Graphical Analysis on Text Mining Unstructured Data Using D-Matrix

Fault dependency (D-matrix) is used as a diagnostic model that identifies the fault system data and its causal relationship at the hierarchical system-level. It consists of dependencies and relationship between identified failure modes and symptoms related to a system. Constructing such D-matrix fault detection model is time overwhelming task .A system is proposed that describes associate ontology based text mining on unstructured data using D-matrix for automatically constructing D-matrix by mining many repair verbatim text data (typically written in unstructured text) collected throughout the identification process. And also graphical model generation for each generated D-matrix. Initially we construct fault diagnosis ontology and then text mining techniques are applied to spot dependencies among failure modes and identified symptom. D-matrix is represented in graph so analysis gets easier and faulty parts becomes simply detectable. The proposed methodology are implemented as a prototype tool and validated by using real-life information collected from the automobile domain

DISSOLUTION ENHANCEMENT OF POORLY WATER-SOLUBLE DRUG BY CYCLODEXTRINS INCLUSION COMPLEXATION

Objective: Solubility of a drug is an important property that mainly influences the extent of oral bioavailability. Enhancement of oral bioavailability of poorly water-soluble drugs is the most challenging aspects of drug development. It is very important to find appropriate formulation approaches to improve the aqueous solubility and bioavailability of poorly aqueous soluble drugs. Ezetimibe is a new lipid lowering agent in the management of hypercholesterolemia. The drug is water-insoluble, lipophilic, and highly permeable according to the pharmaceutical classification system. Therefore, the bioavailability of ezetimibe may be improved by increasing its solubility. Methods: In present work solubility of ezetimibe was increased with inclusion complexes by a different technique like physical mixture, co-grinding and modified kneading method. The physical properties of the prepared inclusion complex of ezetimibe were characterised by Differential scanning calorimetry (DSC), X-ray diffraction spectroscopy (XRD), Fourier transform infra-red spectroscopy (FTIR) and in vitro dissolution studies. Results: From the dissolution studies of ezetimibe with HP-β-cyclodextrin(1:1 and 1:2), we conclude that the prepared complexes of ezetimibe with HP-β-cyclodextrin (1:2) by modified kneading method showed higher release i.e. 88.35% in 60 min. than in (1:1) 76.75% in 60 min. So, ezetimibe with HP-β-cyclodextrin (1:2) inclusion complex was used to formulate tablet by direct compression method. Conclusion: From the dissolution data of formulated tablets was observed that drug release was more in tablet dosage form as compared to plain ezetimibe and especially formulation in a ratio of 1:2 was found the promising result. Also from one-month stability data shows no significant change compared to the initial result

IMPROVEMENT OF PARAMETERS OF STACKED MICROSTRIP PATCH ANTENNA USING EDGE COUPLED PARASITIC PATCHES AND METAMATERIAL SUPERSTRATE

High directive stacked multilayer and edge coupled planar microstrip patch antenna made from a single-layer helical resonating metamaterial superstrate has been investigated. Metamaterials are artificial materials whose properties not found in nature. These materials have negative permittivity and permeability and negative index of refraction over a frequency band. In this paper, an innovative design of stacked rectangular microstrip patch antenna using four edge coupled parasitic patches and helical resonating metamaterial superstrate is explored. The Rogers RO3003 material of dielectric constant 3 has been used as the substrate of the antenna. Investigation is carried out related to bandwidth, gain and directivity enhancement by using edge coupled patches and metamaterial superstrate also the study of highest reduction in the size of helical resonator is carried out and highest reduction in size of helical resonator is achieved at a metallic fill ratio of 0.2. The proposed antenna exhibits wide percentage bandwidth of approximately 72.62%

Evaluation of Mass Drug Administration to Eliminate Lymphatic Filariasis in Surguja and Surajpur District, Chhattisgarh

Background: Mass Drug Administration of a single dose of DEC was launched on June 5, 2004 by the Government of India. MDA coverage increased gradually from 72.42% in 2004 to 88.96% in 2014. However, compliance has remained relatively low in most of the endemic areas as in 9 endemic Districts in State of Chhattisgarh. In Chhattisgarh State, Lymphatic Filariasis affected 14,818 people in the year 2011 and 13921 in the year 2013 with demonstrated manifestation. Objectives: To assess the coverage and compliance along with factors affecting compliance regarding MDA implementation in Surguja and Surajpur District of Chhattisgarh. Methods: A cross-sectional descriptive study was conducted from July-September 2021 in two district of Chhattisgarh. The division of segments and selection of the households was done based on the WHO criteria of coverage evaluation survey field guide in which from 30 villages, 450 households were covered. Result:  The overall coverage rate was 95.55% in Surguja and 89.16% in Surajpur District. The overall compliance was 89.3% with Coverage-Compliance gap of 4.12. The Effective Coverage Rate was 89.3% in 2243 eligible population of Surguja and Surajpur District. Coverage and Compliance was found more in females as compared to males but was found to be statistically not significant.  Coverage and Compliance was found more in Surguja district as compared to Surajpur district. Conclusion: Training programme for drug distributors should emphasize more on how to address the fear of side effects among beneficiaries and other reasons of low compliance for the benefit of the MDA programme

Employing reflective writing as a tool to enhance development of critical thinking skills among medical students

In medical education delivery, reflective writing is a process where medical students evaluate their thoughts and personal experiences. Critical thinking helps medical students and clinicians reach a provisional diagnosis, and decide about the management plans. Reflective writing encourages medical students to deeply analyze their clinical and learning experiences, and this entire process is vital for development of critical thinking skills. Acknowledging the need to train medical students in critical thinking, it is the need of the hour that teachers must explore and encourage students to reflect upon their learning experiences and in the process aid in the attainment of learning outcomes

Optimizing Service Placement and Enhancing Service Allocation for Microservice Architectures in Cloud Environments

With the increasing popularity of microservice architecture, there is a growing need to deploy service-based applications efficiently in cloud environments. Traditional cluster schedulers often fail to optimize service placement adequately, as they only consider resource constraints and overlook traffic demands between services. This oversight can lead to performance issues such as high response times and jitter. To address this challenge, we propose a novel approach to optimize the placement of service-based applications in clouds. Our approach involves partitioning the application into segments while minimizing overall traffic between them, and then strategically allocating these segments to machines based on their resource and traffic demands. We have developed a prototype scheduler and conducted extensive experiments on test bed clusters to evaluate its performance. The results demonstrate that our approach surpasses existing container cluster schedulers and heuristic methods, significantly reducing overall intermachine traffic and improving application performance

An Online Platform to Help the Needy People

Helpneedy.com, is an aspiration for online donation integration platform. It is an online platform for donor to work directly and check the status. Helpneedy.com is designed with all features which are very useful for NGO. Most of us are so plenteous with the resources in our home that we have not used on the other hand 30 % of our population starve for the very basic need of life on daily basis. Helpneedy.com provides transparency for donor. Donor will visit the website if he is pleased with the teams and condition then he\she can donate the stuff. All the donation information will be saved in the database and the donation stuff will be incremented in stock and donor will get the ACK message. The whole detail of stuff on monthly bases will be maintain by administer

FPGA SoC Implementation of Adaptive Deep Neural Network-Based Multimodal Edge Intelligence for Internet of Medical Things

In emergency healthcare services, accurate and timely decision-making is critical for the patient's life and death. The emergence of edge intelligence enables these service goals achievable for Internet of Medical Things (IoMT) compared with cloud-centric approaches. To assist medical personnel in intensive care units (ICU), we present the design of a network edge gateway that performs resource-efficient, real-time data analytics. We develop a cloud-configurable deep neural network (DNN) intellectual property (IP) core with an adaptable hardware architecture that executes four different types of analysis on an edge gateway. Our developed IP core adaptively switches from one architecture to another only in one clock cycle, based on the type of input features. The proposed IP core analyzes raw multimodal signals such as ECG, PPG, accelerometer, and other to discover anomalies in critically ill patients and their surroundings. We have validated the robustness of our developed model by comparing it with benchmark machine learning models and their previous implementations. The results show that our adaptive DNN model has obtained a software accuracy of 99.2% for ECG, 91.4% for PPG, 95% for activity classification, and 98.7% for smoke detection with a five-fold cross-validation strategy. Three versions of adaptive DNN IP cores (8-bit, 16-bit, 24-bit) are implemented on SoC/FPGA and compared together to study the effect of bit precision on accuracy, resource utilization, and power consumption. The developed adaptive DNN IP cores with 16-bits require 680 nanoseconds with a power consumption of 309 milliwatts for a single inference with a speed of 1.47 mega samples per second. Our analysis shows that the decentralization of intelligence in the IP core reduces data size from 96.25% to 98.75%. This flexible IP core has achieved significant power and resource utilization performance compared to independent implementation without compromising latency and throughput.</p

Implementation of addictive learning in medical education

Addictive learning represents a specific type of learning wherein medical students become highly motivated and engaged to continuously remain involved in the learning process. Considering the volume, depth, and complexity of information that a medical student must acquire, there is an immense need for sustained engagement to aid students in absorbing this information, and there lies the importance of addictive learning. Acknowledging the significance of addictive learning in medical education delivery and in facilitating the attainment of learning competencies among students, many teaching-learning strategies have been proposed and implemented globally. In conclusion, the concept of addictive learning in medical education can significantly enhance student engagement and thus play a crucial role in gaining knowledge and acquisition of skills. The need of the hour is to adopt different strategies to promote the implementation of addictive learning by creating a conducive learning environment for medical students