Feedbacks between vegetation pattern and resource loss dramatically decrease ecosystem resilience and restoration potential in a simple dryland model

Conceptual frameworks of dryland degradation commonly include ecohydrological feedbacks between landscape spatial organization and resource loss, so that decreasing cover and size of vegetation patches result in higher water and soil losses, which lead to further vegetation loss. However, the impacts of these feedbacks on dryland dynamics in response to external stress have barely been tested. Using a spatially-explicit model, we represented feedbacks between vegetation pattern and landscape resource loss by establishing a negative dependence of plant establishment on the connectivity of runoff-source areas (e.g., bare soils). We assessed the impact of various feedback strengths on the response of dryland ecosystems to changing external conditions. In general, for a given external pressure, these connectivity-mediated feedbacks decrease vegetation cover at equilibrium, which indicates a decrease in ecosystem resistance. Along a gradient of gradual increase of environmental pressure (e.g., aridity), the connectivity-mediated feedbacks decrease the amount of pressure required to cause a critical shift to a degraded state (ecosystem resilience). If environmental conditions improve, these feedbacks increase the pressure release needed to achieve the ecosystem recovery (restoration potential). The impact of these feedbacks on dryland response to external stress is markedly non-linear, which relies on the non-linear negative relationship between bare-soil connectivity and vegetation cover. Modelling studies on dryland vegetation dynamics not accounting for the connectivity-mediated feedbacks studied here may overestimate the resistance, resilience and restoration potential of drylands in response to environmental and human pressures. Our results also suggest that changes in vegetation pattern and associated hydrological connectivity may be more informative early-warning indicators of dryland degradation than changes in vegetation cover.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Analysis of the Brominated Dioxin and Furan Emission Congener Pattern from Different Sources

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) have been studied for several decades and are well-known as unintentionally generated persistent organic pollutants (POPs), which pose serious health and environmental risks on a global scale1. Polybrominated dibenzo-p-dioxins and dibenzofurans (PBDD/F) have similar properties and effects to PCDD/F, as they are structural analogs with all the chlorine atoms substituted by bromine atoms. PBDD/F have been found in various matrices such as air, sediments, marine products, and human adipose samples.

Fixation pattern analysis with microperimetry in nystagmus patients

Objective: To assess the usefulness of microperimetry (MP) as an additional objective method for characterizing the fixation pattern in nystagmus. Design: Prospective study. Participants: Fifteen eyes of 8 subjects (age, 12–80 years) with nystagmus from the Lluís Alcanyís Foundation (University of Valencia, Spain) were included. Methods: All patients had a comprehensive ophthalmologic examination including a microperimetric examination (MAIA, CenterVue, Padova, Italy). The following microperimetric parameters were evaluated: average threshold (AT), macular integrity index (MI), fixating points within a circle of 1° (P1) and 2° of radius (P2), bivariate contour ellipse area (BCEA) considering 63% and 95% of fixating points, and horizontal and vertical axes of that ellipse. Results: In monocular conditions, 6 eyes showed a fixation classified as stable, 6 eyes showed a relatively unstable fixation, and 3 eyes showed an unstable fixation. Statistically significant differences were found between the horizontal and vertical components of movement (p = 0.001), as well as in their ranges (p < 0.001). Intereye comparison showed differences between eyes in some subjects, but only statistically significant differences were found in the fixation coordinates X and Y (p < 0.001). No significant intereye differences were found between microperimetric parameters. Between monocular and binocular conditions, statistically significant differences in the X and Y coordinates were found in all eyes (p < 0.02) except one. No significant differences were found between MP parameters for monocular or binocular conditions. Strong correlations of corrected distance visual acuity (CDVA) with AT (r = 0.812, p = 0.014), MI (r = –0.812, p = 0.014), P1 (r = 0.729, p = 0.002), horizontal diameter of BCEA (r = –0.700, p = 0.004), and X range (r = –0.722, p = 0.005) were found. Conclusions: MP seems to be a useful technology for the characterization of the fixation pattern in nystagmus, which seems to be related to the level of visual acuity achieved by the patient.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.