Sustainable management of a marine protected area in Vietnam: An application of visitors’ willingness to pay for conservation in Cu Lao Cham

As an important area in terms of biodiversity and fish stocks in Vietnam, Cu Lao Cham or Cham Islands (CLC) marine protected area (MPA) is under increasing threat from human activities. Conservation efforts have recently been implemented but economic analysis of the CLC MPA’s natural values did not investigate what may affect policy decisions on CLC MPA’s sustainable development. Using a structured questionnaire and a payment card technique (based on the contingent valuation method), we estimate visitors’ willingness to pay (WTP) for conservation of marine resources in the CLC MPA. The log-normal regression results indicate that the visitors’ WTP for conservation is affected by their characteristics, perceptions of environmental protection and conservation, and their assessment of the current CLC MPA entrance fee. Visitors are WTP an additional amount of US$2.26 per person for conservation while visiting the CLC MPA. This indicates that with the introduction of the proposed entrance fee (the mean WTP added to the current entrance fee), the CLC MPA management would not only have sufficient funds for their management and conservation programmes, but also that the number of visitors may decline slightly, which would reduce some of the pressure on the environment, natural resources, and coral reefs.

Influence Diagnostics in Log-Normal Regression Model with Censored Data

Dealing with the biological data, the skewed distribution is approximated by the Log-Normal Regression model (LNRM). Traditional estimation techniques for the LNRM are sensitive to unusual observations. These observations greatly affect the model analysis, which makes imprecise conclusions. To overcome this issue, we proposed to develop diagnostics measures based on local influence diagnostics to identify such curious observations in the LNRM under censoring. The proposed measures are derived by perturbing the case weight, response, and explanatory variables. Furthermore, we also consider the One-Step Newton-Raphson method and generalized cook’s distance. We study the Monte Carlo simulation and its application to real data to illustrate the developed approaches.

Mental health prevalence and predictors among university students in nine countries during the COVID-19 pandemic: a cross-national study

The student population has been highly vulnerable to the risk of mental health deterioration during the coronavirus disease (COVID-19) pandemic. This study aimed to reveal the prevalence and predictors of mental health among students in Poland, Slovenia, Czechia, Ukraine, Russia, Germany, Turkey, Israel, and Colombia in a socioeconomic context during the COVID-19 pandemic. The study was conducted among 2349 students (69% women) from May–July 2020. Data were collected by means of the Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-8), Perceived Stress Scale (PSS-10), Gender Inequality Index (GII), Standard & Poor's Global Ratings, the Oxford COVID-19 Government Response Tracker (OxCGRT), and a sociodemographic survey. Descriptive statistics and Bayesian multilevel skew-normal regression analyses were conducted. The prevalence of high stress, depression, and generalized anxiety symptoms in the total sample was 61.30%, 40.3%, and 30%, respectively. The multilevel Bayesian model showed that female sex was a credible predictor of PSS-10, GAD-7, and PHQ-8 scores. In addition, place of residence (town) and educational level (first-cycle studies) were risk factors for the PHQ-8. This study showed that mental health issues are alarming in the student population. Regular psychological support should be provided to students by universities.

The Importance of Country-of-Origin Information on Food Product Packaging

This study aimed to identify selected predictors of country-of-origin (COO) information placed on food packaging. The dependent variable was operationalized in two ways: (1) as a Likert-style question about COO importance in general, and (2) as an indication of COO as the most important food attribute at first purchase, which I called top-of-mind COO importance. The survey was conducted with the use of the internet panel of a research agency in a representative sample of 1051 Polish consumers. In bivariate analyses, I identified the characteristics of consumer segments attaching high importance to each type of COO information. In a multivariate log-normal regression, general COO importance was affected to the largest extent by the product originating from Poland, which confirmed the strong relation between COO importance and consumer ethnocentrism. In multivariate logit regressions, top-of-mind COO importance depended also on the Polish origin of the product to the largest extent. The remaining predictors were sex (men were over 1.5 times more likely to indicate COO as the most important attribute) and age (each year of life contributing to a 2% increase in the likelihood of indicating top-of-mind COO). A theoretical implication is to differentiate between general and top-of-mind COO measures, as different results were obtained depending on whether the COO effect was measured as a response to questions such as “How important is the product COO for you?” or “What is the most important product attribute for you?—COO” Not only were the answer patterns different, but their determinants also varied.

Arsenic, Lead, Cadmium, Mercury and Hypertension among Asian Populations in the United States: NHANES 2011-2016

Background:Asian Americans have been reported having higher arsenic (As), lead (Pb), cadmium (Cd) and mercury (Hg) levels, comparing with non-Asian Americans. Exposure to these metals/metalloids have been associated with risk of hypertension, however, limited studies have explored the associations of As, Pb, Cd and Hg levels with hypertension among general Asian Americans. This study aimed to investigate the associations of As, Pb, Cd and Hg levels with blood pressure and prevalence of hypertension among Asian Americans who participated in the 2011-2016 National Health and Nutrition and Examination Survey (NHANES).Methods:The study included participants aged 20 years and older with determinations of As, Pb, Cd and Hg in blood (n=7913) and urine (n=3954). These metals levels were measured by inductively coupled plasma mass spectrometry. Systolic (SBP) and diastolic blood pressure (DBP) levels were examined through a standardized protocol. Censored normal regression model and logistic regression model were employed to explore the associations of As, Pb, Cd and Hg levels with blood pressure levels and hypertension respectively, and potential confounders were also adjusted in these regression models.Results:In multivariable-adjusted models, blood Hg levels were associated with increased SBP (tertile 2 vs. tertile 1: β=-0.9 mmHg, 95%CI:-6.1, 4.4; tertile 3 vs. tertile 1: β=5.6, 95%CI: 0.0, 11.2; P trend=0.003), DBP level (tertile 2 vs. tertile 1: β=4.1 mmHg, 95% CI: 0.1, 8.1; tertile 3 vs. tertile 1: β=5.8 mmHg, 95% CI: 1.9, 9.7; P trend=0.003) and prevalence of hypertension (tertile 2 vs. tertile 1: OR=1.2, 95% CI: 0.4, 3.4; tertile 3 vs. tertile 1: OR=2.8, 95% CI: 1.1, 7.3; P trend=0.001) among Asian Americans. The positive associations were also found between blood methyl-Hg level and blood pressure levels and prevalence of hypertension, whereas we did not identify significant associations of As, Pb and Cd levels in blood and urine with blood pressure levels and hypertension among Asian Americans. Conclusions:Highly exposed to Hg level among Asian Americans might associated with increased blood pressure levels and prevalence of hypertension. Further prospective studies with larger sample size should be performed to warrant the results.

Sedimentology and Stratigraphy of an Upper Permian Sedimentary Succession: Northern Sydney Basin, Southeastern Australia

This study integrates sedimentological and stratigraphic insights into the Upper Permian sedimentary rocks of the Wittingham, Tomago and Newcastle Coal Measures in the Northern Sydney Basin, Australia. Facies analysis documented fifteen facies that belong to seven facies associations. These facies associations correspond to different depositional environments and sub-environments including prodelta, delta-front, upper, lower delta-plain and fluvial. The stratigraphic development points to a shallowing upward trend and is reflected with fluvial deposits sitting on top of the deltaic deposits. The fluvio-deltaic contact is represented by an unconformity and displays an upward increase in sediment caliber. The delta front is mainly controlled by wave, storms- and/or river currents, even though the contribution of tides also occurs in the form of sedimentary structures that suggest tidal influence. In contrast, prodelta and delta-plain are significantly modulated by tidal currents. The impact of tides in the delta plain is fading away upward and therefore, the upper delta plain is much less impacted compared to the lower delta plain. The low abundance of wave ripples suggests that the wave action was not very important in the delta plain. Steep topographic gradients and increased sediment input are suggested, based on the limited or absent evidence of tides in the fluvial realm, related to the growing New England Orogen. In sequence stratigraphic terms, the deltaic system accumulated during highstand normal regression, while the deposition of the overlying fluvial system occurred during lowstand normal regression. The two systems are separated by a subaerial unconformity developed during an intervening forced regression. Short periods of transgression are inferred from the presence of higher frequency cycles in the delta-front.

Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm

Finite mixtures normal regression (FMNR) models are widely used to investigate the relationship between a response variable and a set of explanatory variables from several unknown latent homogeneous groups. However, the classical EM algorithm and Gibbs sampling to deal with this model have several weak points. In this paper, a non-iterative sampling algorithm for fitting FMNR model is proposed from a Bayesian perspective. The procedure can generate independently and identically distributed samples from the posterior distributions of the parameters and produce more reliable estimations than the EM algorithm and Gibbs sampling. Simulation studies are conducted to illustrate the performance of the algorithm with supporting results. Finally, a real data is analyzed to show the usefulness of the methodology.

On the Regression Model for Generalized Normal Distributions

The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is often ineffective. This drawback can be overcome by using a generalized normal regression model that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in modeling data with a highly skewed response. Furthermore, we study in some details the structural properties of the proposed generalizations of the normal distribution. The maximum likelihood method is used for estimating the parameters of the proposed method. The performance of the maximum likelihood estimators in estimating the distributional parameters is assessed through a small simulation study. Applications to two real datasets are given to illustrate the flexibility and the usefulness of the proposed distributions and their regression models.