First-Countability and its generalizations

Not availabl

Study of clinical, electrocardiographic and echocardiographic profile in patients with chronic obstructive pulmonary disease

Background: Chronic obstructive pulmonary disease is the fourth leading cause of mortality worldwide. It is defined as a disease formal categorized by airflow limitation that is not fully reversible. Patients with chronic obstructive pulmonary disease (COPD) are at increased risk of cardiovascular disease. Electrocardiography (ECG) carries information about cardiac disease and prognosis in COPD patients. However, Echocardiography provides a rapid, noninvasive, portable, and accurate method to evaluate cardiac functions.Methods: A 100 patients of COPD fulfilling the inclusion criteria coming to OPD/wards of Medicine, Gitanjali medical college and Hospital, Udaipur were recruited. They were staged by pulmonary function test (PFT) and evaluated by electrocardiography and echocardiography. Statistical analysis of correlation was done with chi square test and statistical significance was taken p<0.05.Results: Mean age was 52.54±9.55 years, with male preponderance, male to female ratio 4.55:1. Mean duration of disease was 6.36±4.14 years. The common symptoms were Breathlessness (100%). Most common ECG and ECHO finding was RAD (52%) and PAH (54%) respectively. Statistically Right axis deviation and Poor ‘r’ wave progression was significantly correlated with disease severity by ECG findings while R.A. dilatation, R.V. dilatation and Pulmonary hypertension were significantly correlated with disease severity by ECHO findings (p<0.05).Conclusions: COPD is more common in males and in the 5th and 6th decade of life. Most of the patients have fairly advanced disease at presentation. The incidence of abnormalities of ECG and echocardiography increases with COPD severity. ECG and echocardiography are better tools than clinical methods in detecting R.V. dysfunction in COPD

HYBRID DATA APPROACH FOR SELECTING EFFECTIVE TEST CASES DURING THE REGRESSION TESTING

In the software industry, software testing becomes more important in the entire software development life cycle. Software testing is one of the fundamental components of software quality assurances. Software Testing Life Cycle (STLC)is a process involved in testing the complete software, which includes Regression Testing, Unit Testing, Smoke Testing, Integration Testing, Interface Testing, System Testing & etc. In the STLC of Regression testing, test case selection is one of the most important concerns for effective testing as well as cost of the testing process. During the Regression testing, executing all the test cases from existing test suite is not possible because that takes more time to test the modified software. This paper proposes new Hybrid approach that consists of modified Greedy approach for handling the test case selection and Genetic Algorithm uses effective parameter like Initial Population, Fitness Value, Test Case Combination, Test Case Crossover and Test Case Mutation for optimizing the tied test suite. By doing this, effective test cases are selected and minimized the tied test suite to reduce the cost of the testing process. Finally the result of proposed approach compared with conventional greedy approach and proved that our approach is more effective than other existing approach

Some results on vertex-edge neighborhood prime labeling

Let G be a graph with vertex set V (G) and edge set E(G). For u ∈ V (G), NV (u) = {w ∈ V (G)|uw ∈ E(G)} and NE(u) = {e ∈ E(G)|e = uv, for some v ∈ V (G)}. A bijective function f : V (G) ∪ E(G) → {1, 2, 3, . . . , |V (G) ∪ E(G)|} is said to be a vertex-edge neighborhood prime labeling, if for u ∈ V (G) with deg(u) = 1, gcd {f(w), f(uw)|w ∈ NV (u)} = 1 ; for u ∈ V (G) with deg(u) > 1, gcd {f(w)|w ∈ NV (u)} = 1 and gcd {f(e)|e ∈ NE(u)} = 1. A graph which admits vertex-edge neighborhood prime labeling is called a vertex-edge neighborhood prime graph. In this paper we investigate vertex-edge neighborhood prime labeling for generalized web graph, generalized web graph without central vertex, splitting graph of path, splitting graph of star, graph obtained by switching of a vertex in path, graph obtained by switching of a vertex in cycle, middle graph of path.Publisher's Versio

A Novel Deep Learning, Camera, and Sensorbased System for Enforcing Hand Hygiene Compliance in Healthcare Facilities

Hospital-acquired infections are a major cause of death worldwide, and poor hand hygiene compliance is a primary reason for their spread. This paper proposes an artificial intelligence, microcontroller, and sensor-based system that monitors and improves staff hand hygiene compliance at various critical points in a hospital. The system uses a Convolutional Neural Network (CNN) to detect and track if staff have followed the WHO hand rub/hand wash guidelines at alcohol dispensers, hospital sinks, and patient beds. The system also uses RFID tags, vibration motors, LEDs, and a central server to identify staff, alert them of their cleaning requirements, monitor their cleaning activity, and report compliance data. We obtain an accuracy of 90.6% in classifying all steps of the WHO-stipulated hand wash/hand rub guidelines and a testing accuracy of 89.8% on Ivanovs et al.’s dataset. The system ensures that hospital staff stay compliant to all WHO hand hygiene guidelines, saving countless lives