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Corn is one of the most popular food grains in the India and crop loss due to diseases substantially affects the Indian economy and threatens the food availability. Recent access of smart devices can be utilized to provide automatic diagnosis of corn diseases and prevent severe crop losses. This paper presents a real time method based on deep convolutional neural network for corn leaf disease recognition. Deep neural network performance is improved by tuning the hyper-parameters and adjusting the pooling combinations on a system with GPU. Further, the number of parameters of the developed model is optimized to make it suitable for real time inference. The pre-trained deep CNN model was deployed onto raspberry pi 3 using Intel Movidius Neural Compute Stick consisting dedicated CNN hardware blocks. During the recognition of corn leaf diseases, the deep learning model achieves an accuracy of 88.46% demonstrating the feasibility of this method. The presented corn plant disease recognition model is capable of running on standalone smart devices like raspberry-pi or smart-phone and drones. [ABSTRACT FROM AUTHOR]

Drastic increases in oil prices and need to reduce global warming are issues that have raised huge public awareness, which in turn encouraged many developed and developing countries to adopt new energy policies. Renewable energy resources are gaining fast attention in solving power supply problems in almost every area to shield the environment. In solving power supply problem standalone microgrid systems including renewable energy sources plays a key role. For a stand-alone renewable power delivery system, battery is the crucial component which allows storing excess renewable energy and further using the energy as and when needed by the load. In order to achieve the best performance and utilization the state of charge of the batteries should be assessed. This paper presents heuristic approaches to evaluate the state of charge (SOC) of lead–acid battery through optimal power generation in a standalone hybrid wind-solar renewable power delivery system. The mathematical analysis is made by the application of modified heuristic techniques, i.e. tuned genetic algorithm (TGA) and improved colliding body optimization(ICBO), for a specific loading condition in India. More optimized power is generated by the renewable distributed energy sources when evaluated by ICBO than that achieved by TGA, for both winter and summer load demands. These optimized value of power generated by the application of ICBO, resulted in low average value of SOC. The low values of SOC further prevent the battery from malfunctioning for a longer period of time. In order, to explore more optimal generation pertaining to good value of SOC parametric studies of modified algorithms, i.e. TGA and ICBO have been carried out. A set of critical operational parameters have also been obtained through parametric study of TGO and ICBO for a specific real time load profiles. Thus the reliable and optimal values lead to a better prospect in utilizing standalone renewable power delivery system for Indian future power scenario. Moreover, a comparative study has also been performed in analysing the performances of conventional and non-conventional methods in this regard. It has been found that the heuristic techniques, viz ICBO and TGA delivered 13% and 17% better result in comparison to that obtained by linear programming method respectively. Thus it can be surmise that the heuristic techniques perform well in considering the uncertainties and approximations of real world problem of generation renewable power, pertaining to better values of SOC. [ABSTRACT FROM AUTHOR]

We have installed a multi-wavelength allsky airglow imager over the Indian Himalayan region at Hanle, Leh Ladakh, (32.7°N, 78.9°E; dip lat. ∼24.1°N). This is the first of its kind that is installed in the Himalayan region at an altitude of around 4200 m above the mean sea level. The high sensitive thermoelectrically cooled CCD based imager is equipped with two interference filters (557.7 nm and 630.0 nm with a bandwidth of 2 nm). The airglow imaging observations have been carried out during moonless nights since June 2018. This paper presents the detailed steps of the image processing techniques to transform the raw images into the geospatially-calibrated images and discusses the process for the estimation of gravity wave parameters (horizontal wavelength, apparent phase velocity and periodicities) from the processed images. In addition, we report a few interesting events like Ripple-type structures, gravity waves and mesospheric bores in the mesopshere and lower thermosphere (MLT) region and the plasma disturbances in the ionospheric F region observed for the first time over Hanle, Leh Ladakh. [ABSTRACT FROM AUTHOR]

Drilling and blasting have been the important methods of making underground tunnels for irrigation, subways, metro-lines, drainage and defense. Of late TBM (tunnel boring machine) is considered as the best positive alternative, in view of several advantages offered such as smooth profile, automated faster tunneling rates, easy to implement controls, safe and economic tunneling under favorable conditions. It is a gigantic high performing machine, with minimum hassles of operation if selected and operated judiciously. Thus, this equipment is the best possible choice to obtain a high penetration rate at minimum cost. TBM performs various operations such as rock cutting, discharge of muck, ring building and shifting of rail track (TBM movement). These operations are connected serially having a provision to function in parallel. Considering the high initial cost and import dependence, development of methodologies to establish the reliability of the system is mandatory. The reliability analysis using markov modelling was carried out for an EPBTBM (earth pressure balance tunnel boring machine), being used in an irrigation tunnel, considering its suitability to analyze a continuous production system. Accordingly, the failure and repair rates of the different subsystems were critically analyzed and variability was fixed. Maximum availability can be obtained by increasing reliability and maintainability of the equipment. This paper reports the study on the reliability, maintainability and availability of an EPBTBM deployed for excavating an irrigation tunnel located in Central India and the impacts of these parameters on the progress of tunnel. Availability of EPBTBM was found to be 61% which increased to 70% by proper maintenance and proper planning. • EPBTBM performance depends on its reliability, maintainability and availability. • Markov analysis concludes 40% decrease in reliability in 1.5 h of operation. • Maintainability, M(t) increases with time from M s (1) = 0.09 to M s (20) = 0.99. • Availability of EPBTBM is under study around 61%. • Preventive maintenance increases availability of the EPBTBM from 0.61 to 0.70. [ABSTRACT FROM AUTHOR]

Accurately predicting slope reliability under a rainfall/rainstorm event is an important prerequisite for preventing rainfall-induced landslide hazards. However, the predicted probability of slope failure under the rainfall/rainstorm event is often larger than the observed frequency of slope instability. The spatial variability of multiple soil parameters was rarely accounted for. To address this issue, this paper proposes an efficient sequential probabilistic back analyses approach for learning multiple spatially varying soil parameters using Bayesian Updating with Subset simulation (BUS) method. Two survival records of a real slope in India (i.e., the slope stays stable before the rainfall and the slope keeps stable after a 57-day weak rainfall) are successively used in the sequential probabilistic back analyses of soil parameters. The results indicate that the proposed sequential probabilistic back analyses approach can effectively update the distributions of multiple spatially variable soil parameters by the fusion of slope survival records. More accurate statistics of soil parameters can be obtained when additional slope survival records are used in the probabilistic back analyses. Furthermore, two slope failure records under a 3-day heavy rainfall event and a rainfall event ranging from May 1, 2016 to June 30, 2016 in Chibo, India are, respectively, used to predict the slope reliability and further validate the effectiveness of the proposed approach. The predicted probabilities of slope failure under the target rainfall events are well consistent with the actual observation frequency. The proposed approach can provide a powerful and versatile tool for determining the statistics of soil parameters and early warning of landslide hazards under the future rainfall events. • An efficient back analysis approach is proposed for learning multiple spatially variable soil parameters under rainfall. • The BUS method and surrogate models greatly facilitate the sequential probabilistic back analyses of soil parameters. • A real unsaturated slope is investigated to illustrate the effectiveness of the proposed approach. • The proposed approach can effectively update the distributions of soil parameters and slope reliability prediction. • The using of field observations can reduce the uncertainties of soil parameters and enhance the slope reliability prediction. [ABSTRACT FROM AUTHOR]

Early rainfall prediction is very important to ensure the economic balance of any agriculture-dominated country, such as India. Deep learning has recently received considerable attention for predicting rainfall using historical data. However, rainfall depends on multiple factors like ElNino, rising sea levels, warming of the Atlantic Ocean, etc. The changing climate has also significantly increased rainfall variability. The current deep-learning approaches are either based on a limited set of predictors or do not utilize the time-series properties for the recurrence of similar climate conditions every 12 months. Besides, they perform poorly in predicting accurate rainfall estimates for the diverse areas of India due to limited data availability across various regions. Therefore, this paper presents a groundbreaking solution to address the challenge of limited data by introducing RfGan (Rainfall GAN), a novel variant of generative adversarial networks. RfGan aims to fulfill the crucial need for abundant data to ensure the effectiveness of model training. Moreover, a novel rainfall prediction method, RfGanNet, has been introduced that leverages the strengths of the proposed RfGan for data augmentation, long-short-term memory, and convolutional neural networks for rainfall prediction. The performance of RfGanNet has been validated for India and its clustered regions in terms of nine parameters: precision, recall, specificity, Matthews correlation coefficient, accuracy, F1-score, mean absolute error, mean square error and computation time. The experimental results of RfGan are compared against original and synthetic datasets generated from state-of-the-art methods, namely tabular gan (TGAN), Wasserstein gan (WGAN), and conditional tabular gan (CTGAN). The experimental results indicate that the proposed RfNet model, when trained using RfGan-generated data, surpassed all the other considered methods by achieving 97.22 % accuracy. Moreover, RfNet has been compared with other related deep Learning approaches to validate its efficacy. Additionally, an ablation study investigating various LSTM–CNN combinations has been conducted. The findings of the study indicate that oceanic and climate-related predictors have a noteworthy influence on Indian rainfall. Moreover, the study highlights the suitability of combining Bidirectional LSTM, CNN, and multiple predictors for accurate rainfall prediction. • Novel variant of generative adversarial network- RfGan is proposed. • Different combinations of LSTM and CNN for rainfall prediction are tested. • Novel classifier — RfNet is tested on different augmented datasets. • K-means clustered regions of India have been identified. [ABSTRACT FROM AUTHOR]

Autonomous driving is the future of the automotive industry across the globe. Many challenges must be resolved in designing and developing successful Autonomous Driving Systems (ADS), especially in developing countries with poor road infrastructure. Detecting obstacles in lanes is essential for robust ADS to decide whether to change lanes, slow down, or even stop. Developing countries like India present challenges, including poor or no lane line demarcation, poor traffic management, and diverse driving behaviors. That brings many challenges to developing robust lane-keeping or lane-changing decision systems in an ADS. Publicly available driving datasets have an inherent bias towards the road infrastructure of developed countries and hence cannot cater to the needs of the Indian requirements. That makes the existing obstacle-lane segment detection models unsuitable for road infrastructures in developing countries. Due to these factors, the rate of advancement of ADS and Advanced Driver Assistance Systems (ADAS) in developing countries is meager compared to other developed countries. This makes it even more critical for future research to focus on these developing countries. This paper proposes a deep-learning-based novel decision-making network named LaneScanNET to assist the ADS in lane-changing or lane-keeping decision-making. The proposed system assists ADS in detecting obstacles, localization of the Ego Vehicle (EGV) on roads, and estimating lane status in its Field of View (FOV). The proposed LaneScanNET uses a parallel pipeline with an Obstacle Detection Network (ODN) and a Lane Detection Network (LDN) to simultaneously process the incoming image frames for detecting obstacles and segmenting lane lines, respectively. Further, the Obstacle-Lane Fusion Network (OLFN) fuses these results to predict the status of the obstacle lane in the FOV of autonomous vehicles. Vellore Institute of Technology's (VIT's) real-time driving dataset on indigenous roads has been collected to train the proposed LaneScanNET with 2464 obstacle-lane images obtained by driving over 60 km. The dataset generated for obstacle detection and its corresponding lane detection has also been made publicly available to promote research work for these developing countries. The proposed system outperforms all existing networks on Indian roads with an accuracy of 75.28% in obstacle detection and 91.36% in lane detection. Additionally, the proposed system can analyze the lane status with an accuracy of 92.54%. The proposed network performs exceptionally well in unforeseen circumstances like shadows, fog, dust, and occlusions making LaneScanNET a robust network that can be an add-on for ADS to make lane-keeping or lane-changing decisions. The LaneScanNET can be integrated into real-time vehicles to assist drivers or ADS. [ABSTRACT FROM AUTHOR]

In which settings can bicycles help to improve girls' education in low-income countries? This paper analyzes the complementarity between all-weather roads and a bicycle program in India aimed at increasing girls' secondary school enrollment. Using a triple-difference strategy, I find that the program benefits girls living 3–10 km away from schools with all-weather road connections, increasing their enrollment by 60 percent and reducing the gender enrollment gap by 51 percent. There are no effects for girls in villages without all-weather roads or girls living more than 10 km from school. The findings emphasize the importance and interdependence of road infrastructure, mode of transport, and distance to school for improving girls' education in India. • Bicycle program for girls in Bihar, India, reduces the gender education gap. • Bicycle program is only effective if high-quality road infrastructure is available and schools are nearby. • Interdependence of mode of transport (bicycles), road infrastructure, and nearby schools for girls' education choice in India. • Triple-difference estimation. [ABSTRACT FROM AUTHOR]

This paper studies the impact of India's "model" school program which aimed to provide high quality education to economically disadvantaged students. Model schools combine better infrastructure with more accountability, contract teachers, and lower per-pupil spending than regular public schools. Using a fuzzy Regression Discontinuity Design based on entrance exam cutoffs, I find attending a model school for five years increases test scores in math by 0.38 standard deviations, in science by 0.26 sd, and in social science by 0.26 sd on average. Furthermore, model schools increase the probability of joining pre-university by 11.5 percentage points. The results suggest it is possible to deliver substantial improvement of outcomes in public schools at a slightly lower level of school spending through a package of reforms. [ABSTRACT FROM AUTHOR]

• The APPEL (Applied Plasma Physics Experiments in Linear) device is a mid-scale plasma device established at IPR, India, and now in the operational phase. The main objective of the APPEL device is to simulate the plasma surface interactions that occur in the nuclear fusion reactor. • Recently, APPEL device demonstrated the high density of the order 1017-1018 m−3 helium plasma column of length 3.5 m with a DC power of 0.5 kW using a cold hollow cathode-constricted anode plasma source and an axial magnetic field of a few millitesla, which corresponds to the ion flux of the order 1021-1022 m−2s−1 and is suitable for plasma surface interaction studies. • A maintenance-free magnet system provides the magnetic field of 0.41 T for the operating current of 720 A. The APPEL device (Applied Plasma Physics Experiments in Linear Device), recently commissioned at the Institute for Plasma Research (IPR), is an intermediate setup towards building a Plasma Material Interaction (PMI) facility. A particular application of this device is to develop and test high-density, low-pressure plasma sources that can be operated under magnetic fields; optimize the plasma sources for working under various magnetic field configurations; and perform physics experiments relevant to electron heating and plasma interactions with external electrodes. The setup consists of 16 electromagnets that provide a flexible, steady-state axial magnetic field in excess of 0.4 T over an axial distance of 3.5 m, with 2 % radial uniformity across 32.0 cm diameter. This paper gives a brief overview of the APPEL device, emphasizes on the APPEL magnet system, and presents the characteristics of a 3.5 m long, high-density (1017–1018 m−3) helium plasma column, using a hollow cathode plasma source obtained in the pressure range below i.e., p < 2.0 Pa. The corresponding ion flux is found to be in the range of 1021 -1022 m−2s−1, similar to other linear systems currently in use for plasma surface interaction studies. A qualitative discussion is given to explain the characteristics of the plasma column. [ABSTRACT FROM AUTHOR]

[Display omitted] • A high-density (HD) SNP array for goats (Axiom_Cahi) was designed comprising of 626,975 SNPs. • The average coverage of SNPs in the array is one SNP per four kilobase (kb). • Performance of the array was validated by genotyping 443 samples from 26 indigenous goat breeds/populations. • 95.83% markers were highly informative and polymorphic in Indian goats. • No sampling bias and an improved discriminatory power was demonstrated by the goat HD array designed. Goats are the supporting pillars of rural economy contributing significantly to meat and milk production in India. It is a species targeted for fulfilling the interdependent goals of poverty reduction and creation of employment for supporting the rural income. The increased demand for goat products necessitates their genetic characterization and improvement to augment the production of native breeds. Bi-allelic, genome wide, densely placed single nucleotide polymorphism (SNP) markers are most suitable for this purpose. This paper describes the design and validation of an Affymetrix Axiom-based high-density (HD) SNP chip for goats. The array was designed using a panel of 225 samples from 15 diverse goat breeds of India. In total, more than 38 million high quality SNPs were subjected to stringent filtering and 626,975 SNPs were finally tiled on the array. The average coverage of SNPs in our chip is one SNP per four kilobase (kb), providing a denser coverage of the goat genome than previously available arrays. The HD chip (Axiom_Cahi) was validated by genotyping 443 samples from 26 indigenous goat breeds/populations. The results revealed 95.83% markers to be highly informative and polymorphic in Indian goats. Multivariate analysis indicated population structuring, as 15 breeds could be segregated using the designed array. Phylogenetic analysis suggested stratification of breeds by geographic proximity. This HD SNP chip for goats is a valuable resource for genomic selection, genome wide association as well as population genetic studies in goats. [ABSTRACT FROM AUTHOR]

The application of compressed biogas (CBG) in day-to-day livelihood has garnered significant attention in modern day era. It is considered as a potential solution to curb the increasing energy demand and other environmental concerns. The process involves anaerobic digestion (AD) (hydrolysis, acidogenesis, acetogenesis and methanogenesis) of organic substrate under an optimum operating condition (pH, temperature, etc.) via anaerobic microorganisms. This paper aims to describe some major facts about different technological options related to pre-treatment, AD, cleaning and up-gradation system. It also highlights the practical challenges related to high investment cost, inadequate infrastructure, clearance and approval issues, poor feedstock supply chain management etc. in adaption of CBG plants in India. The review examines the significance of both dry and wet continuous digester systems, which includes its advantages and challenges, considering factors, such as feedstock characteristics, process stability, and operational efficiency. It was also found that the type of feedstock and its availability in existing market plays a vital role in smooth functioning of CBG plants. Therefore, the study presents a comprehensive review discussing potential feedstock readily available for CBG plants in Indian market. It was concluded that the usage of agricultural waste (such as Napier grass) has higher CBG potential compared to other feedstock. At the same time, the study presents a broader aspect to policy makers and governing authorities to reframe the existing structure for financial assistantship, regulatory laws, subsidiary schemes etc. for CBG plant owners. It also gives an opportunity to technocrats, industrialist and investors to come-up with an indigenous technology with in-house manufacturing units to promote CBG production in India. [Display omitted] • India has 46 compressed biogas plants with an overall capacity of 272 tonnes/day. • Elaborated the significance of both dry and wet continuous digester systems. • Setting-up a compressed biogas plants requires high investment and operational cost. • Potential market for bio-fuels and bio-fertilizer needs to be explored in India. • Existing financial assistantship and subsidiary schemes need transformation. [ABSTRACT FROM AUTHOR]

Humans receive a significant portion (˃50%) of the total dose attributed to all the natural radiation sources from indoor radon (222Rn), thoron (220Rn), and their progeny. While progeny contributes an overwhelming part to the dose, in most surveys, only radon gas is measured because of the simplicity of measurement. Progeny concentration is usually estimated by multiplying gas concentration with an assumed factor, called the equilibrium factor, and taken from literature. Recently, results of the measurements of equilibrium factors for 222Rn and 220Rn were reported from various parts of the globe. In India, many such studies have been conducted in the current decade. The studies show a wide variation of equilibrium factors which suggests that they depend on environmental factors and measurement conditions. Therefore, they should be determined site specifically if accurate site-specific dose estimation is targeted. This paper summarizes concepts, definitions, and methods to determine equilibrium factors and reviews literature about reported equilibrium factors worldwide, focusing on data reported from India. • Concepts, definitions and methods to determine 222Rn and 220Rn equilibrium factors. • Review of literature about 222Rn and 220Rn equilibrium factors reported from India. • A significant variation in the equilibrium factors is reported across the country. • Site-specific values of 222Rn equilibrium factors should be used in the dose estimation. • Dose due to 220Rn should be estimated from the directly measured progeny concentrations. [ABSTRACT FROM AUTHOR]

• Indian freshwater ecosystems are not well studied, are degraded due to expansive agriculture and are contaminated with pesticides. • Mesocosms identified malathion was the dominant stressor among co-occurring stressors. • In mesocosms malathion reduced richness, organism densities and caused community homogenisation. • Toxicology test comparisons between countries identify that Indian taxa may be relatively pesticide tolerant. Globally, river pesticide concentrations are associated with regional and local stream invertebrate diversity declines. Pesticides often co-occur with elevated nutrients (e.g. nitrogen and phosphorus) and sediments related to agriculture, making their individual effects difficult to disentangle. These effects are also less well studied in Asia, than in other geographic regions. Within Asia, India is one of the largest producers and users of pesticides and has approximately 60% of total land mass used for agriculture. Here we examine the responses of Indian river invertebrate communities subjected to malathion, nutrients, and sediment additions in a semi-orthogonal design, in three sequential (through time) short-term (120 h) mesocosm experiments. Additionally, a series of single-species toxicity tests were run that used 24 h exposure and 72 h recovery to examine the sensitivity of 13 local invertebrate taxa to malathion, and 9 taxa to cypermethrin, comparing these results to those from other biogeographic regions. Mesocosm results indicate that malathion exposure had a major effect compared to other stressors on communities, with a lesser effect of nutrients and/or sediments. In mesocosms, taxa richness, total abundance and the abundance of sensitive species all declined associated with malathion concentrations. Comparisons of organism sensitivities from other geographic locations and those in the current paper suggest taxa in India are relatively tolerant to malathion and cypermethrin. Our results further reinforce that the high observed aquatic pesticide concentrations known to occur in Asian freshwater ecosystems are likely to be negatively affecting biodiversity, homogenising biota towards those most stress tolerant. [ABSTRACT FROM AUTHOR]

We aimed to examine the rate and characteristics of retracted articles in infectious diseases and clinical microbiology. Using the Retraction Watch Database, we conducted a cross-sectional study for retracted publications categorized as 'Infectious Disease' or 'Microbiology' until June 30 2022. We included publications for which citation information was available through the Web of Science database. Study characteristics, retraction trends and number of citations before and after the retraction year were analysed. Overall, 1004 retracted publications were included, retracted between August 1968 and June 2022. The number of retractions climbed through the years, peaking in 2020–2021. A total of 614 retractions originated from USA, China, and India, of total 183 736 PubMed publications from these countries. Overall, 378 (38%) were retracted because of errors; 182 (18%) because of plagiarism; and 142 (14%) because of falsification/fabrication. Specific reasons included 'concerns/issues about data' (158, 16%); 'duplication of image' (127, 13%); and 'unreliable results' (116, 12%). Of the 347 retractions during 2020 to June 2022, 91 (26%) were COVID-19 related. Fifty of 895 (5.6%) first authors had two retracted papers, and 14 (1.6%) had ≥2 retractions. Of 824 publications cited at least once, 466 (57%) were cited more often after retraction. Retractions of infectious diseases and clinical microbiology publications are increasing. Concerning reasons such as plagiarism, falsification/fabrication and errors are not uncommon. Nonetheless, these publications continue to be commonly cited after being retracted. [ABSTRACT FROM AUTHOR]

The increasing nature of biomedical waste and its improper disposal has become a significant concern, resulting in a negative impact on the environment. These are associated with the direct consumption of input materials along with generated co-products and wastewater. This paper aims to address the environmental burden of existing biomedical waste management in the northern part of India. It also aims to suggest some constructive steps which result in a less impactful environment through the Life cycle Assessment (LCA) methodology. LCA is a widely used sustainable assessment tool to evaluate the environmental performance of various waste management systems. The present study's functional unit is all the waste collected at a common treatment and disposal facility. A gate-to-gate approach has been performed based on the foreground data gathered from field surveys, semi-structured interviews, and background data of Eco invent database v1.03. The data has been further evaluated using LCA software SimaPro 9.1.0.4 utilizing the CML-IA impact assessment method. The impact categories include Abiotic Depletion (AD), Abiotic Depletion (fossil fuels) (ADF), Global Warming (GWP), Ozone Layer Depletion (OLD), Human Toxicity (HT), Freshwater Aquatic Ecotoxicity (FAE), Marine Aquatic Ecotoxicity (MAE), Terrestrial Ecotoxicity (TE), Photochemical Oxidation (PO), Acidification (AF), and Eutrophication (EP). The results indicate that the integrated system, including incineration with energy recovery, autoclave-shredder, chemical disinfection, and Effluent Treatment Plant (ETP), reduces the environmental burden compared to the present state. The reliability of the model is tested using sensitivity and uncertainty analysis. The analysis identifies the influential key processes, parameters, and impact categories. This paper suggests that LCA is a beneficial tool for the decision-makers and managers to take strategic decisions for reducing environmental burden and developing a life cycle inventory database for the developed economies. [Display omitted] [ABSTRACT FROM AUTHOR]

• The study depicts societal issues associated with the development projects. • A road map developed that focuses on avenues related to establishment of new development projects. • The SEETA model, based on Multi-Criteria Decision Making techniques is proposed. • Model is demonstrated using comparison of fourteen hydropower plants. • The hydropower plants are compared around five main and fifteen sub-criteria. Selecting an appropriate location for a large infrastructure project poses difficult situations. It shall satisfy sustainability indicators and establish harmony among multiple goals of multiple stakeholders. In such a situation, Multi-Criteria Decision Making techniques allow assessing qualitative and quantitative attributes, analysing and removing subjective biases and help in arriving at objective decisions. However, a single Multi-Criteria Decision Making technique may not be an effective tool to generate concrete results as there is no validation of the results. By utilising hybrid Multi-Criteria Decision Making methods, one can incorporate, gauge, and assess the range of social, economic, and environmental impacts precisely and achieve an accurate result by validating results by other methods. Therefore, this paper develops a model called as Social, Economic, Environmental and Technical Assessment model. It combines four fuzzy Multi-Criteria Decision Making techniques viz. Fuzzy Stepwise Weighted Assessment Ratio Analysis, Fuzzy Multi-Objective Optimization by Ratio Analysis, Fuzzy Weighted Aggregated Sum Product Assessment and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution. This paper utilises the case of hydropower plants in India, how the policy and decision-makers can arrive at the selection of the best location for an infrastructure project. According to the assessment value, Teesta Low Dam IV (Darjeeling, West Bengal) is the preferred hydropower plant for all selected methods. The State could bring social justice, ecological stability; achieve economic benefits and sustainability by appropriating this proposed model in other development fields. [ABSTRACT FROM AUTHOR]

Abstract: The advances in robotics, in the last ten years, have enabled robot technology to solve many practical problems that humans encounter in day-to-day activities. But, even today manual scavenging of the sewage is practiced in urban areas of India, wherein man enter the manholes and clean the scales and clogs in the sewage pipelines manually with virtually no technical equipment. This practice might jeopardize the lives of humans; therefore, a sewage cleaning robot is essential to replace the human intervention. The conventional sewage cleaning robots available are not capable and effective in cleaning a variable diameter pipelines. In order to overcome this issue, an attempt has been is made to design and develop a reconfigurable type sewage cleaning mobile manipulator, which can efficiently clean the scales and clogs formed in the variable diameter sewage pipeline. A conceptual model of the manipulator has been created in solid model using solid works. It will give a clear understanding of the manipulator and its subsystem interactions. A prototype model of the manipulator has been developed based on the design concept and its working environment i. e, various goals that robot has to do after entering the sewage pipe and hence the functional requirements are finalized. It consists of various links and joints. The joints are drive through the various motors which are discussed in the paper. Preliminary investigations are carried out on the developed prototype model and some of the results are discussed in the paper. [Copyright &y& Elsevier]

Abstract: The paper describes the role of ideological change in India''s economic liberalization and provides evidence for it. Since the 1980s the most prevalent ideology has changed from condemning commerce and profit as anti-social, to tolerating—even applauding—commercial success. The paper reports on a content analysis of the most popular Hindi film each year since 1955, and finds that characters of rich merchants have changed from being portrayed as villains to being portrayed as heroes. [Copyright &y& Elsevier]

Abstract: The main objective of the paper is to developed a methodology, named as vague Lambda-Tau, for reliability analysis of repairable systems. Petri net tool is applied to represent the asynchronous and concurrent processing of the system instead of fault tree analysis. To enhance the relevance of the reliability study, vague set theory is used for representing the failure rate and repair times instead of classical(crisp) or fuzzy set theory because vague sets are characterized by a truth membership function and false membership functions (non-membership functions) so that sum of both values is less than 1. The proposed methodology involves qualitative modeling using PN and quantitative analysis using Lambda-Tau method of solution with the basic events represented by intuitionistic fuzzy numbers of triangular membership functions. Sensitivity analysis has also been performed and the effects on system MTBF are addressed. The methodology improves the shortcomings of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The washing unit of a paper mill situated in a northern part of India, producing approximately 200ton of paper per day, has been considered to demonstrate the proposed approach. The results may be helpful for the plant personnel for analyzing the systems'' behavior and to improve their performance by adopting suitable maintenance strategies. [Copyright &y& Elsevier]

Abstract: This paper contributes to the important but small body of research on the role of private schools in Indian education. It uses a household dataset from India with a rich set of household covariates and student performance data on reading, writing, and mathematics. For both rural and urban India the results from regression analyses indicate that private school students perform better on tests controlling for covariates. In both contexts, however, the private school benefit becomes largely, statistically, insignificant after conducting multivariate analysis on data balanced using the propensity score matching technique. The paper also makes an initial attempt to identify ‘low-fee’ private schools; within the regression framework it finds that children in such schools may perform no better than their public school counterparts. The data and methods used in this paper are not without limitations; however these analyses call into question the claim that private school effect may be unequivocally positive and highlights the potential heterogeneity in private school performance in the Indian context. [Copyright &y& Elsevier]

Abstract: Every year during the winter and pre-monsoon periods most of the Indian peninsula, Bay of Bengal and the adjoining Indian Ocean are affected by a polluted regional haze, known as the South Asian Winter Haze (SAWH). This article is the first of a series of three papers collectively anticipated at the central idea of retrieving aerosol component type and effective radius of the SAWH by incorporating experimental data of ground-based solar radiometer into Mie theory, and validation by collocated in-situ measurements. The work presented in this paper provides an approach to retrieve optical depths and their spectral curvature for SAWH at a tropical coastal station, Visakhapatnam (17.7°N, 83.3°E). A dataset comprised of 92 wintertime atmospheric optical depths are obtained from spectral extinction measurements of direct solar flux by means of a Multi-Wavelength Solar Radiometer during the period of October 2002 to February 2004. The inter-channel correlations established a linear relation and the airmass conditions specific to this dataset suggested a two-component linear model; the stable and variable components of which are analysed to obtain the mentioned optical parameters. Results indicate that the spectral curvature is best represented by a Lorentz model with a central wavelength of 0.40425±0.02945μm, a width of 0.40382±0.10253μm, a constant of 0.92623±0.33793, and a wavelength independent component of 0.95778±0.15678. Resonance-like structure of the obtained spectral curvature resembles wavelength dependent extinction characteristic of absorbing soot particles; thereby supports the key findings from INDOEX campaign. [Copyright &y& Elsevier]

With the emergence of World Wide Web (WWW) as the primary reference channel of information, the need for making it barrier-free for all categories of users has evolved into a critical factor. Making the web resources barrier-free for users requires action across various dimensions such as accessibility and readability. This paper presents an analysis of accessibility, readability, and site-ranking of top ranked (N = 20) government websites of India. The accessibility analysis has been carried out using aChecker and WAVE tools. The readability of the contents of the website is measured with six different indices such as Flesch-Kincaid reading ease, Flesch-Kincaid grade level, Gunning fog, SMOG, Coleman-Liau index and Automated Readability Index. The ranking of sites by National Informatics Centre (NIC) has been utilized to select the top ranked websites and their corresponding rankings are compared with global site ranking services such as Alexa. The correlation among these three factors of accessibility, readability, and site-ranking has been carried out with Spearman's rank correlation method and the inferences derived from the results are presented. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop an intelligent cyber-physical system for the waste management in a locality which will not only keep records of the use of the trash bins in the but also ensure they have an additional blockchain based verification system which will ensure the proper working even in the presence of an adversary. The proposed system is an ensemble of the database server, blockchain server, embedded system based clients and an android app. This is inspired by the recent global trend in smartphone popularity which has piqued interest in the above mentioned division. The advent of smartphone popularity helps us to create an ideal system for a plethora of reasons such as processing capability and native storage etc. Furthermore, blockchain technology, introduced by Satoshi Nakamoto in 2009 ensures decentralization and security of transactions due to its use of cryptographic hash functions and public key cryptographic system. To further excite the situation, India recently revised its solid waste management policy in 2016 after 16 long years. All of the above factors together pointed to creation of a smart waste management system to prevent further exacerbation of India's existing waste management crisis. [ABSTRACT FROM AUTHOR]

• Classification of datasets on diabetes and its complications are considered. • Five feature selection algorithms are utilized for choosing significant features. • A hybrid rule-extraction method generating comprehensible rule sets is developed. • Experiments were performed on six datasets: one new and five public. • The proposed approach outperforms ten state-of-the-art classifiers. Diabetes mellitus is a major non-communicable disease ever rising as an epidemic and a public health crisis worldwide. One of the several life-threatening complications of diabetes is hypertension or high blood pressure which mostly remains undiagnosed and untreated until symptoms become severe. Diabetic complications can be greatly reduced or prevented by early detection of individuals at risk. In recent past, several machine learning classification algorithms have been widely applied for diagnosing diabetes but very few studies have been conducted for detecting hypertension among diabetic subjects. Specifically, existing rule-based models fail to produce comprehensible rule sets. To resolve this limitation, this paper endeavours to develop a hybrid approach for extracting rules from support vector machines. A feature selection mechanism is introduced for selecting significantly associated features from the dataset. XGBoost has been utilized to convert SVM black box model into an apprehensible decision-making tool. A new dataset has been obtained from Pt. JNM, Medical College, Raipur, India comprising of 300 diabetic subjects with 108 hypertensives and 192 normotensives. In addition, five public diabetes-related datasets have been taken for generalization of the results. Experiments reveal that the proposed model outperforms ten other benchmark classifiers. Friedman rank and post hoc Bonferroni-Dunn tests demonstrate the significance of the proposed method over others. [ABSTRACT FROM AUTHOR]

• A conceptual framework was designed for a bioenergy system for rural India. • Agro waste potential was estimated through survey. • Techno-economic analysis of an integrated system was conducted. • Uncertainty was assessed through Monte-Carlo simulation. This paper presents a holistic approach to promote bioenergy in India by designing a conceptual framework that combines resource, technology and market. The proposed concept is an attempt to integrate pelletization and gasification technology for bioenergy system development through an end-to-end approach. The potential of bioenergy resource (i.e. agro waste) was estimated based on survey. The study further assessed the economic feasibility of agro waste pelletization. The economic evaluation was made using indicators such as net present value (NPV), internal rate of return (IRR), discounted payback period (DPBP) etc. Pellet plant capacity of 0.5 ton h−1 showed acceptable economics and the NPV , IRR and DPBP were ₹9.35 million ($0.13 million), 41% and 2.8 years, respectively. Moreover, the larger capacity plants (>2 ton h−1) were subjected to more risk under low pellet prices (< ₹5 kg−1 or $71.4 ton−1). The cash flow statement showed a strong debt paying ability for the project. Pellet price was the most sensitive factor followed by annual operating days on pellet plant economics. Monte Carlo simulation predicted an average NPV of ₹9.3 ± 2.0 million ($133.2 ± 29.1 thousand). The economics of fuel pellets utilization in a gasifier for energy applications was also evaluated. The pellet fed gasifier system appeared to be cost competitive with commercial liquefied petroleum gas (LPG) and wood at a pellet price range of ₹6.3–8.8 kg−1 ($90–126 ton−1) in a select scenario. Overall, the designed framework appears to reduce over-dependency on wood or fossil sources, and facilitate bioenergy promotion in rural areas. [ABSTRACT FROM AUTHOR]

This paper discusses about the results of a short term monitoring study of sea coast contaminated with oil spill. Collision of two oil-cargo ships, namely BW Maple and MT Dawn Kanchipuram near the Ennore Port, Chennai had released an approximately 200 tonnes of heavy fuel oil. We analyzed four different oil spill samples collected from Chennai beaches for different time intervals (1st, 3rd, 6th, 10th day of oil spill) from 1 km away from the source of the off shore spill location. The physicochemical characteristics of spilled oil sludge, concentration of total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs) present in the spilled oil were investigated. The variations in pH, moisture content and volatile hydrocarbon content, viscosity and density of the spilled oil were in the range of 7 ± 0.2, 255–575 g/kg, 118 to 850 PaS and 1.04 to 0.98 g/cm3 respectively. Elemental analysis showed that C, H, N and S were in the range of 9.9–13.3 (%), 0.63 to 1.76 (%), 1.88 to 1.02 (%) and 0.29 to 0.27 (%) respectively. Analysis of oil spill samples using gas chromatography and mass spectrometry (GCMS) shows the presence of recalcitrant and hazardous PAHs such as fluoranthene and pyrene. Ocean sediments were collected at different locations along the coast line from Ennore to Cooum after 60 days of oil spill and analyzed for TPH in GCMS. The results showed that the residual concentrations of TPH varied from 0.19 to 3.43 mg/g. The presence of heavy PAHs, which are highly toxic and recalcitrant, could pose a severe threat to the marine environment and ecology.The long term effects of these compounds were unexplored on marine environment. This environmental monitoring study gives an overview about the post oil spill activities and steps taken to remediate the sludge along the coastline. Image 1 • This study highlights the immediate fate and characteristics of the Chennai oil spill over a short period. • Total petroleum hydrocarbon concentration increased from third day to sixth day and then stabilized. • Viscosity of the spilled oil increased eight times from day1 to day 10 after the oil spill. • Priority pollutants such as Fluoranthene and pyrene were identified in beach sediments using GC-MS. [ABSTRACT FROM AUTHOR]

• India had about 53 million symptomatic dengue in India in 2016. • The overall cost of dengue in 2016 was about US$5.71 billion with 14.3% due to fatal cases and 85.7% to non-fatal cases. • The cost shares of non-fatal cases were; hospitalized-62.9%, ambulatory-17%, and non-medical cases-5,8% of total costs. • The aggregate cost estimate for 2016 is almost triple the original 2013 estimate. This paper concerns a systematic analysis of dengue illness and costs in India from 2013 to 2016. A previous study for 2006–2012 found that that projected annual number of dengue cases (5.78 million) was 282 times the officially reported number (20,474), highlighting enormous uncertainty. This study updated primary data for India from 2012 to 2014 and synthesized the latest epidemiological and economic literature through 2018 using the country and global estimates. The first empirically-based publication of dengue costs by country (in 2016) estimated India experienced 18,618,706 symptomatic dengue cases and 1602 deaths, and cost $1.51 billion in 2013. With a combination of increased incidence, more refined methods, and better data this 2018 study raised the latest estimates to 53,210,706 cases, 22,527 deaths, and $5.71 billion economic costs for 2016, representing increases over the previous publication of 186%, 1306%, and 278%, respectively. When consistent methods and data were used to compare 2013 to 2016, the corresponding changes were only +29%, −9%, and +12%, respectively over these 3 years. India's burden of dengue is substantially greater than previously estimated. Although uncertainty intervals remain wide, these latest estimates reinforce the health and economic benefits that India would realize if dengue were substantially controlled. [ABSTRACT FROM AUTHOR]

Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space–time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM 2.5 removed natural dust (PM 2.5_No Dust) concentrations and the corresponding population's PM 2.5_No Dust exposure (PPM 2.5 E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM 2.5_No Dust -polluted regions, whose spatial relative magnitude of PM 2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM 2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM 2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM 2.5_NoDust concentrations and PPM 2.5 E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM 2.5_NoDust concentrations and PPM 2.5 E were greater in the medium stage than in the early and later stages. The local trends of PM 2.5_NoDust concentration and PPM 2.5 E in the two higher PM 2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM 2.5_NoDust concentration and PPM 2.5 E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM 2.5 E to Europe. The PPM 2.5 E trend in Africa generally increased during the study period. • First presentation of a Bayesian space-time hierarchy piecewise regression model detecting self-adaptively transitions. • Firstly applies a developed Bayesian space-time model in investigating the global population's exposure to PM 2.5. • Deeply analyses the spatiotemporal trends of the global continental PM 2.5 pollution removed natural dust. • Details the spatiotemporal trends of the global population's exposure to anthropogenic PM 2.5. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation into the mechanical properties of YSt-310 cold-formed steel hollow sections, which is one of the most widely used structural steel in India, under steady state elevated temperatures ranging from approximately ambient to 800 °C. Elevated temperature stress–strain curves and reduction factors of key material parameters such as Young's modulus; yield stress; stresses corresponding to 0.5%, 1.5% and 2.0% strains; tensile strength and corresponding strain (%); fracture strain (%) and Ramberg-Osgood parameter are reported. Further, the reduction factors obtained from the current experimental programme are compared with predictions based on current fire design standards as well as those reported in literatures for cold-formed steel at elevated temperature. Statistical analysis was carried out to check the reliability of the elevated temperature reduction factors and it was found that the existing design rules are inaccurate and not applicable to cold-formed hollow steels at elevated temperature. A new set of fire design reduction factors are then proposed for cold-formed steels hollow sections based on lower bound values, considering both the current and previously reported experimental data. The newly proposed reduction factors are shown to offer improved mean (conservative) and reliable predictions and therefore recommended for inclusion in future revisions of fire design codes. • Studied mechanical properties of YSt-310 tubular sections at elevated temperatures. • Derived elevated temperature retention factors based on test results. • Design retention factors are proposed based on current and literature test data. [ABSTRACT FROM AUTHOR]

• In this article described the process to build the 170 GHz, 0.1MW short pulse gyrotron. • Indian tubes community progresses towards the first short pulse prototype has been assembled at India. • Numbers of joining operations completed through laser welding, brazing, TIG welding. • The integration and vacuum processing of demountable gyrotron have been done. In this paper, the development of 170 GHz, 0.1 MW short pulse gyrotron has been explained. All components of 170 GHz gyrotron such as MIG, cavity, beam tunnel, collector and RF window with radial output have been designed and developed. A quasi optical mode converter consists of smooth wall mode launcher, 3-metallic mirrors and vacuum chamber with view front of window has been successfully developed. The integration and vacuum processing of demountable 170 GHz gyrotron have been carried out. The overall length of gyrotron is around 2.2 m and weight is around 200 kg without cooling ducts. This is the first step of development of an ITER gyrotron. [ABSTRACT FROM AUTHOR]

Highlights • Impacts of land, water and energy on agricultural sustainability are analysed. • Specialised cultivation of paddy and potato decreases agricultural sustainability. • Pulses show negentropy and higher sustainability level than other crops. • Grey water footprint of crops indicates significant level of water pollution. • Energy should be invested to improve soil health and environmental quality. Abstract Agro-ecosystem is an open system that represents a complex interaction with the environment in term of energy transformation. Growing human population and resultant demand for greater output are increasing the rate of energy flow in agro-ecosystem to facilitate more energy transformation, which is deteriorating different compartment of ecosystem and environment. This paper intends to analyse agricultural sustainability at micro level by developing a composite indicator of land, water and energy used for crop production. Cropland footprint (CF), water footprint (WF) and entropy overproduction (E op) are selected as indicators of land, water and energy respectively with the objective to explore man-land interaction by CF, while WF and E op represent the impact of that interaction. In the study area, cropland surplus or deficit is inversely related with WF and E op. Surplus crops like paddy and potato have higher WF and E op , hence are responsible for agricultural unsustainability. WF and E op are highest for paddy (8912 m3/ha) and potato (117,565 MJ/ha) respectively. Grey water footprint (WF c,grey) is the highest for potato (3708 m3/ha). Pulses (with negative E op of −9124 MJ/ha) are proved the most sustainable crop with the highest potentiality to boost agro-ecosystem sustainability. The results derived from the analysis indicate that increasing use of chemicals and more specialization of water and energy exhaustive crops are reducing agricultural sustainability. The study also suggests for necessary transformations of cropping pattern with special emphasis on incorporation of pulses, oilseeds etc. and non-productive energy investment to reinstall natural quality of agro-ecosystem, not only in the studied areas, but also in other areas facing similar site and situation. [ABSTRACT FROM AUTHOR]

Rapid industrialization, urbanization, and motorization lead to hiking of air pollution in various mega cities including Delhi, India. Studies done to analyze pollution suggests that vehicle exhaust emissions are major contributor to the urban air quality along with many other comparable sources. Hence to check instantaneous pollution increment, government aims to reduce vehicle emission. Traditional policies are working at times for holding sudden increase but long-term effects are not seen which need meso-spatial and small temporal policy. For optimizing meso-spatial policies implementation, many intricacies related to moving and stationary sources in near real-time have to be monitored including vehicular emission, itself. Analyzing these moving source emissions on the meso-spatial level is challenging. Therefore, the present study is an attempt to first understand the need of meso-spatial and near real-time checking policy and then leveraging the usage of previous region specific pollutant survey reports to map real-time pollution monitoring parameters. For that, a real-time vehicle monitoring strategy is proposed to map city-wide traffic congestion and emission using crowd-source data of Google Maps. For comparative study of different meso-spatial regions real-time moving source pollutant parameter 'μ' is calculated using a factor 'F'. Also, the different effects of congestion on vehicular emission in two different meso-spatial areas of Delhi is presented using comparative case study. Analysis of case study revealed that Anand Vihar area is more prone to vehicle emission variability in case of congestion fluctuation. Further the discussion of short temporal and spatial emission calculation, for weekly pollution evaluation is presented in the paper. This will be helpful to the authorities in formulating technological, institutional and traffic management policies. • Traditional pollution checking policy and different emission models are discussed for Delhi region. • A methodology to map real-time congestion for city-wide area on small tempo-spatial basis is provided. • A factor 'F' is introduced for relating congestion to vehicular and dust emission. • Vehicle proportionate area specific moving source emission 'μ' is calculated and analyzed by comparative emission study. • Suggestions on checking policy and the methodology to judge their effectiveness for immediate action is provided. [ABSTRACT FROM AUTHOR]

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and informal settlements using street intersection data. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics of informal settlements. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context. To enable this model to be used as a pragmatic tool for future prediction or further research concerning informal settlements and slums, we have made the python code for the pre-trained ANN models of the five studied cities. • A modelling framework is developed for predicting informal settlements. • The model only requires street intersection data as input, making it broadly applicable. • The model is applied to five cities; Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. • By training the model using data from one city, the model can predict informal settlements in another city. • Using ground truth data of planning status for validation, the model achieves accuracy of between 82.1 and 97.6%. [ABSTRACT FROM AUTHOR]

Abstract The physiological factors like the age, gender and body mass index (BMI) of individuals have seen to affect the thermal comfort of occupants in indoor environment. However, very few studies aimed at determining the effect of these physiological factors on the factors of thermal comfort were conducted, especially in India. In this paper, the results of the field survey based thermal comfort with the objectives of studying the effect of these physiological factors on the thermal comfort factors of clothing insulation, thermal sensation votes, comfort temperature and preferred temperature of the subjects are presented. The mean clothing insulation for female subjects was lower (0.83 clo), than that for male subjects (0.87 clo), while the mean clo for senior subjects (0.94) was higher than mid aged (0.86) and young aged (0.85), respectively. A lower comfort temperature for the male subjects (11.1–35.7°C) than for the female subjects (11.5–36.1°C) was noted. Similarly the mean comfort temperature was seen to be higher in younger subjects (20.4°C), than mid-aged subjects (17.5°C) and senior aged subjects (16.7°C), respectively. Likewise, the underweight subjects had a higher comfort temperature (mean 22.8°C) than normal (mean 20.9°C), overweight (20.3°C) and obese subjects (mean 19.7°C), respectively. [ABSTRACT FROM AUTHOR]

This review article briefly brings out the historical development of atmospheric sodium (Na) measurements over India and the importance of coordinated measurements with multiple techniques to address physical processes in the Earth's upper atmosphere. These measurements were initiated in the early 1970s by observing Na airglow emission intensities with broad band airglow photometer from Mt. Abu, a low-latitude hill station in India. Considerable amount of night-to-night variations in nocturnal emission intensities of the Na airglow were observed. Later, investigations regarding the dependence with the magnetic activities from the equatorial and low latitudes were carried out and double-humped structures in the nocturnal variation of intensities were reported. With the advent of Na lidar at Gadanki around 2005, the measurements of atmospheric neutral Na atom concentration became possible and more frequent occurrences of sporadic Na layers over the magnetic low latitude station compared to other latitudes were detected indicating the role played by electrodynamics. Later, a possible relationship between E-region field aligned plasma irregularities and the concentration of neutral Na atoms was investigated using coordinated measurements of VHF radar and Na lidar. Further, simultaneous measurements with Na lidar and a narrow band airglow photometer with narrow field of view brought out the importance of coordinated observation wherein the characterization of gravity waves could be carried out and also revealed the importance of collisional quenching due to ambient molecules in the Na airglow emission processes. In addition, combining the ground based measurements of Na lidar and meteor wind radar along with satellite measurements made possible to hypothesize the over-turning Kelvin–Helmholtz billow in the Na layer manifests "C-type" or inverted lambda shape structures in the height-time-concentration map of neutral Na atoms. This review paper presents a synoptic view mostly based on the previously reported observations of Na airglow emission, Na lidar and coordinated Na airglow and Na lidar observations from the Indian sector and highlights the importance of simultaneous measurements of mesospheric Na and its emissions along with satellite-borne measurements to address interesting geophysical processes in the Earth's upper atmosphere. [ABSTRACT FROM AUTHOR]

The Multinomial Logit (MNL) model is popular, but a semi-parametric specification of its link/utility function has seldom been used in empirical applications. This is primarily because of the resource intensive nature of semi-parametric estimation. In this paper we propose and implement a parallel computation algorithm to estimate the semi-parametric kernel MNL model. This algorithm reduces model estimation time by a factor of 2–10, depending on the size of the dataset and the available resources for computation. These computational gains make the estimation of this model feasible for large datasets. Additionally, using a Monte Carlo study we show that the kernel MNL outperforms the traditional linear MNL model in terms of fit and predicted choice probabilities. We demonstrate how kernel-based specification can unearth important heterogeneities in the effect of covariates through an empirical exercise. We use data from a nationally representative household survey (N = 157,804) to analyze the factors associated with institutional births (as opposed to home births) in India. Our revealed-preference results indicate that maternal education, household assets, distance to formal health facility, and birth order play an essential role in determining birth location choice. Although the directions of impact are similar across both the linear and the kernel MNL specifications, there are significant differences in the marginal effects of different factors across the two models. These differences, which arise due to the flexibility afforded by the semi-parametric specification, potentially bring additional nuance to policy discussions. [ABSTRACT FROM AUTHOR]

Abstract Biomass has often been regarded as a promising renewable energy resource to mitigate climate change. However, what will be the impact on global surface temperature if biomass replace fossil fuel remain unclear. This paper, through a case study in Uttar Pradesh, India address this issue by assessing biomass resource potential to reduce fossil fuel demand and mitigate climate change. Impact of biomass power related GHG emissions on global surface temperature is estimated using the Absolute Global Temperature change Potential (AGTP) climate metric. Uttar Pradesh can produce 71 Mt surplus biomass annually, equivalent to 7298 MW power. Power density of biomass range between 0.10 and 0.25 Wm-2. Significant amount of GHG emissions reduction is possible if biomass replace fossil fuel as a power source in the region. Annual GHG emissions associated with 1 MWh of power derived from coal, natural gas and biomass would cause 4.10 × 10−7, 2.40 × 10−7 and 1.25 × 10−7 mK rise in global surface temperature, 100 years after an initial emission, indicating potential of biomass power to mitigate climate change. Variation in spatio-temporal distribution and low power density could be delimiting factors in large-scale deployment of biomass power. Graphical abstract Image 1 Highlights • Biomass resources and power potentials are assessed for Uttar Pradesh, India. • GHG emissions from biomass power is assessed and compared with fossil fuels. • Climatic impact of power generation is assessed via novel climate metric. • Biomass power can mitigate climate change by replacing fossil fuels. • Spatio-temporal variability and low power density are limitations of biomass. [ABSTRACT FROM AUTHOR]

Abstract Indian Space Research Organization (ISRO) has developed an indigenous system named Indian Regional Navigation Satellite System (IRNSS) or NavIC (Navigation with Indian Constellation), that consists of 7 satellites and transmits navigation signal in L and S bands. ISRO, for validation of the system, has installed many IGS (IRNSS/GPS/SBAS) receivers scattered over the Indian region. Using preliminary data from two geographically widely separated stations over India, this paper presents the results on studies on parameters of IRNSS signal quality and discusses how these parameters may be used to study the ionospheric behavior over the Indian region. The results show the importance and advantages of using IRNSS data for such studies. [ABSTRACT FROM AUTHOR]

Highlights • The paper deals with two different water quality indicators for the Bakreswar reservoir. • The main indicators chosen here are dissolved oxygen and zooplankton abundance. • Artificial neural network modelling has been used to make predictions of DO and zooplankton abundance. • ANN are capable of accurately predicting the dependent variable based on predictor variables. • ANN modelling is better suited in the current scenario over traditional approaches like multiple regression. Abstract Presence of optimal levels of dissolved oxygen (above critical level of 4.5 mg L−1) and presence of zooplankton community are indicators of good water quality of an aquatic ecosystem and also of the health of the same. Reservoirs being artificially created water bodies present hybrid systems containing features of both lotic and lentic systems and thus have unique organization that are representative of both rivers and lakes. Since any reservoir is primarily a fresh water system, presence of a large array of zooplankton (diverse community structure) implies its good health and also presence of optimal dissolved oxygen levels supports sustenance of life. In this study, artificial neural network modelling approach has been utilized to predict the level of dissolved oxygen and zooplankton abundance in the Bakreswar reservoir and their variation in relation to the environmental factors. Use of neural network modelling is exceedingly capable of determining correlation among apparently non correlated environmental data and in the current study these are capable of accurately predicting the variations in the levels of dissolved oxygen as well as the abundance of zooplankton. From this study, it has been observed that chemical factors like productivity, nitrates, salinity, pH , phosphates, total dissolved solids, etc. are mostly responsible for control of dissolved oxygen and zooplankton variation at certain points of the study site (stations 1 and 3) whereas at other points (station 2) physical factors like solar radiation, humidity, etc. are more effective. These models are capable of finding the important environmental controllers of such variations and prove to be a powerful alternative to traditional approaches like multiple regression analysis. [ABSTRACT FROM AUTHOR]

Abstract Distribution transformer is a most important component of the distribution system to provide uninterrupted power supply to the consumers and it should be highly reliable. Distribution transformer failure affects the reliability and power quality of the supply system. This paper presents distribution transformer failure analysis to find out causes of failures responsible for very high rate of failure in a state owned electric utility Punjab State Power Corporation Limited (PSPCL) in India. Detailed failure analysis is conducted on 348 distribution transformers failed from 2010 to 2015 in a city based sub division area. Failure analysis is carried out in accordance with IEEE standards and summarized the results statistically in the first part. In second part, the failure modes, effect and criticality analysis (FMECA) is used to analyse distribution transformer failure modes, causes of failure and effects of failure. Further, criticality analysis is performed and a risk priority number is assigned based upon the severity classification, probability of occurrence and probability of detection to identify most critical parts of transformer. FMECA leads to identify preventive actions to be taken to reduce the risk of the transformer in future by eliminating the causes of failure, reducing severity and probability of occurrence. Highlights • Analysis of distribution transformer failure in conjunction with a real time data of failure in public sector utility. • Failed transformers are analysed for month wise, year wise, age wise, component wise failure and causes of failure. • Failure Modes, effect and criticality analysis (FMECA) to identify most critical components of transformer. • Compensatory provision are provided to save the distribution transformer from premature failure. [ABSTRACT FROM AUTHOR]

Abstract Indian region is expected to receive signal from multiple satellite-based navigation systems for multi-GNSS hybrid operation. Even in presence of GPS, GLONASS and GALILEO signals together, it is regularly found that, during some parts of the day no GNSS satellite is available above 60° elevation angle from large parts of India. This may cause problem in seamless navigation solutions in the locations, where visibility of the satellites is also obstructed in the lower elevation angles, which is a common feature in Indian urban conditions; the situation also degrades satellite geometry. IRNSS/NavIC is Indian indigenous regional navigation system operational since 2016. This paper presents theoretical, simulated and initial results of real-time studies on IRNSS/NavIC visibility from India using a IRNSS-GPS-SBAS (IGS) receiver. The studies show that, in such situations of inferior GNSS satellite visibility, IRNSS/NavIC operating together with GPS, can augment this time-dependent degraded visibility conditions. The Indian satellite navigation system ensures presence of at least one satellite above 60° elevation for use with GPS over the Indian region, supports multi-GNSS operation towards seamless solution, and improves satellite geometry. [ABSTRACT FROM AUTHOR]

Abstract Mining of coal has always been a very risky task from older times and, is often associated with various fatal accidents in form of rock bursts, gas outbursts, slope/bench failure. Studies carried out by various safety agencies around the world such as MSHA (USA), DGMS (India), State Administration (China), have reported higher fatality rates in underground mines, and are mostly resulting from the sudden roof falls. Most of the roof falls occur suddenly in the running mine due to support failure or adverse strata conditions. The present study focuses on the monitoring of the roof falls in the longwall mine of Central India, as the presence of stable roof has resulted in several roof fall related accidents in various Indian longwall panels including Churcha mine, Kothadi mine and GDK-11A incline. The aforesaid accidents was also responsible for the poor performance of longwall mines in India, which is considered to be one of the best mining practices in majority of the coal producing countries of the world. The paper incorporates the spatial distribution and magnitude of microseismic events released before/during the roof falls and surface blasting in terms of Fractal Dimension as well as b -value. These two parameters helped in getting precursory signatures for spatio-temporal forecasting of minor and major roof falls, as well as helped in monitoring strata behavior during surface blasting. A total of sixty-four minor falls (LF1-LF64), seventy-six major falls (RF1-RF76) and fifteen surface blasting (SB1-SB15). The results showed decreasing trend in the both Fractal Dimension (D) and b -value before roof fall, as microseismic events emitted were highly clustered and had higher magnitude. Whereas, higher Fractal Dimension (D) and lower b -value was seen during surface blasting, when the emitted microseismic events had higher magnitude but were found to be dispersive in nature. Graphical abstract Unlabelled Image Highlights • Microseismic study for monitoring dynamic roof displacements. • Application of Fractal Dimension and b -value in roof fall forecasting. • Monitoring roof strata behavior during surface blasting. • Microseismic activity and its magnitude study during underground mining. • Application of microseismicity for enhancing longwall mine safety. [ABSTRACT FROM AUTHOR]

Abstract Now-a-days biodiesel is gaining attention for its positive impact on the environment. The vegetable oils are major sources of biodiesel. For biodiesel programme in India, jatropha (Jatropha curas) has been identified as one of the most suitable tree borne non-edible oil seed. But its major drawback is its poor oxidation stability. The mahua (Madhuca indica) is another tree borne non-edible oil seed found in India and its production potential is about 60 MT per annum. Its oil content is around 35–40% and its calorific value is close to that of mineral diesel. In this paper, an attempt has been made to study and compare the oxidation and storage stability of mahua biodiesel with that of jatropha biodiesel and mineral diesel. To improve the oxidation stability, the biodiesels have been blended with mineral diesel and it has been found that 20% blended jatropha biodiesel (JB20) and 30% blended mahua biodiesel (MB30) satisfy the EN-590 limit of 20 hrs. When both the biodiesels are blended in different proportions, the 50:50 mixtures by volume satisfy the EN-14214 limit of 6 hrs. For the storage stability study, the different biodiesel samples have been stored in a specified conditions for 8 fortnights (120 days) period and the different properties like density, kinematic viscosity, flash point and acid number of the samples have been measured. It has been found that the rate of change in values of the properties is higher in case of jatropha biodiesel and its blends than that of mahua biodiesel and its blends. [ABSTRACT FROM AUTHOR]

Abstract This paper investigated the performance of the latest International Reference Ionosphere model (IRI-2016) over that of IRI-2012 in predicting total electron content (TEC) at three different stations in the Indian region. The data used were Global Positioning System (GPS) data collected during the ascending phase of solar cycle 24 over three low-latitude stations in India namely; Bangalore (13.02°N Geographic latitude, 77.57°E Geographic longitude), Hyderabad (17.25°N Geographic latitude, 78.30°E Geographic longitude) and Surat (21.16°N Geographic latitude, 72.78°E Geographic longitude). Monthly, the seasonal and annual variability of GPS-TEC have been compared with those derived from International Reference Ionosphere IRI-2016 and IRI-2012 with two different options of topside electron density: NeQuick and IRI01-corr. It is observed that both versions of IRI (i.e., IRI-2012 and IRI-2016) predict the GPS-TEC with some deviations, the latest version of IRI (IRI-2016) predicted the TEC similar to those predicted by IRI-2012 for all the seasons at all stations except for morning hours (0500 LT to 1000 LT). This shows that the effect of the updated version is seen only during morning hours and also that there is no change in TEC values by IRI-2016 from those predicted by IRI-2012 for the rest of the time of the day in the Indian low latitude region. The semiannual variations in the daytime maximum values of TEC are clearly observed from both GPS and model-derived TEC values with two peaks around March-April and September-October months of each year. Further, the Correlation of TEC derived by IRI-2016 and IRI-2012 with EUV and F10.7 shows similar results. This shows that the solar input to the IRI-2016 is similar to IRI 2012. There is no significant difference observed in TEC, bottom-side thickness (B0) and shape (B1) parameter predictions by both the versions of the IRI model. However, a clear improvement is visible in hmF2 and NmF2 predictions by IRI-2016 to that by IRI-2012. The SHU-2015 option of the IRI-2016 gives a better prediction of NmF2 for all the months at low latitude station Ahmedabad compare to AMTB 2013. [ABSTRACT FROM AUTHOR]

Abstract The problem of informal areas is the most significant urban challenge facing third world countries. Most of the urban population has become informal areas dwellers. These areas consume many resources and suffer from bad conditions. Therefore the need arose to transform them into sustainable societies in order to save resources and improve their quality of life. This paper discusses the sustainability as means of achieving quality, efficiency and improvements in informal areas using the three pillars of sustainability: social, economic and environmental. It also aims at carrying out a comparative analysis of case studies for sustainable upgrading of informal areas in Brazil and India to observe the impact on their dwellers' life after upgrading. Analysis is examined on three levels: economic, social and environmental. Moreover, the comparison and evaluation of results ends with suggestions to be applied on Egyptian informal areas in order to achieve more efficient and sustainable upgraded areas. [ABSTRACT FROM AUTHOR]

Abstract Wildlife habitat corridors are components of landscapes, which facilitate the movement of organisms and processes between areas of intact habitat, and thus provide landscape corridor as well as serve as an ideal component to study and understand physiological ecology. Corridors are thus regions within a given landscape that generally comprise native vegetation, and connect otherwise fragmented, disconnected, non-contiguous wildlife habitat patches in the landscape. The purpose of designing corridors as a conservation strategy is primarily to counter, and to the extent possible, mitigate the impacts of habitat fragmentation and loss on the biodiversity of the landscape, as well as support continuance of land use for essential local and global economic activities in the region of reference. In this paper, we use game theory and graph theory to model and design a wildlife corridor in the Central India – Eastern Ghats landscape complex, with tiger (Panthera tigris tigris) as the focal species. We construct a graph using the habitat patches supporting wild tiger populations in the landscape complex as vertices and the possible paths between these vertices as edges. A cost matrix is constructed to indicate the cost incurred by the tiger for passage between the habitat patches in the landscape (based on Shelford's Law of Tolerance), by modelling a two-person Prisoner's Dilemma game. A minimum spanning tree is then obtained by employing Kruskal's algorithm, which would suggest a feasible tiger corridor network for the tiger population within the landscape complex. Additionally, analysis of the graph is done using various centrality measures, in order to identify and focus on potentially important habitat patches, and their potential community structure. Correlation analysis is performed on the centrality indices to draw out interesting trends in the network. [ABSTRACT FROM AUTHOR]

The increasing coverage of Internet has created opportunities and advantages for different aspects of society. However, there come new threats and challenges to information security. One of the typical types of attacks that has increasingly occurred is the APT attack (Advanced Persistent Threat). APT is dangerous with clear purposes. APT attacks employ different sophisticated methods and techniques attacking targets in order to steal confidential and sensitive information. In the past, hackers attacked information systems with personal and financial motives. However, there are nowadays other motives such as political ones and they are potentially backed by governments or nations. Nations that own advanced technologies such as United States, India, Russia, UK are also suffering from special purpose attacks. APT is an advanced type of attacks that consists of many stages and concrete strategies. Besides, techniques and technologies employed in APT attack are usually new and developed by hackers in order to break through the monitoring of security software. However, APT is normally implemented through concrete steps and stages. If one of the steps or stages fails, the entire APT attack will fail. This paper presents a method of detecting APT attacks based on monitoring accesses to unknown domains. This detection method results into high effectiveness in the initial stage of APT attacks. [ABSTRACT FROM AUTHOR]

Abstract Lack of infrastructures, rapid growth in population, high urbanization and unsegregation makes the management of municipal solid waste a difficult task for local authorities. The paper proposes an approach for selecting the waste transfer station locations under unsegregated and segregated waste scenarios in an economical manner. The analytical approach is a combination of two basic components, (i) a mathematical optimization model for the overall cost of municipal solid waste management; and (ii) tools of a geographic information system for creating the dataset of the mathematical model. The novelty in the developed model includes strategic allocation of transfer stations for three scenarios: (i) solid waste collected from sources without segregation (Scenario (I)); (ii) waste collected considering source separation and transfer stations taking only one type of wastes (Scenario (II)); (iii) waste collected under source separation and transfer stations having multiple halls for multiple types of waste (Scenario (III)). The proposed approach has been verified by applying on city of Bilaspur, India. Optimization model selects six best locations for Scenario (I), (II) and five for Scenario (III). Results also show that among the three scenarios studied, scenario (III) is the best as it is providing more profit than scenario (II) and (I). Validity of the model is tested by ANOVA analysis and it suggested key parameters (frequency, operating cost and land value) as significant. This finding suggests that transfer stations with or without multiple halls under source separation environment can earn the profit from solid waste while solving the problem of high collection cost and landfills. Highlights • Mathematical model to find best locations for transfer stations. • Source separation of municipal solid waste along with variation in land cost. • Model validated by applying on a case study. • Significance of key parameters has been tested by ANOVA analysis. [ABSTRACT FROM AUTHOR]

Abstract Many Climate Smart Agricultural (CSA) technologies fail to achieve their full potential impact due to low levels of adoption by smallholder farmers and difficulties in scaling CSA. This paper presents how small and medium-sized enterprises (SMEs) can act as change agents for the uptake of CSA technologies where their business models may be seen as adoption and scaling mechanisms. Drawing upon our fieldwork in Punjab (India) during which over 100 respondents have been interviewed, critical issues and enabling factors for the business model of two types of SMEs, i.e. farmer cooperatives and individual service providers of climate smart technologies have been identified. Enabling factors supporting adoption are driven by scientific and practical evidence of CSA technologies, good partnership between SMEs and research institutes, good customer relationships and effective channels through farmers' field trials. Critical issues consist of distortive government subsidies on energy and the lack of market intelligence affecting the profitability of the business model. Scaling is enhanced through market intelligence and a favouring regulatory landscape. However, difficult socio-economic circumstances and distortive government subsidies limit the role of SMEs business model as mechanism for scaling. Highlights • Small and medium-sized enterprises foster scaling of climate smart agriculture. • Energy subsidies hinder business models in adoption of climate smart agriculture. • Market intelligence is key for business models to scale climate smart agriculture. • Youth forms a niche market for climate smart agriculture. [ABSTRACT FROM AUTHOR]