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Aims To examine the use and effects of multiple simulations in nursing education. Design A mixed study systematic review. Databases (CINAHL, Medline, PubMed, EMBASE, ERIC, Education source and Science Direct) were searched for studies published until April 2020. Method Researchers analysed the articles. Bias risk was evaluated using the Critical Appraisal Skills Programme and Cochrane Risk of Bias tool. Results In total, 27 studies were included and four themes identified. Students participated in multiple simulation sessions, over weeks to years, which included 1–4 scenarios in various nursing contexts. Simulations were used to prepare for, or partly replace, students’ clinical practice. Learning was described in terms of knowledge, competence and confidence. Conclusion Multiple scenario‐based simulation is a positive intervention that can be implemented in various courses during every academic year to promote nursing students’ learning. Further longitudinal research is required, including randomized studies, with transparency regarding study design and instruments.

Population growth, coupled with climate and social shifts, has resulted in a global phenomenon of water scarcity. Yet, the effect of social factors on water resources has been poorly studied. Hence, this study aimed to identify the key parameters in social systems that significantly impact hydrological system change and presents the best scenario for water management. The system dynamic (SD) approach was employed in this research to construct a combined framework of policies based on scenarios, which aimed to ensure social sustainability and coupled human-water systems. For this purpose, the SD model was simulated on the Gavshan Basin in the west of Iran for the long-term period 2020-2050. The results indicate that the water resources in the Gavshan Basin cannot meet the growth of the population. Meanwhile, about 20% of the water stored in the Gavshan Dam is not effectively used and flows out of the irrigation network as wastewater. The result of the sensitivity analysis showed that in scenarios 3 and 4, the policy of wastewater reuse in the agricultural sector significantly increases available water resources, has a major impact on water supply, and increases crop yields. These findings can be applied by policy-makers. Instead of making efforts only to change hydrological systems, policies need to first focus on socio-hydrology systems sustainability. It is suggested that national organizations' support should be implemented to prevent the adverse consequences of wastewater reuse in agriculture and reduce treated wastewater risks.

Introduction In recent years, researchers have recognized the need to examine the relative effectiveness of different simulation approaches and the experiences of physicians operating within such environments. The current study experimentally examined the reflective judgments, cognitive processing, and clinical reasoning performance of physicians across live and video simulation environments. Methods Thirty-eight physicians were randomly assigned to a live scenario or video case condition. Both conditions encompassed two components: (a) patient encounter and (b) video reflection activity. Following the condition-specific patient encounter (i.e., live scenario or video), the participants completed a Post Encounter Form (PEF), microanalytic questions, and a mental effort question. Participants were then instructed to re-watch the video (i.e., video condition) or a video recording of their live patient encounter (i.e., live scenario) while thinking aloud about how they came to the diagnosis and management plan. Results Although significant differences did not emerge across all measures, physicians in the live scenario condition exhibited superior performance in clinical reasoning (i.e., PEF) and a distinct profile of reflective judgments and cognitive processing. Generally, the live condition participants focused more attention on aspects of the clinical reasoning process and demonstrated higher level cognitive processing than the video group. Conclusions The current study sheds light on the differential effects of live scenario and video simulation approaches. Physicians who engaged in live scenario simulations outperformed and showed a distinct pattern of cognitive reactions and judgments compared to physicians who practiced their clinical reasoning via video simulation. Additionally, the current study points to the potential advantages of video self-reflection following live scenarios while also shedding some light on the debate regarding whether video-guided reflection, specifically, is advantageous. The utility of context-specific, micro-level assessments that incorporate multiple methods as physicians complete different parts of clinical tasks is also discussed.

Sustainable land use within river basins is essential for maintaining ecosystem functionality and mitigating environmental degradation. This study analyzes historical and projected land use and land cover (LULC) changes in the Dumai River Basin, Western Indonesia, over a 30-year period (1994–2024) and projects future changes for 2034 and 2050. An object-based image analysis (OBIA) approach was applied for LULC classification, while the Cellular Automata–Markov (CA–Markov) model simulated spatial transitions under two scenarios: the Natural Development Scenario (NDS) and the Ecological Safeguard Scenario (ESS). The NDS assumes business-as-usual land conversion without conservation intervention, whereas the ESS integrates ecological zoning and controlled spatial development. Results show that from 1994 to 2024, forest cover declined from 53.75% to 10.87%, while built-up land expanded from 5.29% to 24.67%. Under the NDS, forest area is projected to decrease further to 1.62% by 2050, accompanied by an expansion of built-up land to 31.53%. Conversely, under the ESS, forest cover could recover to 14.31%, and built-up land growth remains limited to 28.89%, indicating improved ecological balance. Model validation achieved a high predictive accuracy (Kappa coefficient = 0.87). These findings emphasize the effectiveness of integrating OBIA and CA–Markov modeling for scenario-based land use simulation in tropical wetlands. The results also highlight the necessity of adopting ecological safeguard measures to harmonize development with conservation goals, thereby supporting sustainable land resource management and climate-resilient regional planning.

As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use to change year by year, which has also caused changes to the whole land use pattern of the entire island of Hokkaido. To realize the sustainable use of land resources in Hokkaido, past and future changes in land use patterns must be investigated, and target-based land use planning suggestions should be given on this basis. This study uses remote sensing and GIS technology to analyze the temporal and spatial changes of land use in Hokkaido during the past two decades. The types of land use include cultivated land, forest, waterbody, construction, grassland, and others, by using the satellite images of the Landsat images in 2000, 2010, and 2019 to achieve this goal to make classification. In addition, this study used the coupled Markov-FLUS model to simulate and analyze the land use changes in three different scenarios in Hokkaido in the next 20 years. Scenario-based situational analysis shows that the cultivated land in Hokkaido will drop by about 25% in 2040 under the natural development scenario (ND), while the cultivated land area in Hokkaido will remain basically unchanged in cultivated land protection scenario (CP). In forest protection scenario (FP), the area of forest in Hokkaido will increase by 1580.8 km2. It is believed that the findings reveal that the forest land in Hokkaido has been well protected in the past and will be protected well in the next 20 years. However, in land use planning for future, Hokkaido government and enterprises should pay more attention to the protection of cultivated land.

The rapid growth of electric vehicles (EVs) requires the deployment of modular fast charging stations that balance charging performance, grid limitations, and investment costs. This study develops design scenarios for modular EV fast charging stations and introduces a risk-aware performance analysis framework under power and grid quality constraints. A simulation-based approach evaluates 286 station configurations with ten charging outlets (20–50 kW), grouped into 16 representative classes based on three key dimensions: total installed power, dominant charger type, and peak load risk. Performance metrics such as efficiency of charger utilization, load factor, and overload risk are used to construct Pareto frontiers and identify optimal trade-offs between capacity and operational safety. Results indicate that medium-power configurations (251–350 kW) achieve the best compromise between efficiency (>82%) and load factor (>50%) without exceeding safe operating limits, while high-power configurations enable maximum throughput at the expense of elevated overload risk. Sensitivity analysis confirms the robustness of the proposed grouping approach under variations in arrival rates, battery sizes, and grid constraints (400–600 kW). The findings provide practical insights into the design and risk management of modular charging stations, supporting urban planners and power engineers in developing efficient and reliable EV charging infrastructure.

AgriRover was developed to address key operational constraints faced by smallholder vineyards in Peru, including sandy and saline soils, labor shortages, and limited access to advanced agricultural machinery. The platform features an articulated, all-wheel-drive chassis designed to ensure mobility and stability on loose terrain while minimizing soil compaction. This study presents the simulation-driven development of a digital pre-twin, conceived as a virtual prototype prepared for future sensor integration but currently operating without real-time data feedback. The pre-twin was implemented in MATLAB/Simulink (vers. 2024b) using a multibody dynamics model and evaluated through eight scenario-based simulations, varying field geometry, soil type, and slope conditions. The results show stable operation on slopes up to 10°, wheel sinkage values ranging between approximately 20 and 45 mm depending on terrain conditions, and a moderate battery state-of-charge reduction across most scenarios, with higher power demand observed on sandy soils. A scenario-based comparison indicates a potential reduction of approximately 50% in total monitoring time relative to manual field scouting, while advanced sensing, autonomous navigation, and AI-based analytics remain part of future developments. The current pre-twin provides a validated, low-cost foundation for context-specific phenological monitoring and early-stage precision agriculture applications in developing regions.

Mortality prediction in respiratory health is challenging, especially when using large-scale clinical datasets composed primarily of categorical variables. Traditional digital twin (DT) frameworks often rely on longitudinal or sensor-based data, which are not always available in public health contexts. In this article, we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset. This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System, including both COVID-19 and non-COVID-19 patients. Four classification models—extreme gradient boosting (XGBoost), logistic regression, random forest, and a deep neural network (DNN)—are trained using cost-sensitive learning to address class imbalance. The models are evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC) related to the receiver operating characteristic (ROC). The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality. Additionally, we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity. A Python library has been developed to ensure reproducibility. All models achieve AUC-ROC values near or above 0.85. XGBoost yields the highest accuracy (0.84), while the DNN achieves the highest recall (0.81). Scenario-based simulations reveal how key clinical factors, such as intensive care unit admission and oxygen support, affect predicted outcomes. The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone. This framework provides a foundation for data-driven explainable DTs in public health, even in the absence of time-series data.

Background Traditional teaching in undergraduate dermatology and venereology often prioritizes theoretical knowledge over clinical competency development. To address this limitation, we implemented a hybrid instructional model that combines scenario-based simulation with lecture-based learning (Sim + LBL). Methods This prospective cohort study included two consecutive undergraduate cohorts from Xiangya School of Medicine, Central South University. The 2018 cohort ( n  = 164) received traditional LBL, while the 2019 cohort ( n  = 182) was taught using Sim + LBL in the dermatology and venereology course. Both groups completed the same sexually transmitted diseases (STDs) module. Academic performance was assessed through a post-class test and an anonymous questionnaire. An eXtreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP) was used to identify factors associated with test performance, and mediation analysis was applied to investigate the mechanisms through which the teaching model produced its effects. Results The Sim + LBL group achieved higher post-class scores than those in the LBL group (median difference = + 6.0, 95% CI [4.0–8.0], δ = − 0.39, medium effect). The XGBoost–SHAP model showed good predictive performance on the test set ( R ² = 0.82) and indicated that knowledge mastery and learning interest were the strongest contributors to academic outcomes. Mediation analysis revealed significant indirect effects of the teaching modality via knowledge mastery (Q6: β  = 6.48, 95% CI 5.18–7.94, P  < 0.001) and learning interest (Q7: β  = 3.16, 95% CI 2.01–4.28, P  < 0.001). Conclusion The Sim + LBL model was associated with better academic performance and greater student engagement by fostering cognitive and motivational gains.

Threshold concepts, as a transformative learning approach, are crucial for mastering new knowledge and skills. They help students change their understanding of subjects and promote the development of professional competencies. This study aims to identify and understand the key threshold concepts in undergraduate clinical medical education and to explore their effectiveness in scenario-based simulation teaching. Using a comparative experimental design, students were divided into two groups: one receiving threshold concept training and the other receiving traditional reinforcement training. The study combined scenario-based simulation, semi-structured interviews, and open-text surveys to assess student performance before and after training. Students who received threshold concept training performed significantly better in tests than those in the traditional methods group. In Case 1, the threshold concept group scored 251(203, 259), significantly higher than the traditional methods group's 201(181, 249) (  < 0.05). In Case 2, the threshold concept group scored 245(236, 251), significantly higher than the traditional methods group's 232(228, 237) (  < 0.05). The results indicate that threshold concept training significantly improves student test scores, with statistically significant differences. This study identified key threshold concepts in undergraduate clinical education and validated their effectiveness in scenario-based simulation teaching. Threshold concepts not only guide the development of disease diagnosis and treatment skills but also provide new directions for teaching design and learning activities, facilitating the transition of medical students into competent doctors.