A scalable and accessible photoactive formulation with a low synthetic complexity (SC) index is utilized in organic photovoltaic (OPV) fabrication. The formulation readily dissolves in nonchlorinated solvents, and the corresponding photoactive films can be processed by various coating methods to fabricate devices with power conversion efficiencies (PCEs) of 16.1% and 15.2% when using vacuum‐based molybdenum oxide and solution‐processable conducting polymer as the hole transporting layer in the inverted structure, respectively. This prepared device shows superior stability under light exposure. The PCE is maintained 94% of the initial values after 1080 h of light soaking at 100 mW cm−2. Furthermore, the figure of merit based on the ratio of the SC index and PCE indicates the benefit of this formulation for OPV manufacturing, showing the feasibility of commercialization. Eventually, a PCE of 10.3% is demonstrated for a mini‐module fabricated under ambient conditions, with an active area of 32.6 cm2. To our knowledge, this PCE is one of the largest values reported to date for a green solvent and an all‐solution‐processed OPV module with an inverted architecture.

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Solution‐processable hole‐transporting materials are demonstrated to improve the performance of nonfullerene‐based organic photovoltaic devices in an inverted structure. A vanadium oxide (VOX) precursor, used as a sol–gel, is mixed with commercial poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) to form a well‐dispersed VOX:PEDOT:PSS solution. The work function and molecular distribution of the VOX:PEDOT:PSS thin film are examined by ultraviolet photoelectron spectroscopy (UPS) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), respectively. Unlike conventional PEDOT:PSS, VOX:PEDOT:PSS not only is compatible with highly hydrophobic photoactive layers but also aligns well with the highest occupied molecular orbital (HOMO) level of the polymer donor, reaching a power conversion efficiency of 10% (≈100% boost) and achieving an excellent device stability.

In this paper, we propose a simple but effective human identification method based on gait features using frame difference history image (FDHI). Before constructing the FDHI feature, a sequence-based silhouette normalization scheme and an alignment pre-processing step are applied. After that, a post-processing step is devised for getting more representative gait signatures for human identification. Two types of FDHI templates are then extracted and represented more compactly by a dimensionality reduction technique, i.e., Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA). The transformed feature vectors are then respectively classified by individual K-Nearest Neighbor (KNN) classifiers. Lastly, the final classification decision is made by a fusion technique. Experimental results are provided to prove the superiority of the proposed method.

The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate redundant features plays a significant role. This paper proposes a new lightweight Convolution method Cross-Stage Lightweight (CSL) Module, to generate redundant features from cheap operations. In the intermediate expansion stage, we replaced Pointwise Convolution with Depthwise Convolution to produce candidate features. The proposed CSL-Module can reduce the computation cost significantly. Experiments conducted at MS-COCO show that the proposed CSL-Module can approximate the fitting ability of Convolution-3x3. Finally, we use the module to construct a lightweight detector CSL-YOLO, achieving better detection performance with only 43% FLOPs and 52% parameters than Tiny-YOLOv4.