Search Results - (( (staten OR stat) python code analysis ) OR ( stateeeeeeny python code analysis ))

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries.

Authors: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. et al.

Source: Future Internet; Sep2025, Vol. 17 Issue 9, p412, 28p

Subject Terms: LANGUAGE models, EXPERIMENTAL design, PYTHON programming language, GENERATIVE pre-trained transformers, QUANTITATIVE research, BENCHMARK problems (Computer science), CODE generators

Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python.

Authors: Dunker, Fabian Mendoza, Emil Reale, Marco

Source: Mathematics (2227-7390); Dec2025, Vol. 13 Issue 23, p3764, 29p

Subject Terms: PYTHON programming language, MAXIMUM likelihood statistics, CONSUMPTION (Economics), MATHEMATICAL programming, MULTILEVEL models, SCIENTIFIC computing

A statistical approach to automated analysis of the low‐contrast object detectability test for the large ACR MRI phantom.

Authors: Golestani, Ali M. Gee, Julia M.

Source: Journal of Applied Clinical Medical Physics; Jul2025, Vol. 26 Issue 7, p1-9, 9p

Subject Terms: LINEAR statistical models, IMAGING phantoms, QUALITY control, PYTHON programming language, MAGNETIC resonance imaging

Primary data for BJC Carbonic anhydrase IX is a pH-stat that sets an acidic tumour extracellular pH in vivo

Authors: Griffiths, JR Lee, Shen-Han McIntyre, Dominick John et al.

Index Terms: Carbonic anhydrase IX, Extracellular pH, Intracellular pH, ISUCA, Magnetic Resonance Spectroscopy, MRS, pH-stat, Tumour pH, Dataset

URL: https://www.repository.cam.ac.uk/handle/1810/361916
https://doi.org/10.17863/CAM.26029
https://doi.org/10.1038/s41416-018-0216-5
https://www.repository.cam.ac.uk/handle/1810/287141
https://doi.org/10.1038/s41416-018-0216-5
https://www.repository.cam.ac.uk/handle/1810/287141

Python Programming: Numerical Analysis/Machine Learning Series.

Authors: Doskas, Constantinos

Source: ISSA Journal; Oct2023, Vol. 21 Issue 10, p34-38, 5p

Subject Terms: PYTHON programming language, NUMERICAL analysis, MACHINE learning

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions.

Authors: Stoica, Alexandru Stefan Babiceanu, Daniel Mihaescu, Marian Cristian et al.

Source: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3589, 19p

Subject Terms: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code

Entropy, Fidelity, and Entanglement During Digitized Adiabatic Quantum Computing to Form a Greenberger–Horne–Zeilinger (GHZ) State.

Authors: Jansen, Nathan D. Hunt, Katharine L. C.

Source: Entropy; Sep2025, Vol. 27 Issue 9, p891, 21p

Subject Terms: ENTROPY, QUANTUM entanglement, STATISTICAL accuracy, HAMILTONIAN mechanics, ISING model, QUANTUM computing, QUANTUM computers

Company/Entity: INTERNATIONAL Business Machines Corp.

An optimization method for klystron magnetic focusing system.

Authors: Wang, Yiao Zhou, Zusheng Xiao, Ouzheng et al.

Source: Radiation Detection Technology & Methods; Dec2025, Vol. 9 Issue 4, p662-671, 10p

Uncertainty quantification for severe-accident reactor modelling: Results and conclusions of the MUSA reactor applications work package

Authors: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Contributors: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Source: ISSN: 0306-4549 ; Annals of Nuclear Energy ; https://hal.science/hal-04777159 ; Annals of Nuclear Energy, 2025, 211, pp.110962. ⟨10.1016/j.anucene.2024.110962⟩.

Subject Terms: Nuclear reactor, Severe accident, Modelling, Uncertainty quantification, MUSA, Source Term, [PHYS]Physics [physics], [SPI]Engineering Sciences [physics], [STAT]Statistics [stat]

Relation: info:eu-repo/grantAgreement//847441/EU/Euratom research and training programme 2014 – 2018/Euratom

Availability: https://hal.science/hal-04777159
https://hal.science/hal-04777159v1/document
https://hal.science/hal-04777159v1/file/Final_version_ANE20225.pdf
https://doi.org/10.1016/j.anucene.2024.110962

GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model.

Authors: Li, Jingmao Xu, Yaqing Ma, Shuangge et al.

Source: Bioinformatics; Sep2025, Vol. 41 Issue 9, p1-9, 9p

Subject Terms: CANCER research, GENOTYPE-environment interaction, DIAGNOSTIC imaging, DEEP learning, EMPIRICAL research, COMPUTER simulation

The state of the late Quaternary megafauna extinction debate: a systematic review and analysis.

Authors: Stewart, Mathew Peters, Carli Ziegler, Michael J. et al.

Source: Frontiers in Mammal Science. 2025, p1-27. 27p.

Genome-wide analyses reveal intricate genetic mechanisms underlying egg production efficiency in chickens.

Authors: Tan, Lizhi Cai, Xinyu Kong, Yuan et al.

Source: Journal of Animal Science & Biotechnology. 8/11/2025, Vol. 16 Issue 1, p1-21. 21p.

Programmed differently? Testing for gender differences in Python programming style and quality on GitHub.

Authors: Brooke, Siân

Source: Journal of Computer-Mediated Communication; Jan2024, Vol. 29 Issue 1, p1-13, 13p

Subject Terms: PYTHON programming language, PYTHONS, RANDOM forest algorithms, GENDER

An extensive study of the effects of different deep learning models on code vulnerability detection in Python code.

Authors: Wang, Rongcun Xu, Senlei Ji, Xingyu et al.

Source: Automated Software Engineering; Jun2024, Vol. 31 Issue 1, p1-35, 35p

DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 Generate Correct Code for LoRaWAN-Related Engineering Tasks.

Authors: Fernandes, Daniel Matos-Carvalho, João P. Fernandes, Carlos M. et al.

Source: Electronics (2079-9292); Apr2025, Vol. 14 Issue 7, p1428, 24p

Subject Terms: LANGUAGE models, GENERATIVE pre-trained transformers, GENERATING functions, RESEARCH questions, NATURAL languages

Automation of Software Code Analysis Using Machine Learning Methods.

Authors: Moshkin, V. S. Dyrnochkin, A. A. Yarushkina, N. G.

Source: Pattern Recognition & Image Analysis; Sep2023, Vol. 33 Issue 3, p417-424, 8p

Data-Driven Early Warning Approach for Antimicrobial Resistance Prediction–Anomaly Detection Based on High-Level Indicators.

Authors: Csorba, Szilveszter Vribék, Krisztián Farkas, Máté et al.

Source: Veterinary Sciences. Oct2025, Vol. 12 Issue 10, p935. 17p.

Survival Modelling Using Machine Learning and Immune–Nutritional Profiles in Advanced Gastric Cancer on Home Parenteral Nutrition.

Authors: Matysiak, Konrad Hojdis, Aleksandra Szewczuk, Magdalena

Source: Nutrients. Aug2025, Vol. 17 Issue 15, p2414. 16p.

Sex-Specific Dietary Predictors of Blood Glucose Identified Through Decision Tree Modeling in Adults.

Authors: Gautney, Joanna Aguilar, Christina Chan, Julian et al.

Source: Nutrients. Oct2025, Vol. 17 Issue 19, p3119. 12p.

Inferring leader-follower dynamics in three shark species using acoustic telemetry data.

Authors: Kreuter, Nils Fernández-Gracia, Juan Eguíluz, Víctor M. et al.

Source: Movement Ecology. 8/28/2025, Vol. 13 Issue 1, p1-13. 13p.