Simulation Scenarios Research Articles

Assessing rainwater harvesting scenarios to mitigate potable water storage needs in Algerian individual houses under intermittent supply.

Urban areas in Algeria are increasingly suffering from water scarcity due to climate change and rapid urbanization, prompting the government to introduce an intermittent water supply system. This situation requires households to store potable water during supply interruptions. This study investigates the potential of rainwater harvesting (RWH) to reduce the need for such storage in single-family homes in the municipality of Beni-Mared (north of Algeria). Through five simulated scenarios: "Current" which represents the existing situation without RWH; "Maximum" in which all available rainwater is captured; "Switch" in which the existing potable water storage tanks are used for rainwater; "Optimal" in which the size of the storage tanks and efficiency are balanced; and "Outdoor" in which the focus is on rainwater for external purposes such as watering. The results show that the "Maximum" scenario has the greatest potential, as it reduces the need for potable water storage by up to 84.5%, although it requires large and potentially expensive tanks. The "Optimal" scenario offered a practical solution in which a 2.7 m³ tank covers up to 33% of the storage. The "Switch" scenario, where existing tanks were repurposed, reduced storage requirements by 15.07%, while the "Outdoor" scenario, designed for external uses, achieved a more modest reduction of 5.85% to 6.71%. These results demonstrate the importance of implementing an adapted RWH approach supported by a comprehensive development strategy, community involvement and financial support to effectively address water scarcity in Algeria.

Improving Interprofessional Provider Perceptions About Opioid Use Disorder in the Acute Care Setting Through a Blended Educational Simulation Intervention

Background The influx of patients in the acute care setting with opioid use disorder (OUD) has outpaced many hospitals’ ability to educate interprofessional staff. This creates distressing experiences for interprofessional staff and patients, leading to moral distress and burnout in staff and poor patient outcomes. Objective The purpose of this study was to improve interprofessional staff knowledge, attitudes, and perceptions toward working with patients who have OUD using a blended classroom-simulation–based curriculum. Methods A preintervention and postintervention design was selected. Interprofessional staff (n = 46) participated in a blended classroom-simulation educational intervention focused on the neurobiology of OUD, stigma reduction, pain management, and harm reduction principles, followed by 3 high-fidelity simulation scenarios. Participants completed the Drug and Drug Problems Perceptions Questionnaire to measure staff self-perceived knowledge, skills, and attitudes when working with patients who use drugs before, immediately after, and again 3 and 6 months postintervention. Results Most respondents were nurses with an average of 7.6 (SD, 9.6) years of experience. The majority did not have prior training in substance use disorder before (75.6%). There was a statistically significant decrease in mean Drug and Drug Problems Perceptions Questionnaire scores across the total score mean: 55.2 (95% confidence interval, 52.2-58.3) versus 45.5 (95% confidence interval, 43.9-47.1), P < .001. Decreased score indicates improved attitude and perception. Discussion A curriculum consisting of a blended classroom-simulation intervention was successful at improving several domains regarding perceptions of caring for patients with OUD. This educational intervention can serve as a model for health care systems with goal of improving patient outcomes and staff well-being.

A Study on the Effectiveness of Climate Adaptation Materials for Urban Heat Islands using Digital Twin and Computational Fluid Dynamics

This study analyzed the impact of climate adaptation materials on the mitigation of urban heat islands (UHI) using digital twin visualization and computational fluid dynamics (CFD) technology. The effects of UHI due to increased impervious surfaces have become more pronounced, and climate-adaptation materials are gaining attention as potential solutions. In this study, the impact of climate adaptation materials on UHI mitigation was analyzed using the CFD program STAR-CCM+ based on the finite volume method (FVM). The selected research site was Byeoryang-dong, Gwacheon City, and four simulation scenarios were created based on the emissivity values of the asphalt and concrete materials. The results showed that application of climate adaptation materials decreased the surface and aboveground (1.5 m) temperatures by 6.3 and 0.8 °C, respectively. This study suggests that climate adaptation materials can significantly mitigate UHI.

Larval Transport Pathways for Lutjanus peru and Lutjanus argentiventris in the Northwestern Mexico and Tropical Eastern Pacific

Understanding how ocean currents influence larval dispersal and measuring its magnitude is critical for conservation and sustainable exploitation, especially in the Tropical Eastern Pacific (TEP), where the larval transport of rocky reef fish remains untested. For this reason, a lagrangian simulation model was implemented to estimate larval transport pathways in Northwestern Mexico and TEP. Particle trajectories were simulated with data from the Hybrid Ocean Coordinate Model, focusing on three simulation scenarios: (1) using the occurrence records of Lutjanus peru and L. argentiventris as release sites; (2) considering a continuous distribution along the study area, and (3) taking the reproduction seasonality into account in both species. It was found that the continuous distribution scenario largely explained the genetic structure previously found in both species (genetic brakes between central and southern Mexico and Central America), confirming that the ocean currents play a significant role as predictors of genetic differentiation and gene flow in Northwestern Mexico and the TEP. Due to the oceanography of the area, the southern localities supply larvae from the northern localities; therefore, disturbances in any southern localities could affect the surrounding areas and have impacts that spread beyond their political boundaries.

Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers

This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes.

Influence of climate change on water quality in Terra Nova River basin, Pernambuco, Brazil

Climate changes tend to intensify water scarcity in arid and semi-arid regions, making water management in these areas more challenging, directly influencing hydrological dynamics, causing impacts on ecosystems and society. The Terra Nova River basin is crucial for the water security of the semi-arid region of Pernambuco, especially after the construction of the São Francisco River Integration Project (PISF), which saw the construction of six reservoirs to supply water for human consumption, agricultural, and industrial activities. Therefore, this study aims to analyzes the impacts of climate changes on water quality in the Terra Nova River basin in Pernambuco, using long-term data from the Hydrological Response Unit System for Pernambuco (SUPer). The ARIMA (Autoregressive Integrated Moving Averages) model is employed to predict water quality parameters in climate change scenarios, using data from the Intergovernmental Panel on Climate Change (IPCC) and the Oswaldo Cruz Foundation (Fiocruz). The variables include air temperature, precipitation, dissolved oxygen, nitrogen, and phosphorus. Simulated scenarios were compared with CONAMA No. 357/2005 limits, revealing potential decreases in dissolved oxygen and phosphorus concentrations, alongside an increase in nitrogen concentrations. Irregular rainfall rates, high air temperatures, and evapotranspiration, combined with conflicts by water resources, may exacerbate water access issues in the semi-arid region, worsening the water crisis and threatening water security.

Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

Research on the Possibilities of Expanding the Photovoltaic Installation in the Microgrid Structure of Kielce University of Technology Using Digital Twin Technology

Global challenges related to sustainable development are increasingly focusing on the use of digital twin technology as a universal tool for optimizing and monitoring renewable energy installations. This article discusses digital twin technology as a support for sustainable development based on the analysis of microgrid structures. Digital twins allow the creation of virtual models of physical systems. This capability facilitated the accurate replication of the microgrid model at Kielce University of Technology using ETAP (Electrical Transient Analyzer Program) software (version 22.5). The operational parameters of the microgrid structure were analyzed for the examined power range of the photovoltaic installation to determine the possibilities of expanding the existing installation. The impact of the photovoltaic installation’s power on the operational parameters of the microgrid structure was visualized, and final conclusions were formulated. Moreover, the integration of digital twin technology into renewable energy systems not only enhances operational efficiency but also plays a pivotal role in advancing sustainability objectives. Through real-time monitoring and predictive maintenance, digital twin technology facilitates the optimization of energy production and distribution, thereby reducing waste and contributing to the overall sustainability of energy systems. This technology enables the simulation of various scenarios, such as fluctuations in energy demand or the integration of new renewable sources, which can inform more sustainable decision-making processes. In the context of microgrids, digital twin technology ensures that energy production is closely aligned with consumption patterns, minimizing energy losses and enhancing grid resilience. Furthermore, digital twin technology supports the sustainable expansion of renewable installations by providing detailed insights into potential environmental impacts and the long-term sustainability of various energy configurations. As the demand for clean energy continues to grow, digital twin technology will be indispensable in achieving a balance between energy needs and environmental stewardship, ensuring that the expansion of renewable energy sources contributes positively to global sustainability objectives.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving

Artificial neural networks (ANNs) are artificial intelligence techniques that have made autonomous driving more efficient and accurate; however, autonomous driving faces ongoing challenges in the accuracy of decision making based on the analysis of the vehicle environment. A critical task of ANNs is steering angle prediction, which is essential for safe and effective navigation of mobile robots and autonomous vehicles. In this study, to optimize steering angle prediction, NVIDIA’s architecture was adapted and modified along with the implementation of the Swish activation function to train convolutional neural networks (CNNs) by behavioral cloning. The CNN used human driving data obtained from the UDACITY beta simulator and tests in real scenarios, achieving a significant improvement in the loss function during training, indicating a higher efficiency in replicating human driving behavior. The proposed neural network was validated through implementation on a differential drive mobile robot prototype, by means of a comparative analysis of trajectories in autonomous and manual driving modes. This work not only advances the accuracy of steering angle predictions but also provides valuable information for future research and applications in mobile robotics and autonomous driving. The performance results of the model trained with the proposed CNN show improved accuracy in various operational contexts.

Does Prosocial Automation Increase Driver’s Well-being?

As societies transition to hybrid mobility systems, interactions between automated vehicles (AVs) and human users in public spaces become more complex, highlighting the critical role of prosocial behaviors. These behaviors are essential for the seamless operation of interdependent transportation networks, helping to address integration challenges of AVs with human-operated vehicles and enhancing well-being by creating more efficient, less stressful, and inclusive environments. This study explores the impact of receiving prosocial behaviors on the cognition, riding performance, and well-being of micromobility users in simulated urban traffic scenarios. Using a mixed-method design, the research contrasts two types of social interactions—prosocial and asocial—across three temporal conditions: relaxed, neutral, and urgent. The Structural Equation Modeling underscored the intricate relationships between prosocial behaviors, satisfaction, cognition, riding performance, and well-being. Incorporating social factors into driving systems could enhance safety and improve urban mobility solutions.

Improving Teacher Training Through Emotion Recognition and Data Fusion

ABSTRACTThe quality of education hinges on the proficiency and training of educators. Due to the importance of teacher training, the innovative platform Teacher Moments creates simulated classroom scenarios. In this scenario‐based learning, confusion is an important indicator to detect users who struggle with the simulations. Through Teacher Moments, we gathered 7975 audio recording responses from participants who self‐labelled their recordings according to whether they sounded confused. Our dataset stands out for its size, for not including actor‐generated audio, and for measuring confusion, a neglected emotion in artificial intelligence (AI). Our experiments tested unimodal approaches and feature‐level, model‐level and decision‐level fusion. Feature‐level fusion demonstrated superior performance to unimodal methods, achieving a balanced accuracy of 0.6607 on the test set. This outcome highlights the necessity for further investigation in the overlooked area of confusion detection, particularly employing realistic datasets like the one used in this study and exploring new methods. Beyond teacher training, the insights of this research also extend to other directions, such as other professionals making critical decisions, user interface design or adaptive learning systems.

Innovative system to maximize methane production from fruit and vegetable waste.

Anaerobic digestion of fruit and vegetable waste (FVW) offers an environmentally friendly alternative for waste disposal, converting it into methane for energy recovery. Typically, FVW digestion is conducted in a continuously stirred tank reactor (CSTR) due to its ease of use and stability with solid concentrations between 5 and 10%. However, CSTRs are limited to organic loading rates (OLRs) of about 3kg COD/m3.day, resulting in large reactor volumes, low methane productivity, and costly wet digestate handling. This work introduces a novel method for methane production from FVW using a high-rate reactor system. The proposed approach involves grinding, centrifuging, and/or pressing the FVW to separate it into liquid and solid phases. The liquid phase is then digested in an up-flow anaerobic sludge blanket (UASB) reactor, while the solid phase undergoes digestion in a dry methanization reactor. A model incorporating all biological reactors was implemented in the Anaerobic Digestion Model 1 (ADM1) to provide a theoretical basis for the experimental development of this system. The current simulation scenarios offer initial references for operating the experimental system, which will, in turn, generate data for further model refinement. For instance, constrained liquid-gas mass transfer was considered for dry fermentation, with additional potential biochemical kinetic limitations to be incorporated following on experimental evidence. The success of this system could enable energy recovery in 72 Central Wholesale Markets across Brazil, offering a critical tool for planning, operating, and optimizing such systems.

AI-driven adaptive analysis for finding emergent behavior in military capability design

This article introduces and demonstrates a new methodology for searching for emergent behavior in simulation-based analysis as rare, extreme events using adaptive techniques enabled by recent advances in Bayesian machine learning (ML). The new methodology, Low-cost Adaptive exploratioN to Track down Extreme, Rare events using Numerical optimization (LANTERN), supports analysis activities in defense planning that iteratively build up the understanding of new technology and concept alternatives in complex military scenarios. Central to this process is emergent behavior—hard-to-predict but highly important behaviors that present problems or opportunities. These unexpected behaviors can be generated in complex military scenarios and are crucial to decision-making, but are often difficult to find and work with due to the expense and cost of the existing approaches for working with high-fidelity military simulation. To address this challenge, LANTERN is formulated to accelerate the discovery of emergent behavior as rare, extreme events by combining human expert understanding with new artificial intelligence (AI)-driven adaptive experimentation techniques in iterative analysis. A demonstration of the methodology is presented using military agent-based simulation scenarios developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). The demonstration highlights how analysis can focus directly on searching for emergent behavior and shows substantial improvements over brute-force Monte Carlo approaches.

Dynamic Simulation of Carbon Emission Peak in City-Scale Building Sector: A Life-Cycle Approach Based on LEAP-SD Model

Systematically predicting carbon emissions in the building sector is crucial for formulating effective policies and plans. However, the timing and potential peak emissions from urban buildings remain unclear. This research integrates socio-economic, urban planning, building technology, and energy consumption factors to develop a LEAP-SD model using Shenzhen as a case study. The model considers the interrelationship between socio-economic development and energy consumption, providing more realistic scenario simulations to predict changes in carbon emissions within the urban building sector. The study investigates potential emission peaks and peak times of buildings under different population and building area development scenarios. The results indicate that achieving carbon peaking by 2030 is challenging under a business as usual (BAU) scenario. However, a 10% greater reduction in energy intensity compared to BAU could result in peaking around 2030. The simulation analysis highlights the significant impact of factors such as population growth rate, per capita residential building area, and energy consumption per unit building area and the need for a comprehensive analysis. It provides more realistic scenario simulations that not only enhance theories and models for predicting carbon emissions but also offer valuable insights for policymakers in establishing effective reduction targets and strategies.

Integration of plant and microbial oil processing at oilcane biorefineries for more sustainable biofuel production

AbstractOilcane—an oil‐accumulating crop engineered from sugarcane—and microbial oil have the potential to improve renewable oil production and help meet the expected demand for bioderived oleochemicals and fuels. To assess the potential synergies of processing both plant and microbial oils, the economic and environmental implications of integrating microbial oil production at oilcane and sugarcane biorefineries were characterized. Due to decreased crop yields that lead to higher simulated feedstock prices and lower biorefinery capacities, current oilcane prototypes result in higher costs and carbon intensities than microbial oil from sugarcane. To inform oilcane feedstock development, we calculated the required biomass yields (as a function of oil content) for oilcane to achieve financial parity with sugarcane. At 10 dw% oil, oilcane can sustain up to 30% less yield than sugarcane and still be more profitable in all simulated scenarios. Assuming continued improvements in microbial oil production from cane juice, achieving this target results in a minimum biodiesel selling price of 1.34 [0.90, 1.85] USD∙L−1 (presented as median [5th, 95th] percentiles), a carbon intensity of 0.51 [0.47, 0.55] kg CO2e L−1, and a total biodiesel yield of 2140 [1870, 2410] L ha−1 year−1. Compared to biofuel production from soybean, this outcome is equivalent to 3.0–3.9 as much biofuel per hectare of land and a 57%–63% reduction in carbon intensity. While only 20% of simulated scenarios fell within the market price range of biodiesel (0.45–1.11 USD∙L−1), if the oilcane biomass yield would improve to 25.6 DMT∙ha−1∙y−1 (an equivalent yield to sugarcane) 87% of evaluated scenarios would have a minimum biodiesel selling price within or below the market price range.

Robustness of Dry‐Assembled Precast Wall and Coupled Wall‐Frame High‐Rise Structures With Voided Prestressed Slabs

ABSTRACTPrecast concrete structures are recognized among the structural typologies that may mostly suffer from progressive collapse as a consequence of local unpredicted damage, also due to past accidents occurred in buildings employing old precast technologies neglecting the basic robustness criteria and characterized by poorly detailed and/or executed joints and connections. Although vertical ties may relatively easily be provided in both frame and wall panel structures by employing ductile connection devices, dry‐assembled precast systems avoiding concrete pouring in the whole superstructure typically struggle to provide horizontal ties as strong as required by the current standards concerning progressive collapse, in which they are not fully framed yet. Nevertheless, these systems may exploit alternative sources of robustness to stop collapse progression arising from their structural arrangement, the shape of the slab elements employed, and the slab mechanical connections. This article presents the results of a numerical investigation focused on the structural behavior of a dry‐assembled precast floor system consisting of prestressed box‐section slab elements employed in either wall panel or coupled wall‐frame structural systems following the loss of one or multiple primary load‐bearing vertical elements. Nonlinear static simulations of different loss scenarios occurring in prototypes of 100‐m‐tall high‐rise buildings were carried out based upon spread plasticity modeling of the structural elements and experimentally calibrated nonlinear behavior of mechanical connections.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

Assessing the Impact of Climate Change on Methane Emissions from Rice Production Systems in Southern India

The impact of climate change on methane (CH4) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH4 emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH4 emissions (range: from 36.9 to 59.3 kg CH4 ha−1 season−1) compared with SRI (range: from 2.2 to 12.8 kg CH4 ha−1 season−1). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R2 (0.92). The validated model was then used to develop future CH4 emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH4 emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH4 emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH4 production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India.

Group virtual reality simulation in the adult nursing curriculum: student and lecturer experiences.

Virtual reality (VR) simulation technology was rapidly integrated into pre-registration adult nursing programmes in response to the pandemic and a reduction in clinical placements. The UK's regulatory body for nursing has recognised its value in nursing education by increasing the possible number of simulated practice hours that can replace clinical placements to 600 hours. This article reports on an evaluation study of a novel approach using screen-based VR simulations for groups of students in a classroom setting. This study aims to evaluate student and lecturer experiences of screen-based VR with the aim of informing and sharing insights from this approach. Students and lecturers responded to an evaluation survey comprising both closed- and open-ended questions. A large approved education institute in the East of England. Pre-registration adult nursing master's students and adult nursing lecturers who had experience of screen-based VR with groups. Quantitative data were analysed using descriptive methods, and qualitative data using thematic analysis. Student and lecturer experiences of screen-based VR were overwhelmingly positive, overcoming many challenges of simulation documented in the literature. The group-working approach promoted development of non-technical or essential 'soft' skills such as communication, decision-making and teamwork. Screen-based VR for classroombased learning offers an effective, engaging and cost-effective method of incorporating VR simulation scenarios in adult nursing education. Thoughtful consideration of pedagogical aspects are key to its successful and effective integration into the pre-registration adult nursing curriculum.