Search Results (21,764)

In wind tunnel testing, an active vibration suppression system based on piezoelectric actuators is an effective means to ensure stable operation. However, in a cryogenic wind tunnel testing environment, the performance of piezoelectric actuators degrades significantly when they are exposed to cold temperatures and subjected to uneven cooling. This is particularly problematic during real-time changes in the attack angle of a test model. To ensure the reliable operation of wind tunnel tests, an active–passive hybrid thermal control method is proposed in this paper. First, the insulation and heating structure was designed based on the thermal analysis results. Then, combining simulation and measured data, the temperature field was reconstructed in real time using a recurrent neural network algorithm. Next, considering the non-uniform heat dissipation of the system, a thermal allocation module was designed based on digital–physical integration to actively control the overall and localized heat. Finally, a heat preservation performance test platform was established to conduct cooling experiments in a small-scale cryogenic wind tunnel. The results indicated that the proposed thermal control method reduced the average cooling rate of the system by 97% and improved the overall temperature uniformity by approximately 94.23%. Full article

Robust unmanned aerial vehicle (UAV) detection in real low-altitude anti-UAV scenarios remains challenging due to motion blur, extreme illumination, cluttered backgrounds, and tiny target sizes. Most existing UAV detectors rely on RGB imagery, but their performance often degrades severely under these adverse conditions. Event cameras, as a neuromorphic sensing modality, capture motion-sensitive responses with high temporal resolution and thus provide complementary cues for robust UAV detection. However, existing RGB–event fusion detectors usually employ homogeneous feature extraction and generic fusion mechanisms, which are insufficient to handle heterogeneous modality degradation and exploit reliable cross-modal cues. To address this limitation, we propose SMG-UAV, a sparse mutual guided RGB–event fusion network for robust small-UAV detection. The proposed method integrates a hybrid dual-branch backbone for modality-specific representation learning, a Sparse Mutual Guided Bridge for bidirectional sparse cross-modal refinement, and a Selective Gated Pyramid Neck for multiscale enhancement of weak UAV responses. Experiments on the Florence RGB-Event Drone Dataset (FRED) and the Neuromorphic-RGB Drone Detection Dataset (NeRDD) demonstrate that SMG-UAV achieves state-of-the-art performance, outperforming the strongest competing method by an average of 5.2 points in ${\mathrm{AP}}_{50}$, while delivering stronger robustness under multiple challenging anti-UAV conditions. Full article

Accurate prediction of multivariate effluent quality is essential for achieving reliable operation and sustainable management of wastewater treatment processes (WWTPs). However, the strong nonlinearity, coupling relationships, and non-prioritized multi-input multi-output (MIMO) characteristics of WWTP pose significant challenges to conventional prediction methods. To address these issues, a vectorized evidential reasoning-based multivariate effluent quality (VER-MEQ) prediction method is proposed. First, a VER model is developed, in which the nonlinear mapping between multiple process variables and multiple effluent quality indicators is established through a vector evidence matrix (VEM), enabling simultaneous online prediction of multiple outputs within a unified inference framework. Subsequently, a structured hybrid initialization (SHI) strategy is introduced to improve the initialization quality of the genetic algorithm, and the VER inference process is incorporated into parameter optimization to enable online model parameter updating, thereby improving prediction performance. The proposed method is validated under sunny, rainy, and stormy operating scenarios. Experimental results demonstrate that VER-MEQ achieves competitive prediction accuracy, provides a transparent belief-based inference process, and maintains preliminary anti-interference performance under the tested conditions. By providing transparent and credible prediction results for effluent ammonia nitrogen (NH₃-N_e) and total nitrogen (TN_e), the proposed framework can support proactive operational decision-making, improve effluent compliance, reduce the risk of nutrient discharge, and contribute to the sustainable operation of WWTPs. Full article

This study developed a hybrid fuzzy decision-making algorithm based on the Fuzzy Best-Worst Method (FBWM) and the Fuzzy Square-Root-based Evaluation Method (FSREM). Despite the widespread application of the 8D methodology in engineering practice, the importance of its disciplines has not been sufficiently investigated; therefore, the aim of this study is to determine their significance and priority. The proposed fuzzy algorithm was applied to three companies operating within the automotive supply chain. FBWM was used to determine the criteria weights, while FSREM was applied to rank the 8D disciplines. Sensitivity analysis showed that the expert teams from the three considered companies perceived the problem in a very similar manner. The results of applying the proposed algorithm in all three companies showed that the discipline Identify and Verify Root Cause (D4) has the greatest influence on problem-solving effectiveness. In two of the three companies, Prevent Recurrence (D7) was ranked as the second most influential discipline, while in one company Define Permanent Corrective Actions (D5) was identified as the second most influential discipline. It can be concluded that the results demonstrated a high degree of consistency, while minor ranking deviations can be attributed to different quality management system approaches within each company. Full article

While aspect-based sentiment analysis (ABSA) has made significant progress in the identification of explicit opinion targets, the more challenging case of implicit aspects remains insufficiently studied. Implicit aspect extraction is particularly challenging, as it relies on contextual and semantic cues and requires systems to infer what reviewers mean rather than what they state explicitly. A four-component hybrid pipeline is proposed for explicit and implicit aspect extraction, formulating the task as controlled text generation. The pipeline combines (i) a fine-tuned decoder-only large language model as a generative baseline, (ii) an iterative residual generation strategy that recovers multiple aspects through successive masked generation passes, (iii) paraphrase-based input transformation to broaden the contextual signal, and (iv) domain-specific knowledge graphs activated by linguistic signals to infer implicit aspects. The novelty lies not in the individual components themselves but in their principled orchestration and the linguistically motivated gating logic governing the activation of each stage. Extensive experiments are conducted on eight benchmark ABSA datasets spanning both English and Arabic: SemEval-2014, SemEval-2015, SemEval-2016, ACOS, and M-ABSA for English; and SemEval-2016, HAAD, and M-ABSA for Arabic. The proposed solution outperforms strong baseline methods and recent state-of-the-art models on English datasets, with $F_1$-scores of 0.8533, 0.713, 0.7859, 0.793, and 0.664, respectively. On Arabic datasets, the best-performing configurations achieve $F_1$-scores of 0.7632, 0.4765, and 0.7656 on SemEval-2016, HAAD, and M-ABSA, respectively, with the knowledge-graph component providing consistent and statistically significant gains for implicit aspect identification in both languages. These results demonstrate the effectiveness of generative modeling, iterative generation, paraphrasing, and structured knowledge for aspect extraction and highlight the potential of the proposed approach for implicit aspect identification, in particular for morphologically rich languages such as Arabic, where annotated resources remain scarce. Full article

With the increasing penetration of distributed renewable energy, coordinated operation between distribution networks and multiple integrated energy microgrids (IEMs) has become increasingly important for improving operational economy and maintaining system security. To address the insufficient integration of network constraints, P2P energy sharing, and contribution-based benefit allocation, this paper proposes a hybrid game-based optimal scheduling model for multi-IEM systems under distribution network constraints. In the upper level, a Stackelberg game is established between the distribution system operator (DSO) and the multi-IEM alliance to coordinate electricity trading and distribution network operation. In the lower level, an asymmetric Nash bargaining-based cooperative game is developed to enable peer-to-peer (P2P) energy sharing and allocate cooperative benefits according to the actual energy-sharing contributions of individual IEMs. Furthermore, a distributed solution framework combining the Success-History Adaptive Differential Evolution (SHADE) algorithm and an improved alternating direction method of multipliers (ADMM) is adopted to preserve data privacy and improve computational efficiency. Case studies based on the modified IEEE 33-bus distribution system demonstrate that the proposed method eliminates voltage violations and reduces network losses by 9.0%. Meanwhile, the proposed cooperative mechanism decreases the total operating cost of the IEM alliance by 7815.8 CNY and yields a more contribution-consistent profit allocation among participating microgrids. In addition, the improved ADMM reduces computation time by 42.1% compared with the conventional serial ADMM. The results demonstrate the effectiveness of the proposed method in enhancing distribution network security, promoting renewable energy sharing, and improving the economic performance of multi-IEM systems. Full article

Human innovation has continually expanded the boundaries of knowledge, from mastering atomic science to reaching the Moon and now into the era of Industry 4.0, where artificial intelligence, the Internet, and advanced additive manufacturing turn imagination into reality. Among these achievements, hypersonic vehicles represent a pinnacle of technological advancement. Modern vehicles reach speeds exceeding Mach 27 (approximately 9300 m/s), where the air at the leading edges transforms into a chemically reactive, thermally ionized plasma. At such velocities, stagnation temperatures climb to 9000–12,000 K (8726.85–11,726.85 °C), creating one of the most extreme environments encountered by any human-made system—conditions under which conventional materials cannot survive without advanced cooling strategies. To address this challenge, researchers worldwide have developed and experimentally validated a range of thermal protection and leading-edge cooling techniques. This review presents the historical evolution of hypersonic vehicles, highlights recent advancements, examines the key challenges posed by sustained hypersonic flight, and surveys state-of-the-art cooling strategies. The discussion emphasizes methods that combine passive, active, adaptive, and hybrid approaches to protect vehicle integrity under extreme thermal loads, providing insight into the current and future capabilities of hypersonic thermal manageme nt. Full article

In recent years, it has become inevitable to dig underneath existing highways when excavating tunnels. The soil settlement induced by ground excavation may adversely affect existing highways. In this study, a settlement monitoring system is used to obtain the settlement sequence of multiple measurement points on the pavement. Multifractal detrended fluctuation analysis (MF-DFA) is used to focus on analyzing the multiple fractal features of the pavement settlement rate. The results show that the settlement rates of the highway caused by the tunnel excavation and construction process all show multiple fractal characteristics. The fluctuations in the measurement points above and near the entrance of the tunnel are more complex and intense. Based on the moving-average method (MA), convolutional neural network (CNN), and Extreme Learning Machine (ELM), MA-CNN and MA-ELM prediction models are constructed to predict the settlement value sequences of the fluctuating points. The results indicate that the MA-ELM prediction model demonstrates superior predictive performance (with R² values of 0.956, 0.950, and 0.979 on the test set). Further, with the help of the Dung Beetle Optimizer (DBO), a meta-heuristic algorithm for parameter optimization, the hybrid model DBO-MA-ELM greatly improves the prediction performance (R² of 0.975, 0.997, 0.998 for the testing set). Full article

Multibody dynamics (MBD) simulations involving hundreds to thousands of bodies give rise to large-scale, sparse, and structurally indefinite linear systems. Traditional direct solvers incur prohibitive memory and computational costs, while iterative methods suffer from slow convergence due to severe ill-conditioning. This paper proposes HPI-MBD, a hybrid preconditioned iterative framework. It combines an algebraic multigrid (AMG) for global error smoothing with a block Jacobi preconditioner tailored to the kinematic constraint graph. The framework exploits graph topology to construct a block-diagonal Schur complement approximation, incorporates Tikhonov regularisation for redundant constraints, and maintains $\mathcal{O}\left(n\right)$ work per iteration, where n is the number of degrees of freedom. A rigorous spectral analysis supports the problem-size independent convergence of the Minimal Residual (MINRES) solver. Evaluated on five benchmark systems with ${10}^4$ to ${10}^6$ degrees of freedom, the HPI-MBD achieves speedups up to $12.7\times$ and memory reductions up to 68% against MA57, with comparable gains against PARDISO. All solutions maintain relative residuals below ${10}^{-6}$. Comparisons against ILU(0)-preconditioned Generalised Minimal Residual (GMRES), Finite Element Tearing and Interconnecting method (FETI-1), and a block-Jacobi-only variant confirm the essential role of AMG. The framework exhibits near-linear scalability and strong parallel efficiency on up to 32 processors, along with robust performance under redundant constraints and varying time step sizes. These results position HPI-MBD as a scalable, memory-efficient alternative for real-time simulation in virtual prototyping, robotics, and biomechanics. Full article

The contamination of aquatic ecosystems by synthetic dyes such as Safranin O poses significant environmental and health risks. This study reports the synthesis of TiO₂-ZnO heterostructures via a Ruta graveolens-mediated sol–gel method, where the plant extract acts as a structure-directing agent and precursor for residual carbon species. The resulting bio-hybrid catalyst achieved a degradation efficiency of 94% ± 2% under simulated solar irradiation, outperforming UV light (78% ± 3%) and visible light alone (81.18%). The optimal catalyst loading was determined to be 1.0 g L⁻¹, with maximum performance observed at near-neutral pH (6–7). Optical characterization revealed a direct bandgap of 2.69 eV, representing a significant red-shift from pristine TiO₂ and ZnO. The catalyst maintained 90% of its initial degradation efficiency after five consecutive regeneration cycles, demonstrating excellent reusability. Kinetic analysis confirmed pseudo-first-order behavior, while radical scavenging experiments identified superoxide radicals (•O₂⁻) as the dominant reactive species. This work establishes that plant-derived carbon precursors can effectively modify the electronic properties of TiO₂-ZnO heterojunctions, offering a sustainable approach for photocatalytic water remediation. Full article

Friction stir welding (FSW) began as a fairly specialized joining method, but over the past three decades it has evolved into something considerably more versatile, a manufacturing platform that now handles complex multi-material assemblies and solid-state additive processes with reasonable reliability. This review follows this evolution, paying particular attention to friction stir additive manufacturing (FSAM) and the persistent difficulties that arise when joining dissimilar systems, such as aluminum to steel or metals to polymers, where the fate of the joint is largely decided by how well the intermetallic compounds are kept under control. Machine learning, artificial intelligence, and high-fidelity numerical models are reducing the reliance on trial-and-error that once dominated parameter selection and defect prediction, bringing FSW closer to the operating principles of Industry 4.0. Hybrid variants, including ultrasonically assisted and underwater FSW, also receive attention here, as they offer researchers finer control over heat generation and plastic flow than the standard process allows. Throughout the study, microstructural observations are directly connected to mechanical results, with the aim of analyzing the current state of solid-state manufacturing and identifying the questions that most urgently need answering. Full article

Microscopic vision-based alignment measurement is a key procedure in micro-/nanoscale positioning, and its measurement repeatability mainly depends on the stability of subpixel edge–center estimation. However, in practical microscopic imaging, defocus and contamination can cause edge broadening and pseudo-gradient peaks, making it difficult for conventional methods to accurately estimate the edge center of alignment marks. To address this problem, this paper proposes an adaptive edge-response modeling method. First, an amplitude function is constructed by combining the gradient peak and the slope of the edge-transition region, enabling adaptive adjustment of the response amplitude and suppressing its coupling with other parameters. On this basis, the proposed model overcomes the limitation that the Sigmoid model is only suitable for single-edge fitting and enables unified modeling of practical multi-edge hybrid bonding marks. It also suppresses the interference caused by edge pseudo-peaks and abrupt gradient variations, thereby improving the accuracy of subpixel fitting and localization. Experimental results show that, compared with conventional methods, the proposed method improves the repeatability of subpixel edge localization under degraded microscopic imaging conditions by approximately 52%, meeting the requirements of high-precision microscopic vision-based alignment. Full article

To meet the conflicting demands of real-time simulation and high fidelity for thermal modeling of IGBT modules in digital twin applications, this paper presents a dynamic thermal network parameter updating strategy. A hybrid thermal model is constructed by combining a high-fidelity finite-element-method reference model with a 3-D compact network. Initial thermal resistance and capacitance parameters are obtained via offline calibration and validated against the transient thermal impedance curve. A dynamic identification method based on recursive least squares with precomputed sensitivity matrices is then proposed. It dynamically updates each independent thermal branch using only real-time chip junction temperature measurements. The Vincotech full-bridge IGBT module is used for simulation validation. The proposed method achieves steady-state identification errors of 3.2% for the IGBT chip thermal resistance and 4.5% for the freewheeling diode chip thermal resistance, outperforming particle swarm optimization and dual Kalman filter in both convergence speed and steady-state accuracy. Thus, it satisfies the requirements of real-time tracking and dynamic evolution for thermal models in digital twin systems. Full article

This paper addresses the challenge of ensuring physically feasible and reliable real-time control of hybrid microgrids in harsh desert environments. A physics-constrained multi-agent Deep Q-Network (MA-DQN) is proposed for energy management of a grid-interactive microgrid in the Moroccan Sahara. The method embeds operational constraints directly into learning through action filtering, penalty-aware rewards, and coordinated PCC control. The results show a reduction in operational cost from 1250 MAD to 1120 MAD (−10.4%) and CO₂ emissions from 318.9 kg to 272.5 kg (−14.6%), while maintaining voltage within ±10% limits and eliminating PCC oscillations. The framework delivers stable, reliable, and deployment-ready control. Full article

Visual programming using blocks and diagrams facilitates understanding of fundamental programming concepts, which is particularly important for children with special educational needs because it reduces their cognitive load and encourages interactive learning. This study aimed to develop and apply a hybrid multi-criteria framework to evaluate, rank, and select visual programming software solutions intended for children with special educational needs. Based on an analysis of the educational context and the target population’s needs, a set of criteria was defined to evaluate and select the most suitable software solution. Data for the analysis were collected using a structured questionnaire, from which a decision matrix was developed. Within the proposed hybrid multi-criteria decision-making (MCDM) framework, criterion weights were determined using the Rank Order Centroid (ROC) method, and the ranking of alternatives was performed using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II). Additionally, a sensitivity analysis was conducted to assess the stability and robustness of the obtained rankings in relation to changes in the criterion weights. The results indicate a stable ranking of alternatives and the identification of the most favorable solution in the majority of scenarios. The projection quality of 91.1% in the Geometrical Analysis for Interactive Aid (GAIA) plane confirmed the reliability of the visual interpretation of the results. The proposed framework improves the decision-making process and provides a foundation for further research in educational software evaluation. Full article