Treffer 161 - 180 von 17.518

JeLLyFysh-Version1.0 — a Python application for all-atom event-chain Monte Carlo.
Höllmer, Philipp ; Qin, Liang ; Faulkner, Michael F. ; et al.
Computer Physics Communications. Aug2020, Vol. 253, pN.PAG-N.PAG. 1p.

SASSIE: A program to study intrinsically disordered biological molecules and macromolecular ensembles using experimental scattering restraints
Curtis, Joseph E. ; Raghunandan, Sindhu ; Nanda, Hirsh ; et al.
In Computer Physics Communications 2012 183(2):382-389

PyPDB: a Python API for the Protein Data Bank.
Gilpin W
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE

CIF2Cell: Generating geometries for electronic structure programs
Björkman, Torbjörn
In Computer Physics Communications 2011 182(5):1183-1186

Multithreaded event-chain Monte Carlo with local times.
Li, Botao ; Todo, Synge ; Maggs, A.C. ; et al.
Computer Physics Communications. Apr2021, Vol. 261, pN.PAG-N.PAG. 1p.

Sailfish: A flexible multi-GPU implementation of the lattice Boltzmann method
Januszewski, M. ; Kostur, M.
In Computer Physics Communications September 2014 185(9):2350-2368

FaVAD: A software workflow for characterization and visualizing of defects in crystalline structures.
von Toussaint, Udo ; Domínguez-Gutiérrez, F.J. ; Compostella, Michele ; et al.
Computer Physics Communications. May2021, Vol. 262, pN.PAG-N.PAG. 1p.

Python, SQL, Tableau : integrating Python, SQL, and Tableau
Packt Publishing, production company. ; Ganchev, Martin, speaker.

X-ray spectrometer simulation code with a detailed support of mosaic crystals.
Šmíd, Michal ; Pan, Xiayun ; Falk, Katerina
Computer Physics Communications. May2021, Vol. 262, pN.PAG-N.PAG. 1p.

Spinney: Post-processing of first-principles calculations of point defects in semiconductors with Python.
Arrigoni, Marco ; Madsen, Georg K.H.
Computer Physics Communications. Jul2021, Vol. 264, pN.PAG-N.PAG. 1p.

Customer Behaviour Analysis Using Machine Learning Algorithms
Krishan, Ram, author
Digital Transformation, Strategic Resilience, Cyber Security and Risk Management. 111B:133-142

cij: A Python code for quasiharmonic thermoelasticity.
Luo, Chenxing ; Deng, Xin ; Wang, Wenzhong ; et al.
Computer Physics Communications. Oct2021, Vol. 267, pN.PAG-N.PAG. 1p.

NDEx: Accessing Network Models and Streamlining Network Biology Workflows.
Pillich, Rudolf ; Chen, Jing ; Churas, Christopher ; et al.
Current Protocols in Neuroscience. 1(9)

DarkCapPy: Dark matter capture and annihilation.
Green, Adam ; Tanedo, Philip
Computer Physics Communications. Sep2019, Vol. 242, p120-131. 12p.

PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach (PEPPERMINT)
Britta Trautwein, Resident doctor
PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
Fernandez-Bustamante A, Frendl G, Sprung J, Kor DJ, Subramaniam B, Martinez Ruiz R, Lee JW, Henderson WG, Moss A, Mehdiratta N, Colwell MM, Bartels K, Kolodzie K, Giquel J, Vidal Melo MF. Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators. JAMA Surg. 2017 Feb 1;152(2):157-166. doi: 10.1001/jamasurg.2016.4065.
Ferreyra GP, Baussano I, Squadrone V, Richiardi L, Marchiaro G, Del Sorbo L, Mascia L, Merletti F, Ranieri VM. Continuous positive airway pressure for treatment of respiratory complications after abdominal surgery: a systematic review and meta-analysis. Ann Surg. 2008 Apr;247(4):617-26. doi: 10.1097/SLA.0b013e3181675829.
Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017 Mar 1;118(3):317-334. doi: 10.1093/bja/aex002.
Abbott TEF, Fowler AJ, Pelosi P, Gama de Abreu M, Moller AM, Canet J, Creagh-Brown B, Mythen M, Gin T, Lalu MM, Futier E, Grocott MP, Schultz MJ, Pearse RM; StEP-COMPAC Group. A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications. Br J Anaesth. 2018 May;120(5):1066-1079. doi: 10.1016/j.bja.2018.02.007. Epub 2018 Mar 27.
Ball L, Pelosi P. Predictive scores for postoperative pulmonary complications: time to move towards clinical practice. Minerva Anestesiol. 2016 Mar;82(3):265-7. Epub 2015 Sep 4. No abstract available.
Nithiuthai J, Siriussawakul A, Junkai R, Horugsa N, Jarungjitaree S, Triyasunant N. Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? An observational study at a single university hospital. Perioper Med (Lond). 2021 Dec 8;10(1):43. doi: 10.1186/s13741-021-00214-3.
Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240.
Szabo M, Bozo A, Darvas K, Soos S, Ozse M, Ivanyi ZD. The role of ultrasonographic lung aeration score in the prediction of postoperative pulmonary complications: an observational study. BMC Anesthesiol. 2021 Jan 14;21(1):19. doi: 10.1186/s12871-021-01236-6.
van Sloun RJG, Demi L. Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results. IEEE J Biomed Health Inform. 2020 Apr;24(4):957-964. doi: 10.1109/JBHI.2019.2936151. Epub 2019 Aug 19.
Brusasco C, Santori G, Tavazzi G, Via G, Robba C, Gargani L, Mojoli F, Mongodi S, Bruzzo E, Tro R, Boccacci P, Isirdi A, Forfori F, Corradi F; UCARE (Ultrasound in Critical care and Anesthesia Research Group). Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema. J Clin Monit Comput. 2022 Feb;36(1):131-140. doi: 10.1007/s10877-020-00629-1. Epub 2020 Dec 12.
Trautwein B, Beer M, Blobner M, Jungwirth B, Kagerbauer SM, Gotz M. Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. PLoS One. 2025 Aug 19;20(8):e0329076. doi: 10.1371/journal.pone.0329076. eCollection 2025.

SOME PATTERN RECOGNITIONS FOR A RECOMMENDATION FRAMEWORK FOR HIGHER EDUCATION STUDENTS' GENERIC COMPETENCE DEVELOPMENT USING MACHINE LEARNING.
Chi-ho So, Joseph ; Ka-lok Wong, Adam ; Kia Ho-yin Tsang ; et al.
Journal of Technology & Science Education. 2023, Vol. 13 Issue 1, p104-115. 12p.

PyArc: A python package for computing absorption and radiative coefficients from first principles.
Xu, Siyuan ; Liu, Zheng ; Xu, Xun ; et al.
Computer Physics Communications. Dec2024, Vol. 305, pN.PAG-N.PAG. 1p.

Easy-to-Use Cloud Computing for Teaching Data Science.
Kim, Brian ; Henke, Graham
Journal of Statistics & Data Science Education. 2021 Supplement, Vol. 29, pS103-S111. 9p.

Pilgrim: A thermal rate constant calculator and a chemical kinetics simulator.
Ferro-Costas, David ; Truhlar, Donald G. ; Fernández-Ramos, Antonio
Computer Physics Communications. Nov2020, Vol. 256, pN.PAG-N.PAG. 1p.

Practical Programming: An Introduction to Computer Science Using Python 3.
Shamp Winstel, Bonnie J.
Technical Communication. Feb2015, Vol. 62 Issue 1, p68-69. 2p.