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Addressing the issues of low accuracy and large size for tiny objects like pedestrians, non-motor vehicles, and vehicles in traditional object detection models for autonomous driving. An advanced variant of the YOLOv7-tiny model for automatic driving object detection is proposed in this study. The VoVEGSCSP module, designed for cross-layer integration within local networks, is introduced. The SPPCSPC module was replaced with the Space Pyramid Pool Fast (SPPF). This reduces parameters and calculations while preserving the diversity of the original feature scale. CARAFE is a lightweight universal upsampling operator that replaces nearest neighbor interpolation in the upsampling module, thereby reducing feature information loss during upsampling. Results demonstrate that on the KITTI dataset, the improved YOLOv7-tiny algorithm reduces parameters by 35.7% and computation by 34.1%, with only a 0.7% decrease in mAP@0.5 accuracy.

Bioplastics are adopted to replace fossil-based plastics because they are microplastic-free and self-degradable without releasing greenhouse gasses. Despite having many benefits, the main applications of bioplastics are packaging and kitchenware. Moreover, the utilization of bioplastics in electronic applications is still underexplored. Consequently, the development of bioplastics for electronic applications, especially heterojunctions, is essential. Here, we report a novel molybdenum disulfide (MoS[sub.2])/cobalt oxide (Co[sub.3]O[sub.4]) heterojunction based on bioplastic semiconductors, with agar as a matrix. This work also exposes the effect of carrier concentration on the mechanism of an energy band. Using the density of state in three dimensions, Anderson’s rule, and the Fermi energy level calculated by carrier concentration, we find that the energy gaps of the MoS[sub.2]/Co[sub.3]O[sub.4] heterojunction at various concentrations almost match the energy gap evaluated by Tauc’s relation. Additionally, leveraging the MoS[sub.2]/Co[sub.3]O[sub.4] heterojunction as a photodetector, the optimized device indicates an ideality factor of 1.59, a response time of 127 ms, and a recovery time of 115 ms. Our work not only represents a significant step towards using bioplastics in electronic applications but also reveals the mechanism of the energy band affected by carrier concentration.

In recent years, the realm of deep learning has witnessed significant advancements, particularly in object detection algorithms. However, the unique challenges posed by remote sensing images, such as complex backgrounds, diverse target sizes, dense target distribution, and overlapping or obscuring targets, demand specialized solutions. Addressing these challenges, we introduce a novel lightweight object detection algorithm based on Yolov5s to enhance detection performance while ensuring rapid processing and broad applicability. Our primary contributions include: firstly, we implemented a new Lightweight Asymmetric Detection Head (LADH-Head), replacing the original detection head in the Yolov5s model. Secondly, we introduce a new C3CA module, incorporating the Coordinate Attention mechanism, strengthening the network’s capability to extract precise location information. Thirdly, we proposed a new backbone network, replacing the C3 module in the Yolov5s backbone with a FasterConv module, enhancing the network’s feature extraction capabilities. Additionally, we introduced a Content-aware Feature Reassembly (content-aware reassembly of features) (CARAFE) module to reassemble semantic similar feature points effectively, enhancing the network’s detection capabilities and reducing the model parameters. Finally, we introduced a novel XIoU loss function, aiming to improve the model’s convergence speed and robustness during training. Experimental results on widely used remote sensing image datasets such as DIOR, DOTA, and SIMD demonstrate the effectiveness of our proposed model. Compared to the original Yolov5s algorithm, we achieved a mean average precision (mAP) increase of 3.3%, 6.7%, and 3.2%, respectively. These findings underscore the superior performance of our proposed model in remote sensing image object detection, offering an efficient, lightweight solution for remote sensing applications.

The world production of plastic exceeded 360 million tonnes in 2020 alone, a considerable amount of which is not properly disposed of. The significant pressures and damages posed by conventional plastic to human and environmental health suggest that alternatives are urgently needed. One of them is “bioplastic”, which is defined as bio-based plastic that is (or not) biodegradable. This paper reports on a study on the perceptions of bioplastics among consumers in 42 countries to identify their levels of information and concerns. The results suggest that most respondents have positive expectations regarding the future of bioplastics to replace conventional plastics fully or partially, especially for food containers, kitchenware, and boxes and bags for packaging. They also reported that the low costs and increased availability of bioplastic products on the market are likely to be the main drivers for their wide-scale adoption. However, many participants are unsure whether they would buy bio-based and biodegradable products if they are expensive. Overall, whereas a rather positive attitude to bioplastics has been identified, greater efforts are needed to address the many information needs of consumers towards upscaling the adoption of bioplastics. Relevant policies are therefore needed to encourage investments in the large-scale manufacture and market uptake of bioplastics. The paper reports on an initial study of consumer behavior, in a sample of countries spread across all geographical regions.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we introduce Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we integrate Carafe into the widely used Skyline tool. Accurate spectral libraries are essential for analyzing data-independent acquisition (DIA) proteomics data. Here, the authors present Carafe, which trains on DIA data to build experiment-specific spectral libraries, boosting peptide detection across diverse datasets.

IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.

The detection of infrared vehicle targets by UAVs poses significant challenges in the presence of complex ground backgrounds, high target density, and a large proportion of small targets, which result in high false alarm rates. To alleviate these deficiencies, a novel YOLOv7-based, multi-scale target detection method for infrared vehicle targets is proposed, which is termed YOLO-ViT. Firstly, within the YOLOV7-based framework, the lightweight MobileViT network is incorporated as the feature extraction backbone network to fully extract the local and global features of the object and reduce the complexity of the model. Secondly, an innovative C3-PANet neural network structure is delicately designed, which adopts the CARAFE upsampling method to utilize the semantic information in the feature map and improve the model’s recognition accuracy of the target region. In conjunction with the C3 structure, the receptive field will be increased to enhance the network’s accuracy in recognizing small targets and model generalization ability. Finally, the K-means++ clustering method is utilized to optimize the anchor box size, leading to the design of anchor boxes better suited for detecting small infrared targets from UAVs, thereby improving detection efficiency. The present article showcases experimental findings attained through the use of the HIT-UAV public dataset. The results demonstrate that the enhanced YOLO-ViT approach, in comparison to the original method, achieves a reduction in the number of parameters by 49.9% and floating-point operations by 67.9%. Furthermore, the mean average precision (mAP) exhibits an improvement of 0.9% over the existing algorithm, reaching a value of 94.5%, which validates the effectiveness of the method for UAV infrared vehicle target detection.