动态场景下基于视觉的SLAM技术研究

针对视觉同时定位与地图构建(SLAM)技术在动态环境中存在定位精度低、地图虚影等问题,提出了一种基于深度学习的动态SLAM算法。该算法利用网络参数少且目标识别率高的YOLOv8n改善系统的视觉前端,为视觉前端增加语义信息,提取动态区域特征点。然后采用LK光流法识别动态区域的动态特征点,剔除动态特征点并保留动态区域内的静态特征点,提高特征点利用率。此外,该算法通过增加地图构建线程,剔除YOLOv8n提取的动态物体点云,接收前端提取的语义信息,实现静态语义地图构建,消除由动态物体产生的虚影。实验结果显示,在动态环境下该算法与ORB-SLAM3相比,定位精度提升92.71%,与其他动态视觉SLAM算法相比,也有小幅度改善。     

融合GMS检测和置信度判别的TLD目标跟踪

针对目标在遮挡、尺度变化等复杂场景下易产生模型漂移问题,基于跟踪学习检测(TLD)框架提出一种结合基于网格的运动统计(GMS)检测和置信度判别的长时目标跟踪算法.首先在跟踪模块中采用快速判别尺度空间的相关滤波器(fDSST)作为跟踪器,利用位置滤波器和尺度滤波器对上一帧目标进行位置与尺度的判别,并依据TLD算法中跟踪模...     

聚N-异丙基丙烯酰胺接枝聚苯乙烯高分子刷的构象

当长链高分子高密度接枝到一个表面上时,由于分子链间的相互作用使得接枝的高分子链扩张而形成伸直链的构象,这种形态被称为高分子刷.     

Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition

The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.     

Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis

Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some “dark genes” that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.     

基于PLC控制的自动化生产线的产能自调节系统研究

自动生产线上输送带的速度与生产计划以及系统废品率之间无法互相调节,导致了生产效率低、废品率高等现象.为了提高自动分拣生产线的产能,本文设计了产能自调节系统;叙述了构建系统的研究思路;搭建了系统的模型;分析了系统控制方案的工作原理以及影响产能的主要因素;确定了系统的主要技术指标;构建了特定的控制算法模型;运用PLC、步进...     

Autonomous-Strengthening Adhesive Provides Hydrolysis-Resistance and Enhanced Mechanical Properties in Wet Conditions

The low-viscosity adhesive that is used to bond composite restorative materials to the tooth is readily damaged by acids, enzymes, and oral fluids. Bacteria infiltrate the resulting gaps at the composite/tooth interface, demineralize the tooth, and further erode the adhesive. This paper presents the preparation and characterization of a low-crosslink-density hydrophilic adhesive that capitalizes on sol-gel reactions and free-radical polymerization to resist hydrolysis and provide enhanced mechanical properties in wet environments. Polymerization behavior, water sorption, and leachates were investigated. Dynamic mechanical analyses (DMA) were conducted using water-saturated adhesives to mimic load transfer in wet conditions. Data from all tests were analyzed using appropriate statistical tests (α = 0.05). The degree of conversion was comparable for experimental and control adhesives at 88.3 and 84.3%, respectively. HEMA leachate was significantly lower for the experimental (2.9 wt%) compared to control (7.2 wt%). After 3 days of aqueous aging, the storage and rubbery moduli and the glass transition temperature of the experimental adhesive (57.5MPa, 12.8MPa, and 38.7 °C, respectively) were significantly higher than control (7.4MPa, 4.3 MPa, and 25.9 °C, respectively). The results indicated that the autonomic sol-gel reaction continues in the wet environment, leading to intrinsic reinforcement of the polymer network, improved hydrolytic stability, and enhanced mechanical properties.     

Controllable Construction of Amino-Functionalized Dynamic Covalent Porous Polymers for High-Efficiency CO2 Capture from Flue Gas

The design of high-efficiency CO₂ adsorbents with low cost, high capacity, and easy desorption is of high significance for reducing carbon emissions, which yet remains a great challenge. This work proposes a facile construction strategy of amino-functional dynamic covalent materials for effective CO₂ capture from flue gas. Upon the dynamic imine assembly of N-site rich motif and aldehyde-based spacers, nanospheres and hollow nanotubes with spongy pores were constructed spontaneously at room temperature. A commercial amino-functional molecule tetraethylenepentamine could be facilely introduced into the dynamic covalent materials by virtue of the dynamic nature of imine assembly, thus inducing a high CO₂ capacity (1.27 mmol·g⁻¹) from simulated flue gas at 75 °C. This dynamic imine assembly strategy endowed the dynamic covalent materials with facile preparation, low cost, excellent CO₂ capacity, and outstanding cyclic stability, providing a mild and controllable approach for the development of competitive CO₂ adsorbents.     

Phosphatidylcholine Cation—Tyrosine π Complexes: Motifs for Membrane Binding by a Bacterial Phospholipase C

Phosphatidylinositol-specific phospholipase C (PI-PLC) enzymes are a virulence factor in many Gram-positive organisms. The specific activity of the Bacillus thuringiensis PI-PLC is significantly increased by adding phosphatidylcholine (PC) to vesicles composed of the substrate phosphatidylinositol, in part because the inclusion of PC reduces the apparent K_d for the vesicle binding by as much as 1000-fold when comparing PC-rich vesicles to PI vesicles. This review summarizes (i) the experimental work that localized a site on BtPI-PLC where PC is bound as a PC choline cation—Tyr-π complex and (ii) the computational work (including all-atom molecular dynamics simulations) that refined the original complex and found a second persistent PC cation—Tyr-π complex. Both complexes are critical for vesicle binding. These results have led to a model for PC functioning as an allosteric effector of the enzyme by altering the protein dynamics and stabilizing an ‘open’ active site conformation.     

Virtual Screening of Artemisia annua Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a −7 kcal/mol binding energy score, the top-2 docked complex with a −6.9 kcal/mol binding energy score, and the top-3 docked complex with a −6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were −24.23 kcal/mol, −26.38 kcal/mol, and −25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as −17.23 kcal/mol (top-1 complex), −24.75 kcal/mol (top-2 complex), and −24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.