Use of Mercury Intrusion Porosimetry (MIP) Technique to Measure the Porosity of Anodes in Solid Oxide Fuel Cell (SOFC)

The present research is aimed to calculating the porosity of anodes in solid oxide fuel cell through Mercury Intrusion porosimetry (MIP). There are various techniques used to measure the porosity of the solid oxide fuel cell (SOFC). MIP is a method used to find the porosity of anodes due to its high accuracy, and some additional information which includes particle size distribution, pore size distribution, average pore size and bulk density. The working principal of MIP is that when sample is filled with mercury then high pressure is applied which makes the mercury to penetrate into the pores of the sample. The instrument measures the pore volume with the help of capacitive system as the pressure gradually increases to its maximum value and then decrease to its lowest value. This system calculates the volume of mercury intruded for each pressure whether the pressure is increasing or decreasing. The instrument is connected to a computer with dedicated software which calculates the percentage porosity of the sample. The results suggested the importance of PH and agitator on porosity. What we have to provide is the sample mass, sample density and the temperature of the laboratory. However for cleaning purposes of mercury, ethanol could be used instead of acetone, as mercury intrusion porosimeter involves few plastic parts like dilatometer holder and cap. Whereas acetone has catastrophic effect on them, and these parts are very expensive to replace. Keywords: Mercury, Intrusion, Solid oxide, Fuel cell, Porosity, Anodes, Porosimetr

MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG

Seizure type identification is essential for the treatment and management of epileptic patients. However, it is a difficult process known to be time consuming and labor intensive. Automated diagnosis systems, with the advancement of machine learning algorithms, have the potential to accelerate the classification process, alert patients, and support physicians in making quick and accurate decisions. In this paper, we present a novel multi-path seizure-type classification deep learning network (MP-SeizNet), consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory neural network (Bi-LSTM) with an attention mechanism. The objective of this study was to classify specific types of seizures, including complex partial, simple partial, absence, tonic, and tonic-clonic seizures, using only electroencephalogram (EEG) data. The EEG data is fed to our proposed model in two different representations. The CNN was fed with wavelet-based features extracted from the EEG signals, while the Bi-LSTM was fed with raw EEG signals to let our MP-SeizNet jointly learns from different representations of seizure data for more accurate information learning. The proposed MP-SeizNet was evaluated using the largest available EEG epilepsy database, the Temple University Hospital EEG Seizure Corpus, TUSZ v1.5.2. We evaluated our proposed model across different patient data using three-fold cross-validation and across seizure data using five-fold cross-validation, achieving F1 scores of 87.6% and 98.1%, respectively

COVID-19 Detection System: A Comparative Analysis of System Performance Based on Acoustic Features of Cough Audio Signals

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people's daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance on COUGHVID and Virufy datasets for COVID-19 detection.Comment: 8 pages, 3 figure

Vision Transformer Based Model for Describing a Set of Images as a Story

Visual Story-Telling is the process of forming a multi-sentence story from a set of images. Appropriately including visual variation and contextual information captured inside the input images is one of the most challenging aspects of visual storytelling. Consequently, stories developed from a set of images often lack cohesiveness, relevance, and semantic relationship. In this paper, we propose a novel Vision Transformer Based Model for describing a set of images as a story. The proposed method extracts the distinct features of the input images using a Vision Transformer (ViT). Firstly, input images are divided into 16X16 patches and bundled into a linear projection of flattened patches. The transformation from a single image to multiple image patches captures the visual variety of the input visual patterns. These features are used as input to a Bidirectional-LSTM which is part of the sequence encoder. This captures the past and future image context of all image patches. Then, an attention mechanism is implemented and used to increase the discriminatory capacity of the data fed into the language model, i.e. a Mogrifier-LSTM. The performance of our proposed model is evaluated using the Visual Story-Telling dataset (VIST), and the results show that our model outperforms the current state of the art models.Comment: This paper has been accepted at the 35th Australasian Joint Conference on Artificial Intelligence 2022 (Camera-ready version is attached

Sewing Needle as Foreign Body in Urethra of an Adolescent Boy: Case Report

Self-insertion of a foreign body in the urethra is an uncommon presentation clinically. The cases usually arise due to fulfillment of sexual desire, for recreation, play, or exploration, or the foreign body insertion may take place accidentally. We present a case of an adolescent boy with a foreign body urethra presenting to the emergency room with urinary retention, pain, and dysuria. Attending urologist suspected urethral stricture and ordered ultrasonography to investigate which turned out to be a sewing needle in his urethra. The patient was then enquired about the foreign body. He tried to self-dilate his urethra as he was experiencing lower urinary tract symptoms. The sewing needle was removed by endoscopy and he was administered with antibiotics and painkillers. The urethral foreign bodies may present with pain, dysuria, or urinary incontinence and these foreign bodies are mostly seen in the male population in the adolescent age group

Macro-Economic Variables And Stock Prices In India

This study investigates the effects of macroeconomic variables on stock prices in India using annual data for the period from January 1979 to December 2011. The multivariate regression was run using thirteen macroeconomic variables on BSE Sensex using six different models. The null hypothesis which states that macroeconomic variables collectively do not accord any impact on the share prices is rejected at 0.05 level of significance in overall and post-liberalization case but is accepted in pre-liberalization case. The results indicate that out of six models in all the three cases the model with higher R2. has been selected for further analysis which justifies higher explanatory power of macroeconomic variables in explaining stock prices. Consistent with similar results of the developed as well as emerging market studies, inflation rate and exchange rate react mainly negatively to stock prices in the Indian Stock Exchange. The negative effect of Treasury bill rate implies that whenever the interest rate on Treasury securities rise, investors tend to switch out of stocks causing stock prices to fall. However, lagged money supply variables do not appear to have a strong prediction of movements of stock prices while stocks do not provide effective hedge against inflation specially in Manufacturing, Trading and Diversified sectors in the CSE. These findings hold practical implications for policy makers, stock market regulators, investors and stock market analysts

SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scans

Abdominal Aortic Calcification (AAC) is a known marker of asymptomatic Atherosclerotic Cardiovascular Diseases (ASCVDs). AAC can be observed on Vertebral Fracture Assessment (VFA) scans acquired using Dual-Energy X-ray Absorptiometry (DXA) machines. Thus, the automatic quantification of AAC on VFA DXA scans may be used to screen for CVD risks, allowing early interventions. In this research, we formulate the quantification of AAC as an ordinal regression problem. We propose a novel Supervised Contrastive Ordinal Loss (SCOL) by incorporating a label-dependent distance metric with existing supervised contrastive loss to leverage the ordinal information inherent in discrete AAC regression labels. We develop a Dual-encoder Contrastive Ordinal Learning (DCOL) framework that learns the contrastive ordinal representation at global and local levels to improve the feature separability and class diversity in latent space among the AAC-24 genera. We evaluate the performance of the proposed framework using two clinical VFA DXA scan datasets and compare our work with state-of-the-art methods. Furthermore, for predicted AAC scores, we provide a clinical analysis to predict the future risk of a Major Acute Cardiovascular Event (MACE). Our results demonstrate that this learning enhances inter-class separability and strengthens intra-class consistency, which results in predicting the high-risk AAC classes with high sensitivity and high accuracy.Comment: Accepted in conference MICCAI 202