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1,930 results for “matrix”
Structure of single-walled carbon nanotube reinforced polymer matrix composites
<p><strong>Structure of single-walled carbon nanotube reinforced polymer matrix composites</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>A composite material, also called a composite, is a solid material that results when two or more different substances, each with its own characteristics, are combined to create a new substance whose properties are superior to those of the original components in a specific application. The term composite more specifically refers to a structural material within which a fibrous material is embedded. The remarkable properties of composites are achieved by embedding fibers of one substance in a host matrix of another. In materials science, a polymer matrix composite is a composite material composed of a variety of short or continuous fibers bound together by a matrix of organic polymers. Polymer matrix composites are designed to transfer loads between fibers of a matrix. Some of the advantages with polymer matrix composites include their light weight, high resistance to abrasion and corrosion, and high stiffness and strength along the direction of their reinforcements. The function of the matrix in polymer matrix composites is to bond the fibers together and transfer loads between them. Polymer matrix composites matrices are typically either thermosets or thermoplastics. Thermosets are by far the predominant type in use today. Thermosets are subdivided into several resin systems including epoxies, phenolics, polyurethanes, and polyimides. Of these, epoxy systems currently dominate the advanced composite industry. Unlike fiber-reinforced polymer matrix composites, nanomaterials reinforced polymer matrix composites are able to achieve significant improvements in mechanical properties at much lower loadings. Carbon nanotubes in particular have been intensely studied due to their exceptional intrinsic mechanical properties and low densities. In particular carbon nanotubes have some of the highest measured tensile stiffnesses and strengths of any material due to the strong covalent bonds between carbon atoms. However, in order to take advantage of the exceptional mechanical properties of the nanotubes, the load transfer between the nanotubes and matrix must be very large. Like in fiber-reinforced composites, the size dispersion of the carbon nanotubes significantly affects the final properties of the composite. Long carbon nanotubes lead to an increase in tensile stiffness and strength due to the large-distance stress transfer and crack propagation prevention. On the other hand, short carbon nanotubes do not lead to any enhancement of properties without any interfacial adhesion. However once modified, short carbon nanotubes are able to further improve the stiffness of the composite, however there is still very little crack propagation countering. In general, long and high aspect ratio carbon nanotubes lead to greater enhancement of mechanical properties, but are more difficult to process. Aside from size, the interface between the carbon nanotubes and the polymer matrix is of exceptional importance. In order to achieve better load transfer, a number of different methods have been used to better bond the carbon nanotubes to the matrix by functionalizing the surface of the carbon nanotube with various polymers. These methods can be divided into non-covalent and covalent strategies. Non-covalent carbon nanotube modification involves the adsorption or wrapping of polymers to the carbon nanotube surface, usually via van der Waal's or π-stacking interactions. In contrast, covalent functionalization involves direct bonding onto the carbon nanotube. This can be achieved in a number of ways, such as oxidizing the surface of the carbon nanotube and reacting with the oxygenated site, or using a free radical to directly react with the carbon nanotube lattice. Covalent functionalization can be used to directly attach the polymer to the carbon nanotube, or to add an initiator molecule which can then be used for further reactions.</p>
G-matrix stability of clinally diverging populations of an annual weed
<p>How phenotypic and genetic divergence among populations is influenced by the genetic architecture of those traits, and how microevolutionary changes in turn affect the within-population patterns of genetic variation, are of major interest to evolutionary biology. Work on <em>Ipomoea hederacea</em>, an annual vine, has found genetic clines in the means of a suite of ecologically important traits, including flowering time, growth rate, seed mass, and corolla width. Here we investigate the genetic (co)variances of these clinally varying traits in two northern range-edge and two central populations of <em>Ipomoea hederacea </em>to evaluate the influence of the genetic architecture on divergence across the range. We find 1) limited evidence for clear differentiation between Northern and Southern populations in the structure of <strong>G</strong>, suggesting overall stability of <strong>G</strong> across the range despite mean trait divergence and 2) that the axes of greatest variation (g<sub>max</sub>) were unaligned with the axis of greatest multivariate divergence. Together these results indicate the role of the quantitative genetic architecture in constraining evolutionary response and divergence among populations across the geographic range.</p>
No Man's Sky Patch Keywords for the article "Adapting the Harris Matrix for Software Stratigraphy"
<p>Full list of <em>No Man's Sky</em> patch keywords for the article "Adapting the Harris Matrix for Software Stratigraphy" published in <em>Advances in Archaeological Practice.</em></p>
No Man's Sky Patch Notes and Links for the article "Adapting the Harris Matrix for Software Stratigraphy"
<p>This file contains the HTML of all of the <em>No Man's Sky</em> patch notes, plus links, as supplemental online material for "Adapting the Harris Matrix for Software Stratigraphy" as published in <em>Advances in Archaeological Practice</em> 2018.</p>
No Man's Sky Patch Types and Subtypes for the article "Adapting the Harris Matrix for Software Stratigraphy"
<p>Full data set of <em>No Man's Sky</em> patch types and subtypes used in the article "Adapting the Harris Matrix for Software Stratigraphy" published in <em>Advances in Archaeological Practice</em>.</p>
Energy Security: Global analysis of Energy Matrix demand Mozambique case
<p>Energy system modelling, energy security, energy transition, renewable <br>energy, climate change, OSeMOSYS. </p>
Road distances and trip duration matrix for Brazilian municipalities
<p>This dataset presents a matrix with road distance and travel duration estimates for all the trips combination among the Brazilian municipalities. More details about the methodology are available <a href="https://rfsaldanha.github.io/data-projects/brazil_road_distances.html" target="_blank" rel="noopener">here</a>.</p> <p>This version was generated considering the road network at the OSRM project on May 2024.</p> <p><strong>Variable dictionary</strong></p> <ul> <li>orig: Code of the municipality of origin (IBGE 7-digits) </li> <li>dest: Code of the municipality of destiny (IBGE 7-digits) </li> <li>dist: Road distance of the shortest route, in meters</li> <li>dur: Travel time estimation, in minutes.</li> </ul> <p><strong>Files</strong></p> <ul> <li>dist_brasil.rds : R serialized object</li> <li>dist_brasil.parquet : Parquet format</li> <li>dist_brasil.zip : Compressed CSV file. Semi-colon ( ; ) field delimiter and point ( . ) as decimal separator</li> </ul>
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Dataset: Golden Matrix Group, Inc. (GMGI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Matrix Service Company (MTRX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 1. Markov Chain Model&Figure 2. Transition matrix-Study of a Random Navigation on the Web Using Software Simulation
<p>For a good simulation it is very important to find methods for<br> navigating through the web (Levene and Wheeldon, 2004). John Kemeny and Laurie Snell have<br> proposed the use of Markov models for web simulations (Kemeny and Snell, 1960). Cadez et al. (2000)<br> used Markov models for classifying the sessions into different categories for browsers. Some other<br> proposed techniques choose to combine different order Markov models for obtaining low state<br> complexity and improving accuracy, as Deshpande and Karypis (2004). Dongshan and Junyi (2002)<br> used for predicting the access providing good scalability and high coverage a hybrid-order tree-like<br> Markov model. As an alternative to the Markov model Pitkow proposed a longest subsequence model<br> (Pitkow and Pirolli, 1999), also for predicting the next page accessed by the user Sarukkai chose<br> Markov models (Sarukkai, 2000).<br> Transitions are simulated using the Markov Chain nodes, Google matrix and an arbitrary initial<br> probability distribution. Examples can be seen in Figure 1 and Figure 2.</p>
Figure 2. PAM250 matrix for the encoded sequence-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The PAM matrix (Dayhoff et al., 1978) describes the probability that original amino acid<br> will be replaced by another amino acid over a defined evolutionary interval. The unit of<br> evolutionary divergence is defined as the interval in which 1% of the amino acids have been<br> changed between two sequences. The work uses PAM250, which assumes the occurrence of 250-<br> point mutations per 100 amino acids.<br> So, for the given the protein sequence GIVEQCCASVCSLYQLENYCN, A will be replaced<br> by 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 as shown in Figure 2.</p>
Estimated particle parameters of an aluminium matrix composite
<p>The dataset consists of section profile parameters in tabular form (assumed to come from prolate spheroids) of ellipses fitted to measured particles of an aluminium matrix composite from metallographic analysis. The dataset can be used to reconstruct the trivariate spatial spheroid (prolate form) distribution.</p>
Expert-based literature review on RRI indicators for science education assessment: PERFORM analysis matrix
<p>The document contains the main variables and categories of analysis of the expert-based literature review conducted as part of the assessment impact developed in the PERFORM project. This literature review globally aimed to identify and characterize assessment frameworks used in the context of science learning and engagement with young people. Specifically, it examined the operationalization of: i) RRI values and process requirements, ii) transversal competences, iii) experiential aspects, and iv) cognitive aspects. In doing that assessment gaps and challenges where identified relevant to the context of PERFORM and, more broadly, to the development of science education assessments incorporating the RRI dimension. By assessment framework we refer to a set of interlinked criteria, practices and concepts providing a systematic way of data collection, analysis and interpretation to the study of science learning and engagement. The template for data collection was organised in different sections approaching the following specific review questions and sub-questions:</p> <ol> <li><em>What assessment frameworks can be identified in the selected sample?</em> <ol> <li>On which disciplines are they based?</li> <li>What is being assessed in these frameworks?</li> <li>How it is the evaluation conducted?</li> <li>What are the challenges of each approach for assessing science learning and engagement?</li> </ol> </li> <li><em>How are transversal competences, RRI and emotional factors included in these frameworks?</em> <ol> <li>How are these notions operationalised?</li> <li>What kinds of evaluation indicators are applied for data collection, if any?</li> </ol> </li> </ol>
WP6 SIA Model Matrix dataset
<p>There is no single right answer as to which model might be most appropriate for assessing the impact of Maker initiatives. Instead, we discuss the different parameters that are relevant for choosing the appropriate SIA model and provide a matrix of 69 SIA models with their respective approaches and parameters in this dataset and in deliverable <a href="http://make-it.io/deliverables/d6-2-societal-impact-analysis-and-sustainability-scenarios/">D6.2</a>.</p> <p>See also: <a href="http://make-it.io/open-data-api/">http://make-it.io/open-data-api/</a></p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 10. Detailed accuracy separated by classes and a confusion matrix which belongs to the dataset of Coiflet 1 applied by our main method (ANNSVM)
<p>With regard to accuracy values of each class as presented in Figure 10, we observed that the accuracy of the two-dimensional chart class was the lowest (i.e., 0.875), while others were over 0.9. Results here suggested that both the bar and pie classes have their own unique characteristics, as opposed to the 2Dchart class. For example, the graph images that contained some rectangles were individually categorized in the bar graph class. A similar phenomenon occurred for circles in the pie chart class. In contrast, the 2Dchart class contained mixed types of graphs; hence, the graph characteristics belonging to the 2Dchart class varied. </p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 6. Confusion matrix of multilayer feedforward network
<p>Multilayer feedforward network was used for classification with ANN. In this application, the ANN structure and parameters were obtained after a review of previous studies and very much number of trial runs. There is a total of 10 neurons in the hidden layer in ANN. The Bayesian regularization backpropagation was used for learning algorithm and the mean square error function was also used the performance algorithm. 134 samples in the data set were used for training data, 29 samples were used for validation data, and 29 samples were used for testing data. The confusion matrix obtained at the end of the classification was given in Figure 6. As seen from the confusion matrix, the accuracy of classification 82.8% was achieved in the ANN classification. The ANN performed with a sensitivity of 92.40% and a specificity of 88.82%.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 7. Confusion matrix of convolutional neural network
<p>The final layer of CNN is the classification layer. This layer uses the possibilities returned by the softmax activation function for each input to mutually assign one of the special classes. The confusion matrix obtained at the end of the classification was given in Figure 7. As seen from the confusion matrix, the accuracy of classification 97.9% was achieved in the CNN classification. The CNN performed with sensitivity of 99.47% and specificity of 98.42%.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.