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Data for: Information accessibility, accounting manipulation, and sustainable development of digital enterprises: Based on double moderating effect model and panel PSM-DID method

A theoretical mechanism was analyzed from the micro perspective of the enterprise to explore how information accessibility moderates the effect of accounting manipulation on the sustainable development of digital enterprises. Using data from 1200 listing digital enterprises in China and the DEA-Malmquist index method, the efficiency value of digital enterprises in 2007–2021 was estimated to represent the index of sustainable development of digital enterprises. The accounting manipulation was detected using the panel PSM-DID method based on the Administrative Measures for the Recognition of High-tech Enterprise's policy. The information accessibility value was estimated based on the MDA method. Empirical studies were conducted using text analysis, the panel PSM-DID method, and the double moderating effect model. The results showed that: (1) Accounting manipulation had a negative impact on the sustainable development of "true" digital enterprises and the "fake" digital enterprises; (2) Information accessibility directly and positively enhanced the technological progress and scale efficiency of digital enterprises, and its moderating effect was heterogeneous, with a significant moderating effect on the "true" digital enterprises and a negative effect on the "fake" ones.

opencc-by-4.0Dec 2023View details →

A Refined Supply-demand Framework to Quantify variability in Ecosystem Services Related to Surface Water in support of Sustainable Development Goals

The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang.

Data from: Shift from an extensive to an intensive root nutrient-acquisition mode with stand development of three Pinus species

Plant roots employ diverse strategies to acquire soil nutrients, including direct nutrient uptake through absorptive fine roots and root hairs, scavenging nutrients by forming symbioses with mycorrhizal fungi, and mining nutrients by releasing root exudates. However, whether these three strategies are differently coordinated among phylogenetically closely-related tree species and how this coordination shifts with stand development remains largely unclear. To fill these knowledge gaps, we measured 13 root morphological, architectural, physiological and mycorrhizal traits tightly related to nutrient-acquisition of three Pinus species (P. sylvestris var. mongolica, P. densiflora and P. tabuliformis) at young (20-year-old) and mature (50-year-old) stages planted in nutrient-impoverished sandy soils, in Northeast China. We found that young trees had thinner absorptive fine roots and a higher specific root length than mature trees across three Pinus species, indicating that roots become morphologically less efficient in "do it yourself" scavenging nutrients during stand development. Moreover, young stands had greater root length density and root area index, and mature stands had faster root-exudation rates, suggesting that young stands rely more on expanding soil volumes to scavenge nutrients and mature stands depend more on root exudation to 'mine' nutrients. The three Pinus species exhibited different nutrient-acquisition strategies at the mature stage; P. densiflora had higher root length density and root area index, and the other two Pinus species had greater ectomycorrhizal colonization rates. Synthesis. Our findings highlight that phylogenetically closely-related tree species may exhibit different nutrient-acquisition strategies, and suggest a shift from an extensive nutrient-acquisition mode depending more on absorptive fine roots to an intensive nutrient-acquisition mode relying more on root exudation during stand development.

Data for: Adaptive tail-length evolution in deer mice is associated with differential Hoxd13 expression in early development

Variation in the size and number of axial segments underlies much of the diversity in animal body plans. Here, we investigate the evolutionary, genetic, and developmental mechanisms driving tail-length differences between forest and prairie ecotypes of deer mice (Peromyscus maniculatus). We first show that long-tailed forest mice perform better in an arboreal locomotion assay, consistent with tails being important for balance during climbing. We then identify six genomic regions that contribute to differences in tail length, three of which associate with caudal vertebra length and the other three with vertebra number. For all six loci, the forest allele increases tail length, indicative of the cumulative effect of natural selection. Two of the genomic regions associated with variation in vertebra number contain Hox gene clusters. Of those, we find an allele-specific decrease in Hoxd13 expression in the embryonic tail bud of long-tailed forest mice, consistent with its role in axial elongation. Additionally, we find that forest embryos have more presomitic mesoderm than prairie embryos, and that this correlates with an increase in the number of neuromesodermal progenitors (NMPs), which are modulated by Hox13 paralogs. Together, these results suggest a role for Hoxd13 in the development of natural variation in adaptive morphology on a microevolutionary timescale.

Leaf and shoot apical meristem transcriptomes of quinoa (Chenopodium quinoa Willd.) in response to photoperiod and plant development

Our study focused on identifying key genes regulating flowering time and photoperiod response in quinoa. We examined the timing of photoperiod-induced floral transition and analyzed transcriptomes in photoperiod-sensitive and -insensitive quinoa accessions' leaf and shoot apical meristems. Histological analysis showed that floral transition in quinoa initiates two to three weeks after sowing. We found four groups of differentially expressed genes annotated in the QQ74-V2 reference genome that responded to plant development and floral transition, (i) 222 genes responsive to photoperiod in leaves, (ii) 1,812 genes differentially expressed between accessions under long-day conditions in leaves, (iii) 57 genes responding to developmental changes between weeks under short-day conditions in leaves, and (iv) 911 genes responding to floral transition within the shoot apical meristem. Interestingly, among numerous candidate genes, two putative FT orthologues and others (e.g., SOC1, COL, AP1) have been reported as key regulators of flowering time in other species. Additionally, we used co-expression networks to associate novel transcripts to a putative biological process based on the annotated genes within the same co-expression cluster. The candidate genes in this study would benefit quinoa breeding by identifying and integrating their beneficial haplotypes in crossing programs to develop adapted cultivars to diverse environmental conditions. We examined the timing of photoperiod-induced floral transition and analyzed transcriptomes in photoperiod-sensitive and -insensitive quinoa accessions' leaf and shoot apical meristems. Histological analysis revealed floral transition initiating two to three weeks after sowing. Differentially expressed genes were categorized in the QQ74-V2 reference genome, encompassing 222 genes responsive to photoperiod in leaves, 1,812 genes under long-day conditions, 57 genes during short-day conditions, and 911 genes during floral transition in shoot apical meristems. Notably, among numerous candidates, two putative FT orthologues and others (e.g., SOC1, COL, AP1) implicated in flowering time regulation in various species were identified. Co-expression networks associated novel transcripts with putative biological processes based on annotated genes in the same cluster. The candidate genes identified have potential applications in quinoa breeding, facilitating the incorporation of beneficial haplotypes in crossbreeding programs to develop cultivars adapted to diverse environmental conditions.

opencc-zeroMar 2024View details →

Genome evolution is associated with nutrition-responsive regulatory development in horned dung beetles

The Scarabaeinae, or true dung beetles, are a hyper-diverse clade of insects of ecological, evolutionary, and agricultural significance and have long served as informative models of evolutionary ecology and development. Perhaps the most conspicuous of their unique traits are head horns, novel structures that serve as secondary sexual weapons, exhibit extraordinary developmental plasticity, and have fueled one of the most dramatic morphological radiations in the animal kingdom. In this study, we investigate the evolutionary basis for dung beetle traits - including horns - via comparative genomic and developmental assays. We present chromosome-level genome assemblies of three dung beetle species in the species-rich Onthophagini tribe (> 2500 extant species) including Onthophagus taurus, Onthophagus sagittarius, and Digitonthophagus gazella. Contrasting these assemblies with seven other species across the order Coleoptera identifies rapidly evolving gene families associated with metabolic regulation of developmental plasticity and metamorphosis. Intraspecific comparisons of chromatin accessibility in developing head horns of O. taurus identify distinct cis-regulatory architectures underlying sex- and nutrition-responsive development of this novel trait, including a large proportion of recently evolved regulatory elements sensitive to horn morph determination. Binding motifs of diverse developmental transcription factors are enriched in these nutrition-responsive open chromatin regions, including the early embryonic patterning gene twist. Using RNA interference (RNAi), we show twist has been co-opted into the beetle horn regulatory network to mediate differential horn morphogenesis in alternate male morphs via its interactions with nutrition-sensitive DNA-binding sites, highlighting the utility of this approach in identifying new developmental regulators of morphological evolution. These results demonstrate gene networks are highly evolvable transducers of environmental and genetic signals critical for the formation and diversification of developmental traits, established in part by condition-responsive chromatin accessibility. Further, this work provides new reference-quality genome assemblies of three dung beetles that will bolster future developmental, ecological, and evolutionary studies of this insect group.

openmit-licenseMar 2024View details →

A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions

The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and  14 days post-booster vaccination). The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions. A) Multiomics CMI-PB dataset: We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and  14 days post booster vaccination. With the obtained samples processed, we generated omics data by: <ul> <li> Bulk PBMCs transcriptomics, </li> <li> Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors, </li> <li> Cell frequency in PBMCs using flow cytometry, </li> <li> Tdap-specific antibodies levels </li> </ul> B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv” C) Datasets for model building and making predictions:     Data files are divided into two categories: 1) raw dataset and 2) computable matrices. <ol> <li> Raw dataset: This raw-most dataset is divided into training and test datasets.  </li> <li> Computable matrices: There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset. </li> </ol> D) Submission evaluation This folder contains all submitted models with ranking files and code for evaluating these models. To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.

opencc-by-4.0Dec 2023View details →

Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method

This dataset supports the article "Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method" . The data are provided in MatLab format.

opencc-zeroMar 2024View details →

Osmia lignaria larval development response to heatwave treatments

Heatwaves are expected to increase in frequency, intensity, and duration due to climate change. For organisms like insects with discrete development, sensitivity may differ among life stages. Thermal sensitivity is of particular concern for species like bees that provide critical ecosystem services. Although social bees moderate nest temperatures through worker behavior, solitary bees do not thermoregulate their nests, making immobile developing offspring especially vulnerable to such extreme events. We studied the effects of heatwaves on larval development in the solitary bee, Osmia lignaria, an important orchard pollinator and model species for solitary bee biology. We used a factorial design to assess the impacts of heatwave temperature and duration on larval mortality and development rate. Larvae were exposed to heatwaves under realistic diel temperature regimes, with daytime maxima of 31˚C or 37˚C for four or seven days at the beginning of development. Heatwave temperature strongly affected larval mortality. Exposure to 37˚C heatwaves increased larval mortality by 130%, but the cooler 31˚C heatwaves did not significantly impact mortality. Heatwave duration did not impact larval mortality. Larval development time also was affected by heatwave exposure. Compared to the no-heatwave-control, bees in the 31˚C heatwave developed faster, and bees in the 37˚C heatwave developed slower. Our study reveals the importance of stage-specific effects of extreme events and suggests that the timing and maximum temperature of projected heatwaves may be more detrimental to populations than heatwave duration.

opencc-zeroMar 2024View details →

Semi-structured interviews about sustainable and equitable development in the USA

We conducted semi-structured interviews with 23 individuals working in the broad field of sustainable and equitable urban development in the U.S.A. between August and September 2022. We used purposive and snowball sampling, targeting people with experience working in this field across multiple sectors, states, and types of organizations. Participants were informed and consented and interviews were audio-recorded and transcribed. Participants reported working in the following professions: Built environment, Transport & Mobility, Health and social care, Housing, Culture, Parks and Recreation, Water, Education, Energy, Workforce support, Sanitation, Waste, and Others. Participants' organization types included: local government, non-profit organizations, private sector, and others. Participants lived in various U.S. states.

Datos de la investigación: La voz del rehén: adapting creative sustainable development goal-based physical-artístico project during COVID pandemic situation

This article assesses the effects on creativity of an interdisciplinary physicall-artistic programme and their effects during pandemic context. The sample comprised 97 individuals –54 men and 43 women– with an average age of 21.37±1.56, all of whom were studying a Primary Education Teacher Training Degree at Zaragoza University –Spain–. It was a pre-experimental study with measures pre-post programme –adapted to pandemic situation, replacing the acrosport content to skipping rope, juggling and aerial dance–. The creative skills assessment instrument was the PIC-A. A Student’s T-test and ANOVA test were performed to discover significant differences in pre-post in both academic years. Results showed that: i) participants obtained significant improvements in total score of creativity in both academic years; ii) the adaptations made to the intervention programme during the pandemic year were effective in developing creativity. These studies confirm the importance of incorporating interdisciplinary creativity programmes into the university system.

opencc-by-4.0May 2023View details →

Challenges in Developing a Real-Time Bee-Counting Radar

Contained herein is the data used in preparation of the paper titled “Challenges in Developing a Real-time Bee-counting Radar.” These data were gathered as part of a PhD program provisionally titled “Machine Learning to Count and Predict Bee Behaviour and Movement” due for submission in early 2024 at Bangor University, Wales, United Kingdom. The purpose of this data was to evaluate the possibility of creating a radar system, with companion computing system, to classify honeybee movement the entrance of the hive in real-time. Samples from core classes were recorded both by hand and later by automatic extraction tools. These core classes included: Inward, outward, hovering, and no-bee samples.

Literature review - flood model development

This is a spreadsheet containing a review of common flood models and data used in studies between 1980-2023. "Model Physics": Collection of different simplification methods to the shallow water equation and the simulation scales (both space and time). "Coupled Model": Method of coupling flood models to atmospheric models, land surface models, human health models, socioeconomic models, geomorphological models, ecological models. "Model Data": Different direct/indirect measurements for flood model evaluation in these studies. "Flood Data": Curated commonly used flood evaluation data, ranging from in-situ, remote sensing, indirect ground measurements, and model simulated results.

DCASE 2024 Challenge Task 7 Development Dataset : Environmental Sound Scene Synthesis

<h1>Description</h1> This dataset comprises embeddings and captions utilized as the development dataset for <a href="https://dcase.community/challenge2024/task-sound-scene-synthesis">DCASE 2024 Challenge Task 7</a>, focusing on 'Environmental Sound Scene Synthesis.' The embeddings are derived from 60 different 4-second audio files formatted as mono 32-bit 32kHz, and are contained in the 'embeddings.tar.xz' file. Captions corresponding to each audio file can be found in 'caption.csv'. This dataset does not comprise the audio files, only the embeddings. Three different types of embeddings are provided: VGGish (vggish), MS-CLAP (clap-2023), and PANNs CNN14 Wavegram-Logmel (panns-wavegram-logmel). Only PANNs CNN14 Wavegram-Logmel (panns-wavegram-logmel) embeddings are used for evaluation in the challenge. For further details, please refer to the challenge website. Contact <ul> <li>Modan Tailleur, modan.tailleur@ls2n.fr</li> <li>Mathieu Lagrange, mathieu.lagrange@ls2n.fr</li> </ul>

DCASE 2024 Task 9: Language-Queried Audio Source Separation | Development Set

== Description ==  The development set is composed of audio samples from FSD50K [1] and Clotho v2 [2] datasets. FSD50K contains over 51k audio clips (~100 hours) manually labeled using 200 classes drawn from the AudioSet Ontology. For each audio clip in the FSD50K dataset, we generated one automatic caption for each audio clip by prompting ChatGPT (GPT-4) with its sound event tags. All audio files should be converted to mono 16 kHz audio for training LASS models.  Clotho v2: <a href="../records/4783391">https://zenodo.org/records/4783391</a> FSD50K: <a href="../records/4060432">https://zenodo.org/records/4060432</a> Automatic captions generated for FSD50K: <ul> <li>fsd50k_dev_auto_caption.json</li> <li>fsd50k_eval_auto_caption.json</li> </ul> Prompt for generating captions: <blockquote> I will give you a number of lists containing sound events. Please write an one-sentence audio caption to describe these sounds. Make sure you are using grammatical subject-verb-object sentences. Directly describe the sounds and avoid using the word “heard”. Please don't describe the temporal order of these sound events. The caption should be less than 20 words. </blockquote> In addition to the development set, participants are free to use any external data (including private data) but are not allowed to use audio in Freesound uploaded between April and October 2023. Participants must specify all external resources utilized in their submission in the technical report. == References == [1] Fonseca E, Favory X, Pons J, et al. FSD50k: an open dataset of human-labeled sound events. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 30: 829-852. [2] Drossos K, Lipping S, Virtanen T. Clotho: An audio captioning dataset. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020: 736-740. == Contact == Xubo Liu, xubo.liu@surrey.ac.uk

Supplementary data for Development of Indicators of Social Vulnerability and Fishing Reliance in English Coastal Communities

Supplementary data for Development of Indicators of Social Vulnerability and Fishing Reliance in English Coastal Communities by Tom Gibson, Chloe Lucas and Angela Muench. Full details are available via the publication and details of folder and file structures are available in the ReadMe file.

opencc-zeroApr 2024View details →

Early, typical, and late talkers: an exploratory study on predictors of language development in the first two years of life

Background: The consensus in scientific literature is that each child undergoes a unique linguistic development path, albeit with shared developmental stages. Some children excel or lag behind their peers in language skills. Consequently, a key challenge in language acquisition research is pinpointing factors influencing individual differences in language development. Methods: We observed children longitudinally from 3 to 24 months of life to explore early predictors of vocabulary size. Based on the productive vocabulary size of children at 24 months, 30 children met our sample selection criteria: 10 late talkers and 10 early talkers, and we compared them with 10 typical talkers. We evaluated interactive behaviors at 3, 6, 9 and 12 months, considering vocal production, gaze at mother's face, and gestural production during mother-child interactions, and we considered mothers' report of children's actions and gestures and receptive-vocabulary size at 15 and 18 months. Results: Results indicated early precursors of language outcome at 24 months identifiable as early as 3 months in vocal productions, 6 months for gaze at mother's face and 12 months for gestural productions. Conclusions: Our research highlights both theoretical and practical implications. Theoretically, identifying the early indicators of belonging to the group of late or early talkers underscores the significant role of this developmental period for future studies. On a practical note, our findings emphasize the crucial need for early investigations to identify predictors of vocabulary development before the typical age at which lexical delay is identified.

opencc-by-4.0Apr 2024View details →

Perception surveys pre and post-trails development in Granada pilot

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Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"

<h1>Id: Data of following publication</h1> title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids", journal = "Composite Structures", volume =" 348", pages = "118503", year = "2025", doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>", author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels" Data doi on 10.5281/zenodo.10829042 <h1>pyRVE</h1> <h2>Python Code for Geometrical Generator for Braided Composites RVE</h2> pyRVE is a code written in Python using the GMSH API that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for Diamond and Regular patterns. <h2>Requirements</h2> To run, it requires: <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> A typical run case must have a file structure, where: <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with GMSH.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the x-y plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as <div> <pre><code>python3 ../../source/mainRVE.py --name --pattern <pattern></code></pre> </div> In this case, the <code>--name</code> refers to the index that will be given to the model, where <code></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>. <blockquote> Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections. Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure. </blockquote> <h3>All Command Line Options</h3> The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are: <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> Following the run options, a few examples are indicated <ul> <li>A basic mesh generation run for the basic data, considering a regular pattern, for a model named 2:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a regular pattern:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the 2 parameters are modified randomly, considering a regular pattern, for a model named 2:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model 2 already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are: <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul>