Abstract
The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model's ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model's decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

Abstract
Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.

Abstract
The impacts of the e-commerce growth have increased the urgency in designing and adopting new alternative delivery strategies. In this context, it is important to consider the particularities of each city like its terrain conditions. This article aims at exploring the impact of road slopes on parcel delivery operations, and how they condition the adoption and implementation of alternative, more sustainable delivery strategies. To this end, a microscopic traffic simulator was used to evaluate different delivery strategies including ICE vans, electric vans, and cargo bikes in three different slope scenarios. This evaluation was based on a medium-sized European city and conducted by comparing the same parcel delivery route at three levels: operational (route length, duration, and waiting time), energy consumption, and emissions. The results revealed that as the road slopes increased, more time was needed to deliver all packages, waiting times grew longer, and vehicles' energy consumption and emissions levels intensified. From the flat terrain to the most sloped terrain, there was an increase in duration of around 5% for traditional and electric vans, 35% for large cargo bikes, and 14% for small cargo bikes. The ICE van suffers a 105% increase in waiting time; the electric van 71%; the large cargo bike 68% and the small cargo bike 52%. Energy consumption also varied, with ICE vans and small cargo bikes consuming nearly 30% more energy, while electric vans and large cargo bikes consumed 4% and 60% more energy, respectively. The ICE van's emissions of CO, HC, PMx, NOx, and CO2 are 13%, 10%, 1%, 20%, and 29% higher, respectively. Moreover, in flatter terrains, the better strategies are the electric van or a large cargo bike, while in more sloped terrains, the most adequate one is the electric van. These findings suggest that the electric van is the best overall strategy for different terrains and different decision-making profiles, ranking first in more than 70% of the profiles across all three terrains.

Abstract
The transportation industry focuses on reducing vehicle weight for fuel economy and emissions. This emphasis promotes the use of coaches, which raises concerns about passenger safety in frontal collisions. The proposal is to correlate the crashworthiness of coaches with the replacement of fibreglass composite materials by a state-of-the-art polymer (DCPD). Based on the ECE R29 standard, FEM models solved by Pamcrash (R) assess the vehicle's crashworthiness. Cross-referencing these results with the Eco-Design X technique, two models are evaluated in terms of environmental impact. The LeanDfX methodology involves multiple analyses for design domains, including model optimisation, manufacturing processes, and eco-design. On a performance scale of 0% to 100%, different 'X' domains are evaluated. The Eco-Design study allowed the assessing the environmental impacts of the proposed solution compared to the original models, is conducted using Simapro v9.2.0.2 and the ReCiPe 2016 methodology. The novel design proposed modifications to the models resulted in significant structural behaviour improvements for driver's physical integrity. The cross results of Design-for-Crashworthiness and Design-for-Eco-Design using the innovative LeanDfX framework provide a new perspective to be integrated into the automotive industry. The use of DCPD is expected to lead to a more crashworthy and environmentally friendly design, while ensuring passenger safety.

Abstract
Modern supercomputers host numerous jobs that compete for shared storage resources, causing I/O interference and performance degradation. Solutions based on software- defined storage (SDS) emerged to address this issue by coordinating the storage environment through the enforcement of QoS policies. However, these often fail to consider the scale of modern HPC infrastructures.In this work, we explore the advantages and shortcomings of state-of-the-art SDS solutions and highlight the scale of current production clusters and their rising trends. Furthermore, we conduct the first experimental study that sheds new insights into the performance and scalability of flat and hierarchical SDS control plane designs.Our results, using the Frontera supercomputer, show that a flat design with a single controller can scale up to 2,500 nodes with an average control cycle latency of 41 ms, while hierarchical designs can handle up to 10,000 nodes with an average latency ranging between 69 and 103 ms. © 2024 IEEE.

Abstract
Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system's efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.