Treffer 181 - 200 von 17.534

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

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, Shyue Ping ; Richards, William Davidson ; Jain, Anubhav ; et al.
Computational Materials Science. 68

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.

Textile wastewater: COD removal via Box–Behnken design, Fenton method, and machine learning integration for sustainability
International Journal of Clothing Science and Technology, 2024, Vol. 37, Issue 4, pp. 642-662.

A practical guide to writing a radiative transfer code.
Korkin, S. ; Sayer, A.M. ; Ibrahim, A. ; et al.
Computer Physics Communications. Feb2022, Vol. 271, pN.PAG-N.PAG. 1p.

[Thermometrical determination of the postmortem interval by non-linear optimization method].
Nedugov VG ; Nedugov GV
Publisher: Media Sfera Country of Publication: Russia (Federation) NLM ID: 0404546 Publication Model: Print Cited Medium: Print ISSN: 0039-4521 (Print) Linking ISSN: 00394521 NLM ISO Abbreviation: Sud Med Ekspert Subsets: MEDLINE

GUI4dft — A SIESTA oriented GUI.
Sozykin, S.A.
Computer Physics Communications. May2021, Vol. 262, pN.PAG-N.PAG. 1p.

Assessing the impact of programming workshops on biomedical research reproducibility
Deardorff, Ariel

SMATool: Strength of materials analysis toolkit.
Ekuma, C.E.
Computer Physics Communications. Jul2024, Vol. 300, pN.PAG-N.PAG. 1p.

ElasTool v3.0: Efficient computational and visualization toolkit for elastic and mechanical properties of materials.
Ekuma, C.E. ; Liu, Z.-L.
Computer Physics Communications. Jul2024, Vol. 300, pN.PAG-N.PAG. 1p.

ERSN-OpenMC-Py: A python-based open-source software for OpenMC Monte Carlo code.
Lahdour, M. ; El Bardouni, T. ; El Hajjaji, O. ; et al.
Computer Physics Communications. Jun2024, Vol. 299, pN.PAG-N.PAG. 1p.

Data-analysis software framework 2DMAT and its application to experimental measurements for two-dimensional material structures.
Motoyama, Yuichi ; Yoshimi, Kazuyoshi ; Mochizuki, Izumi ; et al.
Computer Physics Communications. Nov2022, Vol. 280, pN.PAG-N.PAG. 1p.

Enhancing river flow predictions in MOHID-Land through integration of gridded soil data and hydraulic parameters using the MOHID SOIL TOOL.
Sales, Dhiego da Silva ; Costa, David de Andrade ; Lugon Junior, Jader ; et al.
Environmental Modelling & Software. Jan2026, Vol. 196, pN.PAG-N.PAG. 1p.

pyKasso: An open-source three-dimensional discrete karst network generator.
Miville, François ; Renard, Philippe ; Fandel, Chloé ; et al.
Environmental Modelling & Software. Mar2025, Vol. 186, pN.PAG-N.PAG. 1p.

Optimization Problems in Transportation and Logistics: A Practical Guide
Bridgelall, Raj

Univariate machine learning models applied in photovoltaic power prediction using Python.
Bahanni, Caouthar ; Mabrouki, Mustapha
Energy Sources Part A: Recovery, Utilization & Environmental Effects. 2023, Vol. 45 Issue 1, p589-607. 19p.

Computing optimum solar tilt angles for photovoltaic applications through python code simulation.
Sumair, Muhammad ; Pasha, Riffat Asim ; Aized, Tauseef ; et al.
Environmental Progress & Sustainable Energy. Sep2023, Vol. 42 Issue 5, p1-15. 15p.

Integrated modeling of the generation, attenuation, and transport of point-source pollutants at the watershed-scale using SWAT+.
Saló-Grau, Joan ; Estrada, Laia ; Llorente, Oliu ; et al.
Environmental Modelling & Software. Sep2025, Vol. 193, pN.PAG-N.PAG. 1p.

PyStructureFactor: A Python code for the molecular structure factor in tunneling ionization rates.
Song, Shanshan ; Zhu, Mingyu ; Ni, Hongcheng ; et al.
Computer Physics Communications. Nov2023, Vol. 292, pN.PAG-N.PAG. 1p.

ENTOSIM, AN INSECTS LIFE CYCLE SIMULATOR ENCLOSING MULTIPLE MODELS IN A DOCKER CONTAINER.
Rossini, Luca ; Speranza, Stefano ; Mazzaglia, Angelo ; et al.
Environmental Engineering & Management Journal (EEMJ). Oct2021, Vol. 20 Issue 10, p1703-1710. 8p.