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In this paper, a multi-objective optimization strategy for efficient design of turbomachinery blades using sparse active subspaces is implemented for a turbofan stage design. The proposed strategy utilized sparse polynomial chaos expansion on a limited dataset to generate a function from which the differential and the covariance matrix can be obtained. Active subspace was used to compute the active variables via singular value decomposition and a hybrid polynomial correlated function expansion was used to construct a surrogate model on the active subspace. Coupled with freeform method and multi-objective genetic algorithm, an automated optimization loop was run at a single operating condition. An improvement in stage efficiency and total pressure ratio of 2.97% and 1.15% was achieved for the optimum design compared with the baseline. Additionally, total pressure loss coefficient decreased by 5.88%, exit flow angle by 34.65% and shock strength by 5.32%. The coupled effect of change in stagger angle, forward sweep, forward lean, and chord length reduced the recirculation at the hub, and blockage at the shroud due the tip leakage flow by decreasing the blade loading. The threshold value hyperparameter was found to be the most influential and must be accurately determined.

This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the IMOGWO started population initialization based on the Bloch coordinates of qubits to ensure a high-quality initial population. Additionally, it employed a nonlinear convergence factor to facilitate global exploration and integrated the inspiration of Manta Ray Foraging to enhance the information exchange between populations. Finally, associative learning was leveraged for archive updating, allowing for perturbative mutation of solutions in crowded regions of the archive to increase solution diversity and improve the algorithm’s search capability. The proposed IMOGWO was applied to five multi-objective benchmark functions, comprising three two-objective and two three-objective problems, and experimental results were compared with three well-known multi-objective algorithms: the non-dominated sorting genetic algorithm II (NSGA II), MOGWO, and the multi-objective multi-verse optimizer (MOMVO). It is demonstrated that the proposed algorithm had advantages in convergence accuracy and diversity of solutions, which were quantified by the performance metrics (generational distance (GD), inverted generational distance (IGD), Spacing (SP), and Hypervolume (HV)). Furthermore, a multi-objective optimization process coupled with the IMOGWO algorithm and Computational Fluid Dynamics (CFD) was proposed. By optimizing the design parameters of an axial cooling fan, a set of non-dominated solutions was obtained within limited iteration steps. Consequently, the IMOGWO also presented an effective and practical approach for addressing multi-objective optimization challenges with respect to engineering problems.

Future civil aero-engines are expected to feature larger bypass ratios and fan diameters to reduce specific thrust and improve propulsive efficiency, thereby lowering specific fuel consumption. These new configurations introduce design challenges, as the traditional scaling will result in the increase of nacelle drag, weight and the integration effects of aircraft. For this reason, compact nacelles are preferred for integration with next-generation civil turbofan engines. Moreover, designing and optimizing compact aero-engine nacelles is particularly challenging due to the non-linear nature of transonic aerodynamics and the diverse operating conditions experienced across the flight envelope. This work first establishes a mapping relationship between geometric parameters and the nacelle drag coefficient through surrogate models across various operating conditions. Then, sensitivity analyses are performed by applying the Sobol sequence sampling method and variance-based sensitivity analysis within the design bounds. To investigate the impact of compactness on the uncertainty of the drag coefficient, compact nacelles with various length-to-highlight radius ratios are examined. The Sobol-based sensitivity analysis reveals that the ratio $ {\\boldsymbol{r}_{\\boldsymbol{max}}}/{\\boldsymbol{r}_{\\boldsymbol{hi}}} $ r max / r hi dominates the first-order effects, with its total-effect Sobol index remaining above 85% across all configurations. The optimized design shows significantly smaller absolute changes compared to the baseline, particularly in the nacelle cruise drag coefficient $ {\\boldsymbol{C}_{\\boldsymbol{D} - \\boldsymbol{cruise}}} $ C D − cruise at mid-cruise condition, with the variation reduced from 0.0017 to 0.0004, indicating that the optimized nacelle design is more geometric robust and less sensitive to parameter variations. The novelty of this work lies in applying Sobol-based sensitivity analysis to quantify geometric parameter impacts on nacelle drag under uncertainty during cruise conditions. The qualitative and quantitative analysis results will provide valuable insights for determining the design space in the early stages of the design process and developing a robust design optimization framework for three-dimensional compact nacelles.

Transient stability status prediction (TSSP) plays an important role in situational awareness of power system stability. One of the main challenges of TSSP is the high-dimensional input feature analysis. In this paper, a novel two-stage feature selection method is proposed to handle this problem. In the first stage, the relevance between features and classes is measured by normalized mutual information (NMI), and the features are ranked based on the NMI values. Then, a predefined number of top-ranked features are selected to form the strongly relevant feature subset, and the remaining features are described as the weakly relevant feature subset, which can be utilized as the prior knowledge for the next stage. In the second stage, the binary particle swarm optimization is adopted as the search algorithm for feature selection, and a new particle encoding method that considers both population diversity and prior knowledge is presented. In addition, taking the imbalanced characteristics of TSSP into consideration, an improved fitness function for TSSP feature selection is proposed. The effectiveness of the proposed method is corroborated on the Northeast Power Coordinating Council (NPCC) 140-bus system.

Metallic lithium anodes possess the lowest redox potential (−3.04 V vs. SHE) and an ultra-high theoretical capacity (3860 mAh g−1, 2061 mAh cm−3). However, during electrochemical cycling, lithium metal tends to plate unevenly, leading to the formation of lithium dendrites. Moreover, severe electrochemical corrosion occurs at the interface between metallic lithium and traditional copper foil current collectors. To address these issues, we selected corrosion-resistant carbon paper as a lithium metal host and modified a uniform distribution of silver nanoparticles and a F-doped amorphous carbon structure as a highly lithiophilic F-CP@Ag host to enhance lithium-ion transport kinetics and achieve improved affinity with lithium metal. The silver nanoparticles reduced the lithium nucleation energy barrier, while F doping resulted in a LiF-rich solid electrolyte interphase that better accommodated volume changes in lithium metal. These two strategies worked together to ensure uniform and stable lithium metal plating/stripping on the F-CP@Ag host. Consequently, under the conditions of 1 mA cm−2 and 1 mAh cm−2, the symmetric cell exhibited stable cycling with a polarization voltage of 8 mV for up to 1400 h. This work highlights the corrosion problem of lithium metal on traditional copper foil current collectors and provides guidance for the long-term cycling stability of lithium metal anodes.

Background Mounting evidence highlights the critical role of mitochondrial dysfunction, driven by mitochondrial-related genes (MTRGs), in the development, progression, and therapeutic response of cancer. However, a comprehensive analysis linking specific Mitochondria-Related Gene Signature (MTRGS) to Bladder Cancer (BLCA) prognosis and immunotherapy efficacy remains largely unexplored. Therefore, this study aims to investigate the role of MTRGs in BLCA, construct and validate a novel MTRGs-based prognostic signature, and explore its potential for guiding personalized treatment strategies. Materials and methods Leveraging transcriptomic and clinical data from The Cancer Genome Atlas (TCGA-BLCA) cohort, we constructed a mitochondrial-related risk score model using LASSO, univariate and multivariate Cox regression analyses. This model was subsequently validated in an independent Gene Expression Omnibus (GEO) dataset. We then employed integrated bioinformatics approaches (implemented in R with online databases) to characterize features of the tumor microenvironment (TME), immune cell infiltration, Gene Set Enrichment Analysis (GSEA), tumor mutational burden (TMB), and drug sensitivity across different risk groups. Additionally, using data from public databases, we further verified our findings through single-cell RNA sequencing (scRNA-seq) analyses. Results Using 104 mitochondria-related differentially expressed genes (MTR-DEGs), unsupervised non-negative matrix factorization (NMF) clustering stratified BLCA patients into three molecular subtypes (Clusters 1–3). Survival analysis revealed that patients in Cluster 3 had significantly longer overall survival than those in Clusters 1 and 2. Our mitochondrial-related risk model incorporating six core genes ( MAP1B , PYCR1 , HSD3B1 , KLK6 , AKR1B15 , and TAT) - exhibited robust prognostic capability (3-years AUC = 0.695 in TCGA-BLCA, 0.798 in GEO-GSE32894, 0.703 in GSE13507). The risk model revealed distinct immune infiltration patterns between high- and low-risk groups. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenotype score (IPS) analyses demonstrated that integrating risk scores with stromal/immune signatures significantly enhanced immunotherapy benefit prediction across BLCA risk-subgroups. Crucially, the model demonstrated predictive power for therapy response: low-risk patients showed potential benefit from immune checkpoint inhibitors, while high-risk patients exhibited heightened sensitivity to specific chemotherapy agents or targeted therapies (e.g., Tozasertib, Gemcitabine) and may require intensified regimens. Conclusion This validated mitochondrial risk model delivers a clinically actionable biomarker for BLCA prognosis stratification and guides personalized therapeutic selection, enabling precision treatment intensification.

To improve power system risk prediction under complex and extreme operating conditions, a hybrid N-BEATS-based framework is proposed for equipment assessment and loss of load expectation (LOLE) forecasting. The method integrates thermal circuit modeling, online thermal parameter identification via physics-informed neural networks (PINNs), Arrhenius–Weibull temperature–failure mapping, Monte Carlo system risk evaluation, and a hybrid spatiotemporal predictor combining N-BEATS and graph neural networks. Case studies on distribution transformers demonstrate improved thermal parameter identification and reduced LOLE forecasting error compared with benchmark methods. The remainder of the paper is organized as follows: Firstly, it presents thermal modeling and PINN identification; Secondly, it introduces the aging-failure and reliability evaluation; Thirdly, it describes the hybrid forecasting framework; Fourthly, it reports case studies; and finally, it concludes.

The static-voltage-stability region (SVSR) is an effective tool for monitoring the safe operation of grid voltage. Rapidly obtaining the static-voltage-stability-region boundary (SVSRB) of a power system is crucial for the application of SVSR technology. First, this paper reveals the mechanisms underlying the failure scenarios that may occur with conventional boundary tracking methods. Then, an improved method incorporating curve extrapolation and correction conversion strategies is proposed, which enhances the efficiency of constructing the SVSRB. Furthermore, the analytical expression of the SVSRB is derived from the intermediate information obtained during the predictor stage. Finally, simulation examples based on a simple power system, the IEEE 3-machine, 9-bus power system and IEEE 300-bus power system were developed to verify the accuracy and efficiency of the proposed method.

The central Tibet AC interconnection project (CTAIP), which connects the Tibet power grid and the Sichuan power grid through a long distance transmission line of more than 1400 km, has a significant problem of voltage regulation. In order to improve the voltage regulation performance, six sets of ±60 Mvar static VAR compensators (SVC) were installed in the CTAIP. However, the SVCs may lead to electromagnetic oscillation below 50 Hz while improving voltage regulation capability. In this paper, the electromagnetic oscillation modes and the sensitivity of control parameters of SVC are analyzed. Then, the characteristics and influencing factors of the oscillation are discussed. It was found that there is an inherent electromagnetic oscillation mode below 50 Hz in the ultra-long distance transmission system. The employ of SVCs weaken the damping of this mode. Large proportional gain and integral gain (PI) parameters of SVCs can improve the voltage regulation performance, but weaken the electromagnetic oscillation mode damping. Therefore, a suppression method based on SVC PI parameters optimization is proposed to damp the oscillation. The essential of this method is to use the rising time of voltage response and setting time of SVCs as performance indicators of voltage regulation, and take the damping level of the electromagnetic oscillation mode as the performance index of SVC electromagnetic oscillation suppression ability. Combining the two indicators to form a comprehensive optimization index function, an intelligent optimization algorithm is applied. The process of SVC parameter optimization and the steps of multi-SVC parameter optimization in large power grids is proposed. Finally, PSCAD and real-time digital simulation (RTDS) simulation results verified the correctness of the proposed method. The optimization strategy was applied to CTAIP. The artificial grounding short circuit experimental results proved the effectiveness of the proposed strategy.

In recent years, machine learning methods have shown the great potential for real-time transient stability status prediction (TSSP) application. However, most existing studies overlook the imbalanced data problem in TSSP. To address this issue, a novel data segmentation-based ensemble classification (DSEC) method for TSSP is proposed in this paper. Firstly, the effects of the imbalanced data problem on the decision boundary and classification performance of TSSP are investigated in detail. Then, a three-step DSEC method is presented. In the first step, the data segmentation strategy is utilized for dividing the stable samples into multiple non-overlapping stable subsets, ensuring that the samples in each stable subset are not more than the unstable ones, then each stable subset is combined with the unstable set into a training subset. For the second step, an AdaBoost classifier is built based on each training subset. In the final step, decision values from each AdaBoost classifier are aggregated for determining the transient stability status. The experiments are conducted on the Northeast Power Coordinating Council 140-bus system and the simulation results indicate that the proposed approach can significantly improve the classification performance of TSSP with imbalanced data.