Camera traps often produce massive images, and empty images that do not contain animals are usually overwhelming. Deep learning is a machine‐learning algorithm and widely used to identify empty camera trap images automatically. Existing methods with high accuracy are based on millions of training samples (images) and require a lot of time and personnel costs to label the training samples manually. Reducing the number of training samples can save the cost of manually labeling images. However, the deep learning models based on a small dataset produce a large omission error of animal images that many animal images tend to be identified as empty images, which may lead to loss of the opportunities of discovering and observing species. Therefore, it is still a challenge to build the DCNN model with small errors on a small dataset. Using deep convolutional neural networks and a small‐size dataset, we proposed an ensemble learning approach based on conservative strategies to identify and remove empty images automatically. Furthermore, we proposed three automatic identifying schemes of empty images for users who accept different omission errors of animal images. Our experimental results showed that these three schemes automatically identified and removed 50.78%, 58.48%, and 77.51% of the empty images in the dataset when the omission errors were 0.70%, 1.13%, and 2.54%, respectively. The analysis showed that using our scheme to automatically identify empty images did not omit species information. It only slightly changed the frequency of species occurrence. When only a small dataset was available, our approach provided an alternative to users to automatically identify and remove empty images, which can significantly reduce the time and personnel costs required to manually remove empty images. The cost savings were comparable to the percentage of empty images removed by models.

In today’s interconnected world, identity authentication and key agreement are important links in the secure communication process of IoT terminal devices. In the edge computing environment, with the frequent cross-domain authentication and data sharing of IoT devices in different security domains, identity authentication faces a series of challenges and security issues. Most of the traditional identity authentication methods are based on public key infrastructure, which is prone to single point of failure and is not applicable to the distributed architecture of edge computing. In this article, we apply blockchain technology to the identity authentication and key agreement process of IoT terminal devices. In order to meet cross-domain requests from terminal devices in different security domains, a multi-layer blockchain authentication architecture is designed. The hash value of the digital certificate is stored on the blockchain and combined with dynamic accumulator technology to enhance the reliability and authentication efficiency of the digital certificate. Security analysis and experimental results demonstrate that our scheme can achieve efficient and secure authentication and key agreement.

The Orchidaceae family, with its unparalleled species diversity among angiosperms, is integral to ornamental, medicinal, cultural, and ecological value. Multi‐omics techniques have proven invaluable for the identification of candidate genes and the advancement of functional genomics research. Nevertheless, the application of these technologies in Orchidaceae remains severely limited due to the lack of effective platforms that can integrate and analyze multi‐omics data, especially in understanding the mechanisms underlying key traits such as distinctive floral morphology. In this study, we present OrchidMD, the Orchid Multi‐omics Database ( www.orchidcomics.com ), a resource platform that integrates data from five omics layers: genomics, transcriptomics, proteomics, metabolomics, and phenomics, encompassing a total of 213 species. OrchidMD is equipped with 18 specialized statistical and analytical tools, and features a user‐friendly interface that facilitates efficient gene mining, multi‐omics data exploration, and integrative interactive analysis. A case study on the comprehensive identification of the pan‐ARF gene family across Orchidaceae species demonstrates the effectiveness and convenience of OrchidMD. Furthermore, experimental validation further shows that transgenic overexpression of CsiARF04 promotes the differentiation and budding of orchid rhizomes. In addition, another case study using gene editing in orchids, CRISPR Design was employed to predict the CsiPDS target site in Cymbidium sinense . Effective editing was subsequently achieved via Agrobacterium ‐mediated delivery of the CRISPR/Cas9 vector into leaves. These results underscore OrchidMD‘s formidable capacity to discern candidate genes associated with salient traits and elucidate their regulatory mechanisms. Thus, OrchidMD serves as a pivotal platform advancing multi‐dimensional biological research and functional genomics in orchids.

Dense small traffic object detection is essential for intelligent transportation systems but remains challenging because distant targets contain limited visual details, densely distributed objects frequently overlap, and complex road backgrounds introduce substantial interference. To address these limitations, this study proposes GAD-YOLO, a multi-level feature enhancement network based on YOLOv9. Ghost-MSConv performs lightweight multi-receptive-field feature extraction in the backbone, Mixed Local Channel Attention combines local spatial relationships with global channel dependencies during feature refinement, and DySample performs content-adaptive point sampling during feature upsampling. In the primary experiments on a six-class traffic object dataset derived from UA-DETRAC, GAD-YOLO achieved a precision of 78.9%, a recall of 76.4%, an ${\mathrm{mAP}}_{50}$ of 82.8%, and an ${\mathrm{mAP}}_{50:95}$ of 65.5%. Compared with YOLOv9c, precision, recall, ${\mathrm{mAP}}_{50}$, and ${\mathrm{mAP}}_{50:95}$ increased by 5.4, 0.5, 3.1, and 4.8 percentage points, respectively. Under the complexity statistics used in the primary experiments, GAD-YOLO contains 25.455 M parameters and requires 102.4 GFLOPs, compared with 25.442 M parameters and 103.2 GFLOPs for YOLOv9c. Additional experiments on the public VisDrone2019-DET benchmark were conducted to evaluate cross-dataset applicability, small-object performance, scene-density sensitivity, and standardized inference efficiency. On the VisDrone2019-DET test-dev set, GAD-YOLO improved ${\mathrm{mAP}}_{50}$ and ${\mathrm{mAP}}_{50:95}$ from 26.5% and 15.7% to 27.1% and 16.3%, respectively. A COCO-style analysis further showed that ${\mathrm{AP}}_S$ increased from 6.72% to 7.31%, while the dense-subset ${\mathrm{mAP}}_{50:95}$ increased from 13.95% to 14.44%. Under an RTX 4090, batch-size-one, $640\times 640$, FP32 inference protocol, GAD-YOLO achieved a mean latency of 9.98 ms and a throughput of 100.20 FPS. These results show that GAD-YOLO improves the primary traffic object detection task and yields modest positive performance differences on an independent public benchmark under the fixed experimental setting, while maintaining real-time inference capability.

Software vulnerability mining is important for improving software reliability and security. Compared with sequence-based models, graph neural networks (GNNs) can explicitly model program structures such as syntax, control flow, data flow, call relations, and dependency paths, and have therefore been widely studied for vulnerability detection, localization, explanation, and repair. This paper presents a PRISMA-informed structured topical review of 87 studies and addresses five research questions concerning program graph representation, homogeneous and heterogeneous GNN architectures, Graph–LLM integration, evaluation reliability, and future research directions. The reviewed evidence shows that graph-based methods are most effective when vulnerability mechanisms can be faithfully represented through explicit structural relations. However, their reported performance remains strongly affected by duplicated samples, random function-level splits, noisy labels, incomplete repository context, graph-construction errors, and weak explanation protocols. Homogeneous GNNs provide efficient structural message passing but may mix different semantic relations, whereas heterogeneous GNNs preserve relation types more explicitly at the cost of greater graph-quality and computational requirements. Graph–LLM systems can improve semantic reasoning, repository-level analysis, explanation generation, and repair support, but their benefits should be evaluated together with memory consumption, inference latency, deployment complexity, and verification cost. This review further proposes minimum requirements for reliable vulnerability benchmarks and verifiable explanations, and develops a strategic agenda covering leakage-resistant datasets, uncertainty-aware graph construction, repository-level evaluation, cost-effective Graph–LLM collaboration, and graph-guided autonomous vulnerability repair.

Camera traps are widely used in wildlife surveys but often generate numerous empty images due to false triggers, which are time-consuming to filter manually. Microsoft’s MegaDetector (MD) is commonly applied for empty image filtering; however, setting its confidence threshold is challenging because a high threshold increases the false negative rate (FNR), whereas a low threshold raises the false positive rate (FPR), making it difficult to achieve a satisfactory trade-off between the two. To address this dilemma, we proposed three MD-based methods with different performance and computational requirements. For FNR-sensitive users, we proposed MD_CS, which integrated context image similarity to achieve a very low FNR while controlling the FPR. For FPR-sensitive users, we proposed MD_CL, which used MD to generate supervisory signals for training a classification model, thereby reducing the FPR while controlling the FNR. For users requiring both low FNR and low FPR, we proposed MD_CS_CL, an ensemble method that combined the strengths of MD_CS and MD_CL. Experimental results on multiple datasets showed that these three methods achieved a significantly better balance between FNR and FPR compared to the threshold adjustment method of the MD method. Specifically, MD_CS maintained an FNR of 1.8–5.9% while controlling the FPR at 4.9–8.5%; MD_CL kept the FPR at 0.4–2.6% while controlling the FNR at 3.8–8.4%; MD_CS_CL achieved an optimal balance with an FNR of 1.4–4.0% and an FPR of 0.3–0.9%. On a server equipped with an RTX 4090 GPU, the three methods processed 6.7, 5.4, and 4.0 images per second, respectively, and none required manual annotation. Consequently, the proposed methods reduce labor costs, improve work efficiency, and provide reliable technical support for large-scale wildlife monitoring by achieving a superior FNR–FPR trade-off compared to MD’s traditional threshold regulation.