Relation between arterial stiffness and aerobic capacity: Importance of proximal aortic stiffness

Brachial-ankle pulse wave velocity (baPWV) has become a popular modality of arterial stiffness measurement. However, its projected arterial segment does not include the proximal aorta which plays important roles for attenuating cardiac pulsation and reducing afterload. We hypothesised that aerobic capacity would be more strongly associated with PWV including the proximal aorta than that omitting the proximal aorta. To test our hypothesis, we compared the association between aerobic capacity and arterial stiffness parameters omitting vs. including the proximal aorta (i.e. baPWV vs. heart-ankle PWV [haPWV]) in 82 apparently healthy men (18–64 years). Estimated VO_2max significantly correlated with baPWV (r?=??0.394, P?<?.001), and more strongly with haPWV (r?=??0.546, P?<?.001). The forward stepwise multi-regression analysis revealed that haPWV (β?=??0.335), as well as age, heart rate, and body mass index (β?=??0.280 to ?0.297), was a significant independent determinant explaining variance of estimated VO_2max. These results suggest that aerobic capacity is influenced more strongly by the proximal aortic stiffness than distal aortic stiffness.     

A DGS gesture dictionary for modelling on mobile devices

Interactive or Dynamic Geometry System (DGS) is a tool that help to teach and learn geometry using a computer-based interactive environment. Traditionally, the interaction with DGS is based on keyboard and mouse events where the functionalities are accessed using a menu of icons. Nevertheless, recent findings suggest that such a traditional model of interaction has a steep learning curve and is inadequate to develop DGS for devices with multi-touch screens. Thus, we propose a new interaction model for DGS based on a gesture dictionary which enables the construction and manipulation of geometric objects without the need of accessing a menu of icons. The dictionary is divided into three types: (i) kernel gestures: which are the basis for defining gestures; (ii) navigation gestures: related to the manipulation and editing of geometric objects; and (iii) basic gestures of construction: to construct geometric objects. To validate our interaction model, a DGS for mobile device, GeoTouch, has been developed using the proposed dictionary. Usability tests were performed comparing GeoTouch with three other DGS for mobile devices. The results indicate that the GeoTouch interface has fewer usability problems than the other three DGS software and it is easier to learn and interact with.     

Differences in stroke phases, arm-leg coordination and velocity fluctuation due to event, gender and performance level in breaststroke

The purpose of this study was to analyse stroke phases, arm-leg coordination and trunk motion fluctuation during breaststroke in elite male and female 50, 100 and 200 m events at the 9th FINA World Swimming Championships, Fukuoka 2001. Four phases of the arm stroke and three phases of the leg kick as well as phases of simultaneous arm and leg propulsion and recovery were identified from video of swimmers' motions below the surface. The duration of each phase was expressed as a proportion of the whole stroke cycle. Three measures of the arm-leg coordination, percent simultaneous arm-leg recovery time (%SRT), percent arm lag time (%ALT) and percent simultaneous arm-leg propulsion time (%SPT) were calculated. Mean mid-pool swimming hip velocity (V), stroke rate (SR) and stroke length (SL) were also calculated. In addition, the intra-cycle hip velocity of the swimmers was obtained by cinematographic analysis. The SR decreased and SL increased significantly as the event distance increased. For the arm-leg coordination the %ALT, %SPT and %SRT indicated significant differences between event, gender and performance level. In particular, for increasing event distance and for the higher performing swimmer the lower the %SPT and the higher the %SRT. In addition, the range of the intra-cycle hip velocity fluctuation in the lower performing group was greater than the higher performing group. The non-propulsive phase seems to be a key factor for better performance. The breaststroke swimmers must avoid rapid deceleration during the non-propulsive phase by adopting a low resistance posture and stroking technique.     

Using learning analytics in the Amazonas: understanding students’ behaviour in introductory programming

Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students’ performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine-grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016–18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine-grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices.