The current study suggested a thermal treatment as a necessary proactive step in improving the adsorption capacity of bio-waste for contaminants removal in wastewater. This approach was based on the experimental and histological investigation of biowaste pods shell. This investigation showed that these shells compose of parenchyma cells that store secondary metabolites compounds produced from cells were exhibited in present study. The results also reported that these compounds are extracted directly from the cells as soon as they are exposed to an aqueous solution, hampering their use as an adsorbent material. The increase in the weight of bio-waste adsorbent at unit liquid volume increases the production of secondary metabolites compounds

     تعد الملابس وسيلة هامة لكل مايقوم به الانسان في حياته العامة ، فهي الانطباع والكلمة الخارجية عن ذاته الداخلية فهي تعكس فكرة الفرد عن ذاته وعن شخصيته , كما تعد وسيلة تعبير جمالية وفنية , فهي تساعد على اخفاء عيوب الجسد وابراز محاسنه . ويتوقف اختيار الفرد لملابسه على مجموعة  عوامل منها احتياجه , قدراته المالية , سنه , مركزه الاجتماعي , طبيعة عمله ,الظروف الجوية التي يعيش فيها وعلى مايُؤمن به من قيم و

Electrochemical Grinding (ECG) process is a mechanically assisted electrochemical process for material processing. The process is able to successfully machine electrically conducting harder materials at faster rate with improved surface finish and dimensional control. This research studies the effect of applied current, electrolyte concentration, spindle speed and the gap between workpiece and tool on hardness and material removal rate during electrochemical grinding for stainless steel 316. The characteristic features of the electrochemical grinding process are explored through Taguchi-design-based experimental studies. The better hardness can be obtained at 10 A of the current, 150 g/l of the electrolyte concentration, 0.3 mm of gap an

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

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

Abstract:

     The objective of this study, is to attempt to explain the reality of the Structural Imbalances in the Iraqi Economy during the period of research, by providing a quantitative analysis of the most important types of Imbalances, Which are represented by the disruption in the Productive Structure, the imbalance of the structure of Public Budget, and the imbalance of the Structure of Trade. The problem of the research, is the fact that the economy structure in Iraq has long suffered from an Imbalances in its economic structure, which are represented in the unequal relations between its constituent elements, according to the proportions levels defined by the economic theory.

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