Gas sensors are essential for detecting noxious gases that have a detrimental effect on people's health and welfare. Carbon quantum dots (CQDs) are the fundamental component of gas detectors. CQDs and graphene (Gr) were prepared using the electrochemical method. The gas sensitivity of these materials was evaluated at different temperatures (150, 200, 250 °C) to assess their effectiveness. Subsequently, experiments were conducted at different temperatures to ascertain that the combination of CQDs and Gr, with various percentages of Gr and CQDs, exhibited superior gas sensitization properties compared to CQDs alone. This was evaluated based on criteria such as sensitivity, recovery time, and reaction time. Interestingly, the combination was

A field experiment was conducted in an agricultural field in Al-Hindia district, Karbala governorate in a silty clay soil during the year 2020. The research included a study of two factors, the first is the depth of plowing at two levels, namely 13 and 20 cm, which represented the main blocks. The second is the tire inflation pressure at two levels, namely (70 and 140 kPa), which represented the secondary blocks. Slippage percentage, field efficiency, leaf area, and 300 grain weight were studied. The experiment was carried out using a split-plot system under a Randomized complete block design, at three replications. The tillage depth of 13 cm exceeds/transcend by giving it the least slippage of (11.01%), the highest field efficiency of (50.

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m

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134 samples of plants and animals wastes were taken from three different regions outside Baghdad and three different regions in Baghdad. 24 cellulolytic isolates fungi AO, C1, TH1, AN1, R1, TV, PG, AF, B1, L1, AP, TH, AP1, AN3, AO2, A, A1, C, F, AO1, C2, F1, CL and AP2 independent were chosen out of 48 selected fungi. The best optimal conditions for growth were 30ºC and pH 7. The isolates were identified and screened according to the colony diameter, biomass and density of spores in addition of capability to produce the hydrolytic enzymes for cellulose.

The aim of this study was to identify the effectiveness of using generative learning model in learning kinetic series on rings and horizontal bar in artistic gymnastics for men ,Also, the two groups were better in learning the two series of movements on the rings and horizontal bar . The experimental method was used to design two parallel groups with pretested and posttest .The sample included third graders at the College of Physical Education and Sports Sciences - University of Baghdad ,The third class (d) was chosen to represent the control group that applied the curriculum in the college, with (12) students per group. After conducting the tribal tests, the main experiment was carried out for (8) weeks at the rate of two units per week di

The fractional order partial differential equations (FPDEs) are generalizations of classical partial differential equations (PDEs). In this paper we examine the stability of the explicit and implicit finite difference methods to solve the initial-boundary value problem of the hyperbolic for one-sided and two sided fractional order partial differential equations (FPDEs). The stability (and convergence) result of this problem is discussed by using the Fourier series method (Von Neumanns Method).

This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them