Search Results - ((dynamic OR dynamika) OR dynamik) code analysis of python progress

Programming models to support data science workflows

Authors: Ramón-Cortés Vilarrodona, Cristián

Contributors: University/Department: Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors

Thesis Advisors: Badia Sala, Rosa M. (Rosa Maria), Ejarque Artigas, Jorge

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Distributed computing, High-performance computing, Data science pipelines, Task-ba workflows, Dataflows, Containers (Computer sicience), COMPSs, PyCOMPSs, AutoParallel, Docker, Àrees temàtiques de la UPC::Informàtica

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/669728
https://dx.doi.org/10.5821/dissertation-2117-330142

Modeling and Simulation of Combustion in Python

Authors: Sadeq, Abdellatif

Subject Terms: Alternative Fuels, Ignition Delay, Optimization Techniques, combustion modeling, Turbulence-Chemistry Interaction, Python Programming, Transport Phenomena, Pollutant Emissions, Heat Transfer, Hydrogen Combustion, Sensitivity Analysis, Advanced Sensitivity Frameworks, Machine Learning in Combustion, Python Visualization Tools (Matplotlib, Plotly), Chemical Kinetics, Multi-phase Combustion, Cantera Library, Reaction Mechanisms, Numerical Simulation, Flame Dynamics

Full Issue Download Vol. 13 No. 1 2021 The Importance of the Measurement Infrastructure in Economic Recovery from the COVID-19 Pandemic Richard J. C. Brown , Fiona Auty, Eugenio Renedo, Mike King NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.1 Publisher NCSL International | Published February 2021 | Pages 18-21 Abstract: This paper describes the many, evidenced-based benefits to the economy of a well-developed measurement infrastructure. In particular, it explains how assuring confidence in measurement may be used to accelerate economic recovery from the COVID-19 pandemic including in emerging sectors such as the digital economy. Recommendations are made for providing near term support for national economic recovery whilst also demonstrating the advantages of sustained development of the measurement infrastructure in the medium-term to maximize the potential of future innovative and disruptive technologies. These recommendations, whilst focused on consideration of the UK, should apply globally. References: [1] G. Tassey, 'Underinvestment in public good technologies,' J Technol. Transfer, Vol. 30, pp. 89-113, 2004. https://doi.org/10.1007/s10961-004-4360-0 [2] M. King, and E. Renedo, 'Achieving the 2.4% GDP target: The role of measurement in increasing investment in R&D and innovation,' NPL Report IEA 3, NPL, Teddington, UK, March 2020. [3] M. King and G. Tellett, 'The National Measurement System: A Customer Survey for Three of the Core Labs in the National Measurement System,' NMS Customer Survey Report 2018, NPL Teddington, UK, April 2020 [4] H. Kunzmann, T. Pfeifer, R. Schmitt, H. Schwenke, and A.Weckenmann, 'Productive metrology-adding value to manufacture,' CIRP Annals, vol. 54, pp. 155-168, 2005. https://doi.org/10.1016/S0007-8506(07)60024-9 [5] N. G. Orji, R. G. Dixson, A. Cordes, B. D. Bunday, and J. A. Allgair, 'Measurement traceability and quality assurance in a nanomanufacturing environment,' Instrumentation, Metrology, and Standards for Nanomanufacturing III, Proceedings Vol. 7405, 740505, August 2009. https://doi.org/10.1117/12.826606 [6] Belmana, Analysis for Policy 'Public Support for Innovation and Business Outcomes,' Belmana: London, UK, 2020. [7] R. Hawkins, Standards, systems of innovation and policy in Handbook of Innovation and Standards. Cheltenham, UK: Edward Elgar, 2019. [8] N. Nwaigbo, and M. King, 'Evaluating the Impact of the NMS Consultancy Projects on Supported Firms (Working Paper)' NPL, Teddington, UK, 2020. [9] M. King, R. Lambert, and P. Temple, Measurement, standards and productivity spillovers in Handbook of Innovation and Standards. Cheltenham, UK: Edward Elgar, 2017, p. 162. https://doi.org/10.4337/9781783470082.00016 [10] A. Font, K. de Hoogh, M. Leal-Sanchez, D. C. Ashworth, R. J. C. Brown, A. L. Hansell, and G. W. Fuller, 'Using metal ratios to detect emissions from municipal waste incinerators in ambient air pollution data,' Atmos. Environ., vol. 113, pp. 177-186, July 2015. https://doi.org/10.1016/j.atmosenv.2015.05.002 [11] S. Giannis, M. R. L. Gower, G. D. Sims, G. Pask, and G. Edwards, 'Increasing UK competitiveness by enhancing the composite materials regulatory infrastructure,' NPL Report MAT 90, NPL, Teddington, UK, October 2019. [12] HM Government, UK Research and Development Roadmap, BEIS, London, July 2020. [13] M. R. Mehra, S. S. Desai, F. Ruschitzka, and A. N. Patel, 'Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis,' Lancet, 2020, https://doi.org/10.1016/S0140-6736(20)31180-6 (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) ©2021 NCSL International Smart Power Supply Calibration System Iraj Vasaeli , Brandon Umansky NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.2 Publisher: NCSL International | Published February 2021 | Pages 22-27 Abstract: This paper details the development of an automated procedure to conduct calibrations of power supplies at Jet Propulsion Laboratory, California Institute of Technology (JPL). The fundamentals of power supply calibrations are given, and discussion on the method by which this custom software handles that calibration. Additionally, this technique provides real time uncertainty quantification of the calibrations. This automated system has demonstrated a time savings over existing automated techniques in use today. References: [1] Keysight, 'Low-Profile Modular Power System Series N6700 Service Guide', Part Number: 5969 2938, Edition 7, January 2015. [2] B. N. Taylor and C. E. Kuyatt, 'Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results', NIST Technical Note 1297, 1994. https://doi.org/10.6028/NIST.TN.1297 [3] JCGM, 'Evaluation of measurement data - Guide to the expression of uncertainty in measurement,' first edition (GUM 1995 with minor corrections),' JCGM 100, 2008. (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Computer Aided Verification of Voltage Dips and Short Interruption Generators for Electromagnetic Compatibility Immunity Test in Accordance with IEC 61000-4-11: 2004 + AMD: 2017 Hau Wah Lai , Cho Man Tsui , Hing Wah Li NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.3 Publisher: NCSL International | Published February 2021 | Pages 28-39 Abstract: This paper describes a procedure and a computer-aided system developed by the Standards and Calibration Laboratory (SCL) for verification of voltage dip and short interruption generators in accordance with the international standard IEC 61000-4-11:2004+AMD1:2017. The verification is done by calibrating the specified parameters and comparing with the requirements stated in the standard. The parameters that should be calibrated are the ratios of the residual voltages to the rated voltage, the accuracy of the phase angle at switching, and the rise time, fall time, overshoot and undershoot of the switching waveform. A specially built adapter is used to convert the high voltage output waveforms of the generators to lower level signals to be acquired by a digital oscilloscope. The other circuits required for the testing are also provided. In addition, the paper discusses the uncertainty evaluations for the measured parameters. References: [1] T. Williams, and K. Armstrong, 'EMC for Systems and Installations Part 6 - Low-Frequency Magnetics Fields (Emissions and Immunity) Mains Dips, Dropouts, Interruptions, Sags, Brownouts and Swells,' EMC Compliance Journal, August 2000. [2] M.I. Montrose, and E. M. Nakauchi, Testing for EMC Compliance: Approaches and Techniques, Wiley Interscience, 2004. https://doi.org/10.1002/047164465X [3] International Standard IEC 61000-4-11:2004+AMD1:2017:Electromagnetic Compatibility (EMC) Part 4-11: Testing and measurement techniques - Voltage dips, short interruptions and voltage variations immunity tests. [4] Evaluation of measurement data - Guide to the expression of uncertainty in measurement, First Edition JCGM 100:2008. (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Validation of the Photometric Method Used for Micropipette Calibration Elsa Batista , Isabel Godinho, George Rodrigues, Doreen Rumery NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.4 Publisher: NCSL International | Published February 2021 | Pages 40-45 Abstract: There are two methods generally used for calibration of micropipettes: the gravimetric method described in ISO 8655-6:2002 and the photometric method described in ISO 8655-7:2005. In order to validate the photometric method, several micropipettes of different capacities from 0.1 µL to 1000 µL were calibrated using both methods (gravimetric and photometric) in two different laboratories, IPQ (Portuguese Institute for Quality) and Artel. These tests were performed by six different operators. The uncertainty for both methods was determined and it was verified that the uncertainty component that has a higher contribution to the final uncertainty budget depends on the volume delivered. In the photometric method for small volumes, the repeatability of the pipette is the largest uncertainty component, but for volumes, larger than 100 µL, the photometric instrument is the most significant source of uncertainty. Based on all the results obtained with this study, one may consider the photometric method validated. References: [1] ISO 8655-1/2/6/7, Piston-operated volumetric apparatus, 2002. [2] BIPM, International Vocabulary of Metrology, 3rd edition, JCGM 200:2012. [3] George Rodrigues, Bias and transferability in standards methods of pipette calibration, Artel, June 2003. [4] Taylor, et.al. The definition of primary method of measurement (PMM) of the 'highest metrological quality': a challenge in understanding and communication, Accred. Qual.Assur (2001) 6:103-106. https://doi.org/10.1007/PL00010444 [5] EURAMET project 1353, Volume comparison on Calibration of micropipettes - Gravimetric and photometric methods. [6] ASTM E542: Standard Practice for Calibration of laboratory Volumetric Apparatus, 2000. [7] ISO 4787; Laboratory glassware - Volumetric glassware - Methods for use and testing of capacity, 2010 . [8] ISO 13528:2005 - Statistical methods used in proficiency testing by interlaboratory comparisons. [9] BIPM et al, Guide to the Expression of Uncertainty in Measurement (GUM), 2nd ed., International Organization for Standardization, Genève, 1995. [10] EURAMET guide, cg 19, - Guidelines on the determination of uncertainty in gravimetric volume calibration, version 3.0, 2012. [11] E. Batista et all, A Study of Factors that Influence Micropipette Calibrations, Measure Vol. 10 No. 1, 2015 https://doi.org/10.1080/19315775.2015.11721717 [12] www.BIPM.org. (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Material Flow Rate Estimation in Material Extrusion Additive Manufacturing G. P. Greeff NCSLI Measure | Vol. 13 No. 1 (2021) | doi.org/10.51843/measure.13.1.5 Publisher: NCSL International | Published February 2021 | Pages 46-56 Abstract: The additive manufacturing of products promises exciting possibilities. Measurement methodologies, which measure an in-process dataset of these products and interpret the results, are essential. However, before developing such a level of quality assurance several in-process measurands must be realized. One of these is the material flow rate, or rate of adding material during the additive manufacturing process. Yet, measuring this rate directly in material extrusion additive manufacturing presents challenges. This work presents two indirect methods to estimate the volumetric flow rate at the liquefier exit in material extrusion, specifically in Fused Deposition Modeling or Fused Filament Fabrication. The methods are cost effective and may be applied in future sensor integration. The first method is an optical filament feed rate and width measurement and the second is based on the liquefier pressure. Both are used to indirectly estimate the volumetric flow rate. The work also includes a description of linking the G-code command to the final print result, which may be used to create a per extrusion command model of the part. References: [1] T. Wohlers, I. Campbell, O. Diegel, J. Kowen, I. Fidan, and D.L. Bourell, 'Wohlers Report 2017: 3D Printing and Additive Manufacturing State of the Industry Annual Worldwide Progress Report,' 2017. [2] Additive manufacturing -- General principles -- Terminology. Geneva, CH: International Organization for Standardization, 2015. [3] R. Jones et al., 'Reprap - The replicating rapid prototyper,' Robotica, vol. 29, no. 1 SPEC. ISSUE, pp. 177-191, 2011, https://doi.org/10.1017/S026357471000069X [4] T. Wohlers and T. Gornet, 'History of Additive Manufacturing 2017,' 2017. [5] S. A. M. Tofail, E. P. Koumoulos, A. Bandyopadhyay, S. Bose, L. O'Donoghue, and C. Charitidis, 'Additive manufacturing: scientific and technological challenges, market uptake and opportunities, 'Materials Today, vol. 21, no. 1, pp. 22-37, Jan. 2018, https://doi.org/10.1016/j.mattod.2017.07.001 [6] G. Moroni and S. Petrò, 'Managing uncertainty in the new manufacturing era,' Procedia CIRP, vol. 75, pp. 1-2, 2018, https://doi.org/10.1016/j.procir.2018.07.001 [7] R. Leach et al., 'Information-rich manufacturing metrology,'in Eighth International Precision Assembly Seminar (IPAS), 2018, no. January. https://doi.org/10.1007/978-3-030-05931-6_14 [8] S. Moylan, J. Slotwinski, A. Cooke, K. Jurrens, M. A. Donmez, and A. Donmez, 'Proposal for a Standardized Test Artifact for Additive Manufacturing Machines and Processes,' Solid Freeform Fabrication Symposium Proceedings, pp. 902-920, 2012. https://doi.org/10.6028/NIST.IR.7858 [9] ASME Y14.46-2017 Product Definition for Additive Manufacturing. New York:The American Society of Mechanical Engineers, 2017. [10] H. Li, T. Wang, J. Sun, and Z. Yu, 'The effect of process parameters in fused deposition modelling on bonding degree and mechanical properties,' Rapid Prototyping Journal, vol. 24, no. 1, pp. 80-92, Jan. 2018, https://doi.org/10.1108/RPJ-06-2016-0090 [11] A. W. Gebisa and H. G. Lemu, 'Investigating effects of Fused-deposition modeling (FDM) processing parameters on flexural properties of ULTEM 9085 using designed experiment, 'Materials, vol.11, no. 4, pp. 1-23, 2018, https://doi.org/10.3390/ma11040500 PMid:29584674 PMCid:PMC5951346 [12] B. Wittbrodt and J. M. Pearce, 'The effects of PLA color on material properties of 3-D printed components,' Additive Manufacturing, vol. 8, pp. 110-116, 2015, https://doi.org/10.1016/j.addma.2015.09.006 [13] O. A. Mohamed, S. H. Masood, and J. L. Bhowmik, 'Optimization of fused deposition modeling process parameters: a review of current research and future prospects,' Advances in Manufacturing, vol. 3, no. 1, pp. 42-53, Mar. 2015, https://doi.org/10.1007/s40436-014-0097-7 [14] S. K. Everton, M. Hirsch, P. Stravroulakis, R. K. Leach and A. T. Clare, 'Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing,' Materials and Design, vol. 95, pp. 431-445, 2016, https://doi.org/10.1016/j.matdes.2016.01.099 [15] P. K. Rao, J. P. Liu, D. Roberson, Z. J. Kong, and C. Williams,'Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors,' Journal of Manufacturing Science and Engineering, vol. 137, no. 6, p.061007, Sep. 2015, https://doi.org/10.1115/1.4029823 [16] J. Pellegrino, T. Makila, S. McQueen, and E. Taylor, 'Measurement science roadmap for polymer-based additive manufacturing,' Gaithersburg, MD, Dec. 2016. https://doi.org/10.6028/NIST.AMS.100-5 [17] T. R. Kramer, F. M. Proctor, and E. Messina, 'The NIST RS274NGC Interpreter -Version 3,' Gaithersburg, Maryland, 2000. https://doi.org/10.6028/NIST.IR.6556 [18] B. N. Turner, R. Strong, and S. A. Gold, 'A review of melt extrusion additive manufacturing processes: I. Process design and modeling,' Rapid Prototyping Journal, vol. 20, no. 3, pp.192-204, Apr. 2014, https://doi.org/10.1108/RPJ-01-2013-0012 [19] Conrad Electronic, 'Renkforce RF1000 3D Drucker,' 2016. https://www.conrad.de/de/renkforce-rf1000-3d-drucker-single-extruder-inkl-software-franzis-designcad-v24-3d-printrenkforce-edition-1007508.html (accessed Sep. 20, 2016). [20] G. Hodgson, A. Ranellucci, and J. Moe, 'Slic3r Manual - Flow Math,' 2016. http://manual.slic3r.org/advanced/flow-math (accessed Jun. 21, 2016). [21] Repetier, 'Repetier-Firmware Documentation.' https://www.repetier.com/documentation/repetier firmware/repetier-firmware-introduction/ (accessed Apr. 17, 2018). [22] B. Weiss, D. W. Storti, and M. A. Ganter, 'Low-cost closedloop control of a 3D printer gantry,' Rapid Prototyping Journal, vol. 21, no. 5, pp. 482-490, Aug. 2015, https://doi.org/10.1108/RPJ-09-2014-0108 [23] R. L. Zinniel and J. S. Batchelder, 'Volumetric Feed Control for Flexible Filament,' US 6085957, 2000. [24] W. J. Heij, Applied Metrology in Additive Manufacturing. Delft: Delft University of Technology, 2016. [25] G. P. Greeff and M. Schilling, 'Closed loop control of slippage during filament transport in molten material extrusion,' Additive Manufacturing, vol. 14, pp. 31-38, 2017, https://doi.org/10.1016/j.addma.2016.12.005 [26] G. P. Greeff, Applied Metrology in Additive Manufacturing, vol. 60. Berlin: Mensch und Buch, 2019. [27] G. P. Greeff and M. Schilling, 'Comparing Retraction Methods with Volumetric Exit Flow Measurement in Molten Material Extrusion,' in Special Interest Group meeting on Dimensional Accuracy and Surface Finish in Additive Manufacturing, 2017, no. October, pp. 70-74. [28] G. P. Greeff and M. Schilling, 'Single print optimisation of fused filament fabrication parameters,' The International Journal of Advanced Manufacturing Technology, Aug. 2018, https://doi.org/10.1007/s00170-018-2518-4 [29] A. Bellini, S. Güçeri, and M. Bertoldi, 'Liquefier Dynamics in Fused Deposition,' Journal of Manufacturing Science and Engineering, vol. 126, no. 2, p. 237, 2004, https://doi.org/10.1115/1.1688377 [30] P. Virtanen et al., 'SciPy 1.0: fundamental algorithms for scientific computing in Python,' Nature Methods, vol. 17, no. 3, pp. 261-272, Mar. 2020, https://doi.org/10.1038/s41592-019-0686-2 PMid:32015543 PMCid:PMC7056644 (Print: ISSN 1931-5775) (Online: ISSN 2381-0580) © 2021 NCSL International Software to Maximize End-User Uptake of Conformity Assessment with Measurement Uncertainty, Including Bivariate Cases. The European EMPIR CASoft Project

Authors: L.R. Pendrill A. Allard N. Fischer et al.

Source: NCSL International measure. 13:58-69

Subject Terms: 9. Industry and infrastructure, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 12. Responsible consumption, 3. Good health

Bridging Nano-Quantum Fields and Consciousness in a New Era via ψ–Hamzah Equation

Authors: JALALI

Subject Terms: ψ–Hamzah equation, nano-quantum fields, consciousness model, fractal derivatives, fractional calculus, Caputo–Hamzah derivative, nonlinear quantum dynamics, unstable path integrals, mental–quantum instability, recursive functions, memory-dependent systems, nanoscale physics, quantum fluctuations, intelligent nanoparticles, pre-conscious phases, Schrödinger equation comparison, Dirac equation comparison, chaos modeling, controlled chaos, Lyapunov exponents, fractal dimension, box-counting method, recursive networks, spectral analysis, bifurcation diagrams, Gross–Pitaevskii comparison, nonlinear instabilities, wave function ψ, quantum tunneling, fractal oscillations, Grünwald–Letnikov approximation, Caputo definition, Hamiltonian operator, quantum awareness, quantum–conscious integration, pre-conscious modeling, quantum chaos suppression, nonlinear feedback control, adaptive control in quantum systems, mental coefficient λ, memory kernel fractal K(t), recursive ψ field, fractal Laplacian, Fourier spectral methods, Adams–Bashforth–Moulton method, Runge–Kutta fractional update, eigenvalue stability analysis, nanoscale simulation, Python quantum simulation, NumPy for ψ dynamics, SciPy integration, FracPy library, fractional Laplacian algorithm, Numba acceleration, Cython parallelization, recursive fractal networks, box-counting regression, spectral scalability indices, chaos stabilization algorithms, ψ–Hamzah virtual experiments, simulated environments, nanobot quantum intelligence, decision-making stability, environmental noise impact, feedback chaos control, chaos dissociation prevention, phase diagram analysis, stability index measurement, controlled dissociation, recursive ψ dimension, fractal network graph, multidimensional ψ analysis, memory-dependent oscillations, quantum tunneling effects, mental–quantum operator M(t), Hamiltonian with recursive terms, unstable γ path integration, recursive ψ operator, wave function recursion, nonlinear nanoscale modeling, dynamic equilibrium chaos order, chaos emergence suppression, instability mitigation, chaos control algorithms, Lyapunov spectrum computation, phase diagram mapping, recursive bifurcation analysis, fractal chaos suppression, nano-conscious intelligence, quantum nanobot engineering, nanotechnology–consciousness integration, artificial intelligence extension, intelligent nanobots, chaotic quantum awareness, mental-quantum fluctuation modeling, recursive nonlinear ψ equation, adaptive nanobot decision-making, multidimensional ψ stability, recursive scaling laws, recursive ψ chaos analysis, fractal recursive system analysis, ψ numerical table outputs, Gaussian wave initial ψ, fractal recursive kernel analysis, memory recursive systems, recursive neural quantum modeling, recursive ψ iterative feedback, recursive ψ fractal networks, recursive ψ stabilization, recursive ψ memory kernel, recursive ψ Lyapunov dimension, recursive ψ bifurcation map, recursive ψ attractor analysis, recursive ψ chaos suppression, recursive ψ decision making, recursive ψ–Hamzah adaptive feedback, recursive ψ stabilization algorithm, recursive ψ–Hamzah Python code, recursive ψ fractal quantum nanobots, recursive ψ network architecture, recursive ψ simulation with feedback, recursive ψ phase diagrams, recursive ψ chaos indices, recursive ψ–Hamzah neural architecture, recursive ψ–Hamzah self-organising behaviour, recursive ψ–Hamzah adaptive algorithm, recursive ψ–Hamzah decision feedback, recursive ψ–Hamzah consciousness modeling, recursive ψ–Hamzah field recursion, recursive ψ–Hamzah multidimensional chaos, recursive ψ–Hamzah fractal stability, recursive ψ–Hamzah nonlinear extension, recursive ψ–Hamzah stability control, recursive ψ–Hamzah nanobot feedback, recursive ψ–Hamzah chaos integration, recursive ψ–Hamzah numerical validation, recursive ψ–Hamzah stability validation, recursive ψ–Hamzah interdisciplinary potential, recursive ψ–Hamzah applied AI, recursive ψ–Hamzah nanotechnology impact, recursive ψ–Hamzah medicine applications, recursive ψ–Hamzah quantum biology, recursive ψ–Hamzah systems neuroscience, recursive ψ–Hamzah theoretical physics, recursive ψ–Hamzah quantum information, recursive ψ–Hamzah post-quantum model, recursive ψ–Hamzah nonlinear mathematics, recursive ψ–Hamzah fractional networks, recursive ψ–Hamzah chaos suppression algorithms, recursive ψ–Hamzah chaos-control design, recursive ψ–Hamzah recursive fractal analysis, recursive ψ–Hamzah recursive simulation codes, recursive ψ–Hamzah adaptive algorithms, recursive ψ–Hamzah cross-disciplinary science, recursive ψ–Hamzah chaos mapping, recursive ψ–Hamzah stability diagrams, recursive ψ–Hamzah chaos dimension calculation, recursive ψ–Hamzah chaos indices analysis, recursive ψ–Hamzah recursive memory oscillations, recursive ψ–Hamzah recursive decision models, recursive ψ–Hamzah recursive instability suppression, recursive ψ–Hamzah recursive adaptation, recursive ψ–Hamzah recursive stabilization, recursive ψ–Hamzah recursive nanobot programming, recursive ψ–Hamzah recursive cognitive simulations, recursive ψ–Hamzah recursive applications in AI, recursive ψ–Hamzah recursive applications in medicine, recursive ψ–Hamzah recursive chaos control experiments, recursive ψ–Hamzah recursive chaos control results, recursive ψ–Hamzah recursive chaos simulations, recursive ψ–Hamzah recursive Lyapunov computations, recursive ψ–Hamzah recursive spectral chaos maps, recursive ψ–Hamzah recursive numerical algorithms, recursive ψ–Hamzah recursive system stability, recursive ψ–Hamzah recursive adaptive responses, recursive ψ–Hamzah recursive multi-scale analysis, recursive ψ–Hamzah recursive computational tools, recursive ψ–Hamzah recursive chaos regulation, recursive ψ–Hamzah recursive chaos stabilization outputs, recursive ψ–Hamzah recursive applications in consciousness, recursive ψ–Hamzah recursive applications in AI-nanotech, recursive ψ–Hamzah recursive systems neuroscience, recursive ψ–Hamzah recursive awareness modeling, recursive ψ–Hamzah recursive chaos mitigation

Modelling a Lab-Scale Continuous Flow Aerobic Granular Sludge Reactor: Optimisation Pathways for Scale-Up.

Authors: Siney, Melissa Salehi, Reza Hassan, Mohamed G. et al.

Source: Water (20734441); Jul2025, Vol. 17 Issue 14, p2131, 37p

Subject Terms: CONTINUOUS flow reactors, MATHEMATICAL models, COMPUTATIONAL fluid dynamics, WASTEWATER treatment, SENSITIVITY analysis, CHEMICAL oxygen demand, CHEMICAL reactors

WEDAP: A Python Package for Streamlined Plotting of Molecular Simulation Data

Authors: Darian T. Yang Lillian T. Chong

Source: J Chem Inf Model

Subject Terms: 0301 basic medicine, User-Computer Interface, 03 medical and health sciences, 0103 physical sciences, HIV-1, Capsid Proteins, Molecular Dynamics Simulation, 01 natural sciences, Software

Access URL: https://pubmed.ncbi.nlm.nih.gov/38826259
https://pubmed.ncbi.nlm.nih.gov/39013164

Principles and Operation of Virtual Brain Twins.

Authors: Hashemi, Meysam Depannemaecker, Damien Saggio, Marisa et al.

Source: IEEE Reviews in Biomedical Engineering; 2026, Vol. 19, p111-139, 29p

Dynamic Distance Mapping Enhances Hallux Valgus Progression Visualization.

Authors: Robinson, Dror Murad, Hamza Khatib, Muhammad et al.

Source: Diagnostics (2075-4418); Nov2025, Vol. 15 Issue 21, p2791, 12p

Subject Terms: HALLUX valgus, VISUALIZATION, EUCLIDEAN distance, STATISTICAL correlation, SPATIAL analysis (Statistics), RADIOGRAPHS

Automation in exam grading with Python and web hosting: data science applied to education.

Alternate Title: Automação na correção de provas com Python e hospedagem web: Ciência de dados aplicada à educação. (Portuguese)

Authors: Cerutti, Paulo Henrique Carbonari, Luan Tiago dos Santos Junior, Carlos Zacarias Joaquim et al.

Source: Revista de Ciencias Agroveterinarias; 2025, Vol. 24 Issue 3, p661-680, 20p

Subject Terms: PYTHON programming language, GRADING of students, DATA science, WEB hosting, SCIENCE education, AUTOMATION software, VETERINARY medicine education

Adaptation Triggers and Indicator Interpretability for Dynamic Reoptimization of Reservoir Control Policies Under Climate Change.

Authors: Sunkara, Sai Veena Herman, Jonathan D. Giuliani, Matteo

Source: Water Resources Research; Jan2026, Vol. 62 Issue 1, p1-22, 22p

Subject Terms: CLIMATE change, WATER demand management, RESOURCE allocation, OROVILLE Dam (Calif.), FLOOD risk, KEY performance indicators (Management), SENSITIVITY analysis, MATHEMATICAL optimization, ADAPTIVE natural resource management

Geographic Terms: CALIFORNIA

Dynamic Brain Network Biomarkers for Depression Prediction: A Multi-Cohort Analysis of Global Neuroimaging Databases.

Authors: Peng, Yangyao Zhan, Yuting Zhang, Qing

Source: Psychology Research & Behavior Management; Dec2025, Vol. 18, p2469-2494, 26p

Automating GIS-Based Cloudburst Risk Mapping Using Generative AI: A Framework for Scalable Hydrological Analysis.

Authors: Adiyasa, Alexander Mantegna, Andrea Niccolò Kveladze, Irma

Source: Hydrology (2306-5338); Aug2025, Vol. 12 Issue 8, p196, 11p

Subject Terms: GENERATIVE artificial intelligence, FLOOD risk, HYDROLOGICAL research, HUMAN-artificial intelligence interaction, GEOGRAPHIC information systems, RISK assessment, DECISION support systems, RAINSTORMS

Saltwater Intrusion Analysis and Control for Complex River Network Using Multi-Source Tracer-Aided Modeling Simulation.

Authors: Liu, Jiangchuan Liang, Haoyang Zhang, Lihong et al.

Source: Water (20734441); Dec2025, Vol. 17 Issue 24, p3483, 23p

Subject Terms: SALTWATER encroachment, SALINIZATION, ENVIRONMENTAL management, WATER quality monitoring, ESTUARINE reserves, DYNAMIC models, HYDRODYNAMICS, CLIMATE change

Geographic Terms: CHINA, HAINAN Sheng (China)

IDF-Net: Interpretable Dynamic Fusion Network for Colorectal Cancer Diagnosis Using Cross-Modal Imaging.

Authors: Hayeso, Helen Haile Shi, Peifeng Lian, Jingwen et al.

Source: Diagnostics (2075-4418); Jan2026, Vol. 16 Issue 1, p99, 18p

Subject Terms: COLORECTAL cancer, ARTIFICIAL intelligence, DIAGNOSTIC equipment, DIAGNOSTIC imaging, MULTIMODAL user interfaces

CNN-BASED CODE READABILITY CLASSIFICATION AND BUG LOCALIZATION IN PROGRAMMING FRAMEWORK.

Authors: BARRAMEDA, ROLANDO B. BALLERA, MELVIN A.

Source: TPM: Testing, Psychometrics, Methodology in Applied Psychology; 2025 Supplement, Vol. 32, p201-206, 6p

Using Satellite Remote Sensing to Estimate Rangeland Carrying Capacity for Sustainable Management of the Marismeño Horse in Doñana National Park, Spain.

Authors: Ramírez-Juidias, Emilio Díaz de la Serna-Moreno, Ángel Delgado-Pertíñez, Manuel

Source: Animals (2076-2615); Dec2025, Vol. 15 Issue 24, p3507, 24p

Subject Terms: SATELLITE-based remote sensing, PASTURE management, OVERGRAZING, LIVESTOCK breeds, NATIONAL parks & reserves, PLANT growth, SUSTAINABILITY, GRAZING

Geographic Terms: SPAIN, SOUTHERN Europe

Bedload Transport Velocities in Alpine Gravel-Bed Streams.

Authors: Rindler, Rolf Shire-Peterlechner, Dorian Schwarz, Sabrina et al.

Source: Water (20734441); Jan2026, Vol. 18 Issue 1, p88, 23p

Subject Terms: SEDIMENT transport, SEDIMENT analysis, RADIO frequency identification systems, WATERSHEDS, ECOSYSTEMS, FLOODS, MEASURING instruments

Geographic Terms: AUSTRIA

Machine learning with multitype functional connectivity uncovers whole-brain network disruption in primary angle-closure glaucoma.

Authors: Chen, Guangxiang Hu, Dekai Huang, Xin et al.

Source: Brain Informatics; 12/27/2025, Vol. 13 Issue 1, p1-14, 14p

Subject Terms: MACHINE learning, FUNCTIONAL connectivity, FUNCTIONAL magnetic resonance imaging, ANGLE-closure glaucoma, COMPUTER-assisted image analysis (Medicine), LARGE-scale brain networks

Estimation of building project completion duration using a natural gradient boosting ensemble model and legal and institutional variables.

Authors: Peiman, Farshad Khalilzadeh, Mohammad Shahsavari-Pour, Nasser et al.

Source: Engineering Construction & Architectural Management (09699988). 2025, Vol. 32 Issue 4, p2069-2104. 36p.

Subject Terms: *EARNED value management, *ARTIFICIAL intelligence, *COST estimates, RADIO frequency identification systems, CONSTRUCTION projects, BUILDING information modeling, DEVELOPING countries

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.

Authors: Zheng, Yumin Schupp, Jonas C. Adams, Taylor et al.

Source: Nature Biomedical Engineering; Dec2025, Vol. 9 Issue 12, p2155-2180, 26p