Search Results (2,233)

To address the problems of enhanced non-stationarity, significant feature distribution shift, and insufficient cross-condition generalization capability of traditional fault diagnosis methods under variable operating conditions such as varying speed and load, a rolling bearing fault diagnosis method integrating group sparse reconstruction and a multi-strategy improved quantum particle swarm optimization-based relevance vector machine (RVM) is proposed. First, group sparse representation learning is employed to reconstruct the original vibration signals, thereby suppressing background noise and enhancing fault-related impulsive components to improve signal separability and stability. Subsequently, a modal component selection criterion combining kurtosis and correlation coefficients is introduced to optimize and reconstruct the decomposed modal components, enabling the reconstructed signals to retain more fault-sensitive information. On this basis, multiple information entropy features are extracted from the reconstructed signals to construct high-dimensional state feature vectors for comprehensively characterizing the dynamic operating states of rolling bearings. To further enhance the parameter optimization capability, Chebyshev chaotic mapping is incorporated into the quantum particle swarm optimization (QPSO) algorithm to improve the uniformity of population initialization. Meanwhile, a Cauchy mutation strategy is introduced to strengthen the global search capability and avoid premature convergence, thereby forming a multi-strategy improved QPSO algorithm. Finally, the improved optimization algorithm is utilized to adaptively optimize the key hyperparameters of the RVM, resulting in a fault diagnosis model with high accuracy, strong generalization capability, and sparse characteristics. Experimental validation on the HUST and XJTU-SY bearing datasets demonstrates that the proposed MIQPSO-RVM framework achieves diagnostic accuracies of 96.70% and 94.83%, respectively. Compared with several representative intelligent diagnosis methods and deep learning models, the proposed method exhibits superior diagnostic performance, robustness, and generalization capability under complex operating conditions. Full article

Emerging technology topic identification is an important research task in the field of scientific and technological intelligence. To achieve a more comprehensive identification of emerging technology topics, this study proposes a multi-source data fusion framework that integrates three types of data sources: academic publications, patent data, and data from the GitHub open-source platform. In addition, an evaluation indicator system is constructed from four dimensions: growth, novelty, continuity, and impact. During the identification process, the BERTopic topic modeling approach is employed to uncover latent topics within the data, while the entropy weight method is applied for objective weighting, ultimately enabling the identification of emerging technology topics. The results indicate that the identified emerging technology topics include, but are not limited to, large language model-driven intelligent interaction, embodied intelligence perception, context memory management, and multimodal generation. Among the data sources, GitHub data provide earlier signals of technological evolution. Incorporating open-source platform data into the framework can effectively alleviate the lagging issues associated with traditional data sources. The proposed framework provides a more comprehensive research perspective for emerging technology topic identification. Full article

Pump-turbines often experience performance deterioration under high-load conditions beyond their best efficiency point, while the underlying flow mechanisms remain insufficiently understood. In this study, we investigate the relationship between internal flow structures and energy performance in a pump-turbine operating at a rated head of 202 m over a range of guide vane openings. Energy losses are evaluated using an average kinetic energy-based method and compared with an entropy production approach. A threshold-independent rigid vorticity method is adopted for vortex identification, and a streamline-based coordinate system is introduced for spatial quantification of energy loss and blade loading. The results show that hydraulic losses are mainly concentrated in the draft tube (66–75%) and runner (25–30%) under high-load conditions. A coupled vortex system formed by separation vortices and horseshoe vortices governs localized dissipation in the runner. In the draft tube, a columnar vortex rope generates strong shear layers that dominate energy loss in the cone and elbow regions. At high flow rates, negative incidence induces pressure-side separation, forming negative torque regions that reduce net runner torque and lead to output power deterioration. These findings highlight the dominant role of coupled vortex structures and pressure redistribution in performance degradation under high-load operation. Full article

In this paper, we introduce a new three-parameter lifetime distribution that is obtained via a power transformation of the modified bimodal exponential model. The inclusion of an additional shape parameter significantly enhances the flexibility of the baseline distribution, allowing it to capture a wide range of distributional characteristics, including skewness, heavy tails, and varying hazard rate shapes such as increasing, decreasing, and non-monotonic forms. Several important structural properties of the proposed model are derived, including explicit expressions for the probability density function, cumulative distribution function, moments, and moment generating function. Entropy measures such as Rényi entropy, Shannon entropy, and cumulative residual entropy are also obtained. Key reliability characteristics, including the survival function, hazard rate function, cumulative hazard function, reversed hazard rate, and mean residual life function, are investigated in detail. A theoretical result on the modality of the distribution is established, demonstrating its ability to exhibit both unimodal and bimodal shapes. Parameter estimation is carried out using maximum likelihood estimation along with several alternative methods. A comprehensive simulation study is conducted to evaluate the performance of the estimators under different parameter settings. Finally, the applicability and effectiveness of the proposed distribution are demonstrated through the analysis of real datasets from reliability and environmental studies. Comparative results based on goodness-of-fit measures indicate that the proposed model provides a superior fit compared to several existing competing distributions. Full article

Feature selection is a critical step in regression problems where a large number of continuous explanatory variables explain the same target through different dependency structures. Classical filters may remain sensitive to a single form of dependence, a single scale, or a specific discretization scheme; generalized entropy measures, on the other hand, typically require the parameters to be fixed at a single point. This study proposes a framework that evaluates the Sharma–Mittal entropy volumetrically across a two-dimensional parameter region rather than for a single parameter pair. For the continuous target and explanatory variables, the marginal, joint, and conditional densities are obtained using a Gaussian kernel density estimation; the conditional entropy and information gain surfaces are integrated across the region Ω = [0.05, 0.95]² in the α-β plane to define three indices: PICSME, which measures the conditional uncertainty volume; PIGSME, which measures the gain volume; and NIGSME, which is the ratio of this gain to the total entropy volume of the target. The method is supported by bandwidth consistency and the renormalization of conditional densities; thus, the issue of negative gain that can occur in the continuous variables is resolved, yielding positive and interpretable scores across all six datasets. It is formally demonstrated that the fact that the three indices produce the same ranking is not an empirical observation but rather the result of a monotonicity relationship valid under a fixed target entropy volume. The method is compared with Pearson and Spearman correlations, the Shannon information gain, mutual information, and random forest variable importance across six regression datasets (Airfoil Self-Noise, AirQualityUCI, BodyFat, Meteorology, Concrete, and WineQualityWhite) that differ in their sample size, dimensions, and application domain. The evaluation is not limited to ranking consistency; the out-of-sample prediction performance is measured using least-squares models on the top-k subsets, with rankings calculated from the training partition. The findings show that NIGSME exhibits a performance comparable to that of built-in filters, outperforms them on the Concrete and Meteorology datasets, and never ranks as the weakest method on any dataset. The results demonstrate that volumetric entropy metrics defined across the entire parameter space provide a feature-ranking tool that is independent of parameter selection for continuous variables. Full article

Electroencephalography-based visual decoding has important applications in brain–computer interfaces and cognitive neuroscience, yet the relative effectiveness of different feature extraction methods for sustained visual paradigms remains unclear due to the absence of standardized, multi-dataset comparative evaluations. This study systematically compares eight feature extraction methods across three public EEG datasets: MindBigData MNIST, MindBigData MNIST-8B for digit recognition, and MSS for natural image classification. The methods include coherence, Granger causality, directed transfer function, partially directed coherence, transfer entropy, discrete wavelet transform, empirical wavelet transform (EWT), and wavelet scattering transform. Two deep learning architectures, EEGNet and EEG Conformer, were trained using two pre-processing pipelines, with and without artifact removal. EWT achieved the highest classification accuracy, reaching 97.83% for digit-vs-blank and 77.10% for within-session natural image classification. Connectivity-based methods consistently underperformed, with the best connectivity method (coherence) reaching up to 91.67%, suggesting that spectral power information is more discriminative than inter-channel relationships. Cross-subject generalization remained challenging, with best accuracies near 68%. The findings establish wavelet-based adaptive spectral decomposition as a strong baseline for EEG visual decoding and highlight the need for domain adaptation techniques to address cross-subject variability. Full article

Traditional image encryption methods based on static permutation and diffusion are vulnerable to structural cryptanalysis and often exhibit limited robustness under imperfect communication conditions. To address these issues, this paper proposes a robust topology-evolving image encryption algorithm driven by complex hyperchaotic dynamics for secure visual data transmission. First, a two-dimensional Rosenbrock–Schwefel hyperchaotic map is constructed to generate high-quality pseudorandom sequences for both permutation and diffusion. Based on this map, a bidirectional oscillatory spatial permutation mechanism governed by a dynamic linked-list topology is developed. Unlike fixed-path permutation strategies, the proposed topology continuously evolves with the system state during image traversal, thereby increasing nonlinear path complexity and improving resistance to structural attacks. Furthermore, a plaintext-dependent adaptive diffusion mechanism is designed to enhance sensitivity to plaintext variations and produce a strong global avalanche effect. Experimental results demonstrate that the proposed algorithm achieves favorable encryption performance, with an information entropy of up to 7.9994, a Number of Pixels Change Rate (NPCR) of 99.6076%, and a Unified Average Changing Intensity (UACI) of 33.4683%. In addition, the algorithm maintains good recovery performance under cropping attacks and noise interference, indicating its robustness and applicability for secure image transmission in complex communication environments. Full article

Software code testing remains a critically important but labour-intensive process in software quality assurance. Existing research evaluates large language model (LLM)-based unit test generation using various quality metrics, such as correctness, coverage, mutation score, and test code smells. However, these single metrics do not reflect the trustworthiness of the unit test generation process. Therefore, this research formalises the trustworthiness of LLM-based unit test generation as a multidimensional index comprising reliability, hallucination resistance, maintainability, functional completeness, and human-reference alignment. In this research, we investigate the effect of prompt engineering strategies on the trustworthiness of LLM-generated unit tests and compare them with human-written tests for the same focal methods. Each dimension is fed by a distinct artefact-level measurement and grounded in dependability theory and ISO/IEC 25010:2023. A centralised multi-agent system generates, builds, repairs, and measures the tests, so that all inputs are collected automatically. The index is evaluated on real-world C# focal methods across 18 model × prompt configurations and a paired human-written baseline. The human baseline achieves the highest T-UTG value (0.904), and the best configuration, Combined × Gemini, achieves 0.788. Entropy weighting identifies maintainability and hallucination resistance as the most discriminating dimensions, and a rank-acceptability analysis over the whole weight simplex confirms that this ordering does not depend on the chosen weighting scheme. Full article

The growing share of variable renewable energy sources increases the need for operational flexibility in power systems. In regions with cascaded hydropower systems, upgrading existing plants may be a more practical short-term planning option than developing new hydropower facilities. This study employed a multi-criteria decision-making (MCDM) framework based on the VIKOR method to screen and prioritize existing hydropower plants for potential infrastructure upgrading and capacity development in the Skellefte River, located in Northern Sweden. According to this prefeasibility study, six hydropower stations with installed capacities above 50 MW along that river were evaluated using six technical criteria: installed capacity, hydraulic head, efficiency, generation cost, average turbine discharge, and normal annual production; their weights were derived using the Shannon entropy method to minimize subjectivity. The ranking suggests Gallejaur, Kvistforsen, and Bastusel as the most favorable alternatives, mainly due to their strong performance in annual production and hydraulic head. Vargfors, Krångfors, and Selsforsen rank lower because of head and/or production constraints. The proposed pre-feasibility hydropower ranking workflow provides a transparent and reproducible preliminary technical screening tool. More detailed studies, including hydraulic cascade operation, environmental permitting and grid constraints, are required before practical implementation and feasibility assessment studies for future investigations. Full article

The operational reliability of shaft systems in hydropower units has become increasingly critical as these units are frequently engaged in grid regulation under new power systems. This paper presents a sensor-driven method for operational status assessment and trend prediction of Francis turbine generator unit shaft systems using vibration and swing signals. Time domain features are extracted from the sensor-acquired signals to construct a multi-dimensional quantitative index system for characterizing the operational state, and a combined Entropy Weight–Coefficient of Variation–TOPSIS model with dynamic health thresholds is established for adaptive condition assessment. To address the nonlinear and non-stationary characteristics inherent in such signals, a decomposition–prediction–reconstruction fusion framework is developed, incorporating Variational Mode Decomposition (VMD) for signal decomposition and noise reduction, iTransformer for capturing global multi-variable interactions, and Bidirectional Long Short-Term Memory (BiLSTM) for bidirectional temporal feature extraction. The hybrid model achieves a coefficient of determination R² of 0.9845 on complex vibration and swing signals, demonstrating its superior prediction capability. Based on the prediction results, health scores and dynamic thresholds are calculated to perform trend analysis and health early warning. A case study is conducted using real-world monitoring data from a 306 MW Francis turbine unit. The results demonstrate that the proposed method effectively characterizes the shaft system operational state, achieving a closed-loop integration from condition monitoring to fault diagnosis and predictive maintenance. The operational status assessment and trend prediction analyses are in good agreement with actual operating conditions, providing reliable technical support for the intelligent health management of hydropower units. Full article

This paper presents an application and validation case for the recently obtained variational principle of a shock stationed in a duct. The streamline curvature method for the circumferentially averaged through-flow and blading design inverse problem remains fundamentally used in current axial compressor design systems and is indispensable as the generator of multi-stage blade coordinates. However, this method inherently smoothens flow discontinuities and thus, to date, cannot provide the stage stall margin, the key performance indicator most critical in the adjustment of high-loading stages, requiring instead a time-consuming CFD validation afterward. Leveraging the variational principle for shock stationarity, this paper acquires a method to show efficiently the stage stall margin by visualizing rotor passage shock rapidly. In the general coaxial rotating relative motion, by modeling the transonic streamlines as a set of layered quasi-one-dimensional duct flows, a variational principle of flow impulse potential energy for the stationary normal shock is derived. It is found that the factors governing the stationarity and location of the normal shock in relative motion include the variable cross-sectional area, the frictional and other on-way losses, and the variable rotational radius of the duct flow. In the applications to transonic rotor cascades, the frictional and other on-way losses are prescribed. First, the discontinuous entropy generation distributions along the cascades of each transonic layer are set to consider the boundary layer, oblique shock, normal passage shock, shock–boundary layer interference, and trailing edge losses. Second, with the total streamline loss fixed by the through-flow design, all shock locations possessing positional stability are determined via the variational principle for each streamline. Third, by comparing with CFD direct problem resu lts, a dimensionless rule governing the actual entropy generation distribution along the layer cascades is established. In three kinds of design cases of axial compressor stage, this method yields consistently 3D curved-surface structures of passage shock that agree well with CFD direct problem solutions, demonstrating its effectiveness and a certain applicability. Full article

We propose an entropy-stable numerical scheme for an augmented one-dimensional blood flow model by constructing an explicit diffusion operator independent of the reconstruction method used for the scaled entropy variables. The diffusion term plays an important role in entropy-stable schemes, i.e., schemes satisfying a discrete entropy inequality. In general, the diffusion operator involves a diffusion matrix that depends on the scaled right eigenvectors and on the reconstruction of the scaled variables. We derive a simple, explicit expression for this operator that avoids computing the full set of scaled eigenvectors. The performance of the scheme is assessed through numerical experiments, focusing on Riemann problems, which confirm its ability to capture shock waves accurately and provide numerical evidence of entropy decay for non-smooth solutions. Full article

To address the challenges of integrating multi-source heterogeneous data, fragmented fault knowledge, and the limited capability of traditional rule engines in recognizing edge cases for ultra-high voltage (UHV) bushing fault diagnosis, this paper proposes a fault identification and decision-making method based on knowledge graph (KG) rule reasoning and inductive graph convolutional network (Inductive GCN). First, a triple-matching strategy is employed to perform entity extraction and relation mining from fault cases, constructing a fault knowledge graph that transforms unstructured fault case texts into a structured knowledge graph. Second, a rule engine based on a multi-source feature rule set is designed, utilizing the entropy weight method and the RETE algorithm to achieve interpretable symbolic reasoning. On this basis, a double-layer inductive graph convolutional network is introduced to learn implicit fault patterns by aggregating topological information from neighboring nodes, and a confidence-driven dynamic weighted fusion strategy is adopted to achieve complementary advantages between the two models. Finally, a large language model is introduced to generate operation and maintenance decision recommendations. Experimental results demonstrate that the proposed method achieves an identification accuracy of 98.1% on a test set of 159 samples, which is 10.7 percentage points higher than that of a single rule engine and 6.9 percentage points higher than that of a single inductive graph convolution network. The standard deviation of accuracy across different test batches is only 0.0029. These results demonstrate the effectiveness and stability of the proposed method, providing a practical technical solution for UHV bushing fault identification. Full article

High-entropy alloys (HEAs), complex concentrated alloys (CCAs), medium-entropy alloys (MEAs), and multi-principal element alloys (MPEAs) are compositionally complex material classes in which design is based on multiple principal elements present in substantial, often comparable, fractions rather than on a single dominant constituent. In this Entry, the discussion focuses primarily on metallic alloy systems within this broader framework. HEAs are commonly described as alloys containing five or more principal elements and relatively high ideal configurational entropy; CCAs represent a broader, phase-agnostic category of chemically complex alloys in which multiple elements occupy significant fractions and can form solid solutions, ordered phases, intermetallic compounds, or multiphase microstructures; MEAs generally refer to systems containing fewer principal elements or lower configurational entropy than typical HEAs; and MPEAs describe alloys designed around multiple principal elements without requiring a specific entropy threshold. These terms overlap and are not universally hierarchical, and their use depends on composition, phase constitution, and research context. The large compositional space enabled by these materials provides opportunities for designing face-centered cubic, body-centered cubic, hexagonal close-packed, ordered, eutectic, refractory, interstitially alloyed, and precipitation-strengthened alloy systems. Their properties are governed by the complex interactions among composition, phase stability, chemical ordering, processing route, microstructure, and service environment. This Entry summarizes the fundamental concepts, design approaches, structural characteristics, processing methods, modeling strategies, properties, candidate engineering applications, limitations, and future perspectives of HEAs and related compositionally complex alloys. Full article

Digital transformation is crucial to the sustainable development of industries in locked-in specialized peripheral regions. Taking Longyan in Fujian Province as a case study, this study draws on county-level panel data covering the period 2008–2022 and employs the entropy weight method, kernel density estimation, location quotient analysis, Necessary Condition Analysis (NCA), and panel-data qualitative comparative analysis (PD-QCA) to identify the constraints and multiple pathways through which digital transformation drives industrial upgrading in peripheral regions. The findings are threefold. First, industrial upgrading in Longyan has continued to advance, while disparities in industrial development across counties and districts have generally tended to converge. Second, industrial upgrading in Longyan has primarily involved the extension of existing industrial foundations into knowledge-intensive fields, including energy conservation and environmental protection, advanced equipment, and new materials. Third, digital infrastructure, digital transformation environment, core enterprises’ digital technology application, digital technology innovation, intra-regional innovation collaboration, and cross-regional innovation collaboration combine to form five pathways to industrial upgrading. The findings demonstrate that digital transformation can enable peripheral regions to achieve knowledge recombination, functional upgrading, and the reconstruction of development paths on the basis of their existing industrial foundations. Full article