TRIBOELECTRIC NANOGENERATORS (TENG): FACTORS AFFECTING ITS EFFICIENCY AND APPLICATIONS

The demand for energy is increasing tremendously with modernization of the technology and requires new sources of renewable energy. The triboelectric nanogenerators (TENG) are capable of harvesting ambient energy and converting it into electricity with the process of triboelectrification and electrostatic-induction. TENG can convert mechanical energy available in the form of vibrations, rotation, wind and human motions etc., into electrical energy there by developing a great scope for scavenging large scale energy. In this review paper, we have discussed various modes of operation of TENG along with the various factors contributing towards its efficiency and applications in wearable electronics

A CRITICAL REVIEW ON THE MATERIAL ASPECTS OF TRIBOELECTRIC NANOGENERATORS (TENG)

Triboelectric nanogenerators (TENG) take the advantage of coupling effect for harvesting energy in the area of electronics for various self-powered applications. These nanogenerators are capable of converting energy in our surroundings into electrical energy by using the process of electrostatic induction and contact electrification. Triboelectric layers of a TENG are formed basically with the use of various polymers, metals and other inorganic materials like PTFE (Poly tetra fluoro ethylene), PDMS (polydimethyl siloxane), FEP (Fluorinated ethylene propylene) and Kapton. Selection of different materials for the device fabrication is very important since it contribute towards the triboelectric effect and also forms the fundamental structure for the proposed TENG device. In this review article, we emphasis mainly on various triboelectric materials considering factors such as stability, flexibility, power density etc., to improve upon the electrical output of the devices for different applications

Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be attributed to noise or randomness in data (aleatoric) and incorrect model inferences (epistemic). While model uncertainty can be reduced with more data or bigger models, aleatoric uncertainty is more intricate. This work proposes a novel approach that interprets data uncertainty estimated from a self-supervised task as noise inherent to the data and utilizes it to reduce aleatoric uncertainty in another task related to the same dataset via data augmentation. The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task. Our findings demonstrate the effectiveness of the proposed approach in significantly reducing the aleatoric uncertainty in the image segmentation task while achieving better or on-par performance compared to the standard augmentation techniques.Comment: Accepted in IEEE International Symposium on Biomedical Imaging (ISBI) 202

Affective computing: challenges and prospect

Abstract-Affective computing simulates empathy through machine that could recognize, interpret, process and respond to human affects. With the use of sensors and computational devices, it proposes to exhibit either innate emotional capabilities or that is capable of convincingly simulating emotions. The paper focuses on varied challenges and future scope of affective computing. The technologies for affective computing are varied but expression if not natural may not yield 100% accurate results. The systems may lack rotational movement freedom and also ignores situational factors in emotional understanding

Prevalence of hepatitis C in patients with chronic kidney disease at a tertiary care hospital in north India: a retrospective analysis

Background: Hepatitis C and chronic kidney disease (CKD) both present an unsolved public health problem Hepatitis C virus (HCV) is easily transmitted in haemodialysis units and by kidney transplantation. HCV leads to increased mortality and morbidity due to cirrhosis and hepatocellular carcinoma, while accelerating the progression of CKD. The aim of the  study was to describe the demographic, clinical/biochemical profile and prevalence of patients with CKD who have HCV infection.Methods: This was a retrospective analysis of patients with CKD who presented to out/in patient department of medicine in a tertiary care center in Jammu from a period of Feb 2016 to Nov 2018. Detailed clinical history along with previous lab reports were noted and tests for HCV infection were conducted in all patients. Diagnosis of HCV was made via HCV RNA(RT PCR) and positive  Anti HCV IgG serology.Results: Total 67 patients were included with median age of 54 years (range 43-72 years) with majority 76.1% being males, and 71.6% within 41-60 years age group. 31.4% were HCV positive out of which 81% were males. 7 patients were found to have co-infection with HIV and HBsAg. Genotype 1 (72%) was found to be more common than Genotype 3. Ultrasonography and Upper GI endoscopy showcased 57% with dilated spleenoportal axis  and oesophageal varices respectively.Conclusions: Prevalence of HCV infection in CKD patients is high with genotype 1 being commonest. False negative Anti HCV antibody is common hence screening with HCV RNA is recommended. Strict universal precautions should be employed in hospitals and dialysis units to prevent transmission

Understanding Calibration of Deep Neural Networks for Medical Image Classification

In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by providing insights into the model's certainty, identifying cases that require attention, and establishing trust in its predictions. Consequently, the significance of a well-calibrated model becomes paramount in the medical imaging domain, where accurate and reliable predictions are of utmost importance. While there has been a significant effort towards training modern deep neural networks to achieve high accuracy on medical imaging tasks, model calibration and factors that affect it remain under-explored. To address this, we conducted a comprehensive empirical study that explores model performance and calibration under different training regimes. We considered fully supervised training, which is the prevailing approach in the community, as well as rotation-based self-supervised method with and without transfer learning, across various datasets and architecture sizes. Multiple calibration metrics were employed to gain a holistic understanding of model calibration. Our study reveals that factors such as weight distributions and the similarity of learned representations correlate with the calibration trends observed in the models. Notably, models trained using rotation-based self-supervised pretrained regime exhibit significantly better calibration while achieving comparable or even superior performance compared to fully supervised models across different medical imaging datasets. These findings shed light on the importance of model calibration in medical image analysis and highlight the benefits of incorporating self-supervised learning approach to improve both performance and calibration.Comment: Accepted in Computer Methods and Programs in Biomedicine Journa

3-(4-Chloro­anilino)-5,5-dimethyl­cyclo­hex-2-en-1-one

The asymmetric unit of the title compound, C14H16ClNO, contains two independent mol­ecules, both with the cyclo­hexene ring in a sofa conformation. In the crystal, N—H⋯O hydrogen bonds link the mol­ecules related by translation along the a axis into two crystallographically independent chains. Weak C—H⋯π inter­actions are also observed