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In recent times, severe acute malnutrition (SAM) in India is considered a serious issue as per UNICEF 2022 records. In that record, 35.5% of children under age 5 are stunted, 19.3% are wasted, and 32% are underweight. Malnutrition, defined as these three conditions, affects 5.7 million children globally. This research utilizes an artificial intelligence-based image segmentation technique to predict malnutrition in children. The primary goal of this research is to use a deep learning model to eliminate the need for multiple manual diagnostic tests and simplify the prediction of malnutrition in kids. The traditional model uses text-based data and takes more time with continuous monitoring of kids by analysing body mass index (BMI) over different periods. Children in rural areas often miss medical expert appointments, and a lack of knowledge among parents can lead to severe malnutrition. The aim of the proposed system is to eliminate the need for manual blood tests and regular visits to medical experts. This study uses the ResNet-50 deep learning model's built-in shortcut connection to solve the image-based vanishing gradient problem. This makes training more efficient for image segmentation tasks in predicting malnutrition. The model is 98.49% accurate in predicting the kids who are malnourished among the kids who are healthy. It is evident from the results that the proposed system serves better than other deep learning models, such as XG Boost (75.29% accuracy), VGG 16 (94% accuracy), Xception (95.41% accuracy), and MobileNet (92.42% accuracy). Hence, the proposed technique is effective in detecting malnutrition and diagnose it earlier, without using predictive analysis function or advice from the medical experts. [ABSTRACT FROM AUTHOR]

The preliminary intention of this study is to comprehensively understand converting a conventional vehicle into a fully electric vehicle. In today’s technologically forward world of travelling, it is crucial for commuters to travel effectively and help maintain a sustainable environment. To accomplish this, the rider should travel as quickly and effectively as feasible. This study provides information on battery-powered electric scooters, including information on the controller, battery, hub motor and other components. Details on converting a scooter with an Internal Combustion (IC) engine into an Electric vehicle (EV) are provided in this work. This uses electricity as their main energy source. It also draws attention to the scooter's modifications. The battery can be charged by being ejected by the main system. ARAI AIS 039 (Rev 1): Electric Power Train Vehicles– Measurement of Electrical Energy Consumption is the standard used for testing the vehicle. When compared to a traditional vehicle, the scooter's electrical power can provide a longer range, higher performance and no emissions. Major Findings: The experimental range of the vehicle under ARAI test conditions is found to be 15km. The experimental top speed of the vehicle under a total load of 150kg is 30kmph. [ABSTRACT FROM AUTHOR]

Abstract Background The aim of the present study was to develop and validate an inductively coupled plasma optical emission spectroscopic (ICP–OES) method for quantification of elemental impurities, i.e., Lead, Palladium, and Zinc, in voriconazole drug substance, and this method was employed for the regular sample analysis of Lead, Palladium, and Zinc in voriconazole drug substance for pharmaceutical use. The method has been validated using RF power of 1150 W, auxiliary gas of 0.5 L/min, and nebulizer flow of 0.4 L/min and plasma view at axial mode for Lead and Palladium and radial mode for Zinc. The wavelength was monitored for Lead, Palladium, and Zinc at 220.3 nm, 340.4 nm, and 213.8 nm respectively. Results The method is selective and is capable of detecting desired elemental impurities with regulatory acceptance limits in the presence of other elements. The validation experiments involve the demonstration of system suitability, specificity, LOD and LOQ, linearity, precision, and accuracy experiments. The linearity results obtained ˃ 0.9990 for all three impurities. Conclusion The proposed method is simple, sensitive quality control tool for the simultaneous quantitative determination of Lead, Palladium, and Zinc at low levels in voriconazole drug substance. Graphical abstract

In the present study 300 doubled haploid lines from six populations were phenotyped with four yield related traits under water stress at flowering. Quantitative trait loci analysis was done for 263 lines which were genotyped successfully based on individual location and combined locations. For the four yield related traits viz., Anthesis silking interval, ear weight, ears per plant and chlorphyll content a total of 48 significant QTLs were detected based on the separate individual location analysis, of which 26 were detected for Hyderabad location data and 22 detected for Aurangabad location data. In the combined analysis there were 18 QTLs detected in which four QTLs found in population group 1(for ASI), four QTLs in population group 2 (one each for ASI and ears per plant and two for ear weight)and 10 QTLS detected in Pooled population analysis (two for ASI, one for chlorophyll content, five for ears per plant and two for ear weight). However, in combined location QTL analysis one common overlapping QTL was identified on chromosome 8 with same interval for ears per plant and ear weight. Meanwhile eight and three major QTLs were identified in individual location and combined location analysis. These genomic regions could be candidate targets for further fine mapping and marker-assisted breeding in maize.

In this study, a new series of 4-(2,5-dimethyl-1H-pyrrol-1-yl)-N’-(2-(substituted)acetyl) benzohydrazides (5a–n) were prepared and new heterocycles underwent thorough characterization and evaluation for antibacterial activity; some of them underwent further testing for in vitro inhibition of enoyl ACP reductase and DHFR enzymes. The majority of the synthesized molecules exhibited appreciable action against DHFR and enoyl ACP reductase enzymes. Some of the synthesized compounds also showed strong antibacterial and antitubercular properties. In order to determine the potential mode of action of the synthesized compounds, a molecular docking investigation was conducted. The results revealed binding interactions with both the dihydrofolate reductase and enoyl ACP reductase active sites. These molecules represent excellent future therapeutic possibilities with potential uses in the biological and medical sciences due to the compounds’ pronounced docking properties and biological activity.

To meet the increasing demands for food production, the utilization of cost‐effective pesticides like carbofuran (CB) becomes essential. CB poses a significant risk to human health due to its high toxicity. Therefore, accurate quantification of CB residue is essential for environmental safety and human health. Electrochemical sensors have proven to be an outstanding choice for the accurate detection and quantification of carbofuran residues. CB is electrochemically inactive and was first hydrolyzed in an alkaline medium to yield an anodically active phenolic derivative carbofuran phenol (CP). This report details the development of a voltammetric sensor for the accurate detection of CB residues utilizing the cyclic voltammetry technique and the electrochemical reactions of CP at WS2 nanosheet‐modified electrodes in phosphate buffer solution (PBS). The constructed sensor has displayed a linear range of carbofuran detection from 10–90 μM, with a limit of detection (LOD) of 0.38 μM and sensitivity of 0.303 μA μM−1 cm−2. Additionally, the developed sensor has been employed to monitor CB residues in real soil and water samples and it shows a recovery greater than 95 %. The developed sensor enables the cost‐effective monitoring of carbofuran in real samples. [ABSTRACT FROM AUTHOR]