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Nodes are important elements of the cyberspace skeleton map visualization process. However, the quality parameters of the node importance index and topological potential index are difficult to obtain, and skeleton map visualization rarely accounts for the spatial distribution characteristics of nodes. The index synthesis and cluster distribution methods are adopted to solve these problems in this paper. The results are as follows: (1) According to the SIR propagation model, the maximum numbers of recoveries and infections for both the ARPA network and social network equal the TPDomiH maximum, and the TPDomiH index has the largest correlation coefficient. All the results show that the proposed TPDomiH index has certain advantages. (2) Regarding the center, the clustering results obtained for a social network are almost unchanged, whereas the original results exhibit large changes. For the center of gravity, the clustering results decrease gradually. The differences relative to the original results are small. With respect to the information entropy and the maximum amount of geometric information, the clustering results are larger than the original results. As the retention ratio increases, all the differences between the clustering results and the original results gradually narrow. These results indicate that the cyberspace skeleton map obtained after clustering is better than the original map. This research can provide a reference for the development of the field of cyberspace map visualization.

Severe environmental pollution and disease exposure are caused by poor waste management, specifically in urban areas due to urbanization. Additionally, energy shortage has threatened almost all parts of human life in the world. To overcome this problem, a precision agriculture approach using spatial mapping based on social environmental factors and sustainability principles can be used to find the variability of sites with respect to their suitability for waste disposal and energy generation. Therefore, this study aimed to develop a system for selecting suitable areas for municipal waste disposal and energy generation based on several structured criteria as hierarchical weighted factors. The system prototype was developed and tested in a case study conducted in an Indonesian Megapolitan area. The suitability map produced by the system for waste disposal and energy generation had an accuracy of 84.3%. Furthermore, validation was carried out by ground-checking at 102 location points. A future application of the proposed system is to provide spatial data-based analysis to improve regional planning and policy-making for waste disposal and energy generation in certain areas, particularly in Indonesia.

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired by the visual cortex, are characterized by their hierarchical learning structure which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable for qualitative color selectivity analysis.

Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.

The issue of weak interpretability in geological disaster susceptibility assessments using machine learning models has been a long-standing concern. Although SHAP (Shapley Additive Explanations) models have been extensively used in recent years to interpret the decision-making details of models, the specialized skills required and the non-intuitiveness of SHAP plots make their application challenging in practical decision-making environments. In response, our study introduces a map-based SHAP visualization framework to enhance the interpretability of susceptibility assessment results. Utilizing Optuna for hyperparameter tuning, we developed a high-performance XGBoost model to assess the susceptibility of the most impactful disaster in Hangzhou: urban road collapses. In addition to interpreting the contributions of evaluation factors through traditional SHAP summaries and bar plots, we displayed the SHAP values for each evaluation factor using map visualizations, and discussed the model's sensitivity to different values. To validate the alignment between model predictions and physical collapse mechanisms, our study selected typical collapse cases, interpreted these cases combining map visualizations, SHAP force plots at collapse points, and the physical mechanisms of collapse. Our research improves the interpretability of susceptibility assessments with machine learning by using map visualizations, providing new insights into spatial effects and robust support for urban decision-making applications.

Objective: To conduct a bibliometric analysis of the global snakebite literature to provide a reference for the future development of snakebite research. Methods: The Web of Science citation analysis tools, VOSviewer and CiteSpace V were used to carry out the bibliometric analysis of the literature and generate visualization maps. Results: The number of publications has increased at a considerably accelerated rate in the past 8 years. Nine distinct cooperation clusters were formed between institutions and countries. Keyword clustering yielded nine well-structured clusters covering two major topics, i.e., snakebite envenoming and antivenom. Burstiness detection revealed eight keywords with strong emergence, including neglected tropical diseases, Elapidae, Viperidae, and Russell’s viper, which have sustained popularity up to the present. Conclusion: Current research on snakebites has gradually garnered attention from the academic community. Cooperation papers between nations severely affected by snakebite and those with higher economic status received more attention. The continued exploration of therapeutic mechanisms, the development of antivenoms or alternative medicines, and primary prevention of snakebites to ensure the safety of populations in impoverished regions should be prioritized by international scholars. The epidemiological evidence and the timely translation of research findings should be valued by policymakers.

For the Yangtze River Delta (YRD) region of China, exploring the spatio-temporal characteristics of carbon emissions from energy consumption (CEECs) and their influencing factors is crucial to achieving carbon peaking and carbon neutrality as soon as possible. In this study, an improved LMDI decomposition model based on the Tapio model and Kaya’s equation was proposed. Combined with the improved LMDI and k-means cluster analysis methods, the energy structure, energy intensity, unit industrial output value and population size were selected as the driving factors, and the contribution of each driving factor to the CEECs of prefecture-level cities was quantitatively analyzed. Our study found that: (1) By 2020, the total amount of CEECs in the 26 prefecture-level cities in the YRD will stabilize, while their intensity has shown a downward trend in recent years. (2) The decoupling relationship between CEECs and economic development generally showed a trend from negative decoupling to decoupling. The dominant factor in decoupling was generally the shift of DEL values towards urbanization rate and energy intensity and the open utilization of energy technologies. (3) From 2000 to 2010, the dominant factors affecting CEECs in 26 cities were energy intensity and energy structure, followed by industrial output value and urbanization rate. In general, the promotion effect of economic development on carbon emissions in the YRD region was greater than the inhibitory effect. After 2010, the restrictive effect of various factors on CEECs increased significantly, among which the role of gross industrial output was crucial. The research results can provide a scientific policy basis for the subsequent spatial management and control of carbon emission reduction and carbon neutrality in the YRD region at a finer scale.

This study aimed to evaluate the effect of resin type and printing design on the dimensional accuracy of three dimensional (3D) printed orthodontic models, considering their clinical relevance for applications such as in-house aligner fabrication. Since low-cost Liquid Crystal Display (LCD) printers have been increasingly adopted in practice but data on their trueness and precision with different resins and print designs were limited, the study sought to provide evidence-based insights into their reliability. A mandibular model was designed using Blenderfordental (B4D, version 1.1.2024; Dubai, United Arab Emirates) software and fabricated with the Anycubic Photon Mono 7 Pro 14K (Anycubic, Shenzhen, China) LCD printer. The model was printed in vertical orientation using three different print designs at two layer thicknesses (50 µm and 100 µm). Four resins (Elegoo, Anycubic, eSUN, and Phrozen) were used, and each resin was printed with all three designs, yielding 126 models per resin and a total of 504 printed models. Dimensional deviations between the printed and reference models were assessed using root mean square (RMS) values and color-coded deviation maps. Significant differences in trueness were found among resins and print designs at both layer thicknesses (p < 0.001). At a layer thickness of 50 µm, eSUN and Anycubic showed superior trueness, whereas Phrozen exhibited the highest deviations. At a layer thickness of 100 µm, Anycubic, eSUN, and Phrozen generally performed better than Elegoo. Overall, printing at 100 µm yielded better performance than at 50 µm. Precision analysis revealed resin-dependent differences, with eSUN showing significantly higher precision than Elegoo at both layer thicknesses (p = 0.006 at 100 µm, p < 0.001 at 50 µm) and superior precision compared to Phrozen at 50 µm (p = 0.019). Both resin selection and print design significantly affect the dimensional accuracy of 3D-printed dental models.

For more than 20 years, disaster dynamic monitoring and early warning have achieved orderly and sustainable development in China, forming a systematic academic research system and top-down policy design, which are inseparable from the research of China’s scientific community and the promotion of government departments. In the past, most of the research on dynamic disaster monitoring and early warning focused on specific research in a certain field, scene, and discipline, while a few studies focused on research review or policy analysis, and few studies combined macro and meso research reviews in academia with national policy analysis for comparative analysis. It is necessary and urgent to explore the interaction between scholars’ research and policy deployment, which can bring theoretical contributions and policy references to the top-down design, implementation promotion, and academic research of China’s dynamic disaster monitoring and early warning. Based on 608 international research articles on dynamic disaster monitoring and early warning published by Chinese scholars from 2000–2021 and 187 national policy documents published during this period, this paper conducts a comparative analysis between the knowledge maps of international research hotspots and the co-occurrence maps of policy keywords on dynamic disaster monitoring and early warning. The research shows that in the stage of initial development (2000–2007), international research articles are few and focused, and research hotspots are somewhat alienated from policy keywords. In the stage of rising development (2008–2015), after the Wenchuan earthquake, research hotspots are closely related to policy keywords, mainly in the fields of geology, engineering disasters, meteorological disasters, natural disasters, etc. Meanwhile, research hotspots also focus on cutting-edge technologies and theories, while national-level policy keywords focus more on overall governance and macro promotion, but the two are gradually closely integrated. In the stage of rapid development (2016–2021), with the continuous attention and policy promotion of the national government, the establishment of the Ministry of Emergency Management, and the gradual establishment and improvement of the disaster early warning and monitoring system, research hotspots and policy keywords are integrated and overlapped with each other, realizing the organic linkage and mutual promotion between academic research and political deployment. The motivation, innovation, integration, and transformation of dynamic disaster monitoring and early warning are promoted by both policy and academic research. The institutions that issue policies at the national level include the State Council and relevant departments, the Ministry of Emergency Management, the Ministry of Water Resources, and other national ministries and commissions. The leading affiliated institutions of scholars’ international research include China University of Mining and Technology, Chinese Academy of Sciences, Wuhan University, Shandong University of Science and Technology, and other institutions. The disciplines involved are mainly multidisciplinary geosciences, environmental sciences, electrical and electronic engineering, remote sensing, etc. It is worth noting that in the past two to three years, research and policies focusing on COVID-19, public health, epidemic prevention, environmental governance, and emergency management have gradually increased.

The object of the study is the growing interest in visualizing the data of publications on nanotechnology in industrial activities, represented by scientific papers in scientometric databases, in particular Scopus. In scientific practice, the strategic directions of nanotechnology development in industry using modern methodological approaches are not widely represented.The purpose of the study is to formulate strategic directions for the development of nanotechnology in the industry using the Hoshin Kanri model by conducting a bibliometric analysis and researching market trends, which allows for assessing the financial capabilities of countries.As a result of the study, it was found that the Scopus scientometric database contains (n=13164) documents such as articles, abstracts, reviews, and books in various subject areas. The processed document data was used to form a network map of the interconnection of scientific interests in nanotechnology in the industry using the VOSviewer software. It was found that there are 13 such clusters, which include an overview of nanotechnology by life cycle changes, impact on the food industry, chemical and microbiological processes, ecology, biotechnology, nanomedicine, and specialized nanotechnology. It has been established that the countries with the highest financial investments in nanotechnology are the United States, China, India, South Korea, and Japan.The proposed original Hoshin Kanri model of strategic directions of nanotechnology development in the industry will ensure the development of scientific views and their further implementation in the practice of business entities. The described components in such groups as advantages of nanotechnology by industry, priorities, tasks and risks - can be used in practice in any industry to move to a new stage of the technology life cycle. This will speed up the management of business processes, rationalize the use of resources and make effective management decisions.