Coriolis coupling and isotopic effects on the quantum dynamics of H(

We here report the effect of Coriolis coupling (CC) on the dynamics of H($$^2$$S) + NaH(X$$^1\Sigma ^+$$) reaction in the ground electronic state (1 $$^2A^\prime$$), employing a time-dependent wavepacket approach on a new potential energy surface. Initial state-selected and energy-resolved reaction probabilities, integral cross sections and thermal rate constants are obtained by performing the exact CC and centrifugal sudden (CS) calculations. The reactivity of hydride destruction channel (R1) is generally found to be greater than the hydrogen exchange channel (R2), in which the former channel is highly exoergic (by 2.788 eV) and the latter one is thermoneutral. The magnitude of reaction probabilities of R1 channel and the resonance features seen in that are effectively reduced at given energies by the exact CC approach compared to the CS model; on the other hand, CC enhances the reactivity of R2 channel compared to CS results. A comparison of CC and CS results reveals that neglecting the Coriolis coupling overestimates both cross sections and rate constants of R1 channel and underestimates the same for R2 channel. The CC also plays a significant role in the title reaction when the reagent NaH(v, j) molecule gets excited. In addition, the effect of isotopic substitution (H atom by D) on the H + NaH reaction is also examined and noted that the D + NaH reaction is highly reactive than the other isotopologues

Characterization of extracellular amylase enzyme produced by <i>Aspergillus flavus</i> MV5 isolated from mangrove sediment

170-173Mangroves provide a unique ecological niche to different microbes which play various roles in nutrient recycling as well as various environmental activities. Analysis of microbial biodiversity from these ecosystems will help in isolating and identifying new and potential microorganisms having high specificity for various applications. Mangroves sediment soils are a resource of fungi providing an end number of enzymes that finds their use in industrial processes. This study indicates the extracellular production of amylase by Aspergillus flavus MV5 was confirmed on GYP agar plates. Further maximum enzyme activity was optimized and the DNA of fungal strains were isolated to sequence the ITS region of 5.8s rRNA with an ITS primer. The novelty of the strain was checked by a BLAST analysis for submission to GenBank

A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India

In recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed, India. These models included the artificial neural network (ANN), k-nearest neighbour regression model (KNN), extreme gradient boosting (XGBoost) regression model, random forest regression model (RF), convolutional neural network (CNN), and CNN-RNN (convolutional recurrent neural network). The years 2003–2007 are classified as the calibration or training period, while the years 2008–2009 are classified as the validation or testing period for the span of time 2003 to 2009. The available rainfall, maximum and minimum temperatures, and discharge data were collected and utilized in the models. To compare the performance of the models, five criteria were employed: R2, NSE, MAE, RMSE, and PBIAS. The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods (training: R2 is 0.99, NSE is 0.99, MAE is 1.76, RMSE is 3.11, and PBIAS is −1.45; testing: R2 is 0.97, NSE is 0.97, MAE is 2.05, RMSE is 3.60, and PBIAS is −3.94). These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study. The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management, flood control, and environmental planning