Search Results - (( (stavebne OR statnych) python code analysis ) OR ( stat python code analysis ))*

Prediction of Positive Patient Health Questionnaire-2 Screening Using Area Deprivation Index in Primary Care.

Authors: Duarte, Martha Salamanca, Mayra Gonzalez, Juan M. et al.

Source: Clinical Nursing Research. Jun2024, Vol. 33 Issue 5, p355-369. 15p.

Subject Terms: *MENTAL depression risk factors, *RISK assessment, *PREDICTION models, *SOCIAL determinants of health, *RECEIVER operating characteristic curves, *QUESTIONNAIRES, *PRIMARY health care, *RETROSPECTIVE studies, *QUANTITATIVE research, *DESCRIPTIVE statistics, *NURSING interventions, *SURVEYS, *HEALTH behavior, *MEDICAL records, *ACQUISITION of data, *SOCIODEMOGRAPHIC factors, *MACHINE learning, *DATA analysis software, *ACCURACY, *COMPARATIVE studies, *SOCIAL classes, *SENSITIVITY & specificity (Statistics), *SOCIAL isolation, *ALGORITHMS, *REGRESSION analysis, *MENTAL depression, *EVALUATION

Geographic Terms: FLORIDA

Effects of a post-acute care management program on subsequent acute care utilization for hospitalized patients discharged to skilled nursing facilities.

Authors: Armstrong D Agovi AM Gehr AW et al.

Source: BMC health services research [BMC Health Serv Res] 2026 Jan 07. Date of Electronic Publication: 2026 Jan 07.

Publication Type: Journal Article

Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101088677 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1472-6963 (Electronic) Linking ISSN: 14726963 NLM ISO Abbreviation: BMC Health Serv Res Subsets: MEDLINE

TRIQS/SOM 2.0: Implementation of the stochastic optimization with consistent constraints for analytic continuation.

Authors: Krivenko, Igor Mishchenko, Andrey S.

Source: Computer Physics Communications. Nov2022, Vol. 280, pN.PAG-N.PAG. 1p.

Subject Terms: *PYTHON programming language, *GREEN'S functions, *MAXIMUM entropy method, *COVARIANCE matrices, *STATISTICAL ensembles, *PROGRAMMING languages, *REGULARIZATION parameter

Machine learning for prioritizing security code-based Vulnerability discovery

Authors: Ballestas, Rafael García Bedoya, Olmer

Contributors: Ballestas, Rafael García Bedoya, Olmer

Source: instname:Universidad de Bogotá Jorge Tadeo Lozano ; reponame:Expeditio Repositorio Institucional UJTL

Subject Terms: Ingeniería, Máquinas, Automatización, Auditorías, Seguridad, Security auditing

Subject Geographic: Colombia

File Description: 58 páginas; application/pdf

Relation: Alon, U., Zilberstein, M., Levy, O., and Yahav, E. (2019). code2vec: learning distributed representations of code. Proc. ACM Program. Lang., 3(POPL):1– 29.; Antunes, N. and Vieira, M. (2009). Comparing the Effectiveness of Penetration Testing and Static Code Analysis on the Detection of SQL Injection Vulnerabilities in Web Services. In 2009 15th IEEE Pacific Rim International Symposium on Dependable Computing, pages 301–306, Shanghai, China. IEEE.; Chang, Y., Liu, B., Cong, L., Deng, H., Li, J., and Chen, Y. (2019). Vulnerability Parser: A Static Vulnerability Analysis System for Android Applications. J. Phys.: Conf. Ser., 1288:012053.; Chong, S., Guttman, J., Datta, A., Myers, A., Pierce, B., Schaumont, P., Sherwood, T., and Zeldovich, N. (2016). Report on the NSF Workshop on Formal Methods for Security. arXiv:1608.00678 [cs]. arXiv: 1608.00678.; Dauber, E., Caliskan, A., Harang, R., Shearer, G., Weisman, M., Nelson, F., and Greenstadt, R. (2019). Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments. Proceedings on Privacy Enhancing Technologies, 2019(3):389–408. arXiv: 1701.05681; Ferreira, A. M. and Kleppe, H. (2011). Effectiveness of Automated Application Penetration Testing Tools. Technical report, OS3 University of Amsterdam.; FluidAttacks (2020). Integrates.; Free Software Foundation (2020). GNU diffutils; Ghaffarian, S. M. and Shahriari, H. R. (2017). Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey. ACM Computing Surveys, 50(4):1–36.; Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., and Chen, Z. (2018a). SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities. arXiv:1807.06756 [cs, stat]. arXiv: 1807.06756.; Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., and Zhong, Y. (2018b). VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. Proceedings 2018 Network and Distributed System Security Symposium. arXiv: 1801.01681.; Moor, O. d., Verbaere, M., Hajiyev, E., Avgustinov, P., Ekman, T., Ongkingco, N., Sereni, D., and Tibble, J. (2007). Keynote Address: .QL for Source Code Analysis. In Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007), pages 3–16, Paris, France. IEEE; Ng, A. (2016). What Artificial Intelligence Can and Can’t Do Right Now. Harvard Business Review. Section: Analytics.; Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.; Rice, H. G. (1953). Classes of recursively enumerable sets and their decision problems. Trans. Amer. Math. Soc., 74(2):358–366.; Schwartz, E. J., Avgerinos, T., and Brumley, D. (2010). All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask). In 2010 IEEE Symposium on Security and Privacy, pages 317–331, Oakland, CA, USA. IEEE.; Sommer, R. and Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. In 2010 IEEE Symposium on Security and Privacy, pages 305–316, Oakland, CA, USA. IEEE.; Stefinko, Y., Piskozub, A., and Banakh, R. (2016). Manual and automated penetration testing. Benefits and drawbacks. Modern tendency. In 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), pages 488–491, Lviv, Ukraine. IEEE.; Yamaguchi, F., Wressnegger, C., Gascon, H., and Rieck, K. (2013). Chucky: exposing missing checks in source code for vulnerability discovery. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS ’13, pages 499–510, Berlin, Germany. ACM Press.; http://hdl.handle.net/20.500.12010/17241; http://expeditio.utadeo.edu.co

Availability: https://hdl.handle.net/20.500.12010/17241
http://expeditio.utadeo.edu.co

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

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.

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

NDL-v2.0: A new version of the numerical differentiation library for parallel architectures.

Authors: Hadjidoukas, P.E. Angelikopoulos, P. Voglis, C. et al.

Source: Computer Physics Communications. Jul2014, Vol. 185 Issue 7, p2217-2219. 3p.

Subject Terms: *NUMERICAL differentiation, *PARALLEL processing, *MULTICORE processors, *DISTRIBUTED shared memory, *PARAMETER estimation, *PYTHON programming language, *CODING theory

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

Strong correlation of gene counts and differentially expressed genes between a 3′ RNA-Seq and an RNA hybridization platform in transcriptome analyses from canine archival tissues.

Authors: Haake, Alexander F. H. Loriani Fard, Alina K. Jovanovic, Vladimir M. et al.

Source: Frontiers in Veterinary Science. 2025, p1-13. 13p.

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.

Anti-Inflammatory Diet and Probiotic Supplementation as Strategies to Modulate Immune Dysregulation in Autism Spectrum Disorder.

Authors: Naranjo-Galvis, Carlos Andrés Trejos-Gallego, Diana María Correa-Salazar, Cristina et al.

Source: Nutrients. Aug2025, Vol. 17 Issue 16, p2664. 21p.

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

Comparative virulence study of locally circulating peste des petits ruminants virus (PPRV) strains in indigenous Ethiopian goats.

Authors: Aklilu, Fasil Ashenafi, Hagos Kassa, Tesfu et al.

Source: BMC Veterinary Research. 10/6/2025, Vol. 21 Issue 1, p1-11. 11p.

Subjects: Peste des petits ruminants, Microbial virulence, Symptoms, Goats, Comparative studies, Seroconversion, Communicable disease control

Geographic Terms: Ethiopia

Sweet Taste Adaptation to Sugars, Sucralose, and Their Blends: A Human and Rodent Perspective.

Authors: Okoye, Stephanie I. Kim, Minjae Petty, Sara et al.

Source: Nutrients. Oct2025, Vol. 17 Issue 19, p3075. 16p.

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