A Low Cost Two-Tier Architecture Model For High Availability Clusters Application Load Balancing

This article proposes a design and implementation of a low cost two-tier architecture model for high availability cluster combined with load-balancing and shared storage technology to achieve desired scale of three-tier architecture for application load balancing e.g. web servers. The research work proposes a design that physically omits Network File System (NFS) server nodes and implements NFS server functionalities within the cluster nodes, through Red Hat Cluster Suite (RHCS) with High Availability (HA) proxy load balancing technologies. In order to achieve a low-cost implementation in terms of investment in hardware and computing solutions, the proposed architecture will be beneficial. This system intends to provide steady service despite any system components fails due to uncertainly such as network system, storage and applications.Comment: Load balancing, high availability cluster, web server cluster

Comparative Study on Agile software development methodologies

Today-s business environment is very much dynamic, and organisations are constantly changing their software requirements to adjust with new environment. They also demand for fast delivery of software products as well as for accepting changing requirements. In this aspect, traditional plan-driven developments fail to meet up these requirements. Though traditional software development methodologies, such as life cycle-based structured and object oriented approaches, continue to dominate the systems development few decades and much research has done in traditional methodologies, Agile software development brings its own set of novel challenges that must be addressed to satisfy the customer through early and continuous delivery of the valuable software. It is a set of software development methods based on iterative and incremental development process, where requirements and development evolve through collaboration between self-organizing, cross-functional teams that allows rapid delivery of high quality software to meet customer needs and also accommodate changes in the requirements. In this paper, we significantly identify and describe the major factors, that Agile development approach improves software development process to meet the rapid changing business environments. We also provide a brief comparison of agile development methodologies with traditional systems development methodologies, and discuss current state of adopting agile methodologies. We speculate that from the need to satisfy the customer through early and continuous delivery of the valuable software, Agile software development is emerged as an alternative to traditional plan-based software development methods. The purpose of this paper, is to provide an in-depth understanding, the major benefits of agile development approach to software development industry, as well as provide a comparison study report of ASDM over TSDM.Comment: 25 pages, 25 images, 86 references used, with authors biographie

Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations

Self-train Semi-supervised Contour Detection for Automated Monitoring of Surface Water Across the Globe

Surface water is a critical resource that requires constant monitoring due to drastic impacts of human use, climate change, and severe weather phenomena. Existing monitoring techniques are limited and vary in their efficacy. It is proposed, here, that contour detection in the visible range is better for monitoring dynamic and long-term changes to surface water bodies. For that purpose, a semi-automated method for collecting and labeling water contours from Landsat-8 and Sentinel-2 images is presented. Due to the need for human inspection, the method has thus far generated 14K labeled images from more than 200,000 images. Given the cost of data labeling, a deep semi-supervised self-learning system is proposed, which performs learning in two stages, known as the teacher-student model. The teacher is trained on the accurate human-labeled data, then used to pseudo-label the remaining unlabeled data. The student is trained on both human-labeled and machine pseudo-labeled data. A uniquely designed multi-scale UNet classifier that uses fewer parameters and is developed and shown to be more accurate than other state-of-the-art (SOTA) classifiers for both teacher and student. Random augmentations are used to ”noise” the student model and improve its generalization, and normalization schemes are used to blend the human-labeled loss with the machine-labeled loss. Without the self-training, the multi-scale UNet classifier has 69.2% F-score over the SOTA systems, that improves to 73.58% with self-training

Impact of Levels of Education on Perceived Academic Stress and Mental Wellbeing: An Investigation into Online Mode of Learning during Pandemic

A sudden outbreak of the current pandemic COVID-19 has switches the learning to online mode which leads to an increase in perceived academic stress and a serious threat to the mental well-being of the students across the globe. The aim of the current study was therefore, to examine the impact of online learning on perceived academic stress and mental well being of the students with moderating effects of levels of education, during the current pandemic. Measures of the constructs were obtained by the online Google form which consists of the Perceptions of Academic Stress Scale (PASS) by Dalia Bedewy and Adel Gabriel (2015) and Warwick- Edinburg Mental Well-being Scale (2008), from a sample of 150 undergraduate students aged 19-25 years studying in different colleges of Bengaluru, India. Mental well -being constituted the criterion variable whilst academic stress and levels of education were treated as predictor variables. Two-way ANOVA were employed. Results show that academic stress is a significant negative predictor of mental wellbeing (r = -.083; p 0.05). It was concluded that combined academic stress and educational levels have an impact on mental wellbeing of students in online mode of learning during the current pandemic, but this impact is low (only 5.2%)

The study of Pharmacoeconomics analysis on anti tuberculosis drugs Rifampicin & Ethambutol

Cost-benefit analysis can be used to quantify the value of clinical pharmacy services. Providing Effective Therapy and Minimum cost, Quantify costs of care, Quantify outcomes, Assess whether and by how much average costs and outcomes differ among treatment groups, Compare magnitude of difference in costs and outcomes and evaluate “value for costs” by reporting a cost-effectiveness ratio, net monetary benefit, or probability that ratio is acceptable – Potential hypothesis: Cost per quality-adjusted life year saved significantly less than Rs.75,000, To Perform sensitivity analysis. For providing good effective therapy with less adverse drug reaction at affordable price, Cost-Identification, Cost-Effectiveness Analysis, Cost-Utility Analysis, Cost-Benefit Analysis, Clinical outcomes: Cure, comfort and survival, Humanistic outcomes: Physical, emotional, social function, role performance, Economic outcomes, Economic Evaluation, Cost of Illness Evaluation (COI), Cost Benefit Analysis (CBA), Cost Minimization Analysis, Cost Effective Analysis: Cost Utility Analysis

Prevalence of premenstrual syndrome and dysmenorrhea among medical students and its impact on their college absenteeism

Background: Dysmenorrhea and premenstrual syndrome are two most common gynaecological problem leading to college absenteeism seen among female medical students. Aim of the study was to evaluate the factors associated and prevalence of dysmenorrhoea and PMS and its effects on the quality of life, particularly absenteeism from college in female medical students. The health care profession has an obligation to provide and to promote education on menstruation and related subjects.Methods: This is a prospective study, conducted on 100 MBBS students studying in a medical college at Mangalore. All participants were given a preformed questionnaire to complete. Dysmenorrhea was assessed based on WaLiDD scoring system. Diagnosis of PMS in the present study was made according to diagnosis criteria proposed by American College of obstetrician and gynecology. The severity of their condition was assessed based on their absenteeism from college/classes.Results: The average age of the participants was 21 year±1 year. The prevalence of dysmenorrhea was 45% and that of the pre-menstrual syndrome was 68%. Pre-menstrual syndrome (p = 0.05) is significantly associated with overweight, obesity and physical inactivity but not the same for dysmenorrhea. 73% and 60% of students consumed junk food suffered from PMS and dysmenorrhea respectively, 40% of students with dysmenorrhea reported limitation of daily activities and significantly associated with college absenteeism (p = 0.005). The most frequent somatic symptom of PMS in this study was breast tenderness (41%) and affective symptom was irritability (35%).Conclusions: Dysmenorrhea and PMS is highly prevalent among female medical students; it is related to college/class absenteeism. Unhealthy and sedentary lifestyle could be the attributing factors which has to be addressed by health education in order to improve the quality of life and academic performance by the medical students