Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

A restrictive relative clause (RRC hereafter), which is also known as a defining relative clause, gives essential information about a noun that comes before it: without this clause the sentence wouldn’t make much sense. A RRC can be introduced by that, which, whose, who, or whom. Givon (1993, 1995), Fox (1987), Fox and Thompson (1990) state that a RCC is used for two main functions: grounding and description. When a RRC serves the function of linking the current referent to the preceding utterance in the discourse, it does a grounding function; and when the information coded in a RRC is associated with the prior proposition frame, the RRC does a proposition-linking grounding function. Furthermore, when a RRC is not used to ground a new di

Objective: The objective of the study was to test the hypothesis, that umbilical cord blood crude serum applied topically with its high concentration of growth factors may promote an early healing for animal models with burn injury.

Methods:  Fresh human umbilical cord blood UCBS was collected and screened for transmitted diseases such as hepatitis B, hepatitis C and HIV. Mice and rabbits were scalded by boiling water and chemical burning by NaOH, then treated with UCBS. In comparison with a Cetrimide treated group and a control group (without treatment). The UCBS was subjected to microbial testing to demonstrate the presence or absence of extraneous viable contaminating microorganisms.

In this paper, an efficient method for compressing color image is presented. It allows progressive transmission and zooming of the image without need to extra storage. The proposed method is going to be accomplished using cubic Bezier surface (CBI) representation on wide area of images in order to prune the image component that shows large scale variation. Then, the produced cubic Bezier surface is subtracted from the image signal to get the residue component. Then, bi-orthogonal wavelet transform is applied to decompose the residue component. Both scalar quantization and quad tree coding steps are applied on the produced wavelet sub bands. Finally, adaptive shift coding is applied to handle the remaining statistical redundancy and attain e

The process of granting loans by banks is the confidence they give to their customers, but this trust should not be a cornerstone in granting loans even if granted these loans on the basis of sound banking should involve risks that may be exposed to the bank because of the failure of the client to meet The bank's financial obligations to the bank due to the unexpected economic conditions affecting the customers, which makes them in a state of faltering, which weaken the ability of banks to provide loans, which are the most important sources of revenue and profits, so the problem of non-performing loans is one of the main problems facing most of the banks Which impede the functioning of its work and the reasons that led to the agg

Abstract The wavelet shrink estimator is an attractive technique when estimating the nonparametric regression functions, but it is very sensitive in the case of a correlation in errors. In this research, a polynomial model of low degree was used for the purpose of addressing the boundary problem in the wavelet reduction in addition to using flexible threshold values in the case of Correlation in errors as it deals with those transactions at each level separately, unlike the comprehensive threshold values that deal with all levels simultaneously, as (Visushrink) methods, (False Discovery Rate) method, (Improvement Thresholding) and (Sureshrink method), as the study was conducted on real monthly data represented in the rates of theft crimes f

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback