Synthesis of Aerogel with Graphene and Significance with Aerospace Industry

The Primarily focus on Graphene Aerogel, its synthesis and structural integrity together with high electrical conduction. Graphene could be a new nanocarbon that has, single-, bi- or few- layers of carbon atoms forming membered rings. Mechanically powerful and electrically semiconductive graphene aerogels will be produced by either essential drying or freeze of gel precursors integration from the reduction of graphene substance with L-ascorbic acid. In distinction to ways in which utilize physical cross-links between GO, this approach provides valency carbon bonding between the graphene sheets. The graphene aerogels put together possess large surface areas and pore volumes, creating those materials to a feasible possibility to be used in energy repository, catalysis, and sensing applications. We\u27ve additionally showcased some applications for Graphene Aerogel such as their electrical conductivities, Lithium-ion batteries and electrical phenomenon devices, Supercapacitors and photocatalysis

HYBRID ROCKET PROPULSION DEVELOPMENT AND APPLICATION

Satellite technology is advancing. Satellite propulsion requires several desired rocket properties. This is necessary for 1 kg of payload. Launch vehicles no longer need rocket engines. Application includes satellite movement, orbit transfer, and probe and lander propulsion. Space travel is inventive and exciting. Research, production, and use costs must be decreased to make space transportation systems generally available. Security shouldn\u27t be affected. Rocket propellants must be high-performing, non-toxic, and safe. Rocket engines have other parts. Restarting and throttling are crucial. These are inaccessible to solid rocket motors. Developing a liquid rocket engine is hard and expensive, but it can be restarted and throttled. It\u27s being studied for use in hybrid rocket propulsion and other space applications. Space tourist vehicles, lunar and planetary landers, suborbital launch vehicles, satellite maneuvering systems, etc. are potential applications for microsatellites (including orbit transfer). More of these applications are pending approval. Hybrid propulsion is straightforward, safe, and permits regenerative braking, throttling, and restarting. Hybrid propellants are mostly non-toxic and storable. Separate oxidizer and fuel storage enhance safety. This study examines the history, development, current applications, and future of hybrid rocket propulsion. We\u27ll discuss popular fuels and oxidizers. Explains hybrid rocket motor\u27s low regression rate. The article includes the author research

S. Soyucayli

Simultaneous Engineering of Quality Through Integrated Modeling Simultaneous engineering has become the means to ensure quality through incorporation of manufacturing. This proves difficult without quantitative support tools. We present here a modeling-based approach to simultaneously design a product and its production process. We demonstrate an approach of examining sensitivities of output to all inputs of a system, from product design specifications, process variables, arid material specifications. We further develop mathematics of estimating the effects of in-line process control changes to improve quality. We demonstrate a method to choose noise sources to measure and process variables to control in-line based upon these measurements, and estimate the error reduction that such process control changes will provide. The tool allows simultaneous engineering of the product and process to improve quality

Multi-class Brain Tumor Detection using Convolutional Neural Network

Brain tumour detection is one of the most critical and arduous function in the domain of healthcare. Brain tumour, if not detected at an early stage, can be fatal. At the present time, detection and classification of brain tumour is done by the method of Biopsy which is very time-consuming and complex. . By looking at the brain MRI or CT scan, it is possible for the experts to identify whether tumour is present or not and the region of the tumour, but it is difficult to identify the small dissimilarities in the structure of tumour and classify it into types. Hence this manual process gets stuck here for verification of type of tumour. For the sole purpose overcoming the above-mentioned gigantic hurdles we have pursued this research of multi-class brain tumour detection using deep learning. Our project will help doctors in quick decision-making regarding detection of the tumour and its type as well, and due to the early detection of the disease the treatment can be initiated at the right time, resulting in speedy recovery of the patient. We propose a deep learning model employing Convolutional Neural Network architecture which we have implemented using Keras and Tensorflow because it yields to a better performance than the traditional ones. In our research work, CNN gained an accuracy of 94.95%. Further, we have integrated our model with a web-app which we have built using Streamlit. Hence, users can provide their MRI scans via our web-app and get their medical results in a quick and efficient manner

Thinkfinity.org Videos

Follow-up reports on the integration of videos in the Thinkfinity projects. Original Goals: • To establish a rapport with a school in which Thinkfinity.org videos could be produced. • To create a reproducible format that could be used to create future Thinkfinity.org videos. • To create a video series (no less than 5 videos) of interactive whiteboard enhanced lessons based on Thinkfinity.org lesson plans that demonstrate how teachers can utilize both resources

2022-2023 Graduate School Catalog

Graduate students from more than 67 counties are providing outstanding leadership during the pandemic, as they conduct vital research to inform public health, contribute to the greater good, and stimulate the economy. Their scholarship spans 140 programs - from biomedical engineering to business administration, from history to horticulture, and from marine sciences to music performance

QoS-aware Video Transcoding Service Composition Process in a Distributed Cloud Environment

ABSTRACT: In this paper, we address the problem of selecting and composing video transcoding services in a distributed cloud environment. One of the challenging issues for video transcoding service composition is how to find the best transcoding path to route the data flow through while satisfying the viewer requirements and specifications. In a cloud environment, video transcoding service providers provide different video transcoding services that have similar functionality (i.e., format conversion), but with different Quality of Services (QoS) specifications. Since the combination of the QoS specifications, such as frame size, frame rate, video bit rate, and transcoding delay might affect the end user's experience in non-intuitive and subjective way and also might affect the delivering of a high quality video content over any type of network, we propose a QoS-aware model to select and compose the best video transcoding services to satisfy hard constraints on the input and output video formats and comes as close as possible to satisfying soft constraints on the QoS. This model uses an aggregate function to evaluate the QoS for each transcoding service and for each viewer request to explore the best composition path. In this paper, we adapt the Simulated Annealing (SA) algorithm and the Genetic Algorithm (GA) as candidate solutions to help in the composition process. The SA/GA algorithms provide multi-constraints QoS assurance for video transcoding service composition. They also support directed acyclic graph composition topology. We have implemented a prototype of the proposed algorithms and conducted experiments using small-, medium-, and large-scale graphs of video transcoders and sample viewer requests to measure the performance and the quality of the results. The experimental results show that the SA outperforms the GA in terms of performance and success ratio for small-scale graph, while GA outperforms the SA algorithm in terms of performance for medium-and large-scale graphs. The success ratio for the SA and GA algorithms are close to each other for medium-and large-scale graphs. At the end, we provide several directions and suggestions for future work