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Abstract Grasslands are crucial ecosystems that provide numerous ecological services and support biodiversity conservation. Grasslands undergo significant threats from both anthropogenic and natural sources, compromising their ability to maintain biodiversity, ecosystem services, and human well-being. However, grasslands are frequently ignored in sustainable development objectives. Adequate knowledge of how grassland degradation affects ecosystem services is essential for sustainable management and grassland ecological restoration. The Kachchh region in western India harbours a unique grassland ecosystem known as the Banni grassland, which once became the finest grassland in Asia. However, undesirable anthropogenic interventions have accelerated its degradation. This research paper aims to assess the suitability of different land areas in Banni for sustainable grassland restoration, considering ecological value as a primary criterion. In the present research, land suitability for grassland management was assessed using a geographical information system (GIS)-based multi criteria evolution (MCE) method with satellite data and the analytic hierarchy process (AHP). The ground truthing of the soil samples was carried out alongside. Slope, rainfall, infiltration rate, LULC, geomorphology, soil texture, soil organic carbon, water holding capacity, SAR, CEC, pH, EC, and soil nutrients were among the criteria used. The weights for each criterion were calculated using a pairwise comparison matrix, and the scores were allocated to sub criteria based on field work, expert opinions, and a literature review. The proposed method can be very useful for evaluating the state of the land and can help with the best possible planning for grassland development and conservation. Banni grassland has the potential to be developed into a critical zone observatory (CZO) in the future, and the present study, with further inputs, holds promise for furthering the cause of its sustainable management. Overall, this study underscores the importance of assessing land suitability for sustainable grassland management and highlights the potential for maximising the ecological value of grasslands in western India and beyond.

The lithium-ion (Li-ion) battery plays a crucial role in the performance of electric vehicles, owing to its unique properties and compact size. To ensure the prolonged lifespan of these batteries, it is imperative for users to exercise additional precautions. The variable load torque applied to the Permanent Magnet Synchronous Motor (PMSM) drive, influenced by diverse road conditions, adds complexity to the scenario. Assessing the State of Charge (SoC) of the Li-ion battery proves to be a significant challenge, given the multitude of electrical sensors and mechanical components involved in the operation of electric vehicles (EVs). In such instances, the SoC may be subject to noisy measurements, leading to performance degradation of the battery over time. This paper proposes the utilization of the Kalman filter to estimate the actual SoC from the noisy measurements, relying on indirect measurements as a basis for improved accuracy.

The major health care problem in developing world is shifting from communicable diseases to non-communicable disease which causes morbidities more than mortality affecting the personal efficiency. Hypertension is a major health care burden in developing countries like India causing both morbidity and mortality. Few studies shows that the hypertension prevalence has raised from 2% to 15% in rural population and 2% to 25% in urban population in last six decades in India. This research work aims to enumerate the price of commonly available oral antihypertensive drugs in India. It also aims to study the cost variation of oral antihypertensive drugs. The present study was carried out in the department of Pharmacology in a tertiary care medical college in south India. The cost analysis of individual drugs by different manufacturers were done. Percentage of cost variation was analysed. The variation in cost of most of the antihypertensive drugs is more than 100%. It’s found maximum in Amlodipine (5mg) with cost variation of 625%, Atenolol 100 mg with cost variation of 413%. In India there is a wide price variation among antihypertensive drugs. The major gap in the cost variation of antihypertensive drugs to be narrowed for the benefit of the patients. So the government and the prescribing doctors should think about the huge difference in cost and work in the direction of benefitting the patient.

Introduction: The world has been under one vicious COVID-19 wave after the other for nearly 2 years, and the battle is still on. We know that the majority of serious infections have significant high-resolution computed tomography (HRCT) changes; a predictor for them would be a vital tool in this battle! Aims and Objectives: The study aimed to assess the correlation between C-reactive protein (CRP) at the time of presentation and HRCT score in patients with severe COVID-19 infection. Methods: In this prospective, single-center study of 121 patients with severe COVID-19 infection in a tertiary COVID-19 care center. Patients were assessed for the relationship between CRP levels and chest radiography in COVID-19 patients. Results: In patients who survived in severe COVID-19 infection, CRP on admission had a significant correlation with HRCT score with P = 0.019, and in patients who did not survive, CRP on admission had a significant correlation with HRCT score with P = 0.005. CRP at day 5 was also significantly correlated to HRCT score with P = 0.035 in survivors and P = 0.001 in patients who did not survive. Conclusion: In centers where HRCT chest is not available or financial resources are a major concern, we suggest early CRP, repeated after 3–5 days, could be a very useful prognostic factor for significant lung involvement during the course of illness. This, in turn, could be a very useful tool in determining a need for admission, referral to higher centers, or more intensive therapy early on.

Abstract Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7 days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39 weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81–0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.