Search Results (1,177)

The reliable operation of grid-connected distributed generation converters is challenged by severe unbalanced conditions and stringent fault ride-through requirements. To address these issues, this paper presents a sensorless flexible predictive direct power control (SF-PDPC) strategy for converters operating under severe asymmetrical grid faults. The proposed approach combines a frequency-adaptive neural network quadrature signal generator (FANN-QSG)-based virtual-flux estimator with a flexible power-reference generation scheme, enabling predictive control without grid-voltage sensors, conventional synchronization units, or cascaded filtering stages. The key feature of the proposed method lies in a flexible power-reference formulation that exploits the degrees of freedom associated with positive- and negative-sequence power components, allowing continuous regulation of the trade-off among current quality, active-power oscillations, and reactive-power oscillations under unbalanced grid conditions. This enables a unified control framework adaptable to different grid support objectives. The effectiveness of the proposed strategy is validated under a severe type-C voltage sag, grid frequency deviation, and harmonic distortion. Compared with the conventional PDPC, the proposed method reduces current total harmonic distortion from 57.78% to 0.44% while maintaining satisfactory active power tracking performance. Furthermore, the FANN-QSG-based estimator and the overall control structure demonstrate strong robustness under highly disturbed operating conditions. The proposed SF-PDPC enhances the operational flexibility of predictive power control for grid-connected converters operating under highly disturbed and unbalanced grid conditions. Full article

The Q-Learning Relational Game (QLRG) framework provides a theoretically rigorous method for identifying minimal winning coalitions in online social networks (OSNs) under the restrictive assumption of global agent symmetry or uniform matroid structure. Real-world OSNs, however, exhibit significant asymmetry. This paper introduces the Cluster-Based Q-Learning Relational Game (C-QLRG), a practical extension that relaxes the global symmetry requirement by leveraging community structure. We partition the agent set into communities with bounded internal variation and represent the state solely by community membership counts of the seed set. Because the closure operator already captures all eventual influence spread, the problem reduces to a sequential seed selection task where the agent decides, at each step, from which community to add the next seed. We prove that the optimal Q-function of a suitably regularized reach-efficiency objective is Lipschitz continuous and derive a performance bound for the learned policy. The full algorithm is presented, and its complexity is analyzed. Empirical evaluations on a synthetic asymmetric network and Zachary’s Karate Club demonstrate that C-QLRG is highly sensitive to reward parameters, where default settings lead to premature stopping, but parameter tuning combined with a corrected minimality verification recovers high-efficiency coalitions by removing non-contributing agents. With tuned parameters, C-QLRG produces a near-winning coalition of size 11 and 99% reach on the synthetic network, surpassing the greedy baseline’s efficiency (size 12) despite a one-node coverage gap, while identifying the optimal winning coalition of size 1 on the Karate Club dataset, matching all baselines. The framework thus offers a principled trade-off between model fidelity and scalability, with the reward design choice being critical for practical deployment. Full article

Asymmetric SiC membranes with surface pore sizes ranging from 0.12 to 0.31 μm at a constant open porosity of approximately 42% were fabricated by dip-coating SiC support followed by sintering from 1700 to 2000 °C. The effect of membrane structural constants (hydraulic resistance (k₁), pore size exponent (k₂), and shape factor (k₃)) on PWP were evaluated by comparing the symmetric and asymmetric structures. In addition, the experimentally determined values of PWP were quantitatively analyzed by comparing with theoretically predicted values obtained using the Kozeny–Carman (K–C) and Hagen–Poiseuille (H–P) models. Despite having a smaller pore size, the asymmetric membranes exhibited high PWP (1257-3883 LMH) due to decreased flow resistance (low k₁), enhanced pore size effect (high k₂), and improved flow network (high k₃) as compared to symmetric membranes. The hydrophilicity of the prepared membranes improved remarkably, with increasing average surface roughness (102.3 nm to 161.0 nm) due to an increase in pore size, which also caused a decrease in water contact angle (WCA) from approximately 27.44° to 21.67° with increasing sintering temperature (1700–2000 °C). Furthermore, the prepared membrane separation performance was found to be affected by its pore size, and the 1900 °C sintered SiC membrane showed optimal gradient profile and pore structure, demonstrating its practical reusability and scalability for O/W wastewater treatment. Full article

In this article, we construct a framework for analyzing the equilibrium and stability of networked multi-sector economic systems via fixed-point analysis. We represent directional intersectoral dependencies, nonlinear feedback effects, and heterogeneous adjustment dynamics in the model by the coupled and tripled fixed-point theory in the graphically extended suprametric spaces. The graphical structure encodes supply-chain and influence networks, whereas asymmetric and nonuniform interaction strengths are encoded in the suprametric setting. Furthermore, we prove the existence, uniqueness, and convergence of equilibrium solutions under new generalized contraction conditions. We apply the theoretical findings in nonlinear state systems in which prices in interdependent markets are adjusted using integral equations. The results of numerical simulations show consistent convergence, and the sensitivity parameter of the network structure significantly influences the determination of economic stability and speed of adjustment. Full article

With the increasing penetration of renewable energy generation (REG) in novel distribution systems, active distribution networks (ADNs) integrated with microgrids (MGs) play a crucial role in enhancing the flexibility of regulation resources and promoting the accommodation of REG. To meet the operational requirements for efficient collaboration between ADNs and MGs under different dispatch time scales, this paper proposes a collaborative optimal dispatch strategy for multi-microgrid active distribution systems based on a hybrid game and variable time scales. Firstly, a transaction operation framework is constructed for the distribution network operator (DNO) and a multi-microgrid alliance (MMA), considering the peer-to-peer (P2P) transaction mode. On this basis, a day-ahead hybrid game model with a two-layer structure is constructed, the upper layer is a master–slave game with the DNO as the leader and the MMA as the follower, while the lower layer is a cooperative game for MGs within the MMA. An asymmetric Nash bargaining strategy based on contribution degree in P2P transactions is introduced to ensure equitable benefit allocation among cooperative MGs. Secondly, an intra-day rolling optimization model for reactive power and voltage based on variable time scales is proposed, which enhances the system’s responsiveness to real-time source–load power fluctuations by dynamically adjusting the dispatch time scale. Finally, the alternating direction method of multipliers (ADMM), integrated with a strategy separation mechanism, is adopted to efficiently solve the hybrid game model involving numerous 0–1 variables. The case study results indicate that, under the proposed strategy, the MMA’s power purchase cost from the DNO and ESS operational cost are decreased by 9.7% and 11.6%, respectively, while the system’s average deviation rate of node voltage decreases by 0.82%. Therefore, the proposed collaborative dispatch strategy can not only effectively reduce the system’s operational cost and ensure voltage stability but also significantly promote the accommodation of REG. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

Signals generated during tool wear are nonlinear, non-stationary, and easily affected by machining noise, which makes reliable tool condition monitoring difficult in intelligent manufacturing. To address this issue, this study proposes a tool wear degree classification framework, FCTrans-CA, that fuses time-domain and frequency-domain information through a lightweight cross-attention (CA) bridge. Fast Fourier transform (FFT) is first used to obtain frequency-domain representations. The raw time-domain signals are processed by a multi-scale one-dimensional convolutional neural network (MS-CNN) to extract temporal wear features, while the FFT-derived representations provide complementary spectral cues. These two feature streams are fused by an asymmetric CA module in which frequency-domain features guide the selection of wear-sensitive temporal features. K-means clustering is used to divide the measured flank wear (VB) trajectory of each tool into initial-, normal-, and severe-wear stages, thereby reducing subjectivity in label generation. Experiments on the PHM2010 milling dataset show that FCTrans-CA achieves 99.43% classification accuracy on 40,648 test samples. The results indicate that cross-domain feature interaction improves the separability of wear states and provides a reproducible data-driven route for tool wear monitoring. Full article

Symmetrical resistance training induces corticospinal and cortical network plasticity; however, the neural consequences of asymmetrical resistance training (offset loading or OL) remain unclear. In this preliminary study, fourteen healthy adults completed eighteen supervised upper-extremity training sessions over a 6-week intervention period. Participants were allocated to either an offset loading (OL) intervention group (n = 8) or a conventional symmetrical resistance training active control group (AC; n = 6). Electroencephalography (EEG) recordings were acquired during motor task performance at baseline, 3 weeks, and 6 weeks. Directed functional connectivity among predefined cortical regions was quantified, and longitudinal changes were assessed using Friedman tests with false-discovery-rate correction for multiple comparisons. Significant changes in the OL group were observed involving two cortical pathways after correction for multiple testing. Connectivity from the right parietal cortex to the right sensorimotor cortex increased over time (Friedman χ² = 8.00, q = 0.037), with post hoc analyses showing a significant increase between the midpoint and post-training assessments (q = 0.047; effect size r = 0.894). No significant longitudinal changes in cortical connectivity were identified in the AC group. Six weeks of OL training was associated with selective strengthening of directed connectivity within prefrontal-to-sensorimotor and parietal-to-sensorimotor cortical pathways. In contrast, conventional symmetrical resistance training was not associated with detectable changes in connectivity. These findings suggest that asymmetrical loading may induce task-specific reorganization of cortical networks involved in sensorimotor processing and motor control. Full article

Global climate change is causing an increase in extreme rainfall events, which impacts the risk of hydrometeorological disasters. To support disaster mitigation and early warning systems, accurate and reliable rainfall predictions are required. Although ensemble forecasting is widely used to model atmospheric uncertainty, raw ensemble results often exhibit insufficient bias and dispersion. Therefore, post-processing techniques are needed to improve the quality of probabilistic predictions. The most commonly used calibration method is Bayesian Model Averaging (BMA). This study conducted a scoping review of peer-reviewed papers on ensemble forecast calibration using BMA, based on the PRISMA-ScR framework. Furthermore, this study presents a comprehensive bibliometric analysis involving co-authorship networks of productive authors and bibliometric maps with clustered terms. A total of 35 relevant articles were identified from 49 screened publications. The bibliometric analysis revealed that “ensemble forecasting” and “Gaussian distribution” are the most dominant terms in the research network, indicating that Gaussian-based approaches remain more widely used in ensemble forecast calibration studies. In contrast, studies explicitly applying Gamma-based approaches are still relatively limited despite their relevance for modeling asymmetric rainfall data. The results obtained in this study highlight the importance of developing and integrating more appropriate probability distributions, such as those within the Extreme Value Theory framework, into BMA models. These findings suggest that the selection of appropriate probabilistic distributions in BMA-based calibration frameworks plays an important role in improving forecast reliability and the representation of uncertainty in rainfall prediction. Furthermore, the development of more suitable probability distributions, including Extreme Value Theory (EVT)-based distributions, has strong potential to enhance probabilistic calibration performance for asymmetric rainfall data. This approach is expected to improve the accuracy and reliability of extreme rainfall predictions. The findings of this study provide an important contribution to the development of early warning systems for hydrometeorological disasters and support the achievement of Sustainable Development Goals (SDGs). Full article

Multimodal biometric systems have become essential components of modern electronic identity and authentication platforms where robustness under real-world degradation is critical. However, opaque face masks impose severe facial occlusion and attenuate high-frequency spectral components. Conversely, transparent face shields introduce complex specular reflections and act as an acoustic channel distortion source. Addressing these asymmetric degradation challenges, this paper proposes a reliability-aware Dynamic Score Fusion (DSF) for multimodal biometric identification. The proposed method performs sample-level reliability estimation for both face and voice modalities at the input stage. This enables sample-wise adaptive weighting of modality scores based on their estimated reliability. The framework integrates an ElasticFace-Arc backbone for face recognition with an Emphasized Channel Attention, Propagation and Aggregation—Time Delay Neural Network (ECAPA-TDNN) for speaker identification. The proposed approach is evaluated on the FaciaVox dataset, comprising face images and voice recordings acquired under multiple face-covering conditions. Experiments under the Standard to Cross-Condition Protocol (SCCP) and Multi-Condition Protocol (MCP) demonstrate that the proposed DSF consistently outperforms conventional score-level fusion methods, including Weighted Sum Fusion (WSF) and Logistic Regression Fusion (LRF). It achieves average Rank-1 accuracies of 89.6% (SCCP) and 93.7% (MCP), with gains of up to 9.3 percentage points over these baselines. The reliability estimators further demonstrate strong predictive capability, yielding Area Under the Curve (AUC) values above 0.95 for both modalities in distinguishing correctly and incorrectly identified samples under the closed-set identification setting. These findings confirm that sample-wise reliability modeling provides an effective mechanism for enhancing multimodal biometric performance under challenging mask and shield conditions, supporting the deployment of robust AI-driven electronic identification systems. Full article

Aiming at the characteristics of high viscosity and poor fluidity of high waxy ordinary heavy oil, a Janus silica-based nanosheet composite viscosity reducer was designed and prepared in this paper. The viscosity reducer was assembled by asymmetric Gemini viscosity reducer and silica nanosheets through dehydration condensation reaction, and its structure was verified by FT-IR, ¹HNMR, XPS and DLS. The viscosity reduction performance, emulsion stability, interfacial tension and flow performance of the viscosity reducer were systematically evaluated by taking heavy oil with wax content of 35.7% and viscosity of 237 mPa·s at 30 °C as the research object. The results showed that, at an oil-to-viscosity-reducer-solution volume ratio of 3:7 and a viscosity reducer mass fraction of 0.3%, the maximum viscosity reduction rate reached 94.5% at 30 °C, calculated relative to the viscosity of the dehydrated original heavy oil. The oil–water interfacial tension was significantly reduced, and the 24 h bleeding ratio, defined as the volume percentage of separated water relative to the initial aqueous phase volume, was only 7.3%, indicating good emulsion stability. The core flow experiment shows that the resistance coefficient is reduced to the lowest at 0.3% concentration, and the seepage capacity is significantly improved. The analysis of total hydrocarbon gas chromatography showed that the content of high-carbon wax components in the C₂₃-C₃₀ range decreased by 4.79 percentage points after treatment, indicating that the viscosity reducer preferentially interacted with high-carbon wax molecules and promoted wax-crystal dispersion, thereby weakening the three-dimensional wax-crystal network. The viscosity reducer has the synergistic effect of dispersing wax crystals, reducing interfacial tension and stabilizing emulsification, which provides a low-cost and high-performance technical approach for the efficient exploitation of high waxy ordinary heavy oil. Full article

This paper examines how international crude-oil price movements are transmitted to natural-rubber prices through the petrochemical–synthetic-rubber chain, with implications for Thailand as the world’s leading natural-rubber exporter and China as the dominant consumer. Using monthly data from April 2003 to March 2026 on the OPEC reference basket, butadiene, styrene–butadiene rubber (SBR), and the Shanghai natural-rubber benchmark, the analysis combines a nonlinear ARDL specification with a Pesaran–Shin–Smith bounds test, a long-run association decomposition into direct and synthetic-rubber-mediated components with bootstrap inference, and a threshold-NARDL extension that conditions the decomposition on the inventory state. Three findings stand out. First, the synthetic-rubber-mediated component accounts for approximately three-quarters of the estimated oil–natural rubber long-run association (73.5 percent, 95 percent bootstrap CI [60.6, 87.2]), with the residual direct component accounting for the remainder. Second, long-run pass-through is directionally consistent with concentration in the synthetic-rubber component, although Wald tests do not reject symmetry at conventional levels for either the synthetic-rubber component (Wald $p=0.135$) or the direct oil component ($p=0.166$). Third, the synthetic-rubber-mediated share is consistently larger in low-inventory regimes by 26 to 66 percentage points across three alternative regime variables, although the magnitude amplification of asymmetric pass-through itself is not robust. Asymmetric local projections and a Diebold–Yilmaz spillover analysis are reported as complementary horizon-indexed and network checks. The results imply that the synthetic–natural rubber spread, conditioned on the inventory state, may be more informative for natural-rubber price-risk monitoring than crude-oil prices alone. These findings have implications for commodity price-risk monitoring, export-income exposure, and stabilisation design in rubber-exporting economies. Because crude-oil shocks are not externally identified, all estimates are interpreted as decompositions of long-run association rather than causal mediation effects. Full article

Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework, where structured pruning and mixed-precision quantization-aware training (QAT) are jointly optimized with TensorRT–DeepStream for efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured pruning strategy that applies asymmetric channel pruning to convolutional and feature-fusion modules while compressing the Swin Transformer prediction heads and bottleneck stacks, thereby reducing parameters and computation with limited impact on multi-scale representation capability. Second, we develop a hardware-aware mixed-precision QAT scheme that maps computation-intensive backbone layers to INT8 while preserving the Transformer-related modules in FP16, improving inference efficiency while mitigating the accuracy loss caused by uniform low-bit quantization. Third, we compile the optimized network with TensorRT and integrate the resulting inference engine into a DeepStream-based asynchronous video pipeline on the edge platform, enabling end-to-end acceleration by reducing decoding, preprocessing, and memory-transfer overheads. Experimental results on the VisDrone2019-DET dataset and the NVIDIA Jetson Orin NX demonstrate that DUST-YOLO achieves 43.7% mAP@0.5 accuracy with an end-to-end latency of 36.3 ms and a throughput of 27.5 FPS. Compared with the state of the art, DUST-YOLO reduces end-to-end latency by 56.9% and improves end-to-end video throughput by $2.31\times$. Full article

Optical nonreciprocity (ONR) plays an important role in laser technique, optical communications, quantum information, etc. Realizing ONR at the quantum level of few photons or single photons is also essential for quantum communications or quantum networks. In this work, we propose a hybrid configuration composed of a quantum dot–metallic nanoparticle (QD-MNP) composite in a ring cavity to achieve ONR at the few-photon level via optical bistability. With the surface plasmon effect of the MNP, the bistable property and regime of photons producing ONR in an asymmetric ring cavity including a QD and an MNP inside can be significantly improved in comparison with the case of a single QD. By using the bistability effect, giant ONR can be achieved in an optimal window of numbers of input photons. The detuning and the coupling strength coefficients of the hybrid system can be adjusted and utilized to optimize the performance of the quantum nonreciprocity. This work may find promising applications in quantum nonreciprocal devices and photonic quantum circuits. Full article

This study presents a proof-of-concept investigation into the use of proximal policy optimization (PPO), a deep reinforcement learning (DRL) algorithm, for estimating the remaining useful life (RUL) of rolling element bearings. Although DRL has shown growing promise in prognostics, existing applications have predominantly relied on off-policy deterministic actor–critic methods such as deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3); the suitability of on-policy clipped-objective methods such as PPO for this task remains comparatively unexplored. To address this gap, statistical time-domain features are extracted from raw vibration signals and used as input to train a PPO agent with an actor–critic architecture, in which the actor network predicts RUL values and the critic network evaluates prediction quality through state-value estimation. A preprocessing pipeline comprising feature extraction, normalization, and sliding-window segmentation is developed, and the PPO framework incorporates generalized advantage estimation (GAE), a custom-designed reward function, and a policy-clipping mechanism to support stable training. The method is evaluated on a representative bearing (Bearing 2_1) from the XJTU-SY run-to-failure dataset using a chronological train/test split, and benchmarked against long short-term memory (LSTM) networks, multilayer perceptrons (MLPs), and a naive linear regression baseline. Performance is assessed using root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), and a domain-specific asymmetric scoring function that penalizes late predictions more heavily than early ones. Experimental results show that the PPO-based model produces more stable and operationally favourable RUL estimates than the supervised baselines on the unseen late-degradation segment, particularly in the critical end-of-life region. The findings support PPO as a viable on-policy DRL formulation for bearing RUL prediction and motivate further validation across multiple bearings and operating conditions, identified here as essential future work. Full article