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The study explored how PSTs perceived a learning design, using a decision simulator, a self-reflection guide and a peer mentoring guide as tools to mediate reflections on approaches to the teacher’s role. The individual characteristics of PSTs were also considered, including role clarity, self-efficacy and affective commitment, as predictors of the learning design’s perceived relevance.

Informed by earlier research exploring the teacher’s role, a scenario-based simulation was supported by a tool for self-reflection on the results and a peer mentoring tool for the further development of an individual’s teaching role. Using structural equation modelling, the study assessed the statistical strength of the relationships between relevant factors to explore how a decision simulator and associated research-based tools were perceived by preservice teachers (PSTs).

The results indicate that regardless of PSTs’ individual characteristics, the decision simulator and associated peer mentoring tools have the potential to enhance learning and reflection. Therefore, the proposed approach can facilitate peer mentoring and enhance PSTs’ learning potential.

The paper explores the use of innovative approaches to mentoring by introducing peer mentoring grounded in experiences using a decision simulator.

3D-printed devices proved their efficacy across different clinical applications, helping personalize medical treatments. This paper aims to present the procedure for the design and production of patient-specific dynamic simulators of the human knee. The scope of these simulators is to improve surgical outcomes, investigate the motion and load response of the human knee and standardize in-vitro experiments for testing orthopedic devices through a personalized physical representation of the patient’s joint.

This paper tested the approach on three volunteers. For each, a patient-specific mathematical joint model was defined from an magnetic resonance imaging (MRI) of the knee. The model guided the CAD design of the simulators, which was then realized through stereolithography printing. Manufacturing accuracy was tested by quantifying the differences between 3D-printed and CAD geometry. To assess the simulator functionality, its motion was measured through a stereophotogrammetric system and compared with the natural tibio-femoral motion of the volunteers, measured as a sequence of static MRI.

The 3D-printing accuracy was very high, with average differences between ideal and printed parts below ± 0.1 mm. However, the assembly of different 3D-printed parts resulted in a higher average error of 0.97 mm and peak values of 2.33 mm. Despite that, the rotational and translational accuracy of the simulator was about 5° and 4 mm, respectively.

Although improvements in the production process are needed, the proposed simulators successfully replicated the individual articular behavior. The proposed approach is general and thus extendible to other articulations.

The purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.

The mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.

The developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.

This study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

This paper aims to describe a modelling and solving methodology of a (static converter–electric motor–control) system for its sizing by optimization, considering the dynamic thermal heating of the machine.

The electrical drive sizing model is composed of two simulators (electrical and thermal) that are co-simulated with a master−slave relationship for the time step management. The computation is stopped according to simulation criteria.

This paper details a methodology to represent and size an electrical drive using a multiphysics and multidynamics approach. The thermal simulator is the master and calls the electrical system simulator at a fixed exchange time step. The two simulators use a dedicated dynamic time solver with adaptive time step and event management. The simulation automatically stops on pre-established criteria, avoiding useless simulations.

This paper aims to present a generic methodology for the sizing by optimization of electrical drives with a multiphysics approach, so the precision and computation time highly depend on the modelling method of each components. A genetic multiobjective optimization algorithm is used.

The methodology can be applied to size electrical drives operating in a thermally limited zone. The power electronics converter and electrical machine can be easily adapted by modifying their sub-model, without impacting the global model and simulation principle.

The approach enables to compute a maximum operating duration before reaching thermal limits and to use it as an optimization constraint. These system considerations allow to over constrain the electrical machine, enabling to size a smaller machine while guaranteeing the same output performances.

This study aims to optimize electrical systems represented by ordinary differential equations and events, using their frequency spectrum is an important purpose for designers, especially to calculate harmonics.

This paper presents a methodology to achieve this, by using a gradient-based optimization algorithm. The paper proposes to use a time simulation of the electrical system, and then to compute its frequency spectrum in the optimization loop.

The paper shows how to proceed efficiently to compute the frequency spectrum of an electrical system to include it in an optimization loop. Derivatives of the frequency spectrum such as the optimization inputs can also be calculated. This is possible even if the sized system behavior cannot be defined a priori, e.g. when there are static converters or electrical devices with natural switching.

Using an efficient sequential quadratic programming optimizer, automatic differentiation is used to compute the model gradients. Frequency spectrum derivatives with respect to the optimization inputs are calculated by an analytical formula. The methodology uses a “white-box” approach so that automatic differentiation and the differential equations simulator can be used, unlike most state-of-the-art simulators.

This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.

In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.

Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.

The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

To investigate the influence of logistics and transportation workers’ perceptions of their management’s simultaneous safety and operations focus (or lack thereof) on related worker safety and operational perceptions and behaviors.

This multi-method research consisted of two studies. Study 1 aimed to establish correlational relationships by evaluating the impact of individual-level worker perceptions of operationally focused routines (as a moderator) on the relationship between worker perceptions of safety-related routines and workers’ self-reported safety and in-role operational behaviors using a survey. Study 2 aimed to establish causal relationships by evaluating the same conceptual relationships in a behavioral-type experiment utilizing vehicle simulators. After receiving one of four pre-task briefings, participants completed a driving task scenario in a driving simulator.

In Study 1, the relationship between perceived safety focus and safety behavior/in-role operational behavior was strengthened at higher levels of perceived operations focus. In Study 2, participants who received the balanced pre-task briefing committed significantly fewer safety violations than the other 3 treatment groups. However, in-role driving deviations were not impacted as hypothesized.

This research is conducted at the individual (worker) level of analysis to capture the little-known perspectives of logistics and transportation workers and explore the influence of balanced safety and operational routines from a more micro perspective, thus contributing to a deeper understanding of how balanced routines might influence worker behavior when conducting dynamic tasks to ensure safe, effective outcomes.

This study aims to propose a methodology aimed at understanding the cognitive and physiological processes inherent in cadet pilot operations. Through analyzing responses from two cadet pilots with varied experience levels across diverse simulation scenarios, the research uses descriptive statistics, t-test, one-way ANOVA and percentage change analysis to explore crucial variables, including heart rate (HR), heart rate variability (HRV) and respiratory rate (RR).

The investigation meticulously examines HR, HRV and RR under circumstances encompassing resting state, visual flight rules and instrument flight rules with engine failure. Pilots undergo comprehensive analyses employing statistical techniques and visual representations to comprehend cognitive loads and physiological adaptations.

Significant disparities emerge between the two pilots, elucidating the profound impact of experience on cognitive and physiological outcomes. Novice cadet pilots exhibit heightened variability during scenario transitions, while experienced cadet pilot demonstrate controlled responses, indicative of adaptability. Visual flight simulations evoke distinct responses, whereas instrument-based scenarios, particularly those simulating emergencies, lead to pronounced physiological changes.

The findings of this research hold practical significance in introducing the proposed novel methodology for monitoring Cadet pilots to refine pilot training simulation protocols and enhance aviation safety by illuminating the interplay between experience levels and scenario complexities.

This study proposes a novel methodology for investigating cognitive and physiological responses in pilot operations, mainly investigating cadet pilots’ vital parameters through diverse analytical methods and an exploration of scenario-specific demands.

Security is the major issue that motivates multiple scholars to discover security solutions apart from the advantages of wireless sensor networks (WSN) such as strong compatibility, flexible communication and low cost. However, there exist a few challenges, such as the complexity of choosing the expected cluster, communication overhead, routing selection and the energy level that affects the entire communication. The ultimate aim of the research is to secure data communication in WSN using prairie indica optimization.

Initially, the network simulator sets up clusters of sensor nodes. The simulator then selects the Cluster Head and optimizes routing using an advanced Prairie Indica Optimization algorithm to find the most efficient communication paths. Sensor nodes collect data, which is securely transmitted to the base station. By applying prairie indica optimization to WSNs, optimize key aspects of data communication, including secure routing and encryption, to protect sensitive information from potential threats.

The Prairie Indica Optimization, as proposed, achieves impressive results for networks comprising 50 nodes, with delay, energy and throughput values of 77.39 ms, 21.68 J and 22.59 bps. In the case of 100-node networks, the achieved values are 80.95 ms, 27.74 J and 22.03 bps, significantly surpassing the performance of current techniques. These outcomes underscore the substantial improvements brought about by the Prairie Indica Optimization in enhancing WSN data communication.

In this research, the Prairie Indica Optimization is designed to enhance the security of data communication within WSN.

Higher education is undergoing a significant transformation, demanding a rethinking of instructional methods, the establishment of novel assessment frameworks and the incorporation of state-of-the-art technologies to advance the development of learning competencies. The use of simulations is an alternative that offers satisfactory results and favors learning in new generations. This study aims to present a methodology for assessing the advancement of self-directed learning (SDL) in participants through simulation techniques and show that the incorporation of simulations can improve participants’ SDL skills.

This study uses a quantitative approach to evaluate the evolution of various competencies related to SDL among participants. Includes the application of scales specifically designed to measure readiness for SDL, the use of factor analysis to identify and validate essential factors and conducting hypothesis testing to verify shifts in students’ perceptions regarding the significance of SDL-related skills.

The findings of this study reveal a positive impact of simulations on the SDL readiness. Factor analysis identified five key factors: self-management skills, openness to learning opportunities, initiative and independence in learning, self-concept as an effective learner and desire for Learning. The use of simulations significantly improved critical thinking, logical reasoning, analysis and decision-making capabilities. These results demonstrate the effectiveness of this technique as a valuable tool for developing SDL skills and highlight the potential of such interventions in complex and uncertain learning environments.

The study provided valuable insights into the impact of simulations in the logistics field, particularly in the development of SDL-related skills. There is a need for studies in other areas of knowledge that would allow for the generalization of these results. Likewise, it is advisable to use scales that measure the development of other types of skills. Additionally, the study’s focus on short-term effects may not fully capture the long-term development of SDL-related skills. Future research should address these limitations, expand the sample size, conduct longitudinal studies and explore possible contextual and external factors that may influence SDL outcomes in various educational settings, leading to broader and more robust conclusions.

The practical implications of this study are significant for both higher education institutions and educators in the logistics domain. The positive impact of the serious game on participants’ SDL skills highlights the potential of incorporating interactive and engaging learning tools in the curriculum. Educators can leverage serious games to foster critical thinking, logical reasoning and decision-making abilities in their students. Moreover, the identified key factors influencing SDL development provide valuable guidance for designing targeted interventions to enhance students’ self-management, initiative and love for learning.

The social implications of this study underscore the importance of equipping individuals with SDL skills in a rapidly changing world. By integrating serious games and fostering SDL competencies, higher education institutions can contribute to a more adaptable and resilient workforce. Empowering students with critical thinking, innovative thinking and independent learning, they can enhance their ability to navigate the challenges of the labor market. This, in turn, may lead to a more informed and proactive society capable of addressing complex issues and making well-informed decisions.

This article arises from empirical exploration with students from two countries in Latin America (Mexico and Colombia), examining the importance of games in developing SDL skills. Furthermore, it promotes the use of a methodology with a low level of complexity, and that can be replicated with different games. This perspective adds valuable insights to the existing literature, offering practical implications for educators and institutions seeking effective strategies to promote SDL skills among students.