Age and total and free prostate-specific antigen levels for predicting prostate volume in patients with benign prostatic hyperplasia

Objectives: To investigate the predictive values of free prostate-specific antigen (fPSA), total PSA (tPSA) and age on the prostate volume.

Methods: The data of 2148 patients with lower urinary tract symptoms were analyzed retrospectively. The patients who had transrectal ultrasonography guided 10 core biopsies owing to the findings obtained on digital rectal examination and presence of high PSA levels (PSA?=?2.5–10?ng/dl), and proven to have BPH histopathologically were included in the study. Age, tPSA, fPSA and the prostate volumes (PV) of the patients were noted.

Results: One thousand patients that fulfilled the inclusion criteria were included in the study. The PV of the patients were significantly correlated with age, tPSA and fPSA (p?r?=?0.307, p?r?=?0.382, p?r?=?0.296, respectively). On linear regression model, fPSA was found as a stronger predictive for PV (AUC?=?0.75, p?p?p?=?0.013).

Conclusions: Although tPSA is an important prognostic factor for predicting PV, the predictive value of fPSA is higher. PV can easily be predicted by using age, and serum tPSA and fPSA levels.     

Development Disparities and Peculiarities: An Empirical Investigation

The vast, sprawling and densely populated province of Punjab, while predominantly agricultural, is marked by considerable regional disparities. An attempt has been made in this paper to see to what extent the differences in the human and social development in its various districts is there. The study exhibits that those regions which are ranked as lagging districts have high disparities in terms of human development and social development with high urban and rural poverty estimates. It is clearly revealed that aggregate national figures mask vast sub regional disparities and speedy development in some regions may not necessarily elevate lagging regions.     

NPO,Internal Controls,and Supervision Mechanisms in a Developing Country

The premise of this paper is that effective supervision and proper internal control system (ICS) can promote accountability and transparency, and this will attract more donors. However, in a developing country like Malaysia, it is common that proper accounting standards are not available, the laws are not enforced, and charities often struggle for survival. Debates on regulating nonprofit organizations (NPOs) have been sensitive to many and are often left unresolved, and some quarters believe that NPOs should be left to handle their own affairs. This paper provides evidence from a survey of 60 charities on the status of their ICS. It briefly describes the current state of regulations in Malaysia and makes a case for better monitoring of the sector, as this might be the reason NPOs are lacking in their ICS. We propose what type of regulation and assistance can be provided by the authorities to this sector, not only in Malaysia but also in other developing countries facing similar challenges.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.     

Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

Uniform in bandwidth consistency for various kernel estimators involving functional data

The paper investigates various nonparametric models including regression, conditional distribution, conditional density and conditional hazard function, when the covariates are infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic results for kernel estimators of these functional operators. Then, the application issues, involving data-driven bandwidth selection, are discussed.     

Asymptotic expansions for sequential confidence levels in inverse linear regression

Non-linear renewal theory is used to derive second order asymptotic expansions for the coverage probability of a fixed-width sequential confidence interval for an unknown parameter xin the inverse linear regression model. These expansions are obtained for a two-stage sequential procedure, proposed by Perng and Tong (1974) for the construction of a confidence interval for x.     

Some tests for the power series distibutions in one parameter using the kullback-leibler information measure

The purpose of the article is, in case of one sample, to obtain tests concerning the parameter in the power series distribution in one parameter using Ku11back-Leibier information measure. The class of power series distibutions contains a host of discrete distributions. Ve illustrate the general results obtained in case of the geometric distibution.     

Education,identity, and ideology: the Islamic movement and Moslem religious education in Israel

The Islamic Movement, which is called in Arabic Al-harakaat al-islamiyya or Al-haraka al-islamiyya, has, since its foundation in the 1970s, placed emphasis on education, especially the dissemination of the Islamic message. After the movement scored significant successes in local authority elections, its influence increased on the ideological guidelines according to which some of the Arab education system is partially or fully shaped. The article discusses the split in the movement within the State of Israel, and the differences between the southern and northern faction. It also compares Islamic education and Arab education within Israel and abroad in Europe, in countries which have large immigrant Moslem populations.

The education system that the Islamic Movement tries to develop symbolizes the complexity of the relations between it and the state authorities. They are aware that the authorities will not help in differentiation and separation and will not cease from the constant supervision of the movement's educational institutions. Therefore, their choice of a synthesis between formal and informal education or of a partition between pedagogic state education and moral study classes, is a rational, calculated choice, taking into consideration the reality of a cultural – ethnic – national minority.