Additional file 2 simulated_trial.RData. Simulated data file for the R code in Analysis_CRT.R.

Context

The finite element method (FEM) is the most widely used method for solving problems of engineering and mathematical models. But this method is time consuming, especially when the structural model is complicated. For the strength assessment with new loads based on same models, machine learning or neural network can be used for the prediction. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5335020%2F10ccdc195a4f4ce5a945ebb2974e18ff%2FFEA.jpg?generation=1601264196850016&alt=media" alt="">

Content

400 samples created, and each sample includes 6 inputs and 80 outputs. Each output represents a node stress of the FE model, which can be used for the strength assessment.

| ID | Input 1 |Input 2 |Input 3 |Input 4 |Input 5 |Input 6 |Output 1~80 | | ---------- | --- | | | | | | | | | |

Acknowledgements

I have created some regression models, and I hope anyone can help me for improving the precision. THX in advance, this will help me a lot during my work.

Inspiration

Yearly citation counts for the publication titled "Modelling boundary and nonlinear effects in porous media flow".

Additional file 1 Analysis_CRT.R. R code for a sigmoid random effects model for the analysis of a CRT of malaria prevalence.

This repository regroups the data associated with the preprint "Size effects in the toughening of brittle materials by heterogeneities: a non-linear analysis of front deformations" by Mathias Lebihain, Manish Vasoya, and Véronique Lazarus. It contains all the script required to generate the data and produce the figures of Sections 3 and 4. Sections 1, 2, and 5 do not use any data set. All comments or questions are welcomed at mathias.lebihain@enpc.fr.

This data release provides source code and an R workspace with functions comprising a non-linear baseflow separation model, calibrated values of parameters and estimates of the baseflow component of daily streamflow at selected streamflow gages. Parameter values were determined by calibration of the model. Estimates of the baseflow component include daily values and the total baseflow as a fraction of streamflow for the analysis period. The file 'run_bf_sep.zip' in the directory 'Nonlinear Baseflow Model Source Code, Functions, and Scripts" has a complete set of model functions, parameters for 13,208 USGS streamflow gages, and a script to run the model. Instructions and software requirements for running the model are described in the file README.txt

Despite the various standard non-linear measurements used in autonomic modulation (AM) assessments usually being applied to long time-series, such analyses can sometimes be applied to shorter term series. To overcome this disadvantage, chaotic global methods were formulated by putting together heart rate variability (HRV) linear methods. Chaos provides information about vegetative function control related to cardiovascular risks. Applying this method can be useful to investigate the complexity of the health condition after resistance training protocols, as a therapeutic intervention in AM in metabolic syndrome individuals (MetS). This study aimed to compare the effects of two resistance training programs (conventional vs functional) in MetS using nonlinear analysis of AM. MetS subjects (n=50) of both sexes aged 40 to 60 years were randomly divided into two programs; a group of 12 people served as a control group. Both groups performed 30 sessions of training. AM was assessed in the chaos domain by chaotic global techniques. The main results showed that both resistance training, functional and conventional, increased chaos when compared to the control group, respectively, observed by chaotic forward parameter (CFP)1 (13.9±17.9 vs 12.8±14.4 vs -2.23±7.96; P≤0.05) and CFP3 (15.4±19.8 vs 21.9±13.2 vs -4.82±11.4; P≤0.05). In addition, 30 sessions of both resistance programs increased chaos, and non-linear analysis enabled discrimination of AM after interventions when compared to the control group.

This compressed file contains simulation programs, simulation results, figures that depict the results, as well as the animations that illustrate nonlinear effects in peak limiting current mode controlled converters. The simulations are performed using Python 2 programming language using pylab environment (numpy, scipy, matplotlib, ipython) under Ubuntu 18.04 operating system.

Abstract: This code has been used for the numerical experiments in the thesis "Error Analysis of Exponential Integrators for Nonlinear Wave-Type Equations" by Benjamin Dörich, see https://www.doi.org/10.5445/IR/1000130187. TechnicalRemarks: ## Readme This program is intended to reproduce the results from the thesis "Error Analysis of Exponential Integrators for Nonlinear Wave-Type Equations" by Benjamin Dörich Requirements The program is tested with 1) Ubuntu 16.04.7 LTS and Python 3.7.6 and the following version of its modules: - numpy - 1.15.4 - scipy - 1.4.1 - matplotlib - 3.2.1 - tikzplotlib - 0.9.6 (quasilinear only:) - dolfin - 2018.1.0 - fenics - 2018.1.0 2) Ubuntu 18.04.5 LTS and Python 3.6.9 and the following version of its modules: numpy - 1.19.2

This dataset stores the supporting data for the manuscript titled "Effect of Asymmetric Infills on Seismic Performance of Reinforced Concrete Frame Buildings". The manuscript is under review in the Journal of Earthquake Engineering. Abstract: "The torsion due to asymmetric placement of unreinforced masonry (URM) infills is often unnoticed because the stiffness and strength of the infills are usually ignored during design. This study estimates the effect of asymmetric placement of infills on the performance of 8-story reinforced concrete (RC) buildings designed for different code levels. Multiple stripe analysis (MSA) is used to assess the performance. The adverse effects of the infills generally overshadow the torsion introduced by the asymmetric layout, leading to consistently poor performance in buildings with more infilled frames. At higher intensities, most of the eccentricity is lost due to infill damage."

Stochastic Deep Material Networks as Efficient Surrogates for Stochastic Homogenisation of Non-linear Heterogeneous Materials This directory contains the data and algorithms generated in publication1 Table of Contents Dependencies and Prerequisites Structure of Repository Images/Geometries and IB-DMN training data of the 6 SVEs Stochastic analysis - Direct numerical simulations of SVEs Training of the reference IB-DMN Stochastic analysis - Stochastic IB-DMN Reproduce paper[^1] figures Dependencies and Prerequisites Python, pandas, matplotlib, texttabble and latextable are pre requisites for visualizing and navigating the data. For generating mesh and for vizualization, gmsh (www.gmsh.info) is required. For running simulations, cm3Libraries (http://www.ltas-cm3.ulg.ac.be/openSource.htm) is required. Instructions using apt & pip3 package manager Instructions for Debian/Ubuntu based workstations are as follows. python, pandas and dependencies sudo apt install python3 python3-scipy libpython3-dev python3-numpy python3-pandas matplotlib, texttabble and latextable pip3 install matplotlib texttable latextable Pytorch Without GPU pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu With GPU pip3 install torch torchvision torchaudio Libtorch (only when using cm3Libraries) Without GPU: In a local directory (e.g. ~/local with export TORCHDIR=$HOME/local/libtorch) wget wget https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-2.3.0%2Bcpu.zip unzip libtorch-shared-with-deps-2.1.1+cpu.zip With GPU: In a local directory (e.g. ~/local with export TORCHDIR=$HOME/local/libtorch) wget https://download.pytorch.org/libtorch/cu121/libtorch-shared-with-deps-2.1.1%2Bcu121.zip unzip libtorch-shared-with-deps-2.1.1+cu121.zip Structure of Repository 6SVE_Example: Images/Geometries and IB-DMN training data of the 6 SVEs. Vf_Mat: Training of the reference IB-DMN. Stochastic_DNS_LinearHardening: Stochastic analysis - Direct numerical simulations of SVEs Stochastic analysis - Stochastic IB-DMN: Stochastic analysis - Stochastic IB-DMN Images/Geometries and IB-DMN training data of the 6 SVEs: 6SVE_Example 6SVE_Example/6SVE_Data: Images/Geometries and IB-DMN training data of the 6 SVEs 6SVE_Example/6SVE_DNS: 6SVE_Example/6SVE_DNS/DNS_LinearHardening: Direct numerical simulations (require cm3Libraries) on the 6 SVEs in a finite-deformation setting: python3 RVE_Test.py TestKey = 'Shear' or TestKey = 'Tensile' or TestKey = 'UniStrain' in RVE_Test.py to run the test in uniaxial stress, shearing or uniaxial strain loading conditions Results are stored in 6SVE_Example/DNS_LinearHardening/Path_Res 6SVE_Example/6SVE_DNS/DNS_LinearHardening_SmallDefo: Direct numerical simulations (require cm3Libraries) on the 6 SVEs in a small-deformation setting: python3 RVE_Test.py Tensile = False or Tensile = True in RVE_Test.py to run the test in uniaxial stress or uniaxial strain loading conditions Results are stored in 6SVE_Example/6SVE_DNS/DNS_LinearHardening_SmallDefo/Path_Res 6SVE_Example/6SVE_DMN: 6SVE_Example/6SVE_DMN/NNW_Tool.py: Defines the class of IB-DMN and loss function Called in the following files (not stand alone) 6SVE_Example/6SVE_DMN/WriteSingleRVEPara.py: Training IB-DMN for single SVE Training = True for warm start or not level = 5 to select the IB-DMN level Results are written in datafile in directory 6SVE_Example/6SVE_DMN/DMNPara for nonlinear simulations 6SVE_Example/6SVE_DMN/SimulationDMN.py: Nonlinear IB-DMN simulations (require cm3Libraries) in a finite deformation setting Use the trained IB-DMN parameters stored in 6SVE_Example/6SVE_DMN/DMNPara TestKey = 'Shear' or TestKey = 'Tensile' or TestKey = 'UniStrain' in RVE_Test.py to run the test in uniaxial stress, shearing or uniaxial strain loading conditions level = 4, level = 5 or level = 6 specifies the level of the IB-DMN to be used SVE number and training case can be manually specified at the end of the file, e.g; f_Para = './DMNPara/rve6_Level'+str(level)+'_300Data.dat' and Id = prefix+'rve6_Level'+str(level)+'_'+Load+'Res300.csv' Results are stored in 6SVE_Example/6SVE_DMN/DMN_simulation 6SVE_Example/6SVE_DMN/SmallDefoDMN.py: Nonlinear IB-DMN simulations (require cm3Libraries) in a small deformation setting Use the trained IB-DMN parameters stored in 6SVE_Example/6SVE_DMN/DMNPara Results are stored in 6SVE_Example/6SVE_DMN/DMN_simulation_SmallDefo 6SVE_Example/6SVE_DMN/Plot_DNS_DMN.py: Plot the comparison of the results of nonlinear simulations using IB-DMN and DNS Load = 'Tensile' or Load = 'Shear' or Load = 'UniStrain' to vizualize the results in uniaxial stress, shearing or uniaxial strain loading conditions Use LargeDefo = True to plot results in finite-strain setting or use LargeDefo = False for the small strain setting (only Load = 'Tensile' is possible then) 6SVE_Example/6SVE_DMN/DMNPara_rve6.py: Training IB-DMN for SVE 6 with 300 sets of material properties Training = True for warm start or not level = 5 to select the IB-

Network of 46 papers and 66 citation links related to "Modelling boundary and nonlinear effects in porous media flow".

IntroductionThis study uses a non-linear model to explore the impact mechanism of change rates between internet search behavior and confirmed COVID-19 cases. The research background focuses on epidemic monitoring, leveraging internet search data as a real-time tool to capture public interest and predict epidemic development. The goal is to establish a widely applicable mathematical framework through the analysis of long-term disease data.MethodsData were sourced from the Baidu Index for COVID-19-related search behavior and confirmed COVID-19 case data from the National Health Commission of China. A logistic-based non-linear differential equation model was employed to analyze the mutual influence mechanism between confirmed case numbers and the rate of change in search behavior. Structural and operator relationships between variables were determined through segmented data fitting and regression analysis.ResultsThe results indicated a significant non-linear correlation between search behavior and confirmed COVID-19 cases. The non-linear differential equation model constructed in this study successfully passed both structural and correlation tests, with dynamic data fitting showing a high degree of consistency. The study further quantified the mutual influence between search behavior and confirmed cases, revealing a strong feedback loop between the two: changes in search behavior significantly drove the growth of confirmed cases, while the increase in confirmed cases also stimulated the public's search behavior. This finding suggests that search behavior not only reflects the development trend of the epidemic but can also serve as an effective indicator for predicting the evolution of the pandemic.DiscussionThis study enriches the understanding of epidemic transmission mechanisms by quantifying the dynamic interaction between public search behavior and epidemic spread. Compared to simple prediction models, this study focuses more on stable common mechanisms and structural analysis, laying a foundation for future research on public health events.

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

meditation task

Map type

T

This code is used for the numerical experiment in Section 6 of the paper "A unified error analysis for nonlinear wave-type equations with application to acoustic boundary conditions" by Jan Leibold. The computations are done in C++ using the Finite Element library deal.II; the plots then are generated with Matlab. To use this code, deal.II (release 9.3.2) has to be installed, cf. https://www.dealii.org/9.3.0 In order to compile the program, open a terminal session in this folder and call "cmake -DDEAL_II_DIR=/path/to/deal.II .". Next, call "make release" and then "make" to compile the files. Finally, run the program with "make run". This performs the computations and generates the file "results" containing the results of the numerical experiments. After that, the plots can be generated with the Matlab Script 'plot_figures.m'.

R-R intervals (ms) values of the groups.

I offer here both dataset and computing code related to a stem analysis algorithm to reconstruct height growth of trees. First, the dataset has time series records of tree height for Nothofagus alpina ("rauli"), N. dombeyi ("coigue"), N. obliqua ("roble"), and Pseudotsuga menziesii ("Douglas-fir"). The data come from stem analysis sample trees in both southern Chile and the Inland Northwest, USA. These trees are part of the ones used in an article about a new algorithm for reconstructing tree height growth. The article is published in Methods in Ecology and Evolution (https://doi.org/10.1111/2041-210x.13616). Second, I provide an R code implementing the proposed algorithm for a given dataset as example.

Methods See methods and accompanying paper in the following article for more details.

Salas-Eljatib C. 2021. A new algorithm for reconstructing tree height growth with stem analysis data. Methods in Ecology and Evolution 12: 2008-2016.

Abstract In this paper, a geometrically nonlinear analysis of functionally graded material (FGM) shells is investigated using Abaqus software. A user defined subroutine (UMAT) is developed and implemented in Abaqus/Standard to study the FG shells in large displacements and rotations. The material properties are introduced according to the integration points in Abaqus via the UMAT subroutine. The predictions of static response of several non-trivial structure problems are compared to some reference solutions in order to verify the accuracy and the effectiveness of the new developed nonlinear solution procedures. All the results indicate very good performance in comparison with references.

Macroclimatic drivers, such as temperature and rainfall regimes, greatly influence ecosystem structure and function in tidal saline wetlands. Understanding the ecological influence of macroclimatic drivers is important because it provides a foundation for anticipating the effects of climate change. Tidal saline wetlands include mangrove forests, salt marshes, and salt flats, which occupy similar geomorphic settings but different climatic regimes. However, most global- or regional-scale analyses have treated these wetlands as independent systems. Here we used climate and literature-derived ecological data from all three systems, collected across targeted regional-scale macroclimatic gradients, to test hypotheses regarding macroclimatic controls of tidal saline wetland ecosystem properties, specifically canopy height, above-ground biomass, productivity, decomposition, soil carbon density, and soil carbon accumulation. We quantified region-specific climate based ecological thresholds for three data-rich transition zones including eastern North America, eastern Australia, and western Gulf of Mexico. The results of our analyses suggest that small macroclimatic changes might have large ecological implications near climatic thresholds. Our results also demonstrate that relationships between macroclimatic drivers and the ecosystem attributes of tidal saline wetlands are likely to be region-specific. The ecosystem-climate linkages revealed by our analysis should help to characterize important climatic thresholds for ecological regime shifts and could also be used to identify and target for conservation critical transition areas that may be especially sensitive to climate change.

BackgroundCupping therapy has been indicated effective in reducing muscle fatigue after 24 h based on the spectral analyses of surface electromyography (sEMG). However, there is no sufficient evidence showing changes of sEMG nonlinear indexes at more time points after cupping therapy. Furthermore, it is unclear whether the intervention timings of cupping therapy affect the recovery from muscle fatigue. The purpose of this study was to use the sEMG nonlinear analysis to assess the difference of time response of cupping therapy between different intervention timings after muscle fatigue.Materials and methodsThis randomized controlled trial recruited 26 healthy volunteers. Cupping therapy (−300 mmHg pressure for 5 min by the 45 mm-diameter cup) was applied before (i.e., pre-condition) or after (i.e., post-condition) muscle fatigue induced by performing repeated biceps curls at 75% of the 10 repetitions of maximum (RM) on the non-dominant upper extremity. Subjects were randomly allocated to the pre-condition group or the post-condition group. The sEMG signals during the maximal voluntary isometric contractions (MVC) of the biceps were recorded at four time points (i.e., baseline; post 1: immediate after cupping-fatigue/fatigue-cupping interventions; post 2: 3 h after cupping-fatigue/fatigue-cupping interventions; post 3: 6 h after cupping-fatigue/fatigue-cupping interventions). Two nonlinear sEMG indexes (sample entropy, SampEn; and percent determinism based on recurrence quantification analysis, �T) were used to evaluate the recovery from exercise-introduced muscle fatigue. The Friedman test followed by the Nemenyi test and the Mann-Whitney U test were applied in statistics.ResultsThe SampEn and �T change rate did not show any significant differences at four time points in the pre-condition group. However, there were significant delayed effects instead of immediate effects on improving muscle fatigue in the post-condition group (SampEn change rate: baseline 0.0000 ± 0.0000 vs. post 2 0.1105 ± 0.2253, p < 0.05; baseline 0.0000 ± 0.0000 vs. post 3 0.0627 ± 0.4665, p < 0.05; post 1–0.0321 ± 0.2668 vs. post 3 0.0627 ± 0.4665, p < 0.05; and �T change rate: baseline 0.0000 ± 0.0000 vs. post 2–0.1240 ± 0.1357, p < 0.01; baseline 0.0000 ± 0.0000 vs. post 3 0.0704 ± 0.6495, p < 0.05; post 1 0.0700 ± 0.3819 vs. post 3 0.0704 ± 0.6495, p < 0.05). Moreover, the SampEn change rate of the post-condition group (0.1105 ± 0.2253) was significantly higher than that of the pre-condition group (0.0006 ± 0.0634, p < 0.05) at the post 2 time point. No more significant between-groups difference was found in this study.ConclusionThis is the first study demonstrating that both the pre-condition and post-condition of cupping therapy are useful for reducing muscle fatigue. The post-condition cupping therapy can e ffectively alleviate exercise-induced muscle fatigue and there is a significant delayed effect, especially 3 h after the interventions. Although the pre-condition cupping therapy can not significantly enhance muscle manifestations, it can recover muscles into a non-fatigued state.