The Mesopotamian marshlands faced a massive destruction from many years and this lead to effect to ecosystem. In this study a survey was made on the physical chemical and heavy metals characteristics and microbiological analysis of AL Chibaish marsh during the two months. Water analyses revealed unacceptable values for almost all physiochemical and biological properties, according to WHO standard limits for drinking water. Almost all major ions and heavy metal concentrations in water showed a distinct decreasing trend at the marsh outlet station compared to other stations. In general, major and minor ions, as well as heavy metals exhibit higher concentrations in location 1 than in location 3. The concentrations of heavy metals in water show

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

Twisted tape insertion in smooth plain tube is one of types of passive methods that is used to enhance heat transfer. Swirl fluid flow inside tube and related heat transfer characteristics are very complex. ANSYS FLUENT (V 16.1) and ASPEN industrial program are used in analyzing this technique for enhancement heat transfer. A circular plain tube has length L=8534mm and 17 mm inner diameter with twisted tape has twist ratio of y = (H/D) = (150/17) =8.8 along with a plain tube were considered for this study. Eight Reynolds numbers (Re) of 784, 1000, 2000, 3000, 4000, 5000, 6000 and 7000 are used to analyze the response of thermal performance. Crude oil API 28 exit temperature, film heat transfer coefficient, Nusselt number

Twisted tape insertion in the smooth plain tube is one of the types of passive methods that are used to enhance heat transfer. Swirl fluid flow inside the tube and related heat transfer characteristics are very complex. ANSYS FLUENT (V 16.1) and ASPEN industrial program are used in analyzing this technique for enhancement heat transfer. A circular plain tube has length L=8534mm and 17 mm inner diameter with a twisted tape of twist ratio of y = (H/D) = (150/17) =8.8 along the plain tube were considered for this study. Eight Reynolds numbers (Re) of 784, 1000, 2000, 3000, 4000, 5000, 6000 and 7000 are used to analyze the response of thermal performance. Crude oil API 28 exit temperature, film heat transfer coefficient, Nus

4-chloro and 4- nitro substituted phenol and aniline incorporated to a carboxylic group of naproxen a well-known non-steroidal anti-inflammatory drug (NSAID) to increase bulkiness were synthesized for evaluation as  a potential anti-inflammatory agents with expected COX-2 selectivity. In vivo acute anti-inflammatory activity of these compounds (I-IV) was evaluated in rats using egg-white induced edema model of inflammation in a dose equivalent to (2.5 mg/Kg) of naproxen. All tested compounds produced a significant reduction in paw edema with respect to the effect of propylene glycol 50% v/v (control group). Moreover, compounds I and IV might show higher effect comparable to that of naproxen and to that of compounds II & III whic

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi