Objectives: The study aims at finding the effectiveness of dietary habits on urolithiatic patients at Urinary Units
in Baghdad Teaching Hospitals.
Methodology: A quantitative descriptive study was conducted to identify the effectiveness of dietary habits on
(100) of urolithiatic patients in Urinary Units at Baghdad Teaching Hospitals starting from May 2011 to Sep.
2012.Data were collected through the use of constructed check list of the questionnaire format, which
consists of two parts: - The first part: is related to the patient's demographic variables ; the second part: is
constructed to serve the purpose of the study (effectiveness of the dietary habits). The total number of items
of the questionnaire is (69) items. Validity of the questionnaire format was determined through a panel of (23)
experts and the reliability is determined through a pilot study. Descriptive statistical analysis procedures (the
frequency, and the percentage) is used for the data analysis of this study.
Results: The data of this study shows that the urinary tract system (UTS) stone formation (SF) is: {Activated
(increased) by fried foods, soft drinks (Pepsi cola and Coca cola), tea, red meat, eggs, ice cream, tomato,
potato, pepper, urinary tract infection (UTI), obesity, hot areas, low education; and, Inhibited (decreased) by
water, coffee, cacao, natural raisin and apricot ( fresh, syrup, dry), Artificial beverage (Seven up, Miranda),
herbs, milk, cheese and butter, white meat (chicken, fish), vitamins}. So, it would be concluded that there is a
clear effect of ''dietary habit'' on urinary stone formation.
Recommendations: The study recommended that the patients should be given booklets or manual guides
including the following (Type of his/her urinary stone. Accordingly should be advised to: Reduction of his/her
dietary habit by preventing certain materials and increasing others to avoid stone recurrence; Advised to drink
liquids especially water 3-4 l/day, Never delay urine voiding, and add the Milk to tea to decrease tea's
promotion to stone formation because the tea is high content of oxalate).
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Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system
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The relation between faithful, finitely generated, separated acts and the one-to-one operators was investigated, and the associated S-act of coshT and its attributes have been examined. In this paper, we proved for any bounded Linear operators T, VcoshT is faithful and separated S-act, and if a Banach space V is finite-dimensional, VcoshT is infinitely generated.
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This paper deals with the F-compact operator defined on probabilistic Hilbert space and gives some of its main properties.
Equilibrium adsorption isotherm for the removal of trifluralin from aqueous solutions using ? –alumina clay has been studied. The result shows that the isotherms were S3 according Giels classification. The effects of various experimental parameters such as contact time, adsorbent dosage, effect of pH and temperature of trifluralin on the adsorption capacities have been investigated. The adsorption isotherms were obtained by obeying freundlich adsorption isotherm with (R2 = 0.91249-0.8149). The thermodynamic parameters have been calculated by using the adsorption process at five different temperature, the values of ?H, ?G and ?S were (_1.0625) kj. mol-1, (7.628 - 7.831) kj.mol-1 and (_2.7966 - _2.9162) kg.
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In this paper, the error distribution function is estimated for the single index model by the empirical distribution function and the kernel distribution function. Refined minimum average variance estimation (RMAVE) method is used for estimating single index model. We use simulation experiments to compare the two estimation methods for error distribution function with different sample sizes, the results show that the kernel distribution function is better than the empirical distribution function.
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