Cooperative Group In Current Vietnamese Commercial Environment

Purpose: The article researches the compatibility between the cooperative group and the current commercial environment in Vietnam to propose for improvement to the laws and for promotion of development of the cooperative group.   Theoretical framework: Recent literature points out that the cooperative group model is quite suitable for the production and business psychology of citizens in Vietnam. In the current stage of development, however, from the influence and impact of international integration and the increasingly fierce competitive pressure of various types of enterprises, the cooperative group have encountered many difficulties to survive. On the other hand, in the context of judicial reform in Vietnam, the issue of complete law system and expanding the freedom to business has always been paid special attention by the Party and Vietnam State.   Design/methodology/approach: The authors have combined traditional research methods of legal science such as legal analysis method, legal efficiency assessment method and legal comparison method.   Findings: According to research, the cooperative group is well adapted to Vietnam's commercial environment because it is compatible with investor psychology and meets the conditions and circumstances of nature, economy and society. However, since Vietnam's deep integration with the rest of the world, the cooperative group model has faced numerous difficulties and challenges, and if suitable solutions are not found, it is very likely that this collective economy will become increasingly difficult. As a result, the article suggests some important solutions, such as offering cooperative group legal status and requiring more preferential lending policies with long loan terms, simple and quick loan procedures, and easy team cooperation.   Research, Practical & Social implications: This research may support many develop future research in Vietnam.   Originality/value: The study of the freedom to business become more and more urgent and very important in Vietnam

Help or Hurt? The Impact of ESG on Firm Performance in S&P 500 Non-Financial Firms

The paper aims to investigate the impact of ESG practice on firms’ financial performance in the context of U.S. market from 2018 to 2020. The paper examines a sample of 57 U.S. non-financial firms belonging to the S&P 500. The Two-Stage Least Squares (2SLS) estimation is employed with an instrumental variable - the political views of the states where the studied firms are located. The paper shows that having a better practice of ESG could enhance firms’ financial performance measured by ROA, ROE, and TobinQ. These findings are consistent with the stakeholder-focused theory instead of shareholder-focus perspective. In addition, the magnitude of the influence of the ESG practice on TobinQ is significantly higher than that of the ESG-ROA and ESG-ROE relations. It reveals that the ESG benefits could make the firms appear more attractive to investors, creating higher market values of the firms’ assets and then higher TobinQ ratio. Not as the TobinQ enhancement, the significant improvement in ROA and ROE would be realized in the long run rather than short term. The low managerial ownership in the U.S. market may increase the chance of ESG overinvestment by the firms’ managers, hence reducing firm value. However, under the pressure of the investors’ strong demand for socially responsible investing, the U.S. firms tend to become involved in ESG activities, obtaining a strong stakeholder commitment and thus creating additional firm value in the long run

Delving into Ipsilateral Mammogram Assessment under Multi-View Network

In many recent years, multi-view mammogram analysis has been focused widely on AI-based cancer assessment. In this work, we aim to explore diverse fusion strategies (average and concatenate) and examine the model's learning behavior with varying individuals and fusion pathways, involving Coarse Layer and Fine Layer. The Ipsilateral Multi-View Network, comprising five fusion types (Pre, Early, Middle, Last, and Post Fusion) in ResNet-18, is employed. Notably, the Middle Fusion emerges as the most balanced and effective approach, enhancing deep-learning models' generalization performance by +2.06% (concatenate) and +5.29% (average) in VinDr-Mammo dataset and +2.03% (concatenate) and +3% (average) in CMMD dataset on macro F1-Score. The paper emphasizes the crucial role of layer assignment in multi-view network extraction with various strategies

An efficient approach to measure the difficulty degree of practical programming exercises based on student performances

oai:ojs.www.rev-jec.org:article/282This study examines the generality of easy to hard practice questions in programming subjects. One of the most important contributions is to propose four new formulas for determining the difficulty degree of questions. These formulas aim to describe different aspects of difficulty degree from the learner's perspective instead of the instructor's subjective opinions. Then, we used clustering technique to group the questions into three easy, medium and difficult degrees. The results will be the baseline to consider the generality of the exercise sets according to each topic. The proposed solution is then tested on the data set that includes the results of the two subjects: Programming Fundamentals, Data Structures and Algorithms from Ho Chi Minh City University of Technology. The most important result is to suggest the instructors complete various degrees according to each topic for better evaluating student's performance

LAND USE CHANGE AND RELATED PROBLEMS UNDER URBANIZATION IN SUBURBAN AREA OF HANOI CITY (A CASE STUDY OF HOANG LIET COMMUNE, THANH TRI DISTRICT)

Joint Research on Environmental Science and Technology for the Eart

Primary Evaluation on Growth Performances of Stress Negative Piétrain Pigs Raised in Hai Phong Province of Vietnam

peer reviewedThe present study was carried out on 19 stress negative Piétrain pigs (Pietrain ReHal), consisting of 13 gilts and 6 young boars imported from Belgium, raised in the livestock farm of Dong Hiep (Hai Phong) in order to evaluate growth performances and their adaptability in the North of Vietnam. Results showed that the average body weight of the whole herd at 2, 4, 5.5, and 8.5 months old was 19.05, 51.05, 85.82, and 119.47 kg, respectively. During the growing periods, except the first stage, the male grew faster than the female and the pigs of the CT genotype grew faster than those of CC genotype although the difference was not significant (P>0.05). The average daily gain (ADG) was 528.56 grams for the whole herd. The ADG was higher for the male (546.48 grams) than for the female (520.29 grams), and its was higher for the CT than the CC, but the difference was not statistically significant (P>0.05). The feed conversion ratio (FCR) was 2.69 kg. The estimated lean percentage at 8.5 months old was 64.08%. The results indicate that Piétrain stress negative pigs could develop well on the farm conditions in Hai Phong, Vietnam

Comparison of sensory characteristics of green tea in Thai Nguyen and Phu Tho, Vietnam

Green tea is a popular consumption product in Vietnam. Especially, tea which origins from Tan Cuong, Thai Nguyen has been known for long by its better quality than those coming from other regions on the country. The study aims at comparing and finding out if the difference between tea in Thai Nguyen and Phu Tho can be figured by sensory tasting. Two products picked from Tan Cuong, Thai Nguyen province and two others from Phu Ho district, Phu Tho are were evaluated by a panel of twelve judges (eleven women and one man) who was set from a group of thirty eight peoples, had completed a general training and sensory tasting on tea. The experiment on dry tea (eleven descriptors) was carried out separately of the experiment on brewed tea (twenty-one descriptors) and brewed leaf (five descriptors). All attributes are made notes on the sensory unstructured intensity scale. Statistic analyses have shown typical differences by region among all of trees groups of attributes: dry leaf (10/11 attributes), liquor (6/21 attributes) and brewed leaf (5/5 attributes)

Stress and sleep quality in medical students: a cross-sectional study from Vietnam

BackgroundThe COVID-19 pandemic has resulted in significant global social and economic disruptions, as well as changes in personal attitude and behavior. The purpose of this research is to assess the sleep quality and stress levels of medical students.MethodData was collected from medical students over the course of a month in 2021. A total of 4,677 students at the University of Medicine Pham Ngoc Thach were invited to complete an anonymous web-based survey, which included the Pittsburgh Sleep Quality Questionnaire Index (PSQI) for measuring sleep quality and the COVID-19 Student Stress Questionnaire (CSSQ) for evaluating stress.ResultsA total of 1,502 students participated in our survey. More than half of the participants exhibited poor quality of sleep as indicated by their PSQI score. Many students reported going to bed after midnight and spending time on their smartphones. Among the students surveyed, 21.84% experienced low levels of stress (CSSQ ≤6), 63.38% had mild stress (7 ≤ CSSQ score ≤ 14), 14.78% reported high levels of stress (CSSQ >14).ConclusionThis study showed a high prevalence of poor sleep quality in the surveyed students, which could be attributed to changes in their behavior following the COVID-19 outbreak. Mild stress was also frequently observed, and it may be related to sleep disorders in this population. These important findings provide valuable insights for making recommendations, including lifestyle modifications to improve sleep quality

Z-GMOT: Zero-shot Generic Multiple Object Tracking

Despite the significant progress made in recent years, Multi-Object Tracking (MOT) approaches still suffer from several limitations, including their reliance on prior knowledge of tracking targets, which necessitates the costly annotation of large labeled datasets. As a result, existing MOT methods are limited to a small set of predefined categories, and they struggle with unseen objects in the real world. To address these issues, Generic Multiple Object Tracking (GMOT) has been proposed, which requires less prior information about the targets. However, all existing GMOT approaches follow a one-shot paradigm, relying mainly on the initial bounding box and thus struggling to handle variants e.g., viewpoint, lighting, occlusion, scale, and etc. In this paper, we introduce a novel approach to address the limitations of existing MOT and GMOT methods. Specifically, we propose a zero-shot GMOT (Z-GMOT) algorithm that can track never-seen object categories with zero training examples, without the need for predefined categories or an initial bounding box. To achieve this, we propose iGLIP, an improved version of Grounded language-image pretraining (GLIP), which can detect unseen objects while minimizing false positives. We evaluate our Z-GMOT thoroughly on the GMOT-40 dataset, AnimalTrack testset, DanceTrack testset. The results of these evaluations demonstrate a significant improvement over existing methods. For instance, on the GMOT-40 dataset, the Z-GMOT outperforms one-shot GMOT with OC-SORT by 27.79 points HOTA and 44.37 points MOTA. On the AnimalTrack dataset, it surpasses fully-supervised methods with DeepSORT by 12.55 points HOTA and 8.97 points MOTA. To facilitate further research, we will make our code and models publicly available upon acceptance of this paper