The relationship between the performance of the economy and the costing of building projects : a case study of school buildings in Egypt

Cost estimating and cost modelling for building projects have attracted the attention of many scholars. Previous research has laid emphasis on the product physical variables and did not explicitly include the economic variables. This study aims at investigating the impact of the performance of the economy on the cost of building projects by explicitly considering the relevant economic indicators in the cost estimating process. The unique attributes of the National Project for Building Schools in Egypt that is running since 1992, provided the opportunity to focus the light on the economic variables due to the standard design applied to thousands of school buildings. The study started by reviewing the current practice in cost estimating for building projects in Egypt seeking to identify the influential cost factors and to further investigate the level of awareness of the impact of the economic changes on the cost of buildings as perceived by the experts. In addition, the study aimed at developing an explanatory cost model illustrating the relationship between the relevant economic indicators and the cost of school buildings in order to quantify the impact of the economic changes on the costing of buildings. This research adopted a mixed methodology in a triangulation approach that was conducted in two stages. A set of 18 interviews with experts from the industry was followed by a survey covering a sample of 400 schools. The results indicated that the quantity surveyor’s method is the prevailing cost estimating technique in Egypt. Practitioners in general, showed a blurred understanding of the fundamentals of economic and did not explicitly consider the economic indicators in the cost estimates for building projects. The cost modelling of the survey data adopted a multiple regression technique and factor analysis. Two sets of Cost Models including 6 economic indicators as independent variables, besides other product variables, were developed. The results indicated that the economic indicators were significant cost variables. Hence, the impact of the economic changes on the cost estimates of buildings can be quantified. The produced models indicated that the cost of school buildings, expressed in real terms, tend to increase during periods of economic recession. The produced model is useful to cost estimators working for government clients as well as contractors, given the rising application of standard design in various sectors within the construction industry in Egypt. Further work is required to gauge this impact across various sectors of the construction industr

Normal testicular tissue elasticity by sonoelastography in correlation with age

AbstractPurposeThis research aimed to study the correlation between the normal testicular tissue elasticity as detected by real time sonoelastography with the age.Materials and methodsThe study was conducted upon 63 adult healthy volunteers. Each volunteer was subjected to real time sonoelastography measuring the strain ratio of testicular tissues and their elasticity scores.ResultStrain ratios of the examined testes ranged from 0.27 to 0.38, with a mean strain of 0.33 and a standard deviation of 0.03. Elasticity score of the examined testes included ES1 in 62 testes (49%), ES2 in 58 testes (46%) and ES3 in 6 testes (5%). No ES4 or ES5 was elicited in this group. High negative correlation is found between the age and both the testicular volume and the strain ratio, while there is no correlation between age and the elasticity score of testes.ConclusionNormal testicular tissues as studies by sonoelastography show strong correlation between the age and the testicular volume as well as the strain ratio

Effect of selenium on nutritive value of purslane (Portulaca oleracea L.)

Purslane (Portulaca oleracea) one of the auxiliary plants was traditionally consumed in many parts of the world for its nutritional and medicinal benefits. The nutrient components of purslane such as total protein, total carbohydrates and mineral content such as macro elements (Na, K, Ca and Mg) and micro elements (Fe, Cu, Pb and Zn) were estimated at different concentrations of selenium which treated in soil where the plant cultivated. The protein and carbohydrate contents of leaves as well as protein of stems increase with increasing the selenium concentration, while protein and carbohydrate of roots as well as carbohydrate of stems decrease with increasing Se concentration. The mineral content was also affected by Se concentration, Fe, Cu and Zn of leaves decreased with increasing Se concentration, while K, Ca, Mg and Na are directly proportional with Se concentration. In stems, Zn only is inversely proportional with Se concentration. In roots, Fe, Cu, Mg and K are inversely proportional with Se concentration, while Na, Ca and Zn are directly proportional. The findings of this study revealed that carbohydrates, protein and mineral contents of purslane can be affected and controlled by selenium concentration. DOI: http://dx.doi.org/10.5281/zenodo.128341

A Proposed Approach for Predicting Liver Disease

One of the main challenges is to exploit recent technologies in a way that is able to preserve human life. Liver disease is one of the most influencing and largest organs of the human body, which has a great impact on human life, according to the massive number of deaths of this disease. So, it is important to predict liver disease with the maximum possible accuracy, as the current problem is the weak accuracy of predicting liver disease and not predicting the severity of the liver disease. Thus, through this paper, the aim behind our proposed work is to enhance the performance of predicting liver disease, predicting the severity of liver disease, and then building a recommender system that recommends the appropriate medical pieces of advice according to the patients condition using machine learning algorithms and tools like a GridsearchCV tool. Indian liver patients dataset (ILPD) and the hepatitis C virus (HCV) dataset are our training datasets. Hence, the proposed solution enhanced the prediction accuracy of liver disease by 80% and 77 % for extra tree and KNN algorithms when using ILPD datasets. And when using the HCV dataset, the accuracy is achieved by the Gradient boosting algorithm and Logistic Regression by 96% for predicting liver disease, disease severity, and patient recommendation system model

Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System

In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities

Pramipexole protective effect on rotenone induced neurotoxicity in mice

Introduction: 
Pramipexole is a new dopaminergic drug which has been approved for PD treatment. However, we tried to find a new capacity for this drug rather than symptomatic effect. 

Materials and Methods: 
A chronic rotenone model with daily oral dose of 30mg/kg was induced in mice. Pramipexole was tried in a new approach where the treatment began in the middle of rotenone course with oral dose 1mg/kg/day of pramipexole. 

Results: 
Further analysis of behavioral tests and immunohistochemistry revealed success of pramipexole in improving the rotenone intoxicated mice. 

Conclusion: 
These results showed possible beneficial effects of pramipexole against rotenone-induced neurotoxicity

The Impact of Disease Registries on Advancing Knowledge and Understanding of Dementia Globally

To help address the increasing challenges related to the provision of dementia care, dementia registries have emerged around the world as important tools to gain insights and a better understanding of the disease process. Dementia registries provide a valuable source of standardized data collected from a large number of patients. This review explores the published research relating to different dementia registries around the world and discusses how these registries have improved our knowledge and understanding of the incidence, prevalence, risk factors, mortality, diagnosis, and management of dementia. A number of the best-known dementia registries with high research output including SveDem, NACC, ReDeGi, CREDOS and PRODEM were selected to study the publication output based on their data, investigate the key findings of these registry-based studies. Registries data contributed to understanding many aspects of the disease including disease prevalence in specific areas, patient characteristics and how they differ in populations, mortality risks, as well as the disease risk factors. Registries data impacted the quality of patients’ lives through determining the best treatment strategy for a patient based on previous patient outcomes. In conclusion, registries have significantly advanced scientific knowledge and understanding of dementia and impacted policy, clinical practice care delivery