Abstract
This article proposes methods for maximising the detection rates of thermal fiducial markers using thermography. By exploring the combination of image processing techniques with the use of an affordable thermographic camera, the aim is to mitigate the negative effects of thermography and improve accurate marker identification in a variety of mounting and distance conditions. The research identified a diversity of processing techniques capable of improving thermal marker recognition, offering the potential to surpass previous results. The results highlight the possibility of using low-cost thermographic cameras for this purpose, which could democratise and reduce the costs of recognition processes. This methodology validates the proposed approach, providing a robust basis for future improvements in thermal marker detection and promoting the feasibility of practical, low-cost applications in an assortment of fields.

Abstract
Azobenzenes are a class of compounds presenting photoisomerization capabilities that allow the writing and erasure of birefringence along a desired direction. This feature enables applications requiring polarization control, which although have been extensively investigated in the visible light spectrum, poor emphasis has been paid to the infrared region. In this paper, a systematic characterization of induced birefringence creation and relaxation dynamics has been carried out in azopolymers thin films in the infrared telecommunications region of 1550 nm. This study covers both birefringence characterization in terms of wavelength and irradiance of birefringence writing beams. Preliminary results revealed remarkable maximum birefringence values as high as 0.0465 attained during the recording phase, that stabilized at 0.0424 during the relaxation phase, which is quite promising for many applications.

Abstract
This study discusses the intricate relations between generative artificial intelligence (AI) and music composers. Based on a previous rapid review of recent literature, it reinforces a gap and suggests the need to develop human-centered generative AI design strategies prioritizing cultural artistic (and non-artistic) aspects. We posit that AI-based music generation solutions should resonate with the cultural diversity of stakeholders who are impacted by these systems in practice. The paper highlights the significance of metaphorical design as an effective method in human-AI music co-creation by leveraging familiar interfaces and features that are rooted in everyday objects and cognitive models derived from real-world settings. Our insights illustrate possible ways of (re)framing human-AI metaphorical design to shape perceptions and facilitate seamless interactions between humans and intelligent systems in music co-creativity, particularly at the compositional level. At the heart of this research is the alignment of AI-driven music creation systems with user needs, values, and expectations that vary from culture to culture and thus require a continuous and transparent adaptation of the technology in use to accommodate individual preferences and the socio-algorithmic specificities underlying musicians’ activities.

Abstract
The circular economy is increasingly being considered as a potential model to replace the prevailing end-of-life approach by establishing a closed-loop flow. The importance of different supply chain (SC) actors in this process has been recognized as a critical aspect of the development of sustainable production-consumption models. Consumers play a crucial role in this context, as they have a dual function: ensuring the correct disposal of used products; and consuming products from circular sources. However, the different roles consumers play (refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle, recover) in circular SCs are still unclear. Through a systematic literature review, this paper aims to contribute to a better understanding of the influence of consumers on the adoption of circular supply chain (CSC) practices and to identify the main drivers and barriers regarding the adoption of circular practices. The results demonstrate that the topic is recent and has gained ground in the literature. An in-depth qualitative analysis was carried out with the 74 papers identified and shows that the most commonly addressed R-strategies are reuse, recycle and repair. The main motivations and challenges towards a greater adoption of circular practices are related to (or lack of) environmental beliefs and financial benefits.

Abstract
Clustering of source code is a technique that can help improve feedback in automated program assessment. Grouping code submissions that contain similar mistakes can, for instance, facilitate the identification of students' difficulties to provide targeted feedback. Moreover, solutions with similar functionality but possibly different coding styles or progress levels can allow personalized feedback to students stuck at some point based on a more developed source code or even detect potential cases of plagiarism. However, existing clustering approaches for source code are mostly inadequate for automated feedback generation or assessment systems in programming education. They either give too much emphasis to syntactical program features, rely on expensive computations over pairs of programs, or require previously collected data. This paper introduces an online approach and implemented tool-AsanasCluster-to cluster source code submissions to programming assignments. The proposed approach relies on program attributes extracted from semantic graph representations of source code, including control and data flow features. The obtained feature vector values are fed into an incremental k-means model. Such a model aims to determine the closest cluster of solutions, as they enter the system, timely, considering clustering is an intermediate step for feedback generation in automated assessment. We have conducted a twofold evaluation of the tool to assess (1) its runtime performance and (2) its precision in separating different algorithmic strategies. To this end, we have applied our clustering approach on a public dataset of real submissions from undergraduate students to programming assignments, measuring the runtimes for the distinct tasks involved: building a model, identifying the closest cluster to a new observation, and recalculating partitions. As for the precision, we partition two groups of programs collected from GitHub. One group contains implementations of two searching algorithms, while the other has implementations of several sorting algorithms. AsanasCluster matches and, in some cases, improves the state-of-the-art clustering tools in terms of runtime performance and precision in identifying different algorithmic strategies. It does so without requiring the execution of the code. Moreover, it is able to start the clustering process from a dataset with only two submissions and continuously partition the observations as they enter the system.

Abstract
The eDNA autonomous biosampler results from a line of research aimed at developing systems for sampling and collecting marine biological data, and for collecting environmental DNA. Environmental DNA is a tool that has been increasingly used in the biological monitoring of aquatic environments, as it is a non-invasive method with very promising results when it comes to assessing biological diversity. In this sense, the automation of this method has the potential to greatly increase the temporal and spatial resolution of current biological monitoring programs in aquatic environments. The system has been developed in a partnership between research teams at the Centre for Robotics and Autonomous Systems (CRAS - INESC TEC) and CIIMAR and has been tested in multiple operational scenarios, including the Arctic, where it was attached to the AUV IRIS.