Manufacturing industry is constantly looking for ways to improve the efficiency of production processes so as to lower production costs, maintain product quality, and improve production planning. This study aims to provide an integrated optimization and forecasting framework that combines Cutting Optimization Pro5, linear programming, and an Enhanced N-BEATS deep learning model to minimize material waste and manufacturing cost and to predict the number of production items with a price-reduction strategy. The real industrial case study is based on production and pricing data from the Akad factory of the General Company for Electrical and Electronic Products in Iraq for the period from 2014 to 2025, concerning the manufacture of 80 L

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

In this study, composite materials consisting of Activated Carbon (AC) and Zeolite were prepared for application in the removal of methylene blue and lead from an aqueous solution. The optimum synthesis method involves the use of metakaolinization and zeolitization, in the presence of activated carbon from kaolin, to form Zeolite. First, Kaolin was thermally activated into amorphous kaolin (metakaolinization); then the resultant metakaolin was attacked by alkaline, transforming it into crystalline zeolite (zeolitization). Using nitrogen adsorption and SEM techniques, the examination and characterization of composite materials confirmed the presence of a homogenous distribution of Zeolite throughout the activated carbon.

Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit

Global warming and environmental damage have become major problems. The production of Portland cement releases large quantities of gas, which cause pollution to the atmosphere. This problem can be solved via the use of sustainable materials, such as glass powder. This study investigates the effect of partial replacement of cement with sustainable glass powder at various percentages (0, 15, 20, and 25%) by weight of cement on some mechanical properties (compressive strength, flexural strength, absorption, and dry density) of Reactive Powder Concrete (RPC) containing a percentage of Polypropylene fibers (PRPC) of 1% by weight. Furthermore, steam curing was performed for 5 hours at 90oC after hardening the sample directly. The RPC was

Aims: This study aimed to assess complications of caesarean section among pregnant women attending AL Diwaniyah Maternity and Pediatric Hospital; and find out relationship between complications of caesarean section and socio-demographic data of women. Methods: A descriptive cross-sectional study design is conducted for the period of December 26th 2020 to June 1st 2021. The validity of the questionnaire is determine through a panel of experts and reliability was achieved through a pilot study. By a purposive sample is selected among pregnant women who undergo caesarean section, data was collected through the use of questionnaire and interview techniques; and analyzed through the descriptive and inferential statistic. Results: Findings indica

Pregnancy and childbirth are physiological states characterized by sudden hormonal and immunologically described changes. The current study aimed to investigate the influence of maternal variables (age, previous abortion, placental position, and fetal position) on some physiological biomarkers, such as oxytocin (OT), prolactin (PRL), cortisol, and insulin growth factor 2 (IGF -2) and some immune biomarkers such as programmed cell death protein 1 (PD-1), programmed cell death ligand 1 (PD-L1) and interleukin 6 (IL-6) in Iraqi women undergoing caesarean section (CS). Blood samples were collected from 48 pregnant women in the age range (16-43 years) and serum was obtained to determine the levels of the above biomarkers. The effect of

Objectives: 1. Assessment women’s knowledge about caesarean section. 2. Determining women’s knowledge in relation to their demographic characteristics (age, level of education, and economic status). Methodology: A descriptive design was conducted on Assessment Women’ Knowledge about Cesarean Section at Maternity and Pediatric Hospital in AL-Samawa City. This study started from 26th of September 2020 up to 16th March 2021. Sample of (100) married women who were at reproductive age, pregnant (prime or multipara ) who were planned to have birth by elective cesarean section or had previous elective caesarian section without medical indication or women who had cesarean section with medical indication or emergency. Results: Results