Application of Mixed Reality to Ultrasound-guided Femoral Arterial Cannulation During Real-time Practice in Cardiac Interventions.

Mixed reality opens interesting possibilities as it allows physicians to interact with both, the real physical and the virtual computer-generated environment and objects, in a powerful way. A mixed reality system, based in the HoloLens 2 glasses, has been developed to assist cardiologists in a quite complex interventional procedure: the ultrasound-guided femoral arterial cannulations, during real-time practice in interventional cardiology. The system is divided into two modules, the transmitter module, responsible for sending medical images to HoloLens 2 glasses, and the receiver module, hosted in the HoloLens 2, which renders those medical images, allowing the practitioner to watch and manage them in a 3D environment. The system has been successfully used, between November 2021 and August 2022, in up to 9 interventions by 2 different practitioners, in a large public hospital in central Spain. The practitioners using the system confirmed it as easy to use, reliable, real-time, reachable, and cost-effective, allowing a reduction of operating times, a better control of typical errors associated to the interventional procedure, and opening the possibility to use the medical imagery produced in ubiquitous e-learning. These strengths and opportunities were only nuanced by the risk of potential medical complications emerging from system malfunction or operator errors when using the system (e.g., unexpected momentary lag). In summary, the proposed system can be taken as a realistic proof of concept of how mixed reality technologies can support practitioners when performing interventional and surgical procedures during real-time daily practice.

A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties.

Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.

An Insight of Deep Learning Based Demand Forecasting in Smart Grids.

Smart grids are able to forecast customers' consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today's demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks-based on Recurrent Neural Networks-are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.