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Objective: The objective of the study is usefulness of diagnostic MRI in evaluation of spinal tumors. Methodology: This cross-sectional study was carried out in Radiology Department, Dhiraj Hospital, SBKS medical college, Pipariya, Vadodara. The data was collected from July 2021 to March 2022 and total 60 patients were included in the study. Data was collected from MRI diagnosed spinal tumors who attended at Radiology and Imaging department from OPD and indoor patients. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI for evaluation of spinal tumors were detected. Results: Out of 60 patients 31 (51.6%) was intradural extramedullary, 15 (25.0%) was extradural and 13 (21.6%) was intramedullary. Distribution of patients according to MR diagnosis, among the 60 patients 49 were diagnosed spinal tumor and 11 were not spinal tumor by MRI. Among the 49 spinal tumor diagnosed 13 (26.5%) were schwannoma, 02(4.0%) were neuro fibroma, 12(24.4%) were meningioma, 08(16.3%) were ependymoma, 06(12.2%) were astrocytoma, 03(6.1%) were metastasis and 2 (4.0%) was osteoblastoma. Out of all cases 49 were diagnosed as spinal tumour by MRI and among them 48 were confirmed by histopathological evaluation. They were true positive.

Fatty Liver Disease (FLD) is described as the accumulation of triglycerides within cytoplasmic vesicles of hepatocytes exceeding 5 percent of total liver weight. It is generally of two types: Alcoholic or Non-alcoholic FLD (NAFLD). It has a tendency to progress and cause steatohepatitis, brosis, cryptogenic cirrhosis, hepatocellular carcinoma, chronic liver disease, metabolic syndrome, polycystic ovarian syndrome and adenocarcinomas. Objective: To nd association of Sonographic Grading of Fatty Liver Disease with Liver Function Tests and CT Hounseld units. The evaluation of the signicance of Ultrasound and LFTs over Computed Tomography is the aim of this study for the diagnosis of Fatty Liver Disease. Method: patients were undergone CT exams, Ultrasound exams and LFT tests for this this study (mean age: Their hepatic Hounseld units were obtained, Ultrasonographic grades were specied and LFTs were recorded. Results: The sonographic grading of fatty liver is significant/ not, taking HU value as standard. Conclusion: It is concluded that Ultrasound is effective in diagnosing this disease in all grades of FattyLiver Disease along with Liver Function Tests.

The traditional electrical grids deals with generation, transmission and distribution have one-way communication and are centered around the distributor. They also lack in routing and interconnection of different types of generations which lead to a reduction in the reliability of supply. Hence to overcome this issue we propose the comprehensive architecture of Smart Grid and it’s Observation and control using the Internet of Things (IoT) and Cloud Technology. We have Integrated the different sources of energy (thermal and renewable) in a system and introduces the automatic routing of electric power according to peak and low demand. It also incorporates the smart metering facility using IoT and Cloud Technology. It can gather, send and receive the real-time data, which provide two-way communication of data of power being consumed at customer’s end to the service producer. The whole data can be stored, accessed and analyzed on the very secured and customized API platform. We have built and thoroughly tested demonstration model of our project having Wi-Fi-based smart meter, which helped us to monitor and control the electric power and to enhance the reliability of the system.

From military imaging to sharing private pictures, confidentiality, integrity and authentication of images play an important role in the Internet of modern world. AES is currently one of the most famous symmetric cryptographic algorithms. Performing encryption/decryption of high-resolution images with AES is highly computation-intensive and time-consuming due to their large sizes and the underlying complexity of AES algorithm. Earlier increasing cryptographic complexity meant an increase in the processing time for encryption as well as decryption. Now, with the rise of the powerful GPUs containing thousands of high-performance and efficient cores and with the evolvement of GPU computing, the processing time has been reduced to a fraction of the time it used to take earlier. This paper presents a parallel implementation of AES using NVIDIA CUDA and OpenCV to encrypt images rapidly. We achieved an average speed up of four times on GPU as compared to CPU-only.