This study proposes a data visualization strategy for predicting rock mass classification, which only adopts 5 tunnelling parameters related to rock fragmentation as input. For implementation, first, the database of T...
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This study proposes a data visualization strategy for predicting rock mass classification, which only adopts 5 tunnelling parameters related to rock fragmentation as input. For implementation, first, the database of TBM tunnelling parameters was established after data cleaning, in which 4,172 boring cycles from the Yinchao project were acquired. Subsequently, five tunnelling parameters of each cycle were plotted automatically into 9 single images in a defined manner. AIF (Assembled Image Fusion) image was composed of 9 single images. Finally, a convolutional neural network model (ResNet) was employed to recognize AIF fused image for rock mass classification. The results show that: (1) The AIF fused image outperforms single images, with an F1 score of 0.83 compared to 0.77 for the best single image (image of cutterhead torque T vs cycle duration time t). (2) In comparison, LightGBM and RF models, using mean values of tunneling parameters, achieved F1 scores of 0.73 and 0.71, respectively, which are inferior to the AIF fused image approach. (3) The proposed visualization strategy suggests that image plotting should consider the statistical distribution of the dataset for axis scaling, while color choice has minimal impact on recognition. This strategy offers an effective framework for prediction of rock mass classification.
作者：
Liu, TingtingChongqing Normal Univ
Sch Econ & Management Chongqing 401331 Peoples R China Sichuan Univ
Inst New Energy & Low Carbon Technol Business Sch Chengdu 610064 Peoples R China
Energy policy plays a critical role in developing low-carbon and sustainable energy systems, requiring dynamic adjustments to address the challenges of different development stages. Despite extensive global policy pra...
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Energy policy plays a critical role in developing low-carbon and sustainable energy systems, requiring dynamic adjustments to address the challenges of different development stages. Despite extensive global policy practices, a systematic understanding of how renewable energy policies, especially for wind power, evolve across different developmental stages remains limited. This study constructs a comprehensive wind policy dataset derived from official policy documents and employs policy paradigm analysis along with keyword visualization techniques to investigate the evolution of China's wind policy framework. The findings reveal that China has progressively adopted more refined policy mixes at each stage, responding promptly and appropriately to the wind industry's evolving needs. Three core policy features identified include target decomposition and assessment systems, power subsidy reforms, and mechanisms for gradual improvement and reactive intervention. Drawing lessons from results and diverse international practices, three implications are outlined: (1) evaluate grid parity strategies considering regional heterogeneity;(2) design electricity market mechanisms explicitly tailored to integrate variable renewables effectively;(3) accelerate the development of zero-carbon flexibility resources across varying timescales. These insights provide a robust and transferable framework to guide policy formulation and evaluation globally, supporting a broader renewable energy transition.
Accurately assigning formulas to thousands of peaks generated by ultrahigh resolution mass spectrometry in a single analysis poses a significant challenge, especially when dealing with diverse molecular compositions a...
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Accurately assigning formulas to thousands of peaks generated by ultrahigh resolution mass spectrometry in a single analysis poses a significant challenge, especially when dealing with diverse molecular compositions across complex mixtures. This difficulty is further compounded by the lack of an established universal mass calibration and formula assignment method. We have developed HRMS-Viewer, a Python-based software tool designed for processing ultrahigh resolution mass spectrometry data specific to petroleum and natural organic matter (NOM). The software employs an efficient, experience-driven approach for small molecule formula assignment, offering a streamlined yet intuitive workflow. Key features include advanced noise reduction, automatic or manual recalibration, real-time visualization of formula assignment results, and options for manual correction. During the workflow, HRMS-Viewer enables the visualization and manual control of critical steps including noise reduction, recalibration, peak identification, and data review.
data visualization tools are important tools for making health decisions based on large amounts of data. However, very few comparisons of data visualization tools have been done in the context of health-related resear...
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data visualization tools are important tools for making health decisions based on large amounts of data. However, very few comparisons of data visualization tools have been done in the context of health-related research. This paper compares commonly available visualization tools from the perspective of a dashboard developer in the context of scientific health research using real-world data and to provide general suggestions for dashboard developers' future visualization efforts in health-related scientific discoveries. We evaluated four commonly available visualization tools (Tableau, Spotfire, PowerBI, and R Shiny). Each tool was evaluated on visualization outcomes (comparing the generated outputs of tables, scatterplots, correlation plots, treemaps, heatmaps, and geographic maps), usability, Cloud compatibility, analytic integration, and a few other factors. All tools generated comparable visualization outcomes in our tests and were equally usable with the exception of R Shiny which is not recommended for users with limited coding experience. Every tool except R Shiny supports connections to PostgreSQL, gSheets, and Athena without additional configurations. All tools support integration with R and Python;however, PowerBI requires some additional configuration. Every tool is capable of data pre-processing and supports data transformation, null detection and outlier detection. Each tool has its own unique advantages and limitations.
Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene...
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Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene abstractions have been proposed, but they are restricted to specific applications for a limited set of visualization types. A unified and expressive model of data visualization scenes for different applications has been missing. To fill this gap, we present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes, to support applications in scene understanding and augmentation. MSC consists of two parts: a unified object model describing the structure of a visualization scene in terms of semantic components, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three applications: visualization authoring, visualization deconstruction and reuse, and animation specification.
We propose an asymmetry index as a measure of degree of asymmetry of a given dataset. It provides an additional information on a dataset allowing to guide and improve any further analysis. The index reflects the inten...
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We propose an asymmetry index as a measure of degree of asymmetry of a given dataset. It provides an additional information on a dataset allowing to guide and improve any further analysis. The index reflects the intensity of the asymmetric relationships among data resulting from hierarchical data structure. Using the information retrieved by our asymmetry index, one obtains a justification and explanation of the effectiveness of the subsequent asymmetric data analysis methods, as well as helpful preparation to asymmetrizing the tools for the further analysis. The asymmetry index is based on the k-nearest neighbors graph representing the considered data. Therefore, it uses the intrinsic geometry-based information on the data, in this way, providing an insight into the data structure. Our experiments on real data are designed to verify the usefulness of the asymmetry index and the correctness of its theoretical fundamentals. In our empirical validation, we employ the symmetric and asymmetric dimensionality reduction algorithms and evaluate their results on the basis of clustering in the 2-dimensional visualization space. We test, whether our index indeed predicts the level of superiority of the asymmetric methods over their symmetric counterparts.
With the rise of short-form video platforms and the increasing availability of data, we see the potential for people to share short-form videos embedded with data in situ (e.g., daily steps when running) to increase t...
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With the rise of short-form video platforms and the increasing availability of data, we see the potential for people to share short-form videos embedded with data in situ (e.g., daily steps when running) to increase the credibility and expressiveness of their stories. However, creating and sharing such videos in situ is challenging since it involves multiple steps and skills (e.g., data visualization creation and video editing), especially for amateurs. By conducting a formative study (N=10) using three design probes, we collected the motivations and design requirements. We then built VisTellAR, a mobile AR authoring tool, to help amateur video creators embed data visualizations in short-form videos in situ. A two-day user study shows that participants (N=12) successfully created various videos with data visualizations in situ and they confirmed the ease of use and learning. AR pre-stage authoring was useful to assist people in setting up data visualizations in reality with more designs in camera movements and interaction with gestures and physical objects to storytelling.
PurposeUncontrolled hypertension is a significant US health problem, despite existing effective treatments. This study assessed the impact of variations in patterns of blood pressure data on physician perceptions of h...
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PurposeUncontrolled hypertension is a significant US health problem, despite existing effective treatments. This study assessed the impact of variations in patterns of blood pressure data on physician perceptions of hypertension control using different forms of data *** (N = 57) reviewed eight brief vignettes describing a fictitious patient;each vignette included a graph of the patient's blood pressure data. We examined how variations in mean systolic blood pressure (SBP), blood pressure standard deviation (SD), and form of visualization (e.g., line graph with raw values or smoothed values only) affected judgments about hypertension control and need for medication *** successfully reduced visual noise for the physicians. For controlled hypertension, physician judgments were more consistent with clinical guidelines when using the smoothed graph compared with the raw data graph. Judgments about hypertension control with the smoothed graph were similar to judgments made using the raw data graph for cases of uncontrolled *** visualization can direct physicians to attend to more clinically meaningful information, thereby improving their judgments of hypertension control.
In this work, we explored the role of a single electron in the energy of neutral and charged clusters of Na39\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{am...
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In this work, we explored the role of a single electron in the energy of neutral and charged clusters of Na39\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Na}_{39}$$\end{document} using data visualization and statistical techniques as a new insight. Initially, we studied the effects of one electron, time, and temperature on energy using multiple linear regression analysis with dummy variables, and the results demonstrated that all three predictors significantly affected the energy. Time had a positive impact (direct ratio effect) on the energy of Na-39\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{Na}<^>{-}}_{39}$$\end{document}, and Na39,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Na}_{39},$$\end{document} and a negative impact (inverse ratio effect) on the energy of Na+39,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{Na}<^>{+}}_{39},$$\end{document} while temperature had a positive effect on the energy of all three sodium clusters. Then, to study the thermodynamic properties of each cluster, we employed the fuzzy clustering technique. The results verified that each sodium cluster is divided into three groups based on the different temperatures used to investigate the thermodynamic properties of each cluster. Finally, time series analys
Interest in data science education is growing as data becomes more prevalent in our daily lives and plays a central role in making informed decisions and understanding the world. Due to the interdisciplinary nature an...
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Interest in data science education is growing as data becomes more prevalent in our daily lives and plays a central role in making informed decisions and understanding the world. Due to the interdisciplinary nature and broad scope of the field, further research is essential to unravel how K-12 students can effectively interact with data through productive learning experiences. This is particularly true in data visualization activities, in which students must employ a variety of skills to effectively extract and communicate data insights. In this study, we describe key actions involved in creating data visualizations using a block-based programming environment (Playdata). Based on qualitative video analysis, we identified six core data visualization programming moves: program creation, selection of parameters, output inspection, data inspection, program rearrangement, and visual design. Then, using learning analytics techniques and Epistemic Network Analysis, we developed a method for automatically categorizing and characterizing those moves based on fine-grained log data collected from the environment, which allowed the identification of patterns in students' trajectories. We found that students' work is distributed across several micro-tasks, each involving distinct types of interaction with the environment and holding a unique value in the process of engaging in programming, data analysis, and visual design. As students progress, there is a transition among these moves, suggesting the need for activities that ensure comprehensive exposure to all of them. Our study presents two main contributions: a novel approach to automatically categorize and describe learning trajectories in open-ended programming tasks and insights into how K-12 students engage with those tasks in a data-related context, laying a foundation for better supporting learning and research in this emergent area.
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