Abstract
In recent years, Artificial Intelligence (AI) provided essential tools to enhance the productivity of activities related to civil engineering, particularly in design, construction, and maintenance. In this framework, the present work proposes a novel AI computer vision methodology for automatically identifying the corrosion phenomenon on roofing systems of large-scale industrial buildings. The proposed method can be incorporated into computational packages for easier integration by the industry to enhance the inspection activities' performance. For this purpose, a dedicated image database with more than 8k high-resolution aerial images was developed for supervised training. An Unmanned Aerial Vehicle (UAV) was used to acquire remote georeferenced images safely and efficiently. The corrosion anomalies were manually annotated using a segmentation strategy summing up 18,381 instances. These anomalies were identified through instance segmentation using the Mask based Region-Convolution Neural Network (Mask R-CNN) framework adjusted to the created dataset. Some adjustments were performed to enhance the performance of the classification model, particularly defining an adequate input image size, data augmentation strategy, Intersection over a Union (IoU) threshold during training, and type of backbone network. The inferences show promising results, with correct detections even under complex backgrounds, poor illumination conditions, and instances of significantly reduced dimensions. Furthermore, in scenarios without a roofing system, the model proved reliable, not producing any false positive occurrences. The best model achieved metrics' values equal to 65.1% for the bounding box detection Average Precision (AP) and 59.2% for the mask AP, considering an IoU of 50%. Regarding classification metrics, the precision and recall were equal to 85.8% and 84.0%, respectively. The developed methodology proved to be extremely valuable for guiding infrastructure managers in taking physically informed decisions based on the real assets condition.

Abstract

Abstract
Cartilage repair after a trauma or a degenerative disease like osteoarthritis (OA) continues to be a big challenge in current medicine due to the limited self-regenerative capacity of the articular cartilage tissues. To overcome the current limitations, tissue engineering and regenerative medicine (TERM) and adjacent areas have focused their efforts on new therapeutical procedures and materials capable of restoring normal tissue functionalities through polymeric scaffolding and stem cell engineering approaches. For this, the sustainable exploration of marine origin materials has emerged in the last years as a natural alternative to mammal sources, benefiting from their biological properties (e.g., biocompatibility, biodegradability, no toxicity, among others) for the develop-ment of several types of scaffolds. In this study, marine collagen(jCOL)-chitosan(sCHT)-fucoidan(aFUC)/ chondroitin sulfate(aCS) were cryo-processed (-20 degrees C,-80 degrees C, and-196 degrees C) and a chemical-free cross -linking approach was explored to establish cohesive and stable cryogel materials. The cryogels were intensively characterized to assess their oscillatory behavior, thermal structural stability, thixotropic properties (around 45 % for the best formulations), injectability, and surface structural organization. Additionally, the cryogels demonstrate an interesting microenvironment in in vitro studies using human adipose-derived stem cells (hASCs), supporting their viability and proliferation. In both physic-chemical and in vitro studies, the systems that contain fucoidan in their formulations, i.e., C1 (jCOL, sCHT, aFUC) and C3 (jCOL, sCHT, aFUC, aCS), submitted at-80 degrees C, are those that demonstrated most promising results for future application in articular cartilage tissues.

Abstract

Abstract
The challenge is a fundamental aspect of almost every gameplay, and immersion is one of the most widely recognized concepts in the video game industry. Since this is currently a work in progress, this study aims to preliminary research how player's perceived level of challenge affects narrative immersion during gameplay in the role-playing game (RPG) genre. This study will outline the procedures that will be undertaken, including the utilization of the Challenge Originating from Recent Gameplay Interaction Scale (CORGIS) instrument and a questionnaire to measure player immersion. These instruments will enable the assessment of the impact of the perceived challenge on narrative immersion in each use case. © 2023 Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2023, IHCI 2023; Computer Graphics, Visualization, Computer Vision and Image Processing 2023, CGVCVIP 2023; and Game and Entertainment Technologies 2023, GET 2023. All rights reserved.

Abstract
This paper proposes an interactive approach for narrative extraction from semantic graphs. The proposed approach extracts events from RDF triples, maps them to their corresponding attributes, and assembles them into a chronological sequence to form narrative graphs. The approach is evaluated on the Wikidata graph and achieves promising results in terms of narrative quality and coherence. The paper also discusses several avenues for future work, including the integration of machine learning, graph embedding methods and the exploration of advanced techniques for attention-based narrative labeling and semantic role labeling. Overall, the proposed method offers a promising approach to narrative extraction from semantic graphs and has the potential to be useful in various applications, including chatbots, conversational agents, and content creation tools.