Weak Lefschetz property of graded Gorenstein algebras associated to the Apéry set of a numerical semigroup

It has been conjectured that all graded Artinian Gorenstein algebras of codimension three have the weak Lefschetz property over a field of characteristic zero. In this paper, we study the weak Lefschetz property of associated graded algebras A of the Apéry set of M-pure symmetric numerical semigroups generated by four natural numbers. These algebras are graded Artinian Gorenstein algebras of codimension three

Using Pictures As Non-Verbal Language Motivating Students With English Speaking Lessons At Vietnam Primary Schools

The study investigates the reality of students' low participation in speaking activities and the possibility of using pictures to increase their participation in classroom speaking activities if necessary. The reason for this research is the lack of using pictures as the visual aids to teach speaking skill for the students in Vietnam’s context. Although several studies have been conducted on these subjects with different approaches, Vietnamese students, especially the primary ones, are not paid much attention as an investigated object. By using methods of questionnaires and tests (pre-tests and post-tests) combined with qualitative analytical analysis, as well as taking 50 students in grade 4 of a primary school in Hanoi as the objects to assess their current situation of learning English with pictures. The research findings indicate that students are more interested in learning English speaking when using pictures. Moreover, the students’ learning strategies and students’ attitude towards learning English are the two main causes that make them have low motivation in learning. In all, motivation and performance of students after the process of applying pictures in learning English presented an improvement. The results of this study are expected to be beneficial for the teachers in conducting the teaching curriculum as they can apply pictures as effective supporters

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

K.Marx-Engels and Ho Chi Minh Viewpoints on Journalism - and Two Fake News Publishing Cases of Thanhnien.vn and Tuoitre.vn (Online Magazines) in Vietnam and Lessons from Indonesia, Japan Approaches

In this paper, by using qualitative analytical analysis with 2 case examples of Thanh nien and Tuoi tre newspapers (online) in Vietnam, in which there are history of publishing fakes news online from 2015, 2016, 2018, 2022 (with very bad editors Nguyen Ngoc Toan and Dang Thi Phuong Thao), as well as giaoduc.edu.vn and vietnamnet.vn in 2022 so we will address some points in this study based on answers for question: “What are regulatory lessons from Indonesia and Japan approaches on publishing fake news?”. We would suggest that there are penalties for negative behaviors of posting fake news online (any fake information) in the context of covid 19 epidemic. Tapsell (2019) defined ‘hoax news’ as similar to the more globally recognized term ‘fake news’: material deliberately fabricated and masqueraded as truth. At last, we will draw some lessons from K.Marx and Ho Chi Minh viewpoints on journalism for educating young generation in emerging markets such as Vietnam

Publishing Fake Information Online-Case of Online Vietnam Magazines (Thanhnien Newspaper, Tuoi tre newspaper, Vietnamnet.vn, dantri.com.vn, giaoduc.edu.vn, sctv.com.vn, etc.) From an Approach of German and EU Laws and Cybersecurity Regulations

Publishing fake information have at least three bad effects on the community: creating disinformation, anxiety, and disorder in society. Still using qualitative analytical methods with synthesis and inductive methods, the authors will address 2 cases of Vietnam magazines: thanhnien. vn and tuoitre.vn, vietnamnet. vn, giaoduc.edu.vn, dantri.com.vn, (online newspapers) and recently sctv.com.vn with their issue of publishing fake news online, which increasing as a problem in recent years 2015-2020. In this paper, we also use the European approach and laws on exploring the issue of publishing and delivering false information via the internet and social media. Last but not least, the views and ideologies of V.I Lenin and Ho Chi Minh on journalism and journalists are mentioned for educating the young generation

Multi-level damage detection using a combination of deep neural networks

In recent years, bridge damage identification using a convolutional neural network (CNN) has become a hot research topic and received much attention in the field of civil engineering. Although CNN is capable of categorizing damaged and undamaged states from the measured data, the level of accuracy for damage diagnosis is still insufficient due to the tendency of CNN to ignore the temporal dependency between data points. To address this problem, this paper introduces a novel hybrid damage detection method based on the combination of CNN and Long Short-Term Memory (LSTM) to classify and quantify different levels of damage in the bridge structure. In this method, the CNN model will be used to extract the spatial damage features, which will be combined with the temporal features obtained from Long Short-Term Memory (LSTM) model to create the enhanced damage features. The combination successfully strengthened the damage detection capability of the neural network. Moreover, deep learning is also improved in this paper to process the acceleration-time data, which has a different amplitude at short intervals and the same amplitude at long intervals. The empirical result on the Vang bridge shows that our hybrid CNN-LSTM can detect structural damage with a high level of accuracy

Residues located inside the Escherichia coli FepE protein oligomer are essential for lipopolysaccharide O-antigen modal chain length regulation

The Escherichia coli O157 : H7 FepE protein regulates lipopolysaccharide (LPS) O-antigen (Oag) chain length to confer a very long modal chain length of >80 Oag repeat units (RUs). The mechanism by which FepE regulates Oag modal chain length and the regions within it that are important for its function remain unclear. Studies on the structure of FepE show that the protein oligomerizes. However, the exact size of the oligomer is in dispute, further hampering our understanding of its mechanism. Guided by information previously obtained for regions known to be important for Oag modal chain length determination in the homologous Shigella flexneri WzzBSF protein, a set of FepE mutant constructs with single amino acid substitutions was created. Analysis of the resulting LPS conferred by these mutant His6-FepE proteins showed that amino acid substitutions of leucine 168 (L168) and aspartic acid 268 (D268) resulted in LPS with consistently shortened Oag chain lengths of <80 Oag RUs. Substitution of FepE’s transmembrane cysteine residues did not affect function. Chemical cross-linking experiments on mutant FepE proteins showed no consistent correlation between oligomer size and functional activity, and MS analysis of FepE oligomers indicated that the in vivo size of FepE is consistent with a maximum size of a hexamer. Our findings suggest that different FepE residues, mainly located within the internal cavity of the oligomer, contribute to Oag modal chain length determination but not the oligomeric state of the protein.Elizabeth Ngoc Hoa Tran and Renato Moron

Optimization of Logistics Services in Vietnam Through LOGIVAN Application

Logistics is a type of service that plays an important role in promoting the country's economic growth. In addition, it is also of great significance to the transport sector in Vietnam in dealing with the challenges of traffic congestion in large cities. In Vietnam today, logistics services are growing. However, there is one major limitation that still exists, that is freight costs are still high. This does not meet the best requirements of customers. There are many reasons for this problem, one of the reasons is that trucks only carry one-way cargo, but there are no goods to ship backwards. The paper studies the application of LOGIVAN smart transport model in developing Logistics services in Vietnam today. Research results show that LOGIVAN transport model is the optimal model in solving the problem of empty cars in the transport of goods when they go back to the place of departure. This helps minimize Logistics costs for businesses, increases income for drivers and leads towards sustainable transport development. LOGIVAN develops two platform solutions for goods owners and vehicle owners. Accordingly, the author of the article confirms the quality of this model in developing Logistics services in Vietnam and guide the operation for users via applications on personal mobile devices at the same time. Keywords: Logistics; LOGIVAN; Vietnam. DOI: 10.7176/JESD/10-14-01 Publication date:July 31st 202

A Distributed Optimization Method for Optimal Energy Management in Smart Grid

This chapter presents a distributed optimization method named sequential distributed consensus-based ADMM for solving nonlinear constrained convex optimization problems arising in smart grids in order to derive optimal energy management strategies. To develop such distributed optimization method, multi-agent system and consensus theory are employed. Next, two smart grid problems are investigated and solved by the proposed distributed algorithm. The first problem is called the dynamic social welfare maximization problem where the objective is to simultaneously minimize the generation costs of conventional power plants and maximize the satisfaction of consumers. In this case, there are renewable energy sources connected to the grid, but energy storage systems are not considered. On the other hand, in the second problem, plug-in electric vehicles are served as energy storage systems, and their charging or discharging profiles are optimized to minimize the overall system operation cost. It is then shown that the proposed distributed optimization algorithm gives an efficient way of energy management for both problems above. Simulation results are provided to illustrate the proposed theoretical approach