Inverse dynamic analysis of milling machining robot, application in calibration of cutting force

This article presents analysis of inverse dynamics of serial manipulators in milling process. Cutting forces and complicated motion involve to difficulties in solving dynamics problems of robots. In general, cutting forces are determined by using empirical formulas that lead to errors of cutting force values. Moreover, the cutting forces are changing and causing vibration during machining process. Errors of cutting force values affect to the accuracy of the dynamic model. This paper proposes an algorithm to compute the cutting forces based on the feedback values of the robot's motion.   

Empirical Investigation of Omni-channel Customer Behavior: Multiple Mediation Effects of Website and Mobile Interactivity

While existing retail research has focused on retail channels in isolation from a single or multi-channel retailing perspective, there is a need to investigate shopping behavioral intention from an omni-channel and customer-centric retailing perspective. The main of this study is to analyze the customer omni-channel behavior under multiple mediating effects of website and mobile interactivity. Data collected from valid 287 respondents via both online and paper form. Partial least squares structural equation modeling (PLS-SEM) and Smart-PLS software have been used to test proposed hypotheses. The result underlined the significant positive effects of technology literacy, attitude towards website interactivity and attitude towards mobile device interactivity on customer’s behavioral intention. Moreover, website interactivity and augmented reality have highest impact attitude towards website interactivity and attitude towards mobile device interactivity respectively

Performance Investigation of High-Speed Train OFDM Systems under the Geometry-Based Channel Model

The high-speed of train (HST) in combination with the high carrier frequency of HST systems leads to the severe inter carrier interference (ICI) in the HST orthogonal frequency division multiplexing (HST-OFDM) systems. To avoid the complexity in OFDM receiver design for ICI eliminations, the OFDM system parameters such as symbol duration, signal bandwidth, and the number of subcarriers should be chosen appropriately. This paper aims to propose a process of HST-OFDM system performance investigation to determine these parameters in order to enhance spectral efficiency and meet a given quality-of-service (QoS) level. The signal-to-­interference-­plus-­noise ratio (SINR) has been used as a figure of merit to analyze the system performance instead of signal-to-noise ratio (SNR) as most of recent research studies. Firstly, using the non-stationary geometry-based stochastic HST channel model, the SINR of each subcarrier has been derived for different speeds of the train, signal bandwidths, and number of subcarriers. Consequently, the system capacity has been formulated as the sum of all the single channel capacity from each sub-carrier. The constraints on designing HST-OFDM system parameters have been thoughtfully analyzed using the obtained expressions of SINR and capacity. Finally, by analyzing the numerical results, the system parameters can be found for the design of HST-OFDM systems under different speeds of train. The proposed process can be used to provide hints to predict performance of HST communication systems before doing further high cost implementations as hardware designs

Economic Instruments and the Pollution Impact of the 2006-2010 Vietnam Socio-Economic Development Plan

The current study derives optimal growth paths for pollution emission charges, in order to control future water pollution emissions in the Vietnamese manufacturing sector. The study builds on a prior study, which estimated the manufacturing sector pollution impact of the 2006- 2010 SEDP development plan for Vietnam (Jensen et al.; 2008). The current study demonstrates that effective implementation and moderate expansion of optimal emission charges, under certain conditions, could have been used, as part of the 2006-2010 SEDP development plan, to control pollution emissions at 2005 levels. Moreover, such a scenario would have been accompanied by a moderate expansion in fiscal revenues and a relatively minor economy-wide efficiency loss. The current study, therefore, suggests that effective implementation and gradual expansion of pollution emission charges should be incorporated into future SEDP development plans, in order to control pollution emissions as development progresses in Vietnam.Vietnam, manufacturing, CGE

Nutraceutical Properties of Legume Seeds: Phytochemical Compounds

Legume seeds have an important role as nutraceuticals in human health (providing protein, carbohydrates, fiber, amino acids, and micronutrients) and act as sustainable food sources in livestock farming and aquaculture. Legume seeds contain a wide range of bioactive compounds that have significant health benefits, mainly classified under phenolic compounds, phytosterols, oligosaccharides, carbohydrates, and saponins. Some of these compounds play an important role in plant defense mechanisms against predators and environmental conditions. Heat-labile antinutritional factors (protease inhibitors and lectins) and heat-stable antinutritional factors (tannins and phytic acid) can be reduced by thermal treatment or postharvest to eliminate any potential negative effects from consumption. Substantial studies have demonstrated that these bioactive compounds possess multiple biological activities, including antioxidant properties, antibacterial, anticancer, anti-inflammatory, antidiabetic, cardiovascular protective. They also have various values for aquaculture, such as fishmeal alternative. In this review, the main bioactive compounds and important biological functions of legume seeds are summarized, and the mechanism of action is discussed

LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization

Multi-document summarization is challenging because the summaries should not only describe the most important information from all documents but also provide a coherent interpretation of the documents. This paper proposes a method for multi-document summarization based on cluster similarity. In the extractive method we use hybrid model based on a modified version of the PageRank algorithm and a text correlation considerations mechanism. After generating summaries by selecting the most important sentences from each cluster, we apply BARTpho and ViT5 to construct the abstractive models. Both extractive and abstractive approaches were considered in this study. The proposed method achieves competitive results in VLSP 2022 competition.Comment: In Proceedings of the 9th International Workshop on Vietnamese Language and Speech Processing (VLSP 2022

RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification

In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic information related to legal contexts, focusing on fake news as potential input for electronic evidence. The RMDM dataset comprises four labels: real, mis, dis, and mal, representing real information, misinformation, disinformation, and mal-information, respectively. By including these diverse labels, RMDM captures the complexities of differing fake news categories and offers insights into the abilities of different language models to handle various types of information that could be part of electronic evidence. The dataset consists of a total of 1,556 samples, with 389 samples for each label. Preliminary tests on the dataset using GPT-based and BERT-based models reveal variations in the models' performance across different labels, indicating that the dataset effectively challenges the ability of various language models to verify the authenticity of such information. Our findings suggest that verifying electronic information related to legal contexts, including fake news, remains a difficult problem for language models, warranting further attention from the research community to advance toward more reliable AI models for potential legal applications.Comment: ISAILD@KSE 202

Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs

This paper presents a knowledge graph construction method for legal case documents and related laws, aiming to organize legal information efficiently and enhance various downstream tasks. Our approach consists of three main steps: data crawling, information extraction, and knowledge graph deployment. First, the data crawler collects a large corpus of legal case documents and related laws from various sources, providing a rich database for further processing. Next, the information extraction step employs natural language processing techniques to extract entities such as courts, cases, domains, and laws, as well as their relationships from the unstructured text. Finally, the knowledge graph is deployed, connecting these entities based on their extracted relationships, creating a heterogeneous graph that effectively represents legal information and caters to users such as lawyers, judges, and scholars. The established baseline model leverages unsupervised learning methods, and by incorporating the knowledge graph, it demonstrates the ability to identify relevant laws for a given legal case. This approach opens up opportunities for various applications in the legal domain, such as legal case analysis, legal recommendation, and decision support.Comment: ISAILD@KSE 202