Nowadays, magnesium (Mg) composites are gaining much attention in biomedical device applications due to their biocompatibility and biodegradability properties. This research is to study the microstructure, mechanical, corrosive and antibacterial properties of Mg−2.5Zn−0.5Zr/xCNT (x = 0, 0.3, 0.6, 0.9) composites made with mechanical alloying and semi-powder metallurgy (SPM) processes, accompanied by SPS. Based on the microstructural characteristics, CNTs were almost uniformly distributed in the Mg matrix. The results displayed that the hardness and ultimate compressive strength (UCS) of the composites were meaningfully increased compared to a Mg matrix. Moreover, the degradation rate of Mg composites was almost halved in the presence of small amounts of CNTs in the Kokubo simulated body fluid (SBF). Due to the slowed degradation process, the Mg−2.5Zn−0.5Zr/0.6CNT biocomposites exhibited excellent cellular compatibility. Evaluation of antibacterial activity displayed that adding CNTs to the Mg matrix could significantly prevent the growing of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). In general, the research results showed that CNTs are an efficient reinforcement for Mg−2.5Zn−0.5Zr/CNTs biocomposites, which leads to improved mechanical, degradation and antibacterial performances.

The inquiry process of traditional medical equipment maintenance management is complicated, which seriously affects the efficiency and accuracy of medical equipment maintenance management, and causes a lot of waste of manpower and materials. In order to accurately predict the failure of medical equipment, an accurate prediction system for failure life cycle of medical equipment was designed. The system is divided into four modules: the whole life cycle management module constructs the life cycle data set of medical devices from the three parts of the management in the early stage, the middle and the later stage; the status detection module monitors the main operation data of the medical device components through the normal value of the relevant sensitive data in the whole life cycle management module; the main function of the fault diagnosis module is based on the medical equipment whole life cycle management module. The operation data of equipment is diagnosed by inference machine; the fault prediction module builds a fine prediction system based on least square support vector machine algorithm, and uses AFS ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters, and the optimal model is used to predict the medical equipment failure. In order to verify the effectiveness of the design system, comparative experiments are designed to verify. The results show that the designed system can accurately predict the failure of electrocardiogram diagnostic instrument and incubator, and has high support and reliability. Compared with the comparison system, the prediction error of the design system is the smallest and the program running time is the shortest. Therefore, the design system can accurately predict the different failure types and causes of medical devices.

This paper deals with the effects of three low-carbon steel filler metals consisting of ferritic and austenitic phases on the weld joints of the tungsten inert gas (TIG) welding of Hardox 500 steel. The correlation between the microstructure and mechanical properties of the weld joints was investigated. For this purpose, macro and microstructure were examined, and then microhardness, tensile, impact, and fracture toughness tests were carried out to analyze the mechanical properties of joints. The results of optical microscopy (OM) images showed that the weld zones (WZ) of all three welds were composed of different ferritic morphologies, including allotriomorphic ferrite, Widmanstätten ferrite, and acicular ferrite, whereas the morphology of the heat-affected zone (HAZ) showed the various microstructures containing mostly ferrite and pearlite phases. Further, based on mechanical tests, the second filler with ferritic microstructure represented better elongation, yield strength, ultimate tensile strength, impact toughness, and fracture toughness due to having a higher amount of acicular ferrite phase compared to the weld joints concerning the other fillers consisting of austenitic and ferritic-austenitic. However, scanning electron microscopy (SEM) images on the fracture surfaces of the tensile test showed a ductile-type fracture with a large number of deep and shallow voids while on the fracture surfaces resulting from the Charpy impact tests and both ductile and cleavage modes of fracture took place, indicating the initiation and propagation of cracks, respectively. The presence of acicular ferrite as a soft phase that impedes the dislocation pile-up brings about the ductile mode of fracture while inclusions may cause stress concentration, thus producing cleavage surfaces.

Cyberbullying is a form of abuse, manipulation, or humiliation directed against a single person via the Internet. CB makes use of nasty Internet comments and remarks. It occurs when someone publicly mocks, insults, slanders, criticizes, or mocks another person while remaining anonymous on the Internet. As a result, there is a rising need to create new methods for sifting through data on social media sites for symptoms of cyberbullying. The goal is to lessen the negative consequences of this condition. This article discusses a soft computing-based methodology for detecting cyberbullying in social multimedia data. This model incorporates social media data. Normalization is performed to remove noise from data. To improve a feature, the Particle Swarm Optimization Technique is applied. Feature optimization helps to make cyberbullying detection more accurate. The LSTM model is used to classify things. With the help of social media data, the PSO LSTM model is getting better at finding cyberbullying. The accuracy of PSO LSTM is 99.1%. It is 2.9% higher than the accuracy of the AdaBoost technique and 10.4% more than the accuracy of the KNN technique. The specificity and sensitivity of PSO-based LSTM is also higher in percentage than KNN and AdaBoost algorithm.