Uptake of Chloroform by Skin During Short Exposures to Contaminated Water

Uptake of chloroform into hairless rat stratum corneum from dilute aqueous solutions was studied using tape-stripping to determine amounts deposited in the skin under various environmental exposure scenarios. The length of exposure of sedated animals to the chloroform-containing medium, the frequency and duration of tape-stripping, and the number of tape-strips per location were varied to map the stratum corneum substantivity of chloroform. Eight minutes immersion of the rat within a well-stirred solution at 36°C was found to be adequate time for the gradient to be established fully across the stratum corneum. Penetration was progressively deeper as the exposure time increased. Substantial evaporative loss of chloroform from the aqueous medium of application seem to be responsible for lower cumulative amounts taken up when the same solution was held on the rat's skin within a stainless steel template of fixed area. Of the total uptake (29 mg) from a dilute stirred solution of chloroform (0.44 mg/ml) at 36°C, about 95% was systematically absorbed after a 30 min exposure as determined by residuals (measurement of bath concentrations).     

Socio‐cultural Adaptation of Second‐generation Afghans in Iran

The long‐term settlement of Afghan immigrants in Iran, along with their high fertility, has produced an important shift in the composition of their population with the emergence of a “second generation”. This article aims to examine how second‐generation Afghans have adapted to the host society and to what extent their adaptation patterns have correlated with demographic and contextual factors. The data is drawn from the 2010 Afghans Adaptation Survey which covered 520 second‐generation Afghans. Results revealed that second‐generation Afghans have a variety of adaptation patterns. Integration is the most prevalent pattern of adaptation and acculturation (which is observed among 35.8 per cent of respondents) followed by separation (33.3%), assimilation (17.1%) and marginalization (13.8%). Our multivariate analysis showed that such socio‐demographic factors as gender, education, ethnicity, perceived discrimination, family context, neighbourhood characteristics, length and city of residence are associated with their adaptation patterns.

Policy Implications

• Successful implementation of policies and durable solutions for Afghans in Iran rests on the diversity of the adaptation patterns of their second‐generation.
• Restriction on employment opportunities has led to downward assimilation and marginalization of some of the Afghans in Iran. Improvement in labour laws would promote the integration of Afghans in the society.
• Afghan females have relatively better access to a gender‐equitable environment in Iran than they do in Afghanistan, and are less willing to return to their homeland. The Government of Afghanistan should improve service and security provisions for women to ensure their voluntary repatriation.

     

Mixture Model Analysis of Partially Rank‐Ordered Set Samples: Age Groups of Fish from Length‐Frequency Data

We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank‐ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a simple random sample of fish and creates partially rank‐ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional expectation–maximization algorithm is not directly applicable for these observations. We propose a suitable expectation–maximization algorithm to estimate the parameters of the FMMs based on PROS samples. We also study the problem of classification of the PROS sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS samples perform substantially better than their simple random sample counterparts even with small samples. The results are used to classify a fish population using the length‐frequency data.     

What Level of Statistical Model Should We Use in Small Area Estimation?

If unit‐level data are available, small area estimation (SAE) is usually based on models formulated at the unit level, but they are ultimately used to produce estimates at the area level and thus involve area‐level inferences. This paper investigates the circumstances under which using an area‐level model may be more effective. Linear mixed models (LMMs) fitted using different levels of data are applied in SAE to calculate synthetic estimators and empirical best linear unbiased predictors (EBLUPs). The performance of area‐level models is compared with unit‐level models when both individual and aggregate data are available. A key factor is whether there are substantial contextual effects. Ignoring these effects in unit‐level working models can cause biased estimates of regression parameters. The contextual effects can be automatically accounted for in the area‐level models. Using synthetic and EBLUP techniques, small area estimates based on different levels of LMMs are investigated in this paper by means of a simulation study.     

Block censoring scheme with two-parameter exponential distribution

This paper deals with a new censoring scheme, called ‘Block Censoring’ which reduces considerably the total time on test in the life testing experiments with respect to the common used experimental tests such as rightly censored data. This new scheme is analysed when the lifetimes of products follow the two-parameter exponential distribution. Specially, it is proved that the respective spacings are independently distributed exponential. The problem of estimating parameters is investigated in details.A Monte Carlo simulation is conducted for obtaining the optimal block censoring scheme in the sense of the shortest expected test time. Finally, a real data set on times to breakdown of an insulating fluid between electrodes from Nelson [Applied life data analysis. New York: Wiley; 1982. p.105] is analysed.     

Robust surface estimation in multi-response multistage statistical optimization problems

As the ordinary least squares (OLS) method is very sensitive to outliers as well as to correlated responses, a robust coefficient estimation method is proposed in this paper for multi-response surfaces in multistage processes based on M-estimators. In this approach, experimental designs are used in which the intermediate response variables may act as covariates in the next stages. The performances of both the ordinary multivariate OLS and the proposed robust multi-response surface approach are analyzed and compared through extensive simulation experiments. Sum of the squared errors in estimating the regression coefficients reveals the efficiency of the proposed robust approach.