Beef tallow injection matrix for serial crystallography

Serial crystallography (SX) enables the visualization of the time-resolved molecular dynamics of macromolecular structures at room temperature while minimizing radiation damage. In SX experiments, the delivery of a large number of crystals into an X-ray interaction point in a serial and stable manner is key. Sample delivery using viscous medium maintains the stable injection stream at low flow rates, markedly reducing sample consumption compared with that of a liquid jet injector and is widely applied in SX experiments with low repetition rates. As the sample properties and experimental environment can affect the stability of the injection stream of a viscous medium, it is important to develop sample delivery media with various characteristics to optimize the experimental environment. In this study, a beef tallow injection matrix possessing a higher melting temperature than previously reported fat-based shortening and lard media was introduced as a sample delivery medium and applied to SX. Beef tallow was prepared by heat treating fats from cattle, followed by the removal of soluble impurities from the extract by phase separation. Beef tallow exhibited a very stable injection stream at room temperature and a flow rate of < 10 nL/min. The room-temperature structures of lysozyme and glucose isomerase embedded in beef tallow were successfully determined at 1.55 and 1.60 Å, respectively. The background scattering of beef tallow was higher than that of previously reported fat-based shortening and lard media but negligible for data processing. In conclusion, the beef tallow matrix can be employed for sample delivery in SX experiments conducted at temperatures exceeding room temperature.

Research on the Practical Teaching System Reform of Computer Hardware Courses

This study analyzes the current situation of the practical teaching system of computer hardware courses in local undergraduate colleges, scrutinizes the experimental environment construction, contents, and methods of computer hardware courses, and proposes a new practical teaching system for computer hardware courses, so as to meet the needs of transformation and development as well as application-oriented talent training.

Car Following Model and Algorithm Design based on Reinforcement Learning

Based on reinforcement learning technology, this paper establishes a new driverless car following model. DQN algorithm and traffic simulator are mainly used to train the agent, and the following model is finally obtained. Under the precise and controllable experimental environment, the preset optimization targets can achieve the expected assumption and complete the following behavior. This study will contribute to the development of unmanned vehicles in the future.

Experimental Environment to Model, Simulate and Analyze Contagious Diseases as a Diffusion Process in Social Networks

In this paper the novel model of diffusion on networks and the experimental environment are presented. We consider the utilization of the graph and network theory in the field of modelling and simulating the dynamics of contagious diseases. We describe basic principles and methods and show how we can use them to fight against the spread of this phenomenon. We also present our software solution – CARE (Creative Application to Remedy Epidemics) that can be used to support decision-making activities.

DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment

Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.

Design of software-defined network experimental teaching scheme based on virtualised Environment

Aiming to address the shortage of experimental resources, the high cost of large-scale deployment of hardware experimental environment and the difficulty for students to get started in the software-defined network (SDN) course, this article proposes an SDN experimental teaching scheme based on the virtualised environment, and gives a specific experimental scheme design. The scheme utilises virtualisation technology to build a SDN experimental environment quickly, uses a lightweight network simulation platform – that goes by the name of Mininet – to build the SDN network and uses open-source controller Floodlight for centralised control of the SDN network. The scheme is mainly divided into three phases: basic, improvement and synthesis. In the basic phase, experimental projects mainly include the study of SDN basic concepts and the use of relevant tools; in the improvement phase, experimental projects mainly include the use of SDN flow table, group table, etc; in the synthetic phase, we design two innovative experimental projects that use computational intelligence technology to achieve efficient load balancing and accurate malicious attack detection. The difficulty of each phase is increasing. The constantly evolving levels of difficulty allow the individual needs of students with different levels to be met, thereby improving the effect of SDN experimental teaching and cultivating innovative SDN talents.