Conceptual Modeling of Micropollutant Fate in Sewer Systems – A GIS-Based Approach to Define Model Structure

A new approach to automate the challenging task of defining the complexity level for conceptual modelling of micropollutant (MP) fate in sewer system is here presented. The approach combines GIS information and advanced statistical techniques (e.g., cluster analysis) and provides for a realistic description of in-sewer hydraulic residence time (HRT), which is fundamental for simulating MP removal processes occurring during in-sewer transport. The conceptual model was first tested in a full-scale catchment, where HRT distribution was determined based on spatial distribution of discharge sources and following calibration against high-frequency flow rate data. The model was then used to predict the dynamics of an ideal MP (biodegradation half-life = 2.5 h) at the outlet of the sewer system, revealing higher removal during in-sewer transport when considering an average HRT compared to a discrete HRT distribution. These results demonstrate that an intermediate complexity level, between highly detailed hydrodynamic models and simplified models, could be adopted for MP fate predictions while keeping computational demands reasonable. This latter aspect can be also of particular interest when an integrated modelling perspective (e.g., sewer and WWTP) is considered

Métrica Induzida da Correntropia Complexa Comparada ao NESTA no Problema de Amostragem Compressiva / Induced Complex Correntropy Metric Compared to NESTA on the Compressive Sampling Problem

Esse artigo compara ao algoritmo de Nesterov (NESTA) o desempenho da métrica induzida da correntropia complexa (Complex Correntropy Induced Metric - CCIM) enquanto uma aproximação de l0 num problema de amostragem compressiva. As simulações mostram que a CCIM é capaz de reconstruir um vetor esparso complexo usando menos medidas do que o NEST

Sex-specific reproductive behaviours and paternity in free-ranging Barbary macaques (Macaca sylvanus)

In a wide variety of species, male reproductive success is determined by contest for access to females. Among multi-male primate groups, however, factors in addition to male competitive ability may also influence paternity outcome, although their exact nature and force is still largely unclear. Here, we have investigated in a group of free-ranging Barbary macaques whether paternity is determined on the pre- or postcopulatory level and how male competitive ability and female direct mate choice during the female fertile phase are related to male reproductive success. Behavioural observations were combined with faecal hormone analysis for timing of the fertile phase (13 cycles, 8 females) and genetic paternity analysis (n = 12). During the fertile phase, complete monopolisation of females did not occur. Females were consorted for only 49% of observation time, and all females had ejaculatory copulations with several males. Thus, in all cases, paternity was determined on the postcopulatory level. More than 80% of infants were sired by high-ranking males, and this reproductive skew was related to both, male competitive ability and female direct mate choice as high-ranking males spent more time in consort with females than low-ranking males, and females solicited copulations mainly from dominant males. As most ejaculatory copulations were female-initiated, female direct mate choice appeared to have the highest impact on male reproductive success. However, female preference was not directly translated into paternity, as fathers were not preferred over non-fathers in terms of solicitation, consortship and mating behaviour. Collectively, our data show that in the Barbary macaque, both sexes significantly influence male mating success, but that sperm of several males generally compete within the female reproductive tract and that therefore paternity is determined by mechanisms operating at the postcopulatory level

Compressive sensing method for improved reconstruction of gradient-sparse magnetic resonance images

We propose a compressive sensing method for reconstructing gradient-sparse magnetic resonance (MR) images based on the pre-filtering of the input signals in the k-space. A set of filtered versions of the image is reconstructed using the available k-space samples, and a final reconstruction stage generates the desired image from the filtered versions. Our experiments, conducted over real MR images and angiograms, show that the proposed method improves the reconstruction over the total-variation minimization, in terms of signal-to-noise ratio and computation time. The proposed method is particularly appropriate for computing MR angiograms, which are typically sparse under the finite-differences operation