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Improvement of water and sanitation conditions may reduce infant mortality, particularly in countries like India where open defecation is highly prevalent. We conducted a quasi-experimental study to investigate the association between the Swachh Bharat Mission (SBM)—a national sanitation program initiated in 2014—and infant (IMR) and under five mortality rates (U5MR) in India. We analyzed data from thirty-five Indian states and 640 districts spanning 10 years (2011–2020), with IMR and U5MR per thousand live births as the outcomes. Our main exposure was the district-level annual percentage of households that received a constructed toilet under SBM. We mapped changes in IMR and U5MR and toilet access at the district level over time. We fit two-way fixed effects regression models controlling for sociodemographic, wealth, and healthcare-related confounders at the district-level to estimate the association between toilets constructed and child mortality. Toilet access and child mortality have a historically robust inverse association in India. Toilets constructed increased dramatically across India following the implementation of SBM in 2014. Results from panel data regression models show that districts with > 30% toilets constructed under SBM corresponds with 5.3 lower IMR (p < 0.05), and 6.8 lower U5MR (p < 0.05). Placebo, falsification tests and robustness checks support our main findings. The post-SBM period in India exhibited accelerated reductions in infant and child mortality compared to the pre-SBM years. Based on our regression estimates, the provision of toilets at-scale may have contributed to averting approximately 60,000–70,000 infant deaths annually. Our findings show that the implementation of transformative sanitation programs can deliver population health benefits in low- and middle-income countries. [ABSTRACT FROM AUTHOR]

Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station's data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+–HCO3− types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3− + CO32−) outperform strong acids (Cl− + SO42−). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p < 0.05). The results also revealed that river water for irrigation during monsoon is suitable compared to the pre-monsoon season. Anthropogenic interventions including riverbed agriculture, and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river, which needs substantial attention from the various stakeholders in a participatory, and sustainable manner. [ABSTRACT FROM AUTHOR]

Ensuring proper menstrual hygiene management remains a significant challenge for young women in India. The term "exclusive use of hygienic period products during menstruation" refers to relying solely on period products like sanitary pads, tampons, or menstrual cups. Poor menstrual hygiene practices not only increase the risk of reproductive tract infections but also lead to various negative health outcomes, including discomfort and potential complications. This study explores factors associated with the exclusive use of period products during menstruation aged 15–24, investigates geographic disparities, examines rural–urban gaps, and assesses inequality in India. Utilizing data from the fifth National Family Health Survey (NFHS-5), responses from 2,41,180 women aged 15 to 24 were analysed using logistic regression and multivariate decomposition analyses to explore socioeconomic predictors. Moran's I statistics also assessed spatial dependency, while Lorenz curves and Gini coefficients measured inequality. Quintile and LISA maps visualized regional disparities. The study found that 76.15% of women in India reported exclusive use of hygienic period products during menstruation. Rural areas reported a lower percentage of exclusive use of hygienic period products (72.32%) during menstruation compared to urban areas (89.37%). Key factors associated with the exclusive use of hygienic period products among 15–24-year-old women in India include age, education, place of residence, wealth, access to media, and healthcare discussions. Geographically, central districts exhibited the lowest coverage (< 65%), while the Southern region reported the highest (> 85). The GINI coefficient of 0.39 highlighted moderate inequality in distribution. Decomposition analysis revealed that household wealth contributed 49.25% to rural–urban differences, followed by education (13.41%), media access (7.97%), and region (4.97%). This study highlights significant regional disparities and low utilization of hygienic period products among young women in India, particularly in central districts. Policymakers should prioritize interventions targeting these regions, addressing socio-economic disparities. Strategies to promote education, improve media access, and enhance household wealth can facilitate menstrual hygiene adoption. Initiatives to reduce sanitary napkin costs and increase accessibility, particularly in rural areas, are crucial to mitigating geographical disparities nationwide. [ABSTRACT FROM AUTHOR]

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India. [ABSTRACT FROM AUTHOR]

Phytoremediation is a basic eco-friendly technique that uses to treat contaminated water and soil. The plants that remediate the water and soil by their absorption process and improve the water and sediment fertility or decrease the contamination. Form this experiment our finding suggest that the contamination decrease in majority from starting point to end point, it means plants plays the most important role in clean-up the environment and its cost-efficient method to improve the quality of water and soil. This study was carried out on Uben River which is around 50kms long and we covered around 41.88kms of area which divided into six locations. in soil minerals (Ca2+, Mg2+, Na+ and K+) from Up-stream to Down-stream the concentration of minerals is in decreasing order but in heavy metals (Cu2+, Zn2+, Fe2+ and Mn2+) the concentration data is varying. We selected plants that grow around riverbanks belongs to family Cyperaceae, Poaceae, Typhaceae. Most of the plants accumulate high Fe2+ concentrations in their root while in the shoots have low concentration observed from our data. For the statistical validation of data, we perform Grouped Component Analysis (GCA) and Radial Cluster Hierarchy (RCH) analysis. Further we included pollution indices: Contamination factor (CF), Degree of contamination (Cd), Geo accumulation index (Igeo). [ABSTRACT FROM AUTHOR]

Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops. The objective of this work is to utilize modern computer vision technology to identify and map cultivable land for agricultural needs. With increasing population and demand for food, improving the farming sector is crucial. However, the challenge lies in the lack of suitable land for food crops cultivation. To tackle this issue, we propose to use sophisticated image processing techniques on satellite images of the land to determine the regions that are capable of growing food crops. The solution architecture includes enhancement of satellite imagery using sophisticated pan sharpening techniques, notably the Brovey transformation, aiming to transform dull satellite images into sharper versions, thereby improving the overall quality and interpretability of the visual data. Making use of the weather data on the location observed and taking into factors like the soil moisture, weather, humidity, wind, sunlight times and so on, this data is fed into a generative pre-trained transformer model which makes use of it and gives a set of crops that are suitable to be grown on this piece of land under the said conditions. The results obtained by the proposed fusion approach is compared with the dataset provided by the government for different states in India and the performance was measured. We achieved an accuracy of 80% considering the crop suggested by our model and the predominant crop of the region. Also, the classification report detailing the performance of the proposed model is presented. [ABSTRACT FROM AUTHOR]

Few studies have focused sufficiently on the intricate link between functional health and depression among older people aged 60 and above in India. Therefore, the current study investigates the association between functional health and depression among older Indian adults through the mediating role of social disengagement and loneliness and the moderating role of living arrangements using recent data from the Longitudinal Aging Study in India (LASI: 2017–2018). Composite International Diagnostic Interview-Short Form (CIDI-SF) for depression, the Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) for functional health, and the indoor/outdoor activities, visits, and religious events for social disengagement were used. The feelings of loneliness and living arrangements were measured using single-item questions and surveys/interviews of household members. Bivariate analysis, logistic regression, and a Partial Least Squares-Structural Equation Model were adopted. The results show that older persons with functional health had 1.85 times higher odds of depression; similarly, those not engaging in social activities and experiencing loneliness were more likely to feel depressed. Living with someone was negatively linked to depression. A significant moderation by living arrangements in the functional health-depression relationship was also observed. The results also indicate significant mediating roles of social disengagement and loneliness, with 22.0% and 3.08% mediation effects, respectively. Therefore, this study recommends the provision of housing and social interaction among older people. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of obesity in India, body mass index (BMI) has garnered importance as a disease predictor. The current World Health Organization (WHO) body mass index (BMI) cut-offs may not accurately portray these health risks in older adults aged 60 years and above. This study aims to define age-appropriate cut-offs for older adults (60–74 years and 75 years and above) and compare the performance of these cut-offs with the WHO BMI cut-offs using cardio-metabolic conditions as outcomes. Using baseline data from the Longitudinal Ageing Study in India (LASI), classification and regression tree (CART) cross-sectional analysis was conducted to obtain age-appropriate BMI cut-offs based on cardio-metabolic conditions as outcomes. Logistic regression models were estimated to compare the association of the two sets of cut-offs with cardio-metabolic outcomes. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were estimated. Agreement with waist circumference, an alternate measure of adiposity, was conducted. For older adults aged 60–74 years and 75 years and above, the cut-off for underweight reduced from < 18.5 to < 17.4 and < 13.3 respectively. The thresholds for overweight and obese increased for older adults aged 60–74 years old from > = 25 to > 28.8 and > = 30 to > 33.7 respectively. For older adults aged 75 years and above, the thresholds decreased for both categories. The largest improvement in AUC was observed in older adults aged 75 years and above. The newly derived cut-offs also demonstrated higher sensitivity and specificity among all age-sex stratifications. There is a need to adopt greater rigidity in defining overweight/obesity among older adults aged 75 years and above, as opposed to older adults aged 60–74 years old among whom the thresholds need to be less conservative. Further stratification in the low risk category could also improve BMI classification among older adults. These age-specific thresholds may act as improved alternatives of the current WHO BMI thresholds and improve classification among older adults in India. [ABSTRACT FROM AUTHOR]

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates. [ABSTRACT FROM AUTHOR]

While many studies have documented adverse impact of multiple chronic conditions or multimorbidity on COVID-19 outcomes in patients, there is scarcity of report on how physicians managed these patients. We investigated the experiences and challenges of clinicians in managing patients with multimorbidity throughout the COVID-19 pandemic in Odisha state, India. To understand the factors influencing illness management and the adaptive responses of physicians alongside the evolving pandemic, we followed a longitudinal qualitative study design. Twenty-three physicians comprising general practitioners, specialists, and intensivists, were telephonically interviewed in-depth. Saldana's longitudinal qualitative data analysis method was employed for data analysis. COVID-19 pandemic initially diverted the attention of health systems, resulting in reduced care. With time, the physicians overcame fear, anxiety, and feelings of vulnerability to COVID-19 and started prioritising patients with multimorbidity for treatment and vaccination. All physicians recommended teleconsultation and digital health records to benefit chronic illness care during future public health crises. The findings underscore the transformative potential of physician resilience and adaptation during the COVID-19 pandemic, emphasizing the importance of prioritizing patients with multimorbidity, incorporating teleconsultation, and implementing digital health records in healthcare systems to enhance chronic illness care and preparedness for future public health crises. [ABSTRACT FROM AUTHOR]

India experienced several unprecedented floods in the recent decades. The increase in the extreme rainfall events (EREs) is the primary cause for these floods, manifesting its societal impacts. The daily downscaled and bias corrected (DBC) Coupled Model Intercomparison Project Phase 6 (CMIP6) rainfall and sea surface temperature (SST) are prepared for the Indian region and are utilized to examine the characteristics of EREs. The DBC products capture the characteristic features of EREs for the baseline period, which inspired us to assess the EREs over India in CMIP6 future projections. Consistent with the observations, DBC product shows ~ 8% of Indian land found to experienced extremely heavy rainfall associated with the long duration EREs in the baseline period. However, area and extreme rainfall thresholds are projected to increase by about 18(13)% and 58(50)%, respectively in the far future under SSP5-8.5 (SSP2-4.5) emission scenario relative to the baseline period. A two-fold-65(62)% increase in long-duration EREs compared to the short-duration EREs and substantial warming ~ 2.4(2.9) oC of Indian Ocean SSTs in the far future under SSP5-8.5 (SSP2-4.5) emission scenario compared to baseline period are reported. These findings may provide fundamental insights to formulate national climate change adaptation policies for the EREs. [ABSTRACT FROM AUTHOR]

Regional weather and climate are generally impacted by global climatic phenomenon′s. Understanding the impact of global climate phenomenon′s on an atmospheric branch of the hydrological cycle is crucial to make advances in skillful precipitation forecast. The present study adopts a multiscale approach based on wavelets for unravelling the linkages between teleconnections and atmospheric moisture transport over homogeneous regions of Indian sub-continent. We investigated linkages between atmospheric moisture transport quantified as monthly integrated water vapor transport (IVT) during 1951–2022 over selected homogeneous regions and eight large scale climate oscillations using wavelet and global wavelet coherence. Our results indicate significant heterogeneity in linkages across different regions and across multiple timescales. In particular, the Indian Ocean Dipole (IOD) influence monthly IVT at intra-annual to inter-annual scale over all regions. The El Niño–Southern Oscillation (ENSO) have strong connection to monthly IVT at inter-annual scale whereas over west central region both IOD and ENSO strongly influence IVT at inter-decadal scale. While the Atlantic Multi-Decadal Oscillation and Pacific Decadal Oscillation have an impact on IVT in the north-east and southern regions, the Arctic Oscillation and North Atlantic oscillation have a strong inter-annual connection to IVT, majorly in the northwest and hilly regions. Overall, the methodology offers an effective approach for capturing the dynamics of atmospheric moisture transport in time–frequency space and provide a practical reference for prediction of atmospheric moisture transport linked precipitation over different regions of Indian subcontinent. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ETo) is an important component of the hydrological cycle and reliable estimates of ETo are essential for assessing crop water requirements and irrigation management. Direct measurement of evapotranspiration is both costly and involves complex and intricate procedures. Hence, empirical models are commonly utilized to estimate ETo using accessible meteorological data. Given that empirical methods operate on various assumptions, it is essential to assess their performance to pinpoint the most suitable methods for ETo calculation based on the availability of input data and the specific climatic conditions of a region. This study aims to evaluate different empirical methods of ETo in the tropical highland Udhagamandalam region of Tamil Nadu, India, utilizing sixty years of meteorological data from 1960–2020. In this study, 8 temperature-based and 10 radiation-based empirical models are evaluated against ETo estimates derived from pan evaporation observation and the FAO Penman–Monteith method (FAO-PM), respectively. Statistical error metrics indicate that both temperature and radiation-based models perform better for the Udhagamandalam region. However, radiation-based models performed better than the temperature based models. This is possibly due to the high humidity of the study region throughout the year. The results suggest that simple temperature and radiation-based models using minimum meteorological information are adequate to estimate ETo and thus find potential application in agricultural water practices, hydrological processes, and irrigation management. [ABSTRACT FROM AUTHOR]

In winter, the paddy residues become wet during morning and late evening due to dew, which restricts the operation of sowing machines (Happy Seeder and Super Seeder) into paddy residues, as wet residues do not slide on furrow openers/tines. A PAU Smart Seeder (PSS) was developed and evaluated for a four-wheel tractor that can sow wheat with optimum crop establishment in combined harvested rice fields. The PSS were evaluated for its performance under varying straw load, forward speed, and rotor speed in terms of fuel consumption, field capacity, seed emergence, and grain yield. The crop establishment and wheat yield of PSS was also compared with the existing straw management machines Happy Seeder (HS) and Super Seeder (SS) under heavy paddy residue conditions. The effect of the straw load was more pronounced on dependent variables than the effect of the speed index. PSS performance was best at a forward speed of 2.6 km h−1, rotor speed of 127.5 rpm, and a straw load of 6 t ha−1. Average fuel consumption using PSS was lower than SS but higher than HS. Wheat emergence was higher by 15.6 and 25.7% on the PSS plots compared to HS and SS, respectively. Average wheat grain yield in PSS plots was significantly higher by 12.7 and 18.9% than SS and HS, respectively in one experiment, while the grain yield was similar for both PSS and HS in other experiments. PSS has a novel mechanism to manage paddy straw and simultaneously sow wheat into a heavy straw load (> 8 t ha−1) mixture of anchored and loose straw. In conclusion, PSS showed promise for in-situ management of rice straw as it eliminates most of the operational problems encountered by the existing seeders (HS and SS). [ABSTRACT FROM AUTHOR]

Landraces are important genetic resources that have a significant role in maintaining the long-term sustainability of traditional agro-ecosystems, food, nutrition, and livelihood security. In an effort to document landraces in the on-farm conservation context, Central Western Ghat region in India was surveyed. A total of 671 landraces belonging to 60 crops were recorded from 24 sites. The custodian farmers were found to conserve a variety of crops including vegetables, cereals and pulses, perennial fruits, spices, tuber and plantation crops. The survey indicated a difference in the prevalence of landraces across the sites. A significant difference with respect to the Shannon-diversity index, Gini-Simpson index, evenness, species richness, and abundance was observed among the different survey sites. Computation of a prevalence index indicated the need for immediate intervention in the form of collecting and ex situ conservation of landraces of some crops as a back-up to on-farm conservation. The study also identified the critical determinants of on-farm conservation, including (i) suitability to regional conditions, (ii) relevance in regional cuisine and local medicinal practices, (iii) cultural and traditional significance, and (iv) economic advantage. The information documented in this study is expected to promote the collection and conservation of landraces ex situ. The National Genebank housed at ICAR-NBPGR, New Delhi conserves around 550 accessions of landraces collected from the Central Western Ghats region surveyed in this report. Information collected from custodian farmers on specific uses will be helpful to enhance the utilization of these accessions. [ABSTRACT FROM AUTHOR]

Engineering rockmass classifications are an integral part of design, support and excavation procedures of tunnels, mines, and other underground structures. These classifications are directly linked to ground reaction and support requirements. Various classification systems are in practice and are still evolving. As different classifications serve different purposes, it is imperative to establish inter-correlatability between them. The rating systems and engineering judgements influence the assignment of ratings owing to cognition. To understand the existing correlation between different classification systems, the existing correlations were evaluated with the help of data of 34 locations along a 618-m-long railway tunnel in the Garhwal Himalaya of India and new correlations were developed between different rock classifications. The analysis indicates that certain correlations, such as RMR-Q, RMR-RMi, RMi-Q, and RSR-Q, are comparable to the previously established relationships, while others, such as RSR-RMR, RCR-Qn, and GSI-RMR, show weak correlations. These deviations in published correlations may be due to individual parameters of estimation or measurement errors. Further, incompatible classification systems exhibited low correlations. Thus, the study highlights a need to revisit existing correlations, particularly for rockmass conditions that are extremely complex, and the predictability of existing correlations exhibit high variations. In addition to augmenting the existing database, new correlations for metamorphic rocks in the Himalayan region have been developed and presented that can serve as a guide for future rock engineering projects in such formations and aid in developing appropriate excavation and rock support methodologies. [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]

A significant rural-urban disparity in unsafe child stool disposal practices exists in India, yet existing research falls short in identifying the contributing factors to this gap. This study addresses the research gap by contextualizing the rural-urban divide in unsafe child stool disposal using data from the fifth round of the National Family Health Survey (NFHS-5, 2019-21). In particular, the study examines the prevalence and predictors of unsafe disposal practices, exploring associated contributing factors to this gap. The study involves a sample of 78,074 women aged 15-49 with a living child under 2 years, without any missing data related to the study interest. Employing descriptive statistics, the Pearson chi-square test, multilevel logistic regression, and the Fairlie decomposition model, the research aims to fulfill its objectives. The rural-urban gap in unsafe child stool disposal practices among the study participants was 22.3 percentage points (pp), with a more pronounced gap among the Scheduled Tribes (ST). Notably, the gap was particularly wide in Madhya Pradesh (33.9 pp), Telangana (27.5 pp), Gujarat (26.1 pp), and Rajasthan (25.8 pp). Predictors such as mother's education, mass media exposure, household wealth quintile, and sanitation facilities proved significant irrespective of residence. However, religion, social group, and water facility on household premises emerged as significant factors in rural areas only. The study identified that 67% of the explained gap in unsafe child stool disposal practices was attributed to the rural-urban difference in household wealth. Other noteworthy contributors were 'household sanitation facility' (21.3%), 'mother's education level' (3.9%), and 'water facility on household premises' (3.9%). These findings underscore the need for population and area-specific policy interventions, especially for individuals from socio-economically disadvantaged backgrounds, those with lower education levels, and limited exposure to mass media, particularly in states with a high prevalence of unsafe disposal practices. Such interventions are crucial to mitigating the existing rural-urban gap in unsafe child stool disposal practices., (© 2024. The Author(s).)

In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP's efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases., (© 2024. The Author(s).)

Healthcare systems worldwide are grappling with the challenge of providing high-quality healthcare in the face of evolving disease patterns. India, like many other countries, faces a significant treatment gap for various curable impairments, non-communicable diseases (NCDs), and cardiovascular diseases (CVDs). To address their healthcare needs, individuals often relocate in search of better treatment options. However, no studies were conducted to understand the spatial mobility. This paper explores the determinants of spatial mobility for treatment in India using data from NSS 75th round (2017-2018). A total of 64,779 individual medical cases of different diseases were taken into consideration for our analysis. Fixed effect and multinomial regression models were used to understand diseases specific mobility for treatment. It was found that those with CVDs, NCDs, and disabilities are more prone to travel outside their district for medical care. Rural and economically disadvantaged individuals also tend to travel further for treatment. The key factors impacting treatment-seeking mobility include insurance coverage, hospital quality, cost of medicine, and cost of X-rays/surgeries. The study highlights the need for improved policies to address the gap between healthcare needs and infrastructure in India, with a focus on prioritizing the development of local healthcare facilities for disabilities, NCDs, and CVDs., (© 2024. The Author(s).)