Low Carbon Mobility Plans: A Case Study of Ludhiana, India

AbstractTransportation is one of the driving forces of any economy. The reliance on petroleum as a fuel is a main source of concern. The major share of transport sector in petroleum has many consequences. India is the world's 4th largest emitter, responsible for ∼5% of the world's carbon emissions, the major contribution of this emission is from transport sector.In this study a low carbon mobility options for Ludhiana city as a case study is presented. In order to understand the impact of these options a stated preference survey is carried out. The shift to low carbon modes (bicycles, walk and public transport) is quantified. Shifts from petroleum driven vehicles to electric is also discussed. The projection to the future the per capita emissions of each of the scenarios: Scenario 1: Lanes for bus, bicycle and walk, Scenario 2: Scenario 1 plus improved buses, Scenario 3: Scenario 2 +policy against cars, Scenario 4: Electric vehicles (electric equivalents of two-wheelers and cars). It is found from the study that the emissions are least for scenario 3 (when there are parking policies imposed against cars in addition to improved transit policy and buses along with independent lanes for buses, and dedicated paths cycling and walkin

IMAGE-BASED IDENTIFICATION OF MLB DISEASE OF MAIZE

Not AvailableIn recent years, deep learning techniques have become very popular in the field of image recognition and classification. Image-based diagnosis of diseases in crops using deep learning techniques has become trendy in the current scientific community. In this study, a deep convolutional neural network (CNN) model has been developed to identify the images of maydis leaf bight (MLB) (Cochliobolus heterostrophus) disease of maize (Zea mays L.) crop. A total of 1547 digital images of maize leaves (596 healthy and 951 infected with maydis leaf blight disease) have been collected from different agricultural farms using hand-held camera and smartphones. The images have been collected from the experimental plots of BCKV, West Bengal and ICAR-IARI, New Delhi during 2018–19. The architectural framework of popular state-of-the network ‘GoogleNet’ has been used to build the deep CNN model. The developed model has been successfully trained, validated and tested on the above-mentioned dataset. The trained model has achieved an overall accuracy of 99.14% on the separate test dataset.Not Availabl

Developing Standard Treatment Workflows—way to universal healthcare in India

Primary healthcare caters to nearly 70% of the population in India and provides treatment for approximately 80–90% of common conditions. To achieve universal health coverage (UHC), the Indian healthcare system is gearing up by initiating several schemes such as National Health Protection Scheme, Ayushman Bharat, Nutrition Supplementation Schemes, and Inderdhanush Schemes. The healthcare delivery system is facing challenges such as irrational use of medicines, over- and under-diagnosis, high out-of-pocket expenditure, lack of targeted attention to preventive and promotive health services, and poor referral mechanisms. Healthcare providers are unable to keep pace with the volume of growing new scientific evidence and rising healthcare costs as the literature is not published at the same pace. In addition, there is a lack of common standard treatment guidelines, workflows, and reference manuals from the Government of India. Indian Council of Medical Research in collaboration with the National Health Authority, Govt. of India, and the WHO India country office has developed Standard Treatment Workflows (STWs) with the objective to be utilized at various levels of healthcare starting from primary to tertiary level care. A systematic approach was adopted to formulate the STWs. An advisory committee was constituted for planning and oversight of the process. Specialty experts' group for each specialty comprised of clinicians working at government and private medical colleges and hospitals. The expert groups prioritized the topics through extensive literature searches and meeting with different stakeholders. Then, the contents of each STW were finalized in the form of single-pager infographics. These STWs were further reviewed by an editorial committee before publication. Presently, 125 STWs pertaining to 23 specialties have been developed. It needs to be ensured that STWs are implemented effectively at all levels and ensure quality healthcare at an affordable cost as part of UHC

Multi-State Markov Model: An Application to Liver Cirrhosis

The control and treatment of chronic diseases is a major public health challenge, particularly for patients suffering from liver disease. In this paper, we propose a frame to estimate survival and death probabilities of the patients suffering from liver cirrhosis and HCC in the presence of competing risks. Database of the admitted patients in a hospital in Delhi has been used for the study. A stochastic illness-death model has been developed describing two liver illness states (Cirrhosis and HCC) and two death states (death due to liver disease and death due to competing risk). Individuals in the study were observed for one year of life at any age xi. The survival and death probabilities of the individuals suffering from liver cirrhosis and HCC have been estimated using the method of maximum likelihood. The probability of staying in the cirrhotic state is estimated to be threefold higher than that of developing HCC (0.64/0.21) in one year of life. The probability of cirrhotic patient moving to HCC state is twice (0.21/0.11) the probability of dying due to liver disease. HCC being the severe stage, the probability of patient dying due to HCC is three times that of cirrhosis. Markov model proves to be a useful tool for analysis of chronic degenerative disease like liver cirrhosis. It can provide in-depth insight for both the researchers and policy makers to resolve complex problems related to liver cirrhosis with irreversible transitions

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Not AvailableA web based system for Half-Yearly Progress Monitoring (HYPM) of the agricultural scientists working in ICAR institutes has been developed and hosted (http://www.hypm.iasri.res.in) at Indian Agricultural Statistics Research Institute (IASRI), New Delhi. The system has been developed using three-tier web architecture on the ASP.NET technology platform. Authenticated secured access has been given to all concerned users; Scientists, Reporting Officers, Reviewing Officers, Nodal Officers and Research Managers involved in the monitoring process of the scientists. Nodal officer at each Institute is responsible for Institute level customization of HYPM and has the right to assign different roles for monitoring, issue password to scientific personnel and allocation of Scientists for reporting and reviewing under Reporting and Reviewing Officers of their respective institutes. Scientists have facility for online submitting their research targets and achievements under different heads of teaching, training, extension and other prioritized activities. Research Manager Personnel (RMP’s) of ICAR have the flexibility to view reports at all levels, i.e. Institutional, Subject Matter Division (SMD) and consolidated for the entire ICAR institutes on different parameters.Not Availabl

Liver transplantation for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the commonest primary malignancy of the liver. It usually occurs in the setting of chronic liver disease and has a poor prognosis if untreated. Orthotopic liver transplantation (OLT) is a suitable therapeutic option for early, unresectable HCC particularly in the setting of chronic liver disease. Following on from disappointing initial results, the seminal study by Mazzaferro et al in 1996 established OLT as a viable treatment for HCC. In this study, the “Milan criteria” were applied achieving a 4-year survival rate similar to OLT for benign disease. Since then various groups have attempted to expand these criteria whilst maintaining long term survival rates. The technique of living donor liver transplantation has evolved over the past decade, particularly in Asia, and published outcome data is comparable to that of OLT. This article will review the evidence, indications, and the future direction of liver transplantation for liver cancer

Online Progress Monitoring of Agricultural Scientists: e Initiative.

Not AvailableA web based system for Half-Yearly Progress Monitoring (HYPM) of the agricultural scientists working in ICAR institutes has been developed and hosted (http://www.hypm.iasri.res.in) at Indian Agricultural Statistics Research Institute (IASRI), New Delhi. The system has been developed using three-tier web architecture on the ASP.NET technology platform. Authenticated secured access has been given to all concerned users; Scientists, Reporting Officers, Reviewing Officers, Nodal Officers and Research Managers involved in the monitoring process of the scientists. Nodal officer at each Institute is responsible for Institute level customization of HYPM and has the right to assign different roles for monitoring, issue password to scientific personnel and allocation of Scientists for reporting and reviewing under Reporting and Reviewing Officers of their respective institutes. Scientists have facility for online submitting their research targets and achievements under different heads of teaching, training, extension and other prioritized activities. Research Manager Personnel (RMPA's) of ICAR have the flexibility to view reports at all levels, i.e. Institutional, Subject Matter Division (SMD) and consolidated for the entire ICAR institutes on different parameters.Not Availabl

Not Available

Not AvailableIn recent years, deep learning techniques have become very popular in the field of image recognition and classification. Image-based diagnosis of diseases in crops using deep learning techniques has become trendy in the current scientific community. In this study, a deep convolutional neural network (CNN) model has been developed to identify the images of maydis leaf bight (MLB) (Cochliobolus heterostrophus) disease of maize (Zea mays L.) crop. A total of 1547 digital images of maize leaves (596 healthy and 951 infected with maydis leaf blight disease) have been collected from different agricultural farms using hand-held camera and smartphones. The images have been collected from the experimental plots of BCKV, West Bengal and ICAR-IARI, New Delhi during 2018–19. The architectural framework of popular state-of-the network ‘GoogleNet’ has been used to build the deep CNN model. The developed model has been successfully trained, validated and tested on the above-mentioned dataset. The trained model has achieved an overall accuracy of 99.14% on the separate test dataset.Not Availabl