Simulation and experimental study on the Fenotec casting method of the engine block RV95

The new designs or new materials used in foundries for sand casting products in Vietnam now often rely on experience and adopt the try-to-fix method to gradually find the best implementation. This method is very time-consuming, and the product is often unsatisfactory in the first many castings. This study developed a casting simulation model to compare experimental and simulated results using the Fenotec molding technology for the RV95 engine body. The 3D simulation model that was used to simulate the casting process with the same boundary conditions as the experiment also gave similar results. The results show that when making new castings for the first time from experience, there are many casting defects such as cracking, metal deficiency, porosity, etc. In order to improve the casting results, the pouring gate, the arrangement of additional risers and the size of the runner were studied on the simulation to improve the defect results on the casting. After that, the parameters in the simulation model used in the experiment reveal that the molded product meets the required quality and no longer has defects. This molded product, after checking the mechanical and geometric parameters, can be put into mass productio

Direct and ultrafast probing of quantum many-body interactions through coherent two-dimensional spectroscopy: From weak- to strong-interaction regimes

Interactions between particles in quantum many-body systems play a crucial role in determining the electric, magnetic, optical, and thermal properties of the system. The recent progress in the laser-pulse technique has enabled the manipulations and measurements of physical properties on ultrafast timescales. Here we propose a method for the direct and ultrafast probing of quantum many-body interaction through coherent two-dimensional (2D) spectroscopy. Using a two-band fermionic Hubbard model for the minimum two-site lattice system, we find that the 2D spectrum of a noninteracting system contains only diagonal peaks; the interparticle interaction manifests itself in the emergence of off-diagonal peaks in the 2D spectrum before all the peaks coalesce into a single diagonal peak as the system approaches the strongly interacting limit. The evolution of the 2D spectrum as a function of the time delay between the second and third laser pulses can provide important information on the ultrafast time variation of the interaction

Counterfactual Explanation of Brain Activity Classifiers Using Image-To-Image Transfer by Generative Adversarial Network

Deep neural networks (DNNs) can accurately decode task-related information from brain activations. However, because of the non-linearity of DNNs, it is generally difficult to explain how and why they assign certain behavioral tasks to given brain activations, either correctly or incorrectly. One of the promising approaches for explaining such a black-box system is counterfactual explanation. In this framework, the behavior of a black-box system is explained by comparing real data and realistic synthetic data that are specifically generated such that the black-box system outputs an unreal outcome. The explanation of the system's decision can be explained by directly comparing the real and synthetic data. Recently, by taking advantage of advances in DNN-based image-to-image translation, several studies successfully applied counterfactual explanation to image domains. In principle, the same approach could be used in functional magnetic resonance imaging (fMRI) data. Because fMRI datasets often contain multiple classes (e.g., multiple behavioral tasks), the image-to-image transformation applicable to counterfactual explanation needs to learn mapping among multiple classes simultaneously. Recently, a new generative neural network (StarGAN) that enables image-to-image transformation among multiple classes has been developed. By adapting StarGAN with some modifications, here, we introduce a novel generative DNN (counterfactual activation generator, CAG) that can provide counterfactual explanations for DNN-based classifiers of brain activations. Importantly, CAG can simultaneously handle image transformation among all the seven classes in a publicly available fMRI dataset. Thus, CAG could provide a counterfactual explanation of DNN-based multiclass classifiers of brain activations. Furthermore, iterative applications of CAG were able to enhance and extract subtle spatial brain activity patterns that affected the classifier's decisions. Together, these results demonstrate that the counterfactual explanation based on image-to-image transformation would be a promising approach to understand and extend the current application of DNNs in fMRI analyses

SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL

Artificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models. These models have revolutionized various fields, benefiting society in numerous ways, from self-driving cars and intelligent chatbots to automated facial authentication systems. However, in recent years, machine learning models have been the target of various attack methods. One common and dangerous attack method is adversarial attack, where modified input images can cause misclassification or erroneous predictions by the models. To confront that challenge, we present a novel approach called adversarial retraining that uses adversarial examples to train machine learning and deep learning models. This technique aims to enhance the robustness and performance of these models by subjecting them to adversarial scenarios during the training process. In this paper, we survey detection methods and propose a method to detect adversarial examples using YOLOv7, a commonly used intensive research model. By training adversarial retraining and conducting experiments, we show that the proposed method is an effective solution for helping deep learning models detect certain cases of adversarial examples

An Online Distributed Boundary Detection and Classification Algorithm for Mobile Sensor Networks

We present a novel online distributed boundary detection and classification algorithm in order to improve accuracy of boundary detection and classification for mobile sensor networks. This algorithm is developed by incorporating a boundary detection algorithm and our newly proposed boundary error correction algorithm. It is a fully distributed algorithm based on the geometric approach allowing to remove boundary errors without recursive process and global synchronization. Moreover, the algorithm allows mobile nodes to identify their states corresponding to their positions in network topologies, leading to self-classification of interior and exterior boundaries of network topologies. We have demonstrated effectiveness ofthis algorithm in both simulation and real-world experiments and proved that the accuracy of the ratio of correctly identified nodes over the total number of nodes is 100%

Study on hardness and wear resistance of shot peened AA7075-T6 aluminum alloy

AA7075-T6 aluminum alloy samples were shot peened at various shot peening pressures in the range of 10-70 psi to study their mechanical and tribological properties under dry and mineral oil lubrication conditions. The surface roughness of the shot peened AA7075-T6 samples apparently increased with increased shot peening pressure. The best bearing surface was obtained when the shot peening process was carried out at the highest shot peening pressure of 70 psi. It was found that the increased shot peening pressure increased the hardness and decreased the wear of the shot peened samples. The shot peened samples tested under the mineral oil lubrication condition also had lower wear for higher shot peening pressures due to the combined effects of their higher surface wear resistance and better bearing surfaces. The results clearly showed that using the mineral oil lubricant during sliding apparently decreased the wear of the shot peened samples as a result of the lubricating effect of the lubricant. It could be deduced that the tribological properties of the shot peened AA7075-T6 samples under the dry and lubrication conditions were affected by the shot peening pressur

An investigation of the properties of conventional and severe shot peened low alloy steel

The effects of the conventional shot peening and severe shot peening process on the mechanical and tribological properties of shot peened AISI 4340 high strength steel were systematically investigated. Comparing with the conventional shot peened sample, the ultrafine grain surface layer with a depth of about 20 μm generated by the severe shot peening process can enhance the hardness and wear resistance of the treated material. However, deeper dimples generated by the high media velocity in the severe shot peening process resulted in a higher surface roughness, which is considered as a side effect of this method reducing the fatigue life of the material. Applying a smaller shot size with an appropriate intensity can be used to peen the severe shot peened samples to not only reduce the surface roughness and friction coefficient but also improve the wear resistance for these samples

Criblage virtuel sur grille de composés isolés au Vietnam

Criblage virtuel sur grille de composés isolés au Vietna

Exploring knowledge and attitudes toward the hepatitis B virus: an internet-based study among Vietnamese healthcare students

Context: Hepatitis B is a serious global public health problem, especially in developing countries such as Vietnam. Many studies worldwide have focused on health care workers, a population at high risk of infection with the hepatitis B virus (HBV), but there is little research that explores the high levels of risk faced by health care students. Aims: To assess the knowledge and attitudes of Vietnamese undergraduate students toward hepatitis B vaccination. Methods: A cross-sectional study was conducted among 1291 Vietnamese healthcare students between November 2017 and March 2018 via social media. Results: The mean score for knowledge was 4.0 ± 0.4, and the mean score for attitude was lower (3.5 ± 0.6). Levels of knowledge were higher (p<0.001) among sixth-year students, students who had been vaccinated, and students attending public universities. Most participants were well-informed about the diseases caused by HBV, with 1128 (87.4%) agreeing that HBV infection can lead to liver cancer. Nevertheless, 259 students believed that HBV cannot be spread by sharing a toothbrush with an infected person, and 18.7% thought that asymptomatic carriers are incapable of transmitting HBV. Conclusions: Although students had adequate knowledge of HBV, their scores for attitude and their rates of vaccination were low. This study therefore recommends improving the knowledge and attitudes of health care students through orientation and sensitization programs and improvements in their educational environment

Large-scale Vietnamese point-of-interest classification using weak labeling

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%)