This study investigates the potential of inorganic perovskite CsPbBr₃ as a photovoltaic material, highlighting its superior stability compared to that of organic–inorganic hybrid perovskite materials. Conventional methods for preparing CsPbBr₃ perovskite films, such as the two-step method and the dual-source thermal evaporation method, face challenges in obtaining high-purity films due to the decomposition of precursor films and the formation of multiple heterogeneous phases. To address this issue, we synthesized CsPbBr₃ powder material using thermal evaporation deposition, which effectively suppressed decomposition and the formation of heterogeneous phases. Consequently, we achieved uniform and dense CsPbBr₃ perovskite films. By incorporating energy-band engineering modification with CsPbBr₃ quantum dots (QDs), the all-inorganic perovskite solar cells (PSCs) attained a power conversion efficiency (PCE) of 7.01% under standard solar illumination conditions. The device PCE remained at 93% of its initial efficiency under 30% relative humidity conditions for over 100 days, showcasing its durability. The developed method produced an average grain size of 800 nm, resulting in a smooth and uniform film surface, thereby demonstrating the method’s high repeatability. Additionally, the optimized PSCs exhibited a high open-circuit voltage (V_OC) with the champion device reaching a V_OC of 1.38 V and a PCE of 7.01%. This research presents a robust, efficient, and cost-effective approach for fabricating high-quality all-inorganic PSCs.

The noteworthy stability of Dion–Jacobson (DJ) phase two-dimensional perovskites marks them as potential contenders for use in optoelectronic applications. Nonetheless, their proliferation is considerably stymied by the constrained charge transport properties inherent to them. This bottleneck is adeptly navigated by deploying 2D-DJ perovskite top layers, seamlessly integrated on 3D perovskite films. We unveil a novel organic cation salt, 4-(Aminomethyl)piperidine (4AMP), as a potent facilitator for treating perovskite photovoltaic films. By employing the annealing technique, we facilitated the in situ creation of a hybrid 2D/3D architecture. Contrasted with conventional 3D architectures, the delineated perovskite heterojunctions with a 2D/3D structure exhibit superior enhanced charge separation, and mitigate photovoltaic losses by proficiently passivating intrinsic defects. The size-graded perovskite 2D/3D structure engineered herein significantly elevates the charge transfer performance, concurrently attenuating the excess lead iodide induced by bulk defects. This precise method resulted in a significant increase in Power Conversion Efficiency, reaching 23.08%, along with an open-circuit voltage (Voc) of 1.17 V. Remarkably, the unpackaged modified device robustly retains 92% of its initial PCE post a 3000 h sojourn under ambient conditions. This discourse propounds a novel paradigm for constructing stable planar PSC 2D/3D heterojunctions, thereby enriching the blueprint for advanced perovskite-based photovoltaic systems.

Improving the decoding accuracy and information transfer rate (ITR) of steady-state visual evoked potential brain–computer interface (SSVEP-BCI) systems, while enhancing cross-subject generalization and reducing calibration cost, is essential for practical deployment. This study proposes an end-to-end framework that integrates adaptive filtering with semi-supervised domain adaptation. The framework incorporates a Gabor adaptive filter bank (G-AFB) to optimize time–frequency representations and extract features matched to individual neural responses. It also introduces a three-stage semi-supervised domain-adversarial neural network (TriS-DANN), which combines unsupervised pre-alignment and supervised fine-tuning to align cross-subject feature distributions and enable lightweight calibration. On the 1.0 s public benchmark dataset, G-AFB-tCNN achieved 89.13% accuracy, a 4.63 percentage-point improvement over its conventional filter-bank counterpart. On the 0.4 s in-house dataset, G-AFB-tCNN achieved 91.85% accuracy, a 3.22 percentage-point improvement over the conventional fixed filter bank. In transfer learning, TriS-DANN reached 86.60% accuracy using 0.4 s segments extracted from the stimulation period and only 23.07% of the available target-domain training/calibration trials, demonstrating higher efficiency and stability than conventional fine-tuning. These results support the proposed framework as a feasible route toward reliable, low-calibration SSVEP-BCI systems.

Wear of the feedback ball head–ball seat interface changes the contact state and reduces the feedback force in electro-hydraulic servo valves, resulting in output nonlinearity and performance degradation. Existing wear models usually assume fixed surface morphology parameters, which their ability to describe time-varying wear evolution during repeated sliding. To address this issue, this study proposes a hybrid wear-prediction framework integrating fractal contact theory, Archard wear law, Gaussian process regression, and a servo-valve mechanical model. The real contact area and wear coefficient are expressed as functions of fractal parameters, while Gaussian process regression is used to predict their evolution under different loading cycles and displacement loads. Repeated loading tests and white-light interferometry measurements were performed to validate the proposed method. The results show that the fractal dimension of the ball seat increased by approximately 4.01%, whereas that of the ball head decreased by approximately 1.71%. After about 12,000 cycles, the fractal parameters tended to stabilize. The prediction error of the Gaussian process regression model was below 3%, and the wear-depth prediction error remained within approximately 1%. These results indicate that the proposed method can effectively capture the time-varying sliding wear behavior of the feedback ball head–ball seat interface.

Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or discontinuous bandwidth degrades depth resolution and introduces sidelobes and artifacts in OCT images. To address these issues in OCT image reconstruction from gapped spectra, a sparse parameter estimation approach based on Sparse Iterative Covariance-based Estimation (SPICE) is proposed in this study. By utilizing a sparse parameter estimation framework to directly resolve depth-dependent components from discontinuous interferograms, SPICE enhances axial resolution while suppressing sidelobe artifacts inherent in standard interpolation. Experiments on multi-layered tape, oral epithelium, and finger skin show that SPICE visually suppresses gap-induced sidelobe artifacts and improves structural interpretability under representative gap conditions. Quantitative evaluations on multi-layer tape and biological tissues show that SPICE reduces axial FWHM by 30–45%, increases SSIM by 0.15–0.25, and achieves significantly lower computational cost than GAPES (p < 0.01).

In view of the problem that high-voltage transmission lines have poor real-time performance and are difficult to effectively respond to sudden wildfires (smoke) and illegal construction (engineering vehicles) offline, this paper aims to develop a high-voltage line alarm robot system to achieve automatic, accurate, and real-time recognition and alarm of smoke and engineering vehicles in its corridor. In terms of methods, the system is equipped with multiple sensors for environmental perception and innovatively adopts the improved YOLOv8 target detection algorithm as the core recognition engine: first, rich input is constructed by fusing multimodal data; second, the attention mechanism CBAM (Convolutional Block Attention Module) module is introduced to enhance the model's ability to focus on key features and significantly improve the discrimination under complex backgrounds; third, transfer learning and domain adaptation technology are used to use large-scale pre-trained models and fine-tune with small sample data of specific high-voltage corridor scenes, which greatly improves the generalization and robustness of the model; finally, through model pruning and quantization, the optimized lightweight model is deployed to the Edge TPU edge computing unit equipped with the robot to achieve local real-time reasoning. In the real high-voltage line scene test set, the system has a recognition rate of 97.1% ([email protected]) in smoke detection and thick smoke, and the PR curve of engineering vehicles maintains F1>0.92 in the threshold range of 0.45-0.65; the average false alarm rate is controlled at 1.03%, and the average response time is less than 0.9 seconds. This robot system effectively solves the problem of real-time, accurate and automated monitoring of smoke and engineering vehicles in high-voltage line corridors, and significantly improves the efficiency and reliability of line safety protection.