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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

Conocybe belongs to the Bolbitiaceae. The morphological classification and molecular phylogenetics of Conocybe section Pilosellae are not in agreement. In this study, based on the specimens from China, we investigated the sect. Pilosellae and identified 17 species, including 7 new species: Conocybe pilosa, with a densely hairy pileus and stipe; C. reniformis, with reniform spores; C. ceracea, with waxy dehydration of the lamellae; C. muscicola, growing on moss; C. sinobispora, with two-spored basidia; C. hydrophila, with a hygrophanous pileus; C. rufostipes, growing on dung with a brown stipe; and C. pseudocrispa, one new record for China. A key was compiled for the sect. Pilosellae in China. Here, the sect. Pilosellae, and new species and records from China are morphologically described and illustrated. Maximum likelihood and Bayesian analyses were performed using a combined nuc rDNA internal transcribed spacer region (ITS) and nuc 28S rDNA (nrLSU), and translation elongation factor 1-alpha (tef1-α) dataset to reconstruct the relationships of this section. We found that the sect. Pilosellae was the basal clade of Conocybe, and its evolutionary features may shed light on the characteristics of Conocybe. By integrating morphological classification and phylogenetic analysis, we explored the possible phylogenetic relationships among the species of the sect. Pilosellae in China.

Casting defects in turbine blades can significantly reduce an aero-engine’s service life and cause secondary damage to the blades when exposed to harsh environments. Therefore, casting defect detection plays a crucial role in enhancing aircraft performance. Existing defect detection methods face challenges in effectively detecting multi-scale defects and handling imbalanced datasets, leading to unsatisfactory defect detection results. In this work, a novel blade defect detection method is proposed. This method is based on a detection transformer with a multi-scale fusion attention mechanism, considering comprehensive features. Firstly, a novel joint data augmentation (JDA) method is constructed to alleviate the imbalanced dataset issue by effectively increasing the number of sample data. Then, an attention-based channel-adaptive weighting (ACAW) feature enhancement module is established to fully apply complementary information among different feature channels, and further refine feature representations. Consequently, a multi-scale feature fusion (MFF) module is proposed to integrate high-dimensional semantic information and low-level representation features, enhancing multi-scale defect detection precision. Moreover, R-Focal loss is developed in an MFF attention-based DEtection TRansformer (DETR) to further solve the issue of imbalanced datasets and accelerate model convergence using the random hyper-parameters search strategy. An aero-engine turbine blade defect X-ray (ATBDX) image dataset is applied to validate the proposed method. The comparative results demonstrate that this proposed method can effectively integrate multi-scale image features and enhance multi-scale defect detection precision.

Cancer is one of the world’s most burdensome diseases, with increasing prevalence and a high mortality rate threat. Tumor recurrence and metastasis due to treatment resistance are two of the primary reasons that cancers have been so difficult to treat. The epithelial–mesenchymal transition (EMT) is essential for tumor drug resistance. EMT causes tumor cells to produce mesenchymal stem cells and quickly adapt to various injuries, showing a treatment-resistant phenotype. In addition, multiple signaling pathways and regulatory mechanisms are involved in the EMT, resulting in resistance to treatment and hard eradication of the tumors. The purpose of this study is to review the link between EMT, therapeutic resistance, and the molecular process, and to offer a theoretical framework for EMT-based tumor-sensitization therapy.

Environmental technology innovation is a crucial measure of the quality of China’s economic development and sustainable environmental protection. Based on the 2009–2017 provincial panel data from China, this article used the modified projection pursuit model to measure the environmental technology innovation capabilities of various regions. Moreover, this article empirically investigates the threshold effect of outward foreign direct investment on China’s environmental technology innovation under different intellectual property protection levels. The results are as follows. First, the environmental technology innovation capabilities of China’s regions vary significantly, showing an “east-middle-west” gradient decline trend similar to levels of economic development. Second, outward foreign direct investment has a significant reverse environmental technology innovation effect, but this effect has complex nonlinear characteristics. Third, in the process of outward foreign direct investment affecting environmental technology innovation, intellectual property protection has a significant double threshold effect. As the level of intellectual property protection continues to cross the threshold value, the effect direction of outward foreign direct investment on environmental technology innovation undergoes a sudden change from inhibition to promotion. However, when intellectual property protection is too high, the promotion effect is relatively limited. This paper provides some reference points and insights that should aid in establishing a scientific intellectual property protection system and raising the level of environmental technology innovation.

Chronic kidney disease (CKD) is a serious global public health challenge, but there is limited information on the connection between air pollution and risk of CKD. The aim of this study was to investigate the association between long-term exposure to particulate matter (PM) with an aerodynamic diameter of less than [Formula: see text] ([Formula: see text]) and the development of CKD in a large cohort. A total of 100,629 nonCKD Taiwanese residents age 20 y or above were included in this study between 2001 and 2014. Ambient [Formula: see text] concentration was estimated at each participant's address using a satellite-based spatiotemporal model. Incident CKD cases were identified by an estimated glomerular filtration rate (eGFR) of less than [Formula: see text]. We collected information on a wide range of potential confounders/modifiers during the medical examinations. Cox proportional hazard regression was applied to calculate hazard ratios (HRs). During the follow-up, 4,046 incident CKD cases were identified, and the incidence rate was 6.24 per 1,000 person-years. In contrast with participants with the first quintile exposure of [Formula: see text], participants with the fourth and fifth quintiles exposure of [Formula: see text] had increased risk of CKD development, adjusting for age, sex, educational level, smoking, drinking, body mass index, systolic blood pressure, fasting glucose, total cholesterol, and self-reported heart disease or stroke, with an HR [95% confidence interval (CI)] of 1.11 (1.02, 1.22) and 1.15 (1.05, 1.26), respectively. A significant concentration-response trend was observed ([Formula: see text]). Every [Formula: see text] increment in the [Formula: see text] concentration was associated with a 6% higher risk of developing CKD (HR: 1.06, 95% CI: 1.02, 1.10). Sensitivity and stratified analyses yielded similar results. Long-term exposure to ambient [Formula: see text] was associated with an increased risk of CKD development. Our findings reinforce the urgency to develop global strategies of air pollution reduction to prevent CKD. https://doi.org/10.1289/EHP3304.

Background Oats are widely consumed throughout the world because of their nutritive value, with their yield and quality being associated with the developmental process of grain development. However, the underlying molecular mechanisms of the transcriptional dynamics of this process have not yet been fully elucidated. In this study, RNA-seq was performed to investigate the transcriptional dynamics and identify the key genes involved in the development of the oat grain at four different developmental stages. Results A total of 33,197 differentially expressed genes (DEGs), including 1,308 differentially expressed transcription factors (TFs) were identified, 398 DEGs associated with plant hormone signal transduction and 107 DEGs associated with starch and sucrose metabolism. The main concern of this study was to include those genes associated with hormone signaling, and the sucrose and starch metabolism pathways. Conclusions The results of this study provide valuable insights into the genetic resources affecting the molecular mechanism underlying the development of the oat grain, as well as establishing a strong theoretical foundation for its improvement.

Ship target detection is crucial in maritime traffic management, smart ports, autonomous ship systems, environmental monitoring, and ship scheduling. Accurate detection of various ships on the water can significantly enhance maritime traffic safety, reduce accidents, and improve the efficiency of port and waterway management. This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). To meet the high-precision requirements in practical applications, several precision-enhancement strategies are introduced based on YOLOv10. To optimize the feature fusion process, the Iterative Attentional Feature Fusion (iAFF) is integrated with the C2F module in the backbone, resulting in the development of a novel C2F_iAFF module that utilizes a multi-scale channel attention mechanism. Meanwhile, the Mixed Local Channel Attention (MLCA) is introduced after the C2F module at the network neck, which improves the model’s ability to integrate both local and global information. Additionally, the BiFPN module is incorporated after the connection operation at the network neck, utilizing learnable weights to optimize the importance of different input features, thereby further enhancing multi-scale feature fusion. The experimental results demonstrate that YOLO-HPSD achieves excellent detection performance on the ship dataset, with an F1-score of 97.88% and mAP@0.5 of 98.86%. Compared to YOLOv10n, the F1-score, and mAP@0.5 have improved by 1.22% and 0.31%, respectively. Furthermore, the detection time for a single image is only 20.6 ms. These results indicate that the model not only ensures high detection speed but also delivers high-accuracy ship target detection. This study provides technical support for real-time ship target detection and the development of edge computing devices.

This paper studies the AWC (Active Wafer Centering) algorithm for the movement control and wafer calibration of the handling robot in semiconductor manufacturing to prevent wafer surface contact and contamination during the transfer process. The mechanical and software architecture of the wafer-handling robot is analyzed first, which is followed by a description of the experimental platform for semiconductor manufacturing methods. Secondly, the article utilizes the geometric method to analyze the kinematics of the semiconductor robot, and it decouples the motion control of the robot body from the polar coordinates and joint space. The wafer center position is calibrated using the generalized least-square inverse method for AWC correction. The AWC algorithm is divided into calibration, deviation correction, and retraction detection. These are determined by analyzing the robot’s wafer calibration process. In conclusion, the semiconductor robot’s motion control and AWC algorithm are verified through experiments for correctness, feasibility, and effectiveness. After the wafer correction, the precision of AWC is <± 0.15 mm, which meets the requirements for transferring robot wafers.