Mejoramiento del desempeño docente de profesores graduados de carreras de la salud. Presente y futuro

La superación del profesional graduado de carreras no pedagógicas resulta una necesidad de la educación permanente, por cuanto el profesional graduado de ingeniera, medicina u otra carrera que ocupa una plaza como docente en un centro universitario debe formar las competencias necesarias para un desempeño docente de calidad. En esta dirección, las universidades cubanas ejecutan acciones de superación, específicamente cursos de postgrado organizados en diplomados y maestrías, que constituyen una de las principales vías para ese mejoramiento del desempeño docente de ese profesional. Es así que el objetivo de este trabajo fue analizar las acciones de superación pedagógica que desarrolla la Facultad de Ciencias Médicas Miguel Enríquez de la Universidad Médica de La Habana, Cuba para el mejoramiento del desempeño docente de los profesores graduados de carreras no pedagógicas. Se usó la metodología cualitativa mediante el análisis de las opiniones recogidas al terminar cada curso. Se evaluaron la aplicabilidad, asequibilidad y tiempo necesario de aprendizaje a través de una escala de cinco puntos. Se analizaron los doce cursos desarrollados en la Facultad de Ciencias Médicas Miguel Enríquez durante los años 2000-2020 dirigidos al mejoramiento del desempeño docente. La aplicabilidad, asequibilidad y tiempo de aprendizaje fueron los criterios evaluados, como resultado se requiere la reelaboración e los mismos; así como el diseño de otros para el futuro inmediato. A partir del análisis unos cursos son mantenidos y otros añadidos en la estrategia de superación de la facultad.Palabras clave: Desempeño docente, postgrado, profesor graduado de carreras no pedagógicas. AbstractThe overcoming of the professional graduated from non-pedagogical careers is a need of permanent education, since the professional graduated from engineering, medicine or another career who occupies a position as a teacher in a university center, must form the necessary competencies for a teaching performance of quality. In this direction, Cuban universities carry out improvement actions, specifically postgraduate courses organized in diploma and master's degrees, which constitute one of the main ways to improve the teaching performance of this professional. Thus, the objective of this work was to analyze the pedagogical improvement actions carried out by the Miguel Enríquez Faculty of Medical Sciences of the Medical University of Havana, Cuba, for the improvement of the teaching performance of professors graduated from non-pedagogical careers. The qualitative methodology was used by analyzing the opinions collected at the end of each course. The applicability, affordability, and time required for learning were assessed using a five-point scale. The twelve courses developed in the Miguel Enríquez Faculty of Medical Sciences during the years 2000-2020 were analyzed, aimed at improving teaching performance. The applicability, affordability and learning time were the evaluated criteria, as a result, reworking of them is required; as well as the design of others for the immediate future. Based on the analysis, some courses are maintained and others are added to the faculty's overcoming strategy.Keywords: Teaching performance, postgraduate, graduate professor of non-pedagogical careers

Human-like Decision Making and Motion Control for Smooth and Natural Car Following

Car-following is an important driving behaviour for intelligent vehicles and has a significant impact on traffic efficiency and traffic safety. Car-following models are widely developed to characterize the human-drivers car-following manoeuvre actions and adopted in traffic simulation and automated vehicle control system development. Car-following models need to be able to represent the drivers behaviour while following preceding vehicles. On the other hand, car-following controllers are an important component of intelligent vehicle systems, both for autonomous vehicles and connected vehicles. However, Adaptive Cruise Control (ACC) as well as Cooperative Adaptive Cruise Control (CACC) do not include human behaviour, which makes their car-following behaviour not human-like or natural for the on-board driver or passenger. To address this problem, in this study, the human-like Wiedemann car-following model is calibrated and verified with our driving simulator data. A human-like car-following nonlinear model predictive control (MPC) controller is developed based on the calibrated car-following model. Three different scenarios are tested to evaluate the performance of the proposed controller, with which the autonomous vehicle is able to have human-like and smooth trajectories at different phases and within different transition zones

Investigating Pedestrians' Crossing Behaviour During Car Deceleration Using Wireless Head Mounted Display: An Application Towards the Evaluation of eHMI of Automated Vehicles

This study investigated pedestrians’ crossing behaviour in a virtual reality environment. One aim was to develop a framework for evaluating external Human-Machine Interfaces (eHMI) used by automated vehicles for future studies. Pedestrians were provided with a series of two approaching cars, which were travelling at either 25mph, 30mph, or 35mph, with eight manipulated time gaps in between cars, where the second car either decelerated or kept pace. These stimuli were presented in 3 blocks. Pedestrians’ task was to cross (or not) naturally between the approaching cars. Data from decelerating trials were analysed. Results showed 51% of crossings happened before deceleration, 31% of crossings after the car had stopped and only 18% of the crossings during deceleration, leaving a great margin for evaluating the effect of eHMI and changing pedestrian crossing behaviour during deceleration. A learning effect was found, demonstrating a shift of decision making across blocks, whereby crossing increasingly occurred during the approaching vehicle’s deceleration, rather than after it had come to a full stop. Further analyses were conducted to investigate the effect of speed on initiation time, crossing time and safety margin. This study provides guidelines in choosing the appropriate time gaps and speeds that may influence pedestrians’ crossing decisions and behaviour while presenting different designs of eHMI in future studies. The results also provide guidance on how to evaluate safety, efficiency/receptivity and learning effects, when comparing different eHMI designs in VR experiments

Achieving Driving Comfort of AVs by Combined Longitudinal and Lateral Motion Control

As automated vehicles (AVs) are moving closer to practical reality, one of the problems that needs to be resolved is how to achieve an acceptable and natural risk management behaviour for the on-board users. Cautious automated driving behaviour is normally demonstrated during the AV testing, by which the safety issue between the AV and other road users or other static risk elements can be guaranteed. However, excessive cautiousness of the AVs may lead to traffic congestion and strange behaviour that will not be accepted by drivers and other road users. Human-like automated driving, as an emerging technique, has been concentrated on mimicking a human driver’s behaviour in order that the behaviour of the AVs can provide an acceptable behaviour for both the drivers (and passengers) and the other road users. The human drivers’ behaviour was obtained through simulator based driving and this study developed a nonlinear model predictive control to optimise risk management behaviour of AVs by taking into account human-driven vehicles’ behaviour, in both longitudinal and lateral directions

Fluctuating Finite Element Analysis (FFEA): A continuum mechanics software tool for mesoscale simulation of biomolecules

Fluctuating Finite Element Analysis (FFEA) is a software package designed to perform continuum mechanics simulations of proteins and other globular macromolecules. It combines conventional finite element methods with stochastic thermal noise, and is appropriate for simulations of large proteins and protein complexes at the mesoscale (length-scales in the range of 5 nm to 1 μm), where there is currently a paucity of modelling tools. It requires 3D volumetric information as input, which can be low resolution structural information such as cryo-electron tomography (cryo-ET) maps or much higher resolution atomistic co-ordinates from which volumetric information can be extracted. In this article we introduce our open source software package for performing FFEA simulations which we have released under a GPLv3 license. The software package includes a C ++ implementation of FFEA, together with tools to assist the user to set up the system from Electron Microscopy Data Bank (EMDB) or Protein Data Bank (PDB) data files. We also provide a PyMOL plugin to perform basic visualisation and additional Python tools for the analysis of FFEA simulation trajectories. This manuscript provides a basic background to the FFEA method, describing the implementation of the core mechanical model and how intermolecular interactions and the solvent environment are included within this framework. We provide prospective FFEA users with a practical overview of how to set up an FFEA simulation with reference to our publicly available online tutorials and manuals that accompany this first release of the package

Protein docking by Rotation-Based Uniform Sampling (RotBUS) with fast computing of intermolecular contact distance and residue desolvation

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test <url>http://www.ebi.ac.uk/msd-srv/capri</url>, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed.</p> <p>Results</p> <p>In these work we present new computational tools for protein-protein docking. We describe here the RotBUS (Rotation-Based Uniform Sampling) method to generate uniformly distributed sets of rigid-body docking poses, with a new fast calculation of the optimal contacting distance between molecules. We have tested the method on a standard benchmark of unbound structures and we can find near-native solutions in 100% of the cases. After applying a new fast filtering scheme based on residue-based desolvation, in combination with FTDock plus pyDock scoring, near-native solutions are found with rank ≤ 50 in 39% of the cases. Knowledge-based experimental restraints can be easily included to reduce computational times during sampling and improve success rates, and the method can be extended in the future to include flexibility of the side-chains.</p> <p>Conclusions</p> <p>This new sampling algorithm has the advantage of its high speed achieved by fast computing of the intermolecular distance based on a coarse representation of the interacting surfaces. In addition, a fast desolvation scoring permits the screening of millions of conformations at low computational cost, without compromising accuracy. The protocol presented here can be used as a framework to include restraints, flexibility and ensemble docking approaches.</p

Interpreting pedestrians' head movements when encountering automated vehicles at a virtual crossroad

In the future, Automated Vehicles (AVs) may be able to use pedestrians’ head movement patterns to understand their crossing intentions. This ability of the AV to predict pedestrian crossing intention will improve road safety in mixed traffic situations and may also enhance traffic flow, allowing the vehicle to gradually reduce its speed in advance of a yield, eliminating the need for a complete and erratic halt. To date, most of the work conducted on studying pedestrian head movements has been based on observation studies. To further our understanding in this area, this study examined pedestrians' head movements when interacting with AVs during a range of road crossing scenarios, developed in a VR environment. Thirty-eight participants took part in this CAVE-based pedestrian simulator study. Head movements were recorded using stereoscopic motion-tracking glasses, as pedestrians crossed the road in response to an AV which approached from the right (UK-based road). A zebra crossing was included in half of the trials to understand how it affected crossing behaviour. The effect of different approaching speeds of the AV, and the presence of an external Human-Machine Interface (eHMI), on head movements and crossing behaviour was also studied. Results showed that the absolute head-turning rate (change in pedestrians' head-turning angle, per frame) increased significantly at around 1 s before a crossing initiation, reaching a peak at the crossing initiation, where pedestrians presented a “last-second check” before the crossing decision. Another increase in absolute head-turning rate to the right was seen at the end of the crossing (∼1.5 s after crossing initiation), to check the proximity of the approaching vehicle. A higher rate of head-turning was also seen for AV-non-yielding scenarios. Finally, the least number of head turns was seen for the yielding conditions which included an eHMI, in the presence of the zebra crossing. These results show the value of infrastructural and vehicle-based cues in assisting pedestrians’ crossing decisions and provide an insight into how head-turning behaviour can be used by AVs to better predict pedestrians’ crossing intentions in urban settings

A Distributed Simulation Study to Examine Vehicle – Pedestrian Interactions

Current research on vehicle-pedestrian interactions focuses on the reaction of one actor other than the interaction of two actors, and considering the impact of the real-time behaviour of both actors on each other. To address this issue, the current study replicated a natural vehicle-pedestrian interaction to the virtual environment by connecting a high-fidelity driving simulator to a CAVE-based pedestrians' simulator. Behaviours from both actors in response to each other were observed indifferent situations including two crossing locations and five time gaps. The proposed method enabled simultaneous interaction in a controlled and safe environment as well as provided implications for future AV design