An in vitro mechanism study on the proliferation and pluripotency of human embryonic stems cells in response to magnesium degradation.

Magnesium (Mg) is a promising biodegradable metallic material for applications in cellular/tissue engineering and biomedical implants/devices. To advance clinical translation of Mg-based biomaterials, we investigated the effects and mechanisms of Mg degradation on the proliferation and pluripotency of human embryonic stem cells (hESCs). We used hESCs as the in vitro model system to study cellular responses to Mg degradation because they are sensitive to toxicants and capable of differentiating into any cell types of interest for regenerative medicine. In a previous study when hESCs were cultured in vitro with either polished metallic Mg (99.9% purity) or pre-degraded Mg, cell death was observed within the first 30 hours of culture. Excess Mg ions and hydroxide ions induced by Mg degradation may have been the causes for the observed cell death; hence, their respective effects on hESCs were investigated for the first time to reveal the potential mechanisms. For this purpose, the mTeSR®1 hESC culture media was either modified to an alkaline pH of 8.1 or supplemented with 0.4-40 mM of Mg ions. We showed that the initial increase of media pH to 8.1 had no adverse effect on hESC proliferation. At all tested Mg ion dosages, the hESCs grew to confluency and retained pluripotency as indicated by the expression of OCT4, SSEA3, and SOX2. When the supplemental Mg ion dosages increased to greater than 10 mM, however, hESC colony morphology changed and cell counts decreased. These results suggest that Mg-based implants or scaffolds are promising in combination with hESCs for regenerative medicine applications, providing their degradation rate is moderate. Additionally, the hESC culture system could serve as a standard model for cytocompatibility studies of Mg in vitro, and an identified 10 mM critical dosage of Mg ions could serve as a design guideline for safe degradation of Mg-based implants/scaffolds

RELATIONSHIPS OF TEMPERATURE AND HUMIDITY TO THE BIODEGRADATION OF PETROLEUM HYDROCARBONS IN SOILS

This work focused on monitoring CO2production, microbial growth and residual hydrocarbon concentration during bioremediation experiments performed on laboratory soil microcosms. A natural soil was artificially contaminated with hexadecane and adjusted with inorganic nutrients to stimulate biodegradation. Microbial growth, CO2production and residual hexadecane were periodically monitored at different soil water contents ranging from 0.15 to 0.25 g water g_1 of dry soil and at different temperatures ranging from 20 to 25oC. Results showed that the humidity has a greater effect on microbial activity and contaminant degradation than the temperature. The study established the experimental regression equation of temperature and humidity to the hexadecane mineralization rate, an important parameter in assessing the ability to convert organic carbon into inorganic carbon. The difference between the results of the hexadecane mineralization rate obtained from the experiment and calculated from the regression equation is not too high, from 2% to 20%

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

A propensity score matching analysis of the relationship between forest resources and household welfare in Vietnam

Using secondary data from a socio-economic quantitative household survey in of the North Central region of Vietnam, the main aim of our study is to analyze the causal effect of forest resources on household income and poverty. Based on the observed characteristics of a forest-based livelihood and forest-related activities, we use a propensity score matching (PSM) method to control for potential bias arising from self-selection. The PSM results indicate that households with a forest livelihood had a higher level of income and lower level of poverty than did those without. Interestingly, our findings confirm that a forest-based livelihood offers much higher income than any other type of livelihood adopted by local households. Also, the poverty rate among households with a forest livelihood is lower than those earning non-labor income or engaged in wage/crop and crop livelihoods. Moreover, households whose livelihoods depend on timber forest products (TFPs) and animals (non-TFPs) also had higher income and lower levels of poverty than did those lacking these resources. Among households and provinces, we find differing opportunities deriving from forest resources, suggesting that there are potential barriers hindering local households from pursuing a forest livelihood or participating in some forest activities. Therefore, government policy and regulations on forest management should focus on improving the access of households to forest resources, at the same time enhancing the sustainability of these resources

Surveying entrepreneurial readiness of Business Administration students - A case study in the University of Labour & Social Affairs (ULSA)

This research is conducted to explore the entrepreneurial readiness, along with its related factors, of business administration students from ULSA. Based on the Theory of Planned Behavior by Ajzen, I., (1991), the researchers used the survey focusing on the influence of four factors: (1) the Entrepreneurial ability of students; (2) Motives/ Goals for students' entrepreneurship; (3) The impact of society on student entrepreneurship; (4) The impact of activities to support student entrepreneurship on "entrepreneurial readiness of students majoring in Business Administration at ULSA". The yielded results show that most factors have an average impact of 3/5 or more. Regarding the average impact, “Motives/ Goals for students’ entrepreneurship” has the highest rate of 4,06; followed by “The impact of society on student entrepreneurship” at 3,72; “The impact of activities to support student entrepreneurship” at 3,35; “Entrepreneurial ability of students” at 3,29

A propensity score matching analysis of the relationship between forest resources and household welfare in Vietnam

Using secondary data from a socio-economic quantitative household survey in of the North Central region of Vietnam, the main aim of our study is to analyze the causal effect of forest resources on household income and poverty. Based on the observed characteristics of a forest-based livelihood and forest-related activities, we use a propensity score matching (PSM) method to control for potential bias arising from self-selection. The PSM results indicate that households with a forest livelihood had a higher level of income and lower level of poverty than did those without. Interestingly, our findings confirm that a forest-based livelihood offers much higher income than any other type of livelihood adopted by local households. Also, the poverty rate among households with a forest livelihood is lower than those earning non-labor income or engaged in wage/crop and crop livelihoods. Moreover, households whose livelihoods depend on timber forest products (TFPs) and animals (non-TFPs) also had higher income and lower levels of poverty than did those lacking these resources. Among households and provinces, we find differing opportunities deriving from forest resources, suggesting that there are potential barriers hindering local households from pursuing a forest livelihood or participating in some forest activities. Therefore, government policy and regulations on forest management should focus on improving the access of households to forest resources, at the same time enhancing the sustainability of these resources

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

SEASONAL VARIATION OF PHYTOPLANKTON FUNCTIONAL GROUPS IN TUYEN LAM RESERVOIR, CENTRAL HIGHLANDS, VIETNAM

Seasonal changes in freshwater phytoplankton assemblages at Tuyen Lam Reservoir in the Central Highlands of Vietnam were classified into 23 functional groups based on physiological, morphological, and ecological characteristics. A total of 168 species were recorded during 10 surveys from 2015 to 2019 at 7 sampling sites, with Chlorophyta dominating in number of species. Phytoplankton abundance varied from 0.18×105 to 21.2×105 cells/L during the study period, mainly due to cyanobacteria. Seven of the 23 functional groups were considered to be dominant (relative density > 5%).  The dominant functional groups were groups M and G in the dry season and groups M, G, P, and E in the rainy season. Group M (Microcystis aeruginosa) was the most common in both seasons, while group P (Closterium, Staurastrum, Aulacoseira), group E (Dinobryon, Synura), and group G (Sphaerocystis, Eudorina) were more common in the rainy season. The Shannon diversity index (H¢) showed that phytoplankton communities were relatively diverse and that most of the study sites were lightly polluted. However, the ecological status has deteriorated at some locations due to the overgrowth of group M, leading to eutrophication in this reservoir. This study highlights the usefulness of functional groups in the study of seasonal changes in phytoplankton dynamics. Functional groups are applied for the first time at Tuyen Lam Reservoir and can be used to predict early-stage cyanobacterial blooms in future studies

Solitary wave collisions in the regularized long wave equation

Abstract. The regularized long-wave equation admits families of positive and negative solitary waves. Interactions of these waves are studied, and it is found that interactions of pairs of positive and pairs of negative solitary waves feature the same phase shift asymptotically as the wave velocities grow large as long as the same amplitude ratio is maintained. The collision of a positive with a negative wave leads to a host of phenomena, including resonance, annihilation and creation of secondary waves. A sharp criterion on the resonance for positive-negative interactions is found

LEGION: Harnessing Pre-trained Language Models for GitHub Topic Recommendations with Distribution-Balance Loss

Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance the discoverability of the repository networks, i.e., groups of similar repositories, GitHub introduced repository topics in 2017 that enable users to more easily explore relevant projects by type, technology, and more. It is thus crucial to accurately assign topics for each GitHub repository. Current methods for automatic topic recommendation rely heavily on TF-IDF for encoding textual data, presenting challenges in understanding semantic nuances. This paper addresses the limitations of existing techniques by proposing Legion, a novel approach that leverages Pre-trained Language Models (PTMs) for recommending topics for GitHub repositories. The key novelty of Legion is three-fold. First, Legion leverages the extensive capabilities of PTMs in language understanding to capture contextual information and semantic meaning in GitHub repositories. Second, Legion overcomes the challenge of long-tailed distribution, which results in a bias toward popular topics in PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the PTMs. Third, Legion employs a filter to eliminate vague recommendations, thereby improving the precision of PTMs. Our empirical evaluation on a benchmark dataset of real-world GitHub repositories shows that Legion can improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also can suggest GitHub topics more precisely and effectively than the state-of-the-art baseline with an average improvement of 20% and 5% in terms of Precision and F1-score, respectively.Comment: Accepted to EASE'2