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To improve the stability of the bridge structure, we detect bolts in the bridge which cause the symmetry failure of the bridge center. For data acquisition, bolts are small-scale objects under complex background in images, and their feature expression ability is limited. Due to those questions, we propose a new bolt positioning detection based on improved YOLOv5 for bridge structural health monitoring. This paper makes three major contributions. Firstly, according to the calibration anchor boxes of bolts, the size and proportion parameters of the initial anchor boxes are optimized by K-means++ clustering algorithm to solve the initial clustering problem of anchor boxes in object detection. Second, the hypercolumn (HC) technique fuses the low-level global features of the trunk and the high-level local features of three different scales to solve the problem of the inefficient distribution of anchors and insufficient extraction of classification features. In this way, we improve the detection accuracy and speed of bolt detection. Finally, we establish a dataset of bridge bolts through network collection and public datasets, including 1494 images. We compare and verify the new method in the collected bolt dataset. The experimental results show that the precision (P) of the improved YOLOv5x is up to 87.3%, and the average precision (AP) is up to 86.3%, which are 6.5% and 5.9% higher than the original YOLOv5x, respectively.

The safety and reliability of the power grid are related to national power security, economic development and people's daily life. The occurrence of extreme weather changes the external environment greatly. Including generators and transmission lines, many power grid units cannot resist such a huge attack and get damaged easily, which forces units to quit from the power grid running system for a while. Furthermore, if the number of influenced units is high enough, the whole power system will be destroyed by cascading failure caused by extreme weather. Aiming at dealing with the cascading failure emergencies, this paper is trying to improve the traditional power structural vulnerability model so that it can be used to discuss extreme weather and propose a theoretical topological model to help scholars measure the damage caused by extreme cases. Based on previous research in this field, this paper utilizes complex network knowledge to build the power grid topology model. Then, considering extreme cases and the three attack modes simulation process, this paper makes use of the characteristic parameters of the power grid topology model and designs an algorithm, according to the realistic situation of the propagation mechanism of cascading failure of the power grid model as well as extreme weather research. Finally, taking IEEE-30 and IEEE-118 node bus system as examples, which shows that the structural vulnerability method proposed in this paper can properly address the mechanism of unbalanced load of cascading failure of power grid units under extreme conditions and can provide theoretical reference for preventing and reducing the impact of extreme cases on power grid which improves the reliability of the power grid.

In response to the emergent public health event of COVID-19, the efficiency of transport of medical waste from hospitals to disposal stations is a worthwhile issue to study. In this paper, based on the actual situation of COVID-19 and environmental impact assessment guidelines, an immune algorithm is used to establish a location model of urban medical waste storage sites. In view of the selection of temporary storage stations and realistic transportation demand, an efficiency-of-transport model of medical waste between hospitals and temporary storage stations is established by using an ant colony-tabu hybrid algorithm. In order to specify such status, Wuhan city in Hubei Province, China-considered the first city to suffer from COVID-19-was chosen as an example of verification; the two-level model and the immune algorithm-ant colony optimization-tabu search (IA-ACO-TS) algorithm were used for simulation and testing, which achieved good verification. To a certain extent, the model and the algorithm are proposed to solve the problem of medical waste disposal, based on transit temporary storage stations, which we are convinced will have far-reaching significance for China and other countries to dispatch medical waste in response to such public health emergencies.

In order to realize the requirement of precise eye tracking in clinical, a pupil center location algorithm based on the least square method is proposed. First, the eye image was captured by the camera under the infrared light, and then the two-valued image was obtained after preprocessing. Use the number of pixels that form the outline which was Extracting from the two-valued image to carry out the rough filtration of the pupil. The ellipse curve was fitting by the randomly select 6 pixels on the filtered contours, and then calculated the variance of distance between the center and the edge of the ellipse. The ellipse center is ellipse center, where the variance of the distance is Minimum. The algorithm has advantage of accurate identification the pupil center when there is white spot interference and squint. Experiments show that the pupil center can be located quickly and real-timely by this algorithm.

Summary: Aiming at current serious problems that the bolt looseness can cause heavy damages and the manual bolt looseness detection is ineffective, this study presents a bolt looseness detection method based on Canny edge detector. First, HSV (hue, saturation, value) color space is used to extract the area of mark bar. Then, after expanding, eroding and median filtering the image, Canny edge detector is used to segment the marker area to extract its edge. Last, the fitted slope of edge is used to judge whether the bolt is loose. The experimental results show that the proposed method has F1 score of 88.37%, contributing to the automated detection of bolt looseness. [ABSTRACT FROM AUTHOR]

Summary: Welders are widely used in machinery manufacturing and machining industry. In the process of electric welding, accidents are often caused by some unsafe behaviors. This paper focuses on the intelligent recognition method of welding flame, which can reduce the accidents of welding flame to a certain extent. Due to the difficulties in observing workers on job sites, developing an algorithm for intelligent identification of unsafe factors is significant. Firstly, Hue,Saturation and Value (HSV) color space with strong robustness is selected to segment the mark image with threshold value, and the region of interest in which the welding fame is located is segment; secondly, the Progressive Probabilistic Hough transform is used to detect the region of interest, judge the number of existing lines, and finally determine whether there is welding flame in this region. In this way, the welding flame can be better detected, which can reduce the workload of managers and is beneficial to the supervision of the management personnel. [ABSTRACT FROM AUTHOR]

The HIF-1 signaling pathway plays an important role in the pathogenesis of cancer. Many studies have explored the progression of prostate cancer (PCa) under hypoxic conditions based on transcriptome data; few have uncovered the immunogenomic profiling and prostate cancer classification based on the HIF-1 signaling pathway. This pathway may help to identify the optimal subset of PCa patients responsive to immunotherapy/chemotherapy. The immunogenomic PCa subsets were classified based on profiling of the HIF-1 signaling pathway, using four publicly available PCa datasets. Three PCa subtypes that named as HIF-1 High (HIF-1_H), HIF-1 Medium (HIF-1_M), and HIF-1 Low (HIF-1_L) were identified. Functional enrichment was analyzed in each subtype. Several cancer-associated and immune-related pathways were hyperactivated in the HIF-1_H subtypes. In contrast, HIF-1_L subtypes were enriched in cell cycle and cell repair. Compared with other subtypes, HIF-1_H subtypes have greater immune cell infiltration, anti-tumor immune activity, and better survival prognosis. The submap and TIDE algorithm were used to predict the clinical response to immune checkpoint blockade, and GDSC was employed to screen potential chemotherapeutic targets for the treatment of PCa. Several chemotherapy drugs were identified in the GDSC dataset, including ABT 888, Temsirolimus, and EHT 1864. Meanwhile, HIF-1_H was defined as an early PCa marker, which is more likely to respond to immunotherapy. The identification of immunogenomic PCa subtypes based on the HIF-1 signaling pathway has potential clinical implications for PCa treatment. Immunopositive PCa subtypes will help to explore the reasons for the poor response of PCa to immunotherapy, and it is expected that immunotherapy will guide the personalized treatment of PCa patients. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major problem in today's society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion. [ABSTRACT FROM AUTHOR]

In the current close-to-smart power systems, renewable energies are the most significant elements that need to be carefully addressed in power system studies. In this regard, microgrids (MGs) have been introduced recently to activate the large penetration of renewables. However, integration of such generation technologies that are associated with severe uncertainty in their power output would significantly impact the scheduling of energy resources in MGs. Thus, efficient energy management systems are required to be employed in MGs. Therefore, this paper presents a day-ahead scheduling framework for an MG equipped with a solar photovoltaic (PV) unit. In this respect, different climate conditions and their impacts on the power output of the PV unit and the optimal scheduling of the MG have been investigated in this paper. To this end, four different days from the four seasons have been used to extract the data of solar irradiance. The scheduling problem has been formulated in a single-objective optimization framework, where the objective function is defined as minimizing the total operating cost over the scheduling period. An effective optimization algorithm named "modified krill herd (MKH)" algorithm is proposed to solve the mentioned day-ahead scheduling problem, while there are renewable and non-renewable generating units, besides an energy storage system. Furthermore, a comprehensive comparison has been made between the MKH algorithm and some well-known optimization algorithms to verify the superior performance of the suggested method. [ABSTRACT FROM AUTHOR]