Abstract
Web programming education is an important component of modern computer science curricula. Assessing students’ web programming skills can be time-consuming and challenging for educators. This paper introduces Webpal, an automated assessment tool for web programming exercises in entry-level courses. Webpal evaluates web applications coded in HTML, CSS, and Javascript, and provides feedback to students. This tool integrates with Virtual Learning Environments (VLEs) through an API, allowing the creation, storage, and access to exercises while assessing student attempts based on the created exercises. The evaluation process comprises various subcomponents: static assessment, interface matching, functional testing, and feedback management. This approach aims to provide feedback that helps students overcome their challenges in web programming assignments. This paper also presents a demo showcasing the tool’s features and functionality in a simulated VLE environment.

Abstract
Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various wireless channel models, it often overlooks the impact of UAV heading. This paper provides an experimental characterization of the Air-to-Ground (A2G) and Ground-to-Air (G2A) wireless channels in an open environment with no obstacles nor interference, considering the distance and the UAV heading. We analyze the received signal strength indicator and the TCP throughput between a ground user and a UAV, covering distances between 50 m and 500 m, and considering different UAV headings. Additionally, we characterize the antenna’s radiation pattern based on UAV headings. The paper provides valuable perspectives on the capabilities of UAVs in offering on-demand and dynamic wireless connectivity, as well as highlights the significance of considering UAV heading and antenna configurations in real-world scenarios.

Abstract
Project Management Maturity Models (PMMM) are considered practical tools to deal with poor Project Management (PM) performance, an issue that concerns academics and practitioners. However, the models that exist are something close to one size fits all. This means that those models might not be suitable for activity sectors with specific requirements, such as construction, in particular, the renovation and remodelling construction firms. The present research proposes a PMMM to assess the PM capabilities of Portuguese renovation and remodelling Project-Based Firms (PBF). To achieve this goal, the authors developed documental research, followed by exploratory research through qualitative analysis. The researchers conducted semi-structured interviews and performed a content analysis of the fully transcribed interviews. Compared with the literature review's findings, qualitative analysis results made it possible to find closure on previous research that indicated two models to have the best fit for an assessment project on construction PBFs: the OPM3 from the PMI and the MMGP-Prado. Based on those findings, the latter has the best fit for an assessment project on construction PBF. However, the model needs adjustments to fit the Portuguese context of renovation and remodelling organisations. This article presents a new PMMM for Portuguese renovation and remodelling construction firms based on obtained results. Furthermore, regarding construction PM, this article is among the few that studied PMMM on renovation and remodelling construction companies. Unlike large construction companies, these are small organisations that academics do not target for research.

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.