Launaea procumbens Linn. is a plant commonly found in the west India and has been reported to decrease the renal calculi. This study investigated the anti-urolithiatic activity of L. procumbens against ethylene glycol-induced urolithiasis and its possible underlying mechanisms. The crude methanolic extract of L. procumbens leaves was studied using ethylene glycol-induced renal calculi in rat model. Results indicate that ethylene glycol feeding to rats resulted in to hyper oxaluria, hypercalciuria, as well as increased renal excretion of phosphate. Supplementation with methanolic extract of L. procumbens leaves (MELP) significantly prevented changes in urinary calcium, oxalate and phosphate excretion dose-dependently. The increased calcium and oxalate level and number of calcium oxalate crystal in the kidney tissue of calculogenic rats were significantly reverted by supplementation with MELP. The MELP supplementation also prevents the impairment of renal functions. The mechanism underlying this effect is mediated possibly through antioxidant nephroprotection and its effect on urinary concentration of stone forming constituents and risk factor.ConclusionThese results indicate that methanolic extracts of L. procumbens leaves are effective against the urolithiasis.

The design of an electrical machine can be quantified and evaluated by Key Performance Indicators (KPIs) such as maximum torque, critical field strength, costs of active parts, sound power, etc. Generally, cross-domain tool-chains are used to optimize all the KPIs from different domains (multiobjective optimization) by varying the given input parameters in the largest possible design space. This optimization process involves magneto-static finite element simulation to obtain these decisive KPIs. It makes the whole process a vehemently time-consuming computational task that counts on the availability of resources with the involvement of high computational cost. In this paper, a data-aided, deep learningbased meta-model is employed to predict the KPIs of an electrical machine quickly and with high accuracy to accelerate the full optimization process and reduce its computational costs. The focus is on analyzing various forms of input data that serve as a geometry representation of the machine. Namely, these are the cross-section image of the electrical machine that allows a very general description of the geometry relating to different topologies and the the classical way of scalar geometry parametrizations. The impact of the resolution of the image is studied in detail. The results show a high prediction accuracy and proof that deep learning-based meta-models are able to minimize the optimization time. The results also indicate that the prediction quality of an image-based approach can be made comparable to the classical way based on scalar parameters.

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