Asymptotic optimal HEAPSORT algorithm

AbstractHeapsort algorithm HEAPSORT runs in a higher efficiency way. It has been improved to reduce the constant factor of the complexity. An asymptotic optimal heapsort algorithm is given in this paper. When the efficiency becomes the lowest, the constant factor of its complexity will not be more than 43

A bibliometric and visualization study of global research trends in sacral Tarlov cyst from 2000 to 2022

BackgroundSymptomatic sacral Tarlov cyst (STC) exerts a significant negative impact on the patient's quality of life, highlighting the significance of the increasing number of studies on STC. However, bibliometric analyses in this research field are scarce. Thus, this study aims to provide a comprehensive knowledge structure and identify the research trends of STC through bibliometrics.MethodsArticles related to STC from 2000 to 2022 were sourced from the Web of Science Core Collection database. VOSviewer 1.6.16, CiteSpace 6.1.6, GraphPad Prism 8.2.1 and R-package “bibliometrix” were used to analyse the data and generate knowledge maps.ResultsA total of 930 studies on STC from 2000 to 2022 were included. The findings revealed a consistent yet upward trend in the number of annual publications in this field. The United States, China and Turkey were the most prolific and influential countries contributing to this field, with the University of Illinois, the University of Maryland and the National Institute of Standards & Technology being the most notable research institutions. Key journals include World Neurosurgery [Impact Factor (IF) = 2.210], Journal of Vascular Surgery (IF = 4.860) and Journal of Neurosurgery-Spine (IF = 3.467). Additionally, Tarlov Mj, Tarlov E and Zachariah Mr exhibit the highest number of publications, making them the leading authors in this field. A twenty-year retrospection of research trends using keyword analysis reveals four principal directions, namely “definition”, “pathogenesis”, “diagnosis” and “treatment”. Currently, therapeutic surgical intervention is the key treatment for this disease, with future treatments primarily hinging on minimally invasive methodologies rooted in microendoscopic and endoscopic techniques.ConclusionThis pioneering, comprehensive scientific bibliometric study provides a holistic summary of STC research trends and hot spots spanning the past 22 years. The results identify existing research frontiers and chart maps for future studies, serving as a valuable reference for scholars vested in this field

SNP rs3803264 polymorphisms in THSD1 and abnormally expressed mRNA are associated with hemorrhagic stroke

BackgroundThrombospondin Type 1 Domain Containing Protein 1 (THSD1) has been suggested to be a new regulator of endothelial barrier function in the angiogenesis process, preserving vascular integrity. We sought to characterize the association of THSD1 genetic variants and mRNA expression with the risk of hemorrhagic stroke (HS) with population-based evidence.MethodsA case–control study was conducted with 843 HS cases and 1,400 healthy controls. A cohort study enrolled 4,080 participants free of stroke at baseline in 2009 and followed up to 2022. A synonymous variant, the main tag SNP rs3803264 of the THSD1 gene, was genotyped in all subjects, and peripheral leukocyte THSD1 mRNA expression was detected using RT-qPCR in 57 HS cases and 119 controls.ResultsIn the case–control study, rs3803264 AG/GG variations are associated with a decreased risk of HS with odd ratio (OR) and 95% confidence interval (CI) of the dominant model of 0.788 (0.648–0.958), p = 0.017. In addition, rs3803264 and dyslipidemia had a multiplicative interaction [OR (95% CI) = 1.389 (1.032, 1.869), p = 0.030]. In the cohort study, a similar association strength of rs3803264 dominant model and the risk of HS was observed with the incidence rate ratio (IRR) of 0.734 and p-value of 0.383. Furthermore, the risk of HS showed a non-linear as THSD1 mRNA expression increased (p for non-linearity <0.001). For the subjects without hypertension, we observed THSD1 mRNA expression had a negative correlation with systolic blood pressure (SBP; ρ = −0.334, p = 0.022).ConclusionSNP rs3803264 polymorphisms in THSD1 are associated with the decreased risk of HS and interacted with dyslipidemia, and a non-linear association was observed between THSD1 mRNA expression and the risk of HS

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Robust Stereo Visual Odometry Using Improved RANSAC-Based Methods for Mobile Robot Localization

In this paper, we present a novel approach for stereo visual odometry with robust motion estimation that is faster and more accurate than standard RANSAC (Random Sample Consensus). Our method makes improvements in RANSAC in three aspects: first, the hypotheses are preferentially generated by sampling the input feature points on the order of ages and similarities of the features; second, the evaluation of hypotheses is performed based on the SPRT (Sequential Probability Ratio Test) that makes bad hypotheses discarded very fast without verifying all the data points; third, we aggregate the three best hypotheses to get the final estimation instead of only selecting the best hypothesis. The first two aspects improve the speed of RANSAC by generating good hypotheses and discarding bad hypotheses in advance, respectively. The last aspect improves the accuracy of motion estimation. Our method was evaluated in the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) and the New Tsukuba dataset. Experimental results show that the proposed method achieves better results for both speed and accuracy than RANSAC

Fusing target information from multiple views for robust visual tracking

In this study, the authors address the problem of tracking a single target in a calibrated multi‐camera surveillance system with information on its location in the first frame of each view. Recently, tracking with online multiple instance learning (OMIL) has been shown to give promising tracking results. However, it may fail in a real surveillance system because of problems arising from target orientation, scale or illumination changes. In this study, the authors show that fusing target information from multiple views can avoid these problems and lead to a more robust tracker. At each camera node, an efficient OMIL algorithm is used to model target appearance. To update the OMIL‐based classifier in one view, a co‐training strategy is applied to generate a representative set of training bags from all views. Bags extracted from each view hold a unique weight depending on similarity of target appearance between the current view and the view which contains the classifier that needs to be updated. In addition, target motion on a camera's image plane is modelled by a modified particle filter guided by the corresponding object two‐dimensional (2D) location and fused 3D location. Experimental results demonstrate that the proposed algorithm is robust for human tracking in challenging scenes

Recoverable and Self-healing Electromagnetic Wave Absorbing Nanocomposites

Recent advancements in electronics engineering require materials with the resiliency and sustainability to extend their life time. With this regard, we presented a sustainable multi-functional nanocomposites strategy by introducing dynamic imine bonds based polyazomethine (PAM) as molecular interconnects and Fe3O4-loaded multiwalled carbon nanotubes as electromagnetic (EM) wave absorbing units. Driven by the reversible dynamic imine bonds, our materials show robust spontaneous self-healing with excellent healing efficiencies of 95 % for PAM and 90 % for nanocomposite, and an accelerated recovery under a moderate mechanical stimulus. By adding Fe3O4-loaded multiwalled carbon nanotubes, the hybrids show excellent EM wave absorbing properties with 50% increment on minimum reflection coefficient (-40.6 dB) than the reported value. We demonstrate a full degradability by decomposing a nanocomposite sheet of 100 mg in an acidic solution within 90 min at room temperature. The nanofillers and monomers after degradation can be re-used to synthesis nanocomposites. The testing results for recoverable nanocomposites show a good retention on mechanical property. This novel strategy may shed a light on the downstream applications in EM wave absorbing devices and smart structures with great potential to accelerate circular economy

Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking

Visual object tracking is a critical task in computer vision. Challenging things always exist when an object needs to be tracked. For instance, background clutter is one of the most challenging problems. The mean-shift tracker is quite popular because of its efficiency and performance in a range of conditions. However, the challenge of background clutter also disturbs its performance. In this article, we propose a novel weighted histogram based on neutrosophic similarity score to help the mean-shift tracker discriminate the target from the background. Neutrosophic set (NS) is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS to improve the mean-shift tracker. First, two kinds of criteria are considered as the object feature similarity and the background feature similarity, and each bin of the weight histogram is represented in the SVNS domain via three membership functions T(Truth), I(indeterminacy), and F(Falsity). Second, the neutrosophic similarity score function is introduced to fuse those two criteria and to build the final weight histogram. Finally, a novel neutrosophic weighted mean-shift tracker is proposed. The proposed tracker is compared with several mean-shift based trackers on a dataset of 61 public sequences. The results revealed that our method outperforms other trackers, especially when confronting background clutter