Systematic visual analysis of groundwater hydrographs: potential benefits and challenges

Visual analysis of time series in hydrology is frequently seen as a crucial step to becoming acquainted with the nature of the data, as well as detecting unexpected errors, biases, etc. Human eyes, in particular those of a trained expert, are well suited to recognize irregularities and distinct patterns. However, there are limits as to what the eye can resolve and process; moreover, visual analysis is by definition subjective and has low reproducibility. Visual inspection is frequently mentioned in publications, but rarely described in detail, even though it may have significantly affected decisions made in the process of performing the underlying study. This paper presents a visual analysis of groundwater hydrographs that has been performed in relation to attempts to classify groundwater time series as part of developing a new concept for prediction in data-scarce groundwater systems. Within this concept, determining the similarity of groundwater hydrographs is essential. As standard approaches for similarity analysis of groundwater hydrographs do not yet exist, different approaches were developed and tested. This provided the opportunity to carry out a comparison between visual analysis and formal, automated classification approaches. The presented visual classification was carried out on two sets of time series from central Europe and Fennoscandia. It is explained why and where visual classification can be beneficial but also where the limitations and challenges associated with the approach lie. It is concluded that systematic visual analysis of time series in hydrology, despite its subjectivity and low reproducibility, should receive much more attention.

5-A-SIDE SOCCER: PREVALENCE OF SPORTS INJURIES IN BRAZILIAN TEAM PLAYERS

ABSTRACT Introduction: 5-a-side soccer is a sports modality exclusively for athletes with vision impairment (VI) classified as blind - B1 (Blind 1) by the visual classification process. Type of impairment and high-performance training are factors that contribute to the development of sports injuries. Objective: The purpose of this study was to characterize the prevalence of sports-related injuries in visually disabled athletes of the Brazilian 5-a-side soccer team. Methods: The method was defined as a descriptive, longitudinal, epidemiological study. The sample was composed of ten male athletes, members of the Brazilian 5-a-side soccer team, including two athletes without VI and eight athletes with the B1 visual classification, who participated in competitions in 2014. The questionnaire for the data collection was based on the Sport Injuries Protocol for Paralympic Sports (PLEEP), expressing quantitative data analyzed by descriptive statistics. Results: The results showed that, in 2014, five athletes with VI had seven sports injuries, with a prevalence of 62.5%, clinical incidence of 0.87 injuries per athlete per year, and 1.4 injuries per injured athlete. The main sports injuries were muscle strain (28.6%), groin pull (athletic pubalgia) (28.6%), and shin splints (periostitis) (28.6%). All sports injuries occurred in the lower limbs, affecting legs (71.4%) and hips (28.6%). Overload was the most frequent mechanism (57%), reported after the technical kick movement and linked to the muscle imbalance between dominant and supporting lower limbs, in addition to the postural misalignment typical of people with visual impairments. Conclusions: In summary, the Brazilian 5-a-side soccer athletes presented a pattern of overload injuries resulting from the repetition of the technical kick movement, occurring predominantly in sports competitions. Epidemiological data can contribute to the development of strategies to prevent injuries in this sport. Level of evidence II, Progressive prognostic study.

Adaptive coding of stimulus information in human fronto-parietal cortex during visual classification

Our ability to flexibly adapt to changing demands is supported by flexible coding of task-relevant information in frontal and parietal brain regions. Converging evidence suggest that coding of stimuli and task rules in these regions become stronger as task difficulty increases. Here, we tested whether there is a corresponding change in the representational format as well, an issue that has rarely been addressed directly in past research. Participants performed a visual classification task under varying levels of perceptual difficulty, while we acquired fMRI. Using a model-based representational similarity approach, we tested whether stimulus representations retain exemplar-level information. We expected representations to drop such exemplar-level information as perceptual difficulty increases, which would indicate a focus on representing behaviorally relevant category information. Counter to these expectations, and in contrast to previous research, we found frontal and parietal brain regions contained exemplar-level stimulus information. Interestingly, the anterior intraparietal sulcus (aIPS) retained exemplar-level stimulus information even in perceptually difficult trials, and these representations were directly related to performance. Overall, these findings call for a reassessment of the neural mechanisms underlying human adaptive behavior during visual classification.

An Augmented Reality Periscope for Submarines with Extended Visual Classification

Submarines are considered extremely strategic for any naval army due to their stealth capability. Periscopes are crucial sensors for these vessels, and emerging to the surface or periscope depth is required to identify visual contacts through this device. This maneuver has many procedures and usually has to be fast and agile to avoid exposure. This paper presents and implements a novel architecture for real submarine periscopes developed for future Brazilian naval fleet operations. Our system consists of a probe that is connected to the craft and carries a 360 camera. We project and take the images inside the vessel using traditional VR/XR devices. We also propose and implement an efficient computer vision-based MR technique to estimate and display detected vessels effectively and precisely. The vessel detection model is trained using synthetic images. So, we built and made available a dataset composed of 99,000 images. Finally, we also estimate distances of the classified elements, showing all the information in an AR-based interface. Although the probe is wired-connected, it allows for the vessel to stand in deep positions, reducing its exposure and introducing a new way for submarine maneuvers and operations. We validate our proposal through a user experience experiment using 19 experts in periscope operations.