Generalized Internal Coordinates for Creative Exploration of Interatomic Geometries by Aleksandr V. Marenich (1283298)

“…The analytical first- and second-order derivatives with respect to Cartesian coordinates are built automatically to provide for the seamless integration of such GICs into geometry optimization, potential energy surface searching and scans, and normal-mode analysis in terms of internal coordinates, all without further coding. Our algorithm allows the user to create compound internal coordinates that are functions of other coordinates, as well as special-purpose coordinates for specific classes of problems. …”

Generalized Internal Coordinates for Creative Exploration of Interatomic Geometries by Aleksandr V. Marenich (1283298)

“…The analytical first- and second-order derivatives with respect to Cartesian coordinates are built automatically to provide for the seamless integration of such GICs into geometry optimization, potential energy surface searching and scans, and normal-mode analysis in terms of internal coordinates, all without further coding. Our algorithm allows the user to create compound internal coordinates that are functions of other coordinates, as well as special-purpose coordinates for specific classes of problems. …”

Generalized Internal Coordinates for Creative Exploration of Interatomic Geometries by Aleksandr V. Marenich (1283298)

“…The analytical first- and second-order derivatives with respect to Cartesian coordinates are built automatically to provide for the seamless integration of such GICs into geometry optimization, potential energy surface searching and scans, and normal-mode analysis in terms of internal coordinates, all without further coding. Our algorithm allows the user to create compound internal coordinates that are functions of other coordinates, as well as special-purpose coordinates for specific classes of problems. …”

Dataset: "A Method for Sensitivity Analysis of Automatic Contouring Algorithms Across Different MRI Contrast Weightings Using SyntheticMR" by Lucas McCullum (16521828)

“…In addition, for each contrast weighting (TR and TE combination), the synthetic image generated from SyMRI as well as the model's predicted automatic contours are also included. …”

Smart contract and interface code for Nature Energy "A general form of smart contract for decentralised energy systems management" by Lee Thomas (19655773)

“…This provides the modelled electricity network cost data, the smart contract code, and the Python interface scripts described in the Nature Energy Paper  "A general form of smart contract for decentralised energy systems management." …”

Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined. by Yuanchen Zhao (12905580)

“…The panel shows representative outputs of these algorithms for <i>N</i> = 3 metabolites and for the number of groups indicated at the top. …”

Cellular data inputs and outputs. by Hector Torres (11708207)

Code and Data for 'Fabrication and testing of lensed fiber optic probes for distance sensing using common path low coherence interferometry' by Radu Stancu (21165068)

“…Distance Sensing</p><p dir="ltr">Code and data to demonstrate extracting distance sensing data from A-scans and to generate Fig. 8 using the algorithm described in Fig. 7. …”

Quantum Simulation of Molecular Dynamics ProcessesA Benchmark Study Using a Classical Simulator and Present-Day Quantum Hardware by Tamila Kuanysheva (21546962)

“…This serves as a benchmark and demonstrates that the quantum algorithms and Qiskit codes we developed are accurate. …”

Seismic-Acoustic Dataset of Coastal Bryde’s Whales in the Beibu Gulf by Yue Wang (22435645)

Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Main module structure. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Architecture of MAR-YOLOv9. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Example images from four plant datasets. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”