Pub Date : 2023-10-16DOI: 10.1016/j.adt.2023.101620
M. Buchowiecki
The differential cross sections for the N(4S)-H(2S), N(4S)-H and N(3P)-H(2S) elastic collisions were calculated and reported in the energy range of –3.7 hartree for the studies of transport properties of ionized gases with the Boltzmann equation.
They were verified by comparison with the integrated and momentum-transfer cross sections. The importance of appropriate grid of angles and possibility of neglecting the smallest phase shifts are discussed.
{"title":"Differential cross sections for the N(4S)-H(2S), N(4S)-H+ and N+(3P)-H(2S) elastic collisions","authors":"M. Buchowiecki","doi":"10.1016/j.adt.2023.101620","DOIUrl":"10.1016/j.adt.2023.101620","url":null,"abstract":"<div><p>The differential cross sections for the N(<sup>4</sup>S)-H(<sup>2</sup>S), N(<sup>4</sup>S)-H<span><math><msup><mrow></mrow><mrow><mo>+</mo></mrow></msup></math></span> and N<span><math><msup><mrow></mrow><mrow><mo>+</mo></mrow></msup></math></span>(<sup>3</sup>P)-H(<sup>2</sup>S) elastic collisions were calculated and reported in the energy range of <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math></span>–3.7 hartree for the studies of transport properties of ionized gases with the Boltzmann equation.</p><p>They were verified by comparison with the integrated and momentum-transfer cross sections. The importance of appropriate grid of angles and possibility of neglecting the smallest phase shifts are discussed.</p></div>","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"155 ","pages":"Article 101620"},"PeriodicalIF":1.8,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0092640X23000487/pdfft?md5=aec2d4e0dcb356aac5b674f09adfae94&pid=1-s2.0-S0092640X23000487-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135762258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.1016/j.adt.2023.101619
Dalip Singh Verma , Vivek , Kushmakshi
Davidson et al. has extended Seeger’s mass formula to non-zero excitation energies by introducing temperature-dependent coefficients in the liquid drop energy part of the semi-empirical mass formula, but did not consider the nuclear shape and shell effects. The semi-empirical mass formula of Davidson et al. is applicable for nuclear temperatures less than or equal to 4 MeV. The mass excess calculated using this mass formula with/without nuclear shape and shell effects does not reproduce the ground state mass excesses of the new atomic mass evaluation data AME2020 and/or FRDM(2012) with its coefficients at zero temperature. So, the coefficients of the semi-empirical mass formula with nuclear shape and shell effects are required to be tuned to reproduce the ground state mass excess of all the nuclei available in the recent atomic mass evaluation data AME2020 and/or FRDM(2012). The bulk and neutron–proton asymmetry coefficients of the semi-empirical mass formula of Davidson et al., including the nuclear shape and shell effects, have been tuned to reproduce the mass excess data for all known 9420 nuclei which include all the nuclei of AME2020 (Z 1-118 and A 1-295) and of FRDM(2012) (Z 8-136 and A 16-339, except 3456 nuclei which are also available in the AME2020 data) at zero temperature. The tuned bulk and neutron–proton asymmetry coefficients reproduce the ground state mass excess of the new atomic mass evaluation data AME2020 and/or FRDM(2012) within a difference of less than 1 MeV and can be used for the applications/investigations in the areas of physics where high energies are experienced or nuclei involved are in excited states, e.g., fusion–evaporation and fusion–fission processes in heavy-ion reactions. The mass excess calculated for the excited states of nuclei is compared with the excited state mass excess of the NUBASE2020 evaluation data and is in good agreement with it.
{"title":"Bulk and neutron–proton asymmetry coefficients of the semi-empirical mass formula tuned to ground state mass excess of AME2020 and/or FRDM(2012)","authors":"Dalip Singh Verma , Vivek , Kushmakshi","doi":"10.1016/j.adt.2023.101619","DOIUrl":"10.1016/j.adt.2023.101619","url":null,"abstract":"<div><p>Davidson et al. has extended Seeger’s mass formula to non-zero excitation energies by introducing temperature-dependent coefficients in the liquid drop energy part of the semi-empirical mass formula, but did not consider the nuclear shape and shell effects. The semi-empirical mass formula of Davidson et al. is applicable for nuclear temperatures less than or equal to 4 MeV. The mass excess calculated using this mass formula with/without nuclear shape and shell effects does not reproduce the ground state mass excesses of the new atomic mass evaluation data AME2020 and/or FRDM(2012) with its coefficients at zero temperature. So, the coefficients of the semi-empirical mass formula with nuclear shape and shell effects are required to be tuned to reproduce the ground state mass excess of all the nuclei available in the recent atomic mass evaluation data AME2020 and/or FRDM(2012). The bulk and neutron–proton asymmetry coefficients of the semi-empirical mass formula of Davidson et al., including the nuclear shape and shell effects, have been tuned to reproduce the mass excess data for all known 9420 nuclei which include all the nuclei of AME2020 (Z <span><math><mo>=</mo></math></span> 1-118 and A <span><math><mo>=</mo></math></span> 1-295) and of FRDM(2012) (Z <span><math><mo>=</mo></math></span> 8-136 and A <span><math><mo>=</mo></math></span><span> 16-339, except 3456 nuclei which are also available in the AME2020 data) at zero temperature. The tuned bulk and neutron–proton asymmetry coefficients reproduce the ground state mass excess of the new atomic mass evaluation data AME2020 and/or FRDM(2012) within a difference of less than 1 MeV and can be used for the applications/investigations in the areas of physics where high energies are experienced or nuclei involved are in excited states, e.g., fusion–evaporation and fusion–fission processes in heavy-ion reactions. The mass excess calculated for the excited states of nuclei is compared with the excited state mass excess of the NUBASE2020 evaluation data and is in good agreement with it.</span></p></div>","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"155 ","pages":"Article 101619"},"PeriodicalIF":1.8,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134994408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.1016/j.adt.2023.101611
Shikha Rathi, Lalita Sharma
Excitation energies, transition parameters and lifetimes for 255 levels with 4 configurations of B-like Xe are calculated using the fully relativistic multiconfiguration Dirac–Hartree–Fock (MCDHF) method. Hyperfine structure constants, isotope shift and Landé factors are computed for the and levels. The contribution of the Breit interaction and QED effects, mainly self-energy and vacuum polarization corrections, on these levels is also investigated. The uncertainties in the lifetimes and line strengths are computed, and transitions are classified according to the NIST accuracy nomenclature. A comparison with the previously available results is also carried out. This study provides extensive results for B-like Xe, which are useful for plasma diagnosis.
采用全相对论多构型狄拉克-哈特里-福克(MCDHF)方法计算了类 B Xe49+ 的 n ≤ 4 构型的 255 个水平的激发能、转变参数和寿命。计算了 2s22p、2s2p2 和 2p3 水平的超细结构常数、同位素位移和 Landé gJ 因子。还研究了布雷特相互作用和 QED 效应(主要是自能和真空极化修正)对这些水平的贡献。计算了寿命和线强度的不确定性,并根据 NIST 精确命名法对跃迁进行了分类。此外,还与以前的结果进行了比较。这项研究提供了有关类 B Xe49+ 的大量结果,对等离子体诊断非常有用。
{"title":"Relativistic atomic structure calculations for B-like xenon ion","authors":"Shikha Rathi, Lalita Sharma","doi":"10.1016/j.adt.2023.101611","DOIUrl":"10.1016/j.adt.2023.101611","url":null,"abstract":"<div><p>Excitation energies, transition parameters and lifetimes for 255 levels with <span><math><mi>n</mi></math></span> <span><math><mo>≤</mo></math></span> 4 configurations of B-like Xe<span><math><msup><mrow></mrow><mrow><mn>49</mn><mo>+</mo></mrow></msup></math></span><span> are calculated using the fully relativistic multiconfiguration Dirac–Hartree–Fock (MCDHF) method. Hyperfine structure<span> constants, isotope shift and Landé </span></span><span><math><msub><mrow><mi>g</mi></mrow><mrow><mi>J</mi></mrow></msub></math></span> factors are computed for the <span><math><mrow><mn>2</mn><msup><mrow><mi>s</mi></mrow><mrow><mn>2</mn></mrow></msup><mn>2</mn><mi>p</mi><mo>,</mo><mn>2</mn><mi>s</mi><mn>2</mn><msup><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span> and <span><math><mrow><mn>2</mn><msup><mrow><mi>p</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span> levels. The contribution of the Breit interaction and QED effects, mainly self-energy and vacuum polarization corrections, on these levels is also investigated. The uncertainties in the lifetimes and line strengths are computed, and transitions are classified according to the NIST accuracy nomenclature. A comparison with the previously available results is also carried out. This study provides extensive results for B-like Xe<span><math><msup><mrow></mrow><mrow><mn>49</mn><mo>+</mo></mrow></msup></math></span><span>, which are useful for plasma diagnosis.</span></p></div>","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"155 ","pages":"Article 101611"},"PeriodicalIF":1.8,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135434286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sustainable water quality data are important for understanding historical variability and trends in river regimes, as well as the impact of industrial waste on the health of aquatic ecosystems. Sustainable water management practices heavily depend on reliable and comprehensive data, prompting the need for accurate monitoring and assessment of water quality parameters. This research describes a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. Internet of Things technology and a Eureka water probe sensor is used to collect and reconstruct the water quality dataset for the period from June 2022–February 2023, with Turbidity, Optical Dissolved Oxygen, Dissolved Oxygen Saturation, Spatial Conductivity, Acidity/Basicity, Total Dissolved Solids, Salinity, Temperature, Chlorophyll, and Depth as the recorded parameters from six different stations. The presented dataset comprises a total of 211,322 data points, which are separated into six CSV files. The dataset is then evaluated using the Long Short-Term Memory (LSTM) algorithm with a Mean Squared Error (MSE) of 0.0012256, and Root Mean Squared Error (RMSE) of 0.0350080. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices.
{"title":"Thailand Raw Water Quality Dataset Analysis and Evaluation","authors":"Jaturapith Krohkaew, Pongpon Nilaphruek, Niti Witthayawiroj, Sakchai Uapipatanakul, Yamin Thwe, Padma Nyoman Crisnapati","doi":"10.3390/data8090141","DOIUrl":"https://doi.org/10.3390/data8090141","url":null,"abstract":"Sustainable water quality data are important for understanding historical variability and trends in river regimes, as well as the impact of industrial waste on the health of aquatic ecosystems. Sustainable water management practices heavily depend on reliable and comprehensive data, prompting the need for accurate monitoring and assessment of water quality parameters. This research describes a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. Internet of Things technology and a Eureka water probe sensor is used to collect and reconstruct the water quality dataset for the period from June 2022–February 2023, with Turbidity, Optical Dissolved Oxygen, Dissolved Oxygen Saturation, Spatial Conductivity, Acidity/Basicity, Total Dissolved Solids, Salinity, Temperature, Chlorophyll, and Depth as the recorded parameters from six different stations. The presented dataset comprises a total of 211,322 data points, which are separated into six CSV files. The dataset is then evaluated using the Long Short-Term Memory (LSTM) algorithm with a Mean Squared Error (MSE) of 0.0012256, and Root Mean Squared Error (RMSE) of 0.0350080. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices.","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"4 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73307194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.adt.2023.101583
A. Rodrigo , N. Otuka , S. Takács , A.J. Koning
Experimental isomeric ratios of light (A 4) particle-induced nuclear reactions were compiled for the product nuclides having metastable states with half-lives longer than 0.1 s. The experimental isomeric ratio data were taken from the EXFOR library and reviewed. When an experiment reports isomer production cross sections instead of isomeric ratios, the cross sections taken from the EXFOR library were converted to the isomeric ratios by us. During compilation, questionable data (e.g., preliminary data compiled in EXFOR in parallel with their final data, sum of isomer production cross sections larger than the total production cross sections) were excluded. As an application of the new compilation, goodness-of-fit was studied for the isomeric ratios predicted by the reaction model code TALYS-1.96. A text file and plots of the compiled isomer production cross sections and isomeric ratios are provided as supplemental materials.
{"title":"Compilation of isomeric ratios of light particle induced nuclear reactions","authors":"A. Rodrigo , N. Otuka , S. Takács , A.J. Koning","doi":"10.1016/j.adt.2023.101583","DOIUrl":"10.1016/j.adt.2023.101583","url":null,"abstract":"<div><p>Experimental isomeric ratios of light (A <span><math><mo>≤</mo></math></span> 4) particle-induced nuclear reactions were compiled for the product nuclides having metastable states with half-lives longer than 0.1 s. The experimental isomeric ratio data were taken from the EXFOR library and reviewed. When an experiment reports isomer production cross sections instead of isomeric ratios, the cross sections taken from the EXFOR library were converted to the isomeric ratios by us. During compilation, questionable data (<em>e.g.,</em> preliminary data compiled in EXFOR in parallel with their final data, sum of isomer production cross sections larger than the total production cross sections) were excluded. As an application of the new compilation, goodness-of-fit was studied for the isomeric ratios predicted by the reaction model code TALYS-1.96. A text file and plots of the compiled isomer production cross sections and isomeric ratios are provided as supplemental materials.</p></div>","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"153 ","pages":"Article 101583"},"PeriodicalIF":1.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48825688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Karanikiotis, Themistoklis G. Diamantopoulos, A. Symeonidis
The availability of code snippets in online repositories like GitHub has led to an uptick in code reuse, this way further supporting an open-source component-based development paradigm. The likelihood of code reuse rises when the code components or snippets are of high quality, especially in terms of readability, making their integration and upkeep simpler. Toward this direction, we have developed a dataset of code snippets that takes into account both the functional and the quality characteristics of the snippets. The dataset is based on the CodeSearchNet corpus and comprises additional information, including static analysis metrics, code violations, readability assessments, and source code similarity metrics. Thus, using this dataset, both software researchers and practitioners can conveniently find and employ code snippets that satisfy diverse functional needs while also demonstrating excellent readability and maintainability.
{"title":"Employing Source Code Quality Analytics for Enriching Code Snippets Data","authors":"Thomas Karanikiotis, Themistoklis G. Diamantopoulos, A. Symeonidis","doi":"10.3390/data8090140","DOIUrl":"https://doi.org/10.3390/data8090140","url":null,"abstract":"The availability of code snippets in online repositories like GitHub has led to an uptick in code reuse, this way further supporting an open-source component-based development paradigm. The likelihood of code reuse rises when the code components or snippets are of high quality, especially in terms of readability, making their integration and upkeep simpler. Toward this direction, we have developed a dataset of code snippets that takes into account both the functional and the quality characteristics of the snippets. The dataset is based on the CodeSearchNet corpus and comprises additional information, including static analysis metrics, code violations, readability assessments, and source code similarity metrics. Thus, using this dataset, both software researchers and practitioners can conveniently find and employ code snippets that satisfy diverse functional needs while also demonstrating excellent readability and maintainability.","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"474 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84073986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, the energy landscape of many countries faces transformational challenges driven by sustainable development objectives, supported by the implementation of clean technologies, such as renewable energy sources, to meet the flexibility and diversification needs of the traditional energy mix. However, integrating these technologies requires a thorough study of the context in which they are developed. Furthermore, it is necessary to carry out an analysis from a sustainable approach that quantifies the impact of proposals on multiple objectives established by stakeholders. This article presents a framework for analysis that integrates a method for evaluating the technical feasibility of resources for photovoltaic solar, wind, small hydroelectric power, and biomass generation. These resources are used to construct a set of alternatives and are evaluated using a hybrid FAHP-TOPSIS approach. FAHP-TOPSIS is used as a comparison technique among a collection of technical, economic, and environmental criteria, ranking the alternatives considering their level of trade-off between criteria. The results of a case study in Valle del Cauca (Colombia) offer a wide range of alternatives and indicate a combination of 50% biomass, and 50% solar as the best, assisting in decision-making for the correct use of available resources and maximizing the benefits for stakeholders.
目前,在可持续发展目标的推动下,在可再生能源等清洁技术的实施支持下,许多国家的能源格局面临变革性挑战,以满足传统能源结构的灵活性和多样化需求。然而,集成这些技术需要对开发它们的环境进行彻底的研究。此外,有必要从可持续的方法进行分析,量化提案对利益相关者建立的多个目标的影响。本文提出了一个分析框架,该框架集成了评估光伏太阳能、风能、小型水力发电和生物质能发电资源技术可行性的方法。这些资源用于构建一组备选方案,并使用混合FAHP-TOPSIS方法进行评估。FAHP-TOPSIS被用作技术、经济和环境标准集合之间的比较技术,根据标准之间的权衡程度对备选方案进行排名。哥伦比亚考卡谷(Valle del Cauca)的一项案例研究的结果提供了广泛的替代方案,并表明50%生物质和50%太阳能的组合是最佳的,这有助于正确利用现有资源的决策,并使利益相关者的利益最大化。
{"title":"A Framework for Evaluating Renewable Energy for Decision-Making Integrating a Hybrid FAHP-TOPSIS Approach: A Case Study in Valle del Cauca, Colombia","authors":"Mateo Barrera-Zapata, Fabian Zuñiga-Cortes, Eduardo Caicedo-Bravo","doi":"10.3390/data8090137","DOIUrl":"https://doi.org/10.3390/data8090137","url":null,"abstract":"At present, the energy landscape of many countries faces transformational challenges driven by sustainable development objectives, supported by the implementation of clean technologies, such as renewable energy sources, to meet the flexibility and diversification needs of the traditional energy mix. However, integrating these technologies requires a thorough study of the context in which they are developed. Furthermore, it is necessary to carry out an analysis from a sustainable approach that quantifies the impact of proposals on multiple objectives established by stakeholders. This article presents a framework for analysis that integrates a method for evaluating the technical feasibility of resources for photovoltaic solar, wind, small hydroelectric power, and biomass generation. These resources are used to construct a set of alternatives and are evaluated using a hybrid FAHP-TOPSIS approach. FAHP-TOPSIS is used as a comparison technique among a collection of technical, economic, and environmental criteria, ranking the alternatives considering their level of trade-off between criteria. The results of a case study in Valle del Cauca (Colombia) offer a wide range of alternatives and indicate a combination of 50% biomass, and 50% solar as the best, assisting in decision-making for the correct use of available resources and maximizing the benefits for stakeholders.","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"9 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77221921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Krivoguz, S. Chernyi, Elena Zinchenko, Artem Silkin, A. Zinchenko
This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model training and testing, using Landsat-5 imagery from 1990 as input data. Four machine learning models (deep neural network, Random Forest, support vector machine (SVM), and AdaBoost) are employed, and their hyperparameters are tuned using random search and grid search. Model performance is evaluated through cross-validation and confusion matrices. The deep neural network achieves the highest accuracy (96.2%) and performs well in classifying water, urban lands, open soils, and high vegetation. However, it faces challenges in classifying grasslands, bare lands, and agricultural areas. The Random Forest model achieves an accuracy of 90.5% but struggles with differentiating high vegetation from agricultural lands. The SVM model achieves an accuracy of 86.1%, while the AdaBoost model performs the lowest with an accuracy of 58.4%. The novel contributions of this study include the comparison and evaluation of multiple machine learning models for land use classification in the Kerch Peninsula. The deep neural network and Random Forest models outperform SVM and AdaBoost in terms of accuracy. However, the use of limited data sources such as cadastral data and scientific articles may introduce limitations and potential errors. Future research should consider incorporating field studies and additional data sources for improved accuracy. This study provides valuable insights for land use classification, facilitating the assessment and management of natural resources in the Kerch Peninsula. The findings contribute to informed decision-making processes and lay the groundwork for further research in the field.
{"title":"Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models","authors":"D. Krivoguz, S. Chernyi, Elena Zinchenko, Artem Silkin, A. Zinchenko","doi":"10.3390/data8090138","DOIUrl":"https://doi.org/10.3390/data8090138","url":null,"abstract":"This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model training and testing, using Landsat-5 imagery from 1990 as input data. Four machine learning models (deep neural network, Random Forest, support vector machine (SVM), and AdaBoost) are employed, and their hyperparameters are tuned using random search and grid search. Model performance is evaluated through cross-validation and confusion matrices. The deep neural network achieves the highest accuracy (96.2%) and performs well in classifying water, urban lands, open soils, and high vegetation. However, it faces challenges in classifying grasslands, bare lands, and agricultural areas. The Random Forest model achieves an accuracy of 90.5% but struggles with differentiating high vegetation from agricultural lands. The SVM model achieves an accuracy of 86.1%, while the AdaBoost model performs the lowest with an accuracy of 58.4%. The novel contributions of this study include the comparison and evaluation of multiple machine learning models for land use classification in the Kerch Peninsula. The deep neural network and Random Forest models outperform SVM and AdaBoost in terms of accuracy. However, the use of limited data sources such as cadastral data and scientific articles may introduce limitations and potential errors. Future research should consider incorporating field studies and additional data sources for improved accuracy. This study provides valuable insights for land use classification, facilitating the assessment and management of natural resources in the Kerch Peninsula. The findings contribute to informed decision-making processes and lay the groundwork for further research in the field.","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"59 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84280879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Horvat, G. Gledec, Tomislav Jagušt, Z. Kalafatić
This data description introduces a comprehensive knowledge graph (KG) dataset with detailed information about the relevant high-level semantics of visual stimuli used to induce emotional states stored in the Nencki Affective Picture System (NAPS) repository. The dataset contains 6808 systematically manually assigned annotations for 1356 NAPS pictures in 5 categories, linked to WordNet synsets and Suggested Upper Merged Ontology (SUMO) concepts presented in a tabular format. Both knowledge databases provide an extensive and supervised taxonomy glossary suitable for describing picture semantics. The annotation glossary consists of 935 WordNet and 513 SUMO entities. A description of the dataset and the specific processes used to collect, process, review, and publish the dataset as open data are also provided. This dataset is unique in that it captures complex objects, scenes, actions, and the overall context of emotional stimuli with knowledge taxonomies at a high level of quality. It provides a valuable resource for a variety of projects investigating emotion, attention, and related phenomena. In addition, researchers can use this dataset to explore the relationship between emotions and high-level semantics or to develop data-retrieval tools to generate personalized stimuli sequences. The dataset is freely available in common formats (Excel and CSV).
{"title":"Knowledge Graph Dataset for Semantic Enrichment of Picture Description in NAPS Database","authors":"M. Horvat, G. Gledec, Tomislav Jagušt, Z. Kalafatić","doi":"10.3390/data8090136","DOIUrl":"https://doi.org/10.3390/data8090136","url":null,"abstract":"This data description introduces a comprehensive knowledge graph (KG) dataset with detailed information about the relevant high-level semantics of visual stimuli used to induce emotional states stored in the Nencki Affective Picture System (NAPS) repository. The dataset contains 6808 systematically manually assigned annotations for 1356 NAPS pictures in 5 categories, linked to WordNet synsets and Suggested Upper Merged Ontology (SUMO) concepts presented in a tabular format. Both knowledge databases provide an extensive and supervised taxonomy glossary suitable for describing picture semantics. The annotation glossary consists of 935 WordNet and 513 SUMO entities. A description of the dataset and the specific processes used to collect, process, review, and publish the dataset as open data are also provided. This dataset is unique in that it captures complex objects, scenes, actions, and the overall context of emotional stimuli with knowledge taxonomies at a high level of quality. It provides a valuable resource for a variety of projects investigating emotion, attention, and related phenomena. In addition, researchers can use this dataset to explore the relationship between emotions and high-level semantics or to develop data-retrieval tools to generate personalized stimuli sequences. The dataset is freely available in common formats (Excel and CSV).","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"4 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90351238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study addressed the challenge of training generative adversarial networks (GANs) on small tabular clinical trial datasets for data augmentation, which are known to pose difficulties in training due to limited sample sizes. To overcome this obstacle, a hybrid approach is proposed, combining the synthetic minority oversampling technique (SMOTE) to initially augment the original data to a more substantial size for improving the subsequent GAN training with a Wasserstein conditional generative adversarial network with gradient penalty (WCGAN-GP), proven for its state-of-art performance and enhanced stability. The ultimate objective of this research was to demonstrate that the quality of synthetic tabular data generated by the final WCGAN-GP model maintains the structural integrity and statistical representation of the original small dataset using this hybrid approach. This focus is particularly relevant for clinical trials, where limited data availability due to privacy concerns and restricted accessibility to subject enrollment pose common challenges. Despite the limitation of data, the findings demonstrate that the hybrid approach successfully generates synthetic data that closely preserved the characteristics of the original small dataset. By harnessing the power of this hybrid approach to generate faithful synthetic data, the potential for enhancing data-driven research in drug clinical trials become evident. This includes enabling a robust analysis on small datasets, supplementing the lack of clinical trial data, facilitating its utility in machine learning tasks, even extending to using the model for anomaly detection to ensure better quality control during clinical trial data collection, all while prioritizing data privacy and implementing strict data protection measures.
{"title":"Enhancing Small Tabular Clinical Trial Dataset through Hybrid Data Augmentation: Combining SMOTE and WCGAN-GP","authors":"Winston Wang, Tun-Wen Pai","doi":"10.3390/data8090135","DOIUrl":"https://doi.org/10.3390/data8090135","url":null,"abstract":"This study addressed the challenge of training generative adversarial networks (GANs) on small tabular clinical trial datasets for data augmentation, which are known to pose difficulties in training due to limited sample sizes. To overcome this obstacle, a hybrid approach is proposed, combining the synthetic minority oversampling technique (SMOTE) to initially augment the original data to a more substantial size for improving the subsequent GAN training with a Wasserstein conditional generative adversarial network with gradient penalty (WCGAN-GP), proven for its state-of-art performance and enhanced stability. The ultimate objective of this research was to demonstrate that the quality of synthetic tabular data generated by the final WCGAN-GP model maintains the structural integrity and statistical representation of the original small dataset using this hybrid approach. This focus is particularly relevant for clinical trials, where limited data availability due to privacy concerns and restricted accessibility to subject enrollment pose common challenges. Despite the limitation of data, the findings demonstrate that the hybrid approach successfully generates synthetic data that closely preserved the characteristics of the original small dataset. By harnessing the power of this hybrid approach to generate faithful synthetic data, the potential for enhancing data-driven research in drug clinical trials become evident. This includes enabling a robust analysis on small datasets, supplementing the lack of clinical trial data, facilitating its utility in machine learning tasks, even extending to using the model for anomaly detection to ensure better quality control during clinical trial data collection, all while prioritizing data privacy and implementing strict data protection measures.","PeriodicalId":55580,"journal":{"name":"Atomic Data and Nuclear Data Tables","volume":"5 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83725294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}