The Compliance Chronic Renal Failure Patient on Restrictions Liquids in Hemodialysis Therapy

Introduction: Nonadherence is a rampant problem among patients undergoing dialysis and can impact multiple aspects of patient care, including medications, and treatment regimens as well as dietary and fluid restriction. The purpose of this descriptive correlative research, on hemodyalysa patient with chronic renal failure was to know the influencing factors of compliance patient to fluid restriction. Method: This study used descriptive correlative design, Data was analysed by using distibution frequency and chi square for analysys relation between variable. Result: The result revealed there were nor significant statistic difference at p > 0.05 between age, gender, education level, frequency of hemodyalysa and health education from nurse to compliance patient to fluid restriction (p = 0.647; p = 0.717; p = 0.345; p = 0.774; p = 0.273). Discussion: Level of patient adherence to therapy not influenced by demographi factor but by the quality of interaction health workers and other factors. This study recommended for further analysis of the factors that influence the level of compliance of the patient as psychological factors (belieft , motivation), socio-economic, and social support

Common Fixed Point Theorems for Weak Contraction Conditions of Integral Type

In this paper we shall establish two common fixed point theorems for a contractive condition and A-contraction mappings of integral type which improve and extend the results of P.B. Prajapati, R. Bhardwaj, M. O. Olatinwo and many others.Keywords: Common fixed point, general contractive mapps of integral type, weak contraction, metric spaces

Text Modeling in Adaptive Educational Chat Room Based on Madamira Tool

his paper discusses how to enhance the ability of text modeling in Arabic during chat sessions. Hanini and Jabari et al. modeled the text in chat sessions, but there is still a problem when using Arabic , because the Arabic language is very difficult to comprehend, has complex derivative and many ambiguities. This paper enhanced the previous study and added MADAMIRA tool to analyze the Arabic text. Monitoring and modeling has been completed through the text modeling process by evaluating the student expressions within the chat session using MADAMIRA tool and machine learning. MADAMIRA tool enables the modeling process to categorize Arabic text into different categories, which makes it easier to use the levels of the used expressions and discover the importance of the chat session between two peers. The process of the student modeling using MADAMIRA and Machine learning will update the student model which gathers information about the student achievements within the AVCM

Novel Green Micro-Synthesis of Graphene-Titanium Dioxide Nano- Composites with Photo-Electrochemical Properties

Background: Graphene-Titanium dioxide nano-composite forms a very promising material in the field of photo-electrochemical research. Methods: In this study, a novel environment-friendly synthesis method was developed to produce well-distributed anatase nano-titanium dioxide spherical particles on the surface of graphene sheets. This novel method has great advantages over previously developed methods of producing graphenetitanium dioxide nanocomposites (GTNCs). High calcination temperature 650°C was used in the preparation of nano titanium dioxide, and chemical exfoliation for graphene synthesis and GTNC was performed by our novel method of depositing titanium dioxide nanoparticles on graphene sheets using a Y-shaped micro-reactor under a controlled pumping rate with minimal use of chemicals. Results: The physiochemical and crystallographic properties of the GTNC were confirmed by TEM, XRD, FTIR and EDX measurements, confirming process repeatability. Spherical nano-titanium dioxide was produced in the anatase phase with very high crystallinity and small particle diameters ranging from 9 nm to 25 nm, also the as prepared graphene (RGO) exhibited minimal flake folding and a high carbon content of 81.28% with a low oxygen-to-carbon atomic ratio of 0.172 and GTNCs produced by our novel method had a superior loading content, a homogeneous distribution and a 96.6% higher content of titanium dioxide particles on the graphene sheets compared with GTNCs prepared with the one-pot method. Conclusion: For its photoelectrochemical properties, chronoamperometry showed that GTNC sample (2) had a higher peak current of 60 μA compared with that of GTNC sample (1), which indicates that the separation and transfer of electron-hole pairs are better in the case of GTNC sample (2) and according to the LSV results, the generation of photocurrent in the samples can be observed through multiple on-off cycles, which indicates that the electrodes are stable and that the photocurrent is quite reversible

Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data

To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R-2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R-2 Multan = 0.97, R2 Skardu = 0.99, R-2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance

Erratum to: An Entropy Functional for Riemann-Cartan Space-Times

We correct the entropy functional constructed in Int. J. Theor. Phys. 51:362 (2012). The 'on-shell' functional one obtains from this correct functional possesses a holographic structure without imposing any constraint on the spin-angular momentum tensor of matter, in contrast to the conclusion made in the above paper.Comment: 15 pages. These are the preprints of the original paper and its erratum published in Int. J. Theor. Phy

The accuracy of currently used WHO´s Body Mass Index cut-off points to measure Overweight and Obesity in Syrian women: A correlation study

Purpose: Obesity is a common health problem in both developed and developing countries. BMI is commonly used to identify obesity. However, there is increasing evidence that the relationship between BMI and BF% differs among various ethnicities. The main objectives of this study are (1) to evaluate the correlation between BF% as determined by BIA, DEXA, Deuterium oxide (D2O) and BMI, (2) to assess the accuracy of currently used WHOÅLs BMI cut-off points to identify overweight and obesity among Syrian women. Material and Methods: A total of 908 healthy Syrian women aged 18-60 years participated in this study. Weight, height, BMI, BF% assessed by BIA and DEXA, and D2O have been determined. Results: BF% results obtained by BIA and DEXA, and D2O revealed strong correlations. BMI showed a statistically significant correlation with BF% determined by BIA, DEXA and D2O. Obesity when defined as BMI ≥ 30 and as BF% > 35% (derived from BIA, DEXA and D2O) classified 43%, 52.5%, 75.9% and 72.7% of women as obese, respectively. ROC analysis defined BMI cut-off points for overweight and obesity of 22.5 and 25.7, respectively. Using the new BMI cut-off point, the prevalence of obesity among Syrian women was increased by 24%. Conclusions: The current BMI cut-off points recommended by WHO underestimate the prevalence of overweight and obesity among Syrian women. Our data suggests that it is important to lower the proposed WHOÅLs BMI cut-off points for the Syrian women