Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks

The difficulty of mountainbike downhill trails is a subjective perception. However, sports-associations and mountainbike park operators attempt to group trails into different levels of difficulty with scales like the Singletrail-Skala (S0-S5) or colored scales (blue, red, black, ...) as proposed by The International Mountain Bicycling Association. Inconsistencies in difficulty grading occur due to the various scales, different people grading the trails, differences in topography, and more. We propose an end-to-end deep learning approach to classify trails into three difficulties easy, medium, and hard by using sensor data. With mbientlab Meta Motion r0.2 sensor units, we record accelerometer- and gyroscope data of one rider on multiple trail segments. A 2D convolutional neural network is trained with a stacked and concatenated representation of the aforementioned data as its input. We run experiments with five different sample- and five different kernel sizes and achieve a maximum Sparse Categorical Accuracy of 0.9097. To the best of our knowledge, this is the first work targeting computational difficulty classification of mountainbike downhill trails.Comment: 11 pages, 5 figure

Identification of the initial molecular changes in response to circulating angiogenic cells-mediated therapy in critical limb ischemia

BackgroundCritical limb ischemia (CLI) constitutes the most aggressive form of peripheral arterial occlusive disease, characterized by the blockade of arteries supplying blood to the lower extremities, significantly diminishing oxygen and nutrient supply. CLI patients usually undergo amputation of fingers, feet, or extremities, with a high risk of mortality due to associated comorbidities.Circulating angiogenic cells (CACs), also known as early endothelial progenitor cells, constitute promising candidates for cell therapy in CLI due to their assigned vascular regenerative properties. Preclinical and clinical assays with CACs have shown promising results. A better understanding of how these cells participate in vascular regeneration would significantly help to potentiate their role in revascularization.Herein, we analyzed the initial molecular mechanisms triggered by human CACs after being administered to a murine model of CLI, in order to understand how these cells promote angiogenesis within the ischemic tissues.MethodsBalb-c nude mice (n:24) were distributed in four different groups: healthy controls (C, n:4), shams (SH, n:4), and ischemic mice (after femoral ligation) that received either 50 mu l physiological serum (SC, n:8) or 5x10(5) human CACs (SE, n:8). Ischemic mice were sacrificed on days 2 and 4 (n:4/group/day), and immunohistochemistry assays and qPCR amplification of Alu-human-specific sequences were carried out for cell detection and vascular density measurements. Additionally, a label-free MS-based quantitative approach was performed to identify protein changes related.ResultsAdministration of CACs induced in the ischemic tissues an increase in the number of blood vessels as well as the diameter size compared to ischemic, non-treated mice, although the number of CACs decreased within time. The initial protein changes taking place in response to ischemia and more importantly, right after administration of CACs to CLI mice, are shown.ConclusionsOur results indicate that CACs migrate to the injured area; moreover, they trigger protein changes correlated with cell migration, cell death, angiogenesis, and arteriogenesis in the host. These changes indicate that CACs promote from the beginning an increase in the number of vessels as well as the development of an appropriate vascular network.Institute of Health Carlos III, ISCIII; Junta de Andaluci

The macroecology of phylogenetically structured hummingbird-plant networks

Aim To investigate the association between species richness, species' phylogenetic signal, insularity and historical and current climate with hummingbird-plant network structure. Location 54 communities along a c. 10,000 kilometer latitudinal gradient across the Americas (39ºN - 32ºS), ranging from sea level to c. 3700 m asl, located on the mainland and on islands, and covering a wide range of climate regimes. Methods We measured null-modeled corrected complementary specialization and bipartite modularity (compartmentalization) in networks of quantitative interactions between hummingbird and plant species. Using an ordinary least squares multi-model approach, we examined the influence of species richness, phylogenetic signal, insularity, and current and historical climate conditions on network structure. Results Phylogenetically-related species, especially plants, showed a tendency to interact with a similar array of partners. The spatial variation in network structure exhibited a constant association with species' phylogeny (R2=0.18-0.19). Species richness and environmental factors showed the strongest associations with network structure (R2=0.20-0.44; R2138 =0.32-0.45, respectively). Specifically, higher levels of complementary specialization and modularity were associated to species-rich communities and communities in which closely-related hummingbirds visited distinct sets of flowering species. On the mainland, warmer temperatures and higher historical temperature stability associated to higher levels of complementary specialization. Main conclusions Previous macroecological studies of interaction networks have highlighted the importance of environment and species richness in determining network structure. Here, for the first time, we report an association between species phylogenetic signal and network structure at macroecological scale. Specifically, null model corrected complementary specialization and modularity exhibited a positive association with species richness and a negative association with hummingbird phylogenetic signal, indicating that both high richness and high inter-specific competition among closely-related 150 hummingbirds exhibit important relationships with specialization in hummingbird-plant networks. Our results document how species richness, phylogenetic signal and climate associate with network structure in complex ways at macroecological scale

University quality measurement model based on balanced scorecard

A Higher Education Institution (HEI) has the responsibility to track the processes through indicators that guarantee the measurement of the results in almost real time. This article presents the design of a management and quality model of the processes in a university, through the integration of a Balance Scorecard (BSC) and the implementation of an information system. For which it was required: a review of existing tracing and monitoring systems in the academic sector, definition of the requirements of the proposed technological, a diagnosis of the current measurement system of the HEI analyzed, identify measurement indicators and develop a technological tool. The designed model presents a precise and clear methodological guide that can be replicated in any HEI to monitor its processes

Local Search is Underused in Genetic Programming

Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied extensively, with many works proposing bloat control methods. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while also reducing program size. Given these results, a question arises: Why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.authorsversionpublishe

Effective Rheology of Bubbles Moving in a Capillary Tube

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train from considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.Comment: 4 pages, 1 figur