A Balanced Scorecard Framework for Measuring Sustainability of Business Organizations

Sustainability is about meeting the needs of today without compromising the needs of future generations. It involves focus on three main dimensions economic, environmental and social for achieving overall performance. Majority of the companies are adopting sustainability for business growth and boosting their corporate image for long term competitiveness, thereby receiving financial benefits as well. Sustainability is a concept that has come into picture a few years back and presently making a big mark in every field. In the thesis, we propose a balanced scorecard framework for measuring sustainability performance of business organizations. We begin by studying, why the companies should invest in sustainability initiatives and what are the tools used for measuring sustainability. We investigate different scorecards for measuring sustainability and propose a new sustainability scorecard model to measure organization’s overall sustainable performance. Our sustainability scorecard encompasses four main dimensions namely organization, process, core and learning. Each of these dimension comprises of various indicators obtained from GRI and corporate reports of 100 most sustainable companies- Forbes. The application of the sustainability scorecard is performed via multi criteria decision making technique called- Analytical Network Process (ANP). A numerical study is provided. The strength of the proposed model is that, - it overcomes the problems faced by the traditional balanced scorecards in sustainability evaluation of organizations. It provides a strong framework, has great flexibility and allows the opportunity to study the impact of one indicator over the other through the means of sensitivity analysis to identify improvements

Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog

The task of identifying out-of-domain (OOD) input examples directly at test-time has seen renewed interest recently due to increased real world deployment of models. In this work, we focus on OOD detection for natural language sentence inputs to task-based dialog systems. Our findings are three-fold: First, we curate and release ROSTD (Real Out-of-Domain Sentences From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, our examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset. Second, we explore likelihood ratio based approaches as an alternative to currently prevalent paradigms. Specifically, we reformulate and apply these approaches to natural language inputs. We find that they match or outperform the latter on all datasets, with larger improvements on non-artificial OOD benchmarks such as our dataset. Our ablations validate that specifically using likelihood ratios rather than plain likelihood is necessary to discriminate well between OOD and in-domain data. Third, we propose learning a generative classifier and computing a marginal likelihood (ratio) for OOD detection. This allows us to use a principled likelihood while at the same time exploiting training-time labels. We find that this approach outperforms both simple likelihood (ratio) based and other prior approaches. We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.Comment: Accepted for AAAI-2020 Main Trac

Prevalence of anaemia among women of reproductive age group in a rural block of Northern India

Background: Nutritional anaemia is a major public health problem in India and is primarily due to iron deficiency. The National Family Health Survey-3 (NFHS-3) data suggests that anaemia is widely prevalent among all age groups, and is particularly high nearly 55.3% amongst the most vulnerable in all women (15-49 years) Aims & Objectives: 1. To determine prevalence of Anaemia among women of reproductive age group in rural block of Haryana. 2. Effects of anaemia on mean height and weight of women of reproductive age group. Material Methods: Cross-sectional, descriptive. All the women of reproductive age group (15-45 years) of CHC Sample block were included as study participants. Results: The overall prevalence of anaemia was 48.9%. 4302 out of 8590 females had varying severity of anaemia while anaemia was absent in 51.1% of the study participants. Out of the 8590 females, 1612 (18.8%) were mildly anaemic, 2374 (27.6%) were moderately anaemic and 217 (2.5%) were severely anaemic. The study revealed that mean weight and mean height in non anaemic females was more than that of varying degree (severe, moderate, mild) of anaemic females. Conclusion: the present study revealed anaemia to be a major health problem among the women of reproductive age group in rural areas in Haryana affecting their health status

Breastfeeding counselling and support: is it adequate? a descriptive study from Rohtak, India

Background: Antenatal breastfeeding education and motivation along with postnatal encouragement and lactation support are likely to improve rates of early initiation and exclusive breastfeeding. This study was done to know whether antenatal and postnatal visits were utilized for promotion of optimum breastfeeding in addition to the routine obstetric services.Methods: This descriptive cross-sectional type of community based epidemiological study was conducted in rural and urban field practice area attached to Department of Community Medicine, Pt. B. D. Sharma PGIMS, Rohtak. A total of 500 mothers were studied. A pre-tested semi-structured interview schedule was used for interviewing the study subjects and house to house visits were carried out to collect the information.Results: Health functionaries were the source of information in 49.6% mothers in urban area as compared to only 29.2% in rural areas. Breastfeeding related information was better in the mothers counseled by health functionaries than not counseled group.Conclusions: Health functionaries should be instructed to inform all pregnant women about the benefits and management of breastfeeding during antenatal as well as postnatal visits. Existing breastfeeding education and postnatal support is not adequate in the population studied and needs to be strengthened

Determinants of Public Debt for middle income and high income group countries using Panel Data regression

To be able to predict when a nation will go bust has been one of toughest challenges in macroeconomics. Considerable research and effort has been put into this direction but still we are not in a position to say anything with certainty. This paper analyzes panel pool data on 31 countries across the world for the past 30 years on the basis of which the possibility of a sovereign default can be explored. The aim of this study is to understand which all factors influence the public debt in middle and high income group countries using Panel regression. Total effects model, Cross section fixed effects model, Cross section random effects model have been used to understand the factors whereas Autoregressive multiple regression model has been used to forecast the debt figures. The research findings suggest that the most important determinant of debt situation is GDP growth rate for both high and middle income group countries. In addition to this, Central government expenditure, education expenditure and Current account balance are also seen to influence the debt situation for both groups. FDI and Inflation have no impact on debt to GDP ratios among high income group countries but are found to be of more relevance when determining debt situation of middle income group countries. Population density and population above 65 years of age do not have any impact whatsoever on debt to GDP ratios of high and middle income group countries. Forecasts for weighted average public debt for high income group countries indicate steady increase. Debt situation of countries including Switzerland, Korea, Slovak rep, France and Japan is likely to worsen over the next 5 years. The debt situation of Greece and Spain is unlikely to change much whereas Ireland, USA, Canada, Italy, Hungary are expected to get better till 2015

Determinants of Public Debt for middle income and high income group countries using Panel Data regression

To be able to predict when a nation will go bust has been one of toughest challenges in macroeconomics. Considerable research and effort has been put into this direction but still we are not in a position to say anything with certainty. This paper analyzes panel pool data on 31 countries across the world for the past 30 years on the basis of which the possibility of a sovereign default can be explored. The aim of this study is to understand which all factors influence the public debt in middle and high income group countries using Panel regression. Total effects model, Cross section fixed effects model, Cross section random effects model have been used to understand the factors whereas Autoregressive multiple regression model has been used to forecast the debt figures. The research findings suggest that the most important determinant of debt situation is GDP growth rate for both high and middle income group countries. In addition to this, Central government expenditure, education expenditure and Current account balance are also seen to influence the debt situation for both groups. FDI and Inflation have no impact on debt to GDP ratios among high income group countries but are found to be of more relevance when determining debt situation of middle income group countries. Population density and population above 65 years of age do not have any impact whatsoever on debt to GDP ratios of high and middle income group countries. Forecasts for weighted average public debt for high income group countries indicate steady increase. Debt situation of countries including Switzerland, Korea, Slovak rep, France and Japan is likely to worsen over the next 5 years. The debt situation of Greece and Spain is unlikely to change much whereas Ireland, USA, Canada, Italy, Hungary are expected to get better till 2015

Rare Form of Crusted Scabies in Diabetes: A Case Report

Crusted scabies is an uncommon manifestation of parasitic infection caused by Sarcoptes scabiei var. hominis. This variant of scabies is seen in various immunocompromised states. Uncontrolled diabetes is an immunocompromised state which is characterized by increased incidence of various infections, and rarely, may be associated with this rare crusted scabies, which may pose a real diagnostic challenge. A high index of suspicion is required for timely and correct diagnosis in the best interest of the patient and the public health point of view to prevent the spread of this highly contagious infestation

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

CRediT authorship contribution statement: Dr. Aman Arora and Dr. Alireza Arabameri have conceptualized the study, prepared the dataset, and optimized the models. Dr. Manish Pandey has helped in writing the manuscript. Prof. Masood A. Siddiqui, Prof. U.K. Shukla, Prof. Dieu Tien Bui, Dr. Varun Narayan Mishra, and Dr. Anshuman Bhardwaj have helped in improving the manuscript at different stages of this work.Peer reviewedPostprin

NIOSOMES:THE UNIQUE VESICULAR DRUG CARRIERS

Drug targeting is the ability to direct a therapeutic agent specifically to desired site of action with little or no interaction with nontarget tissue. Niosomes are one of the best carriers for drug targeting. Niosomes are self assembled vesicles composed primarily of synthetic surfactants and cholesterol. They are analogous in structure to the more widely studied liposomes formed from biologically derived phospholipids. Niosomes are biodegradable, relatively nontoxic, more stable and inexpensive, an alternative to liposomes. The method of preparation of niosome is based on liposome technology. The basic process of preparation is the same i.e. hydration by aqueous phase of the lipid phase which may be either a pure surfactant or a mixture of surfactant with cholesterol. After preparing niosomal dispersion, unentrapped drug is separated by dialysis centrifugation or gel filtration. Niosomes can be SUV (Small Unilamellar Vesicles), MLV (Multilamellr Vesicles) or LUV (Large Unilamellar Vesicles). Niosomal drug delivery is potentially applicable to many pharmacological agents for their action against various diseases

Cost Estimation Tool for Commercial Software Development Industries

ABSTRACT: As cost associated to the software development is increasing continuously, there is a need to direct attention at better understanding of development process and calibrating previous models and methods making them applicable to existing environment such as proposing hybrid tools using the techniques based on existing models. The main aim of this paper is to improve the cost estimation accuracy of applications at all the phases of a software development lifecycle by calibrating COCOMO using a function point as a size measure instead of SLOC being used in COCOMO model. Here we have discussed the working of proposed software estimation tool that is a hybrid implementation of various software estimation and measurement techniques helping an organization to determine metrics such as Effort, Time and Cost, essential for improving turnaround time. This tool also works towards utilizing these metrics for project planning, scheduling and tracking