Search

The emergence of virtual reality (VR) simulation has brought about a significant shift in the healthcare industry, as it integrates modern technologies with human talents to foster patient-centered care, clinical efficiency, and a compassionate healthcare digital ecosystem. The use of clinical simulation in nursing education has become essential in the current day, revolutionizing the training of future medical professionals. This paper explored the realm of simulation-based learning, illuminating its importance, obstacles, and endless potential in the field of nursing education. Given the ever-changing healthcare landscape, nurses need to possess a broad range of skills and a thorough comprehension of real-world situations. But conventional didactic methods frequently fail to adequately prepare student-nurses for the intricacies of clinical practice. VR is one of the most crucial elements of immersive learning that increases constructive pedagogic engagements due to generative imagination it offers. By using VR headgear or goggles, student-nurses can fully submerge themselves in a customized digital world as if they were truly there, interacting with their surroundings and learn in an interactive and pedagogic informative digital ecosystem. In order to help student-nurses practice problem-solving techniques without really placing themselves in risk, VR can also be used to construct very realistic simulations. In this study, the authors offered the transformative potential of VR, with significances to improve nursing education by offering engaging and interactive learning environments. The paper presented a novel approaches in addressing nursing education through technology familiarization, proposing a cutting-edge simulation platform using actor network (ANT) model for the resource distribution. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) has revolutionized the healthcare sector by introducing new methods of diagnosis, treatment, and medical training. This article presents a comprehensive analysis of VR applications in healthcare, exploring its advantages, challenges, and impacts. The review includes case studies and relevant literature, highlighting areas such as rehabilitation, surgical training, and exposure therapies. The results indicate that VR can enhance the effectiveness of treatments and the quality of medical training, offering immersive experiences that benefit both patients and healthcare professionals. Despite the promising advantages, challenges such as cost and specialized training remain. Future research should focus on developing more accessible solutions and expanding VR applications in medicine to ensure broader patient access to this revolutionary technology. [ABSTRACT FROM AUTHOR]

Light-field (LF) cameras, also known as plenoptic cameras, permit the recording of the 4D LF distribution of target scenes. However, many times, surface errors of a microlens array (MLA) are responsible for degradation in the images captured by a plenoptic camera. Additionally, the limited pixel count of the sensor can cause missing parallax information. The aforementioned issues are crucial for creating accurate maps for Intelligent Autonomous Transport System (IATS), because they cause loss of LF information, and need to be addressed. To tackle this problem, a learning-based framework by directly simulating the LF distribution is proposed. A high-dimensional convolution layer with densely sampled LFs in 4D space and considering a soft activation function based on ReLU segmentation correction is used to generate a superresolution (SR) LF image, improving the convergence rate in the deep learning network. Experimental results show that our proposed LF image reconstruction framework outperforms the existing state-of-the-art approaches; specifically, it is effective for learning the LF distribution and generating high-quality LF images. Different image quality assessment methods are used to evaluate the performance of the proposed framework, such as PSNR, SSIM, IWSSIM, FSIM, GFM, MDFM, and HDR-VDP. Additionally, the computational efficiency was evaluated in terms of number of parameters and FLOPs, and experimental results demonstrated that our proposed framework reached the highest performance in most of the datasets used. [ABSTRACT FROM AUTHOR]

The communication media has undergone major technological advances in recent decades, from telegraph services to the internet of things. These rapid changes have impacted the routine of humanity and its way of seeing the world in an impactful way. Currently, we are entering the fifth generation (5G) of mobile telephony, whose architecture promises extensive exchanges of messages in a short period of time, which was still difficult to obtain in the networks of the previous generation. Given this new technology, this work proposes to investigate its impacts and possible advances in the area of ââInternet of Things, an emerging technology that extends mobile networks to so-called smart objects. [ABSTRACT FROM AUTHOR]

Current implementations of 5G networks consider higher frequency range of operation than previous telecommunication networks, and it is possible to offer higher data rates for different applications. On the other hand, atmospheric phenomena could have a more negative impact on the transmission quality. Thus, the study of the transmitted signal quality at high frequencies is relevant to guaranty the user's quality of experience. In this research, the recommendations ITU-R P.838- 3 and ITU-R P.676-11 are implemented in a network scenario, which are methodologies to estimate the signal degradations originated by rainfall and atmospheric gases, respectively. Thus, speech signals are encoded by the Adaptive Multi-RateWideband (AMR-WB) codec, transmitted and the perceptual speech quality is evaluated using the algorithm described in ITU-T Rec. P.863, mostly known as POLQA. In this work, a novel non-intrusive speech quality classifier that considers atmospheric phenomena is proposed. This classifier is based on Deep Belief Networks (DBN) that uses Support Vector Machine (SVM) with radial basis function kernel (RBF-SVM) as classifier, to identify five predefined speech quality classes. Experimental results show that the proposed speech quality classifier reached an accuracy between 92% and 95% for each quality class overcoming the results obtained by the sole non-intrusive standard described in ITU-T Recommendation P.563. Furthermore, subjective tests are carried out to validate the proposed classifier performance, and it reached an accuracy of 94.8%. [ABSTRACT FROM AUTHOR]

Online social networks provide relevant information on users’ opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a convolutional neural network and a bidirectional long short-term memory - recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices. [ABSTRACT FROM AUTHOR]

In recent years, the shared mobility service has increased in many countries across the world because its low cost and several shared-use mobility applications on mobile devices [1]. Commonly, if a ride is shared between people with similar preferences, users likely feel both more comfortable and safe. In this context, the main goal of this article is to classify users with similar preferences, in automatic manner, to improve user's quality of experience in ridesharing service. To obtain initial data, subjective tests are carried out using questionnaires and their results are used to determine ridesharing profiles. Then, some basic user profile information is extracted from Online Social Networks (OSN) to determine an user profile based on preferences in ridesharing service. The user profile classification is performed through different machine learning algorithms, which use as input the data extracted from OSN. Two case studies of shared-mobility are treated, (i) sharing a ride with a passenger with a similar hobby [2], and (ii) sharing a ride with people that support an opposite football teams. The novelty of this work consists in the application of the Hybrid Discriminative Restricted Boltzmann Machines (HDRBM) algorithm for classification whose performance overperforms widely used algorithms such as Random Forest, SVM and DRBM. The experimental results presented a correctly classified instance of 96:9% and 97:3% for the cases of sharing a ride with people with similar hobby and support different football team, respectively. Finally, a Recommendation System (RS) is proposed, which efficiency is compared with a basic RS, obtaining a Pearson correlation coefficient of 0:97 and 0:71, respectively. [ABSTRACT FROM AUTHOR]