Interactive Tool Research Articles

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.

Automatic and User-Assisted Sphere-Mesh Construction.

A sphere-mesh is a class of geometric proxies defined as the volume swept by spheres with linearly interpolated centers and radii, potentially striking a good balance between conciseness of representation, simplicity of spatial queries, and expressive power. We investigate the semiautomatic generation of sphere-meshes from standard triangular meshes. We improve one known automatic construction algorithm, based on iterative local coarsening operation, by introducing a mechanism to prevent operations that would result in spheres exceeding the target shape; then, we propose a 3-D interface designed to permit users to easily and intuitively modify the automatically generated sphere-meshes. The two phases, an improved automatic algorithm and a novel interactive tool, used in cascade, constitute a viable semiautomatic way to produce high-quality sphere-meshes. We test our method on several inputs tri-meshes, assess their quality, and finally exemplify the usability of our results by testing them in a few downstream applications.

Computational Illusion Knitting

Illusion-knit fabrics reveal distinct patterns or images depending on the viewing angle. Artists have manually achieved this effect by exploiting "microgeometry," i.e., small differences in stitch heights. However, past work in computational 3D knitting does not model or exploit designs based on stitch height variation. This paper establishes a foundation for exploring illusion knitting in the context of computational design and fabrication. We observe that the design space is highly constrained, elucidate these constraints, and derive strategies for developing effective, machine-knittable illusion patterns. We partially automate these strategies in a new interactive design tool that reduces difficult patterning tasks to familiar image editing tasks. Illusion patterns also uncover new fabrication challenges regarding mixed colorwork and texture; we describe new algorithms for mitigating fabrication failures and ensuring high-quality knit results.

Interactive Design of Stylized Walking Gaits for Robotic Characters

Procedural animation has seen widespread use in the design of expressive walking gaits for virtual characters. While similar tools could breathe life into robotic characters, existing techniques are largely unaware of the kinematic and dynamic constraints imposed by physical robots. In this paper, we propose a system for the artist-directed authoring of stylized bipedal walking gaits, tailored for execution on robotic characters. The artist interfaces with an interactive editing tool that generates the desired character motion in realtime, either on the physical or simulated robot, using a model-based control stack. Each walking style is encoded as a set of sample parameters which are translated into whole-body reference trajectories using the proposed procedural animation technique. In order to generalize the stylized gait over a continuous range of input velocities, we employ a phase-space blending strategy that interpolates a set of example walk cycles authored by the animator while preserving contact constraints. To demonstrate the utility of our approach, we animate gaits for a custom, free-walking robotic character, and show, with two additional in-simulation examples, how our procedural animation technique generalizes to bipeds with different degrees of freedom, proportions, and mass distributions.

WILDPOP: AN INTERACTIVE TOOL FOR ESTIMATING OCCUPANCY AND ABUNDANCE OF WILDLIFE POPULATIONS

Species abundance or population size is an ecological parameter of critical importance for wildlife management and conservation decisions. Widely used data collection methods, such as sign surveys and remote cameras, often count non-identifiable individuals or individuals prone to misidentification. In ecological modeling, these individuals are considered unmarked, and a state-of-the-art modeling approach for such data is occupancy-type modeling for unmarked individuals, which explicitly incorporates imperfect detection. Hierarchical modeling of this kind requires advanced statistical analyses, typically conducted using the R software platform and the “unmarked” package. However, these models can be explored only by researchers with programming skills and a thorough understanding of hierarchical analysis of wildlife population data. To help researchers and practitioners implement these models, we have developed a Shiny web-based interactive tool for wildlife population assessment, which works with data collected by scientists and wildlife managers. This app facilitates the use of occupancy-type modeling for unmarked individuals (single-season single species occupancy and N-mixture models) for non-coder users. The app performs simulations for single-season single species occupancy and N-mixture models with or without covariates, and estimates occupancy and abundance employing users provided data. The results are displayed as text, tables, and graphs, helping users understand hierarchical modeling and answering real-life wildlife management questions.

FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia.

As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git.

An Arduino-based interactive tool for pain education: a pilot study

An Arduino-based interactive tool for pain education: a pilot study

The Mindful Interactions (MI) tool: promoting student mental health in tertiary education

This conceptual paper introduces the Mindful Interactions (MI) tool designed to enable university teaching academics to promote student mental health and in so doing, impact positively on academic outcomes. The MI tool is comprised of three elements: understandings to provide theoretical guidance which inform pedagogy, practices to provide strategies for translating theory into practice, and guiding principles which provide a catalyst for critical reflexion, challenge existing beliefs, and create a shared vision from which to work. Relationships are at its core, acknowledging that university students report teacher-student relationships as key to their mental health. Issues related to university student mental health, of which psychological distress is an important component, have been well documented and exacerbated since the onset of COVID-19, increasing demands on universities to meet student mental health needs. It is contended in this paper that understanding the causes of psychological distress, particularly in relation to Adverse Childhood Experiences, offers an alternative lens through which to view student mental health. This lens suggests additional ways of thinking about how university teaching academics and universities might proactively respond to student mental health needs. Cultural Historical Activity Theory informed the theoretical framework for the study, whilst two ‘approaches’ (the ‘Three Pillars of Trauma-informed Care’ and ‘Trust Based Relational Intervention’) along with associated trauma literature underpinned the development of the MI tool.

StreamSAXS: a Python-based workflow platform for processing streaming SAXS/WAXS data.

StreamSAXS is a Python-based small- and wide-angle X-ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user-friendly tool for analysis of both batch data files and real-time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug-in framework, which enables developers to extend the built-in workflow tasks. Another feature is the support for both already acquired and real-time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large-scale acquisition systems for end-to-end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios.