Pharmacological Screening Model and Its Treatment of Peptic Ulcer Disease

Peptic ulcer is a ceaseless sickness influencing up to 10% of the total population. Peptic ulcer created by the unevenness of gastric juice pH and mucosal protections. Two fundamental components delivered Peptic ulcer. Frist is central point it included bacterial disease, for example, Helicobacter Pylori (H. Pylori) and medicine non-steroidal anti-inflammatory medication and synthetic E.g., HCL, Ethanol. Second is minor factor it included pressure, smoking, fiery food and nourishment lopsidedness. Ordinary treatment of Peptic ulcer, for example, proton siphon inhibitor (PPI) and Histamine-2 (H2) Receptor Antagonist. Also, other Hand therapeutic plant and their concoction compound are valuable in the counteraction and treatment of peptic ulcer infection. Various creature models are utilizing to influenced ulcer to identifying the antiulcer activity of many new existed drugs such as Pylorus ligated (shay) rats, Stress ulcers, Restraint ulcer in rats, Water immersion-induced restraint ulcers, Cold and restraint ulcers, Gastric mucosal damage induced by NSAID in rats, Induced solitary chronic gastric ulcer, Acetic acid induced kissing gastric ulcers in rats Histamine induced gastric ulcer in guinea pig, Duodenal anti-ulcer activity, Gastric cytoprotective action

Devices and structures utilizing aerosol jet printing : UV photodetectors, transmission lines and ring resonators

The market for printed electronics is growing continuously. Its low-cost fabrication process, large-area scalability and short processing time makes it interesting for researchers, even though the performance is lower as compared to conventional electronics. Popular printing technologies such as screen printing and inkjet printing are well established, but the upcoming maskless meso-scaled aerosol printing technique promises unique advantages. It allows the direct printing of finer 2D-3D structures, using a wide range of materials with viscosity between 1-1000 cP, while resolving the exhaustive problem of nozzle clogging. However, the implementation of printed electronics in consumer electronics remains a challenge, as device performance has to be improved. New techniques such as aerosol jet printing require additional research to fulfill their promise. This thesis investigates the implementation of fully printed structures using aerosol jet printing, focusing on devices in the fields of optoelectronics and semiconductor packaging. The system components and operation mechanism of the aerosol jet printing system are described and a methodology for process optimization is proposed. Four distinctive regions for process optimization are identified: ink selection, surface treatment, process control and postprocessing. In the preliminary work, the possibilities of the aerosol jet printing process control is explored, such as achievable line width, film thickness, material compatibility and sintering possibilities. Test structures are produced in order to test the fabrication workflow, and to observe the interaction and compatibility of multiple printed layers. The challenges associated with aerosol jet printing are identified, including wetting, alignment, overspray and satellite deposition. A fully printed ultraviolet photodetector with a nanoporous morphology is investigated. Presynthesized Zinc Oxide crystals are printed to reduce the post-annealing temperature. At a temperature of < 150 ◦C, the solvent is evaporated, resulting in a porous structure having high surface area-to-volume ratio. A fully printed photodetector that has comparable performance to the state-of-the-art is demonstrated, while the low-temperature fabrication process maintains compatibility with large area flexible plastic substrates. Next, a fully printed microstrip transmission line with SU-8 as dielectric and silver as conductor is proposed, which can provide high-bandwidth interconnections in packaged semiconductor dies. The metal and dielectric materials are characterized at microwave frequencies upto 18 GHz. It is shown that a good correspondence is reached between the simulated design parameters and the printed structure, which results in good characteristic impedance matching and low transmission losses. The transition of the printed transmission line to a microwave integrated circuit is demonstrated, thereby validating the concept of aerosol jet printed transmission lines inside the package. Lastly, the SU-8 based printed transmission lines are extended into microwave ring resonators, with applications in high frequency sensing. It is envisioned to directly print these structures inside the package, directly connected to a microwave integrated amplifier for high-Q sensing. Therefore, these ring resonator are designed for microwave center frequencies ranging from 15.5 to 21.5 GHz for reduced size which can be integrated inside a package. The material characterization of metal and dielectric materials are carried out up to 26 GHz. The simulated results showed good correspondence with the measured results in terms of center-frequency, insertion loss and Q-factor

Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19

Coronavirus disease 2019 has had a pressing impact on people all around the world. Ceasing the spread of this infectious disease is the urgent need of the hour. A vital method of protection against the virus is wearing masks in public areas. Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people. In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly. A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures. OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification. The method has been implemented on various input images and classification results have been obtained for the same. The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively. Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images