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Mixed reality (MR) offers a unique blend of immersive experiences with real-world applications, positioning it as a transformative tool in medical education. This study evaluated the effectiveness of MR technologies, specifically HoloLens 2 and the Lucina Childbirth Simulator, in telemedicine-assisted childbirth simulations. A quasi-experimental design assessed user experience, educational effectiveness, and technology adoption, involving 17 medical students, two instructors, and two observers. Results demonstrated that MR significantly enhanced students' understanding of complex clinical concepts, with 80.95% finding it more effective than traditional methods and 71.43% willing to adopt MR for future training. However, challenges in user comfort and interface intuitiveness were identified, highlighting areas for improvement. The study concludes that further refinements are needed to fully maximize the impact of MR in medical training. [ABSTRACT FROM AUTHOR]

The coverage of IoT networks may be significantly impaired in the event of a disaster, due to the disruption of the communication network. The use of UAVs as flying base stations for IoT emergency coverage is a viable option due to the flexibility of their deployment and high mobility. However, the mobility of IoT devices and the speed at which a channel changes present challenges to the efficient allocation of communication resources. This paper presents a novel energy harvesting scheme, SWIPT, which is developed on top of the UAV–RIS secret system for the 6G high performance communication system. The proposed algorithm is developed to improve the proposed scheme using reinforcement learning under dynamic communication environments. The simulation results demonstrate the superiority of the proposed algorithm in terms of trade-off efficiency and practicality. [ABSTRACT FROM AUTHOR]

This project tackles the challenge of subjectivity in the nutritional assessment of meals, specifically within the context of the Ecuadorian diet. It introduces a computer vision solution based on advanced convolutional neural networks, utilizing two adapted YOLO V8 models: one for detection and the other for segmentation. The system accurately identifies and analyzes the different food portions in served dishes, categorizing them into three classes: builders foods, regulators, and energy sources, ultimately determining whether the dish is well-balanced. This approach addresses the need for a precise estimate of meal proportions and nutritional composition, which is essential for promoting healthy diets and preventing issues related to nutritional imbalances. The project utilized refined datasets tailored to the Ecuadorian menu, resulting in a system that enhances the accuracy of nutritional assessments based on images of served meals. The findings highlight the effectiveness of the tool and recommend expanding the dataset to include a wider range of typical Ecuadorian foods, thus improving the precision and applicability of the system in various nutritional contexts. [ABSTRACT FROM AUTHOR]

The ability to predict energy consumption in buildings and homes is becoming an increasingly crucial aspect of energy management. This ability could help the environment and the economy by making buildings more energy efficient and reducing operational costs. This paper proposes a system for predicting energy consumption for non-residential buildings. It compares traditional machine learning (ML) models and clustering techniques such as K-means, DBSCAN and Hierarchical. The goal is to improve energy consumption predictions by analyzing energy consumption data. These features are used to group similar buildings together. We evaluate two representative time series for each cluster: an average and the time series of the building close to the centroid. ML models such as Linear Regression (LR), Step-wise LR, Tree, SVM, Efficient LR, Ensemble and Random Forest were evaluated for the two representative time series to identify the best one. Numerical results demonstrate that the Random Forest model delivered the lowest root mean square error (RMSE) of the predicted variable for each cluster, with the exception of cluster 3, where the ensemble ML model exhibited the lowest error. Finally, when evaluating the prediction based on the selected models for the rest of the time series belonging to a cluster, the average time series has the lowest RMSE compared to the time series of the building closest to the centroid of the cluster. [ABSTRACT FROM AUTHOR]

Nowadays, the traffic over the networks is changing because of new protocols, devices and applications. Therefore, it is necessary to analyze the impact over services and resources. Traffic Classification of network is a very important prerequisite for tasks such as traffic engineering and provisioning quality of service. In this paper, we analyze the variable packet size of the traffic in an university campus network through the collected data using a novel sniffer that ensures the user data privacy. We separate the collected data by type of traffic, protocols and applications. Finally, we estimate the traffic model that represents this traffic by means of a Poisson process and compute its associated numerical parameters.