dedicated backup output, operating within TN-S or TN-C-S earthing systems with a maximum grid connection capacity of 21 kW. Full article

Against the backdrop of the digital economy, digital transformation has increasingly evolved from a firm-level upgrading process into a collaborative decision-making issue among supply chain members. From the perspective of intelligent supply chain management, this study develops a two-echelon game model of a vertical manufacturer–retailer supply chain to examine digital collaborative decision-making under heterogeneous power structures. By comparing a centralized cooperative benchmark with decentralized non-cooperative scenarios, the study investigates how power structures affect firms’ digital transformation efforts, pricing decisions, and system-level outcomes, while also considering the role of knowledge spillovers. The results show that, under the same power structure, cooperation leads to higher digital transformation effort levels and greater total supply chain profit than non-cooperation. Knowledge spillovers further strengthen firms’ incentives to invest in digital transformation and improve market demand, consumer surplus, and social welfare. Compared with asymmetric power structures, a balanced power structure generates lower retail prices, higher market demand, and better overall supply chain performance. Numerical simulations further show that higher digital transformation costs weaken collaborative gains, whereas greater market sensitivity to digitalization strengthens them. Overall, this study suggests that digital collaboration contributes to supply chain sustainability by improving coordination efficiency, enhancing adaptive operations, and promoting system-level value realization under heterogeneous governance structures. Full article

Overall Equipment Effectiveness (OEE) measures manufacturing productivity as the product of Availability (A), Performance (P), and Quality (Q). Despite its widespread adoption, the classical OEE formula embeds a structural limitation, i.e., the three components are treated as equally important regardless of operational context. This fixed-weight assumption distorts maintenance prioritisation in environments where one component dominates operational losses. To the best of the authors’ knowledge, no published framework has formally addressed this limitation through a structured, auditable multi-criteria weighting model. This paper proposes Adaptive OEE, a FUCOM–TOPSIS framework that replaces the fixed A × P × Q product with a context-driven weighting model. FUCOM derives context-specific weights for A, P, and Q from expert judgement with minimum elicitation effort and mathematically guaranteed consistency. TOPSIS is adapted from its classical formulation by replacing data-derived ideal solutions with fixed reference poles defined independently of the observed data, ensuring that the effectiveness score of each asset is not influenced by the performance of other assets in the dataset. Three illustrative case studies covering availability-dominant, performance-dominant, and quality-dominant industrial scenarios suggest that classical OEE rankings are not preserved under asymmetric weight configurations, with ranking divergence being most severe when one component carries strongly asymmetric weight, precisely the condition that equal weighting cannot accommodate. The principal contributions are the formalisation of the equal-weight assumption as a formal methodological limitation, the replacement of multiplicative aggregation with a weighted distance measure, and the adaptation of TOPSIS with fixed reference poles for context-independent asset scoring. The framework is directly applicable by maintenance managers and industrial engineers seeking operationally justified equipment rankings without specialised analytical expertise. Full article

Vibration energy harvesting has emerged as a sustainable solution for powering low-energy devices such as wireless sensors and wearable electronics. However, conventional vibration energy harvesters often suffer from narrow operational bandwidth and limited output performance under ultra-low excitation conditions. To overcome these limitations, this study proposes an asymmetric tristable vibration energy harvester integrated with an elastic magnifier (EM), hereafter referred to as the asymmetric TVEH with EM, to enhance energy conversion efficiency under weak excitation. A nonlinear two-degree-of-freedom electromechanical model is developed to describe the coupled dynamics between the cantilever beam and the EM, incorporating nonlinear restoring forces and electromechanical coupling effects. The system performance is investigated using the harmonic balance method (HBM) and time-domain numerical simulations. In addition, parametric studies are conducted to examine the influence of the EM mass and stiffness ratios on the dynamic response and energy harvesting performance. The numerical results demonstrate that the inclusion of the EM significantly amplifies the system response under ultra-low excitation $(f=0.055)$, enabling improved inter-well motion and enhancing energy conversion efficiency by up to 45%. To validate the analytical and numerical findings, an experimental prototype is fabricated and tested. The experimental results confirm the effectiveness of the proposed design, achieving a root mean square voltage of $V_{\mathrm{rms}}=5\mathrm{V}$ across a load resistance of $R_L=100\mathrm{k}\Omega$ under a base acceleration of $1.4{\mathrm{m}/\mathrm{s}}^2$ at 14 Hz, measured over a 30 s window with a low-pass filter cut-off frequency of 100 Hz. The proposed asymmetric TVEH with EM consistently outperforms both the symmetric TVEH with EM and the asymmetric configuration without EM. Overall, the results highlight the pivotal role of the elastic magnifier in enhancing the dynamic response and harvesting performance under weak excitations, demonstrating strong potential for powering low-power electronic devices in practical applications. Furthermore, this work supports the United Nations Sustainable Development Goal SDG 7 (Affordable and Clean Energy) by promoting decentralized and renewable vibration-based energy harvesting technologies. Full article

Accurate production forecasting of oil wells is of great significance for reservoir management, production optimization, and investment decisions. However, complex subsurface dynamics and sudden operational interventions frequently break the temporal symmetry of production sequences, generating highly asymmetric data distributions. Standard deep sequence architectures often suffer from severe phase lag and limited adaptability when modeling such asymmetric regime transitions. To resolve these bottlenecks, we introduce the Signature-Weighted Kolmogorov–Arnold Network with Gated Recurrent Units (SigKAN-GRU). The architecture replaces static node activations with adaptive edge–spline mappings, enabling robust approximation of asymmetric nonlinearities. Path signatures compress high-order asymmetric temporal trajectories into invariant geometric features, a learnable gating kernel filters critical variations, and a final GRU layer enforces explicit sequential memory. This integration bridges long-term depletion trends with abrupt asymmetrical perturbations while maintaining structurally controlled complexity and an interpretable decomposition of nonlinear response and temporal weighting. Validated on two real-world wells with contrasting data characteristics, SigKAN-GRU consistently minimizes absolute error metrics and phase distortions against prevailing baselines. In addition, event-sensitive evaluations further confirm its reliability in peak regions and abrupt shock intervals. The resulting framework translates erratic historical data into robust deterministic forecasts, offering a rigorous quantitative tool for field-level reservoir optimization. Full article

The rapid expansion of the Internet of Things (IoT) has increased the risk of large-scale Distributed Denial-of-Service (DDoS) attacks. In high-availability IoT environments, the operational costs of false positives and false negatives are asymmetric, whereas conventional deep learning models usually optimize static accuracy-based objectives. To address this, we propose CDRL-QNN, a cost-aware and chaos-driven reinforcement learning quantum neural network framework in which a parameterized quantum circuit serves as the action-value function approximator within a Deep Q-Network (DQN) agent. The framework incorporates asymmetric operational penalties through both the reward function and sample-wise weighted Bellman optimization, while a logistic-map-based deterministic perturbation mechanism is used to promote exploration under constrained quantum-circuit training conditions. Evaluated on a computationally constrained balanced subset of the CIC-DDoS2019 dataset, the proposed framework reduced false negatives from 49 to 33 without increasing false positives, improving recall from 0.9673 to 0.9780 and F1-score from 0.9738 to 0.9793 while lowering operational cost. These findings suggest that hybrid quantum representations can be integrated into cost-sensitive reinforcement learning pipelines for IoT intrusion detection under constrained experimental conditions. Full article

Accurate short-term metro passenger flow forecasting plays a key role in urban transit management, supporting train scheduling, crowd control, and operational planning. Jointly modeling station-level inflow/outflow (IO) and inter-station origin–destination flows (OD/DO) has proven effective for improving prediction accuracy, as it allows the model to leverage dependencies across different flow granularities. However, effectively exploiting such dependencies remains nontrivial. Station-level intensity (IO) and inter-station migration patterns (OD/DO) differ substantially in both representation and dynamics, and the dependencies between them are inherently directional and uneven. As a result, commonly used parameter-sharing mechanisms in multi-task learning are often insufficient to capture informative cross-task interactions. To address this issue, we propose CATI (Cross-Attention-based Task Interaction), a unified framework for joint multi-granular metro flow forecasting. CATI first learns task-specific spatiotemporal representations for IO, OD, and DO flows, and then introduces directed cross-attention with Gated Residual Fusion to model selective and asymmetric interactions across tasks. In addition, an aggregation-consistency regularization is employed to maintain structural coherence between station-level and inter-station predictions. Experiments on real-world metro datasets from Hangzhou and Shanghai show that CATI consistently outperforms strong baselines across multiple prediction horizons and tasks. Further analysis indicates that the model learns adaptive attention patterns, task-dependent gating behaviors, and controlled interaction strengths, which together explain its improved performance. These results suggest that explicitly modeling asymmetric cross-task interactions is important for multi-granular spatiotemporal forecasting in metro systems. Full article

The central adjustment coil of a gasdynamic Electron Cyclotron Resonance (ECR) ion source requires wide-range bipolar current regulation over ±100 A with flat-top stability within 0.1% (1000 ppm) and a current rise time below 4 ms. Conventional fully controlled H-bridge converters operating under hard-switching conditions are unable to satisfy these requirements simultaneously, as the switching loss penalty restricts the control bandwidth and degrades flat-top stability. This paper presents an Asymmetrical Half-Bridge Flyback (AHB-Flyback) converter specifically designed for this application. By incorporating a dedicated resonant branch $L_r$–$C_r$ on the primary side, the converter achieves primary-side Zero-Voltage Switching (ZVS) and secondary-side Zero-Current Switching (ZCS) over the full operating range, enabling 100 kHz operation without incurring the switching losses that would otherwise limit control bandwidth. A decoupled energy management architecture is adopted in which the primary circuit pre-charges an energy storage capacitor during idle intervals, and the coil current is subsequently established through an autonomous capacitor-to-coil discharge, effectively decoupling the peak power demand from the upstream supply network. The operating modes of the flat-top maintenance stage are analyzed through time-domain state equations, yielding an explicit closed-form expression for the Mode 3 duty cycle $D_{T3}$. This expression demonstrates that $D_{T3}$ is determined solely by the switching frequency and circuit parameters, independent of the load current setpoint, which is the fundamental mechanism enabling stable wide-range current regulation without parameter re-tuning. Parameter selection guidelines are derived from this result. Simulation results across the 20–100 A operating range and experimental validation on a scaled prototype confirm flat-top current stability within 1000 ppm and a current rise time of 4 ms, demonstrating the suitability of the proposed converter for precision ECR ion source power supply applications. Full article

Background/Objectives: The STAT (Signal Transducer and Activator of Transcription) family of seven transcription factors mediates cytokine and growth-factor signaling, regulating proliferation, differentiation, and immunity. While STAT3/STAT5 are established oncogenes and STAT1/STAT2 are classically viewed as tumor suppressors, emerging evidence indicates context-dependent roles in tumorigenesis. This study aimed to integrate evolutionary analysis with bulk transcriptomic, regulon, single-cell, and exploratory chromatin-binding analyses of the STAT family in human solid tumors. Methods: Orthologs and paralogs of human STAT genes (81 sequences total) were retrieved across vertebrates and invertebrates; a phylogenetic tree was constructed using MUSCLE alignment and Neighbor-Joining in MEGA12. Differential expression was assessed in TCGA solid tumors versus GTEx normal tissues. Master-regulator activity was inferred using the corto algorithm. Single-cell RNA-seq datasets were used to compare malignant and non-malignant cell populations. STAT1 chromatin binding was examined via ChIP-seq in interferon-stimulated HeLa and K562 cells. Results: Phylogeny resolved seven conserved vertebrate clades, with endocrine-responsive STAT3/STAT5 showing higher conservation and immune-associated STAT1/STAT2/STAT4/STAT6 exhibiting faster divergence. The majority of STAT genes were frequently upregulated across multiple solid tumors, with activated regulons confirming functional transcriptional engagement. Single-cell analysis demonstrated tumor-cell-autonomous upregulation of STAT1 and STAT2 in the HNSCC dataset. STAT1 ChIP-seq revealed asymmetric forward/reverse-strand read density around peak summits, supporting non-canonical DNA recognition. Conclusions: The STAT family operates as an evolutionarily conserved, broadly activated transcriptional module in human solid cancers, combining quantitative upregulation with qualitative shifts in DNA-binding dynamics. These findings refine our understanding of JAK/STAT signaling in oncology and highlight opportunities for network-targeted therapies. Full article

Bank performance depends not only on capital strength but on the governance environment in which that capital operates. Yet existing studies treat capital buffers and institutional quality as parallel, additive drivers, thereby underexploiting their interaction. This study examines how capital adequacy and governance quality jointly shape bank performance across five Asian banking systems, Hong Kong, South Korea, Taiwan, Malaysia, and Vietnam, using 1628 bank-year observations from 123 deposit-taking institutions between 2010 and 2022. Return on assets, net interest margins, non-performing loans, and loan-to-deposit ratios capture performance. System GMM estimation with Bayesian diagnostics addresses endogeneity and dynamic persistence. Stronger Tier 1 capital reliably enhances profitability while compressing margins, consistent with a resilience–spread trade-off. Governance quality exhibits conditional and non-linear effects, beneficial in mid-capacity systems such as Malaysia and Vietnam, but plateauing or attenuating in mature regimes. Islamic banks demonstrate weaker responsiveness to governance reforms, reflecting contractual distinctiveness that standard prudential frameworks overlook. Post-COVID-19 interventions further attenuate capital’s profitability effect, underscoring the context-dependence of regulatory mechanisms. Integrating the Resource-Based View with Institutional Theory, the study advances a contingent resource-in-context framework in which capital functions as a portable safeguard while governance acts as an institution-dependent multiplier, offering regulators a basis for calibrating capital and governance policy asymmetrically. Full article