Impact Assessment of Nutritional Supplement Program in Urban Settings: A study of under nutrition in Slum Community of Mumbai

Malnutrition among 0-6 years is an impending problem, especially among economically backward communities in urban India. In an attempt to alleviate malnutrition, within the target community, the Foundation for Mother and Child, India, prepared a special food supplement, using cheap and locally available resources. A brief evaluation study was conducted to evaluate the impact of this food supplement on improvement in parameters like weight, height, and mental abilities among the participants. The study was conducted between August 2010 to October 2010 (n=51), with the participants divided in four different age groups. The height and weight parameters were compared to ICMR standards for the age to understand the impact of the program. The mean improvement in height was 0.84 cms (SD-0.6) while the mean improvement in weight was 0.57 kgs (SD-0.8). A significant variance was observed due to different consumption levels of the supplement and differing levels of under-nutrition. A strong correlation was observed between the consumption level and the improvement in height and weight parameters. In addition, a positive correlation was observed between the extent of deviation from ICMR standards and levels of improvement in height and weight parameters. No differential in terms of gender was observed within the community. No significant findings were observed with reference to improvement in mental ability, primarily as the study duration was short. The study has laid the roadmap for a larger and multifaceted study to be conducted to critically evaluate the effectiveness of the nutritional supplement

Private Sector in Indian Healthcare Delivery: Consumer Perspective and Government Policies to promote private Sector

This research paper attempts to collate literature from various sources, in an attempt to answer three pertinent questions related to healthcare in India. Firstly, what is it meant by ‘private sector’ in healthcare delivery system of India, secondly how has the private sector evolved over the decades and what has been the role of the government in propelling the growth. Finally, the paper tries to highlight some of the factors that have promoted the growth of private sector in India with specific reference to quality of medical care. The paper explicitly indicates that the deficiencies in the public health delivery system of India, was the key to growth of private infrastructure in healthcare. The shift of hospital industry for ‘welfare orientation’ to ‘business orientation’ was marked by the advent of corporate hospitals, supported by various policy level initiatives made by the government. Today, there are over 20 international healthcare brands in India with several corporate hospitals. However, a large section of the ‘private healthcare delivery segment’ is scattered and quality of medical care continues to remain a matter of concern. This paper tracks the various government initiatives to promote private investment in healthcare and attempts to explore the reasons for preference of the private sector. Surprisingly, in contrast to contemporary belief, quality of medical care doesn’t seem to be the leading cause for preference of the private sector. Except for a few select corporate and trust hospitals, quality of medical care in private sector seems to be poor and at times compromised

Performance Evaluation of MPLS in a Virtualized Service Provider Core (with/without Class of Service)

The last decade has witnessed a major change in the types of traffic scaling the Internet. With the development of real-time applications several challenges were faced within traditional IP networks. Some of these challenges are delay, increased costs faced by the service provider and customer, limited scalability, separate infrastructure costs and high administrative overheads to manage large networks etc. To combat these challenges, researchers have steered towards finding alternate solutions. Over the recent years, we have seen an introduction of a number of virtualized platforms and solutions being offered in the networking industry. Virtual load balancers, virtual firewalls, virtual routers, virtual intrusion detection and preventions systems are just a few examples within the Network Function Virtualization world! Service Providers are trying to find solutions where they could reduce operational expenses while at the same time meet the growing bandwidth demands of their customers. The main aim of this thesis is to evaluate the performance of voice, data and video traffic in a virtualized service provider core. Observations are made on how these traffic types perform on congested vs uncongested links and how Quality of Service treats traffic in a virtualized Service Provider Core using Round Trip Time as a performance metric. This thesis also tries to find if resiliency features such as Fast Reroute provide an additional advantage in failover scenarios within virtualized service provider cores. Juniper Networks vSRX are used to replicate virtual routers in a virtualized service provider core. Twenty-Four tests are carried out to gain a better understanding of how real-time applications and resiliency methods perform in virtualized networks. It is observed that a trade-off exists when introducing QoS on congested primary and secondary label switched paths. What can be observed thru the graphs is having Quality of Service enabled drops more packets however gives us the advantage of lower Round Trip Time for in-profile traffic. On the hand, having Quality of Service disabled, permits more traffic but leads to bandwidth contention between the three traffic classes leading to higher Round-Trip Times. The true benefit of QoS is seen in traffic congestion scenarios. The test bed built in this thesis, shows us that Fast Reroute does not add a significant benefit to aid in the reduction of packet loss during failover scenarios between primary and secondary paths. However, in certain scenarios fast reroute does seem to reduce packet loss specifically for data traffic

An Improved Robust Optimization Approach for Scheduling Under Uncertainty

In practice, the uncertainty in processing time data frequently affects the feasibility of optimal solution of the nominal production scheduling problem. Using the unit-specific event-based continuous time model for scheduling, we develop a novel multi-stage robust approach with corrective action to ensure robust feasibility of the worst case solution while reducing the conservatism arising from traditional robust optimization approaches. We quantify the probability of constraint satisfaction by using a priori and a posteriori probabilistic bounds for known and unknown uncertainty distributions, consequently, improving the objective value for a given risk scenario. Computational experiments on several examples were carried out to measure the effectiveness of the proposed method. For a given constraint satisfaction probability, the proposed method improves the objective value compared to the traditional robust optimization approaches

An Improved Robust Optimization Approach for Scheduling Under Uncertainty

In practice, the uncertainty in processing time data frequently affects the feasibility of optimal solution of the nominal production scheduling problem. Using the unit-specific event-based continuous time model for scheduling, we develop a novel multi-stage robust approach with corrective action to ensure robust feasibility of the worst case solution while reducing the conservatism arising from traditional robust optimization approaches. We quantify the probability of constraint satisfaction by using a priori and a posteriori probabilistic bounds for known and unknown uncertainty distributions, consequently, improving the objective value for a given risk scenario. Computational experiments on several examples were carried out to measure the effectiveness of the proposed method. For a given constraint satisfaction probability, the proposed method improves the objective value compared to the traditional robust optimization approaches

Worst-Case Optimal Covering of Rectangles by Disks

We provide the solution for a fundamental problem of geometric optimization by giving a complete characterization of worst-case optimal disk coverings of rectangles: For any $\lambda\geq 1$, the critical covering area $A^*(\lambda)$ is the minimum value for which any set of disks with total area at least $A^*(\lambda)$ can cover a rectangle of dimensions $\lambda\times 1$. We show that there is a threshold value $\lambda_2 = \sqrt{\sqrt{7}/2 - 1/4} \approx 1.035797\ldots$, such that for $\lambda<\lambda_2$ the critical covering area $A^*(\lambda)$ is $A^*(\lambda)=3\pi\left(\frac{\lambda^2}{16} +\frac{5}{32} + \frac{9}{256\lambda^2}\right)$, and for $\lambda\geq \lambda_2$, the critical area is $A^*(\lambda)=\pi(\lambda^2+2)/4$; these values are tight. For the special case $\lambda=1$, i.e., for covering a unit square, the critical covering area is $\frac{195\pi}{256}\approx 2.39301\ldots$. The proof uses a careful combination of manual and automatic analysis, demonstrating the power of the employed interval arithmetic technique.Comment: 45 pages, 26 figures. Full version of an extended abstract with the same title accepted for publication in the proceedings of the 36th Symposium on Computational Geometry (SoCG 2020

Conjugated linoleic acid rich vegetable oil production from linoleic rich oils by heterogeneous catalysis

A heterogeneous catalytic vacuum distillation process is used under high temperature conditions to isomerize linoleic acid in triacylglyceride vegetable oils to CLA to produce CLA-rich oils. After processing, the catalyst may be removed by filtration or centrifugation to obtain high quality, CLA-rich oils. The CLA-rich oils may then serve as a potent and bioactive nutraceutical and can be incorporated into various food products, such as a CLA-rich dressing, margarine or chips

Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression

This paper aims at determining the various economic and non-economic factors that can influence the voting behaviour in the forthcoming United States Presidential Election using Lasso regression, a Machine learning algorithm. Even though contemporary discussions on the subject of the United States Presidential Election suggest that the level of unemployment in the economy will be a significant factor in determining the result of the election, in our study, it has been found that the rate of unemployment will not be the only significant factor in forecasting the election. However, various other economic factors such as the inflation rate, rate of economic growth, and exchange rates will not have a significant influence on the election result. The June Gallup Rating, is not the only significant factor for determining the result of the forthcoming presidential election. In addition to the June Gallup Rating, various other non-economic factors such as the performance of the contesting political parties in the midterm elections, Campaign spending by the contesting parties and scandals of the Incumbent President will also play a significant role in determining the result of the forthcoming United States Presidential Election. The paper explores the influence of all the aforementioned economic and non-economic factors on the voting behaviour of the voters in the forthcoming United States Presidential Election. The proposed Lasso Regression model forecasts that the vote share for the incumbent Republican Party to be 41.63% in the 2020 US presidential election. This means that the incumbent party is most likely to lose the upcoming election