Search

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).)