Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, ., can retard their growth.

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation.

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ).

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal.

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models.

Groundwater potential mapping in Trans Yamuna Region, Prayagraj, using combination of geospatial technologies and AHP method.

In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI, lineament density, rainfall, soil types, land use & land cover, and topographic wetness index maps. For the prediction output to have a non-trivial degree of accuracy, multicollinearity tests were run before integrating the layers.

Modeling ocean surface chlorophyll-a concentration from ocean color remote sensing reflectance in global waters using machine learning.

The spatial and temporal variations of Chlorophyll-a (Chl-a) in clear and coastal waters are critical for assessing the health of the marine environment. Machine learning models have been proven to model complex relationships and provide better accuracy estimates of the derived parameters compared to traditional empirical models. The present study proposes a novel approach to derive Chl-a by using multi-layer perceptron Neural Network (MLPNN) with Resilient backpropagation method based on the four ocean color bands existent in most of the ocean color sensors.

The role of cations in uranyl nanocluster association: a molecular dynamics study.

Actinyl ions can self-assemble in aqueous solution to form closed cage clusters ranging from 1.5 to 4.0 nm in diameter.

A molecular dynamics investigation of actinyl-ligand speciation in aqueous solution.

Actinyl ions (AnO2n+), the form in which actinides are commonly found in aqueous solution, are important species in the nuclear fuel cycle. These ions can form stable complexes with ionic ligands such as OH-, NO3-, Cl-, and even other actinyl ions in the aqueous phase. Knowledge of the relative stabilities of these complexes is important for the efficient design of separation processes used in recycling.

Dynamics of actinyl ions in water: a molecular dynamics simulation study.

The dynamics of actinyl ions (AnO2(n+)) in aqueous solutions is important not only for the design of advanced separation processes but also for understanding the fate of actinides in the environment. The hazardous nature of actinides makes it difficult to measure transport and thermodynamic properties experimentally, so predictive simulations are an attractive method for studying these systems. Here, we report the results of atomistic-level molecular dynamics simulations of actinyl ions (of U, Np, Pu, and Am) in their mono- and dication states in aqueous solution.

Development and application of effective pairwise potentials for UO2(n+), NpO2(n+), PuO2(n+), and AmO2(n+) (n = 1, 2) ions with water.

Intra- and intermolecular force field parameters for the interaction of actinyl ions (AnO2(n+), where, An = U, Np, Pu, Am and n = 1, 2) with water have been developed using quantum mechanical calculations. Water was modeled with the extended simple point charge potential (SPC/E). The resulting force field consists of a simple form in which intermolecular interactions are modeled with pairwise Lennard-Jones functions plus partial charge terms.