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Design and fabrication of electrochemical energy storage systems with both high energy and power densities as well as long cycling life is of great importance. As one of these systems, Battery‐supercapacitor hybrid device (BSH) is typically constructed with a high‐capacity battery‐type electrode and a high‐rate capacitive electrode, which has attracted enormous attention due to its potential applications in future electric vehicles, smart electric grids, and even miniaturized electronic/optoelectronic devices, etc. With proper design, BSH will provide unique advantages such as high performance, cheapness, safety, and environmental friendliness. This review first addresses the fundamental scientific principle, structure, and possible classification of BSHs, and then reviews the recent advances on various existing and emerging BSHs such as Li‐/Na‐ion BSHs, acidic/alkaline BSHs, BSH with redox electrolytes, and BSH with pseudocapacitive electrode, with the focus on materials and electrochemical performances. Furthermore, recent progresses in BSH devices with specific functionalities of flexibility and transparency, etc. will be highlighted. Finally, the future developing trends and directions as well as the challenges will also be discussed; especially, two conceptual BSHs with aqueous high voltage window and integrated 3D electrode/electrolyte architecture will be proposed. The fundamental scientific principle, structure, and possible classification of battery‐supercapacitor hybrid devices (BSHs), outlining the recent advances on various existing and emerging BSHs, with the focus on materials and electrochemical performances, and finally providing the future developing trends and directions as well as the challenges are addressed in this review.

Polymetallic transition metal phosphides (TMPs) exhibit quasi-metallic properties and a high electrical conductivity, making them attractive for high-performance hybrid supercapacitors (HSCs). Herein, a nanohoneycomb (NHC)-like FeNi layered double hydroxide (LDH) array was grown in situ on 3D current collector nickel foam (NF), which is also the nickel source during the hydrothermal process. By adjusting the amount of NaH2PO2, an incomplete phosphated FeNi(OH/P) nanosheet array was obtained. The optimized FeNi(OH/P) nanosheet array exhibited a high capacity up to 3.6 C cm−2 (408.3 mAh g−1) and an excellent long-term cycle performance (72.0% after 10,000 cycles), which was much better than FeNi LDH’s precursor. In addition, the hybrid supercapacitor (HSC) assembled with FeNi(OH/P) (cathode) and polypyrrole (PPy/C, anode) achieved an ultra-high energy density of 45 W h kg−1 at a power density of 581 W kg−1 and an excellent cycle stability (118.5%, 2000 cycles), indicating its great potential as an HSC with a high electrochemical performance.

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.

Intensified complementary metal-oxide semiconductor (ICMOS) sensors involve multiple steps, including photoelectric conversion and photoelectric multiplication, each of which introduces noise that significantly impacts image quality. To address the issues of insufficient denoising performance and poor model generalization in ICMOS image denoising, this paper proposes a systematic solution. First, we established an experimental platform to collect real ICMOS images and introduced a novel noise generation network (LD-NGN) that accurately simulates the strong sparsity and spatial clustering of ICMOS noise, generating a multi-scene paired dataset. Additionally, we proposed a new noise evaluation metric, KL-Noise, which allows a more precise quantification of noise distribution. Based on this, we designed a denoising network specifically for ICMOS images, MAST-Net, and trained it using the multi-scene paired dataset generated by LD-NGN. By capturing multi-scale features of image pixels, MAST-Net effectively removes complex noise. The experimental results show that, compared to traditional methods and denoisers trained with other noise generators, our method outperforms both qualitatively and quantitatively. The denoised images achieve a peak signal-to-noise ratio (PSNR) of 35.38 dB and a structural similarity index (SSIM) of 0.93. This optimization provides support for tasks such as image preprocessing, target recognition, and feature extraction.

Current catalysts face challenges with low formate selectivity at high current densities during the CO 2 electroreduction. Here, we showcase a versatile strategy to enhance the formate production on p -block metal-based catalysts by incorporating noble metal atoms on their surface, refining oxygen affinity, and tuning adsorption of the critical oxygen-bound *OCHO intermediate. The formate yield is observed to afford a volcano-like dependence on the *OCHO binding strength across a series of modified catalysts. The rhodium-dispersed indium oxide (Rh/In 2 O 3 ) catalyst exhibits impressive performances, achieving Faradaic efficiencies (FEs) of formate exceeding 90% across a broad current density range of 0.20 to 1.21 A cm −2 . In situ Raman spectroscopy and theoretical calculations reveal that the oxophilic Rh site facilitates *OCHO formation by optimizing its adsorption energy, placing Rh/In 2 O 3 near the volcano-shaped apex. A bipolar electrosynthesis system, coupling the CO 2 reduction at the cathode with the formaldehyde oxidative dehydrogenation at the anode, further boosts the FE of formate to nearly 190% with pure hydrogen generation under an ampere-level current density and a low cell voltage of 2.5 V in a membrane electrode assembly cell. Developing high-productive and energy-saving systems in formate electrosynthesis is attractive but challenging. Here, the authors report an efficient Rh-dispersed In 2 O 3 catalyst for CO 2 reduction, benefiting the ampere-level formate coproduction when pairing with anodic aldehyde oxidative dehydrogenation.

Given a complete edge-weighted undirected graph G ( V ,  E ,  W ), clique partitioning problem (CPP) aims to cluster all vertices into an unknown number of disjoint groups and the objective is to maximize the sum of the edge weights of the induced subgraphs. CPP is an NP-hard combinatorial optimization problem with many real-world applications. In this paper, we propose a novel two-model local search algorithm with a self-adaptive parameter (TMLS _ SA) to solve CPP. First, a simple solution is presented, that is, one vertex per group is used. Then, we present a two-model local search that is used to improve the solution which comprises a move operator model and an exchange operator model. In the local search phase, a gain function is used to guide the search toward a possible best solution, and a lock mechanism is also applied to prevent the local search from immediately returning to visited solutions. Finally, we execute a perturbation procedure to increase the diversity. The perturbation strength is updated self-adaptively according to the solutions obtained. Our algorithm TMLS _ SA is compared with several representative algorithms, and the experimental results show that TMLS _ SA is superior to competitors on almost all test instances with respect to the solution quality.

Tool wear is a critical factor that directly impacts product performance, making accurate and timely detection essential for ensuring machining quality. In particular, under conditions of shallow cutting depth, tool tip wear significantly exceeds edge wear, yet the detection of tool tip wear has received little attention. Therefore, this paper proposes an image segmentation algorithm for detecting milling cutter tip wear, enabling precise measurement of tool tip wear. Initially, Valley-emphasis method is employed for initial segmentation of ground images to detect and segment the bottom edges. Subsequently, the detected edges serve as masks for parallel computation, achieving precise edge segmentation. Finally, the XOR result of the finely segmented edges and the mask is used to determine the wear region. Compared to existing detection algorithms, this method enhances edge detection accuracy without increasing detection time. The maximum error compared to manual measurement is within 0.007 mm, with a minimum accuracy rate of 97.92%. Additionally, the algorithm’s runtime has been reduced to 15.53 s, a decrease of approximately 94.68%. These results substantiate the efficacy of the proposed approach.

Considering that the ferritic stainless steel Z10C13 support plate material in nuclear power equipment tends to undergo fretting wear during service, this paper systematically investigates the effect of varying normal loads (10–50 N) and displacement amplitudes (15–75 μm) on its fretting response and wear mechanisms. Through ball-on-flat fretting wear experiments, together with macro- and micro-scale observations of wear scars, it is revealed that normal load primarily controls the contact intensity and the extent of adhesion, whereas displacement amplitude mainly affects the slip amplitude and features of fatigue damage. The results show that the fretting system’s dissipated energy increases nonlinearly with both load and amplitude, and their coupled effect significantly exacerbates interfacial damage. The wear scar morphology evolves from a shallow bowl shape to a structure characterized by multiple spalling pits and propagating fatigue cracks. An equivalent hardness-corrected Archard model is proposed based on the experimental data. The model captures the nonlinear dependence of equivalent material hardness on both load and amplitude. As a result, it accurately predicts wear volume (R2=0.9838), demonstrating its physical consistency and modeling reliability. Overall, this study elucidates the multi-scale damage evolution mechanism of Z10C13 under fretting conditions and provides a theoretical foundation and methodological support for wear-resistant design, life prediction, and safety evaluation of nuclear power support structures.

Background: In this study, we explored the interplay between exogenous orienting attention and emotional audiovisual integration (AVI) via electroencephalography (EEG). Methods: We designed a 2 (cue validity: valid, invalid) × 3 (emotion types: happiness, neutral and sadness) × 3 (modality: visual, auditory, audiovisual) discrimination task on the basis of the cue–target paradigm. Twenty-two participants (average age: 21.71 ± 1.84 years; 13 females, 9 males) were enrolled in this experiment. Participants were asked to respond to three emotional stimuli presented in different modalities by pressing a corresponding key. Results: The results indicated faster responses to multisensory stimuli than to unisensory stimuli and to the valid cue condition than to the invalid cue condition, which indicated multisensory advantage and cueing effect occurred. In addition, happiness stimuli induced the fastest response compared with neutral and sadness emotion stimuli. EEG findings indicated a reduction in audiovisual integration induced by valid exogenous orienting in the frontal, central and parietal lobe regions. Moreover, neutral emotional stimuli elicited greater audiovisual integration than stimuli expressing happiness and sadness did. Conclusions: Overall, valid exogenous cues and emotional processing decreased audiovisual integration. The present study sheds light on how exogenous attention modulates emotional audiovisual integration and highlights the complex interactions among attention, sensory processing, and the emotional context in multisensory perception.

There is currently a lack of efficient heat transfer analysis methodologies for spiral channel regenerative cooling that has been increasingly applied in liquid rocket engines. To figure out the heat transfer characteristics of the spiral channel regenerative cooling thrust chamber, a simple 1D method based on the traditional semi-empirical formula after correcting the flow velocity is proposed. The accuracy of this approach is verified by the 3D numerical simulation. The verified method is further used to analyze the distribution of inner wall temperature in the test case and optimize the channel’s parameters. The research shows that the maximum inner wall temperature cooled by the spiral channel is 8.5% lower than that of the straight channel under the same channel size and boundary condition, indicating that the application of the spiral channel significantly improves the cooling effect. In addition, the 1D model combined with the second-order response surface model is employed to optimize the channel width, channel height, pitch, and inner wall thickness aiming for the best cooling effect. The calculated maximum temperature of the inner wall after the structure optimization is 586.6 K, which is 29.8% lower than the initial structure before optimization.