Alpine Cold Vegetation Response to Climate Change in the Western Nyainqentanglha Range in 1972–2009

The Tibetan Plateau is regarded as one of the most climatic-sensitive regions all over the world. Long-term remote sensing data enable us to monitor spatial-temporal change in this area. The vegetation changes of the western Nyainqentanglha region were detected by using RS and GIS techniques. And the vegetation coverage was derived by the NDVI-SMA (spectral mixture analysis) methods. An incensement of vegetation was observed in the mountain areas during 1972–2009 with a mean vegetation coverage of 24.87%, 35.89%, and 42.88% in 30/09/1972, 14/09/1991, and 30/08/2009, respectively. The vegetation fraction increased by 18% in the period of 1972–2009. The bin with the elevation between 4400 and 5200 m had the highest vegetation coverage. This may be the result of the mountain effect. Alpine vegetation had a trend to increase and expand to higher altitudes with the climate change in the past 40 years. The variation appears to be associated with an increase in mean temperature of 0.05°C per year and an increase in precipitation of 1.83 mm per year in the growing season of the past four decades. The results provide further evidence of alpine ecosystem change due to climate change in the central Tibetan Plateau

A bolt defect detection method for transmission lines based on improved YOLOv5

To solve the problem of bolt defects in unmanned aerial vehicle inspection that are difficult to identify quickly and accurately, this paper proposes a defect detection method based on the improved YOLOv5 anchor mechanism. Firstly, the Normalized Wasserstein distance (NWD) evaluation metric and the Intersection over Union evaluation metric are combined, and the experiment determines the appropriate weight for this combination. This way, the sensitivity of using IoU alone to small objecet detection anchor box threshold changes was reduced. Furthermore, Convolutional Block Attention Module is included into the head network architecture of yolov5 in order to prioritize significant information and suppress irrelevant features. Omni-dimensional Dynamic Convolution (ODConv) is used to replace convolution in MobileNetv2. The combination module is used as the new backbone of the YOLOv5 model. It simultaneously enhances the model’s capability to extract bolt defect object information, minimizes calculation requirements, and achieves lightweight detection across the entire model. Compared with the original algorithm, the model detection Accuracy Precision (AP) is increased by 30.1%, the mean Accuracy Precision is increased by 30.4%. Other evaluation metrics of the model, such as GFlOPs and Parameters, all decreased slightly. The above results show that the improved algorithm proposed in this paper greatly improves the detection accuracy of the model on the premise of ensuring that the model is as small as possible

Research on large-scale clean energy optimal scheduling method based on multi-source data-driven

With the large-scale growth and grid connection of intermittent renewable energy such as wind and solar, the problem of increasing renewable energy curtailment rate and system backup flexibility has become increasingly prominent. In order to solve the problem of high proportion of renewable energy scientific consumption and flexible and stable operation of energy system. We propose a flexible and economical dispatch method based on data-driven multi-regional power system. For the problem of economic dispatch of multi-area power system, a mathematical calculation model is established to satisfy the constraints of unit output, system power balance, unit ramp rate, and valve point effect, and to consider the requirement of minimizing the cost of multi-area power load comprehensively. Based on data-driven, this paper adopts an improved fruit fly optimization algorithm to quickly find the global optimal solution. The calculations are performed by IEEE6 simulation test system, and the results verify the feasibility of the proposed algorithm. The improved fruit fly optimization algorithm is compared and analyzed with other algorithms considering the quality of the obtained solutions. The results show the effectiveness and superiority of the proposed algorithm in solving multi-area economic dispatching problems in real power systems

Nosocomial outbreak of KPC-2- and NDM-1-producing Klebsiella pneumoniae in a neonatal ward: a retrospective study

Sequencing for MLST. (DOCX 21 kb

Research on detection of transmission line corridor external force object containing random feature targets

With the objective of achieving “double carbon,” the power grid is placing greater importance on the security of transmission lines. The transmission line corridor has complex situations with external force targets and irregularly featured objects including smoke. For this reason, in this paper, the high-performance YOLOX-S model is selected for transmission line corridor external force object detection and improved to enhance model multi-object detection capability and irregular feature extraction capability. Firstly, to enhance the perception capability of external force objects in complex environment, we improve the feature output capability by adding the global context block after the output of the backbone. Then, we integrate convolutional block attention module into the feature fusion operation to enhance the recognition of objects with random features, among the external force targets by incorporating attention mechanism. Finally, we utilize EIoU to enhance the accuracy of object detection boxes, enabling the successful detection of external force targets in transmission line corridors. Through training and validating the model with the established external force dataset, the improved model demonstrates the capability to successfully detect external force objects and achieves favorable results in multi-class target detection. While there is improvement in the detection capability of external force objects with random features, the results indicate the need to enhance smoke recognition, particularly in further distinguishing targets between smoke and fog

The Performance of Pleural Fluid T-SPOT.TB Assay for Diagnosing Tuberculous Pleurisy in China: A Two-Center Prospective Cohort Study

The performance of T-SPOT.TB (T-SPOT) assay in diagnosing pleural tuberculosis (plTB) is inconsistent. In this study, we compared the performance of peripheral blood (PB) and pleural fluid (PF) T-SPOT assay in diagnosing plTB. Between July 2017 and March 2018, 218 and 210 suspected plTB patients were prospectively enrolled from Wuhan (training) and Guangzhou (validation) cohort, respectively. PB T-SPOT, PF T-SPOT, and other conventional tests were simultaneously performed. Our data showed the performance of PB T-SPOT in diagnosing plTB was limited, especially with low sensitivity. However, the results of early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) in PF T-SPOT were significantly increased compared with those in PB T-SPOT in plTB patients. If using 76 as the cutoff value of MAX (the larger of ESAT-6 and CFP-10) in Wuhan cohort, the sensitivity and specificity of PF T-SPOT to diagnose plTB were 89.76 and 96.70%, respectively. The diagnostic accuracy of PF T-SPOT was better than other routine tests such as pathogen detection methods and biochemical markers. The diagnostic accuracy of PF T-SPOT in Guangzhou cohort was similar to that in Wuhan cohort, with a sensitivity and specificity of 91.07 and 94.90%, respectively. Furthermore, CD4+ T cells were more activated in PF compared with PB, and the frequency of mycobacterium tuberculosis-specific CD4+ T cells in PF was significantly higher than that in PB in plTB patients. In conclusion, the performance of PF T-SPOT is obviously better than PB T-SPOT or other laboratory tests, which suggests that PF T-SPOT assay has been of great value in the diagnosis of pleural tuberculosis

Determination of reference intervals of serum levels of human epididymis protein 4 (HE4) in Chinese women

The detailed numbers of subjects from different centers. (DOCX 17.3 kb