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AbstractThe study predicts friction stir process (FSP) parameters using an artificial neural network (ANN) model. A design of experiment (DoE) approach is used to conduct experiments on FSP and to evaluate the best operating parameters of FSP. ANN uses 30 neurons, 1 hidden layer, and an input and output layer in the Matlab® environment. ANN predicts the output parameters with R values of 0.999, 0.995, and 0.992 for training, validation, and test datasets, respectively, while the overall R-value is 0.997. GRA is used to optimize and rank the parameters of the processes and revealed that the rotational tool speed should be 1180 rpm, traverse feed rate 38 mm/min, and tool tilt angle 1° for best results. The optimized values obtained are 380 MPa, 3.8 µm, 138 HV, and 14% for tensile strength, grain size, microhardness, and elongation, respectively. According to the analysis of variance (ANOVA) and grey relational analysis, the order of influencing parameters on output factors was rotational speed (80%), followed by transverse feed (19%) and tool tilt angle (1%). An ANN is further used to predict using the two most significant parameters—rotational speed and traverse feed. The modified ANN has nearly the same R values as the original ANN. Thus modified ANN may be used for prediction.

The doxorubicin, an anthracycline derivative, is a cytotoxic agent with proven efficacy in various malignancies. The clinical utility has been limited due to its dose - dependent cardiac toxicity. To evaluate the role of Chlorophytum borivilianum L. on doxorubicin-induced cardiotoxicity in rats and to predict the role of Chlorophytum borivilianum L. by insilico and in vivo methods. Invitro studies were conducted on Chlorophytum borivilianum L. Cardiotoxicity was produced by administration of doxorubicin (Dox-15 mg/kg ip. for two weeks). Ethanolic extract and fractions of Chlorophytum borivilianum L. (250 and 500 mg/kg, p.o.) were administered as pretreatment for 15 days followed by Doxorubicin 2.5 mg/kg i.p. on alternate day for two weeks. The parameters like body weight, food and water consumption, cardiac specific markers like Creatine Kinase (CK-MB), Lactate Dehydrogenase (LDH) and Cardiac Troponin-I (cTnl), ECG changes, antioxidant parameters like superoxide dismutase (SOD), glutathione (GSH), catalase (CAT) and lipid peroxidation (MDA) were monitored. Histopathological studies of the heart were also performed to evaluate myocardial toxicity. Dox treatment results in cardiomyopathy characterised by elevated cardiac biomarkers and deficiency of antioxidant enzymes. By reducing the elevated levels of biomarker enzymes like LDH and CK-MB and the absence of cTnI, pretreatment with the EECB (500mg/kg) significantly protected the myocardium from the toxic effects of Dox. In addition, the EECB increased the reduced levels of GSH, SOD, and CAT while decreasing the elevated levels of malondialdehyde (MDA) in cardiac tissue. [ABSTRACT FROM AUTHOR]

In crop improvement, delineating inheritance pattern of a character guides a plant breeder to choose appropriate breeding procedure, which enables ease of improving phenotypic expression of the traits. To deduce the inheritance pattern of naked seed or fuzzless character, four parental lines involving two purely naked seed types (DHN‐1 and DHN‐10), one partial naked seed type (SP46CC) and one fully fuzzy type (OSB415) were used to develop three different types of crosses, namely, naked × naked seed type cross (N × N), partial naked × naked seed type cross (PN × N) and fuzzy × naked seed type cross (F × N). We observed that naked trait segregates in the ratio of 11 naked: 5 partial naked in N × N cross, 22 naked: 42 partial naked in PN × N cross and 22 naked: 42 partial naked: 192 fuzzy in F × N cross. Our study reports a novel four‐gene model to understand inheritance nature of naked seed character where the presence of single basic dominant fuzzy gene negates the effect of genes controlling nakedness resulting fuzzy phenotype irrespective of the number of partial fuzzyness/nakedness imparting alleles. This indicates that nakedness is recessive to fuzzyness. Based on our results, we infer observing gradations of nakedness on seeds depends on the presence of number of partial fuzzyness imparting alleles, which led to a conclusion that the trait exhibited dose‐dependent expression with one basic gene for fuzziness. [ABSTRACT FROM AUTHOR]

This study presents a novel hybrid deep neural network (DNN) with the White Shark optimization (WSO) algorithm to predict and optimize the gas sensing performance of Ni0.6−xMgxCo0.4Fe2O4 nanoparticles at different sizes (x = 0.1, 0.2, 0.3, 0.4, and 0.5). In order to create Ni0.6−xMgxCo0.4Fe2O4, the sol–gel process was employed, with x varying from 0.1 to 0.5 ppm. This study utilized precursors such as Ni(NO3)2·6H2O, Mg(NO3)2·6H2O, Co(NO3)2·6H2O, and Fe(NO3)3·9H2O. Response–recovery curves were analyzed, and a hybrid DNN-WSO model was created to predict gas sensing properties for Ni0.6−xMgxCo0.4Fe2O4 ferrite materials with different Mg levels. Fit statistics like the sum of squares (SSE), R-squared (R2), degrees of freedom (DFE), adjusted R2, and RMSE were calculated. In XRD analysis, the (311) peak shifted from 35.6561° to 35.5614° for Ni0.6−xMgxCo0.4Fe2O4. At 200 °C, the sensor is more responsive to NO2 gas than NH3 gas, with Ni0.6−xMgxCo0.4Fe2O4(x = 0.5) showing the highest response due to increased oxygen adsorption sites for gas oxidation. Smaller particle sizes (x = 0.1) in SEM analysis exhibited well-defined nanocrystalline structures with high crystallinity and smooth surfaces, enhancing sensitivity. The TGA results confirm the stability and functionality of the proposed materials up to the operating temperature of 250 °C. The Ni0.6−xMgxCo0.4Fe2O4(x=0.2) concentration exhibited the best predictive accuracy with high R2, high adjusted R2, enhanced DFE, and low RMSE. The hybrid DNN-WSO algorithm effectively predicts and optimizes gas sensing performance, which is significant for advancing efficient gas sensor development. [ABSTRACT FROM AUTHOR]

In this paper, an attempt has been made to correlate and optimise the plasma transferred arc welding parameters such as transferred arc current (TC), powder feed rate (PF), speed of oscillation (SO), welding speed (WS) and plasma gas (PG) so as to enhance the surface characteristics. The investigations are carried out by considering width-to-reinforcement ratio (W/R) and surface hardness as key responses. The empirical relationship of responses isdeveloped using response surface method (RSM). From this investigation, it is found that the maximum hardness of 47.59 HRC could be achieved with a moderate 4.5 W/R for the deposit. The optimised processing conditions show the transferred arc current (TC) 126 A, welding speed (WS) 100 mm/min, powder feed rate (PF) 13 g/min, speed of oscillation (SO) 600 mm/min, and plasma gas (PG) 2.9 L/min. [ABSTRACT FROM AUTHOR]