Estimation of Thermodynamic Stability of Methane and Carbon Dioxide Hydrates in the Presence of Hydrogen Sulfide

.Peer reviewedPublisher PD

A practical approach to adult-onset white matter diseases, with illustrative cases

Aim. To evaluate five illustrative cases and perform a literature review to identify and describe a working approach to adult-onset white matter diseases (WMD).State of the art. Inherited WMD are a group of disorders often seen in childhood. In adulthood, progressive WMDs are rare, apart from the common nonspecific causes of hypertension and other cerebrovascular diseases. The pattern of WMDs on neuroimaging can be an important clue to the final diagnosis. Due to the adoption of a combined clinical-imaging-laboratory approach, WMD is becoming better recognised, in addition to the rapidly evolving field of genomics in this area.Clinical implications. While paediatric WMDs have a well-defined and literature-based clinical-laboratory approach to diagnosis, adult-onset WMDs remain an important, pathologically diverse, radiographic phenotype, with different and distinct neuropathologies among the various subtypes of WMD. Adult-onset WMDs comprise a wide collection of both acquired and inherited aetiologies. While severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neurological complications are emerging, we are as yet unaware of it causing WMD outside of post-anoxic changes. It is important to recognise WMD as a potentially undefined acquired or genetic syndrome, even when extensive full genome testing reveals variants of unknown significance.Future directions. We propose a combined clinical-imaging-laboratory approach to WMD and continued exploration of acquired and genetic factors. Adult-onset WMD, even given this approach, can be challenging because hypertension is often comorbid. Therefore, we propose that undiagnosed patients with WMD be entered into multicentre National Organisation for Rare Diseases registries to help researchers worldwide make new discoveries that will hopefully translate into future cures

Comparison Of Object Detection Models - to detect recycle logos on tetra packs

Background: Manufacturing and production of daily used products using recyclable materials took a steep incline over the past few years. The recyclable packages that are being considered for this thesis are Tetra Packs. Tetra packs are widely used for packaging liquid foods. A few recyclable methods are being used to recycle such tetra packs which use the barcode behind them to scan and give which recyclable method the particular tetra pack has to go through. In some cases, the barcode might get worn off due to excessive usage leading to a problem. Therefore there needs to be a research that has to be carried out to address this problem and find a solution to the same.  Objectives: The objectives to address and fulfill the aim of this thesis are : To find/create the necessary data set containing clear pictures of the tetra packs with visible recyclable logos. To draw bounding boxes around the objects i.e., logos for training the models. To test the data set by applying all four Deep Learning models. To compare each of the models on speed and the performance metrics i.e, mAP and IoU and identify the best algorithm among them.  Methods: To answer the research question we have chosen one research methodol- ogy which is Experiment.Results: YOLOv5 is considered as the best algorithm among the four algorithms we are comparing. Speed of YOLOv5, SSD and Faster-RCNN were found to be similar i.e, 0.2 seconds whereas Mask-RCNN was the slowest with the detection speed of 1.0 seconds. The mAP score of SSD is 0.86 which is the highest among the four followed by YOLOv5 at 0.771, Faster-RCNN at 0.67 and Mask-RCNN at 0.62. IoU score of Faster-RCNN is 0.96 which is the highest among the four followed by YOLOv5 at 0.95, SSD at 0.50 and Mask-RCNN at 0.321. On comparing all the above results YOLOv5 is concluded as the best algorithm among the four as it is relatively fast and accurate without any major draw-backs in any category.  Conclusions: Amongst the four algorithms Faster-RCNN, YOLO, SSD and Mask- RCNN, YOLOv5 is declared as the best algorithm after comparing all the models based on speed and the performance metrics mAP, IoU. YOLOv5 is considered as the best algorithm among the four algorithms we are comparing.

Comparison Of Object Detection Models - to detect recycle logos on tetra packs

Background: Manufacturing and production of daily used products using recyclable materials took a steep incline over the past few years. The recyclable packages that are being considered for this thesis are Tetra Packs. Tetra packs are widely used for packaging liquid foods. A few recyclable methods are being used to recycle such tetra packs which use the barcode behind them to scan and give which recyclable method the particular tetra pack has to go through. In some cases, the barcode might get worn off due to excessive usage leading to a problem. Therefore there needs to be a research that has to be carried out to address this problem and find a solution to the same.  Objectives: The objectives to address and fulfill the aim of this thesis are : To find/create the necessary data set containing clear pictures of the tetra packs with visible recyclable logos. To draw bounding boxes around the objects i.e., logos for training the models. To test the data set by applying all four Deep Learning models. To compare each of the models on speed and the performance metrics i.e, mAP and IoU and identify the best algorithm among them.  Methods: To answer the research question we have chosen one research methodol- ogy which is Experiment.Results: YOLOv5 is considered as the best algorithm among the four algorithms we are comparing. Speed of YOLOv5, SSD and Faster-RCNN were found to be similar i.e, 0.2 seconds whereas Mask-RCNN was the slowest with the detection speed of 1.0 seconds. The mAP score of SSD is 0.86 which is the highest among the four followed by YOLOv5 at 0.771, Faster-RCNN at 0.67 and Mask-RCNN at 0.62. IoU score of Faster-RCNN is 0.96 which is the highest among the four followed by YOLOv5 at 0.95, SSD at 0.50 and Mask-RCNN at 0.321. On comparing all the above results YOLOv5 is concluded as the best algorithm among the four as it is relatively fast and accurate without any major draw-backs in any category.  Conclusions: Amongst the four algorithms Faster-RCNN, YOLO, SSD and Mask- RCNN, YOLOv5 is declared as the best algorithm after comparing all the models based on speed and the performance metrics mAP, IoU. YOLOv5 is considered as the best algorithm among the four algorithms we are comparing.

Design of a simple device for accurate measurement of human blood viscosity in oxygenated and deoxygenated conditions

The purpose of this research is to design, fabricate, and test a simple device that can accurately measure the viscosity of whole blood in both an oxygenated and a deoxygenated environment. The ideal device is easy to operate, inexpensive to fabricate, and is usable outside of a laboratory setting. The microfluidic rheometer presented here was fabricated using a wet chemical etching method. Using the channel dimensions, the known viscosity of a reference fluid, and the velocity of fluid flow of the sample and a reference fluid through the microchannels the unknown viscosity of a sample fluid is calculated

Transition from intravenous to enteral ketamine for treatment of nonconvulsive status epilepticus.

BACKGROUND: Nonconvulsive status epilepticus (NCSE) is a diagnosis that is often challenging and one that may progress to refractory NCSE. Ketamine is a noncompetitive N-methyl-d-aspartate antagonist that increasingly has been used to treat refractory status epilepticus. Current Neurocritical Care Society guidelines recommend intravenous (IV) ketamine infusion as an alternative treatment for refractory status epilepticus in adults. On the other hand, enteral ketamine use in NCSE has been reported in only 6 cases (1 adult and 5 pediatric) in the literature to date. CASE PRESENTATION: A 33-year-old woman with a history of poorly controlled epilepsy presented with generalized tonic-clonic seizures, followed by recurrent focal seizures that evolved into NCSE. This immediately recurred within 24 h of a prior episode of NCSE that was treated with IV ketamine. Considering her previous response, she was started again on an IV ketamine infusion, which successfully terminated NCSE. This time, enteral ketamine was gradually introduced while weaning off the IV formulation. Treatment with enteral ketamine was continued for 6 months and then tapered off. There was no recurrence of NCSE or seizures and no adverse events noted during the course of treatment. CONCLUSION: This case supports the use of enteral ketamine as a potential adjunct to IV ketamine in the treatment of NCSE, especially in cases without coma. Introduction of enteral ketamine may reduce seizure recurrence, duration of stay in ICU, and morbidity associated with intubation