Students‘ perceptions of professional social work ethics and the need for the development of competence-based ethics standards in undergraduate training program in Vietnam

As a newly recognized profession in Vietnam over the last two decades social work has undergone significant development, both in training and practice. However, there are many challenges in its professionalization, in which ethics plays a key role. This article presents the key findings of a study about students‘ perceptions of ethics and developing competence-based ethical standards in social work programs. It first analyses the students‘ attitudes and understanding of professional ethics. Second, it clarifies the situation of ethics education in training institutions. Finally, it provides suggestions for the implementation of social work competence-based ethical standards for Vietnam and beyond.     

Impacts of off‐farm employment on welfare,food security and poverty: Evidence from rural Vietnam

How off‐farm employment can enhance welfare in terms of food consumption and poverty alleviation is a critical question facing many developing countries. This study addressed that question by pursuing two objectives: (i) to quantify the impact of off‐farm employment on rural households’ welfare, food security and poverty; and (ii) to examine the factors that affect their decision to work off‐farm. Using panel data, we estimated a difference‐in‐difference combined with a propensity score matching model. The findings show that off‐farm employment improves income, ensures food security and contributes to poverty alleviation. The results also show that age, marital status, education, labour, financial capital, land, location, market access and losses from natural disasters are significant contributing factors to the decision to participate in off‐farm employment. The findings suggest that to improve the welfare of rural households, the Vietnamese government should proceed with policies that enhance their opportunities for participation in off‐farm employment.     

Bound and exact methods for assessing link vulnerability in complex networks

Assessing network systems for failures is critical to mitigate the risk and develop proactive responses. In this paper, we investigate devastating consequences of link failures in networks. We propose an exact algorithm and a spectral lower-bound on the minimum number of removed links to incur a significant level of disruption. Our exact solution can identify optimal solutions in both uniform and weighted networks through solving a well-constructed mixed integer program. Also, our spectral lower-bound derives from the Laplacian eigenvalues an estimation on the vulnerability of large networks that are intractable for exact methods. Through experiments on both synthetic and real-world networks, we demonstrate the efficiency of the proposed methods.     

The globalization of production,national labour regulations and income inequality in the global North and South, 1980–2013

A central concern over global value chains (GVCs) is whether the integration of national firms into GVCs exacerbates income inequality within countries. However, despite decades of research, the distributional consequences of GVCs remain unclear in the empirical literature. Drawing on panel data from 96 countries between 1980 and 2013, we examine the effects of GVC integration on market income inequality and whether national labour regulations moderate these effects. We find integration increases inequality in the global North and South. More importantly, we find labour regulations amplify the inequality effects of integration in Southern countries by expanding the size of the informal sector while suppressing these effects in Northern countries by promoting unionization. This suggests institutional power from national labour regulations may enhance the bargaining power of labour in the North through increasing collective resources while disempowering labour in the South through reinforcing labour market segmentation between formal and informal sectors.     

“Susie Bubble is a Sign of The Times”

This essay takes as its point of critical departure the digital stardom of Susanna Lau, aka Susie Bubble, the most recognizable and respected fashion blogger in the world, in order to consider the historical formation of success in the postmillennial digital economy. By examining Lau as an embodied sign rather than an exceptional figure of success, I am concerned with the technical, cultural, and economic forces that give shape to hegemonic notions of success and the ideal subject it produces. More specifically, I hope to demonstrate the cultural frames that structure our ways of seeing and recognizing successful individuals in the post millennium by highlighting the gendered and racial construction of the ideal Web 2.0 subject. The aim of my discussion is to show that the discursive construction of Lau's success is reflective of emerging global patterns born out of the rising significance of Asians and young women (especially young Asian women) as consumers and producers in the digital economy.     

Maximizing k-submodular functions under budget constraint: applications and streaming algorithms

Journal of Combinatorial Optimization - Motivated by the practical applications in solving plenty of important combinatorial optimization problems, this paper investigates the Budgeted k-Submodular...     

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.     

A Combined D.C. Optimization—Ellipsoidal Branch-and-Bound Algorithm for Solving Nonconvex Quadratic Programming Problems

In this paper we propose a new branch-and-bound algorithm by using an ellipsoidal partition for minimizing an indefinite quadratic function over a bounded polyhedral convex set which is not necessarily given explicitly by a system of linear inequalities and/or equalities. It is required that for this set there exists an efficient algorithm to verify whether a point is feasible, and to find a violated constraint if this point is not feasible. The algorithm is based upon the fact that the problem of minimizing an indefinite quadratic form over an ellipsoid can be efficiently solved by some available (polynomial and nonpolynomial time) algorithms. In particular, the d.c. (difference of convex functions) algorithm (DCA) with restarting procedure recently introduced by Pham Dinh Tao and L.T. Hoai An is applied to globally solving this problem. DCA is also used for locally solving the nonconvex quadratic program. It is restarted with current best feasible points in the branch-and-bound scheme, and improved them in its turn. The combined DCA-ellipsoidal branch-and-bound algorithm then enhances the convergence: it reduces considerably the upper bound and thereby a lot of ellipsoids can be eliminated from further consideration. Several numerical experiments are given.     

A Note on Optical Network with Nonsplitting Nodes

We study the problem of computing the minimum total weight multicast route in an optical network with both nonsplitting and splitting nodes, and present a simple approximation with performance ratio 3, which is better than existing one in the literature.