Neural Network Precept Diagnosis on Petrochemical Pipelines for Quality Maintenance

Pipeline tubes are part of vital mechanical systems largely used in petrochemical industries. They serve to transport natural gases or liquids. They are cylindrical tubes and are submitted to the risks of corrosion due to high PH concentrations of the transported liquids in addition to fatigue cracks. Due to the nature of their function, they are subject to the alternation of pressure-depression along the time, initiating therefore in the tubes body  micro-cracks that can propagate abruptly to lead to failure by fatigue.On to the diagnostic study for the issue the development of this prognostic process employing neural network for such systems bounds to the scope of quality maintenance. Keywords: Percept, Simulated results, Fluid Mechanic

Neural Network Precept Diagnosis on Petrochemical Pipelines for Quality Maintenance

Pipeline tubes are part of vital mechanical systems largely used in petrochemical industries. They serve to transport natural gases or liquids. They are cylindrical tubes and are submitted to the risks of corrosion due to high PH concentrations of the transported liquids in addition to fatigue cracks. Due to the nature of their function, they are subject to the alternation of pressure-depression along the time, initiating therefore in the tubes' body micro-cracks that can propagate abruptly to lead to failure by fatigue. On to the diagnostic study for the issue the development of this prognostic process employing neural network for such systems bounds to the scope of quality maintenance. Keywords: Percept, Simulated results, Fluid Mechanic

Opracowanie bezprądowego nanoszenia powłok niklowo-fosforowych na włókna sizalowe

Improving the properties of natural fibers can be interesting from a technological point of view. Modifications can result in a high specific strength, electromagnetic interference (EMI) shielding, antistatic brush etc. Plant fiber composites would make them an option for use in components such as rifle stocks and knife handles etc. The greatest challenge would be to ensure that the components are all weather capable, but today several materials exist that have a high moisture tolerance. Moreover with proper surface coating techniques, most natural fibers materials can be toughened up to use as reinforcement. An attempt was made using a proprietary experimental set-up to coat natural fiber with electroless nickel phosphorus (ENi-P), since sisal fiber is a non-conducting material. In this article the surface morphology, ingredients and cross-section images of the modified natural fibers were characterised by a scanning electron microscope equipped with EDAX.Poprawa właściwości włókien naturalnych jest interesująca z technologicznego punktu widzenia. Wynikiem modyfikacji może być wysoka wytrzymałość, poprawa ekranowania pól elektromagnetycznych (EMI), poprawa właściwości antystatycznych itp. Powłoki niklowo-fosforowe na włóknach roślinnych umożliwiają wytworzenie różnego rodzaju kompozytów, min. o powierzchni odpornej na wilgoć. Morfologia powierzchni kompozytów była badana za pomocą SEM-EDAX

Biological and structural properties’ interpretation on antitumour drug 3-(2-aminoethyl) indole (tryptamine) using molecular spectroscopy and computational tools

In this work, the known and unknown structural as well as biological properties of 3-(2-aminoethyl) indole (tryptamine) were interpreted using molecular spectroscopy (FT-IR, FT-Raman, NMR and UV–Visible) and cheminformatic tools. The supportive drug-related information was gained by analysing the obtained data which will be useful for the drug chemist for the pharmaceutical research. The important biological properties of the present chemical species satisfied the Lipinski five rules and it was opt to fabricate complex antibiotic compounds. The acquired charge potential load for creating antibiotic strain on compositional parts was keenly observed from the obtained data and it was evaluated by the vibrational analysis and Mulliken charge profile. From the NMR data, the chemical nodal points were noted and their movement around the molecule was carefully monitored. The degenerate and non-degenerate energy profile of orbital interaction system was studied and the link of chemical reactivity path was identified. The significance of excited electronic transitions among non-bonding molecular orbital system was justified and their transitional energy coefficient was determined. The toxicity level was checked from the chirality characteristics and enantiomer structure obtained from vibrational circular dichroism profile

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells