Abstract
Currently, lung cancer is one of the deadliest diseases that affects millions of people globally. However, Artificial Intelligence is being increasingly integrated with healthcare practices, with the goal to aid in the early diagnosis of lung cancer. Although such methods have shown very promising results, they still lack transparency to the user, which consequently could make their generalised adoption a challenging task. Therefore, in this work we explore the use of post-hoc explainable methods, to better understand the inner-workings of an already established multitasking framework that executes the segmentation and the classification task of lung nodules simultaneously. The idea behind such study is to understand how a multitasking approach impacts the model's performance in the lung nodule classification task when compared to single-task models. Our results show that the multitasking approach works as an attention mechanism by aiding the model to learn more meaningful features. Furthermore, the multitasking framework was able to achieve a better performance in regard to the explainability metric, with an increase of 7% when compared to our baseline, and also during the classification and segmentation task, with an increase of 4.84% and 15.03%; for each task respectively, when also compared to the studied baselines.

Abstract
Methylation is considered one of the proteins' most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.

Abstract
Despite the prevailing paradigm of user-friendliness and enjoyment in mainstream game design and user interface design, intentional friction in game user interfaces that can be used to create meaningful experiences and to encourage reflection in players. This work aims to explore such use of intentional friction, providing designers with a valuable resource to generate unconventional game interfaces. As a starting point, we previously identified six strategies for intentional friction: (1) exploit memory shortcomings; (2) faulty feedback; (3) mismatched mental models; (4) impairment of ability; (5) deliberate inefficiency; and (6) oppressive constraints. Afterwards, to help operationalise these strategies and identify others, we ran co-creation workshops with game and UI designers, which lead to the development of a tool composed of three decks of cards, combining additional friction strategies, intentions, emotions, and ideation triggers, and enabling designers to create expressive game interfaces that intentionally incorporate friction as a design strategy. The Friction Firestarter toolkit is intended to inspire designers to explore various options and think creatively about friction in UI design. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
The detection of low-mass planets orbiting the nearest stars is a central stake of exoplanetary science, as they can be directly characterized much more easily than their distant counterparts. Here, we present the results of our long-term astrometric observations of the nearest binary M-dwarf Gliese 65 AB (GJ65), located at a distance of only 2.67 pc. We monitored the relative astrometry of the two components from 2016 to 2023 with the VLTI/GRAVITY interferometric instrument. We derived highly accurate orbital parameters for the stellar system, along with the dynamical masses of the two red dwarfs. The GRAVITY measurements exhibit a mean accuracy per epoch of 50-60 ms in 1.5 h of observing time using the 1.8 m Auxiliary Telescopes. The residuals of the two-body orbital fit enable us to search for the presence of companions orbiting one of the two stars (S-type orbit) through the reflex motion they imprint on the differential A-B astrometry. We detected a Neptune-mass candidate companion with an orbital period of p = 156 +/- 1 d and a mass of mp = 36 +/- 7 M circle plus. The best-fit orbit is within the dynamical stability region of the stellar pair. It has a low eccentricity, e = 0.1 - 0.3, and the planetary orbit plane has a moderate-to-high inclination of i > 30 degrees with respect to the stellar pair, with further observations required to confirm these values. These observations demonstrate the capability of interferometric astrometry to reach microarcsecond accuracy in the narrow-angle regime for planet detection by reflex motion from the ground. This capability offers new perspectives and potential synergies with Gaia in the pursuit of low-mass exoplanets in the solar neighborhood.

Abstract
This article addresses the problem of wheelchair path planning. In particular, to minimize the length of the trajectory within an environment containing a variable number of obstacles. The positions and quantities of these obstacles are pre-determined. To tackle this challenge, we present a methodology that integrates optimisation techniques and heuristic algorithms to find trajectories both optimal and collision-free. The effectiveness of this methodology is illustrated through a practical example, demonstrating how it successfully generates a collision-free trajectory, even when a large number of obstacles is present in the workspace. In the future, we intend to continue investigating the same problem, taking into account energy consumption as well as time minimisation.

Abstract
The high level of precision and consistency required for pallet detection in industrial environments and logistics tasks is a critical challenge that has been the subject of extensive research. This paper proposes a system for detecting pallets and its pockets using the You Only Look Once (YOLO) v8 Open Neural Network Exchange (ONNX) model, followed by the segmentation of the pallet surface. On the basis of the system a pipeline built on the ROS Action Server whose structure promotes modularity and ease of implementation of heuristics. Additionally, is presented a comparison between the YOLOv5 and YOLOv8 models in the detection task, trained with a customised dataset from a factory environment. The results demonstrate that the pipeline can consistently perform pallet and pocket detection, even when tested in the laboratory and with successive 3D pallet segmentation. When comparing the models, YOLOv8 achieved higher average metric values, with YOLOv8m providing better detection performance in the laboratory setting.