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Paper is a kind of renewable material that exists widely and has important application prospects. However, previous studies have mostly focused on the macromechanical properties of paper but lack micro theory based on paper fiber networks. We present a comprehensive experimental and computational study on the mechanical properties of fibers and fiber networks under the influence of microstructure. A beam-spring model was established based on a beam-fiber network to simulate the behavior of fiber networks. Simulations were performed to demonstrate the influence of fiber microstructural parameters such as fiber bond strength, stiffness, failure strength, size, and network density on mechanical features. Mechanical experiments verified that the fiber bond strength had a greater influence on the paper properties than did the fiber strength. This result is highly consistent with that of the model. All the simulations were validated by experimental measurements. Finally, we provided computational insights into the interfiber bond damage pattern with respect to different fiber microlevels and demonstrated that the proposed beam-spring model can be used to predict the response of fiber networks of paper materials. The above research can be used to optimize the formulation, process, and treatment of paper to meet specific application needs. [ABSTRACT FROM AUTHOR]

The switch to online learning required a creative solution to allow for the experiential learning outcomes of the program to be satisfied when access to physical spaces and equipment was restricted. This paper describes a collaborative process between technical and support staff as well as research and teaching faculty that led to the creation of meaningful experiential learning opportunities for over one thousand stakeholders. The implemented solutions included the development of hardware and software, the creation of documentation and training procedures for teaching assistants and designing a support system for the students. [Articles in this journal were presented at the University of Calgary Conference on Postsecondary Learning and Teaching.]

Artificial intelligence (AI) is now affecting all aspects of our social lives. Without always knowing it, we interact daily with intelligent systems. They serve us invisibly. At least that is the goal we assign to them: to make our lives better, task by task. Artificial intelligence has the potential to make biology education more engaging, personalized, and effective by providing students with interactive simulations, personalized learning experiences, and other tools that help them understand complex biological concepts. In this paper, we discuss the integration of AI into the virtual classroom, which significantly enhances student learning experiences in various ways. The study shows that an effective integration of technology into the virtual classroom requires a thoughtful approach that aligns with educational goals and the specific needs of students. In fact, interactive simulations can help make biology more engaging and memorable for students. Besides, personalized learning AI algorithms can help biology students receive a more tailored and effective learning experience, helping them to better understand the course material and develop a deeper appreciation for the natural world. In this work, we will discuss the use of AI to enhance interactive simulation-based cellular processes, with additional application in anatomy, physiology, and ecology teaching. Moreover, this paper discusses how AI could be used to analyze student data and propose personalized learning using adaptive assessments, content recommendations, and data sciences. This paper illustrates examples of AI algorithms that could be useful for teaching biology.

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness., (© 2024 John Wiley & Sons Ltd.)

Immersive technology plays an increasingly important role in design education, supporting digital literacy and experiential learning in higher education, particularly in the post-COVID-19 context. Many design disciplines, such as Architecture and Landscape Design, which used to rely heavily on physical field trips, and dialogic studios as signature pedagogies, had to adapt to the proliferation of innovative educational technologies due to the suspension of face-to-face teaching and learning. Augmented reality and virtual reality are now extensively used in technology and design education to support a more interactive, collaborative, and student-centred approach to learning. This paper expands on a pilot research study on the transition from traditional signature pedagogies of studio-based design education to technology-enhanced collaborative learning to support experiential learning. Based on Kolb's experiential learning framework concerning four learning modes, namely, concrete experience (CE), reflective observation (RO), abstract conceptualisation (AC), and active experimentation (AE), this paper analyses data from 75 undergraduate students across four cohorts in three different higher education institutions in Hong Kong. They were surveyed on their experiences of synchronous hybrid virtual experiential learning with design and technology curricula. Furthermore, six students were interviewed about their experiences. The research findings confirm two research hypotheses showing that immersive experiential learning could constructively align RO and AE, thus bringing an overall positive impact on the experiential learning process, but shows no conclusive influence on promoting CE, especially AC. The quantitative findings and qualitative results gave new insights into the discussion of the theoretical and practical implications of the study.

Researchers and educators have been exploring innovative methods in entrepreneurship education to address traditional approach failure at engaging students and developing their skills. Educational technology has shown promising results in early studies. Yet, there is a scarcity of reviews on their effectiveness in developing entrepreneurial competencies. This paper systematically reviews the empirical literature on the outcomes of educational technology used to teach entrepreneurship at higher education institutions. From an initial 316 search hits, 26 articles were selected for in-depth review. The learning outcomes were translated into entrepreneurial competencies, using the EntreComp Framework as a reference. Findings show that educational technologies, in general, positively impacted entrepreneurial competencies, especially financial and economic literacy, motivation and perseverance, and initiative taking. Gaming and simulations were the most researched, having an overall positive influence, while online learning and MOOCs showed limited and sometimes contradictory effects. The study fills the scholarly gap by connecting educational technology, entrepreneurship education, and entrepreneurial competencies. It provides a basic mapping linking each educational technology to the competencies it develops and advocates for a competency-based pedagogy in the delivery and assessment of entrepreneurship education. Educators can apply the findings and the mapping developed in this study to design and deliver entrepreneurship courses, incorporating educational technologies more insightfully and effectively in their pedagogies.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community., (© 2024 Wiley Periodicals LLC.)

Purpose: MR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR-STAT. In this work we leverage GPU hardware to reduce MR-STAT reconstruction times. A highly optimized, GPU-compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well., Methods: The Julia programming language was used to develop a flexible yet highly performant and GPU-compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix-free) modification of a previously presented (matrix-free) MR-STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR-STAT reconstruction times on GPU hardware are compared to previously presented MR-STAT reconstruction times., Results: The BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU-accelerated partially matrix-free MR-STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two-dimensional (2D slice)., Conclusion: By combining the proposed Bloch simulation toolbox and the partially matrix-free reconstruction algorithm, 2D MR-STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non-random missingness. Using worst case identification bounds that do not make assumptions about the non-response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non-response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals of estimators. Its application however can be prohibitively demanding in settings involving large data. In addition, modern causal inference estimators based on machine learning and optimization techniques exacerbate the computational burden of the bootstrap. The bag of little bootstraps has been proposed in non-causal settings for large data but has not yet been applied to evaluate the properties of estimators of causal effects. In this article, we introduce a new bootstrap algorithm called causal bag of little bootstraps for causal inference with large data. The new algorithm significantly improves the computational efficiency of the traditional bootstrap while providing consistent estimates and desirable confidence interval coverage. We describe its properties, provide practical considerations, and evaluate the performance of the proposed algorithm in terms of bias, coverage of the true 95% confidence intervals, and computational time in a simulation study. We apply it in the evaluation of the effect of hormone therapy on the average time to coronary heart disease using a large observational data set from the Women's Health Initiative., (© 2024 John Wiley & Sons Ltd.)

The U.S. Food and Drug Administration (FDA) has launched Project Optimus to shift dose selection from the maximum tolerated dose (MTD) to the dose that produces the optimal risk-benefit tradeoff. One approach highlighted in the FDA's guidance involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This article focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. The resulting design is referred to as MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy). Simulation studies demonstrate that MERIT has desirable operating characteristics, and a sample size between 20 and 40 per dosage arm often offers reasonable power and type I errors to ensure patient safety and benefit. To facilitate the implementation of the MERIT design, we provide software, available at https://www.trialdesign.org., (© 2024 John Wiley & Sons Ltd.)

Patients are often switched between generic formulations of the same drug, but in some cases generic interchangeability is questioned. For generic drugs to be approved, bioequivalence with the innovator drug should be demonstrated, but evidence of bioequivalence is not required in the intended patient population or relative to other approved generics., Aim: We aim to identify pathophysiological pharmacokinetic subpopulations for whom there is a difference in comparative bioavailability compared to a healthy population., Methods: We used simulated exposures from a nonparametric model of multiple generics and the originator gabapentin. Exposure was simulated for virtual populations with pharmacokinetic characteristics beyond those of healthy subjects with regard to rate of absorption, volume of distribution and reduced renal function. Virtual parallel design bioequivalence studies were performed using a random sample of 24 simulated subjects, with standard acceptance criteria., Results: Results indicated increased pharmacokinetic variability for patient populations with a lower rate of absorption or a reduced renal function, but no change in the average comparable bioavailability ratio. This increased variability results in a reduced likelihood of demonstrating bioequivalence. Observations were similar for comparisons between all different formulations, as well as between subjects who received the identical formulation in a repeated fashion. No relevant effect was observed for simulations with increased volume of distribution., Conclusion: Our simulations indicate that the reduced likelihood of demonstrating bioequivalence for subjects with altered pharmacokinetics is not influenced by a formulation switch, nor does the average comparable bioavailability ratio change, therefore these results support generic interchangeability and current approval requirements for generics., (© 2022 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Purpose: Inclusive growth in the education sector is still a major challenge in some countries because of limited access to technologies and internet connectivity, among other reasons. However, as a technology, augmented reality (AR) is expected to be widely used in the field of education in the future. The main purpose of this paper is to explore the use of AR for inclusive growth in education as well as identify the challenges, particularly in countries like India and Tanzania. Design/methodology/approach: This paper applies a systematic literature review by analysing and synthesising relevant documents, mainly journal articles, books and conference papers. Descriptive statistics and cross-tabulation were used for the analysis. Content analysis was used to evaluate the contents of the reviewed literature. Findings: For the use of AR, it is important to have adequate digital infrastructure, access to universal internet or broadband facilities and the digital empowerment of citizens. Major challenges to inclusive growth in education include the lack of trained teacher educators and students' preference for practical or project-based curriculum. Practical implications: Practitioners in both countries may consider the use of AR for inclusive growth in education. Originality/value: This paper specifically examines the use of AR in higher education and the related challenges based on a review of two countries, namely India and Tanzania.

Landscape garden design is an important design foundation course for undergraduate students of landscape architecture in China, but the current classroom teaching has the problem of limited spatial dimension of the course. This paper proposes a virtual scene model construction method through the computer software with the creation of the need to display the scene. This virtual reality technology has immersive, interactive, and risqué characteristics. This paper focuses on the key points of virtual reality technology, discusses the specific implementation method of virtual reality technology in the "landscape garden design" course, and introduces the interactive control principle. The results of the research show that virtual reality technology to assist teaching can cultivate students' spatial thinking and enhance students' spatial simulation experience, greatly reducing the cost of traditional teaching.

In recent years, Game-Based Learning (GBL) has been widely adopted in various educational settings. This paper aims to review empirical studies that adopt GBL in the field of AI education and explore its future research perspectives. After a systematic keyword search in the online database and a snowballing approach, a total of 125 empirical papers with 133 studies were targeted as samples. Results indicated that the games in AI education are mainly fell into five categories: Puzzle games are the most used in the curriculum (27.07%), followed by Reasoning strategy games (23.31%), Robot games (18.05%), Role-playing games (9.02%) and Simulation games (6.77%). Among them, 22.39% of games were with real characters, 11.94% were with virtual characters and 64.18% were with no characters. Besides, games were used in three main forms in AI education: games as teaching tools (78.95%), games as student works (12.03%), and games as a competing mechanism (9.02%). Researchers mainly paid attention to the effect of GBL on students' Opinions and Attitude (52.96%) and Learning achievement (24.04%), while the other three categories such as Skills and ability, Interaction, and Cognition were not extensively measured. The cross-sectional analysis, research gaps, and potential directions for future research were also discussed.

Social-emotional deficits in school-aged children diagnosed with autism spectrum disorder (ASD) greatly hinder these children from fully participating in various school activities in the inclusive education setting. Previous studies have demonstrated evidence regarding the effectiveness of using virtual reality (VR) for enhancing the children's affective expression and social reciprocity. However, considering the technical and logistical complexity of the enabling hardware and software systems, how such approaches can be effectively and sustainably delivered in the school setting remains underexplored. This paper presents a study that utilised VR headsets to enhance affective expression and social reciprocity for children with ASD and explored how the approach could be effectively and sustainably delivered at schools. A total of eight VR learning scenarios were designed based on Kolb's experiential learning framework. 176 children aged 6-12 with a clinical diagnosis of ASD participated in the study. The statistical analyses showed that the participants who received the intervention significantly improved in affective expression and social reciprocity, compared to those who were in the control group. Moreover, the approaches to enhance long-term sustainability have also been presented and discussed in this paper.

This is a small-scale qualitative participatory research study conducted in two large primary schools in Ireland with classes for autistic students. The paper reports how a menu of immersive technology learning experiences was thoughtfully designed and employed with autistic children who were communicating through their behavior an aversion to the learning environment and many aspects of conventional learning activities. Through this study, students were enabled to explore and respond to learning through immersive learning environments. The Universal Design for Learning (UDL) infused design of these immersive learning experiences was tailored to remove barriers to learning, thus counteracting the observable disruptive behaviors of the students. The study explores a purposive sample of six participating teachers' perspectives on the affective gains (engagement, motivation, and self-efficacy) achieved through the use of these immersive experiences and their impact on students' use of disruptive behaviors as a means of communication. Semi-structured interviews with teacher participants were analyzed using the theory and a general inductive approach for qualitative data analysis. This paper contributes to the literature on the use of immersive technologies in education and highlights the potential benefits of incorporating these technologies into the classroom.

Background: Augmented reality (AR) is receiving increasing interest as a tool to create an interactive and motivating learning environment. Yet, it is unclear how instructional support affects performance in AR. Objectives: This study sought to explore how varying the instructional support in AR can affect performance-related behaviours of students with low cognitive abilities during assembly work. Methods: A total of 90 Belgian secondary school students repeatedly executed four different realistic assembly tasks. Three levels of instructional support (low, medium, and high) in AR as well as a control condition with paper instructions with a high level of detail were systematically varied across tasks and participants. Results and Conclusions: Multilevel regression analyses showed that AR instructions yielded lower assembly times and a lower perceived physical effort than paper instructions. Additionally, participants perceived tasks as less complex when given AR instructions with a high or a medium level of detail than when given a low level of detail. No effects of instructional support were established for other performance-related behaviours, namely necessary assistance, error-making, cognitive load, competence frustration, and stress. Effect sizes were small, at least among the instructional support conditions studied, yielding a limited base for adaptivity. Presumably, tailoring the instructional support in AR is only beneficial for highly complex tasks. The results might be useful for the design and implementation of AR in educational settings.

This paper delves deeply into the creative pedagogies which support cutting edge digital STEAM practice across primary and secondary school settings. It contextualises the research within current STEAM agendas including transdisciplinarity, and STEAM and technology and goes on to offer insight from the novel context of ocean learning to develop and extend a theorisation of creative pedagogies as entwining both creative teaching and teaching for creativity as embodied, democratic, dialogic and material processes. Intra-action between theory, praxis, nature, culture, the digital and humans enables an emergent perspective about changing the dynamics of power to develop ocean or environmental learning and related activism. Derived from research into an ocean education project, which aimed to develop students' ocean literacy through the combined educative principles of creative pedagogies and digital technologies (Augmented and Virtual Realities), the research draws on data from six projects across primary and secondary school settings in Denmark, Spain and England. It used a "diffractive" analytic technique, inspired by new materialist theory, to explore the messy mixtures of natural, cultural and technological environments that were being learned through. This involved the development of four material-dialogic assemblages each including diffractive switches. Each is presented first through a "piece" which demonstrates each assemblage's connection to the core question, followed by "ripples," which briefly articulate the new learning and questions arising from that assemblage. The four assemblages cover the irresistibility of making kin, the relationships between lively bodies and virtual environments, the importance of spacetimematter in environmental edu-activism and trajectories between transience, stability and dialogic space. The paper leaves the reader/engager with a selection of prompts to highlight the research's contribution to current STEAM agendas related to changing power dynamics, and to provoke reader/engagers' own practices. These can include new pedagogies and activisms, as well as theoretical developments to the combined educative principles of creative pedagogies and digital technologies within STEAM education.

This paper describes a case study of an online physics laboratory course implemented for 254 engineering students at Walailak University in Thailand in response to the COVID-19 pandemic. The laboratory sequence was designed based on the United Kingdom Professional Standards Framework and covered eight experiments on fundamental concepts of motion, electricity, waves, and fluid mechanics. The experiments were conducted at students' homes using physics education technology simulations, Tracker for video analysis, smartphones, and home-based apparatuses. During the experiments, students shared their progress with classmates and lecturers through screen sharing, which facilitated exchanges of ideas and difficulties. The paper discusses the successful implementation of the online laboratory course and the challenges encountered, such as student engagement, resource limitations, and practical skill development. The insights gained from this case study may be useful for online laboratory education beyond the pandemic.

Water-sensitive papers (WSPs), as an artificial target, have been generally used to evaluate spray quality in pesticide applications. Though many tools were available to analyze WSPs, they were not effective in processing the presented overlapping droplets, which prevented accurate quantification of spray quality. Here, a novel methodology was proposed to analyze WSPs, focusing on the segmentation of overlapping droplets based on concave point detection and ellipse fitting. Up to 553 WSPs obtained from field trials in apple trees were used to validate this methodology. A high overall segmentation accuracy of 77.8% was achieved for the WSPs with coverage below 25%, which allowed to precisely characterize the corresponding deposition pattern. A universal linear relationship was observed between the droplet density and coverage, independent of the sprayer and canopy characteristics. In addition, the droplet size distributions for all spray applications showed a similar trend. As the proposed methodology was not effective to segment the complex overlapping spots on the WSP with high coverage (>25%), simulated WSPs were generated to estimate the deposition pattern of high coverage based on the droplet size distribution deposited on the WSPs with low coverage. A linear relationship with a much higher slope was observed between droplet density and coverage for the high deposition, which enabled an approximate estimation of the corresponding droplet density. In general, the proposed approaches allowed to obtain more accurate spray quality indicators from the WSP collectors and could be widely used for spray application evaluation to promote precision spraying. • A novel approach was proposed to analyze WSPs with the split of overlapping droplets. • A high segmentation accuracy of 77.8% was observed for the WSPs with low coverage. • A linear relationship was observed between the droplet density and spray coverage. • The applied droplet pattern showed a remarkable effect on the spray distribution. • The droplet density at high coverage was estimated based on the computer simulation. [ABSTRACT FROM AUTHOR]

This work provides an overview of the use of Virtual Reality (VR) technology in the context of training through the documentation of a project produced by Ohio University's Game Research and Immersive Design (GRID) Lab involving the development of a cine-VR series for police officer training in areas such as suicide prevention, mental health, crisis intervention, domestic violence resolution, and school violence prevention. The article starts by introducing the concept of VR training with its subdivisions and its benefits, including the promotion of learning and empathy through immersion. Followingly, it describes the process of planning for a VR training. Subsequently, it breaks down the several stages of creating VR training content from scenario exploration, scriptwriting, recording, to postproduction. In conclusion, this paper recognizes the challenges of creating compelling VR content such as storytelling, editing, and resources. Additionally, it attempts to pave the way for similar VR training projects by providing recommendations and emphasizing the importance of communication, adaptability, and experimentation.

Virtual Reality (VR) as an interactive, illustrative, and immersive learning environment has ample opportunities for the development of language skills. Despite a growing literature on VR-assisted language learning, the benefit of VR for L2 listening is in need of further investigation. To fill this lacuna, the current study surveyed the impact of using 360° monoscopic VR videos as pre-listening tasks on language learners' development of listening comprehension. Sixty English as a foreign language (EFL) learners participated in the study as the control and the experimental groups. Their entry level of listening comprehension was evaluated by the B1 Preliminary listening paper before the study. Listening was taught to both groups by the comprehension-based model consisting of a pre-during-post listening cycle. The pre-listening activities of the experimental group included watching and working on 360° VR videos related to the topics of the listening parts of the textbook. The pre-listening phase of the control group included the warmup activities of the textbook. Both groups' listening comprehension was evaluated again at the end of the study. The quantitative data analysis revealed a significant difference between the participants' listening comprehension in favor of those who had worked with VR video as pre-listening tasks. Also, larger effect sizes for Parts 3 and 4 of the test that focused on understanding monologues were evident. The experimental group members were asked to express their perceptions of the experience of working with 360° VR videos in listening instruction by taking part in a structured interview. The results showed that almost all participants found the activities to be inspiring and pedagogically valuable.

The United States faces persistent threats conducting malicious cyber campaigns that threaten critical infrastructure, companies and their intellectual property, and the privacy of its citizens. Additionally, there are millions of unfilled cybersecurity positions, and the cybersecurity skills gap continues to widen. Most companies believe that this problem has not improved and nearly 44% believe it has gotten worse over the past 10 years. Threat actors are continuing to evolve their tactics, techniques, and procedures for conducting attacks on public and private targets. Education institutions and companies must adopt emerging technologies to develop security professionals and to increase cybersecurity awareness holistically. Leveraging Virtual/Augmented/Mixed/Extended Reality technologies for education, training, and awareness can augment traditional learning methodologies and improve the nation's cybersecurity posture. This paper reviews previous research to identify how distance and remote education are conducted generally, and how Virtual/Augmented/Extended/Mixed reality technologies are used to conduct cybersecurity awareness training, cybersecurity training, and conduct operations. Finally, barriers to adopting these technologies will be discussed. Understanding how these technologies can be developed and implemented provides one potential way of overcoming the cybersecurity workforce gap and increasing the competencies and capabilities of cybersecurity professionals.

This study explores the transformative journey of higher education towards smart universities, emphasizing integrating cutting-edge technologies such as augmented reality, virtual reality, artificial intelligence, and biometric systems. This evolution responds to the evolving demands of society, aiming to significantly enhance the educational experience while reimagining campus life and community involvement. It sheds light on the creation of immersive, interactive, and personalized learning landscapes that accommodate a diverse array of student requirements, including those with disabilities, setting a new standard in educational inclusivity. The discourse extends to the pivotal role of smart campuses, which are instrumental in forging safer, more inclusive, and environmentally sustainable educational realms. Furthermore, the paper highlights the importance of digital platforms in nurturing social bonds and community engagement, especially through the potent medium of alumni networks. By transitioning into centers of innovation and technological excellence, universities are equipping students for a future shaped by technology. The review highlights the transition's challenges and opportunities, emphasizing the need to balance technological advancements with human-centric values, ensure equitable technology access, and maintain privacy. It presents the rise of smart universities as a pivotal step towards more interconnected, intelligent, and sustainable communities, significantly influencing education and societal development.

We used computer simulations of growth, mating and death of cephalopods and fishes to explore the effect of different life-history strategies on the relative prevalence of alternative male mating strategies. Specifically, we investigated the consequences of single or multiple matings per lifetime, mating strategy switching, cannibalism, resource stochasticity, and altruism towards relatives. We found that a combination of single (semelparous) matings, cannibalism and an absence of mating strategy changes in one lifetime led to a more strictly partitioned parameter space, with a reduced region where the two mating strategies co-exist in similar numbers. Explicitly including Hamilton's rule in simulations of the social system of a Cichlid led to an increase of dominant males, at the expense of both sneakers and dwarf males ("super-sneakers"). Our predictions provide general bounds on the viable ratios of alternative male mating strategies with different life-histories, and under possibly rapidly changing ecological situations., (© 2024. The Author(s), under exclusive licence to the Society for Mathematical Biology.)

Complex diseases are often analyzed using disease subtypes classified by multiple biomarkers to study pathogenic heterogeneity. In such molecular pathological epidemiology research, we consider a weighted Cox proportional hazard model to evaluate the effect of exposures on various disease subtypes under competing-risk settings in the presence of partially or completely missing biomarkers. The asymptotic properties of the inverse and augmented inverse probability-weighted estimating equation methods are studied with a general pattern of missing data. Simulation studies have been conducted to demonstrate the double robustness of the estimators. For illustration, we applied this method to examine the association between pack-years of smoking before the age of 30 and the incidence of colorectal cancer subtypes defined by a combination of four tumor molecular biomarkers (statuses of microsatellite instability, CpG island methylator phenotype, BRAF mutation, and KRAS mutation) in the Nurses' Health Study cohort., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully., (© 2024 The Authors. Clinical Pharmacology & Therapeutics © 2024 American Society for Clinical Pharmacology and Therapeutics.)

Background: Non-inferiority trials comparing different active drugs are often subject to treatment non-adherence. Intention-to-treat (ITT) and per-protocol (PP) analyses have been advocated in such studies but are not guaranteed to be unbiased in the presence of differential non-adherence., Methods: The REMoxTB trial evaluated two 4-month experimental regimens compared with a 6-month control regimen for newly diagnosed drug-susceptible TB. The primary endpoint was a composite unfavorable outcome of treatment failure or recurrence within 18 months post-randomization. We conducted a simulation study based on REMoxTB to assess the performance of statistical methods for handling non-adherence in non-inferiority trials, including: ITT and PP analyses, adjustment for observed adherence, multiple imputation (MI) of outcomes, inverse-probability-of-treatment weighting (IPTW), and a doubly-robust (DR) estimator., Results: When non-adherence differed between trial arms, ITT, and PP analyses often resulted in non-trivial bias in the estimated treatment effect, which consequently under- or over-inflated the type I error rate. Adjustment for observed adherence led to similar issues, whereas the MI, IPTW and DR approaches were able to correct bias under most non-adherence scenarios; they could not always eliminate bias entirely in the presence of unobserved confounding. The IPTW and DR methods were generally unbiased and maintained desired type I error rates and statistical power., Conclusions: When non-adherence differs between trial arms, ITT and PP analyses can produce biased estimates of efficacy, potentially leading to the acceptance of inferior treatments or efficacious regimens being missed. IPTW and the DR estimator are relatively straightforward methods to supplement ITT and PP approaches., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two-step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study., (© 2024 John Wiley & Sons Ltd.)

There has been ongoing debate about the efficacy and mechanism of action of neuromodulation devices in pain relief applications. It has recently been suggested that both issues may be resolved if electromagnetic theory is incorporated into the understanding and application of this technology, and we therefore undertook an in silico analysis to further explore this idea. We created a CAD replication of a standard neuromodulation electrode array with a generic linear 3/6 mm 8-contact lead, developed a parameterized algorithmic model for the pulse delivered by the device and assigned material properties to biologic media to accurately reflect their electromagnetic properties. We then created a physical simulation of the device's output both in air and in the biophysical environment. The simulations confirmed the presence of an electromagnetic field (EM field). Variations in programming of the device affected the strength of the EM field by orders of magnitude. The biologic media all absorbed the EM field, an effect which was particularly pronounced in cerebrospinal fluid and muscle. We discuss the implications of all these findings in relation to the literature. We suggest that knowledge of electromagnetic theory and its application within the biophysical space is required for the optimal use of neuromodulation devices in pain relief applications., (© 2024 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

Aims: Amikacin requires therapeutic drug monitoring for optimum efficacy; however, the optimal model-informed precision dosing strategy for the area under the concentration-time curve (AUC) of amikacin is uncertain. This simulation study aimed to determine the efficient blood sampling points using the Bayesian forecasting approach for early achievement of the target AUC range for amikacin in critically ill patients., Methods: We generated a virtual population of 3000 individuals using 2 validated population pharmacokinetic models identified using a systematic literature search. AUC for each blood sampling point was evaluated using the probability of achieving a ratio of estimated/reference AUC at steady state in the 0.8-1.2 range., Results: On day 1, the 1-point samplings for population pharmacokinetic models showed a priori probabilities of 26.3 and 45.6%, which increased to 47.3 and 94.4% at 23 and 15 h, respectively. Using 2-point sampling at the peak (3 and 4 h) and trough (24 h) on day 1, these probabilities further increased to 72.3 and 99.5%, respectively. These probabilities were comparable on days 2 and 3, regardless of 3 and 6 sampling points or estimated glomerular filtration rate. These results indicated the higher predictive accuracy of 2-point sampling than 1-point sampling on day 1 for amikacin AUC estimation. Moreover, 2-point sampling was a more reasonable approach than rich sampling., Conclusions: This study contributes to the development of an efficient model-informed precision dosing strategy for early targeting of amikacin AUC in critically ill patients., (© 2024 British Pharmacological Society.)

Background: Painting is the foundational expression across all art forms and is one of the key creative practices for fostering students' aesthetic ability and creativity within fine arts courses. Collaborative painting in the form of socially shared regulation of learning (SSRL) can be recognized as an effective strategy for enhancing creativity in both individual and group work. However, the absence of contextual experiences and collaborative spaces poses challenges for students in cultivating their creativity in painting. Objectives: The present study explores students' creativity, socially shared regulation (SSR) and positive painting behaviours using an SSRL-based collaborative painting approach in the metaverse (Meta-CP). Methods: Via a quasi-experimental design, a total of 40 Chinese students in fifth grade were recruited and were randomly divided into an experimental group and a control group, with 20 students utilizing the Meta-CP approach, while the remaining 20 students followed the conventional SSRL-based paper-and-brush collaborative painting (C-CP) approach. The Meta-CP approach not only offers an authentic painting context and a collaborative space but also facilitates the collaborative process through the SSRL framework. The data collection included students' creative tendency, painting works and painting process. Results and Conclusions: The Meta-CP approach effectively enhances students' creativity concerning adventure, curiosity and imagination, as well as results in the creation of more distinctive and logical artistic works. Furthermore, the approach significantly improved the quality of students' SSR. Additionally, students utilizing the Meta-CP approach displayed more positive painting behaviours compared with those employing the C-CP approach.

Informal science activities occurring at various non-traditional learning sites present a pivotal model for involving youths in the science learning process. The dynamic landscape of emerging technologies has prompted the exploration of innovative methodologies to bolster and refine informal science education. However, the expanse of this field and its attendant complexities has led to a lack of empirical evidence on the subject matter necessitating further research. This paper presents the findings of a systematic literature review, spanning the years 2017 to 2022, focusing on empirical inquiries into the deployment and impact of digital tools and technologies on the cognitive, affective, and behavioral dimensions of student learning. The investigation pertains specifically to unconstrained, extracurricular learning environments, encompassing science centers and museums and outdoor locations. A methodical search of renowned electronic databases, including ACM Digital Library, ERIC, Google Scholar, Sage, and Web of Science, supplemented by rigorous cross-referencing, yielded a corpus of seventeen (n = 17) relevant studies. These were subjected to a thorough qualitative content analysis. A diverse array of scientific concepts was found to have been investigated using hardware-based, software-based, or mixed approaches. Notably, augmented reality (AR) emerges as a preeminent focal point within the purview of digital interfaces explored by researchers. The synthesis of findings underscores the affirmative influence of digital tools and technologies on pivotal facets such as engagement, motivation, attitude towards science, and comprehension of scientific principles. In culmination, this study delineates prospective research focus for future studies in informal science education.

This paper examines the utility of virtual simulation as an inclusive learning activity. Using a framework derived from literature, the Virtual Simulated International Placements (VSIP) were developed, and we explored the experiences of learners and facilitators to identify the pedagogical practices that they adopted during the learning activities with an approach inspired by phenomenography. Data from focus groups with 6 learners and 6 facilitators who participated in the VSIP were analysed along with 64 survey responses from learner participants. The findings of the study indicate there were three key concepts of the virtual simulation which supported inclusive learning environments. Overall, the participants' reflections indicated that the VSIP offered an accessible and usable way to engage in international placements that supported a personalised learning journey. Finally, the findings suggested VSIP should incorporate a diverse range of perspectives, backgrounds, and experiences to create and promote an inclusive learning environment. Further research can explore if these activities create a greater acceptance of different peoples' perspectives and cultures as well as impact stereotypes and biases. In addition, key concepts that made this teaching and learning activity inclusive could aid future investigation into how and why virtual simulations can be inclusive.

This paper presents "rational inattention" as a new, transdiagnostic theory of information seeking in neurodevelopmental conditions that have uneven cognitive and socio-emotional profiles, including developmental language disorder (DLD), dyslexia, dyscalculia and autism. Rational inattention holds that the optimal solution to minimizing epistemic uncertainty is to avoid imprecise information sources. The key theoretical contribution of this report is to endogenize imprecision, making it a function of the primary neurocognitive difficulties that have been invoked to explain neurodivergent phenotypes, including deficits in auditory perception, working memory, procedural learning and the social brain network. We argue that disengagement with information sources with low endogenous precision (e.g. speech in DLD, orthography-phonology mappings in dyslexia, numeric stimuli in dyscalculia and social signals in autism) constitutes resource-rational behaviour. We demonstrate the strength of this account in a series of computational simulations. In experiment 1, we simulate information seeking in artificial agents mimicking an array of neurodivergent phenotypes, which optimally explore a complex learning environment containing speech, text, numeric stimuli and social cues. In experiment 2, we simulate optimal information seeking in a cross-modal dual-task paradigm and qualitatively replicate empirical data from children with and without DLD. Across experiments, simulated agents' only aim was to maximally reduce epistemic uncertainty, with no difference in reward across information sources. We show that rational inattention emerges naturally in specific neurodivergent phenotypes as a function of low endogenous precision. For instance, an agent mimicking the DLD phenotype disengages with speech (and preferentially engages with alternative precise information sources) because endogenous imprecision renders speech not conducive to information gain. Because engagement is necessary for learning, simulation demonstrates how optimal information seeking may paradoxically contribute negatively to an already delayed learning trajectory in neurodivergent children.

In this paper, we propose a method of generating the elemental image array (EIA) for the integral imaging (InIm) based light field display (LFD). Firstly, we reveal the intrinsic voxel spatial distributions of the InIm‐based LFD and analyze how the voxel space affects the display performances such as spatial resolution, depth of field, and smoothness of parallax. Then, we present an algorithm to generate an EIA from a pair of RGB and depth (RGBD) images and an optimally selected voxel space. In the experiments, we tested the display performances of the voxels on different depth planes and obtained results consistent with the theoretical analyses. We also experimented with a computer 3D model and a real‐world scene on two InIm‐based LFD prototypes working in different modes, one in real mode and the other in virtual mode, and obtained favorable 3D display effects. The proposed method simplifies the light field data acquisition and image synthesis process, and supports handling both the virtual and the real‐world 3D scenes. We expect the proposed system to help the practical application of the InIm‐based LFD technology. [ABSTRACT FROM AUTHOR]

Storey's estimator for the proportion of true null hypotheses, originally proposed under the continuous framework, has been modified in this work under the discrete framework. The modification results in improved estimation of the parameter of interest. The proposed estimator is used to formulate an adaptive version of the Benjamini-Hochberg procedure. Control over the false discovery rate by the proposed adaptive procedure has been proved analytically. The proposed estimate is also used to formulate an adaptive version of the Benjamini-Hochberg-Heyse procedure. Simulation experiments establish the conservative nature of this new adaptive procedure. Substantial amount of gain in power is observed for the new adaptive procedures over the standard procedures. For demonstration of the proposed method, two important real life gene expression data sets, one related to the study of HIV and the other related to methylation study, are used., (© 2024 Wiley-VCH GmbH.)

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings., (© 2024. The Author(s).)

Developing new imputation methodology has become a very active field. Unfortunately, there is no consensus on how to perform simulation studies to evaluate the properties of imputation methods. In part, this may be due to different aims between fields and studies. For example, when evaluating imputation techniques aimed at prediction, different aims may be formulated than when statistical inference is of interest. The lack of consensus may also stem from different personal preferences or scientific backgrounds. All in all, the lack of common ground in evaluating imputation methodology may lead to suboptimal use in practice. In this paper, we propose a move toward a standardized evaluation of imputation methodology. To demonstrate the need for standardization, we highlight a set of possible pitfalls that bring forth a chain of potential problems in the objective assessment of the performance of imputation routines. Additionally, we suggest a course of action for simulating and evaluating missing data problems. Our suggested course of action is by no means meant to serve as a complete cookbook, but rather meant to incite critical thinking and a move to objective and fair evaluations of imputation methodology. We invite the readers of this paper to contribute to the suggested course of action., (© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Objective: Extended reality (XR) has emerged as an innovative educational modality that offers the potential for the creation of more interactive and engaging forms of patient education experiences and products. The purpose of this article is to describe the field of XR technologies and review its potential through a critical lens as well as its possible adoption as a mainstream technology for providing patient education in the future. Method: A review of the literature was undertaken to summarise the emerging evidence concerning the effectiveness of XR as a patient education modality. The findings of several reviews are summarised and a critical discussion of potential issues and challenges in the adoption and use of XR among particular marginalised populations are explored. Results: The emerging evidence suggests that different forms of XR technology applications have the potential to create immersive and engaging patient education experiences that can lead to enhanced patient satisfaction, positive educational outcomes and reduced patient anxiety. Nonetheless, there have been calls for greater consideration of how patient characteristics, including socioeconomic status, gender, cultural and generational differences, influence the learning effects of virtual reality educational applications, as well as its adoption and implementation for patient education purposes. Conclusion: The evidence surrounding the effectiveness of XR in patient education is growing; however, various factors could influence the successful adoption and implementation of XR in different patient populations who have traditionally experienced challenges with digital health literacy. The paper offers some recommendations for enhancing the evidence base and potential approaches to advance the design and evaluation of XR applications in patient education.

Simulation is a powerful approach that plays a significant role in science and technology. Computational models that simulate learner interactions and data hold great promise for educational technology as well. Amongst others, simulated learners can be used for teacher training, for generating and evaluating hypotheses on human learning, for developing adaptive learning algorithms, for building virtual worlds in which students can practice collaboration skills with simulated pals, and for testing learning environments. This paper provides the first systematic literature review on simulated learners in the broad area of artificial intelligence in education and related fields, focusing on the decade 2010-19. We analyze the trends regarding the use of simulated learners in educational technology within this decade, the purposes for which simulated learners are being used, and how the validity of the simulated learners is assessed. We find that simulated learner models tend to represent only narrow aspects of student learning. And, surprisingly, we also find that almost half of the studies using simulated learners do not provide "any" evidence that their modeling addresses the most fundamental question in simulation design -- is the model valid? This poses a threat to the reliability of results that are based on these models. Based on our findings, we propose that future research should focus on developing more complete simulated learner models. To validate these models, we suggest a standard and universal criterion, which is based on the lasting idea of Turing's Test. We discuss the properties of this test and its potential to move the field of simulated learners forward.

Online program design is important for cross-cultural learning and cultural awareness and responsiveness. Because of the importance of these areas in journalism and similar fields, this research studies an intercultural "bridge" project with journalism and media-related students at a southwest U.S. university and four universities in Yemen. The paper presents an analysis of the students' experience of the cultural exchange on the platform, Slack, of journalism, and other types of media. Data demonstrated the impact of learning about each culture, and interculturally, through students' and instructors' perspectives versus news coverage provided by mainstream media. Intercultural bridge building and relationship development through collective activities and journalistic work also offered promise. The study also revealed impediments to seamless cultural exchanges and learning, and barriers to effective intercultural communication.

Background: Virtual reality (VR) offers much potential for learning, but it challenges learners' orientation. Objectives: This paper investigates whether it is possible to use light or movement cues to facilitate orientation in a search task in a desktop-VR environment so that participants can better attend to the learning content presented simultaneously. Methods: In two pre-registered online experiments, we investigated the effects of cueing (light and movement) on search time, learning, and several evaluation variables. Participants were asked to find tools in a virtual workshop, while information about the respective tool was narrated. Experiment 1 (N = 60) used a within-subject design, that is, the objects were alternately highlighted by light, movement or not. For Experiment 2 (N = 159) the narration was substantially shortened, and a between-subject design was used. Cognitive load and presence were measured additionally. Results and conclusions: In Experiment 1, only the movement cue decreased search time, indicating automatic guidance of learners' attention. There was no effect of cueing on learning, which may be due to the average search time being substantially shorter than the narration, leaving sufficient time to attend to the narration exclusively. In Experiment 2 search times were significantly faster for both cueing methods, but only the light cue resulted in better learning outcomes, which could be explained by the slightly lower presence in the movement cue condition. Implications: Results imply that it is important to develop cues that automatically guide attention without reducing presence.

Background Study: Virtual reality (VR) is becoming a popular technology for safety training in construction. Several VR training prototypes have been designed and tested, which show they can perform better than traditional training tools. However, most of these existing tools are not underpinned by clear pedagogical theory, and studies assessing the impact of pedagogical theories on the effectiveness of VR prototypes are still rare in the literature. Objectives: This study aims to investigate if and how the productive failure theory and the directive instruction theory have an impact on the effectiveness of VR safety training for confined space workers. Methods: The study used a randomized controlled method involving 74 participants. The effectiveness of these two training methods was assessed in terms of knowledge acquisition and retention. Results and Conclusions: The results illustrate that the productive failure training design performed better in terms of knowledge acquisition and retention. This paper introduces the Productive Failure Theory and shows the great potential of this approach for self-service VR safety training in the field of construction.