Malignant pleural mesothelioma (MPM) is associated with a history of heavy, long-term exposure to asbestos. However, MPM may also be associated with simian virus 40 (SV40), a polyomavirus. The association between SV40 and MPM remains unclear. The present study was conducted in order to investigate the proportion of SV40 presence in the histological specimens of Vietnamese patients with MPM. Histological specimens were obtained from 45 patients (19 men and 26 women) with MPM at the Pham Ngoc Thach Hospital in Ho Chi Minh City, Vietnam. The specimens were processed and examined in order to detect the presence of the SV40 large T antigen (SV40 Tag) expression using immunohistochemistry. Of the 45 patients, 23 (51%) were epithelioid, 7 (16%) were biphasic, 6 (13%) were sarcomatoid, 4 (9%) were desmoplastic, 4 (9%) were well-differentiated papillary and 1 (2%) was the anaplastic subtype. In total, 9/45 patients (20%) demonstrated SV40 Tag expression. The proportion of patients that demonstrated SV40 Tag expression was not significantly different between the epithelioid subtype and the other subtypes (22 vs. 18%; P=1.000) or between the patients with stage IV disease and other stages (20 vs. 20%; P=1.000). The median survival time was not significantly different between the patients with or without SV40 Tag expression (196 vs. 236 days, P=0.8949). In summary, a 5th of the Vietnamese patients with MPM were associated with infection with SV40. SV40 may be a potential cause of MPM in Vietnam and this potential association requires additional studies.

Nowadays, network intrusion detection is an essential problem because cyber-attacks are increasing in both the number and extent of the danger. Network intrusion techniques often use various methods to bypass the oversight of anomaly detection and surveillance systems. This paper proposes to use behavior analysis techniques, machine learning, and deep learning algorithms for the task of detecting network intrusions. The practical and scientific significance of our paper includes two issues: (1) Regarding the process of selecting and extracting features: instead of using typical abnormal behaviors of attacks, this study will use statistical behaviors that are easy to calculate and extract while still ensuring the effectiveness of the method; (2) Regarding the detection process, this study proposes to use the Random Forest (RF) classification algorithm, the Multilayer Perceptron (MLP) and the Convolutional Neural Network (CNN) deep learning model. The experimental results in Section IV have proven that our proposal in this paper is completely correct and reasonable. Based on the results shown in Section IV, this study has provided network surveillance systems with a number of abnormal behaviors as the basis for detecting network intrusions.

The stator current control loop plays an important role in ensuring the quality of electric drives interm of producing fast and adequate required torque. When the current controller provides ideal responses, speed control design subsequently is in charge of improving the system performances. Classical PID control is commonly used in current loop design, this paper presents the comparative analysis of current stator controller using proportional integral control and predictive current control (PCC) in field-oriented control-based induction motor drives, with rigidly coupled loads. The experimental results show system responses with PID and PCC. Informative experiment-based analysis provides primary guidance in selection between the two controls.

Induction motor is widely used in industrial applications due to its low cost, simple design, and reliability. In this paper, the induction motor control structure FOC will be implemented on the FPGA platform for the drive system using GaN devices. By using GaN technology, the switching frequency can be up to 100 kHz instead of 2 to 20 kHz when using IGBT transistors. It leads to a significant reduction in switching loss as well as increasing the power density of the power electronic converter. The control structure will be programmed in VHDL language on the system-on-chip environment of Xilinx Zybo z7010 FPGA development board. The validity of the research is verified by some results when operating with HIL device.

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.

Nowadays, the application of data mining in the healthcare industry is necessary. Data mining brings a set of tools and techniques that can be applied to discover hidden patterns that provide healthcare professionals an additional source of knowledge for making decisions. In more detail, clustering the patients that have the same status helps discovering new disease, but the suitable number of clusters is not often obvious. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Then, an improved algorithm is presented for learning k while clustering. Finally, we evaluate the algorithm, apply to dataset of patients and results show its efficiency.

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