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Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime. [ABSTRACT FROM AUTHOR]

River connectivity is often described as a key requirement for maintaining the health and biodiversity of associated wetlands of rivers. A comparative study was undertaken to understand the influence of river connectivity on plankton diversity and abundance in an open (Chharaganga Beel) and a closed wetland (Kalobaur Beel) situated on the banks of River Ganga in West Bengal, India. During the present investigation, data revealed that the major groups of phytoplankton recorded were Cyanobacteria (33.12%), Bacillariophyta (29.07%), and Chlorophyta (22.34%) from the open wetland, and Bacillariophyta (47.40%), Euglenozoa (30.68%), and Chlorophyta (10.12%) from the closed one. Shannon diversity (H') and Margalef's species richness index indicate that phytoplankton diversity in the Chharaganga ecosystem was higher than in Kalobaur Beel. A one-way ANOVA showed significant station-wise difference for water turbidity (p<0.05) in both of the beels, and student t test revealed that differences in physico-chemical parameters between the studied beels was statistically significant (p<0.05) for depth, turbidity, conductivity, total hardness, nitrate, total N, phosphate-P, silicate, Ca2+, Mg2+and chlorophyll a,whereas abundance of phytoplankton groups was significantly different for Bacillariophyta (p<0.05) and Euglenozoa (p<0.05). The positive correlation of total N with Euglenozoa (r=0.496; p<0.05) might be the cause of favourable conditions for a higher abundance of Euglenozoa in the closed Kalobaur Beel,which is on the verge of partial eutrophication. The hydrological flushing by river water in the Chharaganga wetland maintains the ecological stability. Therefore, restoration of river connectivity is recommended to improve the health of the disconnected wetlands. [ABSTRACT FROM AUTHOR]

Length–weight relationships (LWRs) were determined for seven riverine fish species from the river Ganga, India. Specimens were collected on a bi‐monthly basis from April 2017 to December 2018 using gill nets (mesh size 22–34 mm), cast nets (mesh size 16 mm) and bag nets (mesh size 14–22 mm). Total length was measured to the nearest 0.1 cm using a digital caliper and weight was recorded to the nearest 0.01 g on an electronic balance. From estimated length–weight relationships, the values for parameter "a" ranged from 0.004 (Bregmaceros mcclellandi and Setipinna tenuifilis) to 0.014 (Brachirus pan). Likewise, the values for the parameter "b" of the equation ranged from 2.958 (Bagarius bagarius) to 3.124 (Bregmaceros mcclellandi) and r2 from 0.978 (Gonialosa manmina) to 0.996 (Brachirus pan). [ABSTRACT FROM AUTHOR]