Abstract
Objectives Space, an extreme environment, poses significant challenges to human physiology, including adverse effects on oral health (e.g., increase of periodontitis prevalence, caries, tooth sensitivity). This study investigates the differences in oral health routines and oral manifestations among analog astronauts during their daily routines and simulated space missions conducted on Earth. Materials and methods This research focused on scientist-astronaut candidates of the International Institute for Astronautical Sciences (IIAS) and analog astronauts from other institutions. The study used a cross-sectional methodology with a descriptive component. A total of 16 participants, comprising individuals aged between 21 and 55 years, were invited to complete an online questionnaire. A comparison was made between the subjects' oral hygiene practices in everyday life (designated as Earth in this research) and their oral hygiene routines during their space analog missions. Results (i) Toothbrushing duration was mostly 1-3 minutes (n = 13; 81.30% on Earth; n = 11; 68.80% on a mission); (ii) time spent was the greatest difficulty in maintaining oral hygiene routine on a mission (n = 9; 53,6%); (iii) There were more experienced oral symptoms on Earth (n = 12; 75%) than on mission (n = 7; 43.80%); (iv) The most frequent frequency of oral check-ups was > 12 months (n = 6; 37,5%); (v) Oral health materials were scarce on the mission (n = 9; 56.30%); (vi) For the majority, personal oral hygiene was classified as good (n = 9; 56.30% on Earth; n = 7; 43.80% on the mission). Conclusion and Clinical relevance This research contributes to increasing knowledge of oral hygiene measures in extreme environments, but further research is needed as this topic remains relatively understudied. This study represents an initial contribution to oral health in analog space missions, aiming to propose guidelines for future missions, including deep space missions and expeditions to extreme environments.

Abstract
Radiography is the primary modality for diagnosing canine hip dysplasia (CHD), with visual assessment of radiographic features sometimes used for accurate diagnosis. However, these features typically constitute small regions of interest (ROI) within the overall image, yet they hold vital diagnostic information and are crucial for pathological analysis. Consequently, automated detection of ROIs becomes a critical preprocessing step in classification or segmentation systems. By correctly extracting the ROIs, the efficiency of retrieval and identification of pathological signs can be significantly improved. In this research study, we employed the most recent iteration of the YOLO (version 8) model to detect hip joints in a dataset of 133 pelvic radiographs. The best-performing model achieved a mean average precision (mAP50:95) of 0.81, indicating highly accurate detection of hip regions. Importantly, this model displayed feasibility for training on a relatively small dataset and exhibited promising potential for various medical applications.

Abstract

Abstract
Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage.

Abstract

Abstract
Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low -structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real -life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real -life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators.