EMPLOYMENT AT TAN AN ISLET, CA MAU PROVINCE, VIETNAM

Tan An, an islet in the Ca Mau province, is located on the historic Hon Khoai island, which serves as the nation's southernmost outpost. The economic condition of the residents there is quite poor, with employment being the most urgent issue. Because this location is crucial for national marine strategies, the paper hopes to contribute to providing the necessary knowledge for those who propose socio-economic development plans for the local economy and national defense.  Article visualizations

THE EFFECT OF ECOLOGICAL FACTORS ON GROWTH OF Sterculia foetida L. IN BUON DON AND EA SUP DISTRICTS, DAK LAK PROVINCE

Accurate and reliable assessment of site suitability for the growth of selected plant species is critical for afforestation projects and programs to improve rural livelihoods. This research examines the relationship between growth of Sterculia foetida L. and six ecological factors in Buon Don and Ea Sup districts, Dak Lak Province. The purpose of the research is to predict the growth in average height and trunk diameter of the trees using ecological factors that are easy to measure and observe. These include topography (slope) and soil characteristics (clay ratio, depth of soil layer, surface and subsurface rock concentrations, and agglomeration ratio). The ecological factors were evaluated by multivariable regression analysis of growth data from 31 experimental plots, 16 in Buon Don district and 15 in Ea Sup district. The results show that four of the six factors affect average growth in tree height and that five factors affect average trunk diameter growth. The findings are of practical value for households and agricultural extension officers to consider before planting Sterculia foetida L. in the two areas

Cash holding, state ownership and firm value: The case of Vietnam

Using a sample of 650 listed firms on the Vietnamese stock exchange over the period 2008-2015, we examine the effect of cash holding level on firm value. The results find out the cash holding has an impact on firm value in an inverted U-shaped form. Furthermore, this study investigates whether the state ownership influences firm value. We point out that there is a statistically insignificant positive relationship between state ownership and firm value unless the state ownership’s advantages are utilized. The findings have implications of cash management in state-owned firms. © 2016, Econjournals. All rights reserved

CLAffinity:A software tool for identification of optimum ligand affinity for competition-based primary screens

[Image: see text] A simplistic assumption in setting up a competition assay is that a low affinity labeled ligand can be more easily displaced from a target protein than a high affinity ligand, which in turn produces a more sensitive assay. An often-cited paper correctly rallies against this assumption and recommends the use of the highest affinity ligand available for experiments aiming to determine competitive inhibitor affinities. However, we have noted this advice being applied incorrectly to competition-based primary screens where the goal is optimum assay sensitivity, enabling a clear yes/no binding determination for even low affinity interactions. The published advice only applies to secondary, confirmatory assays intended for accurate affinity determination of primary screening hits. We demonstrate that using very high affinity ligands in competition-based primary screening can lead to reduced assay sensitivity and, ultimately, the discarding of potentially valuable active compounds. We build on techniques developed in our PyBindingCurve software for a mechanistic understanding of complex biological interaction systems, developing the “CLAffinity tool” for simulating competition experiments using protein, ligand, and inhibitor concentrations common to drug screening campaigns. CLAffinity reveals optimum labeled ligand affinity ranges based on assay parameters, rather than general rules to optimize assay sensitivity. We provide the open source CLAffinity software toolset to carry out assay simulations and a video summarizing key findings to aid in understanding, along with a simple lookup table allowing identification of optimal dynamic ranges for competition-based primary screens. The application of our freely available software and lookup tables will lead to the consistent creation of more performant competition-based primary screens identifying valuable hit compounds, particularly for difficult targets

NEW STOCHASTIC AND RANDOMIZED ALGORITHMS FOR NONCONVEX OPTIMIZATION IN MACHINE LEARNING

The goal of this dissertation is to develop efficient stochastic and randomized first-order methods to solve composite nonconvex problems arising from modern machine learning applications. The content of this dissertation is divided into four main chapters. Firstly, we motivate our research topics by briefly introducing our interested problems and their challenges. We also review necessary mathematical concepts and tools used throughout this dissertation. Our first contribution is in Chapter 2, where we propose ProxSARAH, a new framework that uses a variance reduced stochastic gradient estimator called SARAH, to develop new algorithms for solving the stochastic composite nonconvex problems. Our analysis shows that our methods can achieve the best-known convergence results and even match the lower bound complexity. We also provide extensive numerical experiments to illustrate the advantages of our methods compared to existing ones. Next, we study a policy gradient strategy in reinforcement learning in Chapter 3. We propose a new proximal hybrid stochastic policy gradient algorithm, called ProxHSPGA, using a new policy gradient estimator built from two different estimators. ProxHSPGA makes uses of a newly hybrid stochastic estimator introduced in Tran-Dinh et al. (2019b), and apply it to reinforcement learning. This new algorithm is able to solve the general composite policy optimization problem which includes regularization or constraint on the policy parameters. It also achieves the best-known sample complexity compared to existing methods. Our experiments on both discrete and continuous control tasks show that our proposed methods indeed are advantageous over existing ones. Then, in Chapter 4, we focus on a new machine learning paradigm, called federated learning (FL), where multiple agents collaboratively train a machine learning model in a distributed fashion. We propose two new algorithms, FedDR and asyncFedDR, for solving the nonconvex composite optimization problem which can handle convex regularizers in FL. Our algorithms rely on a novel combination between a nonconvex Douglas-Rachford splitting method, randomized block-coordinate strategies, and asynchronous implementation. Unlike previous primal-dual based method for FL, our algorithms allow not only partial participation at each communication round but also asynchronous updates between agents which greatly improves their practicality. Our convergence analyses show that the new algorithms match the communication complexity lower bound up to a constant factor under standard assumptions. Our numerical experiments illustrate the advantages of our methods compared to existing ones on various datasets. Finally, we summarize our contribution, further discuss some notable points of our results, and outline some ongoing and possible future directions. One of our ongoing works is to develop a class of accelerated Douglas-Rachford splitting algorithms for federated learning.Doctor of Philosoph

Solid Waste Management Practices in the Street Food Sector in Thu Duc District, Ho Chi Minh City

A survey on the solid waste management practices of the street food sector was conducted in Thủ Đức District, Ho Chi Minh City. All the street food vendors in the area were recorded, classified according to the nature of their stalls, and also categorized according to the type of food vended. A survey by interview was conducted with 62 random stalls to determine the solid waste management practices of the street food vendors and their customers. Waste samples from 32 different stalls were collected for weight and volume measurements as well as visual determination of waste composition. About 1158 stalls were recorded in the area, generating about 8.20 - 12.66 tons of wastes per day, roughly 3% – 5% of the total municipal solid waste in the district. Biodegradable waste accounted for about 89% by weight followed by non-biodegradable wastes, and recyclable wastes at 7% and 4% respectively. Reused grocery bags were the most common waste receptacle used by vendors. Segregation is limited to the materials that vendors can reuse or that the informal sector of recyclers buy and is prevalent only in stalls selling beverages, with plastic bottles and metal cans as the most recycled components. The rest of the wastes are commonly wrapped in bags or in burlap sacks for bulky wastes and left on roadsides awaiting collection. Keywords: street food, street food vendors, mobile vendors, hawkers, street food vending, solid waste management, food wast