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The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India. [ABSTRACT FROM AUTHOR]

All landscapes, including estuarine islands, normally try to restore their geomorphic isostasy in all anthropogenic interventions on land dynamics. Munroe Island has been experiencing drastic environmental degradation, such as land subsidence, severe tidal/monsoon flooding, subsidence of build-ups and a drastic decay in agricultural productivity. This paper examines the role of anthropo-geomorphic interventions causing for the land degradation in Munroe Island through a multidisciplinary approach. Multidated, multiresolution satellite products and published maps, spanning a period of about six decades from 1960 to 2021, were used to understand the different geomorphic and geographical processes in the study area. Evaluation of the temporal bathymetric datasets, salinity measurements of the river and estuary, borehole data logs of the area and electrical resistivity surveys of the island were analyzed to find out the causative factors for the disturbances in the land neutrality, along with the tidal hydrodynamic changes in the region. The study shows about 14% of the total land area was vanished during the study period, and more than 25% of the area is under stress, leading to further land degradation. More than 500 households are forced to vacate their residence due to land subsidence/flooding. Lack of required freshwater and sediment supply from the Kallada river after the construction of the Thenmala reservoir in the Kallada river as well as the uncontrolled sand mining prevailed are the key factors for the environmental degradation of Munroe Island. The paper describes the role and colinkages of human-induced hydrogeomorphic interventions on a geomorphic system, in charge of the environmental degradation and land subsidence crisis of an estuarine island ecosystem and discusses the concerns related to the management strategies of such region. [ABSTRACT FROM AUTHOR]

A diet high in sodium contributes to a significant proportion of Disability Adjusted Life Years (DALYs) due to cardiovascular diseases. This paper describes the awareness, behaviour and determinants related to dietary salt intake in an adult population of 18–69 years that were assessed as part of the National NCD Monitoring Survey (NNMS) in India. A sub-sample of 3000 adults selected through simple random sampling from 150 nationally representative Primary Sampling Units (PSUs) was included. Data regarding awareness and behaviour related to dietary salt intake were collected. Urinary sodium excretion in spot urine samples was estimated and used to calculate dietary salt intake. The dietary salt intake's sociodemographic, behavioural and metabolic determinants were also analysed. Less than one-third of the adults of both genders in all age groups in rural and urban areas were aware that daily high salt intake could affect health. The estimated mean daily salt intake was 8.0 g (8.9 g/day for men and 7.1 g/day for women). The salt intake was significantly higher in men [Adjusted OR = 17.66 (5.24–59.46)], rural areas [Adjusted OR = 6.14 (1.83–20.60)], overweight and obese respondents [Adjusted OR = 17.62 (3.17–98.07)]. The perception of the harmful effects of high salt intake and practices to limit salt intake was low in the study population. The mean daily salt intake was higher than the WHO recommendation of up to 5 g daily. The mean dietary salt intake is high in the Indian population, which calls for planning and implementing control of dietary salt consumption measures. [ABSTRACT FROM AUTHOR]

This paper presents new data on the salt tolerance and avoidance mechanisms among various groups of halophytes in India. The halophytic flora in general has positive effect of high saline environments on growth and physiology. The coastal area of the Kachchh district in Gujarat includes about 350 km of shoreline along the Gulf of Kachchh. This study presents data on the elemental accumulation mechanisms in soil and halophytic flora (succulent and non-succulents). The halophytes were divided into two groups namely succulent with thick and fleshy leaves and stems as well as non-succulents with thin leaves and stem. The succulent halophytes included species such as Salicornia brachiata, Suaeda fruticosa and Suaeda nudiflora. The non-succulent halophytes include Aeluropus lagopoides and Urochondra setulosa. Plant parts namely leaves (Phylloclade for Salicornia), stems and roots were analyzed during the monsoon season. The results of soil and plant mineral ion contents differed widely across the intertidal zones in the same habitat. Likewise, the intra species have varied in all nutrient levels and salt concentration. The accumulation of elemental concentration was high during the monsoon season in the succulent Salicornia brachiata, especially in leaves that showed Na+ reaching high up to 7.6 meq g−1, whereas Cl− was noted to be 4.34 meq g−1. In the non-succulent halophytes, the accumulation of mineral ion concentration was lower when compared to succulent plants. [ABSTRACT FROM AUTHOR]

This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map. [ABSTRACT FROM AUTHOR]

This paper investigates the role of nuclear energy in promoting ecological sustainability in India, focusing on three ecological indicators: ecological footprint (EF), CO2 emissions (CO2), and load capacity factor (LF). In addition to nuclear energy, the study considers the influence of gas consumption and other drivers of ecological sustainability using data spanning from 1970 to 2018. The analysis also takes into account the impact of the 2008 global financial crisis on the model, employing the autoregressive distributed lag (ARDL) and frequency domain causality approaches to assess the relationships. Unlike previous studies, this research evaluates both the Environmental Kuznets Curve (EKC) and load capacity curve (LCC) hypotheses. The ARDL results support the validity of both the EKC and LCC hypotheses in the Indian context. Furthermore, the findings reveal that nuclear energy and human capital contribute positively to ecological quality, while gas consumption and economic growth have a negative impact on ecological sustainability. The study also highlights the increasing effect of the 2008 global financial crisis on ecological sustainability. Additionally, the causality analysis demonstrates that nuclear energy, human capital, gas consumption, and economic growth can serve as predictors of long-term ecological sustainability in India. Based on these findings, the research presents policy recommendations that can guide efforts towards achieving SDGs 7 and 13. [ABSTRACT FROM AUTHOR]

For countries with high maternal mortality and morbidity, on-time initiation of antenatal care (ANC) is indispensable. Therefore this paper aims for studying the median survival time (MST) of first ANC among pregnant women as well as understanding the contextual factors that influence a mother's decision to access ANC services in India. The study used cross-sectional survey data obtained from the NFHS-4 conducted in 2015–2016. The MST of the timing of the first ANC visit was estimated using the Kaplan-Meir estimate. A multivariate Cox-proportional hazard regression model was used to identify the factors related to the timing of the first ANC visit with a 95% confidence interval (CI). Overall at least one ANC checkup was assessed by 60.15% of women and the median survival time for the first ANC checkup was found to be 4 months. Early initiation of ANC in pregnant women increased by 37% (AHR: 1.37, CI:1.34–1.39) for primary education, and 88% (AHR:1.88, CI:1.86–1.90) for secondary education compared to women having no formal education. Results of the current study revealed that the median survival time of the first ANC visit was 4 months in India which is delayed compared to recommendations of WHO. Therefore boosting the access and utilization of antenatal care coverage among pregnant women can ensure the best health outcomes for their pregnancy. [ABSTRACT FROM AUTHOR]

The growing number of end-of-life vehicles (ELVs) engenders a genuine concern for achieving sustainable development. Properly recycling ELV is paramount to checking pollution, reducing landfills, and conserving natural resources. The present study evaluates the sustainability of India's ELV recycling system from techno-socio-economic and environmental aspects as an instrumental step for assessing performance and progress. This investigation has performed the Strength-Weakness-Opportunity-Threat (SWOT) analysis to evaluate ELV recycling in the long-term viability and examine the critical factors and potential. This research makes practical recommendations for effectively encountering persistent challenges in the ELV recycling system based on Indian values. This research adopts an explorative and Integrated bottom-up mixed approach; it interfaces qualitative and quantitative data and secondary research. This study reveals that the social, economic, technological, and environmental aspects of the sustainability of India's ELV recycling system are comparatively limited. The SWOT analysis demonstrates that potential market size and resource recovery are more significant strengths, whereas lack of an appropriate framework and limited technology are major challenges in the recycling of ELVs in India. Sustainable development and economic viability have emerged as great opportunities, while informality and environmental impact have surfaced as primary potential threats to ELV recycling in India. This paper offers insights and yields critical real-world data that may assist in rational decision-making and developing and implementing any subsequent framework. [ABSTRACT FROM AUTHOR]

The overarching goal of this paper is to shed light on the human influence on the changing patterns of heat waves in India using the Heat Wave Magnitude Index daily (HWMId). The HWMId obtained from the observational data sets shows a large increase in the heat waves during the past decades. Investigating the effects of natural (e.g., solar variations and volcanic forcings) and anthropogenic (e.g., greenhouse gas emissions, anthropogenic, land use, and land cover) forcings revealed that the anthropogenic factors have cause a two-fold increase in the occurrence probability of severe heat waves in central and mid-southern India during twentieth century. The spatial distribution of maximum HWMId values under natural and all forcings (including anthropogenic) indicates that in most places human activities have increases the frequency, duration and intensity of extreme heat waves. Under the Representative Concentration Pathway (RCP) 4.5, the risk of heat waves is projected to increase tenfold during the twenty-first century. More than ~ 70% of the land areas in India is projected to be influenced by heat waves with magnitudes greater than 9. Furthermore, we find a significant relationship between heat waves and deficits in precipitation. Results show that concurrent heat waves and droughts are projected to increase in most places in India during the twenty-first century. [ABSTRACT FROM AUTHOR]

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc. [ABSTRACT FROM AUTHOR]

Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability. [ABSTRACT FROM AUTHOR]

The present paper utilizes entropy theory and Google earth engine cloud computing technique to investigate system state and river recovery potential in two large sub-basins of the Mahanadi River, India. The cross-sectional intensity entropy (CIE) is computed for the post-monsoon season (October–March) along the selected reaches. Further, a normalized river recovery indicator (NRRI) is formulated to assess the temporal changes in river health. Finally, NRRI is related to a process-based variable-LFE (low flow exceedance) to comprehend the dominating system dynamics and evolutionary adjustments. The results highlight the existence of both threshold-modulated and filter-dominated systems based on CIE and NRRI variabilities. In addition, the gradual decline in CIE and subsequent stabilization of vegetated landforms can develop an 'event-driven' state, where floods exceeding the low-flow channel possess a direct impact on the river recovery trajectory. Finally, this study emphasizes the presence of instream vegetation as an additional degree of freedom, which further controls the hierarchy of energy dissipation and morphological continuum in the macrochannel settings. [ABSTRACT FROM AUTHOR]

This paper presents the step-by-step development and implementation of real-time data transmission from a distant Observatory. Using Python programming language, we have developed an algorithm for automated data transmission of high resolution real-time magnetic data from Choutuppal (CPL) and Hyderabad (HYB), India to INTERMAGNET Edinburgh GIN, with minimal internet services. We have designed a system to transfer the data in a secured and encrypted pattern with SSH keys and save the same dataset in the local server at CSIR-NGRI. Both the Observatories data are transmitted to GIN in real-time with a time frame of less than 300 s. After successfully transmitting real-time 1 min geomagnetic data from CPL and HYB Observatories to Edinburgh GIN, CPL's 1 s real-time data is also initiated and is one of the first Indian observatories to transmit real-time 1 s data to GIN. The details of algorithm development, function libraries and the challenges faced for 1 s and 1 min data transmission at both the Observatories are discussed. [ABSTRACT FROM AUTHOR]

Coalbed methane (CBM) production is effectively achieved by utilizing two processes, viz. primary and secondary recovery. In this paper, the primary recovery of CBM was studied using the adsorption isotherm while CO2-ECBM process for the secondary recovery was simulated with realistic parameters. The adsorption isotherm for CH4 was drawn up to the pressure of 1200 psi for four coal samples and Langmuir isotherm curves for both CH4 and CO2 was measured for one sample up to 2000 psi. The adsorption isotherm of four samples was further utilized for finding the primary recovery factor of methane, showing that the average primary recovery is ~ 54% with the highest recovery factor of ~ 76% for one sample. Hence, CO2-ECBM process could be further implemented to enhance gas recovery. Then, a 3D heterogeneous coalbed model at a depth of 3219 ft was constructed using the COMET3 simulator to demonstrate the potential of CO2-ECBM recovery technique. A concept of break-even time was introduced in this study for the comprehension of CO2-ECBM process. It is found that coalbed reservoirs may opt to implement this technology with economically sound recovery. [ABSTRACT FROM AUTHOR]

The Khangchendzonga Landscape (KL), a part of 'Himalayan Biodiversity Hotspot', is known for its unique biodiversity assemblage. In recent years, the KL is experiencing threats to biodiversity due to the biological overdominance of native Maling bamboo (Yushania maling). In the present study, we investigated the impacts of the overdominance of Y. maling on the forest composition of Singalila National Park (SNP), Eastern Himalaya, India. Elevational habitats 2400 to 3400 m asl were sampled by laying 69 (10 m × 10 m) forest plots including 51 bamboo plots and 18 non-bamboo plots. Bamboo plots showed significantly (p < 0.05) low species richness and density in both shrub and herb layers which further manifested the low seedling density. Generalized Additive Model (GAM) estimated a significant (p < 0.0001) decline in species richness and density with increasing bamboo density in SNP. Our study projects the overdominance of Y. maling has a significant negative impact on forest structure and composition. Therefore, management of invasiveness of Y. maling is essential through its optimized removal from the protected areas and utilization in making handicrafts, paper industries etc. to create ecological and economic benefits. Further long-term studies assessing the impacts of Y. maling overdominance on forest ecosystems and soil dynamics are recommended. [ABSTRACT FROM AUTHOR]

In a culturally different and low-resource setting, where lifestyle habits, including dietary pattern and physical activities differ from those in high-income countries, the association between physical activity and cognition is expected to differ. We aimed to investigate the association between physical activity and cognitive functioning after controlling for potential confounders among older adults in India. Furthermore, gender differences in this relationship were analyzed. Using a national-level data from the Longitudinal Ageing Study in India (2017–2018), this paper employed propensity score matching (PSM) approach to examine the association between physical activities and cognitive functioning among Indian older adults. Cognitive impairment was measured through five broad domains (memory, orientation, arithmetic function, executive function, and object naming). We limit our sample to older adults aged 60 + years, and our final dataset contains 31,464 participants (men = 16,366, and women = 15,098). The results indicated that older adults who engaged in frequent physical activity have greater cognitive functioning than older adults without physical activity after adjusting for various individual, health, lifestyle, and household factors. This association holds true for both older men and older women. The results from the PSM revealed that the cognitive function score was increased by 0.98 and 1.32 points for the frequently physically active older men and women population, respectively. The results demonstrate the possible beneficial effects of frequent physical activity on cognitive functioning among older adults. Thus, regular physical activity can be considered as an effective lifestyle factor to promote healthy cognitive aging. [ABSTRACT FROM AUTHOR]

The Novel Coronavirus which emerged in India on January/30/2020 has become a catastrophe to the country on the basis of health and economy. Due to rapid variations in the transmission of COVID-19, an accurate prediction to determine the long term effects is infeasible. This paper has introduced a nonlinear mathematical model to interpret the transmission dynamics of COVID-19 infection along with providing vaccination in the precedence. To minimize the level of infection and treatment burden, the optimal control strategies are carried out by using the Pontryagin's Maximum Principle. The data validation has been done by correlating the estimated number of infectives with the real data of India for the month of March/2021. Corresponding to the model, the basic reproduction number R 0 is introduced to understand the transmission dynamics of COVID-19. To justify the significance of parameters we determined the sensitivity analysis of R 0 using the parameters value. In the numerical simulations, we concluded that reducing R 0 below unity is not sufficient enough to eradicate the COVID-19 disease and thus, it is required to increase the vaccination rate and its efficacy by motivating individuals to take precautionary measures. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Financial inclusion plays an important role in helping households manage risks, but its role in mitigating climate risks is unexplored. Access to formal financial institutions in regions with high climate risks increases households' access to liquidity that they need to buffer against climate shocks. Using longitudinal data from 1082 rural households located in the semi-arid tropics in India, we find that households facing high climate risks hold a higher proportion of assets in liquid form. Access to formal financial services, however, reduces the need to keep liquid assets to be able to respond to high climate variability. Our results suggest that expanded financial inclusion in regions with high climate variability can reallocate resources held in unproductive liquid assets to invest in climate adaptation. [ABSTRACT FROM AUTHOR]

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ( R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's I = - 0.0395 and p < 0.01 ) in the residuals. It is found that more than 86% of local R 2 values are larger than 0.60 and almost 68% of R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found. [ABSTRACT FROM AUTHOR]