Relationship Between Socio-Economic Values and Wellbeing: An Overview Research in Asia

This paper investigates ten Asian nations to consider how socio-economic values affect happiness and satisfaction. Moreover, it considers whether economic factors can strongly affect wellbeing under certain conditions. Males in Asia are said they have more opportunities to obtain higher happiness and satisfaction but it does not happen in the current study. Unemployment has negative and significant impact with regard to happiness and satisfaction in developing countries yet it does not have the same effect in developed nations. It is believed cultural value positively affects happiness and satisfaction in East Asia but the result in the models is different with no clear relationship between this variable and wellbeing. Furthermore, some people declare that a relative increase of income compared to the lowest group will lead them to lower life satisfaction.     

Exploring Parameter Relations for Multi‐Stage Models in Stage‐Wise Constant and Time Dependent Hazard Rates

Single cohort stage‐frequency data are considered when assessing the stage reached by individuals through destructive sampling. For this type of data, when all hazard rates are assumed constant and equal, Laplace transform methods have been applied in the past to estimate the parameters in each stage‐duration distribution and the overall hazard rates. If hazard rates are not all equal, estimating stage‐duration parameters using Laplace transform methods becomes complex. In this paper, two new models are proposed to estimate stage‐dependent maturation parameters using Laplace transform methods where non‐trivial hazard rates apply. The first model encompasses hazard rates that are constant within each stage but vary between stages. The second model encompasses time‐dependent hazard rates within stages. Moreover, this paper introduces a method for estimating the hazard rate in each stage for the stage‐wise constant hazard rates model. This work presents methods that could be used in specific types of laboratory studies, but the main motivation is to explore the relationships between stage maturation parameters that, in future work, could be exploited in applying Bayesian approaches. The application of the methodology in each model is evaluated using simulated data in order to illustrate the structure of these models.     

Semiparametric Estimation of a Change-point for Recurrent Events Data

One particular recurrent events data scenario involves patients experiencing events according to a common intensity rate, and then a treatment may be applied. The treatment might be effective for a limited amount of time, so that the intensity rate would be expected to change abruptly when the effect of the treatment wears out. In particular, we allow models for the intensity rate, post-treatment, to be at first decreasing and then change to increasing (and vice versa). Two estimators of the location of this change are proposed.     

A survey of time series analysis through parametric models

The paper is a survey of recent works on time series analysis using parametric models. The main emphasis is on linear models, in particular the ARMA model. Usual me¬thods of parameter estimation, goodness of fit tests and the choice of model order are con¬sidered. Some extensions of the methods to related problems are briefly discussed     

Penalized spline joint models for longitudinal and time-to-event data

The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models.     

Sustainable landscaping programs in the United States and their potential to encourage conservation and support ecosystem services

Sustainable landscaping programs are voluntary initiatives that recommend a set of practices to improve the environmental quality of urban ecosystems by providing landscaping materials, guidelines, and educational resources. Our goal was to examine the recommendations and outcomes of these programs in the United States and their value for encouraging conservation practices in residential ecosystems. We conducted a comprehensive web search and identified 193 sustainable landscaping programs in the United States. Programs aim to increase native species richness, affordably manage stormwater runoff, and offer residents meaningful experiences with nature. Sustainable landscaping programs present many opportunities to engage people with local ecological knowledge and conservation practices, provide accessible spaces for environmental education, cultivate interdisciplinary research collaborations, and advance inclusion in conservation. However, assessments of their ecological value are rare, leaving many questions surrounding the benefits to biodiversity and water quality afforded by participation. Many programs also require investments in landscaping materials and certification fees that might limit participation by some households. Future work should examine how recommended practices influence urban biodiversity, identify and address barriers to participation, and generate social-ecological knowledge that can inform future programs.

     

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.     

“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.     

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.     

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.