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Is lithium from geothermal brines the sustainable solution for Li-ion batteries?

The rising demand for Li, paramount for energy storage, necessitates expanded supply. As the supply is concentrated in a few countries, this poses supply chain risks for Li-ion battery makers. To diversify suppliers, alternative Li <a title="Learn more about ore deposits from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/ore-deposit">ore deposits</a> such as geothermal brines are being explored. However, Li extraction from geothermal brines is challenging due to the unique chemistry and elevated temperatures. Since Li-extraction from geothermal brines is in its infancy, data availability and quality are still poor, hampering <a title="Learn more about life cycle assessments from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/life-cycle-assessment">life cycle assessments</a>. Hence, this study provides a parametrized life cycle inventory model of Li carbonate production from geothermal brines. The model accounts for site-specific environmental conditions and technological features. Life cycle impacts at the Salton Sea in the US (1686 cases) and the Upper Rhine Graben in Germany (1982 cases) are quantified. The high case numbers are chosen to mitigate the high uncertainties in input parameters. Specifically, the brine chemistry, adsorption yield, drilling required and energy inputs are varied. <a title="Learn more about Climate change impacts from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/climate-change-impact">Climate change impacts</a> of selected cases vary within 18–59 kg CO2eq/kg Li carbonate at the Salton Sea and within 5.3–46 kg CO2eq/kg Li carbonate at the Upper Rhine Graben, compared to 2.1–11 kg CO2eq/kg Li carbonate in existing ecoinvent data sets. The wide range of potential impacts underscore the necessity of early-stage assessments of the technologies. In case of high drilling demand and use of <a title="Learn more about fossil from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/fossil">fossil</a> energy, climate change impacts of Li-ion batteries using Li carbonate from geothermal brines can increase by 30–41 % compared to literature values.

Implementation and Results of a "Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem"

1. Introduction In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) with and without FFA applied to the Quadratic Assignment Problem. These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied. Jiayang Chen, Zhize Wu, Sarah L. Thomson, and Thomas Weise. Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. In 16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24). November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress. To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case. 2. Directory Structure This archive contains the following directories: <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> 3. License The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive. 4. Contact If you have any questions or suggestions, please contact Mr. Jiayang CHEN (陈嘉阳) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:cjy65820607@163.com">cjy65820607@163.com</a>.

Urocanic acid as a novel scaffold for next-gen nature-inspired sunscreens: II. Time-resolved spectroscopy under solution conditions - dataset

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Extended Hubbard Model with spin-valley polarization DMRG solution

The data set for containing observables obtained by means of Density Matrix Renormalization Group for the Extended Hubbard model at filling n=2/3 and with spin-valley polarization included in the single-particle term. For all the considered case the on-site Hubbard repulsion U is set to 15|t| and |t| = 1. The phase for hopping termi is 2*pi/3. Folders naming convention is following: VXYZ V stands for intersite interaction and XYZ is its magnitude.   In each folder there are following files: - n_single columns: i  j Re_up Im_up Re_dn Im_dn Where: i,j are site indicies Re(Im)_up(dn) stand for the real(imaginary)  part of single particle equal-time Green function for up(dn) spin - nn columns: i  j  Re_up Im_up Re_dn Im_dn Re_(up+dn) Im_(up+dn) Where: i,j are site indicies Re(Im)_up(dn) stand for the real(imaginary)  part of two particle equal-time Green function <n_i,up(dn) n_j,up(dn)> for up(dn) spin and two last columns correspond to <(n_i,up+n_i,dn)(n_j,up+n_j,dn)> - ss columns: i  j  Re  Im Where: i,j are site indicies Re(Im) stand for the real(imaginary)  part of two particle equal-time Green function <S_i S_j> where S_i is total spin operator on site "i" - szsz columns: i  j  Re  Im Where: i,j are site indicies Re(Im) stand for the real(imaginary)  part of two particle equal-time Green function <S_z S_z> where S_z is z-th component of  spin operator on site "i" - spsm columns: i  j  Re  Im Re Im Where: i,j are site indicies Re(Im) stand for the real(imaginary)  part of two particle equal-time Green function <S_i^(+)S_j^(-)> and <S_i^(-)S_j^(+)> where S_i^(+/-) are spin ladder operators

T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data, Part 2

Raw and processed MRI data of the study entiled: T1234-Part 2 Authors:  Chung (Kenny) Kan1, Rüdiger Stirnberg2, Marcela Montequin1, Omer Faruk Gulban3,4, A Tyler Morgan1, Peter Bandettini1, Laurentius (Renzo) Huber1 <ol> <li>NIMH, NIH, Bethesda, United States,</li> <li>German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,</li> <li>CN, FPN, University of Maastricht, The Netherlands,</li> <li>Brain Innovation, Maastricht, The Netherlands</li> </ol> Purpose: High-resolution fMRI at 7T is limited by limited registration quality between functional data with structural scans. We aim to provide a fast acquisition method that provides distortion-matched, artifact mitigated structural reference data. Methods: We developed an efficient sequence approach with adjustable distortions, termed T1234: T1-weighted 2-inversion 3D-EPI with 4 directions for high-resolution fMRI. A forward Bloch model is implemented for T1 quantification and protocol optimization. 20 participants were scanned on 7T with structural and functional protocols to evaluate the utility of T1234. Results: We find that a fast protocol provides reliable data for whole-brain segmentations in EPI-space  in 3:00-3:40 min). It is robust across sessions, participants, and three 7T SIEMENS scanners. T1234 allows layer fMRI signal analysis with higher laminar precision. Conclusion: This structural mapping approach allows precise registration with fMRI data. T1234 is implemented, validated, and tested to serve users of our sequence (locally and >50 centers worldwide).  Part 1 of the data set is here: https://zenodo.org/doi/10.5281/zenodo.13366783

Segment and Support: a dual-purpose Deep Learning solution for Limited Angle Holographic Tomography - dataset

This dataset constains training data of two types: <ol> <li>Synthetic volumes: Containing randomly generated and placed spheres of various sizes. The dataset was augmented by adding noise, rotation, holes, flipping, blurring and shifting. Data generated this way is treated as the input data. An output pair for each datapoint has been generated by inducing missing cone artifacts, by masking the 3D Fourier spectrum of each volume (to mimic an experimental scenario). </li> <li>Experimental data: This part of the dataset contains erroneous Direct Inversion reconstructions of cells [1], organoids [2], phantoms [3-4]. The mask has been generated through a thresholding step of a GTVIC [5] algorithm.</li> </ol> Each volume has the shape (128,128,128), and is of float32 precision. They are saved as .tiff files, which can be easily read via the tifffile Python library. Additionally, the dataset contains: <ul> <li>SnSNet_best_loss_model.pth - PyTorch file containing the weights of the model, whose loss was the lowest during training. </li> <li>SnSNet_best_metric_model.pth - PyTorch file containing the weights of the model, whose metrics were the highest during training.  </li> <li>SnSNet_config.yml - YAML file containing all config information about the model, its training and dataset. It is necessary for inference and model recreation.</li> <li>Inference.py - Short Python script for quick inference. The input should be a floating point, erroneous DI reconstruction. It produces a binary mask of the object with corrected geometry and saves it into the same directory as the input file, with the prefix PRED_.</li> <li>reqs.txt - text file, containing the library requirements necessary for running Inference.py. It is compiled, such that the script performs the inference on GPU, using CUDA 11.8 on Windows 10. If the configuration does not enable running on GPU, it will run on CPU and will be indicated in the logs.</li> <li>training.xlsx - Excel file, containing data about the training process.</li> </ul>   [1] M. Baczewska, W. Krauze, A. Kuś, P. Stępień, K. Tokarska, K. Zukowski, E. Malinowska, Z. Brzózka, and M. Kujawińska, “On-chip holographic tomography for quantifying refractive index changes of cells’ dynamics,” in Quantitative Phase Imaging VIII, vol. 11970 Y. Liu, G. Popescu, and Y. Park, eds., International Society for Optics and Photonics (SPIE, 2022), p. 1197008. [2] P. Stępień, M. Ziemczonok, M. Kujawińska, M. Baczewska, L. Valenti, A. Cherubini, E. Casirati, and W. Krauze, “Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging,” Biomed. Opt. Express 13, 5709–5720 (2022). [3] M. Ziemczonok, A. Kuś, P. Wasylczyk, and M. Kujawińska, “3d-printed biological cell phantom for testing 3d quantitative phase imaging systems,” Sci. Reports 9, 1–9 (2019). [4] M. Ziemczonok, A. Kuś, and M. Kujawińska, “Optical diffraction tomography meets metrology — measurement accuracy on cellular and subcellular level,” Measurement 195, 111106 (2022). [5] W. Krauze, P. Makowski, M. Kujawińska, and A. Kuś, “Generalized total variation iterative constraint strategy in limited angle optical diffraction tomography,” Opt. Express 24, 4924–4936 (2016).

opengpl-3.0-or-laterAug 2024View details →

PMcardio ECG Image Database (PM-ECG-ID): A Diverse ECG Database for Evaluating Digitization Solutions

The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions. PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below: <ul> <li>metadata.csv: This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns: <ul> <li>Image name: image name with extension,</li> <li>ECG ID: this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the leads.npz and rhythms.npz,</li> <li>Image relative path: relative path to the image in question,</li> <li>Image page: page number of the particular image (starting from 0),</li> <li>ECG number of pages: number of pages in the whole ECG,</li> <li>ECG number of columns per page: number of columns per page in the ECG,</li> <li>ECG number of rows per page: number of rows in the ECG,</li> <li>ECG number of rhythm leads:  number of rhythms in the ECG,</li> <li>ECG format: short version of the ECG format.</li> </ul> </li> <li>data folder:  <ul> <li>leads.npz: NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.</li> <li>rhythms.npz: NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.</li> </ul> </li> <li>visual_data folder:   This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The metadata.csv file provides a direct link to each image and its associated ECG information.</li> </ul>

Acrolein solution and gas phase configurations and OpenMM inputs

Dataset for acrolein solution phase and gas phase spectrum calculations with the ESPF-DRF method. Included in the dataset are: <ul> <li>OpenMM input files for generating configurations together with the trajectory file.</li> <li>The extracted configurations for gas and aqueous solution simulations.</li> <li>QM/MM ESPF-DRF data and input files for PyESPF + PySCF for several different water models.</li> </ul>

B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests

We provide the data and online Appendix for the paper "B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests".  File list: <ul> <li>Appendix.pdf: including the proofs for the theorems in our paper.</li> <li>data.zip: including datasets and generated code solutions/test cases used for our replicated package, which is available at <a href="https://github.com/ZJU-CTAG/B4">https://github.com/ZJU-CTAG/B4</a>. </li> </ul>

opennotspecifiedSep 2024View details →

FIGURE 1 in There and Back Again: On the Homonymy, Solution and Re-Homonymy of Pampa Walker 1854 (Hexapoda: Lepidoptera: Zygaenidae) and Pampa Reichenbach, 1854 (Aves: Apodiformes: Trochilidae)

FIGURE 1. Timeline depicting the Principle of Priority and Principle of First Reviser for Pampa Walker, 1854 and Pampa Reichenbach, 1854. The image summarizes the relation between the multiple names assigned to both genera but does not show the nomenclatural conflicts between the species groups in Campylopterus.

Effect of Performance Enhancing Treatment on Ga-Pt Supported Catalytically Active Liquid Metal Solutions (SCALMS) for Propane Dehydrogenation

The dataset for manuscript entitled 'Effect of Performance Enhancing Treatment on Ga-Pt Supported Catalytically Active Liquid Metal Solutions (SCALMS) for Propane Dehydrogenation'   The raw data in ‘Dataset of Figure 2a-c.rar’ is used for plotting the spectra in Fig. 2 and Fig. S1, S2, S3 and S5 The raw data in ‘t1-t7_Fig. 3 & Fig. S6-S13.rar’ is used for plotting the spectra in Fig. 3 and Fig. S6-S13 The raw data in 'Fig S14.rar' is used for plotting the spectra in Fig. S14 The raw data in 'Fig S15.rar' is used for plotting the spectra in Fig. S15

Identification of crashworthy designs combining active learning and the solution space methodology - Validation and training dataset

Dataset and Python codes for training a crashworthiness classifier.

opencanada-crownSep 2024View details →

Montreal climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

Data file: Equal performance of HTK-based and UW-based perfusion solutions in sub-normothermic liver machine perfusion

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opencanada-crownSep 2024View details →

Toronto climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

Communities Creating Climate Solutions for a Healthy Planet and Healthy People - A Case Study of an Ecovillage in Sothern Germany

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SPARCS_WP3_Espoo_City_SPARCS-WP3 number of promising solutions for PEDs identified in Espoo

Number of promising solutions for PEDs identified during SPARCS Work Package 3 (WP3) work

opencc-by-4.0Oct 2024View details →

EVALUATION OF THE EFFECTIVENESS OF TCHNICAL AND SANITARY-HYGIENIC SOLUTIONS FOR STUDYING AND IMPROVING WORKING CONDITIONS IN GREENHOUSES.

At present, one of the most pressing issues is the rapid growth of the world's population and their production, ensuring the safe operation of all aspects of the tasks involved in the development of agro-industrial systems. how much work is being done. The Action Strategy pursued by the President for 2021-2024 is of great importance as a key link in the radical reform of the agro-industrial system, the creation of safe conditions for workers in the cultivation of safe and competitive agricultural products.

opencc-by-4.0Oct 2024View details →

SUSTAINABLE FINANCING AS A FACTOR INFLUENCING ECO-FRIENDLY BUSINESS TRANSFORMATION: EXISTING CHALLENGES AND POTENTIAL SOLUTIONS

This study underscores the slow adoption of eco-friendly practices, particularly in high-emission sectors ("brown" businesses), and emphasizes the role of sustainable financing in implementation of environmentally sustainable business practices, with a particular interest in such types of corporate financing as sustainability-linked loans and a relatively new type of financing provided to companies with core business falling under eligible green criteria (loans for green businesses). The purpose was to identify existing challenges preventing promotion of these two types of sustainable financing and borrowers’ sustainable transformation, and propose potential solutions addressing these challenges. The analysis was performed based on a vast literature review. The study revealed the existence of such challenges as borrowers’ profit-driven motives, lack in regulatory transparency, concerns of financial institutions about their profitability and risk of greenwashing, and insufficient governmental support. To address these challenges, regulatory measures are recommended to incentivize financial institutions to offer reduced interest rates on loans for green business and sustainability-linked loans, with governments compensating such losses through imposing higher taxes on “brown” businesses. The study also proposes a potential action plan on amending the regulatory framework with the main focus on the need to develop respective changes with the involvement of all relevant stakeholders. It also addresses the need of establishing mandatory criteria for recognizing businesses as green to qualify for green business loans, and obligatory KPIs for each industry outlining clear targets for improving a company's sustainability profile to be eligible for sustainability-linked loans. These obligatory criteria and KPIs may be adjusted by financial institutions depending on aspects being material for the borrower. The borrower’s compliance with criteria set for green business loans should be regularly reported and verified by an expert company, similarly to the existing rules for sustainability-linked loans. Further, the study proposes incorporating a regulatory body to oversee compliance of financial institutions with these regulations and introducing penalties for their violations, and performing monitoring and adaptation of a regulatory framework, if necessary. The suggested amendments, being subject to a comprehensive feasibility study, are intended to promote the considered types of sustainable financing and boost transit to eco-friendly business practices.