Interactive Tool Research Articles

Acceptability of interactive post-endodontic restoration decision making application among undergraduate dental students

Background/PurposeSelecting an appropriate coronal restoration after root canal treatment requires careful consideration, however, there is a lack of established web-based tool that is based on clear guidelines for suitable post-endodontic restorations. Hence, we aimed to design and determine the acceptability of an interactive application to aid the decision making process for post-endodontic restoration among the undergraduate dental students. Materials and methodsA web-based post-endodontic restoration decision making interactive application, (RestoSmart) was developed after a detailed review of available literature. Seventy undergraduate dental students of IMU University were recruited in the study. A validated questionnaire was given to the participants before and after the use of RestoSmart to answer the provided mock clinical scenarios. Obtained data were statistically analyzed using IBM SPSS version 26 software package. ResultsThe statistical analysis demonstrated that RestoSmart was acceptable among the undergraduate dental students (P < 0.05). Initially, participants recognized the potential benefits of designing an interactive application, 74% believing that it may enhance decision making and 76% emphasizing its user-friendliness. Additionally, close to half of the participants expressed the willingness to use interactive tools in future. Following the use of RestoSmart, there was a notable increase in favorability, with 94.2% finding it helpful in the decision making and 94.3% acknowledging its user-friendliness. Furthermore, 87.2% expressed intent to use RestoSmart in future. ConclusionRestoSmart was useful for decision making and user friendly. While overall response was positive, there exist some areas for improvement such as ease of use, updating evidence-based guidelines, and establishing its helpfulness in teaching and learning.

Multi-sensory stimulation in speech development for neuroatypical children: A therapist’s perspective

The speech of young children is a rich area of study and discussion because it serves as a remarkable tool for communication and interaction with their environment. Consequently, disorders in speech acquisition draw considerable attention from speech therapists. This article explores the issue of polysensory stimulation, considering developmental sequences in speech acquisition. It describes the therapeutic use of sound-imitative expressions, natural gestures, and systemic gestures (e.g., Makaton). The article emphasizes the importance of music in developing speech and sensory stimulation, particularly focusing on vestibular and proprioceptive senses. It also presents measurable outcomes of therapeutic interventions through individual case studies.

Looking Deeply into the Magic Mirror: An Interactive Analysis of Database Index Selection Approaches

Indexes are important data structures for database tuning. However, finding the best indexes for a given workload is challenging. In this demonstration, we present our extensible open-source index selection evaluation platform and the corresponding interactive result analysis tool. The platform provides an automatic setup of the database, workload, and cost evaluation, which is otherwise often tedious work when evaluating index selection approaches. Users can also connect the platform to their own existing database and evaluate indexes for custom workloads. Our platform comprises multiple state-of-the-art index selection approaches, which can be used as baselines for new index selection proposals. Further, we present an application for thoroughly analyzing the selected database indexes. One can observe which indexes are used for which queries and their effect on processing costs. Also, it is possible to adapt the resulting index selections (i.e., add, remove, or change an index) and observe the impact. In this process, the application helps to understand the effects of indexes, improve index selections, and craft new index selection approaches.

ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions

IntroductionBreast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. ObjectivesUsing DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. ResultsARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR=15.43, 95 % CI: 9.57–24.88, P=3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95 % CI: 0.805–0.883), AUC5-year = 0.816 (95 % CI: 0.775–0.857), and C-index = 0.803 (95 % CI: 0.776–0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95 % CI: 1.038–1.083) and 1.090 (95 % CI: 1.057–1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 0.019, NB5-year = 0.062; NR3-year = 69.21 %, NR5-year = 56.01 %). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. ConclusionARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

Release notice - Perinatal Health Indicators (PHI) Data Tool.

The Maternal and Infant Health Section of the Public Health Agency of Canada (PHAC) is pleased to announce an update to the Perinatal Health Indicators (PHI) Data Tool. The interactive Data Tool on the PHAC Infobase website presents statistics on maternal, fetal and infant health in Canada based on data from the Canadian Institute for Health Information's (CIHI) Discharge Abstract Database (DAD), the Canadian Community Health Survey (CCHS), and the Canadian Vital Statistics (birth, stillbirth and death databases). The data include 20 indicators grouped into four key health domains: health behaviours and practices, health services, maternal outcomes, and infant outcomes. For this update, five new indicators were added and three existing ones were modified. To access the latest Perinatal Health Indicators Data Tool, visit https://health-infobase.canada.ca/phi/.

A Visual Environment for Data Driven Protein Modeling and Validation.

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

SceneDirector: Interactive Scene Synthesis by Simultaneously Editing Multiple Objects in Real-Time.

Intelligent tools for creating synthetic scenes have been developed significantly in recent years. Existing techniques on interactive scene synthesis only incorporate a single object at every interaction, i.e., crafting a scene through a sequence of single-object insertions with user preferences. These techniques suggest objects by considering existent objects in the scene instead of fully picturing the eventual result, which is inherently problematic since the sets of objects to be inserted are seldom fixed during interactive processes. In this article, we introduce SceneDirector, a novel interactive scene synthesis tool to help users quickly picture various potential synthesis results by simultaneously editing groups of objects. Specifically, groups of objects are rearranged in real-time with respect to a position of an object specified by a mouse cursor or gesture, i.e., a movement of a single object would trigger the rearrangement of the existing object group, the insertions of potentially appropriate objects, and the removal of redundant objects. To achieve this, we first propose an idea of coherent group set which expresses various concepts of layout strategies. Subsequently, we present layout attributes, where users can adjust how objects are arranged by tuning the weights of the attributes. Thus, our method gives users intuitive control of both how to arrange groups of objects and where to place them. Through extensive experiments and two applications, we demonstrate the potentiality of our framework and how it enables concurrently effective and efficient interactions of editing groups of objects.

ISLAT: the Interactive Spectral-line Analysis Tool for JWST and Beyond

We present the Interactive Spectral-Line Analysis Tool (iSLAT), a python-based graphical tool that allows users to interactively explore, inspect, and fit line emission observed in molecular spectra. iSLAT adopts a simple slab model in LTE that simulates emission spectra with a small set of parameters (temperature, emitting area, column density, and line broadening) that users can adjust in real time for multiple molecules or multiple thermal components of a same molecule. A central feature of iSLAT is the possibility to interactively inspect individual lines or line clusters to visualize their properties at high resolution and identify them in the population diagram. iSLAT provides a number of additional features, including the option to identify lines that are not blended at the instrumental resolution, the possibility to save custom line lists selected by the user, and to fit and measure their properties (line flux, width, and centroid) for later analysis. In this paper we launch the tool and demonstrate it on infrared spectra from the James Webb Space Telescope and ground-based instruments that provide higher resolving power. We also share curated line lists that are useful for the analysis of the forest of water emission lines observed from protoplanetary disks. iSLAT is shared with the community on GitHub.

Distance‐Based Smoothing of Curves on Surface Meshes

AbstractThe smoothing of surface curves is an essential tool in mesh processing, important to applications that require segmenting and cutting surfaces such as surgical planning. Surface curves are typically designed by professionals to match certain surface features. For this reason, the smoothed curves should be close to the original and easily adjustable by the user in interactive tools. Previous methods achieve this desired behavior, e.g., by utilizing energy‐minimizing splines or generalizations of Bézier splines, which require a significant number of control points and may not provide interactive frame rates or numerical stability. This paper presents a new algorithm for robust smoothing of discrete surface curves on triangular surface meshes. By using a scalar penalty potential as the fourth coordinate, the given surface mesh is embedded into the 4D Euclidean space. Our method is based on finding geodesics in this lifted surface, which are then projected back onto the original 3D surface. The benefits of this approach include guaranteed convergence and good approximation of the initial curve. We propose a family of penalty potentials with one single parameter for adjusting the trade‐off between smoothness and similarity. The implementation of our method is straightforward as we rely on existing methods for computing geodesics and penalty fields. We evaluate our implementation and confirm its robustness and efficiency.

Exploring the Role of ChatGPT in Shaping an Innovative Organizational Culture, Responsiveness to Change, and Enhancing Human Resource Development

The rapid development of digital technology has prompted organizations to adopt new tools and systems to enhance operational efficiency and effectiveness. One of the latest technologies garnering widespread attention is artificial intelligence (AI), including language models like ChatGPT, capable of processing and generating text naturally. This research aims to further explore how ChatGPT can be implemented within organizations to cultivate an innovative culture, responsiveness to change, and effectively support human resource development. This study employs a literature review method with a qualitative approach. Data were collected from articles published on Google Scholar between 2021 and 2024, considering Google Scholar as a comprehensive and credible academic database. The findings indicate that ChatGPT plays a crucial role in fostering innovation by serving as a catalyst for creative ideas and new solutions. Furthermore, ChatGPT has proven to enhance organizational responsiveness to changes in the business environment by providing accurate insights based on real-time data. On the other hand, utilizing ChatGPT as an interactive training tool also aids in accelerating employee competency development. However, it is essential to consider the ethical challenges and considerations in the use of this technology, including data privacy issues and overreliance.

The New Mixed Teaching Exploration of Low-Voltage Electrician Training Course under the Background of New Engineering

In the context of New Engineering, the exploration of a novel blended teaching approach for low-voltage electrician training courses aims to address the challenges of modern engineering education by integrating New Engineering concepts and modern teaching technologies to achieve comprehensive innovation in course content and teaching methods. This strategy leverages the advantages of combining online and offline learning to provide a diverse and personalized learning experience, thereby enhancing students' knowledge and practical skills. Online course resources, including interactive courseware, virtual experiments, and simulations, enrich the learning content and increase students' interest and engagement. Offline classes focus on interactivity and practical application through discussions, case studies, and project collaborations, helping students deepen their understanding and apply their knowledge. Flexible scheduling allows students to learn at their own pace, improving learning efficiency and effectiveness. Personalized learning path design, through preliminary surveys and assessments, helps students set individualized learning goals. Utilizing big data analysis and intelligent recommendation algorithms, the system provides the most suitable learning resources. Students can autonomously adjust their learning paths, fostering self-directed learning, while teachers offer targeted guidance and support to ensure the successful completion of learning tasks. Interactive and collaborative learning environments are strengthened through online and offline interaction mechanisms, group collaborative learning, and the use of various interactive tools, enhancing classroom interaction and participation. The establishment of online learning communities promotes resource sharing and experience exchange, creating a positive learning atmosphere. Assessment and feedback employ formative and practical assessments through online tests, assignments, and project work, comprehensively reflecting students' learning progress and outcomes. Multi-dimensional feedback mechanisms and data-driven evaluation provide detailed learning analysis and improvement suggestions for both teachers and students, enhancing teaching effectiveness.

Anterior interhemispheric vs. pterional approach in the microsurgical management of anterior communicating artery aneurysms: a comparative analysis employing a novel multidimensional matching-tool

The surgical management of anterior communicating artery aneurysms (AcomA) is challenging due to their deep midline position and proximity to complex skull base anatomy. This study compares the pterional craniotomy with the interhemispheric approach based on the specific aneurysm angulation. A total of 129 AcomA cases were analyzed, with 50 undergoing microsurgical clipping via either the pterional or interhemispheric approach. All selected cases had computed tomography-angiography with sagittal imaging slices and 2D-angiography. Using an interactive tool, 14 cases treated via the interhemispheric approach were matched with 14 cases approached pterionally based on clinical and morphological parameters, emphasizing intracranial aneurysm (IA) dome angulation relative to the frontal skull base. Outcomes included IA occlusion, temporary clipping incidence, intraoperative rupture, postoperative strokes, hemorrhages, hydrocephalus, vasospasm, and patient functionality. Matched cohorts had consistent demographics. Both approaches resulted in similar IA occlusion rates, but the interhemispheric approach led to improved clinical outcomes, measured by the modified Rankin Scale. It also had a lower incidence of hydrocephalus and reduced need for permanent ventriculoperitoneal shunt placement. Vasospasms and postoperative infarction rates were comparable between the groups. Our findings suggest potential advantages of the interhemispheric approach in managing AcomA, depending on aneurysm angulation. Despite a small sample size, the results highlight the importance of customized surgical decision-making based on the unique traits of each aneurysm and the surgeon's expertise.

An Interactive Tool to Learn the Sensors and Transducers Subject for Kinaesthetic Domain Students

An Interactive Tool to Learn the Sensors and Transducers Subject for Kinaesthetic Domain Students

Learners’ psychological needs in online learning environment for executive education: role of cognitive overload and learning self-efficacy

ABSTRACT The emergent transition from face-to-face to online teaching and training has swayed the design and operating model of executive education globally. Reports indicate that this change has adversely impacted the learners’ satisfaction with executive education programmes and desired learning outcomes. Scholars believe that learners’ learning outcomes in the online learning environment are driven primarily by the satisfaction and frustration of their universal psychological needs (UPN) (autonomy, competence, and relatedness). Using, the theoretical view of Self-Determination Theory and Cognitive Load Theory, this study examines, how cognitive overload and online learning self-efficacy affect the positive impact of behavioural engagement on executive learners’ UPNs and learning outcomes in an online learning environment. Through this combined theoretical lens, we studied the dynamics of behavioural engagement, UPN satisfaction (frustration), and learning outcome relationships from the viewpoint of resource misfit. Hypotheses for direct and indirect effects were empirically validated by surveying online learners enrolled in a premier business school executive education programme. The findings indicate that in an online learning environment, executive programme designers and educators should balance the availability of information, interaction and learning tools to help learners attain UPN satisfaction and ultimately achieve desired learning outcomes.

Штучний інтелект як інструмент підвищення ефективності дистанційного навчання

Given the rapid development of technology and the need to ensure quality and accessible education for all, the study of the impact of AI on distance learning is extremely relevant and important for further improving educational processes in the modern world. The article deals with the issue of improving the efficiency of distance learning with the help of artificial intelligence. Theoretical and practical approaches to the use of AI to personalise the learning process, automate assessment and support students in real time are explored. The importance of integrating AI into educational platforms to improve the accessibility and quality of education, especially in the context of distance learning, is substantiated. The article analyses the impact of AI on the educational process, including the optimisation of educational programmes and the reduction of the workload of teachers. The key advantages of using AI in education are identified, such as individualisation of learning, flexibility in the choice of teaching methods, and increasing student motivation through interactive tools. It is established that the effective implementation of AI in education requires addressing a number of challenges, including ethical and technical aspects, as well as training teachers to work with new technologies. A promising area for further research is to investigate the potential of AI to support students with disabilities or those with limited access to traditional education.

TME-analyzer: a new interactive and dynamic image analysis tool that identified immune cell distances as predictors for survival of triple negative breast cancer patients

Spatial distribution of intra-tumoral immune cell populations is considered a critical determinant of tumor evolution and response to therapy. The accurate and systemic search for contexture-based predictors would be accelerated by methods that allow interactive visualization and interrogation of tumor micro-environments (TME), independent of image acquisition platforms. To this end, we have developed the TME-Analyzer, a new image analysis tool, which we have benchmarked against 2 software tools regarding densities and networks of immune effector cells using multiplexed immune-fluorescent images of triple negative breast cancer (TNBC). With the TME-Analyzer we have identified a 10-parameter classifier, predominantly featuring cellular distances, that significantly predicted overall survival, and which was validated using multiplexed ion beam time of flight images from an independent cohort. In conclusion, the TME-Analyzer enabled accurate interactive analysis of the spatial immune phenotype from different imaging platforms as well as enhanced utility and aided the discovery of contextual predictors towards the survival of TNBC patients.

Incidence of Metaphorical Virtual Classrooms and Interactive Learning Objects in the Interaction of Online Students: An Ecuadorian Case Study

This study explored the incidence of metaphorical virtual classrooms and interactive learning objects in the interaction of students in online mode. The main objective was to analyze how these digital tools, driven by a set of strategies to promote their use, affect the interaction of students in the virtual classroom system and their derived effects. To this end, the latest version of Moodle was implemented in conjunction with gamification plugins and interactive tools in the higher education institution used as a case study. The methodology consisted of data collection through ordinal instruments applied to the teachers and student performance metrics gathered using a plugin developed to extract accurate metrics of each student’s usage and performance through direct queries to the Moodle database and its processing through a neural network. This facilitated the collection of standardized data on the actual metrics of each virtual classroom at the end of the teaching of each subject from both the previous LMS and the newly implemented one. This data was then analyzed using advanced statistical techniques, including Mahalanobis distances, confirmatory factor analysis, and the Wilcoxon signed-rank test. These methods provided a compelling comparison between the old and new systems, revealing significant improvements in the metrics and factors evaluated. The results showed a significant improvement in teachers’ perceptions of the usability of the virtual classroom system and an increase in students’ academic performance, interaction, progress, and time spent learning in virtual contexts. These results provide solid empirical evidence of the added value of these educational tools as effective strategies for improving student interaction, performance, and motivation in online education.

Enhancing Engagement and Achievement in Nursing Education: The Effect of Combining Online PBL and Design Thinking on Student Motivation and Satisfaction - A Qualitative Analysis.

This study explores the integration of design thinking into online problem-based learning (PBL) within the context of nursing education, leveraging Google Jamboard as a facilitative interactive tool. By prioritizing creativity, empathy, and iteration, this innovative approach seeks to bolster student motivation and satisfaction. Employing a quasi-experimental design, the research involved third-year nursing students and compared the efficacy of traditional PBL methods against a hybrid model that integrates design thinking principles. Notably, the experimental group's interaction with Google Jamboard significantly enhanced their engagement, resulting in notable improvements in both motivation and satisfaction. The findings underscore the benefits of merging online PBL with design thinking, particularly when supported by digital tools, in creating a more vibrant and effective learning environment. This methodology not only stimulates active learning but also fosters vital competencies such as critical thinking and collaboration, which are crucial in the nursing profession. The results of this study advocate for the implementation of cutting-edge teaching methodologies in nursing education and pave the way for further investigation into the enduring impacts of this educational amalgamation.

Clinical Informatics in Pre-Registration Nurse Education.

This case study explores the pivotal role Clinical Informaticians in Nursing and Midwifery in Wales can have within pre-registration education. It underscores the necessity for nurses and midwives to adapt to digital transformations in healthcare delivery and discusses the potential digital career paths within the often-misunderstood domain of digital nursing. The initiative aimed to enhance awareness at both national and local levels, collaborating with educational institutions to incorporate digital education into pre-registration nursing programs. In partnership with the University of South Wales, sessions were tailored to the existing curriculum to highlight digital career opportunities and foster digital understanding among future nurses. The session design was aligned with course guidelines to emphasize the role of digital technology in quality improvement and leadership. Evaluations using interactive tools facilitated continuous improvement and provided insights, shaping the future of digital integration in nursing education.

Implementation of ChatGPT to enhance pre-travel consultation in a specialist tertiary centre in Singapore.

We designed and implemented an interactive artificial intelligence tool based on ChatGPT to complement the pre-travel consultation in a specialist tertiary centre that cares for a range of travellers including those who have undergone solid organ or bone marrow transplantation, as well as persons living with HIV.