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Supplemental Material to "The Small-Amplitude Dynamics of Spontaneous Tropical Cyclogenesis. Part II: Linear Stability Analysis"

This is the supplemental material to the manuscript "The Small-Amplitude Dynamics of Spontaneous Tropical Cyclogenesis. Part II: Linear Stability Analysis". It deposits: (1) A handwritten math derivation note (math_derivation_note.pdf). (2) A movie version of Figure 1 (Part_II_movie.avi). (3) The Fortran code for amplifying the longwave radiative feedback in CM1 (Radiation_amplification_RAD_parameter_in CM1.txt). (4) The MATLAB postprocessing codes for generating data figures (postprocessing_code.zip). (5) The MATLAB solver of the four-layer QG model (four_layer_QG_solver.m). (6) Supplemental figures (supplement_figures.pdf).   Please contact Dr. Hao Fu (haofu@uchicago.edu or haofu736@gmail.com) if you have any questions.

Accelerated mechanochemical bond scission and stabilization against heat and light in carbamoyloxime mechanophores

Underpinning data of manuscript and Supplemental Information sorted after Figures and Tables. Additionally, coordinate files from computational investigations.

Raw data: Amphiphilic nanogels as versatile stabilizers for Pickering emulsions

Raw data for Journal article: "Amphiphilic nanogels as versatile stabilizers for Pickering emulsions" Abstract:  Pickering emulsions (PEs) are stabilized by particles at the water/oil interface and exhibit superior long-term stability compared to emulsions with molecular surfactants. Among colloidal stabilizers, nano-/microgels facilitate emulsification and can introduce stimuli-responsiveness. While increasing their hydrophobicity is connected to phase inversion from oil-in-water (O/W) to water-in-oil (W/O) emulsions, a predictive model to relate this phase inversion to the molecular structure of the nano/microgel network remains missing. Addressing this challenge, we developed a library of amphiphilic nanogels (ANGs) that enable adjusting their hydrophobicity while maintaining similar colloidal structures. This enabled us to systematically investigate the influence of network hydrophobicity on emulsion stabilization. We found that W/O emulsions are preferred with increasing ANG hydrophobicity, oil polarity, and oil/water ratio. For non-polar oils, increasing emulsification temperature enabled the formation of W/O PEs that are metastable at room temperature. We connected this behavior to interfacial ANG adsorption kinetics and quantified ANG deformation and swelling in both phases via atomic force microscopy. Importantly, we developed a quantitative method to predict phase inversion by the difference in Flory-Huggins parameters between ANGs with water and oil ( χwater − χ oil). Overall, this study provides crucial structure-property relations to assist the design of new nano-/microgels for advanced PEs.

Mechanical Measurements of IntI1 Synaptic Complex Stability

These data sets are associated with the publication "The recombination efficiency of the bacterial integron depends on the mechanical stability of the synaptic complex" Preprint available here: https://doi.org/10.1101/2024.04.09.588808 Single-molecule force spectroscopy data of various IntI1 variants forming complexes with different attC-stite variants. ZIP files contain h5 data recoreded on a LUMICKS C-Trap device. <ul> <li>IntI1wt.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1wt with aadA7bs attC sites</li> <li>IntI1Y312F-Alanine.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F-double Alanine mutant with aadA7bs attC sites</li> <li>IntI1Y312F-mEGFP.zip contains correlative force spectroscopy and confocal imaging data (h5 files) of IntI1Y312F-mEGFP binding and moving on single-stranded DNA</li> <li>IntI1Y312F-Truncated.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F-C-terminal truncation with aadA7bs attC sites</li> <li>IntI1Y312F_aadA7-bs.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F with aadA7bs attC sites</li> <li>IntI1Y312F_aadA7-ts.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F with aadA7ts attC sites</li> <li>IntI1Y312F_aadA7bs-L2.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F with aadA7bs-L2 hybrid attC sites</li> <li>IntI1Y312F_aadA7bs-VCRwt.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F with aadA7bs-VCRwt hybrid attC sites</li> <li>IntI1Y312F_L2.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F with L2 attC sites</li> <li>IntI1Y312F_VCRwt.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F with VCRwt attC sites</li> <li>IntI1Y312F_VCRinv.zip contains force  spectroscopy data (indivdual h5 traces) of IntI1Y312F with VCRinv attC sites</li> </ul> README.txt contains information about how to read the individual h5 files with e.g. a Python script.

opencc-by-4.0Oct 2024View details →

ERA5 atmospheric stability and Geostrophic wind shear for usage in WAsP

The dataset "ERA5-meso.nc" is obtained by loading the variables needed for calculating the temperature scale from hourly ERA5 files. These files are available in grib format that have been obtained from the Copernicus Data Store (CDS). They are opened using xarray and cfgrib and processed using the functions stability_histogram from the python package PyWAsP. Because a conditional mean based on the 50% highest wind speeds must be calculated, all values are binned according to wind speed at 100 m and this histogram is then used to calculate the mean and root-mean-square of the temperature scale. The boundary layer height scale is calculated in a similar fashion. For more documentation see the accompanying paper. A validation of the WAsP model using these data is available in the references. The file "ERA5-baro.nc" contains the geostrophic wind shear. The mean magnitude and direction is obtained sector-wise in similar fashion as described above. The geostrophic wind shear can be calculated from the pressure level geopotential height. The way to do this is described here: <a href="https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w" target="_blank" rel="noopener">https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w</a> These data are for estimating atmospheric stability conditions, if you are looking for data to estimate air density, please refer to the item "ERA5 data for air density calculations in WAsP" (related materials item 5). The methods for this are described in related materials item 7. v1-v2: Version corresponding to paper before review (related materials 3), do not use these. v3: Final version that corresponds to the published version of the paper (related materials 6): https://doi.org/10.1007/s10546-023-00803-3 This is slightly different then the first version due to Eq. 9 v4: Updates to load the files using PyWAsP versions specifically suited for use in pywasp with the variable names adopted in PyWAsP. For ERA5-baro.nc NaNs are filled with 0.0, i.e. assuming a barotropic atmosphere. Mirror of: https://data.dtu.dk/articles/dataset/ERA5_atmospheric_stability_for_usage_in_WAsP_12_8/19576042

opencc-by-4.0May 2024View details →

Loading Stability Benchmark for Pallet Loading Problems

<h3>Description</h3> The dataset contains information about approx. 32.000 pallet loading problem (PLP) cargo layouts derived from a physical simulation with the MSC ADAMS software. The question of the study is to compare and benchmark multiple static stability algorithms: full base support, partial base support, static mechanical equilibrium, and physical simulation. As there are no real-world cargo loading datasets available, we decided to approximate real-world loading. We used a multibody simulator (MSC ADAMS) and simulated cargo loadings for a set of layouts. The benchmark simulation was adjusted in an iterative process, in which we visually tested simulations and incorporated insights from literature about physical cargo properties until we were satisfied with the plausibility of the results. However, the benchmark simulation depends on many parameters that need to be adjusted, and our adjustments might be unprecise. The dataset contains 2 sub-datasets: Dataset 1 is based on the <a href="https://github.com/fbrandt/ACLPP" target="_blank" rel="noopener">ACLPP instances</a> generated by Brandt & Nickel (2019). Dataset 2 is based on the <a href="https://www.sciencedirect.com/science/article/pii/S1568494623011869" target="_blank" rel="noopener">instances</a> from Ali et al. (2024). For every sub-dataset, we assembled three complexity scenarios with four stages. Scenario 1 is the easiest scenario, which assumes all items (boxes) have uniform density and no displaced center of mass (in relation to their geometric center). Scenario 2a moves the CoM now to a random position in the items' dimensions according to a Gaussian distribution around the item's geometric center. Scenario 2b now applies the same procedure as Scenario 2a but employs a uniform distribution. <h3>Stages</h3> Stage 0 ("0_input") are the raw data from both datasets. We did not include the raw data but provided the link to the dataset in the meta-information here. Stage 1 ("1_AeULDs") are the input cargo items with itemLabel, weight, (box)-shape with width, height, depth, loading coordinate (x,y,z), sequence, and center of mass (x, y, z). We imposed a cap on the number of items of 20. If a ULD exceeds this threshold, we include only the first 20 items. Stage 2 ("2_AeJobs") transfers the ULDs to an executable format and includes assessment information (i.e., stability approaches). Stage 3 ("3_AeResults") are the results from the different static stability algorithms with a uniform static stability score between 0 (first item unstable) and 1 (all items stable) and a runtime.  Stage 4 ("4_Benchmark") are the benchmark data from our multibody simulation. The first folder ("done_raw") is raw output of our ADAMS simulation, which tracks relevant physical characteristics such as angular momentum, angular velocity, acceleration, position, velocity (about the center of mass), translation, rotation, and contact forces with other items. We measured multiple observations per loading sequence, which all have an assigned time step. The total simulation length is 0.3 s. The second folder ("done_intermediate") now filters the raw data, such that we track the largest translation and rotation in x, y, and z- directions per loading step per item. We also calculate the maximal translation and rotation within the simulation. The third folder ("done") transforms the intermediate steps into a final quantified stability outcome, in case any item exceeds the threshold values for translation and rotation. The final outcome is normalized, such that 1 represents a stable cargo layout and 0 represents a layout in which the first item is unstable. Divide the number of stable loading steps by the total number of items in the cargo layout. The fourth folder ("done_sensitivity_analysis") computes the benchmark results for different epsilon_translations and epsilon_rotation values for sensitivity analyses. We filtered the data for the analysis, such that only ULDs with a minimal width, depth, and height of every item are included for the final analysis. Further, we filtered out ULDs that contained less than two items. Stage 5 ("5_Final_results") contains aggregated results, such as the aggregated number of correct predictions, underestimations, overestimations, and sensitivity analyses results. <h3>Further references</h3> The paper (preprint) describing the study can be found <a href="https://papers.ssrn.com/abstract=4778113" target="_blank" rel="noopener">here</a>. The code for data generation, simulation, and analysis is in the <a href="https://github.com/philippmaz/palletizing_stability_benchmark/" target="_blank" rel="noopener">GitHub Repository</a> (also linked below). <h3>Changelog:</h3> <table> <tbody> <tr> <td>Version</td> <td>Change</td> </tr> <tr> <td>0.0.3</td> <td>This version fixes a bug during item dimension mapping in dataset 2 that made a re-simulation necessary. We imposed a cap on the number of items of 20. If a ULD exceeds this threshold, we include only the first 20 items. For the analysis, we included a minimal item level per ULD of 2. We set the minimal item dimensions to 10 (previously: 15). We removed macOS-specific files from the archive.</td> </tr> </tbody> </table>

opencc-by-4.0Dec 2023View details →

Physical stabilization of water-soluble PVA nanofibrous materials functionalized with biologically active substances

Tissue engineering aims to develop materials that enhance biological activity and promote tissue healing and regeneration. One promising approach is to functionalize nanofibrous materials with antimicrobial substances, such as lipophosphonoxin (LPPO), and use water-soluble polymers like polyvinyl alcohol (PVA) to incorporate bioactive molecules into fibers. However, water-soluble materials often face the issue of "burst release," releasing over 90% of the active substances within the initial 24 hours. This research focuses on preparing functionalized nanofibrous materials based on PVA containing the experimental antimicrobial compound LPPO and subsequent physical stabilization of the materials using the "Heat treatment" method. The applied stabilization successfully reduced the incorporated substance's release rate by up to 50%. The resulting materials have the potential to provide functional cross-linked PVA nanofiber scaffolds for regenerative medicine applications in large and chronic skin injuries.

opencc-by-4.0Nov 2024View details →

Dataset for publication "What does it take to stabilize a naphthalene anion?"

Dataset for the publication What does it take to stabilize a naphthalene anion?  <div> <div><a href="https://doi.org/10.1063/5.0230131" target="_blank" rel="noopener">https://doi.org/10.1063/5.0230131</a></div> </div>  containing excel file with file assignments, raw data binaries (tfb), corresponding ascii files (asc) and metadata files in text format (tfa).

opencc-by-4.0Nov 2024View details →

Long-term biochar and soil organic carbon stability– Evidence from field experiments in Germany-ROW DATA

Row data for researcher paper Long-term biochar and soil organic carbon stability– Evidence from field  experiments in Germany

opencc-by-4.0Jul 2021View details →

Data for "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities"

Data for the paper "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities" which is in minor revision in Ecology Letters (manuscript id:ELE-00589-2021.R1). A doi will be provided upon publication. Current citation: Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E., & Fontaine, C. (In revision) Species richness and food-web structure jointly drive total biomass and its temporal stability in fish communities Minor revision in Ecology Letters. The repository constains data describing fish community monitoring across stream sections in metropolitan France over the period 1995-2018 by the French Office of Water and Aquatic Ecosystems (ONEMA) using electrofishing. The repository contains: <ul> <li> description of fishing: fishing_protocol.csv <ul> <li>surface: sampled surface</li> <li>opcod: fishing operation code, a unique identifier for each sampling event</li> <li>station: unique identifier for each site</li> <li>nb_sp, nb_ind: number of species, number of individuals</li> </ul> </li> <li>geographical information: station_basin.csv <ul> <li>X, Y: spatial coordinates of the station, expressed in metres in Lambert93 (epsg:2154)</li> <li>basin: name of the hydrographic basin</li> </ul> </li> <li>environment: environment.csv ( _mean: mean, _med: median, _cv: coefficient of variation) <ul> <li>alt: altitude</li> <li>d_source: distance to source</li> <li>strahler: strahler order</li> <li>BOD: Biological Oxygen Demand</li> <li>temperature: water temperature</li> <li>flow: water flow</li> </ul> </li> <li>community data: community_data.csv <ul> <li>species: three digits code corresponding to a given species (see Table S1, Danet et al. in revision)</li> <li>nind: number of individuals</li> <li>biomass: biomass in gram</li> </ul> </li> <li>Length of each fish individual: fish_length.csv <ul> <li>length: length of the fish in millimeter</li> </ul> </li> <li>Inferred food-web: class_network.rda <ul> <li>data: <ul> <li>class_id: size class of a fish individual</li> </ul> </li> <li>network: these data.frame can be handled by igraph::graph_from_data_frame() <ul> <li>from, to: "to" eats "from"</li> </ul> </li> <li>composition: <ul> <li>sp_class: concatenation of species and class_id columns</li> <li>bm_std: biomass reported to the sampled surface</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Jun 2021View details →

A comparative study of red brick powder and lime as soft soil stabilizer (Dataset)

This data is the result of laboratory CBR testing in soaked and unsoaked conditions with or without additional stabilization

opencc-by-4.0Jul 2021View details →

Experimental sloshing pressure data from "Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses"

3D sloshing in a prismatic tank under translational coupled surge-sway (X and Y axis) motions. The main dimensions are height HT = 0.54m, width WT = 0.84m and length LT = 0.72m. The filling ratio of 50% (HF = 0.27m). The periods of surge and sway excitations are Ts = 1.25s with amplitude motions of Ax = 0.0144m (0.02 x length) and Ay = 0.0168m (0.02 x width). Files: experimental_slosh_h50_20cycles_p1_dt0p000100.txt: Experimental pressure data at sensor P1 slosh_3d_exp_timer: Experimental movie sloshing_tank_dimensions.pdf: Tank main dimensions   The experimental data was used in: Cheng, L.Y., Amaro Junior, R.A., Favero, E.H. (2021). Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses. Engineering Analysis with Boundary Elements, 131, 118-145. Available at. doi: <a href="https://doi.org/10.1016/j.enganabound.2021.06.018">https://doi.org/10.1016/j.enganabound.2021.06.018</a>

opencc-by-4.0Aug 2014View details →

Figure 1. Plate 8 in Reversal of precedence for Scarabaeus monoceros Nicolson, 1776, in favor of Scarabaeus oblongus Palisot de Beauvois, 1807 to stabilize the nomenclature of Strategus oblongus (Palisot de Beauvois) from Hispaniola (Coleoptera: Scarabaeidae: Dynastinae)

Figure 1. Plate 8 from Nicolson (1776) illustrating Scarabaeus monoceros in Fig. 5. Illustration courtesy of the John Carter Brown Library at Brown University.

Fig. 1 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River

Fig. 1. Locations of the two studied lagoons in the right bank of the lower Orinoco river, between the cities of Puerto Ordaz and Ciudad Bolívar, Bolívar State, Venezuela. The arrows in black indicate the lagoons.

Fig. 3 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River

Fig. 3. Percentage abundance of total species (S) and number of species for orders in each habitats of the lagoons. The abbreviations of the habitats are explained in the Fig. 2.

Fig. 4 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River

Fig. 4. Mean values (+ confidence interval) of abundance, biomass and richness by habitats and hydrological phases between lagoons. The abbreviations of the hydrological phases and habitats are explained in the Fig. 2.

Fig. 6. nMDS analysis during high waters and low waters. a and b in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River

Fig. 6. nMDS analysis during high waters and low waters. a and b = gill nets sampling; c and d = seine net sampling. Each symbol represents one sample, filled symbols belongs to Las Arhuacas (arh) and those of open symbols to Los Cardonales (car). The dissimilarity between the sampling is approximately proportional to the distance, that is to say to greater distance greater dissimilarity. The abbreviations of the habitats are explained in the Fig. 2.

Fig. 2 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River

Fig. 2. Percentage distribution abundance and biomass of fish by habitats in each hydrological phase in both lagoons. DW = Descent water, LW = Low water, RW = Raise water and HW = High water. RO = Rocky outcrops, FGF = Flooded grass fields, FF = Floodplain forests, B = Beach, AV = Aquatic vegetation, LZFT = Littoral zone with fallen trunks and LZOW = Littoral zone and open waters.

opencc-by-4.0Jun 2006View details →

Fig. 4 in Modeling energy flow in a large Neotropical reservoir: a tool do evaluate fishing and stability

Fig. 4. Simulated Total catch (solid line) and catch values observed (triangles). Simulations performed on ITAIPU-2 model, under constant fishing effort (values were close to 1998). Simulations made in Ecopath with Ecosim (Subroutine: Run Ecossim, module: Results).