Detailed Overview of Software Smells

This document provides an overview of literature concerning software smells covering various dimensions of smells along with their corresponding references

Leveraging Sentiment Analysis for Twitter Data to Uncover User Opinions and Emotions

Huge amounts of emotion are expressed on social media in the form of tweets, blogs, and updates to posts, statuses, etc. Twitter, one of the most well-known microblogging platforms, is used in this essay. Twitter is a social networking site that enables users to post status updates and other brief messages with a maximum character count of 280. Twitter sentiment analysis is the application of sentiment analysis to Twitter data (tweets) in order to derive user sentiments and opinions. Due to the extensive usage, we intend to reflect the mood of the general people by examining the thoughts conveyed in the tweets. Numerous applications require the analysis of public opinion, including businesses attempting to gauge the market response to their products, the prediction of political outcomes, and the analysis of socioeconomic phenomena like stock exchange. Sentiment classification attempts to estimate the sentiment polarity of user updates automatically. So, in order to categorize a tweet as good or negative, we need a model that can accurately discern sarcasm from the lexical meaning of the text. The main objective is to create a practical classifier that can accurately classify the sentiment of twitter streams relating to GST and Tax. Python is used to carry out the suggested algorithm

Definitions of a Software Smell

Many authors have defined smells from their perspective. This document attempts to provide a consolidated list of such definitions

Lixisenatide: a once-daily glucagon-like peptide-1 receptor agonist

Lixisenatide (AVE0010) is a once-daily glucagon-like peptide-1 (GLP-1) receptor agonist used in the treatment of type 2 diabetes. Phase II dose-finding and pharmacodynamic studies identified the 20 µg once-daily dose as having the optimum combination of efficacy, convenience and tolerability. Lixisenatide was prospectively investigated in a series of 11 multinational, randomised, controlled phase III trials (GLP-1 agonist AVE0010 in patients with type 2 diabetes mellitus for Glycemic control and safety evaluation [Getgoal] programme) that included a direct head-to-head study with exenatide. The Getgoal programme established the efficacy and safety profile of lixisenatide 20 µg once daily across the spectrum of patients with type 2 diabetes, including patients not treated with anti-diabetic agents, those failing on oral agents and as an adjunct to basal insulin therapy. The main efficacy endpoints were met in all studies, with the baseline to endpoint reductions in HbA1c consistently ranging from 0.7% to 1.0%. In a head-to-head comparison with exenatide 10 µg twice daily, lixisenatide 20 µg once daily was non-inferior for HbA1c reduction, achieved with threefold fewer patients with symptomatic hypoglycemia events and better gastrointestinal tolerability. Three randomised trials of lixisenatide treatment added to basal insulin showed significantly improved glycemic control over placebo, with pronounced postprandial glucose reductions and good tolerability. Discontinuations for adverse events were consistently low, ranging from 2.5% to 10.4%. As the provision of individualized care moves center stage in diabetes management, lixisenatide with once-daily dosing, a single maintenance dose and fixed-dose pens offers an important treatment option for type 2 diabetes

QUANTIFICATION OF EXEMESTANE ACCUMULATION DURING MICROBIAL BIOCONVERSION BY TLC IMAGE ANALYSIS

Objective: The present study was aimed at developing a rapid, cost effective and accurate method for quantification of exemestane using thin layer chromatography (TLC) separation followed by image analysis and to test it for monitoring the accumulation of exemestane during microbial bioconversion.Methods: After microbial bioconversion and TLC separation of products formed, exemestane was quantified using ImageQuant TL v2003 image analysis software and the results were compared with high performance liquid chromatography (HPLC) analysis.Results: The percentage error between TLC and HPLC analyses was ranged from (-) 5.18 to (+) 5.51. Bacterial strains Arthrobacter simplex IAM 1660, Nocardia sp. MTCC 1534, Pseudomonas putida MTCC 1194 and Rhodococcus rhodochorus MTCC 291 respectively yielded 79.7 (72 h), 63.9 (72 h), 69.8 (96 h) and 83.2 (96 h) mole percent bioconversion of 6-methylene androstenedione to exemestane. Conclusion: Rhodococcus rhodochorus MTCC 291 was found to be the most suitable organism for the bioconversion and may be used to develop an eco-friendly route to replace chemical synthesis that eliminates the use of toxic chemicals and side products

Lean Implementation on Indian manufacturing firm

In today’s market every manufacturing industry is trying to implement ‘Lean’ in its operations. This emergent need of reducing waste and getting efficient production had created a boom for the Lean Production (LP). Many people from corporate firms and management associates want a productive tool for achieving this task. Value Stream Mapping (VSM) is the solution to such emerging need, it identifies the source of waste practices and tries to scale down them analytically. VSM in this way measures all the value and non-value added processes, to evaluate the origin of wastes, their effect on different operation of industry and the processes in between. In this paper we have implemented VSM technique on a small scale industry to showcase its effect on cost of production, production lead time and the developing some procedure for reduction of root cause of this loss. Considering the current processes of the industries operations Current State map is developed to show how actual production is taking place at the industry before implementing any lean procedure. A Future State Map is finally developed considering the lean behaviours to reduce the waste production and to increase its productivity. This is inspected along with its takt time calculation. Thus, by curbing these wasteful practices we will show how manufacturing performance of the company can be upgraded by employment of VSM

SUBSTRATE CARRIERS FOR C-1(2)-DEHYDROGENATION OF 6-METHYLENE ANDROSTENEDIONE TO EXEMESTANE BY GROWING AND IMMOBILIZED ARTHROBACTER SIMPLEX NCIM 2449

Objective: Permeability of hydrophobic steroid substrates across cell membrane is a critical factor during microbial bioconversion. To increase substrate intake, the feasibility of some organic solvents and emulsifiers as substrate carrier on the bioconversion of 6-methylene androstenedione to exemestane was assessed.Methods: Androstenedione, a commonly available steroid precursor, was chemically converted 6-methylene androstenedione. The time course of exemestane accumulation was estimated after addition of 6-methylene androstenedione dissolved in some organic solvents or dispersed with emulsifiers by growing and immobilized cells of Arthrobacter simplex NCIM 2449 in shake flask cultures.  Results: The use of substrate carriers for addition of 6-methylene androstenedione enhanced the bioconversion several folds. With growing bacterium in triplicate flasks, a peak mol % bioconversion recorded was- ethanol (67.25, 72 h); soybean oil + tween 80 (50.37, 48 h); acetone (38.84, 48 h); soybean oil (38.36, 48 h); lecithin (32.73, 48 h), methanol (32.71, 48 h) and tween 80 (10.37, 48 h). As compared to the growing cells, the bioconversion with Ca-alginate immobilized cells was delayed and peak mol % bioconversion was recorded as ethanol (60.78, 120 h); soybean oil + tween 80 (42.98, 120 h);  methanol (40.50,  72 h);  soybean oil (38.36, 48 h);  acetone (31.18, 72h ) and lecithin (33.67, 120 h); tween 80 (13.87, 120 h).Conclusion: The use of substrate carriers for addition of 6-methylene androstenedione increased the permeability of substrate and may be used to increase the yield of exemestane and reduce incubation time

Multi-Purpose Agriculture Machine

The paper aims on the design, development and the fabrication of the vehicle which can dig the soil, sow the seeds, leveler to close the soil and pump to spray water, these whole systems of the vehicle works with the battery and solar power, the vehicle is controlled by toggle switch. In recent years the development of the autonomous vehicles in the agriculture has experienced increased interest. The advantages of these vehicles are hands-free and fast input operations. In the field of agricultural autonomous vehicle, a concept is been developed to investigate if multiple small autonomous machine could be more efficient than traditional large tractors and human forces. Keeping the above ideology in mind, a unit with the following feature is designed, Ploughing is one of the first steps in farming. During this process we till the land and make it ready for the seed sowing. By tilling we mean that a plough will be used which will have teeth’s like structure at the end and will be able to turn the top layer of soil down and vice-versa. Seed sowing comes next where the seeds need to be put in ground at regular intervals and these needs to be controlled automatically. Limiting the flow of seeds from the seeds chamber is typically doing this. soil leveler is fitted to close the seeds to the soil and to level the ground. Water pump is used to spray the water

Quantifying Outlierness of Funds from their Categories using Supervised Similarity

Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection, as well as by fund managers for peer analysis and competitive positioning. As a result, a (unintended) miscategorization or lack of precision can significantly impact allocation decisions and investment fund managers. Here, we aim to quantify the effect of miscategorization of funds utilizing a machine learning based approach. We formulate the problem of miscategorization of funds as a distance-based outlier detection problem, where the outliers are the data-points that are far from the rest of the data-points in the given feature space. We implement and employ a Random Forest (RF) based method of distance metric learning, and compute the so-called class-wise outlier measures for each data-point to identify outliers in the data. We test our implementation on various publicly available data sets, and then apply it to mutual fund data. We show that there is a strong relationship between the outlier measures of the funds and their future returns and discuss the implications of our findings.Comment: 8 pages, 5 tables, 8 figure