Economics

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Public Subsidy on Education, Welfare and Wage Inequality in a Small Open Developed Economy: A Two-period Analysis

World University root node

Indian Journal of Human Development

Non-Hospitalisation Care in West Bengal: A Demand-Side Assessment

New software tools powered by Artificial Intelligence (AI) are going to be dominant components of near-future healthcare. Soon, medical practitioners and researchers will routinely adopt a wide range of machine-learning techniques in most of their daily tasks. Here, I show some of the most exciting results obtained in this space and discuss how future developments of AI will radically change the way we diagnose and cure people. Artificial Intelligence (AI) is going to heavily affect how we will research, diagnose and cure diseases in the very near future. The adoption of machine learning tools and algorithms by physicians and researchers will be of incredible benefit for the whole community. It will allow prompter and more accurate diagnoses, help doctors navigate the plethora of new medical research in order to better define their curing strategies and allow researchers to develop new cures and better identifying new patterns in complex data. Two main components are responsible for the rapid adoption of AI in healthcare: the availability of large volumes of personal health data and the massive advancements in computing technology. Big and deep Data Until few years ago, the only way to measure most health biomarkers required systems and devices available only in the hospital environment. As a consequence, health data records in the past were obtained in a hospital environment only in cases where the actual analyses/scans were required (i.e. not in a continuous fashion all-over the daily life of the patient). But the growing use of cheap and always-recording sensors embedded on wearable smart devices and smartphones today for personal health data collection and sharing is changing this. These sensors are increasingly efficient and accurate, often competing in quality with their medical-level counterparts.The affordability of such devices and their capacity for continuously collecting data has provoked an explosion in the amount of personal-health data available to researchers and practitioners. Moreover, an easy and continuous monitoring of biomarkers outside the hospital environment can provide physicians with a much more frequent measure of their patients wellbeing. Of course, this raises a number of ethical and privacy concerns, which will be discussed later. Meanwhile, analysis and patient examination technology in healthcare facilities is becoming more detailed and able to record huge amounts of information in single scans. As an example, DNA sequencing is no more an inaccessibly expensive exam as it used to be and even a simple blood analysis is able to return dozens of different biomarker measurements. However, when all this personal health data is aggregated in order to obtain a more complete picture of a patient's health status, the number of parameters to be studied can largely exceed the number that humans are able to evaluate. Most diagnoses are based on finding patterns in symptoms that can be linked to known diseases. The better the mapping of these symptoms, the lower the likelihood of a wrong diagnosis (assuming that the diseases'

Foreign Direct Investment in Developing Countries

FDI in Healthcare

FDI, SEZ and Agriculture

General Equilibrium Models: Usefulness and Techniques of Application

Role of FDI in Developing Countries: Basic Concepts and Facts

Mimeo, Indian Statistical Institute, Delhi Centre

Culture, discrimination and women's work force participation: a study on Indian labor market

Health Care Quality vs Health Care Quantity: A General Equilibrium Analysis

International Journal of Management Concepts and Philosophy 8 (2-3)

Indian jute manufactures: adaptation and survival in a'sunset'industry

This paper makes an attempt to provide a theory of determination of interest rate in the informal credit market in a less developed economy in terms of a three-sector static deterministic general equilibrium model. There are two informal sectors which obtain production loans from a monopolistic moneylender and employ labour from the informal labour market. On the other hand, the formal sector employs labour at an institutionally fixed wage rate and takes loans from the competitive formal credit market. We show that an inflow of foreign capital and/or an emigration of labour raises (lowers) the informal (formal) interest rate but lowers the competitive wage rate in the informal labour market when the informal manufacturing sector is more capital-intensive vis-{\`{a}}-vis the informal agricultural sector. International factor mobility, therefore, raises the degrees of distortions in both the factor markets in this case. {\textcopyright} 2013 Elsevier B.V.

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Economic Modelling

International factor mobility, informal interest rate and capital market imperfection: A general equilibrium analysis