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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]

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor. [ABSTRACT FROM AUTHOR]

In recent years biochar (BC) has gained importance for its huge carbon (C) sequestration potential and positive effects on various soil functions. However, there is a paucity of information on the long-term impact of BC on the priming effect and nutrient availability in soil with different properties. This study investigates the effects of BC prepared from rice husk (RBC4, RBC6), sugarcane bagasse (SBC4, SBC6) and mustard stalk (MBC4, MBC6) at 400 and 600 °C on soil C priming and nitrogen (N), phosphorus (P), and potassium (K) availability in an Alfisol, Inceptisol, and Mollisol. BC properties were analyzed, and its decomposition in three soil orders was studied for 290 days in an incubation experiment. Post-incubation, available N, P, and K in soil were estimated. CO2 evolution from BC and soil alone was also studied to determine the direction of priming effect on native soil C. Increasing pyrolysis temperature enhanced pH and EC of most of the BC. The pyrolysis temperature did not show clear trend with respect to priming effect and nutrient availability across feedstock and soil type. MBC6 increased C mineralization in all the soil orders while RBC6 in Alfisol and SBC6 in both Inceptisol and Mollisol demonstrated high negative priming, making them potential amendments for preserving native soil C. Most of the BC showed negative priming of native SOC in long run (290 days) but all these BC enhanced the available N, P, and K in soil. SBC4 enhanced N availability in Alfisol and Inceptisol, RBC4 improved N and P availability in Mollisol and P in Alfisol and MBC6 increased K availability in all the soils. Thus, based on management goals, tailored BC or blending different BC can efficiently improve C sequestration and boost soil fertility. [ABSTRACT FROM AUTHOR]

Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned "gamucha" from India's northeast, from counterfeit powerloom imitations. Our study's objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures—VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201—were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven "gamucha"s and counterfeit powerloom imitations—a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products. [ABSTRACT FROM AUTHOR]

In India, the rising double burden of diseases and the low fiscal capacity of the government forces people to resort to hardship financing. This study aimed to examine the factors contributing to the reduction in hardship financing among inpatient households in India. The study relies on two rounds of National Sample Surveys with a sample of 34,478 households from the 71st round (2014) and 56,681 households from the 75th round (2018). We employed multivariable logistic regression and multivariate decomposition analyses to explore the factors associated with hardship financing in Indian households with hospitalized member(s) and assess the contributing factors to the reduction in hardship financing between 2014 and 2018. Notably, though hardship financing for inpatient households has decreased between 2014 and 2018, households with catastrophic health expenditure (CHE) had higher odds of hardship financing than those without CHE. While factors such as CHE, prolonged hospitalization, and private hospitals had impoverishing effects on hardship financing in 2014 and 2018, the decomposition model showed the potential of CHE (32%), length of hospitalization (32%), and private hospitals (24%) to slow down this negative impact over time. The findings showed the potential for further improvements in financial health protection for inpatient care over time, and underscore the need for continuing efforts to strengthen the implementation of public programs and schemes in India such as Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PMJAY). [ABSTRACT FROM AUTHOR]

The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment. [ABSTRACT FROM AUTHOR]

The agroforestry system is the best option to achieve the net zero carbon emissions target for India. Keeping this view, carbon sequestration and credit potential of gamhar based agroforestry system has been assessed. The experiment was carried out in randomized block design in seven different treatments with five replications. Gamhar tree biomass accumulation was higher in gamhar based agroforestry system compared to sole gamhar. Among different tree components, stem contributed a maximum to total gamhar tree biomass followed by roots, leaves and branches. The average contributions of stems, roots, leaves and branches in total tree biomass in two annual cycles (2016–17 and 2017–18) varied between 50 and 60, 19.8 and 20, 19.2 and 20, and 10.7 and 12.7 percent, respectively. In case of crops, above ground, below ground and total biomass was significantly higher in sole intercrops than gamhar based agroforestry system. Total (Tree + interrops + Soil) carbon stock, carbon sequestration, carbon credit and carbon price were significantly affected by treatments, and was maximum in Sole Greengram-Mustard. Net carbon emission was also recorded lowest in Sole Greengram-Mustard for which the values were 811.55% and 725.24% and 760.69% lower than Sole Gamhar in 2016–17, 2017–18 and in pooled data, respectively. [ABSTRACT FROM AUTHOR]

Biosimilar insulin analogues are increasing market access for diabetic patients globally. Scientific establishment of biosimilarity is cornerstone of this key change in the medical landscape. BGL-ASP is a biosimilar insulin aspart developed by BioGenomics Limited, India. BioGenomics has considered a stepwise approach in generating the totality of evidence required to establish similarity with reference product. Insulin aspart is a recombinant rapid-acting human insulin analogue utilised in the treatment of type-1 and type-2 diabetes mellitus. The single amino acid substitution at position B28 where proline is replaced with aspartic acid results in a decreased propensity to form hexamers, thus increasing the absorption rate on subcutaneous administration compared to native insulin. In order to establish the safety and efficacy of BGL-ASP, the critical quality attributes (CQAs) of BGL-ASP are identified based on the impact created on biological activity, pharmacokinetic/pharmacodynamic (PK/PD), immunogenicity and safety. The CQAs of insulin aspart are related to product structure, purity and functionality and are characterised using a series of state-of-the-art orthogonal analytical tools. The primary protein sequence, the secondary, tertiary and quaternary structure are found to be highly similar for BGL-ASP and reference product. The product related impurities of insulin aspart and the assay content are determined using high performance liquid chromatography (HPLC) based analysis and is similar for BGL-ASP and reference insulin aspart sourced from United States of America (US), Europe Union (EU) and India. The safety, efficacy and immunogenicity of BGL-ASP is also found to be comparable with reference product and is confirmed through the clinical trials conducted as recommended by International Council for Harmonisation of Technical Requirements of Pharmaceuticals for Human Use (ICH) and European Medicines Agency (EMA) guidelines. The data encompassed in this study demonstrates that reference insulin aspart and BGL-ASP are highly similar in terms of structural, physicochemical, and biological properties, thus confirming its safety and efficacy for usage as potential alternative economical medicinal treatment for diabetes mellitus. [ABSTRACT FROM AUTHOR]

Hindus and Muslims represent the two largest religions in India, and also differ in nutritional status, health-related habits and standard of living associated with economic disparities. In this context, the present study considered estimated secular changes in body size, proportions, and weight status among Hindu and Muslim Indian men. The data are from anthropological surveys in the 1970s which included measurements of height, weight and sitting height of 43,950 males 18–84 years (birth years 1891–1957). Leg length was estimated; the BMI and sitting height/height ratio were calculated. Heights of men 35 + years were adjusted for estimated height loss with age. Weight status was also classified relative to WHO criteria for the BMI. Anthropometric characteristics of the two groups were compared with MANCOVA with age and geographic region as covariates. Linear regression of height on year of birth was also used to estimate secular change in each group. Heights, weights, and BMIs tended to be, on average, greater among Muslim than Hindu men at most ages, while distributions by weight status between groups were negligible. Sitting height was greater among Muslim men but estimated leg length did not differ between groups; the sitting height/height ratio thus suggested proportionally shorter legs among Muslim men. Results of the regression analyses indicated negligible differences in secular change between groups across the total span of birth years but indicated a decline in adjusted heights of men in both groups born between 1891 through 1930s and little secular change among those born in the 1930s through 1957. The variation in heights, weights and BMIs between Muslim and Hindu men at most ages suggested variation in socio-economic status and dietary habits between the groups, whereas the negligible estimated secular changes in height between groups likely reflected economic, social, and nutritional conditions during the interval of British rule and the transition to independence. [ABSTRACT FROM AUTHOR]

Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) has significantly affected maize crop yields, production efficiency, and farmers' incomes in the Indian Eastern Gangetic Plains region since it was first observed in India in 2018. A lack of awareness by maize growers of the appropriate selection, method, and timing of insecticide application not only creates a barrier to sustainable FAW control but also contributes to increased environmental pollution, reduced human health and increased production costs. We demonstrated that FAW inflicted the most damage in early whorl growth stage of maize, regardless of whether chemical insecticides were applied. FAW egg masses and larvae collected from maize fields in which no insecticides had been sprayed showed high parasitism rates by parasitoid wasps; in contrast fields that had been sprayed had much lower rates of parasitism on FAW. Ten hymenopteran parasitoids were observed in maize fields across the study region, suggesting a diversity of natural methods to suppress FAW in maize at different growth stages. These included two FAW egg parasitoids and eight FAW larval parasitoids. Microplitis manilae Ashmead was the most abundant FAW larval parasitoid species, and Telenomus cf. remus was the dominant FAW egg parasitoid species. Endemic FAW parasitoids such as those observed in this study have great potential as part of a sustainable, cost-effective agroecological management strategy, which can be integrated with other methods to achieve effective control of FAW. [ABSTRACT FROM AUTHOR]

Detecting potholes and traffic signs is crucial for driver assistance systems and autonomous vehicles, emphasizing real-time and accurate recognition. In India, approximately 2500 fatalities occur annually due to accidents linked to hidden potholes and overlooked traffic signs. Existing methods often overlook water-filled and illuminated potholes, as well as those shaded by trees. Additionally, they neglect the perspective and illuminated (nighttime) traffic signs. To address these challenges, this study introduces a novel approach employing a cascade classifier along with a vision transformer. A cascade classifier identifies patterns associated with these elements, and Vision Transformers conducts detailed analysis and classification. The proposed approach undergoes training and evaluation on ICTS, GTSRDB, KAGGLE, and CCSAD datasets. Model performance is assessed using precision, recall, and mean Average Precision (mAP) metrics. Compared to state-of-the-art techniques like YOLOv3, YOLOv4, Faster RCNN, and SSD, the method achieves impressive recognition with a mAP of 97.14% for traffic sign detection and 98.27% for pothole detection. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy and acceptance, driven by social influence, is usually explored by most researchers using exhaustive survey-based studies, which investigate public preferences, fundamental values, beliefs, barriers, and drivers through closed or open-ended questionnaires. Commonly used simple statistical tools do not do justice to the richness of this data. Considering the gradual development of vaccine acceptance in a society driven by multiple local/global factors as a compartmental contagion process, we propose a novel methodology where drivers and barriers of these dynamics are detected from survey participants' responses, instead of heuristic arguments. Applying rigorous natural language processing analysis to the survey responses of participants from India, who are from various socio-demographics, education, and perceptions, we identify and categorize the most important factors as well as interactions among people of different perspectives on COVID-19 vaccines. With a goal to achieve improvement in vaccine perception, we also analyze the resultant behavioral transitions through platforms of unsupervised machine learning and natural language processing to derive a compartmental contagion model from the data. Analysis of the model shows that positive peer influence plays a very important role and causes a bifurcation in the system that reflects threshold-sensitive dynamics. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine (a) the socioeconomic status (SES)-related inequalities associated with handgrip strength (HGS); and (b) the extent to which several demographic, health, and behavioral factors contributed to such SES disparities in HGS among older adults in India. Data were drawn from the 2017–2018 wave 1 of the Longitudinal Ageing Study of India (LASI). The study sample included 27,707 older adults (13,199 men and 14,508 women) aged 60 years and older. HGS was assessed using a handheld Smedley's Hand Dynamometer with a cut-off of 19.5 kg for men and 12.5 kg for women. Bivariate analysis showed the weighted percentage distribution of weak HGS across respondent characteristics. Multivariate logistic regression assessed factors linked to weak HGS. The concentration curve and index (CCI) was used to determine the inequalities in the prevalence of weak HGS by wealth index scores. Wagstaff's decomposition approach was used to test the contribution of each explanatory variable to weak HGS. Around 9% of older adults in this study reported a weak HGS. It was significantly higher among those aged 80 or older (19.21%) and males (15.55%). Weak HGS was concentrated among older adults from poor SES (CCI: 0.05, p < 0.001). A higher percentage of wealth-based inequality in weak HGS was explained by being underweight (38.83%), belonging to the richest wealth quintile (27.95%), and having a higher subjective social status (32.20%). Moreover, about 23.29% of the inequality in weak HGS was explained by Western region and 22.54% by female gender. Additionally, having a secondary level of education explained a higher percentage (22.09%) of inequality, followed by current working status (− 20.68%). Rural residence (13.08%), limitations in instrumental activities of daily living (IADL) (12.21%), and engagement in yoga-related activities (11.55%) explained a higher percentage of wealth-based inequalities. The findings provide evidence of significant SES-related inequalities in HGS and the contribution of various demographic, health, and behavioral factors to such inequality. As such, public health policies and programs focusing on reducing the burden of disability must consider the contribution of social and economic equity to the preservation of muscle strength among older adults. [ABSTRACT FROM AUTHOR]

Durgama Anchalare Malaria Nirakaran (DAMaN) is a multi-component malaria intervention for hard-to-reach villages in Odisha, India. The main component, malaria camps (MCs), consists of mass screening, treatment, education, and intensified vector control. We evaluated MC effectiveness using a quasi-experimental cluster-assigned stepped-wedge study with a pretest–posttest control group in 15 villages: six immediate (Arm A), six delayed (Arm B), and three previous interventions (Arm C). The primary outcome was PCR + Plasmodium infection prevalence. The time (i.e., baseline vs. follow-up 3) x study arm interaction term shows that there were statistically significant lower odds of PCR + Plasmodium infection in Arm A (AOR = 0.36, 95% CI = 0.17, 0.74) but not Arm C as compared to Arm B at the third follow-up. The cost per person ranged between US$3–8, the cost per tested US$4–9, and the cost per treated US$82–1,614, per camp round. These results suggest that the DAMaN intervention is a promising and financially feasible approach for malaria control. [ABSTRACT FROM AUTHOR]

Accurate and in-time prediction of crop yield plays a crucial role in the planning, management, and decision-making processes within the agricultural sector. In this investigation, utilizing area under irrigation (%) as an exogenous variable, we have made an exertion to assess the suitability of different hybrid models such as ARIMAX (Autoregressive Integrated Moving Average with eXogenous Regressor)–TDNN (Time-Delay Neural Network), ARIMAX–NLSVR (Non-Linear Support Vector Regression), ARIMAX–WNN (Wavelet Neural Network), ARIMAX–CNN (Convolutional Neural Network), ARIMAX–RNN (Recurrent Neural Network) and ARIMAX–LSTM (Long Short Term Memory) as compared to their individual counterparts for yield forecasting of major Rabi crops in India. The accuracy of the ARIMA model has also been considered as a benchmark. Empirical outcomes reveal that the ARIMAX–LSTM hybrid modeling combination outperforms all other time series models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. For these models, an average improvement of RMSE and MAPE values has been observed to be 10.41% and 12.28%, respectively over all other competing models and 15.83% and 18.42%, respectively over the benchmark ARIMA model. The incorporation of the area under irrigation (%) as an exogenous variable in the ARIMAX framework and the inbuilt capability of the LSTM model to process complex non-linear patterns have been observed to significantly enhance the accuracy of forecasting. The performance supremacy of other hybrid models over their individual counterparts has also been evident. The results also suggest avoiding any performance generalization of individual models for their hybrid structures. [ABSTRACT FROM AUTHOR]

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities. [ABSTRACT FROM AUTHOR]

India is the second-highest contributor to the post-2000 global greening. However, with satellite data, here we show that this 18.51% increase in Leaf Area Index (LAI) during 2001–2019 fails to translate into increased carbon uptake due to warming constraints. Our analysis further shows 6.19% decrease in Net Primary Productivity (NPP) during 2001–2019 over the temporally consistent forests in India despite 6.75% increase in LAI. We identify hotspots of statistically significant decreasing trends in NPP over the key forested regions of Northeast India, Peninsular India, and the Western Ghats. Together, these areas contribute to more than 31% of the NPP of India (1274.8 TgC.year−1). These three regions are also the warming hotspots in India. Granger Causality analysis confirms that temperature causes the changes in net-photosynthesis of vegetation. Decreasing photosynthesis and stable respiration, above a threshold temperature, over these regions, as seen in observations, are the key reasons behind the declining NPP. Our analysis shows that warming has already started affecting carbon uptake in Indian forests and calls for improved climate resilient forest management practices in a warming world. [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]

The 'Green Revolution (GR)' has been successful in meeting food sufficiency in India, but compromising its nutritional security. In a first, we report altered grain nutrients profile of modern-bred rice and wheat cultivars diminishing their mineral dietary significance to the Indian population. To substantiate, we evaluated grain nutrients profile of historical landmark high-yielding cultivars of rice and wheat released in succeeding decades since the GR and its impacts on mineral diet quality and human health, with a prediction for decades ahead. Analysis of grain nutrients profile shows a downward trend in concentrations of essential and beneficial elements, but an upward in toxic elements in past 50 y in both rice and wheat. For example, zinc (Zn) and iron (Fe) concentration in grains of rice decreased by ~ 33.0 (P < 0.001) and 27.0% (P < 0.0001); while for wheat it decreased by ~ 30.0 (P < 0.0001) and 19.0% (P < 0.0001) in past more than 50 y, respectively. A proposed mineral-diet quality index (M-DQI) significantly (P < 0.0001) decreased ~ 57.0 and 36.0% in the reported time span (1960–2010) in rice and wheat, respectively. The impoverished M-DQI could impose hostile effects on non-communicable diseases (NCDs) like iron-deficiency anemia, respiratory, cardiovascular, and musculoskeletal among the Indian population by 2040. Our research calls for an urgency of grain nutrients profiling before releasing a cultivar of staples like rice and wheat in the future. [ABSTRACT FROM AUTHOR]

Unmet needs for contraception in India have declined over time but the rate has not been uniform among women across geographies and socio-economic strata. Identifying the characteristics of women in communities where unmet need is still high is important to devise appropriate strategies to ensure access and uptake of modern contraceptive methods. The current study examined whether there was a national decline in unmet need over time and if regional disparities exist in unmet need. Demographic variations in unmet need based on factors such as maternal age, education, religion, caste, wealth index quintile, family size, and access to antenatal care (ANC) were also documented. Our approach was to document the prevalence of total unmet need for family planning and unmet need for spacing among married Indian women and quantify variability based on socio-economic and demographic drivers within a hierarchal framework, thus providing both macro and micro perspectives. We used data from the fourth and fifth rounds of the National Family Health Survey (NFHS) collected from all the States and Union Territories (UTs) in India. Quantile regression analysis and multilevel regression techniques were used to understand the predictors for the total unmet need for family planning and the unmet need for spacing. Results show a considerable decline in the prevalence of unmet need for family planning in India from NFHS-4 to 5 (from 12.9 to 9.3%) in the last six6 years. The north-eastern states show a significant reduction in unmet need for family planning in Manipur (17.8%), Nagaland (13.5%), and followed by Sikkim (9.1%). The predictors such as years of schooling, place of residence, caste, religion, wealth quintile, number of antenatal care (ANC) visits, and children ever born have a significant association with unmet needs for family planning and spacing among married women in India. There is a significant association between years of schooling with the total unmet needs for family planning at (q25) quantiles and the unmet need for spacing at (q25, q50) quantiles. Results reveal that the demand for unmet need for spacing and limiting was the highest among the women in the age categories 15–19 (17.8%) and 20–24 (17.3%). The demand for limiting was the highest (6.8%) among Muslim women. Across wealth quantile categories, the overall unmet demand (11.4%) for spacing and limiting was the highest among the women in the lowest socioeconomic groups. We conclude that greater access to frontline health workers among young wives, and significant investment in education in general, will continue to reduce the unmet needs for family planning in India. [ABSTRACT FROM AUTHOR]

Apple cankers are extremely destructive diseases threatening the global apple industry through direct and indirect losses. The population structure of the pathogens is of paramount significance for the development of efficient management strategies. Therefore, phenotypic, pathogenic, and genetic diversity of Diplodia seriata causing black rot canker of apple was investigated in this study. All the isolates were included for investigating the in vitro mycelial growth, conidial dimensions, and pathogenic variability on two-year-old potted apple seedlings. The ISSR approach was used to investigate the molecular diversity of D. seriata. Mycelial growth rates were found to vary significantly amongst the isolates; however, there were no major variations seen between the different geographical groupings of isolates. Pathogenicity tests revealed variations in the size of cankers among the isolates indicating the presence of virulence variability. The isolates were segregated into three virulence groups based on canker length. The Bayesian analyses of ISSR data divided the isolates into two genetic clusters. The genetic clustering of the isolates revealed no relationship with geographical origin of the isolates. Furthermore, no direct relationship of genetic clustering was observed with morphological or pathogenic variability. The ISSR primers revealed very high level of variability in D. seriata; however, no distinct populations of the pathogen existed which is an indication of high level of gene flow between the diverse geographical populations. According to our knowledge, this is the first thorough investigation on the diversity of D. seriata associated with apple black rot canker in India. [ABSTRACT FROM AUTHOR]

Rice–wheat production in the Indo-gangetic plains (IGPs) of India faces major concerns such as depleting resources, rice residue burning, excessive fertilizer use, and decreasing nitrogen use efficiency. These issues threaten sustainable crop production in the future. Therefore, a field study was conducted during the winter seasons of 2020–21 and 2021–22 to evaluate the effect of combined conventional and nano fertilizers on nitrogen application just before or after irrigation to improve wheat productivity, profitability and NUE under conservation tillage. The study evaluated eight treatment combinations of nitrogen application through conventionally applied urea (46% N) and foliar applied nano urea (4% N) under zero tillage with rice residue retention. Results revealed that growth, physiological indices, yield, and quality parameters were enhanced with the application of 150 kg N/ha in three equal splits as basal and just before 1st and 2nd irrigation alone (T2) or along with a spray of nano urea (T5) compared to other treatments. T5 recorded 7.2%, 8.5%, and 7.8% more plant dry matter, number of tillers, and grain yield, respectively, over the conventional practice of applying 150 kg N/ha in three equal splits as basal and 7–10 days after 1st and 2nd irrigation (T3, farmers practice). Although, T2 showed similar results to T5, T5 recorded significantly higher gross ($2542/ha) and net returns ($1279/ha) than the other treatments. However, the benefit–cost ratio of T2 and T5 was same (2.01). A significant and positive correlation coefficient between grain yield and physiological parameters such as CCI and NDVI confirmed that increasing the nitrogen dose enhanced the chlorophyll content, greenness, and plant vigor. Based on the results, it can be concluded that applying 150 kg N/ha in three equal splits as basal and just before 1st and 2nd irrigation under conservation agriculture, along with a single spray of nano urea (4% N) at 60–65 days after sowing, can improve growth, yield attributes, wheat yield, and NUE compared to farmers practice (T3) in India. [ABSTRACT FROM AUTHOR]

In the midst of rapid urbanization and economic shifts, the global landscape witnesses a surge in overweight and obese individuals, even as child malnutrition persists as a formidable public health challenge in low- and middle-income countries (LMICs). This study seeks to unravel the prevalence of the double burden of malnutrition (DBM) within the context of India and delve into the associated disparities rooted in wealth. This study leverages data from the fifth wave of the National Family and Health Survey (NFHS-5), a nationally representative survey conducted in the year 2019–21 in India. This study focuses on mother–child dyads with children under the age of 3 years. Descriptive, bivariate and logistic regression analysis is used to decipher the intricate web of DBM's prevalence and risk factors, as underscored by socio-demographic attributes. Wagstaff decomposition analysis is applied to quantify the contribution of each inequality in the social determinants on the observed income-related inequality in the DBM. Result from bivariate and logistic regression indicated a heightened risk of DBM within households marked by C-section births, affluence, ongoing breastfeeding practices, advanced maternal age, and larger household sizes. Additionally, households harbouring women with abdominal obesity emerge as hotspots for elevated DBM risk. Notably, the interplay of abdominal obesity and geographical disparities looms large as drivers of substantial inequality in DBM prevalence, whereas other factors exert a comparably milder influence. As India grapples with the burgeoning burden of DBM, a conspicuous imbalance in its prevalence pervades, albeit inadequately addressed. This juncture warrants the formulation of dual-purpose strategies, and a slew of innovative actions to deftly navigate the complex challenges poised by the dual burden of malnutrition. Amidst these exigencies, the imperative to forge a holistic approach that encompasses both sides of the malnutrition spectrum remains a beacon guiding the quest for equitable health and nutrition outcomes. [ABSTRACT FROM AUTHOR]

Neonatal resuscitation training can change outcomes of neonatal mortality due to perinatal asphyxia. Recently, in 2021, the advanced NRP course material was changed, and for Basic NRP, a hybrid course was introduced in India. We assessed the instructor's feedback to improve the conduct of the IAP NNF NRP Program as well as get their perception of the effectiveness, usefulness, and pitfalls of the new hybrid Basic NRP course (offline + online). A cross-sectional survey was conducted amongst instructors across India with current status with IAP NRP FGM Office. The data were exported to a Microsoft Excel Spreadsheet. STATA 14.2 was used for descriptive [Frequency (percent) analysis. 827 basic and 221 advanced NRP instructors responded. Bag and mask ventilation was identified as the most important step in basic 468 (56.6%) and advanced 147 (66.5%) courses. In the basic NRP, almost two third (71.0%) participants believe that it is challenging to conduct a case scenario for bag and mask ventilation, whereas, in the advanced course, intubation 116 (52.5%) was considered the most difficult step to teach and medication 80(36.2%) followed by intubation 62(28.1%) are the most difficult steps to conduct case scenario. 725(87.7%) reported that it would be easy to explain them in an offline course after completion of an online course. Most of the instructors were satisfied with the course structure, material, overall quality of the workshop, and support from the IAP NRP office. Constructive suggestions were obtained from the instructors for improvement of the course. [ABSTRACT FROM AUTHOR]

Indian cities have frequently observed intense and severe heat waves for the last few years. It will be primarily due to a significant increase in the variation in heat wave characteristics like duration, frequency, and intensity across the urban regions of India. This study will determine the impact of future climate scenarios like SSP 245 and 585 over the heat wave characteristics. It will present the comparison between heat waves characteristics in the historical time (1981 to 2020) with future projections, i.e., D1 (2021–2046), D2 (2047–2072), and D3 (2073–2098) for different climate scenarios across Indian smart cities. It is observed that the Coastal, Interior Peninsular, and North-Central regions will observe intense and frequent heat waves in the future under SSP 245 and 585 scenarios. A nearly two-fold increase in heat wave' mean duration will be observed in the smart cities of the Interior Peninsular, Coastal, and North Central zones. Thiruvananthapuram city on the west coast has the maximum hazard associated with heat waves among all the smart cities of India under both SSPs. This study assists smart city policymakers in improving the planning and implementation of heat wave adaptation and mitigation plans based on the proposed framework for heat action plans and heat wave characteristics for improving urban health well-being under hot weather extremes in different homogeneous temperature zones. [ABSTRACT FROM AUTHOR]

Guggul is a gum oleo-resin, tapped from a data deficient plant- Commiphora wightii (Arnott.) Bhandari in India. It is extensively used in ayurvedic drugs and formulations since ages. Natural plant-based products; especially aromatic ones like guggul gum oleo-resin deteriorates, qualitatively during its storage and transits before reaching the industry for its value addition. This economical and ecological loss can be avoided if it is stored in proper containers. Physico-chemical degradation of guggul samples stored were analysed by scanned electron microscopy, fourier transformed infra red, thermogravimatric, Powdered X-ray diffraction and elemental analysis for carbon, hydrogen, nitrogen and sulphur. Physico-chemical degradation of guggul oleo-resin occurs with the age of storage and the type of storage containers used. Among the four storage containers (earthen pot, plastic jar, polythene bag, jute bag) evaluated, earthen pot was found to be the best in checking the qualitative loss of guggul even upto 24 months. The qualitative information generated in the study on guggul storage may be useful to the drug industry and guggul traders. It may encourage them practice storing guggul in earthen pots against current practice of using jute bags and polythene bags, to store it. [ABSTRACT FROM AUTHOR]

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3 (95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters. [ABSTRACT FROM AUTHOR]

The efficient detection of nutrient deficiency and proper fertilizer for that deficiency becomes the critical challenges various farmers face. The family Cucurbitaceae includes members cultivated globally as a source of indigenous medicines, food, and fiber. Luffa acutangula (L.) Roxb, generally called Ridge gourd, belongs to the Cucurbitaceae family and is an annual herb originating in several areas of India, particularly in the coastal regions. Nutrient deficiency detection in ridge gourd is essential to improve crop productivity. In agricultural practises, the early identification and categorization of nutrient deficiencies in crops is essential for sustaining optimal growth and production. Addressing these nutrient deficiencies, we applied the Ring Toss Game Optimization with a Deep Transfer Learning-based Nutrient Deficiency Classification (RTGODTL-NDC) to Ridge Gourd (Luffa acutangula). This research proposes a new ring toss game optimization with a deep transfer learning-based nutrient deficiency classification (RTGODTL-NDC) method. The RTGODTL-NDC technique uses pre-processing, segmentation, feature extraction, hyperparameter tuning, and classification. The Gabor filter (GF) is mainly used for image pre-processing, and the Adam optimizer with SqueezeNet model is utilized for feature extraction. Finally, the RTGO algorithm with the deep hybrid learning (HDL) model is applied to classify nutrient deficiencies. The suggested framework has the potential to improve crop management practises by allowing for proactive and targeted interventions, which will result in improved agricultural health, production, and resource utilisation. The outcomes represented by the RTGODTL-NDC method have resulted in improved performance. For example, based on accuracy and specificity, the RTGODTL-NDC methodology rendered maximum a c c u y of 97.16% and specificity of 98.29%. The outcomes show how effective the transfer learning-based model is in identifying nutrient deficits in ridge gourd plants, as seen by its high level of accuracy. [ABSTRACT FROM AUTHOR]

High concentrations of potentially toxic elements (PTEs) in potable water can cause severe human health disorders. Present study examined the fitness of groundwater for drinking purpose based on the occurrence of nine PTEs in a heavy pilgrim and tourist influx region of the Garhwal Himalaya, India. The concentrations of analyzed PTEs in groundwater were observed in the order of Zn > Mn > As > Al > Cu > Cr > Se > Pb > Cd. Apart from Mn and As, other PTEs were within the corresponding guideline values. Spatial maps were produced to visualize the distribution of the PTEs in the area. Estimated water pollution indices and non-carcinogenic risk indicated that the investigated groundwater is safe for drinking purpose, as the hazard index was < 1 for all the water samples. Assessment of the cancer risk of Cr, As, Cd, and Pb also indicated low health risks associated with groundwater use, as the values were within the acceptable range of ≤ 1 × 10−6 to 1 × 10−4. Multivariate statistical analyses were used to describe the various possible geogenic and anthropogenic sources of the PTEs in the groundwater resources although the contamination levels of the PTEs were found to pose no serious health risk. However, the present study recommends to stop the discharge of untreated wastewater and also to establish cost-effective as well as efficient water treatment facility nearby the study area. Present work's findings are vital as they may protect the health of the massive population from contaminated water consumption. Moreover, it can help the researchers, governing authorities and water supplying agencies to take prompt and appropriate decisions for water security. [ABSTRACT FROM AUTHOR]

The Early Jurassic and Cretaceous deposits of India are known for their diverse sauropod fauna, while little is known from the Middle and Late Jurassic. Here we report the first ever remains of a dicraeosaurid sauropod from India, Tharosaurus indicus gen. et sp. nov., from the Middle Jurassic (early–middle Bathonian) strata of Jaisalmer Basin, western India. Known from elements of the axial skeleton, the new taxon is phylogenetically among the earlier-diverging dicraeosaurids, and its stratigraphic age makes it the earliest known diplodocoid globally. Palaeobiogeographic considerations of Tharosaurus, seen in conjunction with the other Indian Jurassic sauropods, suggest that the new Indian taxon is a relic of a lineage that originated in India and underwent rapid dispersal across the rest of Pangaea. Here we emphasize the importance of Gondwanan India in tracing the origin and early evolutionary history of neosauropod dinosaurs. [ABSTRACT FROM AUTHOR]

Leptospirosis, a formidable zoonotic threat spawned by Leptospira, plagues tropical and subtropical realms. This study delves deep into tropical Indian states, namely, Kerala, Gujarat, Karnataka, Maharashtra, and Tamil Nadu, unraveling the dynamics of leptospirosis through a comprehensive mathematical model that embraces temperature-driven growth rates of Leptospira. Sensitivity analysis and parameter estimation techniques fortified the model's accuracy, unraveling the factors shaping leptospirosis transmission. Notably, the numerical results highlight the significant impact of rainfall, fishing, climate, mining, agriculture, and cattle farming on leptospirosis prevalence in the endemic states of India. Finally, our study urges resolute preventive action to control and combat leptospirosis in India. Strengthening surveillance, impactful awareness campaigns, targeted interventions, and improved hygiene practices among high-risk individuals are vital. Embracing these proactive strategies will alleviate the burden of leptospirosis and enhance public health in India and beyond. [ABSTRACT FROM AUTHOR]