ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…The “ADT” program can be efficiently used to (a) formulate analytic functional forms of differential equations for ADT angles and diabatic potential energy matrix and (b) solve the set of coupled differential equations numerically to evaluate ADT angles, residue due to singularity­(ies), ADT matrices, and finally, diabatic potential energy surfaces (PESs). …”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…</p><h4><b>Data sources</b></h4><ul><li><b>BradGinns_SOIL2004_SoilData.csv</b> - Soil measurements from the University of Sydney Llara Campey farm site from 2004, corresponding to sites L1, L3 and L4 describing mid-depth, soil apparent electrical conductivity (ECa), GammaK, Clay, Silt, Sand, pH and soil electrical conductivity (EC)</li><li><b>Llara_Campey_field_boundaries_poly.shp</b> - Field boundary shapes for the University of Sydney Llara Campey farm site</li></ul><h4><b>Dependencies</b></h4><ul><li>This notebook requires Python 3.10 or higher</li><li>Install relevant Python libraries with: <b>pip install mccn-engine rocrate rioxarray pykrige</b></li><li>Installing mccn-engine will install other dependencies</li></ul><h4><b>Overview</b></h4><ol><li>Select soil sample measurements for pH or EC at 45 cm depth</li><li>Split sample measurements into 80% subset to model interpolated layers and 20% to test interpolated layers</li><li>Generate STAC metadata for layers</li><li>Load data cube</li><li>Interpolate pH and EC across site using the 80% subset and three different 2D interpolation methods from rioxarray (nearest, linear and cubic) and one from pykrige (linear)</li><li>Calculate the error between each layer of interpolated values and measured values for the 20% setaside for testing</li><li>Compare the mean and standard deviation of the errors for each interpolation method</li><li>Clean up and package results as RO-Crate</li></ol><h4><b>Notes</b></h4><ul><li>The granularity of variability in soil data significantly compromises all methods</li><li>Depending on the 80/20 split, different methods may appear more reliable, but the pykrige linear method is most often best</li></ul><p><br></p>…”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”