Simulated Environment Research Articles

Nursing students’ perceptions and satisfaction with a self-learning methodology in simulated environments: A mixed-methods study

AimThe aim of this study was to explore the perceptions and satisfaction of last-year nursing students with the Self-Learning Methodology in Simulated Environments (MAES©). BackgroundAs a learning method, clinical simulation uses elements that replicate real clinical practice scenarios and facilitates the acquisition of competencies and learning objectives. Simulations promote critical thinking, knowledge, techniques and teamwork in nursing students. The Self-Learning Methodology in Simulated Environments (MAES©) is a method that integrates problem-based learning with realistic clinical simulation. DesignA cross-sectional mixed-method study implemented a simulation learning method as part of the mandatory nursing training practice; that was registered in Open Science Framework (Registration DOI: https://doi.org/10.17605/OSF.IO/R89PZ). MethodsStudents were invited to complete a cross-sectional questionnaire about satisfaction with the simulation and were asked to participate in an interview about their perceptions on the simulation. All data were collected in December 2023 in a mid-sized southern Spanish university. A total of 69 last-year nursing students were enrolled in the simulation course and were selected using eligibility criteria. ResultsSatisfaction measures showed no significant differences across gender, university access, or age (p>0.05 each). However, strong correlations were found between students' preference for the simulation method and perceived effort value (p<0.001 each). Qualitative analysis identified key themes in different stages of simulation (prebriefing, scenario, briefing, debriefing), simulation benefits (learning, usefulness, positive emotions) and challenges (difficulty, realism, time constraints). Despite some realism concerns, overall, students viewed the methodology positively. ConclusionsThe findings of this study underscore the vital role of simulation-based learning in nursing education. As the field of nursing continues to evolve, so too must the educational methods we employ, with simulation-based learning standing at the forefront of this transformative journey.

Counseling in Virtual Spaces and Into the Metaverse: A Framework for Reflection, Conversation, and Future Scholarship

Counseling in Virtual Spaces and Into the Metaverse: A Framework for Reflection, Conversation, and Future Scholarship

Driving engagement in metaverse-mediated tourism environments: exploring the role of perceived realism

PurposeDespite the promising development and marketing potential of the metaverse, our understanding of how realistic metaverse environments impact user engagement and behaviours remains limited. This study investigates the role of perceived realism in influencing user engagement, thereby affecting external search behaviour and visit intentions.Design/methodology/approachWe surveyed 270 active metaverse users to test the research model. The data were analysed using partial least squares structural equation modelling (PLS-SEM).FindingsThe results of our study show that three dimensions of realism – avatar involvement, perceptual pervasiveness and social realism – significantly enhance user engagement, which in turn influences external search behaviour and visit intention. In contrast, simulation realism and freedom of choice have minimal effects on absorption and dedication.Research limitations/implicationsThis study highlights the role of perceived realism in enhancing user engagement with the metaverse and its impact on physical world behaviours. It contributes to metaverse literature by demonstrating that engagement within the metaverse significantly influences physical world behaviours, including visit intentions and external search behaviours.Practical implicationsThis study offers practical guidance for developers to enhance user engagement in metaverse environments. Specifically, our findings advocate for visual and audio quality enhancements, greater persuasiveness of virtual spaces, improved avatar representativeness and a closer alignment of metaverse activities with real-life events.Originality/valueThis study advances the theoretical understanding of perceived realism by examining how its dimensions – such as visual and audio quality, avatar representativeness and alignment with real-life events – impact user engagement in the metaverse. It also explores how this engagement influences offline behaviours, thus bridging the gap between virtual and real-world interactions.

Aversion to external feedback suffices to ensure agent alignment

Ensuring artificial intelligence behaves in such a way that is aligned with human values is commonly referred to as the alignment challenge. Prior work has shown that rational agents, behaving in such a way that maximizes a utility function, will inevitably behave in such a way that is not aligned with human values, especially as their level of intelligence goes up. Prior work has also shown that there is no “one true utility function”; solutions must include a more holistic approach to alignment. This paper describes apprehensive agents: agents that are architected in such a way that their effective utility function is an aggregation of a partial utility function (built by designers, to be maximized) and an expectation of negative feedback on given states (reasoned about, to be minimized). Agents are also capable of performing a temporal reasoning process that approximates designers’ intentions in function of environment evolution (a necessary feature for severe mis-alignment to occur). We show that an apprehensive agent, behaving rationally, leverages this internal approximation of designers’ intentions to predict negative feedback, and, as a consequence, behaves in such a way that maximizes alignment, without actually receiving any external feedback. We evaluate this strategy on simulated environments that expose mis-alignment opportunities: we show that apprehensive agents are indeed better aligned than their base counterparts and, in contrast with extant techniques, chances of alignment actually improve as agent intelligence grows.

Effect of Different AC Densities on Corrosion Resistance and Mechanism of FeCoNi HEA in Simulated Golmud Soil Environment

Effect of Different AC Densities on Corrosion Resistance and Mechanism of FeCoNi HEA in Simulated Golmud Soil Environment

Million Microfiber Releases: Comparing Washable and Disposable Face Masks.

The extensive use of single-use or disposable face masks has raised environmental concerns related to microfiber contamination. In contrast, research on the potential release and ecological impact of microfibers from washable masks (WMs), suggested as an eco-friendly alternative, is currently lacking. Here, we comprehensively investigated the release of microfibers from disposable and WMs of different types in simulated aquatic environments and real-life scenarios, including shaking, disinfection, hand washing, and machine washing. Using a combination of wide-field fluorescence microscopy, He-ion microscopy, and confocal μ-Raman spectroscopy, we revealed that disposable masks (DMs) released microfibers ranging from 18 to 3042 microfiber/piece, whereas WMs released 6.1 × 104-6.7 × 106 microfibers/piece depending on the simulated conditions above. Another noteworthy finding was the observed negative correlation between microfiber release and the proportion of reinforcement (embossing) on the DM surfaces. Microfibers from tested DMs primarily comprised polypropylene (PP), while WMs predominantly released poly(ethylene terephthalate) (PET) and cellulose microfibers. Furthermore, acute toxicological analyses unveiled that PP microfibers (0.01-50 mg/L) from DMs impacted zebrafish larval swimming behavior, while PET microfibers from WMs delayed early-stage zebrafish hatching. This study offers new insights into the source of microfiber contamination and raises concerns about the environmental implications linked to the use of washable face masks.

Using Psychological Reactance and Bandwagon Cues to Explain High School Sports Coaches’ Resistance to Concussion Communication Policies

Sport-related concussions (SRC) are a significant health risk for athletes amalgamated by issues with underreporting. Coaches play a pivotal role in promoting safety and health, but coach-player SRC communication efforts have been insufficient at improving players’ reporting behaviors. This study considers coaches’ responses to a proposed mandate to engage in SRC communication with athletes via psychological reactance theory (PRT), with bandwagoning cues positioned as a form of social proof that heightens or mitigates persuasion. Two hundred five coaches from 20 distinct sports were exposed to one of four messages addressing a proposed SRC management policy mandating biweekly coach-player communication about SRCs. The message variations incorporated different bandwagon cues (positive, negative, absent, mixed), manipulated through user comments and emoji reactions on a simulated social media environment. Results demonstrate direct and mediated relationships between exposure to negative bandwagon cues, bandwagon perceptions, freedom threat, reactance, and opposition intentions. Our findings provide valuable insights into the dynamics of coaches’ attitudes towards SRC communication in a social media context, bridging gaps in the literature and introducing PRT to the SRC domain.

Sim-to-real transfer of adaptive control parameters for AUV stabilisation under current disturbance

Learning-based adaptive control methods hold the potential to empower autonomous agents in mitigating the impact of process variations with minimal human intervention. However, their application to autonomous underwater vehicles (AUVs) has been constrained by two main challenges: (1) the presence of unknown dynamics in the form of sea current disturbances, which cannot be modelled or measured due to limited sensor capability, particularly on smaller low-cost AUVs, and (2) the nonlinearity of AUV tasks, where the controller response at certain operating points must be excessively conservative to meet specifications at other points. Deep Reinforcement Learning (DRL) offers a solution to these challenges by training versatile neural network policies. Nevertheless, the application of DRL algorithms to AUVs has been predominantly limited to simulated environments due to their inherent high sample complexity and the distribution shift problem. This paper introduces a novel approach by combining the Maximum Entropy Deep Reinforcement Learning framework with a classic model-based control architecture to formulate an adaptive controller. In this framework, we propose a Sim-to-Real transfer strategy, incorporating a bio-inspired experience replay mechanism, an enhanced domain randomisation technique, and an evaluation protocol executed on a physical platform. Our experimental assessments demonstrate the effectiveness of this method in learning proficient policies from suboptimal simulated models of the AUV. When transferred to a real-world vehicle, the approach exhibits a control performance three times higher compared to its model-based nonadaptive but optimal counterpart.

A Study on Differences in Educational Method to Periodic Inspection Work of Nuclear Power Plants

Construction work and regular inspection work at nuclear power plants involve many special tasks, unlike general on-site work. In addition, the opportunity to transfer knowledge from skilled workers to unskilled workers is limited due to the inability to easily enter the plant and various security and radiation exposure issues. Therefore, in this study, we considered the application of virtual reality (VR) as a method to increase opportunities to learn anytime and anywhere and to transfer knowledge more effectively. In addition, as an interactive learning method to improve comprehension, we devised a system that uses hand tracking and eye tracking to allow participants to experience movements and postures that are closer to the real work in a virtual space. For hand-based work, three actions, “pinch”, “grab”, and “hold”, were reproduced depending on the sizes of the parts and tools, and visual confirmation work was reproduced by the movement of the gaze point of the eyes, faithfully reproducing the special actions of the inspection work. We confirmed that a hybrid learning process that appropriately combines the developed active learning method, using experiential VR, with conventional passive learning methods, using paper and video, can improve the comprehension and retention of special work at nuclear power plants.

Achieving Robust Learning Outcomes in Autonomous Driving with DynamicNoise Integration in Deep Reinforcement Learning

The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, where it promises notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to effectively navigate complex driving environments. To tackle this challenge, we propose a novel network called DynamicNoise, which was designed to significantly boost the algorithmic performance by introducing noise into the deep Q-network (DQN) and double deep Q-network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, thus leading to more robust learning outcomes. Our experiments demonstrated a 57.25% improvement in the navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the soft actor–critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, which substantially surpassed the traditional methods. These results confirmed that the DynamicNoise network significantly enhanced the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.

Motion Classification of Objects using Accelerometer and Gyroscope Readings

It can be challenging to properly analyze data from moving objects when there is limited information about the conditions under which the data was collected. The focus of this research is to use accelerometer and gyroscope data as the input to machine learning models to accurately classify data points based on the movement of the object within its environment. Three machine learning models were implemented and tested: a Hidden Markov Model (HMM), a Random Forest Model (RF), and a Multilayer Perceptron Model (MLP). The majority of the data collected via a simulated environment in Gazebo was used to train all three models. The simulated experiments involved a Jackal Robot from Clearpath Robotics driving on three terrains — flat ground and up and down a ramp with a 7-degree slope — that match the classification states. The simulation data was manually classified based on the delta values between orientation data. The models were tested using the remaining 20% of simulated data, data from a physical Jackal, and data from haul trucks working in an open-pit mine. The HMM is based on unsupervised learning which meant that the pre-classified datapoints were not useful in training the model. This model was ineffective at classifying the datapoints because the probabilities of the classification changing from one datapoint to the next were situationally skewed, so it was highly improbable that the state changed between datapoints. Due to the inability of this model to classify the simulated data, no further testing was completed. The RF model was able to classify the simulated data with 99.78% accuracy and the MLP model classified the simulated data with 99.73% accuracy. The RF model and the MLP model require noisier training data to accurately classify real-world data. Further testing is currently being conducted using data from a physical Jackal and haul trucks.

Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

Endoscopic Integrated Multipoint Laser System to Perform Quantitative Measurements for Anterior Skull Base Defects.

Measurements in endoscopic sinus surgery (ESS) are usually obtained with variable accuracy. We aimed to validate endoscopic multipoint integrated laser systems (EIMLS) for use in ESS, which can acquire measurements within one-hundredth of a millimetre. A 4.4 mm flexible endoscopic EIMLS projecting 49 laser points into the view was used to assess simulated anterior skullbase defects. Antero-posterior and lateral measurements were obtained and repeated 20 times by two surgeons. These were compared to measurements with surgical callipers. Intra and inter-observer reliability was assessed. Eighty measurements were obtained of simulated skullbase defects by each otolaryngology surgeon and compared to manual measurements. The mean difference shown was 0.56 cm. Bland-Altman plot shows low bias (0.044) but wide 95% limits of agreement (-1.8-1.9). EIMLS allows reliable and easy to obtain measurements within a simulated ESS environment. Translation of this technology offers promise in a future clinical setting but will require further refinement to improve accuracy.

Distribution characteristics and relationship between virtual- and real-imagery hotspots as influenced by social media: a case study of Changsha City

ABSTRACT Social media closely links virtual and real spaces, and this connection can influence the distribution of a city’s imagery hotspots. This study used point of interest data and site visit data from Tiktok to compare the characteristics of two spatial imagery hotspot types in Changsha as influenced by social media in terms of type characteristics, distribution pattern, and spatial relationships at both the macro- and meso-scales. Using methods such as buffer analysis, hotspot analysis, and bivariate spatial autocorrelation to research on imagery hotspot distribution, correlation, and differentiation in virtual – real spatial interaction, the result shows the following: 1) The impact of social media on different types of the city image is not balanced, having a greater impact on architectural landmarks and commercial consumption; 2) The distribution characteristics of image hotspots in virtual space and real space does not align, virtual space is more in line with the development strategy of cities; 3) Social media has a polarizing effect on city image, but is also less constrained by spatial form; 4) Social media has a certain restructuring effect on city image, and grouped city image in two spatial clustering types – enhanced and diminishing image spaces.

Voice user interfaces for effortless navigation in medical virtual reality environments

In various situations, such as clinical environments with sterile conditions or when hands are occupied with multiple devices, traditional methods of navigation and scene adjustment are impractical or even impossible. We explore a new solution by using voice control to facilitate interaction in virtual worlds to avoid the use of additional controllers. Therefore, we investigate three scenarios: Object Orientation, Visualization Customization, and Analytical Tasks and evaluate whether natural language interaction is possible and promising in each of these scenarios. In our quantitative user study participants were able to control virtual environments effortlessly using verbal instructions. This resulted in rapid orientation adjustments, adaptive visual aids, and accurate data analysis. In addition, user satisfaction and usability surveys showed consistently high levels of acceptance and ease of use. In conclusion, our study shows that the use of natural language can be a promising alternative for the improvement of user interaction in virtual environments. It enables intuitive interactions in virtual spaces, especially in situations where traditional controls have limitations.

Dual-functional paper-like evaporator for boosting interfacial evaporation synchronized with organic pollutant degradation

Solar-driven interfacial evaporation is a sustainable method to produce freshwater, but volatile organic compounds (VOCs) inevitable accumulate in the condensed water, causing secondary pollution. Herein, paper-like photothermal evaporators were constructed by fixing zeolitic diazoate frameworks-8 derived N-doped carbon with different morphologies on the interwoven cellulose nanofibrils (CNFs) membranes for simultaneous steam generation and organic pollutant degradation. The results showed that the evaporator incorporated porous N-doped carbon with rhombic dodecahedron (NC-RD) into CNFs membranes not only increases the roughness to achieve broad spectrum solar absorption, but also capable of ensuring continuous evaporation of water, and maintaining a sufficient water supply. The NC-RD/CNFs membranes exhibit the high evaporation rate of 1.46 kg·m−2·h−1 under one sun. Meanwhile, N-doped carbon enable photothermal-synergistic peroxymonosulfate (PMS) activation to promote the generation of abundant 1O2 and O2∙– for degradation of methylene blue, tetracycline and phenol. Furthermore, the solar-vapor conversion efficiency and the evaporation rate are stable in a simulated seawater environment with 15 wt% NaCl solution. Importantly, an outdoor NC-RD/CNFs-based device was constructed, which realizes a high evaporation rate of 0.659 kg∙m−2∙h−1 under natural light irradiation for 10 h. The present study highlights that the NC-RD/CNFs membranes successfully coupled interfacial evaporation with PMS based-advanced oxidation processes to provide a subtlety strategy to solve the problem of VOCs accumulation in the evaporation process.

"Physiological Adaptations to Weightlessness: The Contractile Properties and Fatiguability of the Human Triceps Surae Muscle After Exposure to Simulated Microgravity Environments"

"Physiological Adaptations to Weightlessness: The Contractile Properties and Fatiguability of the Human Triceps Surae Muscle After Exposure to Simulated Microgravity Environments"

Scratch visibility modeling on flat & textured polymeric surfaces

Polymer materials have gained widespread usage in the marketplace due to their lightweight properties, versatility, and cost-effectiveness. However, their susceptibility to scratches has been a longstanding issue. To overcome this shortcoming, surface texturing is one of the most low-cost, efficient ways to improve scratch performance by utilizing the inherent moldability of polymers. Textures rely on both the improvement of contact properties in terms of reduction in surface friction and the perceived visibility of scratches by masking the scratch-induced deformations. The current testing of the effectiveness of a particular texture design on scratch resistance requires tedious and costly psychophysical and/or experimental verification. In this paper, we present a comprehensive framework for scratch visibility analysis in virtual reality, integrating a novel scheme and finite element methods simulation. Our approach considers material, topographical, and optical properties, replicating perceived scratch visibility within the virtual space. The FEM model was found to overpredict the scratch depth and underpredict the shoulder height. A parametric study based on the material constitutive and surface properties has been carried out to highlight the effect of different material and surface factors on scratch performance and visibility. This study opens the door for a complete virtual design-development-analysis cycle for scratch performance evaluation of polymers.

Reinforcement learning-based drone simulators: survey, practice, and challenge

Recently, machine learning has been very useful in solving diverse tasks with drones, such as autonomous navigation, visual surveillance, communication, disaster management, and agriculture. Among these machine learning, two representative paradigms have been widely utilized in such applications: supervised learning and reinforcement learning. Researchers prefer to use supervised learning, mostly based on convolutional neural networks, because of its robustness and ease of use but yet data labeling is laborious and time-consuming. On the other hand, when traditional reinforcement learning is combined with the deep neural network, it can be a very powerful tool to solve high-dimensional input problems such as image and video. Along with the fast development of reinforcement learning, many researchers utilize reinforcement learning in drone applications, and it often outperforms supervised learning. However, it usually requires the agent to explore the environment on a trial-and-error basis which is high cost and unrealistic in the real environment. Recent advances in simulated environments can allow an agent to learn by itself to overcome these drawbacks, although the gap between the real environment and the simulator has to be minimized in the end. In this sense, a realistic and reliable simulator is essential for reinforcement learning training. This paper investigates various drone simulators that work with diverse reinforcement learning architectures. The characteristics of the reinforcement learning-based drone simulators are analyzed and compared for the researchers who would like to employ them for their projects. Finally, we shed light on some challenges and potential directions for future drone simulators.

How to avoid sinking in swamp: exploring the intentions of digitally disadvantaged groups to use a new public infrastructure that combines physical and virtual spaces

With advances in digital technology, physical and virtual spaces have gradually merged. For digitally disadvantaged groups, this transformation is both convenient and potentially supportive. Previous research on public infrastructure has been limited to improvements in physical facilities, and few researchers have investigated the use of mixed physical and virtual spaces. In this study, we focused on integrated virtual and physical spaces and investigated the factors affecting digitally disadvantaged groups’ intentions to use this new infrastructure. Building on a unified theory of the acceptance and use of technology, we focused on social interaction anxiety, identified the characteristics of digitally disadvantaged groups, and constructed a research model to examine intentions to use the new infrastructure. We obtained 337 valid data from the questionnaire and analysed them using partial least squares structural equation modelling. The results showed positive relationships between performance expectancy, perceived institutional support, perceived marketplace influence, effort expectancy, and facilitating conditions. The influence of psychological reactance was significantly negative. Finally, social interaction anxiety had a regulatory effect on performance expectancy, psychological reactance, perceived marketplace influence, and effort expectancy. Its effects on perceived institutional support and facilitating conditions were not significant. The results support the creation of inclusive smart cities by ensuring that the new public infrastructure is suitable for digitally disadvantaged groups. Meanwhile, this study presents new theoretical concepts of new public infrastructures, mixed physical and virtual spaces, which provides a forward-looking approach to studying digitally disadvantaged groups in this field and paves the way for subsequent scholars to explore the field in theory and literature.