The Local Front Reconstruction Method for direct simulation of two- and three-dimensional multiphase flows

We present a new interface reconstruction technique, the Local Front Reconstruction Method (LFRM), for incompressible multiphase flows. This new method falls in the category of Front Tracking methods but it shares automatic topology handling characteristics of the previously proposed Level Contour Reconstruction Method (LCRM). The LFRM tracks the phase interface explicitly as in Front Tracking but there is no logical connectivity between interface elements thus greatly easing the algorithmic complexity. Topological changes such as interfacial merging or pinch off are dealt with automatically and naturally as in the Level Contour Reconstruction Method. Here the method is described for both two- and three-dimensional flow geometries. The interfacial reconstruction technique in the LFRM differs from that in the LCRM formulation by foregoing using an Eulerian distance field function. Instead, the LFRM uses information from the original interface elements directly to generate the new interface in a mass conservative way thus showing significantly improved local mass conservation. Because the reconstruction procedure is independently carried out in each individual reconstruction cell after an initial localization process, an adaptive reconstruction procedure can be easily implemented to increase the accuracy while at the same time significantly decreasing the computational time required to perform the reconstruction. Several benchmarking tests are performed to validate the improved accuracy and computational efficiency as compared to the LCRM. The results demonstrate superior performance of the LFRM in maintaining detailed interfacial shapes and good local mass conservation especially when using low-resolution Eulerian grids.     

Enhanced performance analysis of inter-satellite optical-wireless communication (IsOWC) system

In this paper, we have reported the improved performance by usage of a square root module. By simulation in OPTISYSTEM™ a distance of 5000 km with 1.25 Gbps was achieved with the same performance representing an enhancement of 48% when compared to the traditional detection.     

月基对地观测跟踪转台设计与分析

针对登月过程严酷的力学条件、月球表面复杂的环境特征以及严格的重量要求,设计了一种可适用于月基观测的跟踪转台.该转台主要由水平轴系及驱动机构和竖直轴系及驱动机构组成,采用霍耳元件作为位置检测元件,其综合指向准确度≤0.1°.基于有限元分析方法确定转台结构方案、优化主要支撑件结构及合理选材,并对驱动机构与轴系对转台刚度、温度适应性和指向准确度的影响做了深入分析与计算.同时进行了整机的力学模拟试验和运动机构的高低温试验,一阶谐振频率为49 Hz,在-40 ℃ ～+80 ℃温度范围内工作正常.结果表明转台各项性能指标满足工程任务要求,具有结构紧凑、可靠性高、重量轻的特点.     

大气激光通信中稳定跟踪技术研究

 为了研究大气激光通信中稳定跟踪所采用的器件及有效跟踪方法,分析了光束定位探测器件(电荷耦合器件和四象限光电探测器)的灵敏度特性,结合影响大气激光通信跟踪系统性能的五种大气湍流效应,分别讨论了光束漂移、光强起伏、光斑弥散、到达角起伏及光束扩展的原理.对质心跟踪算法和形心跟踪算法定位准确度的影响进行分析,得到在大气条件下形心算法的跟踪误差小于质心误差的结论,并通过实验进行了验证.     

一类非线性时滞系统的时滞相关镇定及跟踪控制问题

本文讨论了一类满足Lipschitz条件的非线性时滞系统的镇定与跟踪控制问题.基于非线性状态反馈控制器,利用Lyapunov-Krasovskii泛函和矩阵理论,得到了系统时滞相关全局渐近镇定的新判据,并且保证了输出和状态跟踪控制的误差全局渐近收敛于零.本文推广了文献[9]所得到的结论.因此,本文所研究的模型及所给出的判定条件更具有一般性和实用性.     

Reversible Live-Cell Labeling with Retro-engineered HaloTags Enables Long-Term High- and Super-Resolution Imaging

Self-labeling enzymes (SLE) such as the HaloTag have emerged as powerful tools in high and super-resolution fluorescence microscopy. Newly developed fluorogenic SLE substrates enable imaging in the presence of excess dye. To exploit this feature for reversible labeling, we engineered two variants of HaloTag7 with restored dehalogenase activity. Kinetic studies in vitro showed different turnover kinetics for reHaloTagS (≈0.006 s⁻¹) and reHaloTagF (≈0.055 s⁻¹). Imaging by confocal and stimulated emission depletion microscopy yielded 3-5-time enhanced photostability of reHaloTag labeling. Prominently, single molecule imaging with reHaloTags enabled controlled and stable labeling density over extended time periods. By combination with structured illumination, simultaneous visualization of single molecule diffusion and organellar dynamics was achieved. These applications highlight the potential of reHaloTag labeling for pushing the limits of advanced fluorescence microscopy techniques.     

Efficient detection of refugees and migrants in Turkey using convolutional neural network

At the start of the Syrian Civil War in 2011, NGOs played a big part in giving refugees access to aid and distributing that aid so that people could go to school, get a job, or get medical care. Within the last few years, when tensions rose between Syrian refugees and the Turkish community, many non-governmental organizations switched their attention to fostering community among refugees in Turkey. Over the past two decades, family displacement has become a big problem in various countries due to a rise in the frequency with which natural catastrophes, military conflicts, and terrorist strikes occur. It poses severe difficulties for governing bodies and the organizations that oversee them. This research aims to identify and track refugees in surveillance zones by utilizing artificial intelligence. Refugees are vulnerable to acts of nature and human aggression, which makes their random relocation or encampments challenging to manage. To overcome these challenges, a convolutional neural network deep learning model has been proposed to identify and track refugees in surveillance zones. The proposed solution is integrated with Internet of Things (IoT) technology by equipping the system with IoT sensors to capture real-time data on the location and movements of refugees. This combination of AI and IoT has the potential to improve the efficiency and effectiveness of refugee management efforts. The suggested solution uses a convolutional neural network deep learning model, which can quickly identify a refugee’s face. To assist the government in locating a specific refugee, the system simultaneously connects with the refugees and requests that they regularly update their location. The system alerts security to identify the missing immigrant since the refugee does not update their whereabouts. Without human intervention, the deep learning algorithm makes it simple to recognize immigrants and keep an eye on them.