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Differential cross sections for the N(4S)-H(2S), N(4S)-H+ and N+(3P)-H(2S) elastic collisions N(4S)-H(2S)、N(4S)-H+ 和 N+(3P)-H(2S)弹性碰撞的差分截面
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-10-16 DOI: 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 104–3.7 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.

在 10-4-3.7 hartree 的能量范围内,计算并报告了 N(4S)-H(2S)、N(4S)-H+ 和 N+(3P)-H(2S)弹性碰撞的微分截面,用于用玻尔兹曼方程研究电离气体的传输特性。讨论了适当角度网格的重要性以及忽略最小相移的可能性。
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引用次数: 0
Bulk and neutron–proton asymmetry coefficients of the semi-empirical mass formula tuned to ground state mass excess of AME2020 and/or FRDM(2012) 根据 AME2020 和/或 FRDM(2012)的基态质量过剩调整的半经验质量公式的总量和中子-质子不对称系数
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-09-28 DOI: 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.

戴维森等人通过在半经验质量公式的液滴能量部分引入随温度变化的系数,将西格的质量公式扩展到非零激发能量,但没有考虑核形状和核壳效应。戴维森等人的半经验质量公式适用于核温度小于或等于 4 MeV 的情况。使用该质量公式计算出的质量过剩(包括/不包括核形状和核壳效应)并不能再现新的原子质量评估数据 AME2020 和/或 FRDM(2012) 在零温度下的基态质量过剩及其系数。因此,需要对包含核形状和壳效应的半经验质量公式的系数进行调整,以再现最新原子质量评价数据 AME2020 和/或 FRDM(2012) 中所有原子核的基态质量过剩。戴维森等人的半经验质量公式中的体积和中子-质子不对称系数,包括核形状和壳效应,已经过调整,以再现所有已知 9420 个原子核在零温下的质量过剩数据,其中包括 AME2020(Z = 1-118,A = 1-295)和 FRDM(2012)(Z = 8-136,A = 16-339,但不包括 AME2020 数据中的 3456 个原子核)中的所有原子核。调整后的体态和中子-质子不对称系数再现了新原子质量评估数据 AME2020 和/或 FRDM(2012) 的基态质量过剩,差值小于 1 MeV,可用于经历高能量或涉及激发态原子核的物理学领域的应用/研究,例如重离子反应中的聚变-蒸发和聚变-裂变过程。计算出的核激发态质量过剩量与 NUBASE2020 评估数据的激发态质量过剩量进行了比较,结果非常吻合。
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引用次数: 0
Relativistic atomic structure calculations for B-like xenon ion 类 B 氙离子的相对论原子结构计算
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-09-22 DOI: 10.1016/j.adt.2023.101611
Shikha Rathi, Lalita Sharma

Excitation energies, transition parameters and lifetimes for 255 levels with n 4 configurations of B-like Xe49+ are calculated using the fully relativistic multiconfiguration Dirac–Hartree–Fock (MCDHF) method. Hyperfine structure constants, isotope shift and Landé gJ factors are computed for the 2s22p,2s2p2 and 2p3 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 Xe49+, 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。
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引用次数: 0
Thailand Raw Water Quality Dataset Analysis and Evaluation 泰国原水水质数据集分析与评价
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-09-04 DOI: 10.3390/data8090141
Jaturapith Krohkaew, Pongpon Nilaphruek, Niti Witthayawiroj, Sakchai Uapipatanakul, Yamin Thwe, Padma Nyoman Crisnapati
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.
可持续的水质数据对于了解河流状况的历史变化和趋势以及工业废物对水生生态系统健康的影响非常重要。可持续的水管理实践在很大程度上依赖于可靠和全面的数据,因此需要对水质参数进行准确的监测和评估。本研究描述了一个重建的每日水质数据集,该数据集补充了泰国湄南河沿岸六个站点的罕见历史观测数据。利用物联网技术和Eureka水探针传感器,以浊度、光学溶解氧、溶解氧饱和度、空间电导率、酸碱度、总溶解固形物、盐度、温度、叶绿素和深度作为6个不同站点的记录参数,采集并重建了2022年6月至2023年2月的水质数据集。所呈现的数据集共包含211,322个数据点,这些数据点被分成6个CSV文件。然后使用长短期记忆(LSTM)算法对数据集进行评估,均方误差(MSE)为0.0012256,均方根误差(RMSE)为0.0350080。拟议的数据集为研究河流生态系统的研究人员提供了有价值的见解,支持明智的决策和可持续的水管理实践。
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引用次数: 1
Compilation of isomeric ratios of light particle induced nuclear reactions 轻粒子诱导核反应异构体比率的编制
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-09-01 DOI: 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.

编制了光(A≤4)粒子诱导核反应的实验同分异构体比,得到了半衰期大于0.1 s的亚稳态产物核素。实验同分异构体比值数据取自EXFOR文库并进行了校核。当实验报告同分异构体生成截面而不是同分异构体比率时,我们将EXFOR库中的截面转换为同分异构体比率。在编制过程中,排除了有问题的数据(例如,在EXFOR中与最终数据并行编制的初步数据,大于总生产截面的同分异构体生产截面总和)。作为新编译的应用,对反应模型代码TALYS-1.96预测的同分异构体比率进行了拟合优度研究。本文提供了一份文本文件和编制的同分异构体生产截面和同分异构体比率图作为补充材料。
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引用次数: 2
Employing Source Code Quality Analytics for Enriching Code Snippets Data 利用源代码质量分析丰富代码片段数据
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-08-31 DOI: 10.3390/data8090140
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.
像GitHub这样的在线存储库中的代码片段的可用性导致了代码重用的上升,这种方式进一步支持基于开源组件的开发范例。当代码组件或代码片段是高质量的,特别是在可读性方面,使得它们的集成和维护更简单时,代码重用的可能性就会增加。朝着这个方向,我们开发了一个代码片段的数据集,它同时考虑了代码片段的功能和质量特征。该数据集基于CodeSearchNet语料库,并包含其他信息,包括静态分析指标、代码违规、可读性评估和源代码相似性指标。因此,使用该数据集,软件研究人员和从业人员都可以方便地找到和使用满足各种功能需求的代码片段,同时还具有出色的可读性和可维护性。
{"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}
引用次数: 0
A Framework for Evaluating Renewable Energy for Decision-Making Integrating a Hybrid FAHP-TOPSIS Approach: A Case Study in Valle del Cauca, Colombia 整合混合FAHP-TOPSIS方法的可再生能源决策评估框架:哥伦比亚考卡谷案例研究
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-08-30 DOI: 10.3390/data8090137
Mateo Barrera-Zapata, Fabian Zuñiga-Cortes, Eduardo Caicedo-Bravo
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。
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引用次数: 0
Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models 使用Landsat-5进行准确的历史LULC分类:机器学习模型的比较
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-08-30 DOI: 10.3390/data8090138
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.
本研究探讨了各种机器学习模型在刻赤半岛土地利用和土地覆盖(LULC)分类中的应用。该研究利用档案现场数据、地籍数据和已发表的科学文献进行模型训练和测试,使用1990年的Landsat-5图像作为输入数据。采用深度神经网络、随机森林、支持向量机(SVM)和AdaBoost四种机器学习模型,并通过随机搜索和网格搜索对其超参数进行调优。通过交叉验证和混淆矩阵来评估模型的性能。深度神经网络达到了最高的准确率(96.2%),在水、城市土地、开阔土壤和高植被分类方面表现良好。然而,它在划分草原、裸地和农业区方面面临挑战。随机森林模型的准确率为90.5%,但在区分高植被和农田方面存在困难。SVM模型的准确率为86.1%,而AdaBoost模型的准确率最低,为58.4%。本研究的新贡献包括对刻赤半岛土地利用分类的多种机器学习模型的比较和评估。深度神经网络和随机森林模型在精度方面优于SVM和AdaBoost。然而,使用有限的数据来源,如地籍数据和科学论文,可能会带来局限性和潜在的错误。未来的研究应考虑纳入实地研究和其他数据来源,以提高准确性。该研究为刻赤半岛的土地利用分类、自然资源评估和管理提供了有价值的见解。这些发现有助于知情的决策过程,并为该领域的进一步研究奠定基础。
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引用次数: 1
Knowledge Graph Dataset for Semantic Enrichment of Picture Description in NAPS Database 基于aps数据库的图片描述语义丰富的知识图谱数据集
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-08-24 DOI: 10.3390/data8090136
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).
该数据描述介绍了一个全面的知识图(KG)数据集,其中包含有关用于诱导存储在Nencki情感图片系统(NAPS)存储库中的情绪状态的视觉刺激的相关高级语义的详细信息。该数据集包含5个类别的1356张nap图片的6808个系统手动分配的注释,链接到WordNet同义词集和以表格形式呈现的建议的上层合并本体(SUMO)概念。这两个知识数据库都提供了广泛的、受监督的分类术语表,适合描述图片语义。注释术语表由935个WordNet和513个SUMO实体组成。还提供了数据集的描述以及用于收集、处理、审查和将数据集作为开放数据发布的具体过程。该数据集的独特之处在于,它以高质量的知识分类法捕获了复杂的对象、场景、动作和情感刺激的整体背景。它为各种研究情感、注意力和相关现象的项目提供了宝贵的资源。此外,研究人员可以使用该数据集来探索情绪与高级语义之间的关系,或开发数据检索工具来生成个性化的刺激序列。该数据集以常用格式(Excel和CSV)免费提供。
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引用次数: 0
Enhancing Small Tabular Clinical Trial Dataset through Hybrid Data Augmentation: Combining SMOTE and WCGAN-GP 通过混合数据增强增强小表格临床试验数据集:SMOTE和wggan - gp的结合
IF 1.8 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL Pub Date : 2023-08-23 DOI: 10.3390/data8090135
Winston Wang, Tun-Wen Pai
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.
本研究解决了在小型表格临床试验数据集上训练生成对抗网络(gan)以进行数据增强的挑战,由于样本量有限,这些数据集在训练中存在困难。为了克服这一障碍,提出了一种混合方法,结合合成少数过采样技术(SMOTE),最初将原始数据扩展到更大的规模,以改进随后使用带梯度惩罚的Wasserstein条件生成对抗网络(wggan - gp)进行的GAN训练,该方法被证明具有最先进的性能和增强的稳定性。本研究的最终目的是证明最终wggan - gp模型生成的合成表格数据的质量使用这种混合方法保持了原始小数据集的结构完整性和统计代表性。这一重点与临床试验尤其相关,在临床试验中,由于隐私问题和受试者登记的可及性受限,数据可用性有限,这构成了共同的挑战。尽管数据有限,但研究结果表明,混合方法成功地生成了保留原始小数据集特征的合成数据。通过利用这种混合方法的力量来生成忠实的合成数据,在药物临床试验中加强数据驱动研究的潜力变得明显。这包括对小型数据集进行强大的分析,补充临床试验数据的不足,促进其在机器学习任务中的应用,甚至扩展到使用该模型进行异常检测,以确保在临床试验数据收集过程中更好的质量控制,同时优先考虑数据隐私并实施严格的数据保护措施。
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引用次数: 0
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