Visualization Software Research Articles

Overview of Online Learning Behavior Analysis in the Context of Big Data

Under the Internet era, various technologies such as big data and cloud computing are becoming more and more a hot issue of concern. Big data provides a large number of network resources with great economic as well as social value, which has a certain impact on various fields in society, and its use in education has received more attention from scholars. After the outbreak of the new crown epidemic, online learning has become a normalized way of learning, online learning platform records a large number of learners' learning data, through the processing and analysis of these massive data, not only is conducive to the realization of students' personalized learning, but also helps teachers to understand and master the current situation of students' learning, and then adjust their teaching. In order to have a clear understanding of the current research status of online learning behavior analysis in the context of big data, the article uses the visual analysis software CiteSpace to analyze the literature selected from the China Knowledge Network (CKN), from which the current status of the research, research hotspots, and future research trends in this field are obtained. The analysis results show that the number of publications has gradually decreased in the past two years, and there is less cooperation and communication between the core publication authors as well as the publication institutions; the analysis of learning behavior data on MOOC platforms has become a research hotspot at this stage, and in-depth learning, multi-scenario, and focusing on the students' learning experience are the future research trends.

422 Novel Application of a Mixed-Reality Platform for Learning Neuroanatomy

INTRODUCTION: Neuroanatomy is a difficult and intimidating, but also foundational, subject for many medical specialties, especially Neurosurgery. Yet, few modern tools exist for teaching neuroanatomy. This pilot study sought to evaluate the effectiveness of a novel mixed-reality (MR) neuroanatomy visualization software and headset compared to traditional methods of instruction. METHODS: This was a prospective randomized cross-over study of volunteer participants in a graduate neuroanatomy course. Students were randomly assigned to either traditional (Group-1) or MR (Group-2) modalities for 2 of 4 course units. All students participated in class lectures. Following each lecture, the MR group conducted MR lab modules while the traditional group conducted traditional cadaveric dissections or yarn and paper lab activities. All students then took the traditional Unit-lab exam. The two groups switched modalities between units. For logistical reasons, the MR laboratory sessions were taught in a separate location than traditional modalities with a teaching assistant. Student opinions were collected via survey and exam results were analyzed via Student’s t-test. RESULTS: Thirty-seven students volunteered to participate, with 23 completing the study (Group-1 n = 11 and Group-2 n = 12). Twenty-one students completed the post-course survey. Performance on both lab exams did not differ significantly between traditional and MR groups (p > 0.05). Most participants (95.2%) recommended traditional neuroanatomy teaching methods while 47.6% recommended the MR application. However, 95.2% of participants agreed that using MR with traditional methods would be better than either separately. CONCLUSIONS: Students’ neuroanatomy exam performance did not differ based on laboratory modality, though students preferred the traditional approach. Most students acknowledged the potential usefulness of MR in addition to traditional modalities. Further studies with larger sample sizes and a single location and instructor are needed to more fully elucidate the value of MR in learning neuroanatomy.

Electrical substations mapping for possible communication technologies using QGIS and Google Earth Pro

The visualization of mapping has aided researchers in gaining a better understanding and studying a specific area in a variety of professions. One of the well-known visualization software for mapping is the Geographic Information System (GIS). This work demonstrates the integration between an open-source GIS technology, named QGIS, and the Google Earth Pro programme in the mapping and analysis of electrical distribution substations. This integration shows the electrical substation data, the possible communication technology ranking, and a map that can be displayed and visualized based on the demographics. The QGIS in this study is used to reclassify the demographics into three main demographics: urban, suburban, and rural areas. On the other hand, the Google Earth Pro programme is used to map and visualize the overall electrical distribution substations alongside the communication technology ranking, based on the three main demographics. A case study using QGIS and Google Earth Pro was carried out in Area X, Malaysia for this investigation. Substation locations in Area X are mapped using Google Earth Pro, and the area’s demographics are categorized as part of the process. The features also enable the technology ranking to be displayed upon clicking the respective color-coded areas, where red represents urban area, yellow represents suburban and green represents rural This integration deems beneficial for future references on electrical distribution substations alongside the possible communication technologies.

Curare and GenExVis: a versatile toolkit for analyzing and visualizing RNA-Seq data

Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a cumbersome process often executed manually, hence error-prone and lacking reproducibility. For corresponding data processing, we introduce Curare, an easy-to-use yet versatile workflow builder for analyzing high-throughput RNA-Seq data focusing on differential gene expression experiments. Data analysis with Curare is customizable and subdivided into preprocessing, quality control, mapping, and downstream analysis stages, providing multiple options for each step while ensuring the reproducibility of the workflow. For a fast and straightforward exploration and visualization of differential gene expression results, we provide the gene expression visualizer software GenExVis. GenExVis can create various charts and tables from simple gene expression tables and DESeq2 results without the requirement to upload data or install software packages. In combination, Curare and GenExVis provide a comprehensive software environment that supports the entire data analysis process, from the initial handling of raw RNA-Seq data to the final DGE analyses and result visualizations, thereby significantly easing data processing and subsequent interpretation.

Slimmable transformer with hybrid axial-attention for medical image segmentation

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better; the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning.

Data visualization guidance using a software product line approach

Data visualization aims to convey quantitative and qualitative information effectively by determining which techniques and visualizations are most appropriate for different situations and why. Various software solutions can produce numerous visualizations of the same data set. However, data visualization encompasses a wide range of visual configurations that depend on factors such as the type of data being displayed, the different displays (e.g., scatter plots, line graphs, and pie charts), the visual components used to represent the data (e.g., lines, dots, and bars), and the specific visual attributes of those components (e.g., color, shape, size, and length). A similar problem arises when designing data tables, where the dimensionality of the data and its complexity influence the choice of the most appropriate structure (e.g., unidirectional, bidirectional). Often, this broad spectrum of configurations requires a visualization expert who knows which techniques are best for which type of data source and what is to be conveyed. Typically, researchers and developers lack knowledge of data visualization best practices and must learn the design principles that enable effective communication and the technical details of the specific software tool they use to generate visualizations. This paper proposes a software product line approach to model and realize the variability of the visualization design process, using feature models to encode knowledge about design best practices in graphs and charts. Our approach involves solving visualization design variability through a stepwise configuration process and evaluating the proposal for a specific software visualization tool. Our solution facilitates effective communication of quantitative results by helping researchers and developers select and generate the most effective visualizations for each case. This approach opens up new opportunities for research at the intersection of data visualization and variability.

Bibliometric Analysis of the Relationship Between Breast Cancer and Exercise Based on Citespace

This study aims to conduct a bibliometric analysis of the literature on the relationship between breast cancer and exercise using Citespace software, in order to explore the research hotspots, trends, and deficiencies in this field. Relevant literature including keywords such as “breast cancer” and “exercise” was retrieved from the Web of Science database and analyzed using Citespace software for data processing and visualization. The main conclusions of this study are as follows: In co-occurrence analysis of keywords, researchers mainly focus on the prevention, rehabilitation, and treatment of breast cancer, as well as the effects of different types of exercise on breast cancer. Meanwhile, the cooccurrence patterns of keywords in studies related to exercise and breast cancer indicate an increasing attention to the relationship between breast cancer and exercise, making it gradually one of the hotspots in breast cancer research. The timeline of keywords and burst detection analysis show that researchers have increasingly focused on the relationship between breast cancer and exercise in recent years, with research intensity showing an increasing trend year by year. Furthermore, in recent years, researchers have begun to emphasize the role of exercise in breast cancer rehabilitation, rather than just prevention and treatment. Discipline analysis indicates that research in this field involves multiple disciplinary areas, including medicine, oncology, sports science, nutrition, etc. Additionally, an analysis of papers published by country reveals that the United States leads in research on the relationship between breast cancer and exercise, followed closely by the United Kingdom, China, Canada, Australia, and other countries. Conclusion: Literature on the relationship between breast cancer and exercise shows a trend of increasing quantity year by year. However, there are still some deficiencies in this field, such as lack of uniform research methods, overly limited research samples, and insufficient rigor in research results. Therefore, further strengthening research on the relationship between breast cancer and exercise is highly necessary.

Bibliometric analysis of research challenges and trends in urban underground space

AbstractThe utilization and development of urban underground space play a crucial role in optimizing the layout of civic architecture and enhancing the urban ecological environment, which contributes toward increasing the overall carrying capacity and promoting sustainable development in megacities. To delve into the research progress of urban underground space, knowledge maps were created using the information visualization software VOSviewer. The literature was systematically extracted from three prominent databases, namely, Web of Science, Scopus, and China National Knowledge Infrastructure. According to the bibliometric analysis of the co‐citation and core words co‐occurrence, the trends and challenges of research on urban underground space were identified. As highlighted by the results obtained, it still remains highly challenging to achieve interdisciplinary collaboration in urban underground space research; the research trends of urban underground space consist of collaborative planning and whole life cycle sustainable development, multisource geological data informatization and resource evaluation, infrastructure design optimization, and intelligent construction. The knowledge map, drawn using bibliometric methods, offers a quantitative analysis of literature retrieval across various levels. It is recognized as an essential tool for exploring and identifying challenges and trends in urban underground space.

The Impact of Visual Packaging Design on Perceived Food Product Quality and Brand Preference with Moderating Effect of Gender

The purpose of the study is to explore the influence of visual packaging design on perceived food product quality and brand preference with moderating effect of gender. The study used the fast food industry for analysis and analyze how the packaging (colors, logos, graphics) of fast food products impact on consumer perception about the product. An adopted questionnaire used in the analysis. The study offers directions for understanding the effects of visual packaging on brand preference and perceived quality.

Exploring dimensions of metadata quality assessment: A scoping review

Assessing metadata is of paramount importance for several critical reasons. Metadata plays a pivotal role in various aspects, including data retrieval and search, data organization, interoperability, data preservation, and the overall user experience. The purpose of this scoping review is to identify the most commonly measured dimensions of metadata quality in existing studies on metadata quality assessment. The study also investigates the types of data sources and countries contributing most to the literature on metadata quality assessment and the types of documents used to communicate their findings. The methodology involves the application of PRISMA model for qualitatively evaluating 55 studies on metadata quality assessment. The co-occurrence analysis is made on the title and abstract of selected articles using VOSviewer 1.6.18 version, visualization software. The review found that completeness, accuracy, consistency, accessibility, conformance, provenance, and timeliness are commonly used dimensions in metadata quality assessment. However, there is no consensus on their exact definition and measurement, indicating a need for further investigation into less commonly assessed quality dimensions. Digital repositories and open government data are the most commonly studied data sources, with the United States being the leading contributor and journal articles being the most commonly used document type. The cluster analysis based on co-occurrence of terms in title and abstract found three research areas, “Metadata Quality Assessment,” “Metadata Quality Dimensions,” and “Metadata Quality Applications, Frameworks, and Approaches” as prominent areas of research. The originality of the study lies in its methodology that involves rigorous screening of articles on metadata quality. It is a first attempt to qualitatively synthesize literature on metadata quality. The article emphasizes the importance of metadata quality research and the need to improve the flexibility of metadata quality assessment tools to facilitate better metadata quality assurance measures.

ImageSTEAM: Teacher Professional Development for Integrating Visual Computing into Middle School Lessons

Artificial intelligence (AI) and its teaching in the K-12 grades has been championed as a vital need for the United States due to the technology's future prominence in the 21st century. However, there remain several barriers to effective AI lessons at these age groups including the broad range of interdisciplinary knowledge needed and the lack of formal training or preparation for teachers to implement these lessons. In this experience report, we present ImageSTEAM, a teacher professional development for creating lessons surrounding computer vision, machine learning, and computational photography/cameras targeted for middle school grades 6-8 classes. Teacher professional development workshops were conducted in the states of Arizona and Georgia from 2021-2023 where lessons were co-created with teachers to introduce various specific visual computing concepts while aligning to state and national standards. In addition, the use of a variety of computer vision and image processing software including custom designed Python notebooks were created as technology activities and demonstrations to be used in the classroom. Educational research showed that teachers improved their self-efficacy and outcomes for concepts in computer vision, machine learning, and artificial intelligence when participating in the program. Results from the professional development workshops highlight key opportunities and challenges in integrating this content into the standard curriculum, the benefits of a co-creation pedagogy, and the positive impact on teacher and student's learning experiences. The open-source program curriculum is available at www.imagesteam.org.

INTRICATE GENEALOGIES: WHAT IS SAID IN THE EPIC POEM ABOUT NOGAY PEOPLE?

This article examines communication blueprints of the Forty Knights of Steppe epic poem (original title in Kazakh "Қырымның қырық батыры"). The Forty Knights of Steppe is a cultural heritage of Turkic nations. The epic poem’s text is an assemblage of ballads that are connected with each other. It consists of thirty-five ballads and depicts relationships of around two hundred characters. The epic poem's events have a historical origin that links to the Nogay nobility from the Golden Horde period. The main event in the epic poem develops around Edige and his descendants. The historical aspect of this epic poem positions it with the paragons of heroic literature such as "Shahnameh" and "Manas". The epic poem’s text was first fully recorded in 1942 in Almaty by Muryn Zhyrau (real name ‘Tilegen Sengirbekuly’) by specialists from the Kazakh SSR Academy of Sciences. Unfortunately, the epic poem stayed unpublished for more than 60 years. The full text of the epic poem was published in 2005 after restoring the independence of the Republic of Kazakhstan. This research discovers the epic poem by analyzing their network. It focuses on mapping and visualizing links between characters described in the text. The authors used the methods of text mining, proposition, and semantic triangles for assembling information about contacts between characters into the databases. The databases include basic information, such as the names of characters and their contacts with other characters. The databases also contain information about the direction of every contact, relationship statuses of contactors, contactors' roles inside of genealogical lineage, kinship degrees of contactors inside their genealogies, and contactors' gender. In addition, the databases include information about the connection of contactors to genealogies, and information about the contacts of contactors outside of their genealogies. All databases were built in spatial data analysis compatible format. The databases were launched through data visualization software by switching on the environmental settings required for this type of research. The authors used Gephi data visualization software for this research to visualize databases. The visualization shows an intricate communicative network that covers almost all characters. The visualization demonstrates two types of contacts between characters of the epic poem. The first type is a structured type of contacts. This type of contacts develops inside of genealogies. It is a common type of contacts in the epic poem, and it follows the hierarchical order. The second type is a class-based type of contacts. This type of contacts ties characters from different genealogies and does not correspond to the hierarchical order. However, this type of contacts depends on the characters’ roles and statuses inside their genealogies. Thereby, the second type of contacts is connecting different genealogies into whole network. The types of contacts demonstrate two levels of social communication in the narration of the epic poem. Considering the visualization results and according to the historical origins of the characters, the authors argue that the text of the epic poem shows the patterns of social communication typical for Central Eurasian medieval nomad cultures in the Golden Horde period. On the other side, the stratification into two types of contacts may demonstrate the levels of bureaucracy in social communication in the same historical period. The authors suggest that the social networks of the epic poem describe the transitional form of the chiefdom society with communication levels typical for tribal and chiefdom societies. In general, the paper's authors suppose that the example of this research on the epic poem’s communication structure may give more data to understand the correlation between language and society.

Development Status and Trend Forecast of China's Iron and Steel Industry Based on Multi-Algorithm Coupling Modeling

Iron and steel metallurgy, as the most important weapon of the country, is ushering in a new round of good opportunities under the support of the national big infrastructure strategy. In this paper, we first use Python to crawl the data, and then use the data visualization software Emotional Sky visualization platform to get the big screen of steel industry data, and analyze the future development of the steel industry through the use of multi-algorithm coupled prediction method. The prediction of the future development of the iron and steel industry is realized through multi-algorithm coupling, and a new round of outlook on the iron and steel industry is proposed based on the prediction. Contribute to the early realization of efficient, intelligent and green development of China's iron and steel industry.

Demonstration of computer vision for void characterisation of 3D-printed continuous carbon fibre composites

Microstructural void formation during the processing of continuous fibre-reinforced polymer composites are a significant limitation of processes such as 3D printing, as voids inhibit resultant mechanical properties. Traditional optical microscopy approaches to void characterisation are tedious and are prone to errors due to non-ideal but realistic image conditions, including low contrast typical of microscopic images. This paper proposes a novel application of automated computer vision image processing techniques (i.e. a contrast-limited adaptive histogram equalisation (CLAHE) algorithm and Otsu's method) for detailed void characterisation of continuous fibre-reinforced composites. Microstructural void fraction was determined for both unidirectional and bi-axial laminates of a variety of thicknesses. The void characterisation by computer vision was shown to produce comparable results with manual contrast/brightness control, but with significantly less standard deviation. The computer vision software used in void characterisation is made freely available online at https://github.com/Xiao-ying/VoidDetector.

A survey into the current fitness testing practices of elite male soccer practitioners: from assessment to communicating results.

This study provides insight into the current fitness testing practices in elite male soccer. One hundred and two practitioners from professional soccer leagues across 24 countries completed an online survey comprising 29 questions, with five sections: a) background information, b) testing selection, c) testing implementation, d) data analysis, and e) data reporting. Frequency analysis was used to evaluate the responses to fixed response questions and thematic analysis was used for open-ended questions to generate clear and distinct themes. Strength (85%) and aerobic capacity (82%) represent the most frequently assessed physical qualities. Scientific literature (80%) is the most influential factor in testing selection and practitioners conduct fitness testing less frequently than their perceived ideal frequency per season (3.6 ± 2 vs. 4.5 ± 2). Time and competitive schedule were the greatest barriers to fitness testing administration. Practitioners mostly used a 'hybrid' approach (45%) to fitness testing, blending 'traditional' (i.e., a day dedicated to testing) and 'integrated' (i.e., testing within regular training sessions) methods. Microsoft Excel is the most used software for data analysis (95%) and visualization (79%). An equal use of the combination of best and mean scores of multiple trials (44%) and the best score (42%) was reported. Comparing a player's test performance with previous scores (89%) was the most common method for interpreting test results. However, only 38% considered measurement error. Digital displays and verbal feedback are the most common data reporting methods, with different data reporting processes for coaches and players. Practitioners can use data and findings from this study to inform their current testing practices and researchers to further identify areas for investigation, with the overarching aim of developing the field of fitness testing in elite male soccer.

Equity in park green spaces: a bibliometric analysis and systematic literature review from 2014-2023

With the global increase in population and the accelerated process of urbanization, the equitable access to park green spaces by diverse communities has become a growing concern. In order to provide an overview of the developmental trends, research focal points, and influencing factors in the study of equity in park green spaces, this paper employs bibliometric analysis and the visualization software CiteSpace to systematically analyze relevant literature in the Web of Science core database from 2014 to December 2023. The findings reveal an increasing emphasis on the research of equity in park green spaces, delineated into two distinct phases: a period of gradual exploration (2014–2018) followed by rapid development (2018 to present). Key nations contributing to research in this domain include China, the United States, and Germany. Currently, the research focus in this field primarily centers on the analysis of park green space equity based on primary social fairness, analysis of park green space equity based on vulnerable groups, and the relationship between park green spaces and health. The influencing factors of park green space equity mainly involve regional economic factors and government planning, as well as residents’ economic capabilities and racial discrimination. Future research directions could include studying park green space equity among different demographic groups, emerging assessment methods and data, park green space equity based on perceived accessibility, and the relationship between park green space equity and surface temperature.

Analytical Evaluation of Midjourney Architectural Virtual Lab: Defining Major Current Limits in AI-Generated Representations of Islamic Architectural Heritage

In artificial intelligence (AI), generative systems, most notably Midjourney, have tremendous power to generate creative images of buildings and sites of Islamic architectural heritage through text-to-image generation based on the internet. The AI-generated representations have significant potential for architects, specialists, and everyday users. However, the system has considerable limitations when generating images for some buildings and sites where the representations appear too far from their original represented structures. Evaluating Midjourney as an architectural virtual lab, this research article attempts to define the major current limitations of using Midjourney’s AI system in producing images similar to the actual buildings and sites of Islamic architectural heritage. This research employs prompt engineering techniques based on historical sources as inputs to examine the accuracy of the output of the AI-generated images of selected examples of structures of the Islamic tradition. To validate the research results, it compares the Midjourney output with the original look by employing direct observation through critical analysis of human intelligence (HI) facilitated by the analysis of the latest version of 3M Visual Attention Software and an online survey. It concludes that while Midjourney can represent high-end AI-generated images inspired by the Islamic tradition, it currently falls short of presenting the actual appearance of some of its original structures. Furthermore, it categorizes these limitations into four groups: (1) limits of the prompt, (2) limits of fame, (3) limits of regionality and historical styles, and (4) limits of architectural elements and details. The significance of this research lies in providing timely evaluation factors to architectural heritage practitioners and software developers. This practical article is the second in a series of two sequential articles in the Buildings journal; the first (theoretical) article introduces the concept of Artificial Intelligence Islamic Architecture (AIIA), answering the question: what is Islamic architecture in the age of artificial intelligence?

Blockchain technology for supply chain provenance: increasing supply chain efficiency and consumer trust

PurposeThis study aims to examine the blockchain landscape in supply chain management by drawing insights from academic and industry literature. It identifies the key drivers, categorizes the products involved and highlights the business values achieved by early adopters of blockchain technology within the supply chain domain. Additionally, it explores fingerprinting techniques to establish a robust connection between physical products and the blockchain ledger.Design/methodology/approachThe authors combined the interpretive sensemaking systematic literature review to offer insights into how organizations interpreted their business challenges and adopted blockchain technology in their specific supply chain context; content analysis (using Leximancer automated text mining software) for concept mapping visualization, facilitating the identification of key themes, trends and relationships, and qualitative thematic analysis (NVivo) for data organization, coding and enhancing the depth and efficiency of analysis.FindingsThe findings highlight the transformative potential of blockchain technology and offer valuable insights into its implementation in optimizing supply chain operations. Furthermore, it emphasizes the importance of product provenance information to consumers, with blockchain technology offering certainty and increasing customer loyalty toward brands that prioritize transparency.Research limitations/implicationsThis research has several limitations that should be acknowledged. First, there is a possibility that some relevant investigations may have been missed or omitted, which could impact the findings. In addition, the limited availability of literature on blockchain adoption in supply chains may restrict the scope of the conclusions. The evolving nature of blockchain adoption in supply chains also poses a limitation. As the technology is in its infancy, the authors expect that a rapidly emerging body of literature will provide more extensive evidence-based general conclusions in the future. Another limitation is the lack of information contrasting academic and industry research, which could have provided more balanced insights into the technology’s advancement. The authors attributed this limitation to the narrow collaborations between academia and industry in the field of blockchain for supply chain management.Practical implicationsPractitioners recognize the potential of blockchain in addressing industry-specific challenges, such as ensuring transparency and data provenance. Understanding the benefits achieved by early adopters can serve as a starting point for companies considering blockchain adoption. Blockchain technology can verify product origin, enable truthful certifications and comply with established standards, reinforcing trust among stakeholders and customers. Thus, implementing blockchain solutions can enhance brand reputation and consumer confidence by ensuring product authenticity and quality. Based on the results, companies can align their strategies and initiatives with their needs and expectations.Social implicationsIn essence, the integration of blockchain technology within supply chain provenance initiatives not only influences economic aspects but also brings substantial social impacts by reinforcing consumer trust, encouraging sustainable and ethical practices, combating product counterfeiting, empowering stakeholders and contributing to a more responsible, transparent and progressive socioeconomic environment.Originality/valueThis study consolidates current knowledge on blockchain’s capacity and identifies the specific drivers and business values associated with early blockchain adoption in supply chain provenance. Furthermore, it underscores the critical role of product fingerprinting techniques in supporting blockchain for supply chain provenance, facilitating more robust and efficient supply chain operations.

Comparative evaluation of volumetric changes following rotary and hand files' canal preparation of primary maxillary canine: an in vitro nano-CT analysis.

To evaluate the volumetric changes of two recently introduced paediatric rotary file systems in comparison with conventional hand file systems in primary maxillary canines using an ultra-high-resolution nano-computed tomography. This in vitro study was performed in extracted primary maxillary canines based on certain inclusion and exclusion criteria. Samples were prepared, and working length was determined after the pre-operative scan using a high-resolution nano-CT device (SkyScan 2214, Bruker, Kontich, Belgium). A single well-experienced paediatric dentist prepared the canals using three file systems: Kedo-S plus, Kedo-SG blue and hand K-files. All samples were subjected to post-operative scans performed similar to pre-operative scans. Image reconstruction was performed with NRecon software for 3D volumetric visualisation and analysis of the root canals. Kedo-SG blue file systems had the highest mean difference in the canal volume (4.05%). Hand K-files had the least difference at (3.71%) of canal volume. Kedo-S plus file system had a moderate mean canal volume difference (3.82%) which is closer to hand K-files. Intergroup comparison between the three groups showed that the mean difference in canal volume was statistically significant between all three file systems (p = 0.000). Within the limitations of the current study, rotary file systems produced a significant enlargement of canals as compared to hand files. Kedo-SG blue created a uniform preparation of the canal cervico-apically. Kedo-S plus files were prepared more coronally with minimal preparation apically as close to the preparation of hand files. Trial registration number: IHEC/SDC/PEDO-2103/22/651, Date of registration: 2022.

Nano1D: An accurate computer vision software for analysis and segmentation of low-dimensional nanostructures

Nanoparticles in microscopy images are usually analyzed qualitatively or manually and there is a need for autonomous quantitative analysis of these objects. In this paper, we present a physics-based computational model for accurate segmentation and geometrical analysis of one-dimensional deformable overlapping objects from microscopy images. This model, named Nano1D, has four steps of preprocessing, segmentation, separating overlapped objects and geometrical measurements. The model is tested on SEM images of Ag and Au nanowire taken from different microscopes, and thermally fragmented Ag nanowires transformed into nanoparticles with different lengths, diameters, and population densities. It successfully segments and analyzes their geometrical characteristics including lengths and average diameter. The function of the algorithm is not undermined by the size, number, density, orientation and overlapping of objects in images. The main strength of the model is shown to be its ability to segment and analyze overlapping objects successfully with more than 99 % accuracy, while current machine learning and computational models suffer from inaccuracy and inability to segment overlapping objects. Benefiting from a graphical user interface, Nano1D can analyze 1D nanoparticles including nanowires, nanotubes, nanorods in addition to other 1D features of microstructures like microcracks, dislocations etc.