Abstract
Welding is a challenging, risky, and time-consuming profession. Recently, there has been a documented shortage of trained welders, and as a result, the market is pushing for an increase in the rate at which new professionals are trained. To address this growing demand, training institutions are exploring alternative methods to train future professionals. The emergence of virtual reality technologies has led to initiatives to explore their potential for welding training. Multiple studies have suggested that virtual reality training delivers comparable, or even superior, results when compared to more conventional approaches, with shorter training times and reduced costs in consumables. This paper conducts a comprehensive review of the current state of the field of welding simulators. This involves exploring the different types of welding simulators available and evaluating their effectiveness and efficiency in meeting the learning objectives of welding training. The aim is to identify gaps in the literature, suggest future research directions, and promote the development of more effective and efficient welding simulators in the future. The research also seeks to develop a categorical system for evaluating and comparing welding simulators. This system will enable a more systematic and objective analysis of the features and characteristics of each simulator, identifying the essential characteristics that should be included in each level of classification.

Abstract

Abstract
The global push for environmental sustainability is driving substantial changes in power systems, prompting extensive grid upgrades. Policies and initiatives worldwide aim to reduce CO2 emissions, with a focus on increasing reliance on Renewable Energy Sources (RESs) and electrifying transportation. However, the geographical variability and uncertainties of RESs directly impact power generation and distribution, necessitating adjustments in transmission system planning and operation. This paper presents a Transmission Expansion Planning (TEP) model using the 2021 Texas snowstorm as a benchmark scenario, incorporating wind and solar energy penetration while addressing associated uncertainties. Climate Change (CC) and Extreme Weather Events (EWE) are integrated into the set of scenarios aiming at evaluating the proposed method's effectiveness. Comparisons in extreme operative conditions highlight the importance of network reliability and security, emphasizing the significance of merged grids. All simulations are conducted using the ACTIVSg2000 synthetic test system, which emulates the ERCOT grid, with comparisons made between TEP scenarios considering and disregarding CC and EWEs, supporting the concept of umbrella protection.

Abstract
Autonomous driving can reduce the number of road accidents due to human error and result in safer roads. One important part of the system is the perception unit, which provides information about the environment surrounding the car. Currently, most manufacturers are using not only RGB cameras, which are passive sensors that capture light already in the environment but also Lidar. This sensor actively emits laser pulses to a surface or object and measures reflection and time-of-flight. Previous work, YOLOP, already proposed a model for object detection and semantic segmentation, but only using RGB. This work extends it for Lidar and evaluates performance on KITTI, a public autonomous driving dataset. The implementation shows improved precision across all objects of different sizes. The implementation is entirely made available: https://github.com/filipepcampos/yolomm.

Abstract
Purpose 2-[F-18]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[F-18]FDG PET images.Methods One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[F-18]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation was performed from the remaining sets for training and evaluating a set of candidate models and for selecting the final model. Models of three types of 3D CNNs architectures were trained from random weight initialization (Stacked 3D CNN, VGG-like and Inception-v2-like models) both in original and augmented datasets. Transfer learning, from ImageNet with ResNet-50, was also used.Results The final model (Stacked 3D CNN model) obtained an area under the ROC curve of 0.8385 (95% CI: 0.6455-1.0000) in the test set. The model had a sensibility of 80.00%, a specificity of 69.23% and an accuracy of 73.91%, in the test set, for an optimised decision threshold that assigns a higher cost to false negatives.Conclusion A 3D CNN model was effective at distinguishing benign from malignant pulmonary nodules in 2-[F-18]FDG PET images.

Abstract
In the context of the ongoing energy transition towards renewable sources and the decentralization of generation, multi-carrier energy systems emerge as a comprehensive solution that allows the synergic integration of different energy carriers, such as electricity, natural gas, heat, and storage, offering an effective response to the challenges posed by the variability of renewable generation and the fluctuation of energy demand. In addition, the inherent flexibility of these systems facilitates the management of the variability of renewable generation and adaptation to changes in energy demand, thus contributing to the stability and reliability of supply. In this context, the participation of prosumers who contribute their distributed generation and load flexibility through energy aggregators that effectively coordinate energy supply and demand in real-time ensures a constant balance in the energy system stands out. This paper explores the potential for various prosumer groups, facilitated by multi-carrier energy aggregators, to offer flexible services to electric distribution and natural gas grid utilities, given that natural gas is the prosumers' primary fuel for heating and cooking. The model is formulated as a two-level optimization problem. The upper level results in the emulation of the distribution system, while the lower level minimizes the flexible demand of prosumers. The interaction of the two levels is not through the price of electricity but through prosumer demand. The resulting optimization problem is a mixed-integer linear programming formulation. The results on the IEEE 33-bus distribution and 20-bus natural gas systems allow us to observe that the supply costs in the distribution and natural gas networks are efficiently reduced considering the coordination of prosumers' participation.