Virtual Material Research Articles

Dynamic modeling of sliding joints based on transversely isotropic virtual material and deep neural network

Dynamic modeling of sliding joints based on transversely isotropic virtual material and deep neural network

Stakeholders’ Perspective on the Quality of Virtual Learning Material in Google Classroom

The COVID-19 pandemic stimulated education system worldwide to employ online learning to support learning despite difficult times. To respond to this challenge and to promote Sustainable Development Goal (SDG) 4, advocating quality education, a virtual learning material (VLM) for Biology was articulated in Google Classroom. Accordingly, this study aimed to evaluate its acceptability and conformity to the international standards for online courses using the Open SUNY Course Quality Review (OSCQR) rubric as the questionnaire. Respondents (N=40) involved four stakeholders: Senior High School Students, Pre-service Science Teachers, High School Teachers, and Science Instructors/Professors, with n=10 representatives each group. Their perspectives of the VLM acceptability in terms of Overview and Information, Technology and Tool, Design and Layout, Content and Activities, Interaction, and Assessment and Feedback were obtained through a Google Form by rating the 50-item questionnaire on a 4-point Likert scale together with two open-ended questions. With a grand mean of 3.81(SD=0.40), the findings revealed highly acceptable results. The qualitative responses also substantiated this result. Significant differences in the responses are also discussed, while the Cronbach alpha reliability test is high (α=0.923). Significantly, the VLM conforms with the international standards for online course design, suggesting it can be implemented among target students.

The Design and Development of Learning Media “Circle Time Management” Based on Virtual Reality Games in an Innovative Pedagogical Perspective

The development of increasingly sophisticated technology should be utilized as a medium for educational innovation. One of the technologies currently being developed is virtual reality which provides a real experience to its users. This study describes the analysis, design, development, and validation of virtual reality technology-based learning materials for early childhood education students’ comprehension of classroom management content utilizing the Circle Time model. Three teachers and three experts, including media, early childhood, and circle time experts as product validators, participated in this research approach’s two-year analysis, design, and development phases. The ADDIE model was modified for use in this process. Afterward, quantitative and qualitative data analysis was done on the information gathered through surveys and conversations. Based on the findings of the expert assessment, it can be concluded that learning media for the “Circle time management” based on virtual game reality can be created by adding components of innovative pedagogy such as experiential learning, computational thinking, embodied learning, multiliteracies, and gamification.

Integrating Virtual Teaching in a New Era of Medical Education: Lessons from a Neurology Course.

The COVID-19 pandemic has led to a sudden shift toward virtual learning in neurology education, which presents challenges for educators. However, virtual learning is here to stay for three key reasons: demand among students, ease of dissemination, and potential to improve educational quality. Despite challenges, educators can teach effectively using appropriate virtual tools and methods, with innovative approaches that will ultimately lead to sustained improvements in neurology education. Here, we aim to help educators effectively incorporate virtual instruction into their "new normal" by offering practical, evidence-based tips for balancing in-person and virtual learning, selecting the appropriate tools and methods for virtual teaching, and creating a supportive virtual learning environment. Using a systematic approach, educators can identify specific, achievable goals, determine the content's scope, appropriate assessments, select appropriate teaching methods, and create the session schedule and materials. Here we described evidence-based strategies for best practices, developing virtual material, and creating the appropriate virtual learning environment.

DEVELOPMENT OF VIRTUAL REALITY TEACHING MATERIALS BUILDING MATERIALS PRACTICE COURSES BASED ON INDUSTRIAL NEEDS OF THE SOCIETY 5.0 ERA

This research aims to create a collaborative and participatory classroom atmosphere in the use of technology in the world of Education. The era of Society 5.0 makes educators minimize their role as Learning material providers to become educators as an inspiration for the growth of student creativity. This study examines the validity and effectiveness of virtual reality teaching materials for building materials practice courses based on the needs of industry Society 5.0. This research used a research and development (R&D) approach with the ADDIE development model (Analyze et al., Evaluation). This study resulted in media feasibility from several experts where the learning design score was 3.85, learning material expert 3.77, media design expert 3.86; and the increase in student learning outcomes using media with an average pretest score of 41.12 and an average post-test of 74.80 seen from these values, the learning outcomes of students in building materials practice courses using virtual reality media increased by 33.68 or 87.77%.

Data‐Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases

AbstractSolid electrolyte interphases (SEIs) form as reduction products at the electrodes and strongly affect battery performance and safety. Because SEI formation poses a highly nonlinear, complex multi‐physics problem over various lengths and time scales, traditional modeling approaches struggle to characterize SEI evolution solely with existing physical properties. To improve the characterization of SEIs, it proposes a data‐driven strategy for a virtual material design that learns to represent and characterize SEI formation with physical and data‐driven properties from kinetic Monte Carlo simulations. A Variational AutoEncoder with a property regressor learns data‐driven properties, which represent SEI configurations and correlate with physical target properties. This new neural network design encodes the high‐dimensional structural and reaction spaces into a lower‐dimensional latent space, while the property regressor orders the latent space by physical target properties. The model achieves high correlation scores between target and predicted properties from latent representations, thereby proving that the data‐driven properties enrich the expressiveness of SEI characterizations.

Thriving at university: Designing a coaching psychology programme to promote wellbeing and resilience among undergraduate students

Wellbeing concerns increased during the Covid-19 pandemic, with university students being a population deserving special attention. Considering this, and the positive impact that coaching psychology can make in promoting wellbeing and resilience, we designed Time to Thrive (version 2022), a coaching psychology programme for undergraduate students based on an integrative model of cognitive-behavioural solution-focused coaching, positive psychology coaching and principles of neuroscience. This paper presents 1) the coaching psychology model used as a framework to structure the content of the programme. The model, called EMERALD, is based on the following domains for wellbeing and resilience: Emotions, Meaning and Engagement, Relationships, Achievements, Living better and Driving change; and 2) the co-creation approach that we used to design the programme and its upcoming evaluation, and a thematic analysis of semi-structured interviews with students after a pilot run. Voluntary response sampling was used to recruit first-year undergraduate students across the different faculties of the university. Preliminary results (N=6) indicate that the acceptability of Time to Thrive is high among undergraduate students. Participants commented on perceived outcomes from engaging with the programme, topic content, the virtual learning environment resources and materials, interaction with other students, structure and timeline, and recruitment strategies for increasing the visibility of Time to Thrive and reaching students more broadly. By presenting EMERALD and the initial results of this student-centred project, we hope to contribute to the work of other colleagues aiming to develop similar coaching psychology strategies to promote student wellbeing and resilience at universities and other educational settings.

Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma.

Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis of the aggressive macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic performance of dual-phase contrast-enhanced multiparametric DECT for predicting MTM HCC. Materials and Methods Patients with histopathologic examination-confirmed HCC who underwent contrast-enhanced DECT between June 2019 and June 2022 were retrospectively recruited from three independent centers (center 1, training and internal test data set; centers 2 and 3, external test data set). Radiologic features were visually analyzed and combined with clinical information to establish a clinical-radiologic model. Deep learning (DL) radiomics models were based on DL features and handcrafted features extracted from virtual monoenergetic images and material composition images on dual phase using binary least absolute shrinkage and selection operators. A DL radiomics nomogram was developed using multivariable logistic regression analysis. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC), and the log-rank test was used to analyze recurrence-free survival. Results A total of 262 patients were included (mean age, 54 years ± 12 [SD]; 225 men [86%]; training data set, n = 146 [56%]; internal test data set, n = 35 [13%]; external test data set, n = 81 [31%]). The DL radiomics nomogram better predicted MTM than the clinical-radiologic model (AUC = 0.91 vs 0.77, respectively, for the training set [P < .001], 0.87 vs 0.72 for the internal test data set [P = .04], and 0.89 vs 0.79 for the external test data set [P = .02]), with similar sensitivity (80% vs 87%, respectively; P = .63) and higher specificity (90% vs 63%; P < .001) in the external test data set. The predicted positive MTM groups based on the DL radiomics nomogram had shorter recurrence-free survival than predicted negative MTM groups in all three data sets (training data set, P = .04; internal test data set, P = .01; and external test data set, P = .03). Conclusion A DL radiomics nomogram derived from multiparametric DECT accurately predicted the MTM subtype in patients with HCC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Smart and Novelty Villages as the Quality Place of Virtuality

From the perspective of the recent orientations of virtual geographies, the idea of smart and novelty villages in the context of renovated material rural worlds is conceptualized. The sum of new virtual and new materiality produces virtual and novelty spaces and places, which acquire a precise territorial dimension in the rural policy and politics of smart villages. Smart villages can not only be framed in global smart contexts, but they can also play a fundamental role in de-global territorial horizons as an instrument of resistance to global processes of rural restructuring. The smart political idea or orientation takes shape in each rural community with a different expression in the form of new local materials. The concept of quality virtuality is developed theoretically along three axes: the encounter between smart, novelty and new materials; the smart in the equitable rural community; and the right to disconnection in remote rural areas.

Fracture assessment of U-notched diagonally loaded square plates additively manufactured from ABS with different raster orientations

This paper investigates the fracture behavior of 3D-printed notched specimens under various combinations of mode I and mode II loadings, including pure mode I and pure mode II loadings. The study employs fused deposition modeling (FDM) to fabricate U-notched diagonally loaded square plate (UNDLSP) specimens using Acrylonitrile Butadiene Styrene (ABS) filaments. To experimentally measure the fracture load and fracture initiation angles, 120 UNDLSP specimens are 3D printed with different notch tip radii, notch orientation angles, and [0, 90], [45, −45] raster orientations. Tensile characteristics and translaminar fracture toughness of ABS plates with [0, 90] and [45, −45] raster orientations are obtained using dog-bone and cracked DLSP samples, respectively. Considering the layered nature and anisotropy of the material, the virtual isotropic material concept (VIMC) is utilized along with two well-known stress-based brittle fracture models: the maximum tangential stress (MTS) and the mean stress (MS) criteria. The test results are predicted using the fracture limit curves and fracture initiation angle curves developed for these fracture models in terms of the notch stress intensity factors (NSIFs). Furthermore, a general equation is established to estimate the fracture initiation angle of U-notched ABS specimens under mixed mode I/II loading, irrespective of geometry, raster orientation, and notch tip radius, based on the curves obtained from the MTS and MS criteria. The fracture surfaces of the UNDLSP specimens are examined using scanning electron microscopy (SEM) to study the micro-mechanisms involved in the fracture process. The findings indicate that both the VIMC-UMTS and VIMC-UMS criteria accurately predict the experimentally achieved fracture loads. Additionally, it is observed that the fracture initiation angle curves for both criteria are nearly identical and accurate. The significance of this research is not only providing numerous new experimental results on mixed mode I/II fracture of notched 3D-printed ABS plates, but also providing simple, fast, accurate, and robust criteria for predicting the critical loads of such plates, without the need for conducting layer-by-layer failure analysis using progressive damage models.

Evaluation of solidification cracking susceptibility factors in carbon steel and availability of Ti addition based on theoretical analysis

This study conducted a basic investigation to improve the solidification cracking susceptibility of carbon steel. BTR is one of the susceptibilities to solidification cracking, and it is said that solidification cracking susceptibility is reduced when BTR is smaller. BTR of three commercial filler metals was evaluated by using Trans-Varestraint test. EPMA and EDS analysis of the weld metal microstructure revealed two compounds. The white structure was composed of silicon, manganese, and sulphur, and the black structure was Ti-based compound. Theoretical analysis of BTR was conducted by applying a two-phase peritectic solidification model in FA mode and considering MnS crystallization during solidification segregation. The analysis results indicated that BTR of carbon steel is attributed to the difference in solidification completion temperatures, and carbon is the most deleterious element. Also, MX compound (Ti, C, N) crystallized in addition to MnS at the end of solidification. Theoretical analysis was conducted for Ti virtual addition materials because MX compound (Ti, C, N) crystallization by Ti addition may lead to BTR reduction by trapping C in the liquid phase. The results showed that Ti addition reduced BTR. The amount of C in the residual liquid phase was reduced by an increase in MX compound (Ti, C, N) crystallization due to Ti addition. Based on theoretical considerations, it can be concluded that Ti addition to carbon steel is effective in reducing BTR.

Proposal for New Development Environment Based on Digital Materials and a Case Study of Gasoline Particulate Filter

Carbon dioxide emissions must be reduced as a countermeasure against global warming. A gasoline direct injection (GDI) engine is known for its high fuel economy, but there are more particulate emissions. To trap these particulates, a gasoline particulate filter (GPF) is needed. In this study, numerical simulations of the flow field in the GPF have been performed using the lattice Boltzmann method (LBM). Three types of filters were tested by our proposed approach, named as a digital material technique, where virtual materials are created by the simulation. Focusing on the pore characteristics of the filters, we used filters with the same pore size but different porosity. For comparison, the real internal structures of the actual GPF were detected using an X-ray CT method. The simulated flow field and the pressure drop across the filter wall were investigated, comparing with those of the actual GPF. As a result, it was found that the larger the porosity, the smaller the pressure drop. By setting the pore size and porosity to the same values as the actual GPF, a similar flow field was obtained, and the value of the pressure drop was close to that of the actual GPF.

Equivalent Modeling Method for Screw Joints Based on Stiffness Calculation

Accurate assessment of the mechanical properties of an assembly with screw joints has to rely on the equivalent modeling of the screw joint and accurate kinetic properties analysis. In this paper, an equivalent modeling method for screw joints based on stiffness calculation is proposed. With the solid model of the screw-coupled member, the equivalent virtual material parameters that can characterize the mechanical properties of the joint surface of the coupled member are obtained by calculating the joint surface stiffness, and then the accurate parametric modeling of the screw joints is realized. In addition, the vibration modal test platform for screw joints is built. By comparing the frequency response function obtained from the experiment with that obtained from the equivalent model simulation, it is found that the errors of the 1st-order and 2nd-order bending natural frequencies of the cantilever beam are 3.96% and 5.5%, respectively, and the simulation analysis results are of high accuracy. Furthermore, by comparing the first 6th-order of natural frequencies of the cantilever beam, the maximum frequency deviation between the simulation analysis results and the test results is ≤ 7%, and the average frequency deviation is 4.67%, which can better reflect the mechanical properties of the screw joint.

Student experiences from virtual reality-based chemistry laboratory exercises

This paper describes a practical case study on the benefits and feasibility of a virtual laboratory as a part of chemistry laboratory exercises. Three different objectives that must fit together to create an efficient Virtual Reality (VR) learning experience were found: relevant information content, learning design, and technical feasibility. To achieve these multidisciplinary goals, a simple framework for designing VR learning materials was created. A 360-VR version of a chemistry laboratory exercise was designed and created following this framework. Data on its effectiveness was collected on a laboratory course with over 150 first-year chemical engineering students. The students completed the same laboratory exercise both as a virtual laboratory and in a real student laboratory. In the student feedback, students clearly stated that virtual laboratories cannot replace the experience in a real laboratory, and that the virtual laboratory exercises did not directly increase student motivation. Nevertheless, students showed a very positive attitude towards virtual learning materials and suggested including even more activating materials such as quizzes and interactive videos in the learning materials. Only a few students reported any downsides related to the virtual laboratory exercise. Overall, it was shown that our design principles work in practice as the students reported several real benefits when they completed a virtual laboratory exercise before the real-life laboratory exercise. These benefits included learning the correct way that the laboratory exercise proceeds and how to perform certain tasks correctly.

Calcium scoring on coronary computed angiography with photon-counting technology: predictors of performance

Abstract Funding Acknowledgements Type of funding sources: None. Background Spectral CT imaging and virtual non-contrast (VNC) reconstruction techniques allow coronary artery calcium score (CACS) calculations from coronary CT angiography (CCTA) datasets. A novel virtual material decomposition algorithm (PureCalcium, PC) developed for photon-counting detector CT (PCD-CT) yields the potential of further increasing the accuracy of CCTA-based CACS measurements, however, little is known about the means of optimizing its performance for routine Agatston-scoring. Purpose The aim of the current study was to determine the predictors of the performance of a novel virtual non-iodine algorithm for PCD-CT. Materials and Methods Calcium scoring on non-enhanced scans is predominantly used as a complementary examination to CCTA increasing both acquisition time and radiation dose, thus, acquiring accurate CACS from CCTA datasets are warranted. In the current study, CACS and CAC volumes were calculated using true non-contrast (TNC) images and PC reconstructions in 139 consecutive patients (61.5 ± 10.9 years, 41.7% female) undergoing clinically indicated CCTA. Relative differences (TNC–PC/TNC) of PC-based CACS calculations from non-enhanced scans was calculated for each patient. Predictors of the degree of discrepancy were determined using linear regression analysis. Results Overall, BMI and heart rate did not affect the accuracy of PC measurements. However, the application of a higher tube voltage (140 kVp instead of 120 kVp) significantly decreased the relative difference of PC-based measurements from TNC values (β=-0.33 [95%CI: −0.64 – −0.03]). Furthermore, the average volume/lesion ratio also proved to be an independent predictor of performance (β=-0.01 [95%CI: −0.02 – −0.004]). Conclusions Spectral imaging based PC reconstructions may substitute conventional calcium scoring for a selected population of patients. It seems plausible that its performance can be improved by increased tube voltage protocols, provided spectral information gathered from CTA datasets is enhanced by using 140 kVp. It must be noted that the accuracy of PC may be hindered for smaller calcified lesions.

The Characteristics of Virtual Child Sexual Abuse Material Offenders and the Harms of Offending: A Qualitative Content Analysis of Print Media

Child sexual abuse material (CSAM) has become a global problem. With technological advancements, a sub-type of material has emerged: virtual child sexual abuse material (VCSAM). Despite the far-reaching harms of this material, little is known about VCSAM offenders. Research has found some disconnect between the perceived harmfulness of VCSAM and legislative definitions, compared with CSAM. Given the media’s influential ability to shape public perceptions, this exploratory study aimed to: (1) identify the characteristics of VCSAM offenders and (2) explore whether the harms of VCSAM are represented in this reporting. For the most up-to-date data, the search spanned 1 January 2019 to 23 September 2022. Of the 160 newspaper articles that appeared, 25 met the inclusion criteria. Four themes emerged from the qualitative content analysis: (1) VCSAM is a form of CSAM, (2) potential for escalation in offending from viewing VCSAM, (3) offenders have preferences in specific types of VCSAM (with the sub-theme: written stories and documents are VCSAM too), and (4) offenders “didn’t know” the material was illegal. Overall, these findings were positive; how cases were reported may broadly educate the public about VCSAM offending, with articles signifying the harms of this offending. The current findings have the potential to contribute to prevention and intervention efforts, with utility in policymaking, criminal justice, media, and psychology disciplines.

Data Subjectivation - Self-sovereign Identity and Digital Self-Determination

The purpose of this paper is to explore the philosophical implications of the Self-sovereign identity (SSI), considered one of the most prominent examples in which the process that we define as data subjectivation takes place. We will begin by examining the technical side of the SSI model and then delve into the concepts of identity and sovereignty, which are crucial for defining digital identity. We will explain how these concepts must be understood within a broader theoretical framework that we refer to as data subjectivation. This framework acknowledges the emergence of a new digital subject defined through the materialisation of data, which determines an individual’s rights and powers in their digital life. We scrutinise this process starting from the concept of “virtual materiality” and from the relationship between the physical world and the virtual world defined by Katherine Hayles. We will show that, to achieve the promises that are often associated with the SSI model, such as a decentralised and trusted web inhabited by digitally “sovereign” users, we must frame the vision put forth by the SSI as a socio-technical imagery. We sketch how this discourse can be oriented by employing the idea of digital self-determination, trying to frame the open questions of powers and rights in digital spaces linking the SSI and the process of data subjectivation. Finally, we show how a purely technical solution to the goal of a user that “owns” their data cannot be feasible, and we encourage further exploration of the inextricable links between the technological development of digital identity solutions and the political values that shape them.

A Two-Step Machine Learning Method for Predicting the Formation Energy of Ternary Compounds

Predicting the chemical stability of yet-to-be-discovered materials is an important aspect of the discovery and development of virtual materials. The conventional approach for computing the enthalpy of formation based on ab initio methods is time consuming and computationally demanding. In this regard, alternative machine learning approaches are proposed to predict the formation energies of different classes of materials with decent accuracy. In this paper, one such machine learning approach, a novel two-step method that predicts the formation energy of ternary compounds, is presented. In the first step, with a classifier, we determine the accuracy of heuristically calculated formation energies in order to increase the size of the training dataset for the second step. The second step is a regression model that predicts the formation energy of the ternary compounds. The first step leads to at least a 100% increase in the size of the dataset with respect to the data available in the Materials Project database. The results from the regression model match those from the existing state-of-the-art prediction models. In addition, we propose a slightly modified version of the Adam optimizer, namely centered Adam, and report the results from testing the centered Adam optimizer.

Establishing structure–property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach

The goal of this study is to develop correlations between microstructure morphology and macroscopic material behavior, known as structure–property linkages. These correlations can be used to predict material behavior and enable virtual materials design efforts. In this work the structure–property linkages for the capillary-driven fluid transport through highly porous open-pored polymeric membranes are determined by a data-driven approach. To establish linkages, about 400 porous microstructures with different geometrical features are algorithmically generated and characterized in 3D, using fluid flow simulations and image analysis methods. The data processing pipeline for the generation and analysis of the microstructures is implemented by a generic workflow tool called KadiStudio, which is embedded in the research data infrastructure Kadi4mat. The data-driven analysis enables predictions about the propagation time of a fluid over definable distances when only the porosity and the ligament radius are known as microstructural properties. The generated knowledge can be utilized for an accelerated development of novel polymeric membranes with an optimized pore structure.

The Godmother Project: A Virtual Initiative to Support Pregnant and Postpartum Women in Brazil During the COVID-19 Pandemic.

The COVID-19 pandemic increased maternal distress and demand for social support and educational services, while restriction measures decreased the availability of these services. The Godmother Project, a person-centered, WhatsApp-based support and health education initiative, was created after all in-person perinatal educational activities offered at 3 Brazilian hospitals were canceled in 2020. The project was developed in response to pregnant/postpartum women who called the hospitals with questions for health care professionals (HCPs). Health education nurses teamed up with staff from the communication department to design a project to (1) provide a direct communication channel between women and HCPs to answer health-related questions, (2) offer reliable virtual health education material, (3) identify women in need of additional assessment/referrals, (4) offer each woman continuous support from a specific nurse educator (Godmother), and (5) offer a virtual forum where women going through similar experiences could interact. The project provides direct communication between WhatsApp groups of 25-30 pregnant/postpartum women and a Godmother. It also offers access to a virtual library of educational materials prepared in response to women's needs/demands. The project, which began in July 2020 with 1 WhatsApp group of 5 women and 1 Godmother, currently has 305 groups, 6,942 active participants, and 4 Godmothers. Enrollments and WhatsApp groups continued to increase despite reestablishment of in-person educational activities in 2022. The vast majority of 232 participants surveyed in December 2021 were very satisfied with the project, the Godmothers, and the educational materials. Our findings suggest that WhatsApp groups of pregnant/postpartum women led by dedicated nurse educators can be an important tool to educate and support women during the perinatal period. This type of initiative may be especially important in contexts of physical distancing requirements or situations where social support is unavailable.