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Fig. 9 in Wing geometric morphometrics to distinguish and identify Haematobosca flies (Diptera: Muscidae) from Thailand

Fig. 9. Factor map of the principal components (PC1, 46% as horizontal axis and PC2, 22% as vertical axis) from wing shape variables of test specimens (male and female) and reference data of Haematobosca sanguinolenta and H. aberrans. Squares represent mean values in each group.

Fig. 8 in Wing geometric morphometrics to distinguish and identify Haematobosca flies (Diptera: Muscidae) from Thailand

Fig. 8. Linear regression between centroid size and the first shape-based principal component (PC) of Haematobosca sanguinolenta (A) and H. aberrans (B). Linear regression prediction is shown by the orange dots. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3. The 10 in Wing geometric morphometrics to distinguish and identify Haematobosca flies (Diptera: Muscidae) from Thailand

Fig. 3. The 10 landmarks on the wing of Haematobosca flies used in the wing geometric morphometric analysis.

Fig. 7 in Wing geometric morphometrics to distinguish and identify Haematobosca flies (Diptera: Muscidae) from Thailand

Fig. 7. Hierarchical agglomerative clustering tree based on shape similarities of each individual for male and female Haematobosca sanguinolenta and H. aberrans. Euclidean distances were used for the construction of the tree.

Fig. 1 in Wing geometric morphometrics to distinguish and identify Haematobosca flies (Diptera: Muscidae) from Thailand

Fig. 1. Heads in the lateral view and the pleura of Haematobosca sanguinolenta (A, B) and H. aberrans (C, D). The anterior and posterior katepisternal setae (arrow) were used to distinguish between both species. Photographs were prepared by the authors.

Fig. 5 in Wing geometric morphometrics to distinguish and identify Haematobosca flies (Diptera: Muscidae) from Thailand

Fig. 5. Mean shape of male (A) and female (B) Haematobosca sanguinolenta and H. aberrans after Procrustes superimposition.

Fig. 6 in Wing geometric morphometrics to distinguish and identify Haematobosca flies (Diptera: Muscidae) from Thailand

Fig. 6. Factor map of the first two principal components (PC1, 47% as horizontal axis and PC2, 33% as vertical axis) of wing shape variables (A) and factor map of the first two discriminant factors (DF1, 66.8% as horizontal axis and DF2, 31.8% as vertical axis, the two discriminant factors represent 98.6% of the total discriminant space) of wing shape variables (B). Each point represents the individuals of male and female Haematobosca sanguinolenta and H. aberrans, and each polygon corresponds to a different species and sex. Squares represent the mean values in each group.

opencc-by-4.0Aug 2024View details →

Collection of images and raw coordinates of honey bee (Apis mellifera) wings from the central highlands of Ecuador.

Images and raw coordinates of honey bee (Apis mellifera) wings from the central highlands of Ecuador

opencc-by-4.0Jan 2023View details →

Fig. 3 in Spatiotemporal distribution of the glassy-winged sharpshooter, Homalodisca vitripennis (Hemiptera: Cicadellidae), in a southeastern agroecosystem

Fig. 3. Spatiotemporal distribution patterns of glassy-winged sharpshooters in Gadsden County, Florida, USA. Top = images display red-blue plots based on interpolation of the cluster index for individuals from 2001 to 2003. Red areas indicate significant aggregations (greater than 1.5), and blue areas indicate significant gaps (less than −1.5). Bottom = interpolated density maps display seasonal distribution patterns of glassywinged sharpshooters collected in traps during 2001 to 2003, according to habitat.

opencc-by-4.0Jan 2023View details →

Fig. 2 in Spatiotemporal distribution of the glassy-winged sharpshooter, Homalodisca vitripennis (Hemiptera: Cicadellidae), in a southeastern agroecosystem

Fig. 2. The proportion of glassy-winged sharpshooters captured on yellow sticky card traps along forest edge in Gadsden County, Florida, USA, during 2001 to 2003. The blue line represents a Loess fit.

opencc-by-4.0Jan 2023View details →

Fig. 1 in Spatiotemporal distribution of the glassy-winged sharpshooter, Homalodisca vitripennis (Hemiptera: Cicadellidae), in a southeastern agroecosystem

Fig. 1. Temporal distribution of glassy-winged sharpshooters captured on yellow sticky card traps in Gadsden County, Florida, USA, during 2001 to 2003.

FIGURE 5 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 5 Historical demographic trajectories of Parochlus steinenii within its distribution in the Magellanic Subantarctic Ecoregion (red), South Georgia (yellow) and South Shetland Islands (violet). Left panels: Past demographic changes constructed using Bayesian Skyline Plot approach based on cox1 haplotypes. The y-axis is the product of the effective population sizes (Ne) and generation length in a log scale. The x-axis is the time before present (Myr). The median estimate (solid black line) and 95% highest probability density (HPD) limits (colored area) are shown. The thick dashed line represents the time of the most recent common ancestor (TMRCA). Right panels: Distribution of pairwise differences of cox1 for each demographic unit. Values of Tau are shown.

FIGURE 3 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 3 Haplotype network for Parochlus steinenii based on 151 mtDNA cox1 sequences spanning the species' distribution. Neighbor-joining network illustrating the distribution of haplotypes across lakes in the Magellanic Subantarctic Ecoregion, South Georgia and the South Shetland Islands. Circles sizes are proportional to haplotype frequency.

FIGURE 4 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 4 Spatial genetic structure of Parochlus steinenii from the spatial model in Geneland across the three biogeographic regions sampled. Higher posterior probabilities of population membership are indicated in yellow for each sampling site (A) MSE, (B) SG, (C) MA. Black circles indicate the relative position of the sampling localities. Posterior probabilities of membership were plotted with the shapefiles of Scotia Arc coastline available in the repository in the Antarctic digital database from the British Antarctic survey (BAS). https://add.data.bas.ac.uk.

FIGURE 6 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 6 Phylogenetic reconstruction based on haplotypes cox1 data showing divergence times of Parochlus steinenii across its distribution in sub- and maritime Antarctica. Nodes ages are the median values from both Bayesian Molecular Clock analyses and TMRCA of each clade estimated with Bayesian Skyline Plot. In each clade of interest (MSE, SG and MA), nodes bar indicated the 95% HPD. The colored tip represents the colors of the sampling region.

FIGURE 2 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 2 Phylogenetic reconstruction of Podonominae based on concatenated data. Maximum Likelihood reconstruction, including members of the subfamily Podonominae with emphasis on Parochlus spp. Information in brackets represent the sequence code used for the analysis (within P. steinenii), and sampling site for each sequence. Values for the nodes support are indicated for posterior probability/bootstrap, respectively.

FIGURE 1 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 1 Historical biogeography reconstruction based on 151 cox1 sequences of Parochlus steinenii across the sampling areas. (A) Map of the sampling region in the Magellanic Subantarctic regions (MSE, red); sub-Antarctic Island of South Georgia (SG, orange), and Maritime Antarctic (MA, violet); (B) Bayesian Inference reconstruction using P. steinenii individuals. The values for node support are indicated for posterior probability/bootstrap from BI and ML analyses, respectively.

opencc-by-4.0Sep 2024View details →

Surrogate Modeling Benchmark - Wing weight function

This dataset is related to the wing weight function benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-wing-weight-function/" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-wing-weight-function/</a>. The experimental designs include datasets with 100, 200, 300, 400, and 500 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses. <h2>Description of the dataset file</h2> The dataset file includes two variables: <ul> <li>ExpDesigns, and</li> <li>ValidationSet.</li> </ul> Both variables are Matlab structures with fields X, Y, and nSamples. Variable ExpDesigns is a non-scalar structure sized according to the number of experimental design groups. Each field of X for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the ValidationSet variable, except it contains only one dataset per benchmark case. The structure can be summarized as follows: <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity. For Polynomial Chaos Expansions (PCE), we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP). Polynomial Chaos Kriging (PCK) employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters. We benchmarked both linear and ordinary Kriging, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization. For further details on the settings, please refer to the competitors.m file and UQLab user manuals: <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> The results file contains one variable: Metrics. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows: <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l) <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>. <h2>Additional files</h2> We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called working_with_datafiles.* (the extension depends on the selected language). <h2>Acknowledgment</h2> This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.

opengpl-3.0-or-laterJul 2024View details →

Dataset: Structural Health Monitoring of a Flexible Wing

This Zenodo entry contains the experimental data underlying the journal article Noise-robust Modal Parameter Identification and Damage Assessment for Aero-structures, in preparation. The document XB-2_SHM_Dataset.pdf serves as the explanatory note for the data contained in SHM_XB2.mat