Model Predictive Controller (MPC) technology has been researched and developed to meet varied demands of need to control industrial power plants and petroleum refineries. This development has paved the way for the MPC technology too many other fields like automotive, aerospace, food processing industries in this paper, primary importance has been paid to the development of a MPC for an identified model of Multiple Input and Multiple Output process. In this paper, a Four Tank System has been considered for generation of input-output data. This data i.e. generated input output data is used for the estimation of two polynomial model, name ARX model (Autoregressive exogenous) model and OE (Output Error) model. With each of model output generated, the Fit-Rates of models are compared to find out most efficient model. The model equations are now considered as plant for developing a Model Predictive Controller (MPC). Two sets of results are obtained after the development of MPC and tested. One is without noise and one is with noise. Both sets of results were a success as the output signals traces step input signals after some steady oscillations in real time with in a very short period of time which indicated a good response time. The MPC developed can be applied to any polynomial model with a good Fit-Rate, it predicts and control the process variables automatically. Keywords:Auto regressive exogenous (ARX) Model predictive controller (MPC) Output error (OE)
Context: Since its outbreak, the COVID-19 pneumonia pandemic is rapidly spreading across India; although computed tomography of chest (CT chest) is not recommended as a screening tool, there is a rapid surge in the CT chest performed in suspected cases. We should be aware of the imaging features among the Indian population. Aim: To analyze the CT chest features in Indian COVID-19 patients. Settings and Design: Retrospective study. Subjects and Methods: CT chest of 31 polymerase chain reaction (PCR) verified patients of COVID-19 was assessed for ground-glass opacities (GGO), consolidations, bronchiectasis, pleural effusions, vascular enlargement, crazy paving, and reverse halo sign. Statistical Analysis Used: The data was analyzed in Microsoft Excel 2019. Results: Only one patient showed a normal scan. Multilobar involvements with parenchymal abnormalities were seen in all the patients with bilateral involvement in 74.1%. 42.5% of the lung parenchymal abnormalities were pure GGOs, while 41.6% had GGOs mixed with consolidation. Peripheral and posterior lung field involvement was seen in 70.5% and 65.5%, respectively; 56.8% had well-defined margins. Pure GGOs were seen in all six patients, who underwent CT in the first 2 days of onset of symptoms. Seventeen patients scanned between 3 and 6 days of the illness showed GGOs mixed with consolidation and pure consolidations 76%. Vascular enlargement, crazy paving, and reverse halo sign were seen in 70%, 53%, and 35% of the patients, respectively. Patients scanned after 1 week of symptoms showed traction bronchiectasis along with GGOs and or consolidations. Conclusions: COVID-19 pneumonia showed multifocal predominantly subpleural basal posteriorly located GGOs and/or consolidations which were predominantly well defined. “Crazy paving” was prevailing in the intermediate stage while early traction bronchiectasis among the patients presented later in the course of illness.
The pancreatic tail is an uncommon location for the accessory spleen. Although it is a benign entity, it can mimic and get misdiagnosed as a pancreatic tumor which can lead to unnecessary biopsy and surgery. Here, we present a case who was detected to have a tail of pancreas mass. On CT and MRI, it showed similar density, signal intensity, and matching enhancement pattern with the orthotopic spleen. The ADC value of the mass was found to be similar to that of the spleen and significantly less than that of normal pancreas. A diagnosis of intrapancreatic accessory spleen was hence made and the patient was followed up after 6 months on MRI. No change in lesion morphology and size was noted. Thus, intrapancreatic accessory spleen should be kept in mind as a differential diagnosis while reviewing a case with pancreatic mass.
Convolutional Neural Networks have always given promising results in the use-cases of object detection. Due to the learning capabilities of neural networks, they are able to find hidden patterns in the architecture of the data. Using deep learning models in the image recognition field has always given good results. Using this approach we introduce a presence detection model that finds the presence of a person via camera and predicts the their availability from the trained images. The model first checks for mask detection based on Transfer Learning approach and predicts if the present person is wearing mask or not and further marks the presence.
Background Bone marrow signal is ideally evaluated with magnetic resonance imaging (MRI) due to its high tissue contrast. While advanced MRI quantitative methods can be used for estimating bone density, there are no readily available parameters on routine clinical MRI sequences of the lumbar spine. Purpose To evaluate whether T1 signal intensity (SI) ratio of lumbar vertebral body (VB)/cerebrospinal fluid (CSF) may predict decreased bone density. Material and Methods A retrospective study was conducted. After use of inclusion/exclusion criteria, 36 patients who had an MRI scan of the lumbar spine and a DEXA scan performed as a part of annual health visit were selected. T1 SI of the lumbar vertebral bodies and adjacent CSF were recorded. Ratio of T1 SI of L1–L4 (VB)/CSF was calculated. The corresponding bone-density values on DEXA scan measured as g/cm2 were obtained. Pearson's r correlation statistic was used to determine the correlation between these variables. Results T1 VB/T1 CSF SI ratio was between 1.308 and 2.927 (mean = 2.028). Mean T1 SI value of vertebral bodies (L1–L4) was 264.9 and mean CSF SI value was 131.9. Bone density in g/cm2 was between 0.851 and 1.398 (mean = 1.081). Pearson correlation coefficient was r = −0.619 ( P=0.0001), which shows a negative moderate correlation between the T1 VB/T1 CSF SI ratio and bone density. Conclusion A high T1 VB/T1 CSF SI ratio on routine MRI sequences may indicate decreased bone density. This ratio may be of substantial benefit in unsuspected osteoporosis/osteopenia on routine MRI lumbar spine imaging.
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