Abstract

Abstract
Hydroponics is an advanced agricultural technique that involves growing plants without soil. Instead, plants are cultivated in a nutrient-rich water solution that provides all the essential minerals they need to thrive, allowing plants to grow either with their roots directly in the solution or supported by inert substrates like pine bark, coconut husk fiber, and rice husk. The solid waste generated from hydroponic cultivation is valuable due to its low cost, abundance, biodegradability, and renewability. These residues are rich in lignocellulosic materials, which can be extracted and refined to produce cellulose and nanocellulose (NC). In this work, cellulose and nanocellulose were extracted from residues of coconut husk fiber and a mixture of pine bark and coconut husk fiber, used in tomato and strawberry hydroponics, respectively. The residues were ground, washed, and chemically treated to obtain cellulose and NC. The chemical process involved several stages: (i) acid treatment, alkaline treatment, and bleaching to isolate cellulose, and (ii) acid hydrolysis followed by ultrasonication to obtain NC. Both materials underwent characterization using various techniques such as TGA, DSC, XRD and FTIR-ATR, which confirmed very low levels of lignin and hemicellulose. Morphological characterization through SEM revealed the presence of micro- and nano-crystals in the cellulose and NC samples, respectively, highlighting the effectiveness of the extraction method. The high purity and quality of the extracted materials make them competitive with commercially available products, suitable for applications in healthcare, food packaging, and automotive industries, while supporting recycling and reuse principles.

Abstract
Web applications are widespread and can be accessed from anywhere, in theory, using aweb browser on a computer or smartphone. Primarily due to the diversity of web browsers and frameworks available for developing web application interfaces, testing such applications is a challenging task. With the advent of large language models, several works are utilizing them to automate software engineering tasks, including test case generation. This use of LLMs for test case generation prioritizes unit testing. More recently, we have seen the advent of Generic Artificial Intelligence Agents, which are tools that utilize LLMs and also possess the ability to run additional tools, such as cloning repositories, navigating websites, and compiling programs. In this work, which is part of a research and development project, we evaluate a specific Generic AI Agent Assistant regarding its capability to navigate web applications and create fully automated end-to-end test cases, utilizing Selenium WebDriver and JUnit 5 framework. Results show that, considering a set of nine websites, in overall end-to-end test case generation, Suna configured with DeepSeek V3 produced 165 successful test cases out of 481 generated tests, a success rate of 34.3%. On the other hand, Suna configured with Claude Sonnet 4 produced 336 successful test cases out of 479 generated tests, a success rate of 70.1%, which is very impressive, mainly due to the complexity of creating end-to-end testing. In terms of cost, we used a free and a paid LLM model. The paid model generates successful test cases at an average price of $ 0.15 per test case.

Abstract
The increasing number of connected and autonomous vehicles generates an even greater demand for efficient content delivery in vehicular networks. Estimating the popularity of content is an important task to proactively cache and distribute content throughout the networks to add value to users' experiences and reduce network congestion. This paper presents a novel approach for predicting popular content on vehicular networks based on a Federated Learning-Adversarial Autoencoder model and anonymised data. Unlike prior works that relied on users' raw features, our model protects user privacy through data anonymisation. This allows us to learn from the hidden patterns of content popularity and deliver popular content without compromising user privacy. Experiments showed that our approach exceeded traditional collaborative filtering and deep learning methods in terms of accuracy and robustness, even with sparse data.

Abstract
Nowadays, eXtended Reality (XR) has matured to the point where it seamlessly integrates various input and output modalities, enhancing the way users interact with digital environments. From traditional controllers and hand tracking to voice commands, eye tracking, and even biometric sensors, XR systems now offer more natural interactions. Similarly, output modalities have expanded beyond visual displays to include haptic feedback, spatial audio, and others, enriching the overall user experience. In this vein, as the field of XR becomes increasingly multimodal, the education process must also evolve to reflect these advancements. There is a growing need to incorporate additional modalities into the curriculum, helping students understand their relevance and practical applications. By exposing students to a diverse range of interaction techniques, they can better assess which modalities are most suitable for different contexts, enabling them to design more effective and human-centered solutions. This work describes an Advanced Human-Machine Interaction (HMI) course aimed at Doctoral Students in Computer Science. The primary objective is to provide students with the necessary knowledge in HMI by enabling them to articulate the fundamental concepts of the field, recognize and analyze the role of human factors, identify modern interaction methods and technologies, apply HCD principles to interactive system design and development, and implement appropriate methods for assessing interaction experiences across advanced HMI topics. In this vein, the course structure, the range of topics covered, assessment strategies, as well as the hardware and infrastructure employed are presented. Additionally, it highlights mini-projects, including flexibility for students to integrate their projects, fostering personalized and project-driven learning. The discussion reflects on the challenges inherent in keeping pace with this rapidly evolving field and emphasizes the importance of adapting to emerging trends. Finally, the paper outlines future directions and potential enhancements for the course.

Abstract
The growing reliance on e-commerce and the demand for efficient intralogistics operations have increased the need for automation, while labour shortages continue to pose significant challenges. When combined with the inherent risks of forklift operation, this circumstance prompted businesses to look for robotic solutions for intralogistics tasks. However, robots are still limited when they come across situations that are outside of their programming scope and often need assistance from humans. To achieve the long-term goal of enhancing intralogistics operation, we propose the development of a virtual reality-based teleoperation system that allows remote operation of robot forklifts with minimal latency. Considering the specificities of the teleoperation process and network dynamics, we conduct detailed modelling to analyse latency factors, optimise system performance, and ensure a seamless user experience. Experimental results on a mobile robot have shown that the proposed teleoperation system achieves an average glass-to-glass latency of 368 ms, with capturing latency contributing to approximately 60% of the total delay. The results also indicate that network oscillations significantly impact image quality and user experience, emphasising the importance of a stable network infrastructure.