Affections of Turbine Nozzle Cross-Sectional Area to the Marine Diesel Engine Working

After a long period of use, some important technical parameters of the main marine diesel engines (MDE) gradually become worse, such as the turbine speed, intake pressure, exhaust temperature, engine power, and specific fuel oil consumption (SFOC). This paper studies the affections of the turbine nozzle cross-sectional area (AT) to MDE and presents a method of AT adjustment to improve the performances of MDE. A mathematical model of an engine was built based on the existent engine construction and the theory of the diesel engine working cycle and the simulation was programmed by Matlab/Simulink. This simulation model accuracy was evaluated through the comparison of simulation results and experimental data of the MDE. The accuracy testing results were acceptable (within 5%). The influences of AT on the engine working parameters and the finding optimization point were conducted by using the simulation program to study. The predicted optimization point of the nozzle was used to improve the engine’s performances on board. The integration of the simulation and experiment studies showed its effectiveness in the practical application of the marine diesel engine field

Selection of Exercises to Improve the Effectiveness of Physical Education under the Project of Elective Sports Cockpitching for Female Students K2023 Hanoi Metropolitan University

The interview results have selected 36 exercises belonging to 5 exercise groups to improve the effectiveness of physical education according to the project for female Hanoi Metropolitan University students studying the optional sport of shuttlecock, including Group 1: Exercises to develop strength quickly has 7 exercises; Group 2: Strength development exercises have 6 exercises; Group 3: Exercises to develop endurance has 5 exercises; Group 4: Exercises to develop flexibility and coordination has 5 exercises; Group 5: Exercises with the bridge has 13 exercises with an agreement level of > 70% or more and an average score of > 3.41 - 4.20 points. &nbsp

A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language

Soft Robotic Link with Controllable Transparency for Vision-based Tactile and Proximity Sensing

Robots have been brought to work close to humans in many scenarios. For coexistence and collaboration, robots should be safe and pleasant for humans to interact with. To this end, the robots could be both physically soft with multimodal sensing/perception, so that the robots could have better awareness of the surrounding environment, as well as to respond properly to humans' action/intention. This paper introduces a novel soft robotic link, named ProTac, that possesses multiple sensing modes: tactile and proximity sensing, based on computer vision and a functional material. These modalities come from a layered structure of a soft transparent silicon skin, a polymer dispersed liquid crystal (PDLC) film, and reflective markers. Here, the PDLC film can switch actively between the opaque and the transparent state, from which the tactile sensing and proximity sensing can be obtained by using cameras solely built inside the ProTac link. In this paper, inference algorithms for tactile proximity perception are introduced. Evaluation results of two sensing modalities demonstrated that, with a simple activation strategy, ProTac link could effectively perceive useful information from both approaching and in-contact obstacles. The proposed sensing device is expected to bring in ultimate solutions for design of robots with softness, whole-body and multimodal sensing, and safety control strategies.Comment: Submitted to RoboSoft 2023 for review. Final content subjected to chang

Factors Affecting E-Commerce Customer Satisfaction in Hanoi, Vietnam

The escalating ubiquity and cost-effectiveness of internet facilities have precipitated a notable surge in the adoption of e-commerce among Vietnamese inhabitants, particularly those residing in urban areas. This research endeavours to enhance comprehension of factors affecting e-commerce customer satisfaction in Hanoi, Vietnam. A quantitative survey of this research was conducted in which online questionnaires were distributed to 382 respondents through social media. The amassed data underwent meticulous analysis utilizing Microsoft Excel. The findings of this investigation underscore that information quality, system quality, service quality and price simultaneously influence e-commerce customer satisfaction. Keywords: factors, e-commerce customers, satisfaction DOI: 10.7176/EJBM/16-2-03 Publication date:March 31st 202

Evaluation of genetic diversity and DNA fingerprinting of 19 standard reference rice varieties using SSR markers

Molecular markers are advanced-tools for identifying new varieties at DNA levels. According to the International Union for the Protection of New Varieties ofPlants,  new breeded varieties need to be tested for the Distinctness, Uniformity and Stability (DUS), before being recognized as the new ones. Traditional DUS criteria based on 62 - 65 morphological and biochemical characteristics, which evaluated on comparison of new varieties with 19 standard reference varieties for traits of interest.  Study on the genotypic polymorphism of 19 standard reference rice varieties provides genotypic information of these varieties for the evaluation of new rice varieties based on genotyping analysis.  The reference marker set (30 markers) was used to evaluate the genetic diversity and DNA fingerprinting of 19 standard reference rice varieties. The results showed the similarity coefficient of 19 varieties varied from 0.04 to 0.548. At the genetic similarity coefficient of 0.1, the 19 rice varieties divided into two main groups. Group one included 3 varieties: DH1, DH5, DH13. Group 2 included the remaining 16 varieties. Inside group two, phylogenetic tree divided into two main branches at the genetic similarity coefficient of 0.3. Branch 1 includes 5 varieties including DH2, DH6, DH10, DH11 and DH7. The 11 remaining varieties were in the branch 2. The most closely varieties were DH6 and DH10 with the genetic similarity coefficient of 0.548. This study shows that, the standard reference varieties have high uniformity and high genotypic polymorphism, could used for testing new varieties based on genotyping by DNA fingerprinting combining with phenotype

Modification of natural zeolite by salt to treat ammonia pollution in groundwater

Treating ammonium pollution in ground water by natural zeolite after being modificated to the Naform (Z-Na) is the new way of research that scientists interested in. The experiment results showed that, at pH 6, the efficient of treating ammonium in ground water is the highest. The efficient of treating increase rapidly in the first 5 minutes and remain stable after that. Higher concentration of the Z-Na will increase the treating coefficient of the process. With a water sample that has CNNH4+= 27 mg/L at first, using CZ-Na=13g/L and after 5 minutes, the concentration of ammonium in water was declined to 1mg/L, passed the Vietnamese standard for ground water (QCVN 09:2015- MT/BTNMT). The treating coefficient is 96.30%, the adsorption capacity is 2.07 mg N-NH4 +/1g ZNa. The loaded Z-Na was regenerated using 2g/L NaOH solution, the ammonium recovery ratio exceeded 92%. This means the reuse of Z-Na for ammonium adsorption is very high. The results of the experiment with groundwater samples in Phu Xuyen district, Ha Noi have a concentration of 53 mg/L. In conclusion, Z-Na material is perfectly fit for purpose of treating ammonium in ground water because of it low price, safety, easily to imitate and high efficiency.Xử lí ô nhiễm amoni trong nước ngầm bằng vật liệu zeolite tự nhiên được biến tính bằng muối ăn (Z-Na) là một hướng nghiên cứu mới, được các nhà khoa học rất quan tâm. Kết quả thí nghiệm cho thấy, tại pH 6 thì hiệu quả xử lí amoni trong nước là tốt nhất. Hiệu quả xử lí amoni trong nước tăng rất nhanh trong 5 phút đầu tiên xử lý. Càng tăng nồng độ Z-Na thì hiệu quả xử lí amoni càng cao. Với dung dịch nước ban đầu có nồng độ amoni tính theo nitơ (N-NH4 +) nhỏ hơn 27 mg/L và nồng độ vật liệu Z-Na sử dụng là 13g/L thì nước sau xử lí có nồng độ nhỏ hơn 1mg N-NH4 +/L, đạt QCVN 09-MT:2015/BTNMT, hiệu suất xử lí đạt 96,30%, dung lượng hấp phụ cực đại đạt 2,07 mg NNH4 +/1g Z-Na. Vật liệu Z-Na sau khi xử lý được nghiên cứu giải hấp bằng dung dịch NaOH với nồng độ 2g/L cho thấy hiệu quả giải hấp đạt 92% lượng amoni được hấp phụ. Điều này chứng tỏ khả năng tái sử dụng của vật liệu Z-Na cho hấp phụ amoni là khá cao. Kết quả nghiên cứu đã được thử nghiệm xử lý với mẫu nước ngầm tại huyện Phú Xuyên, Hà Nội có nồng độ N-NH4 + là 53 mg/L. Vì vậy, vật liệu Z-Na hoàn toàn có thể ứng dụng vào thực tiễn để xử lý amoni trong nước ngầm rất an toàn, dễ thực hiện và hiệu quả cao

Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells

Artificial Intelligence (AI) in healthcare, especially in white blood cell cancer diagnosis, is hindered by two primary challenges: the lack of large-scale labeled datasets for white blood cell (WBC) segmentation and outdated segmentation methods. These challenges inhibit the development of more accurate and modern techniques to diagnose cancer relating to white blood cells. To address the first challenge, a semi-supervised learning framework should be devised to efficiently capitalize on the scarcity of the dataset available. In this work, we address this issue by proposing a novel self-training pipeline with the incorporation of FixMatch. Self-training is a technique that utilizes the model trained on labeled data to generate pseudo-labels for the unlabeled data and then re-train on both of them. FixMatch is a consistency-regularization algorithm to enforce the model's robustness against variations in the input image. We discover that by incorporating FixMatch in the self-training pipeline, the performance improves in the majority of cases. Our performance achieved the best performance with the self-training scheme with consistency on DeepLab-V3 architecture and ResNet-50, reaching 90.69%, 87.37%, and 76.49% on Zheng 1, Zheng 2, and LISC datasets, respectively

Retrosynthetic reaction prediction using neural sequence-to-sequence models

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis