Comparison of performance between our next reaction implementation and the Python library from Ref. [3]. حسب Samuel Cure (22250922)

Python implementation of a wildfire propagation example using m:n-CAk over Z and R. حسب Pau Fonseca i Casas (9507338)

Fitting a feast/famine cycle to a glycolysis model. حسب Paul van Lent (16876977)

Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... حسب Ziyan Jin (21021976)

Segmentation module training details. حسب Khelina Fedorchuk (19130089)

Performance Benchmark: SBMLNetwork vs. SBMLDiagrams Auto-layout. حسب Adel Heydarabadipour (22290905)

"…<p>Log–log plot of median wall-clock time for SBMLNetwork’s C++-based auto-layout engine (blue circles, solid fit) and SBMLDiagrams’ implementation of the pure-Python NetworkX spring_layout algorithm (red squares, dashed fit), applied to synthetic SBML models containing 20–2,000 species, with a fixed 4:1 species-to-reaction ratio. …"

MCCN Case Study 3 - Select optimal survey locality حسب Donald Hobern (21435904)

Summary of Tourism Dataset. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Segment-wise Spending Analysis. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Hyperparameter Parameter Setting. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Marketing Campaign Analysis. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Visitor Segmentation Validation Accuracy. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Integration of VAE and RNN Architecture. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

<b>Altered cognitive processes shape tactile perception in autism.</b> (data) حسب Ourania Semelidou (19178362)

"…</p><p dir="ltr">The perceptual decision-making data were collected from 5 different cohorts of mice at different time-points during their active phase of the day. …"

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 حسب Robert Zomer (12796235)

"…</p><p>***********************************************************************************************************************************</p><p dir="ltr"><b>NOTE: </b>The recently released Future Global Aridity Index and PET Database (CMIP_6) is now available at:</p><p dir="ltr">https://doi.org/10.57760/sciencedb.nbsdc.00086</p><p dir="ltr">High-resolution (30 arc-seconds) global raster datasets of average monthly and annual potential evapotranspiration (PET) and aridity index (AI) for two historical (1960-1990; 1970-2000) and two future (2021-2040; 2041-2060) time periods for each of 22 CIMP6 Earth System Models across four emission scenarios (SSP: 126, 245, 370, 585). …"

Microscopic Detection and Quantification of Microplastic Particles in Environmental Water Samples حسب Derek Lam (11944213)

"…Image processing algorithms, implemented in Python using adaptive thresholding techniques, were applied to segment particles from the background. …"

Code حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Core data حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"