Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…In `main.py`, the parameters for the TAMS algorithm are specified (trajectory time, time step, score function, number of particles, type of score threshold, maximum number of iterations, noise level etc.). …”

Identifying cognitive impairment with story recall (Wilson et al., 2024) by Sarah C. Wilson (19370966)

“…</b> Discourse measures and descriptions from the CLAN software (including duration, MLU, type-token ratio, etc.).…”

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling by Shan He (121220)

“…To address this, we created a new data set of NP systems from public sources. Herein, 11 different AI/ML algorithms were used to develop the predictive AI/ML models. …”

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

“…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)

“…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)

“…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)

“…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. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning by Xiaoshi Cheng (14119418)

“…Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. …”

Cut point estimation. by Adrian Verster (5875283)

Latent traversal video still. by Matthew R. Whiteway (11467528)

Functional characterization of expressed ZERO 2.1 –BK channel isoform channel. by Katherine Cordero Padilla (18182244)

Table1_Aortic systolic and pulse pressure invasively and non-invasively obtained: Comparative analysis of recording techniques, arterial sites of measurement, waveform analysis alg... by Daniel Bia (8157270)

“…</p><p>Aim: To evaluate the agreement between aoSBP and aoPP values invasively and non-invasively obtained using different: 1) recording techniques (tonometry, oscilometry/plethysmography, ultrasound), 2) recording sites [radial, brachial (BA) and carotid artery (CCA)], 3) waveform analysis algorithms (e.g., direct analysis of the CCA pulse waveform vs. peripheral waveform analysis using general transfer functions, N-point moving average filters, etc.), 4) calibration schemes (systolic-diastolic calibration vs. methods using BA diastolic and mean blood pressure (bMBP); the latter calculated using different equations vs. measured directly by oscillometry, and 5) different equations to estimate bMBP (i.e., using a form factor of 33% (“033”), 41.2% (“0412”) or 33% corrected for heart rate (“033HR”).…”

DataSheet1_Aortic systolic and pulse pressure invasively and non-invasively obtained: Comparative analysis of recording techniques, arterial sites of measurement, waveform analysis... by Daniel Bia (8157270)

“…</p><p>Aim: To evaluate the agreement between aoSBP and aoPP values invasively and non-invasively obtained using different: 1) recording techniques (tonometry, oscilometry/plethysmography, ultrasound), 2) recording sites [radial, brachial (BA) and carotid artery (CCA)], 3) waveform analysis algorithms (e.g., direct analysis of the CCA pulse waveform vs. peripheral waveform analysis using general transfer functions, N-point moving average filters, etc.), 4) calibration schemes (systolic-diastolic calibration vs. methods using BA diastolic and mean blood pressure (bMBP); the latter calculated using different equations vs. measured directly by oscillometry, and 5) different equations to estimate bMBP (i.e., using a form factor of 33% (“033”), 41.2% (“0412”) or 33% corrected for heart rate (“033HR”).…”

IOWarp v1.0 Beta: Advancing Scientific AI with Intelligent Data Management & MCP Integration by Anthony Kougkas (17069216)

Table_1_Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.docx by Baoyu Yan (8628765)

“…However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. …”

Data_Sheet_1_Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.docx by Baoyu Yan (8628765)

“…However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. …”