Learning environment

AIM: The aim of this paper is to discuss the significance of the intersectionality framework for addressing prejudices, racism and inequalities in nursing education and clinical learning environments. BACKGROUND: Discrimination and racism against nursing students and educators based on their gender, ethnicity, race and social identities is well-documented in the nursing literature. Despite documented discrimination and incivility based on intersectional factors, it is reported that often nurse educators show limited interest in the culture, diverse experiences and values of nursing students with culturally and linguistically diverse backgrounds...

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy...

In this article, we propose a distributional policy-gradient method based on distributional reinforcement learning (RL) and policy gradient. Conventional RL algorithms typically estimate the expectation of return, given a state-action pair. Furthermore, distributional RL algorithms consider the return as a random variable and estimate the return distribution that can characterize the probability of different returns resulted by environmental uncertainties. Thus, the return distribution provides more valuable information than its expectation, leading to superior policies in general...

Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded datasets without online environment interaction. It is unfortunate that extrapolation errors can cause over-optimistic Q -value estimates when learning with a fixed dataset, limiting the performance of the learned policy. To tackle this issue, this article proposes an offline actor-critic with behavior value regularization (OAC-BVR) method. In the policy evaluation stage, the difference between the Q -function and the value of the behavior policy is considered as the regularization term, driving the learned value function to approach the value of the behavior policy...

Intensive care units provide a data-rich environment with the potential to generate datasets in the realm of big data, which could be utilized to train powerful machine learning (ML) models. However, the currently available datasets are too small and exhibit too little diversity due to their limitation to individual hospitals. This lack of extensive and varied datasets is a primary reason for the limited generalizability and resulting low clinical utility of current ML models. Often, these models are based on data from single centers and suffer from poor external validity...

The effectiveness of modern medical education largely depends on the integration and utilization of digital technologies in teaching various disciplines. In this pilot usability study, we introduced 3D printed anthropomorphic dental models, specifically designed for the elective discipline "Digital and Metal-Free Techniques in Dental Technology" from the curriculum of the Dental Technician specialty in the Medical University of Varna. The evaluation focused on dental technician students' perception of this novel learning environment, its influence on their performance, and the potential for future application of these models and related 3D technologies in their professional practice...

Training a model to recognize human actions in videos is computationally intensive. While modern strategies employ transfer learning methods to make the process more efficient, they still face challenges regarding flexibility and efficiency. Existing solutions are limited in functionality and rely heavily on pretrained architectures, which can restrict their applicability to diverse scenarios. Our work explores knowledge distillation (KD) for enhancing the training of self-supervised video models in three aspects: improving classification accuracy, accelerating model convergence, and increasing model flexibility under regular and limited-data scenarios...

UNLABELLED: Universities are active agents of social change through knowledge, providing citizens with the necessary abilities to face professional challenges. This work aims to evaluate and analyse the adaptation of emotional regulation in learning situations of group work in virtual and hybrid (virtual and presential) environments, of a group of students of Physical Activity and Sport Sciences belonging to a Chilean university and a Spanish university. METHOD: A total of 107 students from a Chilean university and a Spanish university, all of them enrolled in the degree in Physical Activity and Sport Sciences, participated in the study...

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human-machine-environment interaction loop...

Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner...

To improve the rapidity of path planning for drones in unknown environments, a new bio-inspired path planning method using E-DQN (event-based deep Q -network), referring to introducing event stream to reinforcement learning network, is proposed. Firstly, event data are collected through an airsim simulator for environmental perception, and an auto-encoder is presented to extract data features and generate event weights. Then, event weights are input into DQN (deep Q -network) to choose the action of the next step...

Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning...

The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare...

BACKGROUND: Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a novel device that uses a machine learning (ML) algorithm to detect and track diabetic neuropathy. The Moveo device comprises 4 sensors positioned on the back of the hands and feet accompanied by a mobile application that gathers data and ML algorithms that are hosted on a cloud platform. The sensors measure movement signals, which are then transferred to the cloud through the mobile application...

The study of neuroarchitecture is concerned with the significant effects of architecture on human behavior, emotions and thought processes. This review explores the intricate relationship between the brain and perceived environments, focusing on the roles of the anterior cingulate cortex (ACC) and parahippocampal place area (PPA) in processing architectural stimuli. It highlights the importance of mirror neurons in generating empathetic responses to our surroundings and discusses how architectural elements like lighting, color, and space layout significantly impact emotional and cognitive experiences...

INTRODUCTION: Simulation-based learning has become an essential element in entry-level perfusion education. While the use of simulation has been demonstrated to improve patient outcomes, few institutions possess the budgetary resources to build and maintain a high-fidelity simulation environment. This project aims to identify novel uses of web conferencing platforms to support in-person, remote, and virtual simulation exercises. METHODS: The Zoom Virtual Meeting platform (Zoom Video Communications, Inc...

Is the hippocampus involved in implicit memory? I argue that contemporary views on hippocampal function, going beyond the classic dichotomy of explicit versus implicit, predict involvement of the hippocampus whenever flexible, predictive associations are rapidly encoded. This involvement is independent of conscious awareness. A paradigm case is statistical learning: the unconscious extraction of statistical regularities from the environment. In line with this, a substantial body of literature on contextual cueing in visual search has established hippocampal involvement in this form of implicit learning...

Theoretical and empirical scholars of cultural evolution have traditionally studied social learning strategies, such as conformity, as adaptive strategies to obtain accurate information about the environment, whereas within social psychology there has been a greater focus upon the social consequences of such strategies. Although these two approaches are often used in concert when studying human social learning, we believe the potential social benefits of conformity, and of social learning more broadly, have been overlooked in studies of non-humans...

Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain. Yet, the static DL model is likely to perform poorly in a continually evolving environment, requiring appropriate model updates. In an incremental learning setting, we would expect that well-trained static models are updated, following continually evolving target domain data-e.g., additional lesions or structures of interest-collected from different sites, without catastrophic forgetting...

Depression, a pervasive global mental disorder, profoundly impacts daily lives. Despite numerous deep learning studies focused on depression detection through speech analysis, the shortage of annotated bulk samples hampers the development of effective models. In response to this challenge, our research introduces a transfer learning approach for detecting depression in speech, aiming to overcome constraints imposed by limited resources. In the context of feature representation, we obtain depression-related features by fine-tuning wav2vec 2...