Hadron Spectra, Decays and Scattering Properties within Basis Light Front Quantization

We survey recent progress in calculating properties of the electron and hadrons within the Basis Light Front Quantization (BLFQ) approach. We include applications to electromagnetic and strong scattering processes in relativistic heavy ion collisions. We present an initial investigation into the glueball states by applying BLFQ with multigluon sectors, introducing future research possibilities on multi-quark and multi-gluon systems.Comment: Presented at LightCone 2017, Mumbai, Indi

Availability of essential medicines, progress and regional distribution in China: a systematic review and meta-analysis

BackgroundEssential medicines are the backbone of healthcare and meet the priority healthcare needs of the population. However, approximately one-third of the global population does not have access to essential medicines. Although China formulated essential medicine policies in 2009, the progress of availability of essential medicines and regional variations remains unknown. Therefore, this study was conducted to evaluate the availability of essential medicines, their progress, and regional distribution in China in the last decade.MethodsWe searched eight databases from their inception to February 2022, relevant websites, and reference lists of included studies. Two reviewers selected studies, extracted data, and evaluated the risk of bias independently. Meta-analyses were performed to quantify the availability of essential medicines, their progress, and regional distribution.ResultsOverall 36 cross-sectional studies conducted from 2009 to 2019 were included, with regional data for 14 provinces. The availability of essential medicines in 2015–2019 [28.1%, 95% confidence interval (CI): 26.4–29.9%] was similar to that in 2009–2014 (29.4%, 95% CI: 27.5–31.3%); lower in the Western region (19.8%, 95% CI: 18.1–21.5%) than Eastern (33.8%, 95% CI: 31.6–36.1%) and Central region (34.5%, 95% CI: 30.6–38.5%); very low for 8 Anatomical Therapeutic Chemical (ATC) categories (57.1%), and low for 5 categories (35.7%) among all ATC groups.ConclusionThe availability of essential medicines in China is low compared with the World Health Organization goal, has not changed much in the last decade, is unequal across regions, and lacks data for half of provinces. For policy-making, the monitoring system of the availability of essential medicines is to be strengthened to enable long-term surveillance, especially in provinces where the data has been missing. Meanwhile, Joint efforts from all stakeholders are warranted to improve the availability of essential medicines in China toward the universal health coverage target.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=315267, identifier: PROSPERO CRD42022315267

FLNet: A Near-shore Ship Detection Method Based on Image Enhancement Technology

In the past few years, Synthetic Aperture Radar (SAR) has been widely used to detect marine ships due to its ability to work in various weather conditions. However, due to the imaging mechanism of SAR, there is a lot of background information and noise information similar to ships in the images, which seriously affects the performance of ship detection models. To solve the above problems, this paper proposes a new ship detection model called Feature enhancement and Land burial Net (FLNet), which blends traditional image processing methods with object detection approaches based on deep learning. We first design a SAR image threshold segmentation method, Salient Otsu (S-Otsu), according to the difference between the object and the noise background. To better eliminate noise in SAR images, we further combine image processing methods such as Lee filtering. These constitute a Feature Enhancement Module (FEM) that mitigates the impact of noise data on the overall performance of a ship detection model. To alleviate the influence of land information on ship detection, we design a Land Burial Module (LBM) according to the morphological differences between ships and land areas. Finally, these two modules are added to You Only Look Once V5 (YOLO V5) to form our FLNet. Experimental results on the SAR Ship Detection Dataset (SSDD) dataset show that FLNet comparison with YOLO V5 accuracy when performing object detection is improved by 7% and recall rate by 6.5%

N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

High-resolution images provided by synthetic aperture radar (SAR) play an increasingly important role in the field of ship detection. Numerous algorithms have been so far proposed and relative competitive results have been achieved in detecting different targets. However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). The N-YOLO includes a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. First, NLC derives and classifies the noise level of SAR images. Secondly, the STPAE module is composed by a CA-CFAR and expansion operation, which is used to extract the complete region of potential targets. Thirdly, the YOLOv5-based detection module combines the potential target area with the original image to get a new image. To evaluate the effectiveness of the N-YOLO, experiments are conducted using a reference GaoFen-3 dataset. The detection results show that competitive performance has been achieved by N-YOLO in comparison with several CNN-based algorithms

FLNet: A Near-shore Ship Detection Method Based on Image Enhancement Technology

In the past few years, Synthetic Aperture Radar (SAR) has been widely used to detect marine ships due to its ability to work in various weather conditions. However, due to the imaging mechanism of SAR, there is a lot of background information and noise information similar to ships in the images, which seriously affects the performance of ship detection models. To solve the above problems, this paper proposes a new ship detection model called Feature enhancement and Land burial Net (FLNet), which blends traditional image processing methods with object detection approaches based on deep learning. We first design a SAR image threshold segmentation method, Salient Otsu (S-Otsu), according to the difference between the object and the noise background. To better eliminate noise in SAR images, we further combine image processing methods such as Lee filtering. These constitute a Feature Enhancement Module (FEM) that mitigates the impact of noise data on the overall performance of a ship detection model. To alleviate the influence of land information on ship detection, we design a Land Burial Module (LBM) according to the morphological differences between ships and land areas. Finally, these two modules are added to You Only Look Once V5 (YOLO V5) to form our FLNet. Experimental results on the SAR Ship Detection Dataset (SSDD) dataset show that FLNet comparison with YOLO V5 accuracy when performing object detection is improved by 7% and recall rate by 6.5%

An efficient layer node attack strategy to dismantle large multiplex networks

Network dismantling aims to identify the minimum set of nodes whose removal breaks the network into components of sub-extensive size. The solution to this problem is significant for designing optimal strategies for immunization policies, information spreading, and network attack. Modern systems, such as social networks and critical infrastructure networks, which consist of nodes connected by links of multiple types can be encapsulated into the framework of multiplex networks. Here we focus on the dismantling problem in multiplex networks under layer node-based attack, and propose an efficient dismantling algorithm based on network decycling. Experiments on synthetic and real-world networks show that the proposed algorithm outperforms existing algorithms by a considerable margin. We also show how the robustness of a multiplex network is affected by the interlayer degree correlation. Our results shed light on the design of more resilient network systems and the effective destruction of harmful networks