GSM Based Health Assistant for People with Chronic Diseases

A GSM based health assistant for people with chronic diseases proposes and implements a prototype to help people with chronic diseases. The system is composed of two components namely, wearable body area network and a microprocessor unit with GSM and GPS. This project can track certain chronic diseases namely, heart problems, asthma, apart from the chronic diseases can also track blood pressure with sensors namely, electrocardiogram (ECG) sensor, temperature sensor, heart beatsensor and blood pressure sensor. This project keeps track of the readings from the sensors and if there are some abnormalities it would send messages via GSM stored in SIM.The location of the patient can be sent using GPS

Saccharification and Single Step Fermentation of Cassava Peel by Mixed Culture of Saccharomycopsis fibuligera NCIM 3161 and Zymomonas mobilis MTCC 92

Abstract The main objective of this study was to utilize the cassava peel by saccharification and single step fermentation (SSF) by mixed culture of Saccharomycopsis fibuligera NCIM 3161 and Zymomonas mobilis MTCC 92. Single step fermentation was performed in conical flasks with various concentrations of cassava peel (30, 50, 70 and 90 g/L), pH (3.5, 4.5 and 5.5), temperature (37, 47 and 57°C), reaction time (72, 720 and 168 h), inoculum size (5, 10 and 15 % v/v) and agitation speed (50, 100 and 150 rpm). Cell growth was identified by measuring optical density at 660 nm. The total reducing sugars were determined by centrifuging at 5000 rpm for 10 min by 3,5-dinitrosalicylic acid method. Starch concentration was estimated by anthrone method. Ethanol concentration was determined by acid dichromate method. The optimum process conditions were substrate concentration of 70 g/L, pH of 4.5, temperature of 37°C, reaction time of 120 h, inoculum size of 10 % (v/v) and agitation speed of 100 rpm. Under these conditions, the highest ethanol concentration of 26.46 g/L was obtained for cassava peel (93.75 % of theoretical yield)

High-Performance Computing for SKA Transient Search: Use of FPGA based Accelerators -- a brief review

This paper presents the High-Performance computing efforts with FPGA for the accelerated pulsar/transient search for the SKA. Case studies are presented from within SKA and pathfinder telescopes highlighting future opportunities. It reviews the scenario that has shifted from offline processing of the radio telescope data to digitizing several hundreds/thousands of antenna outputs over huge bandwidths, forming several 100s of beams, and processing the data in the SKA real-time pulsar search pipelines. A brief account of the different architectures of the accelerators, primarily the new generation Field Programmable Gate Array-based accelerators, showing their critical roles to achieve high-performance computing and in handling the enormous data volume problems of the SKA is presented here. It also presents the power-performance efficiency of this emerging technology and presents potential future scenarios.Comment: Accepted for JoAA, SKA Special issue on SKA (2022

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection

In this research, we proposed a Deep Convolutional Neural Network (DCNN) model for image-based plant leaf disease identification using data augmentation and hyperparameter optimization techniques. The DCNN model was trained on an augmented dataset of over 240,000 images of different healthy and diseased plant leaves and backgrounds. Five image augmentation techniques were used: Generative Adversarial Network, Neural Style Transfer, Principal Component Analysis, Color Augmentation, and Position Augmentation. The random search technique was used to optimize the hyperparameters of the proposed DCNN model. This research shows the significance of choosing a suitable number of layers and filters in DCNN development. Moreover, the experimental outcomes illustrate the importance of data augmentation techniques and hyperparameter optimization techniques. The performance of the proposed DCNN was calculated using different performance metrics such as classification accuracy, precision, recall, and F1-Score. The experimental results show that the proposed DCNN model achieves an average classification accuracy of 98.41% on the test dataset. Moreover, the overall performance of the proposed DCNN model was better than that of advanced transfer learning and machine learning techniques. The proposed DCNN model is useful in the identification of plant leaf diseases