A Study of High Temperature Heat Pipes and the Impact of Magnetic Field on the Flow of Liquid Metal

A study of high temperature heat pipe was conducted to understand its characteristics. A review of working fluid, temperature, wick structure, problems, operational limit and applications was done. Alkali metal were concluded as the most viable candidate for the working fluid. The impact of three parameters namely magnetic field, heat flux and temperature was analyzed on the performance of HTHP (High Temperature Heat Pipe). The presence of magnetic field had the most considerable impact on reducing the pumping limit of the heat pipe while the temperature had almost negligible effect. Magnetic field results in the pressure drop and adversely affect the fluid inside the heat pipe. The adverse impact was characterized due to the conducting nature of the working fluid. Analyzing the Magnetohydrodynamic (MHD) equation showed that the reason for the pressure drop inside heat pipe was Lorentz force. The flow was found to be dependent on three dimensionless number namely Capillary number, Hartmann number and aspect ratio of heat pipe. Thereafter a mathematical model was developed to inquire if the presence of non-uniform magnetic field can increase the capillary limit over the uniform magnetic field. It was found that an exponential varying field along the axial direction of the heat pipe improves the performance of the device. The results corresponding to uniform and non-uniform field were compared and concluded in our study

GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features With Spatial and Channel Attention

Deep learning and Computer vision are extensively used to solve problems in wide range of domains from automotive and manufacturing to healthcare and surveillance. Research in deep learning for food images is mainly limited to food identification and detection. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. This research is intended to expand the horizons of deep learning and semantic segmentation by proposing a novel single-pass, end-to-end trainable network for food segmentation. Our novel architecture incorporates both channel attention and spatial attention information in an expanded multi-scale feature representation using the WASPv2 module. The refined features will be processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or postprocessing

Critical appraisal of speech in noise tests: a systematic review and survey

Speech in noise tests that measure the perception of speech in presence of noise are now an important part of audiologic tests battery and hearing research as well. There are various tests available to estimate the perception of speech in presence of noise, for example, connected sentence test, hearing in noise test, words in noise, quick speech-in-noise test, bamford-kowal-bench speech-in-noise test, and listening in spatialized noise-sentences. All these tests are different in terms of target age, measure, procedure, speech material, noise, normative, etc. Because of the variety of tests available to estimate speech-in-noise abilities, audiologists often select tests based on their availability, ease to administer the test, time required in running the test, age of the patient, hearing status, type of hearing disorder and type of amplification device if using. A critical appraisal of these speech-in-noise tests is required for the evidence based selection and to be used in audiology clinics. In this article speech-in-noise tests were critically appraised for their conceptual model, measurement model, normatives, reliability, validity, responsiveness, item/instrument bias, respondent burden and administrative burden. Selection of a standard speech-in-noise test based on this critical appraisal will also allow an easy comparison of speech-in-noise ability of any hearing impaired individual or group across audiology clinics and research centers. This article also describes the survey which was done to grade the speech in noise tests on the various appraisal characteristics

MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization

Memes are a powerful tool for communication over social media. Their affinity for evolving across politics, history, and sociocultural phenomena makes them an ideal communication vehicle. To comprehend the subtle message conveyed within a meme, one must understand the background that facilitates its holistic assimilation. Besides digital archiving of memes and their metadata by a few websites like knowyourmeme.com, currently, there is no efficient way to deduce a meme's context dynamically. In this work, we propose a novel task, MEMEX - given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme. At first, we develop MCC (Meme Context Corpus), a novel dataset for MEMEX. Further, to benchmark MCC, we propose MIME (MultImodal Meme Explainer), a multimodal neural framework that uses common sense enriched meme representation and a layered approach to capture the cross-modal semantic dependencies between the meme and the context. MIME surpasses several unimodal and multimodal systems and yields an absolute improvement of ~ 4% F1-score over the best baseline. Lastly, we conduct detailed analyses of MIME's performance, highlighting the aspects that could lead to optimal modeling of cross-modal contextual associations.Comment: 9 pages main + 1 ethics + 3 pages ref. + 4 pages app (Total: 17 pages

Estimation of Appearance and Occupancy Information in Birds Eye View from Surround Monocular Images

Autonomous driving requires efficient reasoning about the location and appearance of the different agents in the scene, which aids in downstream tasks such as object detection, object tracking, and path planning. The past few years have witnessed a surge in approaches that combine the different taskbased modules of the classic self-driving stack into an End-toEnd(E2E) trainable learning system. These approaches replace perception, prediction, and sensor fusion modules with a single contiguous module with shared latent space embedding, from which one extracts a human-interpretable representation of the scene. One of the most popular representations is the Birds-eye View (BEV), which expresses the location of different traffic participants in the ego vehicle frame from a top-down view. However, a BEV does not capture the chromatic appearance information of the participants. To overcome this limitation, we propose a novel representation that captures various traffic participants appearance and occupancy information from an array of monocular cameras covering 360 deg field of view (FOV). We use a learned image embedding of all camera images to generate a BEV of the scene at any instant that captures both appearance and occupancy of the scene, which can aid in downstream tasks such as object tracking and executing language-based commands. We test the efficacy of our approach on synthetic dataset generated from CARLA. The code, data set, and results can be found at https://rebrand.ly/APP OCC-results

SERUM HOMOCYSTEINE AS A RISK FACTOR FOR STROKE: A PROSPECTIVE STUDY FROM A RURAL TERTIARY CARE CENTRE

Objective: Stroke is one of the leading causes of mortality and long-term disability in both developed and developing countries. Serum homocysteine level is one of the emerging modifiable risk factors for atherosclerosis which may result into a cerebrovascular accident. This study was designed to study the association of Serum Homocysteine level with the development of acute stroke at a rural tertiary care centre in North India.Methods: The present study was a prospective cross-sectional study conducted in the Department of Medicine, Maharishi Markandeshwar Institute of Medical Sciences and Research, Mullana, Ambala. The study population included 100 patients presenting with Stroke (either ischemic or hemorrhagic) in the indoor and outdoor facilities in the Department of Medicine. 50 age and sex-matched healthy individuals were taken as controls. Serum total Homocysteine level was measured in all the cases and controls.Results: Majority of the patients suffered from ischemic stroke (78%), while only 22% patients had hemorrhagic stroke. The mean Serum Homocysteine level in stroke patients (19.88±8.78 μmol/l) was significantly higher than in controls (10.48±4.39 μmol/l) (p<0.01). In a subgroup analysis, stroke patients with a positive history of smoking had significantly higher homocysteine level as compared to non-smokers (p<0.05).Conclusion: Increased level of Serum Homocysteine is significantly associated with risk of cerebrovascular accident, which is independent of the risk attributed to traditional risk factors.Â

Medical student’s perception and feed-back on virtual classes during COVID-19 pandemic: a multi-centric questionnaire based study

Introduction: The quick turn to online platforms from contact learning during COVID-19 remained challenging for both teachers as well for students. This study was done with the aim to know the perception and feed back of under-graduate medical students on virtual classes during the pandemic. Material & Methods: This was a cross-sectional questionnaire based multi-centric study.  Questionnaire in the form of Google form was distributed to the undergraduate medical students from various MBBS professionals studying in various medical colleges across North India. The completed questionnaire was collected and data was analyzed. Results: 40% students were from government, 52% from private medical colleges and 8% from AIIMS/ SGPGI. Majority of students were using mobile (63.7%) for e learning, using 4G internet (70.6%). Mostly the private medical colleges (73%) and only 4.5% government colleges were conducting the live video classes. Rest of them was providing the soft copy of the study material to the students. Based on the feedback by the students, about one third of the students (36.7%) appreciated the online platform in the current scenario as well for future in the combination with traditional classroom teaching. Discussion: The e-learning was the need of the hour as every day is important for a medical student and the learning has to be uninterrupted. Although helpful, e-learning alone is a far cry from face‐to‐face interaction between students and teachers. Finding the right balance of class-room teaching combined with e-learning should become the norm for future students.   &nbsp

Computed tomography virtual oesophagography for the grading of oesophageal varices in cirrhotic liver disease patients with upper gastrointestinal endoscopic examination as the gold standard : a diagnostic validation study

Purpose: Virtual endoscopy is a postprocessing method using three-dimensional computed tomography (CT), which produces views of the inner surfaces of the human body like those produced by fibreoptic endoscopy. To evaluate and categorise patients who require medical or endoscopic band ligation to prevent oesophageal variceal bleed, a less invasive, less expensive, better tolerated, and more sensitive modality is required, as well as to reduce the use of invasive procedures in the follow-up of patients who do not require endoscopic variceal band ligation. Material and methods: A cross-sectional study was conducted in the Department of Radiodiagnosis in association with the Department of Gastroenterology. The study was conducted over a period of 18 months from July 2020 to January 2022. The sample size was calculated as 62 patients. Patients were recruited on the basis of inclusion and exclusion criteria after giving informed consent. CT virtual endoscopy was performed through a dedicated protocol. Classification of variceal grading was done independently by a radiologist and endoscopist who were blinded to each other's findings. Results: The diagnostic performance of oesophageal varices detection by CT virtual oesophagography was good, with sensitivity: 86%, specificity: 90%, PPV: 98%, NPV: 56%, and diagnostic accuracy: 87%. There was substantial agreement between the 2 methods, and this agreement was statistically significant (Cohen's k = 0.616, p ≤ 0.001). Conclusions: Based on our findings, we conclude that the current study has the potential to change the way chronic liver disease is managed, as well as generate similar medical research endeavours. A multicentric study with a large number of patients is needed to improve the experience with this modality