Search Results (337)

Aims/Background: Polypharmacy, or the concurrent intake of five or more medications, is a significant issue in clinical practice, particularly in multimorbid elderly individuals. Despite its importance for patient safety, medical education often lacks systematic training in recognising and managing polypharmacy within the framework of patient-centred care. We investigated the impact of a structured learning intervention introducing polypharmacy as a chronic condition, assessing whether it enhances medical students’ diagnostic competence, confidence, and interprofessional collaboration. Methods: A prospective cohort study was conducted with 50 final-year medical students who received a three-phase educational intervention. Phase 1 was interactive workshops on the principles of polypharmacy, its dangers, and diagnostic tools. Phase 2 involved simulated patient consultations and medication review exercises with pharmacists. Phase 3 involved reflection through debriefing sessions, reflective diaries, and standardised patient feedback. Student knowledge, confidence, and attitudes towards polypharmacy management were assessed using pre- and post-intervention questionnaires. Quantitative data were analysed through paired t-tests, and qualitative data were analysed thematically from reflective diaries. Results: Students demonstrated considerable improvement after the intervention in identifying symptoms of polypharmacy, suggesting deprescribing strategies, and working in multidisciplinary teams. Confidence in prioritising polypharmacy as a primary diagnostic problem increased from 32% to 86% (p < 0.01), and knowledge of diagnostic tools increased from 3.1 ± 0.6 to 4.7 ± 0.3 (p < 0.01). Standardised patients felt communication and patient-centredness had improved, with satisfaction scores increasing from 3.5 ± 0.8 to 4.8 ± 0.4 (p < 0.01). Reflective diaries indicated a shift towards more holistic thinking regarding medication burden. The small sample size limits the generalisability of the results. Conclusions: Teaching polypharmacy as a chronic condition in medical school enhances diagnostic competence, interprofessional teamwork, and patient safety. Education is a structured way of integrating the management of polypharmacy into routine clinical practice. This model provides valuable insights for designing medical curricula. Future research must assess the impact of such training on patient outcomes and clinical decision-making in the long term. Full article

The education sector in Tanzania faces significant challenges, especially in public primary schools. Unmanageably large classes and critical teacher–pupil ratios hinder the provision of tailored tutoring, impeding pupils’ educational growth. However, artificial intelligence (AI) could provide a way forward. Advances in generative AI can be leveraged to create interactive and effective intelligent tutoring systems, which have recently been built into embodied systems such as social robots. Motivated by the pivotal influence of teachers’ attitudes on the adoption of educational technologies, this study undertakes a qualitative investigation of Tanzanian primary school mathematics teachers’ perceptions of contextualised intelligent social robots. Thirteen teachers from six schools in both rural and urban settings observed pupils learning with a social robot. They reported their views during qualitative interviews. The results, analysed thematically, reveal a generally positive attitude towards using social robots in schools. While commended for their effective teaching and suitability for one-to-one tutoring, concerns were raised about incorrect and inconsistent feedback, language code-switching, response latency, and the lack of support infrastructure. We suggest actionable steps towards adopting tutoring systems and social robots in schools in Tanzania and similar low-resource countries, paving the way for their adoption to redress teachers’ workloads and improve educational outcomes. Full article

The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy. Full article

The space flexible netted pocket capture system provides a flexible and stable solution for capturing non-cooperative space objects. This paper investigates the control problem for the capture of non-cooperative targets undergoing motion. A dynamic model of the capturing net is established based on the absolute nodal coordinate formulation (ANCF) and equivalent plate–shell theory. A contact collision force model is developed using a spring–damper model. Subsequently, a feedforward controller is designed based on the estimated collision force from the dynamic model, aiming to compensate for the collision effects between the target and the net. By incorporating the collision estimation data, an extended state observer is designed, taking into account the collision estimation errors and the flexible uncertainties. A sliding mode feedback controller is then designed using the fast terminal sliding mode control method. Finally, simulation analysis of target capture under different motion states is conducted. The results demonstrate that the spacecraft system’s position and attitude average flutter amplitudes are less than ${10}^{-2}$ m and ${10}^{-2}$ deg. In comparison to standard sliding mode control, the designed controller reduces the attitude jitter amplitude by an order of magnitude, thus demonstrating its effectiveness and superiority. Full article

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the $MSE$ by up to 76.4% and the $MaxAE$ by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article

Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices $K$ is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix $K$ is selected from the control matrix set, and $K$ is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced. Full article

Gamified education, as an emerging educational model, is gradually transforming traditional learning methods and has sparked widespread public discussion about its effectiveness and potential. According to connectivism, thinking and learning occur through the connections and interactions among a large number of units. Gamified education can serve as a form of connection, facilitating learners’ links and knowledge construction across different units through interactions, tasks, and feedback. This study aims to explore the public’s emotional attitudes toward gamified education, particularly analyzing the phenomenon of detachment and return regarding its educational function and essence. Through sentiment analysis and LDA topic modeling, three main themes were identified: the gamification and effectiveness of language learning, programmatic learning under temporal and spatial flexibility, and interactive entertainment and social learning. The study found that the public’s emotional attitude toward gamified education is diverse, reflecting the recognition of its potential in providing flexible learning and enhancing interactive experiences, as well as concerns regarding the entertainment-focused nature of educational functions. Additionally, based on the conclusions drawn, the study offers recommendations for educators and designers of gamified education to address issues such as distraction and excessive entertainment during the promotion of gamified education, helping them gain a deeper understanding of its evolution and challenges. Full article

This paper presents an initial output of the project “Augmented Intelligence in Mathematics Education through Modeling, Automatic Reasoning and Artificial Intelligence (IAxEM-CM/PHS-2024/PH-HUM-383)”. The starting hypothesis of this project is that the use of technological tools, such as mathematical modeling, visualization, automatic reasoning and artificial intelligence, significantly improves the teaching and learning of mathematics, in addition to fostering positive attitudes in students. With this hypothesis in mind, in this article, we describe an investigation that has been developed in initial training courses for mathematics teachers in several universities in Madrid, where students used GeoGebra Discovery automated reasoning tools to explore geometric properties in real objects through mathematical paths. Through these activities, future teachers modeled, conjectured and validated geometric relationships directly on photographs of their environment, with the essential concourse of the automated discovery and verification of geometric properties provided by GeoGebra Discovery. The feedback provided by the students’ answers to a questionnaire concerning this novel approach shows a positive evaluation of the experience, especially in terms of content learning and the practical use of technology. Although technological, pedagogical and disciplinary knowledge is well represented, the full integration of these components (according to the TPACK model) is still incipient. Finally, the formative potential of the approach behind this experience is highlighted in a context where Artificial Intelligence tools have an increasing presence in education, as well as the need to deepen these three kinds of knowledge in similar experiences that articulate them in a more integrated way. Full article

The aim of this work is to develop a discrete-time control algorithm that allows the attitude angle of a satellite with an attached solar panel to track a prescribed periodically changing reference signal with zero asymptotic error. Using the concept of the general regulation theory for the state space setup, combined with a time discretization procedure based on the Cayley–Tustin transformation, we derive an error feedback controller. In our control analysis, we prove and explore several system-theoretic properties that are preserved under this continuous-to-discrete time transformation. The obtained discrete-time controller is then applied as a digital control system, demonstrating zero asymptotic tracking error. The theoretical results are tested on a numerical example and computations are performed within the MATLAB R2024b environment, confirming the highly useful nature of the developed approach. The controller also shows some robustness with respect to parametric uncertainty in the satellite model. Full article

Educational technology courses in teacher education programs are critical as they equip teacher candidates (TCs) with the necessary knowledge, skills, and attitudes to incorporate technology into their teaching. Given the rapid technological advancements, it is essential that these courses implement research-informed and current practices to promote TCs’ preparedness in using educational technologies. Accordingly, the instructional team of the educational technology course in the teacher education program at Brock University—Canada initiated a rigorous process to revise this course. This process included exploring the evolving needs of TCs and their feedback on previous course iterations and consulting with teacher educators who lead other courses in the program to ensure curriculum alignment. This paper aims to achieve the following: (1) document the course revision process, with a focus on how TCs and teacher educators were involved; (2) explore TCs’ evolving needs in educational technology; (3) present the revised educational technology course. The paper presents the findings of a survey administered to 116 TCs, focus groups with TCs, and a survey administered to 14 teacher educators. Findings from TCs’ survey showed high levels of their self-assessment of digital competence and intention to use technology in their future teaching. However, TCs believed that they had not received adequate training to do so, suggesting improvements in the design and delivery of the educational technology course. Drawing on the Voice Theory and instructional design models relevant to educational technology courses, this research offers valuable insights into TCs’ digital competence, the scholarship of teaching and learning, and universities’ response to change. Implications for teacher education research and practice are also discussed based on the course revisions and the adopted process. Full article

In view of the difficulties encountered when tuning parameters and the lack of anti-interference capabilities exhibited by high-precision trajectory-tracking control of quadrotor UAVs in complex dynamic environments, this paper proposes a fusion control framework based on an improved crowned pig optimisation algorithm (ICPO) and preset performance anti-disturbance control (PPC-ADRC). Initially, this paper addresses the limited convergence efficiency of the traditional crowned pig algorithm (CPO) by introducing a dynamic time threshold mechanism and an adaptability-based directed elimination strategy to balance the algorithm’s global exploration and local development capabilities. This results in a significant improvement in the convergence speed and optimisation accuracy. Secondly, a hierarchical control architecture is designed, with the outer loop using a PPC-ADRC controller to dynamically constrain the tracking error boundary using an exponential performance funnel function and a combined state observer (ESO) to estimate the compound disturbance in real time. The inner-loop attitude control uses ADRC, and the 24-dimensional parameters of the ADRC (including the ESO bandwidth and non-linear feedback gain) are optimised autonomously using the ICPO to achieve efficient parameter tuning. The simulation experiments demonstrate that, in comparison with the original CPO, the ICPO attains an average fitness ranking that is superior in the CEC2014–2022 benchmark test, thereby substantiating its global optimisation capability. In the PPC-ADRC controller parameter optimisation, the preset performance of the ICPO-tuned PPC-ADRC controller (PPC-ADRC) is superior to that of the particle swarm optimisation (PSO), genetic algorithm (GA) and original CPO. The ICPO-based PPC-ADRC controller is shown to reduce the total error by more than 45.6% compared to the ordinary ADRC controller in the task of tracking a spiral trajectory, and it effectively reduces the overshoot. Its capacity to withstand complex wind disturbances is notably superior to that of the traditional PID and ADRC architectures. Stability analysis further proves that the system satisfies the Lyapunov convergence condition in a finite time. This research provides a theoretical foundation for the high-precision control of UAVs in complex dynamic environments. Full article

Background/Objectives: In this study, we aimed to analyze the validity, based on the internal structure of the construct, measurement invariance by sex, and reliability of the scores for the “Knowledge in Basic Cardiopulmonary Resuscitation in Peruvian children/adolescents” (KBCPR_P21) and “Attitudes in Basic Cardiopulmonary Resuscitation in Peruvian children/adolescents” (ABCPR_P21) instruments in Spanish. Methods: A cross-sectional and instrumental methodological study was conducted between February and August 2021, with the participation of 415 Peruvian elementary school students between 8 and 13 years of age. Participants responded to surveys on knowledge and attitudes toward CPR. For both instruments, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was used as the estimation method for categorical data. Results: All of the items for both scales have high discriminative capacity (>0.30), and both scales showed high internal consistency (Cronbach’s alpha > 0.87 and McDonald’s omega > 0.90). The validity, based on the internal structure of the construct, implied the existence of a single factor grouping all the items in the two scales (CFI and TLI > 0.95; RMSEA and SRMR < 0.08). Multigroup confirmatory factor analysis also allowed us to satisfactorily verify measurement invariance by sex at the four levels (configural, metric, scalar, and strict) for both scales. Conclusions: We can conclude that the values obtained in our evaluation of the scales favor considering them as valid and reliable instruments with which to measure knowledge and attitudes toward basic cardiopulmonary resuscitation in children/adolescents in Peru, given prior learning. The scales could also be used in the evaluation of knowledge and attitudes around basic cardiopulmonary resuscitation in other countries, providing trainers with rapid feedback on the knowledge and attitudes transmitted in training courses, thus allowing better control over the training activities carried out in these courses. Finally, the availability of the scales would allow researchers to empirically test their psychometric properties in other countries. Full article

Integrating technology-enhanced gamification into teacher education can significantly foster motivation and reshape perceptions toward science learning. This mixed-methods case study explores how a 14-week course for preservice early childhood teachers, supported by the digital gamification platform FantasyClass and enriched with a cohesive narrative structure, impacted students’ motivation and attitudes towards science. The course featured structural gamification elements—such as experience points, digital collectibles, and team-based challenges—combined with immersive storytelling that contextualized scientific tasks within a fantasy adventure. Quantitative data from pre- and post-intervention surveys revealed statistically significant improvements in attitudes toward science and perceived teaching competence. Thematic analysis of qualitative feedback highlighted enhanced engagement, enjoyment, and relevance of science learning. These findings suggest that intelligent integration of gamified technologies and narrative design in science teacher initial training can address motivational barriers and foster positive emotional engagement. While context-specific, this study offers insights into how digital gamified learning environments can support the development of positive attitudes towards science among future early childhood educators. Full article

The architecture, engineering, and construction (AEC) industry is experiencing significant digital transformation, creating a critical need to understand how future professionals perceive and accept emerging technologies. This study applies the Technology Acceptance Model (TAM) to investigate undergraduate construction students’ perceptions of Building Information Modelling (BIM) and examines how these factors influence their views on BIM applications. Using an exploratory mixed-methods approach, we analysed 773 responses from students at an Australian university across AEC disciplines, with 607 providing substantive qualitative feedback. Qualitative thematic analysis provided rich contextual understanding of student perspectives, while quantitative analysis revealed pattern frequencies across disciplines. Findings showed that perceived usefulness (PU) (37.7%) and attitude toward using (ATU) (68.4%) dominated student responses, while perceived ease of use (PEOU) (6.9%) received less attention. Productivity benefits (15.3%) and increased accuracy (7.9%) emerged as primary usefulness drivers. Disciplinary differences were significant, with Civil Engineering students emphasising design validation aspects and Construction Management students focusing on project delivery benefits of BIM. Notably, students exhibited sophisticated ambivalence, recognising BIM’s professional value while expressing concerns regarding the steep learning curve, especially when its adoption is coupled with the integration of emerging technologies such as artificial intelligence. This study contributes to the existing knowledge by: (1) documenting the current state of student perceptions in BIM education; and (2) revealing the complex interplay between technological enthusiasm and socio-professional concerns across both educational and industry settings. These findings provide evidence-based guidance for developing BIM curricula that address both socio-technical competencies and student perceptions, helping bridge the gap between educational outcomes and students’ understanding of industry requirements. Full article

Artificial intelligence has transformed educational environments by facilitating processes such as information retrieval, assisted writing, automated feedback, and personalized tutoring. Within university settings, the adoption of technologies capable of autonomously generating content has increased rapidly, becoming a common academic resource for students. However, this accelerated integration poses ethical challenges, particularly when such tools are used without a clear understanding of their implications. This study aimed to examine how students’ emotional attitudes (affective), understanding (cognitive), and practical use (behavioral) of AI relate to their ethical engagement with these technologies. A structured questionnaire was administered to 833 university students in Ecuador. The instrument showed excellent internal consistency (α = 0.992; Ω = 0.992), and the validity analyses confirmed that the dimensions measured distinct but related constructs. ChatGPT was reported as the most used tool (62.2%), followed by Gemini and Siri. The structural model indicated that emotional and cognitive dimensions substantially influenced ethical behavior (β = 0.413 and β = 0.567, respectively), whereas frequent use alone exhibited no significant effect (β = −0.128; p = 0.058). These results suggest that ethical engagement with AI is primarily driven by reflection and knowledge rather than habit. This study contributes to the literature by modeling how different learning dimensions shape ethical behavior in AI use and underscores the relevance of aligning academic practices with socially responsible uses of emerging technologies. Full article