Bringing Global Sourcing into the Classroom: Experiential Learning via Software Development Project

Global sourcing of software development has imposed new skill requirements on Information Technology (IT) personnel. In the U.S., this has resulted in a paradigm shift from technical to softer skills such as communications and virtual team management. Higher education institutions must, consequently, initiate innovative curriculum transformations to better prepare students for these emerging workforce needs. This paper describes one such venture between Marquette University (MU), U.S.A. and Management Development Institute (MDI), India, wherein IT students at MU collaborated with Management Information Systems (MIS) students at MDI on an offshore software development project. The class environment replicated an offshore client/vendor relationship in a fully virtual setting while integrating communications and virtual team management with traditional IT project management principles. Course measures indicated that students benefited from this project, gained first-hand experience in the process of software offshoring, and learned skills critical for conduct of global business. For faculty considering such initiatives, we describe the design and administration of this class over two semesters, lessons learned from our engagement, and factors critical to success of such initiatives and those detrimental to their sustenance

Bringing Global Sourcing into The Classroom: Experiential Learning Via a Global Software Development Project

The growing trend in offshore software development has imposed new skills requirements on collaborating global partners. In the U.S. this has translated into skill sets that include communications, project management, business analysis, and team management. In a virtual setting, these skills take on a complex proportion. This paper describes an educational initiative in offshore software development between undergraduate students enrolled in a project management course at Marquette University, USA and graduate business students enrolled in an Information Systems Analysis and Design course at Management Development Institute, India. The course replicated an offshore client/vendor relationship in a virtual setting. For faculty considering such initiatives, this paper describes the setting and factors critical to success of this initiative and cautions against others that can be detrimental to such an effort

Project Quality of Offshore Virtual Teams Engaged in Software Requirements Analysis: An Exploratory Comparative Study

The off-shore software development companies in countries such as India use a global delivery model in which initial requirement analysis phase of software projects get executed at client locations to leverage frequent and deep interaction between user and developer teams. Subsequent phases such as design, coding and testing are completed at off-shore locations. Emerging trends indicate an increasing interest in off-shoring even requirements analysis phase using computer mediated communication. We conducted an exploratory research study involving students from Management Development Institute (MDI), India and Marquette University (MU), USA to determine quality of such off-shored requirements analysis projects. Our findings suggest that project quality of teams engaged in pure off-shore mode is comparable to that of teams engaged in collocated mode. However, the effect of controls such as user project monitoring on the quality of off-shored projects needs to be studied further

Sentiment Analysis of Twitter Data Using Naive Bayes Algorithm

Sentiment analysis the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral. Now a days the growth of social websites, blogging services and electronic media con-tributes huge amount of user give messages such as customer reviews, comments and opinions. Sentiment Analysis is an important term referred to collect information in a source by using NLP, computational linguistics and text analysis and to make decision by subjective information extracting and analyzing opinion, identifying positive and negative reviews measuring how positively and negatively an entity (public ,organization, product) is involved. Sentiment analysis is the area of study to analyze people’s reviews, emotion, attitudes and emotion from written languages. We concentrate on field of different opinion classification techniques, performed on any data set. Now a days most popular approaches are Bag of words and feature extraction used by researchers to deal with sentiment analysis i.e used by politician, news groups, manufactures organization, movies, products etc

Analysis of Twitter Data Using Deep Learning Approach: LSTM

Sentiment analysis the procedure of computationally identifying and categorizing evaluations expressed in a chunk of text, especially with a view to decide whether the writer’s mind-set toward a selected subject matter, product, etc. is high-quality, poor, or impartial[1]. Now a days the growth of social websites, running a blog offerings and electronic media con-tributes big amount of consumer supply messages which includes customer reviews, remarks and evaluations. Sentiment evaluation is an important term cited gather facts in a source with the aid of the usage of NLP, computational[2] linguistics and text analysis and to make decision through subjective information extracting and analyzing opinion, figuring out advantageous and bad opinions measuring how definitely and negatively an entity (public ,organization, product) is concerned. in the beyond decade , researcher have performed the sentiment analysis using device getting to know techniques which include guide vector gadget, naive bayes , maximum entropy method etc. Sentient analysis on social media textual content received lot of recognition because it includes pointers and pointers. lately deep gaining knowledge of methods like long short-term memory (LSTM) and convolution neural network (CNN) have gained recognition by means of displaying promising effects for speech and photograph processing, obligations in NLP through learning functions wealthy deep illustration from the facts robotically

Timing feedback-inhibition of the male reproductive hormone axis

Hormonal methods of male contraception have addressed feedback-inhbition of the cascade of hormones that has evolved to regulate sperm production but high concentrations of testosterone (T) in the blood have not worked satisfactorily. We hypothesized that an episodic event, such as degranulation of gonadotropin-releasing hormone (GnRH) neurons in the hypothalamus, could be as well inhibited by episodic interference as by continuously-applied suppression. We used a computational model of hypothalamus-pituitary-gonad axis described by Veldhuis et al. to test the hypothesis that episodic administration of T would inhibit GnRH and/or luteinizing hormone (LH) secretion. A set of stochastic differential equations model feedback as well as feed-forward actions of GnRH, LH and T. The model predicted feedback-inhibition of GnRH pulses in a dose and frequency dependent manner when transdermal patches or injections delivering pulsatile T were applied. Since the published model did not address the effect of chronic GnRH deprivation on the viability of pituitary gonadotrophes, we introduced a function to address this issue. Incorporation of this function in the model allowed the prediction of a “holiday period” in the contraceptive regimen, during which exogenous T would not be needed to protect from conception. Pulsatile T delivered by real-life transdermal delivery system applied as per a parsimonious regimen reduced secretion of LH and T and fertility. The vitality function, proposed to account for survival and proliferation of pituitary gonadotrophes correlated with in vivo observations as extensive apoptosis in the anterior pituitary was observed after application of transdermal T

Hyper Competitive Digital Marketing-An Advancement in Technology & its Future Prospective

Purpose: The purpose of this text is to provide an overview of digital marketing and its significance in today's world. It touches upon the evolving landscape of marketing and the challenges marketers face in adapting to new technologies and consumer behaviors.   Design/methodology/approach: This section discusses the approach taken in the text. It outlines the content's structure and how it presents information about digital marketing, including its focus on communication, customer engagement, and ethical considerations.   Findings: The findings section summarizes key points about digital marketing, emphasizing the increasing importance of mobile devices and the uncertainty surrounding the impact of the Internet of Things (IoT). It also highlights the challenges marketers face in measuring the effectiveness of traditional advertising.   Research, Practical & Social implications: Here, we delve into the implications of digital marketing from three perspectives: research, practical application, and social impact. It explores the need for research agendas related to ethics, privacy, and bias in marketing, as well as how marketing strategies and consumer behavior may evolve.   Originality/value: This section underscores the originality and value of the text by emphasizing the unique insights it offers into the future of marketing, including the intersection of technology, economics, and geopolitics. It suggests that the text provides valuable insights into ethical considerations and potential changes in marketing practices

Analytical Purity Method Development and Validation by gas Chromatography of L-valine Methyl Ester Hydrochloride for Production of Anti-hypertensive Drugs

Gas chromatography is the most widely used technique in pharmaceutical industry. Analytical chemistry research is largely driven by performance of sensitivity, selectivity, robustness, linear range, accuracy, precision. Validation is founded on but not specifically prescribed by regulatory requirements and is best viewed as an important and integral part of GMP (Good Manufacturing Practice). Gas chromatography method has been developed for L-valine methyl ester hydrochloride. It is used for production of anti-hypertensive drug. The Gas Chromatography system was used for method development and method validation with an auto injector and detection was performed by means of flame ionization detector (FID) with capillary column DB-624, 30m length, 0.53mm diameter and 1.0µm thickness. Nitrogen an inert gas was used as carrier gas. The method was validated for precision (system precision and method repeatability), recovery, linearity range, robustness and sample solution stability. The high recovery and low relative standard deviation confirms the suitability of the method for purity of L-valine methyl ester hydrochlride. It has been found from data of validation criteria that the proposed method has adequate reproducibility and specificity therefore suitable in pharmaceutical industry. Key words: Validation, Gas chromatography, FID, GMP, Pharmaceutical industry

Performance Analysis of GaN/AlGaN HEMTs Passivation using Inductively Coupled Plasma Chemical Vapour Deposition and Plasma Enhanced Chemical Vapour Deposition Techniques

In the present paper SiN thin film has been studied as a passivation layer and its effect on AlGaN/GaN HEMTs is investigated using two different deposition techniques i.e PECVD and ICPCVD. AlGaN/GaN HEMTs devices passivated with optimised SiN film have delivered lower gate leakage current (from μA to nA). Device source drain saturation current (Ids) increased from 400mA/mm to ~550 A/mm and the peak extrinsic trans-conductance increased from 100 mS/mm to 170 mS/mm for a 0.8 μm HEMT device. The optimised SiN passivation process has resulted in reduced current collapse and increased breakdown voltage for HEMT devices.