Studying the Structure of Terrorist Networks: A Web Structural Mining Approach

Because terrorist organizations often operate in network forms where individual terrorists collaborate with each other to carry out attacks, we could gain valuable knowledge about the terrorist organizations by studying structural properties of such terrorist networks. However, previous studies of terrorist network structure have generated little actionable results. This is due to the difficulty in collecting and accessing reliable data and the lack of advanced network analysis methodologies in the field. To address these problems, we introduced the Web structural mining technique into the terrorist network analysis field which, to the best our knowledge, has never been done before. We employed the proposed technique on a Global Salafi Jihad network dataset collected through a large scale empirical study. Results from our analysis not only provide insights for terrorism research community but also support decision making in law-reinforcement, intelligence, and security domains to make our nation safer

Crystal Structure of the Hendra Virus Attachment G Glycoprotein Bound to a Potent Cross-Reactive Neutralizing Human Monoclonal Antibody

The henipaviruses, represented by Hendra (HeV) and Nipah (NiV) viruses are highly pathogenic zoonotic paramyxoviruses with uniquely broad host tropisms responsible for repeated outbreaks in Australia, Southeast Asia, India and Bangladesh. The high morbidity and mortality rates associated with infection and lack of licensed antiviral therapies make the henipaviruses a potential biological threat to humans and livestock. Henipavirus entry is initiated by the attachment of the G envelope glycoprotein to host cell membrane receptors. Previously, henipavirus-neutralizing human monoclonal antibodies (hmAb) have been isolated using the HeV-G glycoprotein and a human naïve antibody library. One cross-reactive and receptor-blocking hmAb (m102.4) was recently demonstrated to be an effective post-exposure therapy in two animal models of NiV and HeV infection, has been used in several people on a compassionate use basis, and is currently in development for use in humans. Here, we report the crystal structure of the complex of HeV-G with m102.3, an m102.4 derivative, and describe NiV and HeV escape mutants. This structure provides detailed insight into the mechanism of HeV and NiV neutralization by m102.4, and serves as a blueprint for further optimization of m102.4 as a therapeutic agent and for the development of entry inhibitors and vaccines

Analysis and Protection Studies of Bird Droppings on the Electric Field Distribution Near 330 kV Transmission Line Cathead Towers With Composite Insulators

The increase in bird populations along the Hexi Corridor has led to an increase in flashover faults on 330 kV transmission lines caused by bird droppings. To mitigate the issue of line tripping caused by bird droppings, it is necessary to analyze the distribution of the electric field near composite insulators during the process of bird droppings falling. A three-dimensional model, including the tower, bird droppings, and insulators, was created to assess the impact of bird droppings’ characteristics on the electric field near the insulators. Based on simulation results, a bird-proof cover was designed to modify the trajectory of the bird droppings’ path. The research found that when bird droppings are grounded and continuous, the most severe distortion of the residual air gap field occurs at a distance of 599.34 mm from the insulator axis. In the dynamic process of ungrounded and continuous bird droppings falling downward with a length less than 3200 mm, the electric field intensity in the residual air gap remains below 5.66 kV/cm outside the circular area centered at the insulator skirt with a radius of 740 mm. Based on the simulation results under both single-end grounded and ungrounded conditions of bird droppings, a bird-proof cover structure was devised to alter the location where bird droppings fall. After bird droppings fall along the edges of the bird-proof cover, the average electric field intensity between the bird droppings and the high-voltage terminal is maintained below 3.02 kV/cm. These research findings lay the foundation for subsequent fabrication of physical models of bird-proof covers

Research of Message Scheduling for In-Vehicle FlexRay Network Static Segment Based on Next Fit Decreasing (NFD) Algorithm

FlexRay is becoming the in-vehicle communication network of the next generation. In this study, the main contents are the FlexRay network static segment scheduling algorithm and optimization strategy, improve the scheduling efficiency of vehicle network and optimize the performance of communication network. The FlexRay static segment characteristic was first analyzed, then selected bandwidth utilization as the performance metrics to scheduling problem. A signal packing method is proposed based on Next Fit Decreasing (NFD) algorithm. Then Frame ID (FID) multiplexing method was used to minimize the number of FIDs. Finally, experimental simulation by CANoe. FlexRay software, that shows the model can quickly obtain the message schedule of each node, effectively control the message payload size and reduced bus payload by 16.3%, the number of FID drops 53.8% while improving bandwidth utilization by 32.8%

Design and implementation of partial dynamically reconfigurable FPGA process scheduling

In view of the diverse edge computing requirements of the 6G era, reconfigurable technology based on FPGAs can achieve lower latency and provide diversified services. Based on the idea of local dynamic reconfiguration, the ICAP interface is used to reconfigure FPGA resources, so as to realize the local dynamic reconfigurable scheme on the FPGA logic. Drawing on the idea of software process management in the operating system, based on the concept of introducing hardware processes in the Linux operating system, it is possible to divide a whole block of FPGA resources into multiple small FPGA resource blocks, each small reconfigurable FPGA resource block can be abstracted into a hardware process, the hardware process is actually not running on the CPU but running in the FPGA logical resource area, and is only a software language description of the hardware process on the operating system. As a result, the hardware scheme of CPU plus FPGA is designed to achieve partial reconfigurable system, and verified on Xilinx Zynq series chips, and the FPGA hardware resources are scheduled and allocated in a process manner, which greatly improves the utilization and flexibility of FPGA hardware resources

Design and implementation of communication middleware of heterogeneous processors in STRS system

Aiming at the problems of low real-time performance, large redundancy, and inability to recover from faults in the communication between heterogeneous processors in the Space Telecommunication Radio System (STRS),the distributed data distribution service (DDS) middleware technology is introduced into the STRS architecture to realize the publish/subscribe mode-based communication middleware between the STRS heterogeneous processor waveform application components.Under the premise of being fully compatible with the STRS standard specification, it effectively improves the real-time performance of the communication system based on STRS, reduces the complexity and redundancy of the system, improves the development efficiency, saves the development and maintenance costs of the system, and realizes the dynamic refactoring of global and local modules

Thickening of the Immobilized Polymer Layer Using Trace Amount of Amine and Its Role in Promoting Gelation of Colloidal Nanocomposites

Immobilized polymer layers surrounding nanoparticles are proposed to be of essentially vital importance for the reinforcement of nanofiller to polymer matrices, but there is still a need to clarify its contribution to diverse rheological performance like colloidal stability and gelation. In this study, we find for the first time that introducing a trace amount of secondary/tertiary amine efficiently thickens the immobilized glassy layer in hydrophilic fumed silica (FS) filled polypropylene glycol (PPG) from 1.5 to 4.5 nm, which simultaneously promotes gelation of the liquid-like dispersion even containing extremely low contents of FS (<2 vol %). By coordinately using modulated differential scanning calorimetry and rheology methods, we find strong evidence that (1) the amine-promoted gelation is due to thickening and easy-percolation of the inner glassy layer converted from an outer uncrystallizable layer, and (2) the dispersion rheology could be well normalized within the framework of a two-phase model incorporating effective volume fraction of nanoparticles plus the glassy layers. We also highlight the importance of the surface chemistry of FS for adjusting the polymer immobilization and dispersion rheology

Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network

Vector polygons represent crucial survey data, serving as a cornerstone of national geographic censuses and forming essential data sources for detecting geographical changes. The timely update of these polygons is vital for governmental decision making and various industrial applications. However, the manual intervention required to update existing vector polygons using up-to-date high-resolution remote sensing (RS) images poses significant challenges and incurs substantial costs. To address this, we propose a novel change detection (CD) method for land cover vector polygons leveraging high-resolution RS images and deep learning techniques. Our approach begins by employing the boundary-preserved masking Simple Linear Iterative Clustering (SLIC) algorithm to segment RS images. Subsequently, an adaptive cropping approach automatically generates an initial sample set, followed by denoising using the efficient Visual Transformer and Class-Constrained Density Peak-Based (EViTCC-DP) method, resulting in a refined training set. Finally, an enhanced attention-based multi-scale ConvTransformer network (AMCT-Net) conducts fine-grained scene classification, integrating change rules and post-processing methods to identify changed vector polygons. Notably, our method stands out by employing an unsupervised approach to denoise the sample set, effectively transforming noisy samples into representative ones without requiring manual labeling, thus ensuring high automation. Experimental results on real datasets demonstrate significant improvements in model accuracy, with accuracy and recall rates reaching 92.08% and 91.34%, respectively, for the Nantong dataset, and 93.51% and 92.92%, respectively, for the Guantan dataset. Moreover, our approach shows great potential in updating existing vector data while effectively mitigating the high costs associated with acquiring training samples