The materials include codes and example input / output files for Monte Carlo simulations of lattice chains in the grand canonical ensemble, for determining phase behavior, critical points, and formation of aggregates.
This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-2, +3] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 150 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-5, +20] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 50 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
Bourrianne, Philippe; Chidzik, Stanley; Cohen, Daniel; Elmer, Peter; Hallowell, Thomas; Kilbaugh, Todd J.; Lange, David; Leifer, Andrew M.; Marlow, Daniel R.; Meyers, Peter D.; Normand, Edna; Nunes, Janine; Oh, Myungchul; Page, Lyman; Periera, Talmo; Pivarski, Jim; Schreiner, Henry; Stone, Howard A.; Tank, David W.; Thiberge, Stephan; Tully, Christopher
Abstract:
The detailed information on the design and construction of the Princeton Open Ventilation Monitor device and software are contained in this data repository. This information consists of the electrical design files, mechanical design files, bill of materials, human subject recording and analysis code, and a copy of the code repository for operating the patient monitors and central station.
A code to identify the laser transition for a quantum cascade laser design based on the figure of merit. Variables such as the number of layers, and layer thicknesses, as well the applied electric field, materials composition, number of period repetitions, and layer tolerance ranges to generate random designs are specified. A folder containing a .csv file with all electronic state-pair transitions collected, a .png file of the bandstructure and the laser transition chosen (in red), for all electric field iterations, and a summary .csv file of all these laser transitions for a structure at each electric field is generated by the code. To use, first install ErwinJr2 on your computer. Then locate the "ErwinJr2" folder and copy these 6 files into that directory, overwriting the previous five files (Material.py, QCLayers.py, QCPlotter.py, QuantumTab.py, rFittings.py). Lastly, run the "acej-qcl-layer_10-lwrandom-v23.py" script using Python.
The "summary-fomstar-3lu-eVmiddle-19.csv" file is generated after running the laser transition code, with all of the data collected for one structure at many electric fields. Running the script various times will generate random structures with the same electric field range. Joining these "summary" .csv files makes a QCL dataset.
Amazonian deforestation causes systematic changes in regional dry season precipitation. Some of these changes at contemporary large scales (a few hundreds of kilometers) of deforestation have been associated with a ‘dynamical mesoscale circulation’, induced by the replacement of rough forest with smooth pasture. In terms of decadal averages, this dynamical mechanism yields increased precipitation in downwind regions and decreased precipitation in upwind regions of deforested areas. Daily, seasonal, and interannual variations in this phenomenon may exist, but have not yet been identified or explained. This study uses observations and numerical simulations to develop relationships between the dynamical mechanism and the local- and continental-scale atmospheric conditions across a range of time scales. It is found that the strength of the dynamical mechanism is primarily controlled by the regional-scale thermal and dynamical conditions of the boundary layer, and not by the continental- and global-scale atmospheric state. Lifting condensation level and wind speed within the boundary layer have large and positive correlations with the strength of the dynamical mechanism. The strength of these relationships depends on time scale and is strongest over the seasonal cycle. Overall, the dynamical mechanism is found to be strongest during times when the atmosphere is relatively stable. Hence, for contemporary large scales of deforestation this phenomenon is found to be the prevalent convective triggering mechanism during the dry and parts of transition seasons (especially during the dry-to-wet transition), significantly affecting the hydroclimate during this period.
This is the raw experimental dataset and the corresponding code to reproduce plots from the paper "Shear-induced migration of confined flexible fibers".