Infinite time aggregation for the critical Patlak-Keller-Segel model in R2

We analyze the two-dimensional parabolic-elliptic Patlak-Keller-Segel model in the whole Euclidean space R2. Under the hypotheses of integrable initial data with finite second moment and entropy, we first show local in time existence for any mass of "free-energy solutions", namely weak solutions with some free energy estimates. We also prove that the solution exists as long as the entropy is controlled from above. The main result of the paper is to show the global existence of free-energy solutions with initial data as before for the critical mass 8 Π/Χ. Actually, we prove that solutions blow-up as a delta dirac at the center of mass when t→∞ keeping constant their second moment at any time. Furthermore, all moments larger than 2 blow-up as t→∞ if initially bounded.

A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

Endogenous labor market participation and the business cycle

Existing models of equilibrium unemployment with endogenous labor market participation are complex, generate procyclical unemployment rates and cannot match unemployment variability relative to GDP. We embed endogenous participation in a simple, tractable job market matching model, show analytically how variations in the participation rate are driven by the cross-sectional density of home productivity near the participation threshold, andhow this density translates into an extensive-margin labor supply elasticity. A calibration of the model to macro data not only matches employment and participation variabilities but also generates strongly countercyclical unemployment rates. With some wage rigidity the model also matches unemployment variations well. Furthermore, the labor supply elasticity implied by our calibration is consistent with microeconometric evidence for the US.

Gait Analysis for Classification

This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features.

Data-driven multivariate flood hazard modeling

The present Dissertation shows how recent statistical analysis tools and open datasets can be exploited to improve modelling accuracy in two distinct yet interconnected domains of flood hazard (FH) assessment. In the first Part, unsupervised artificial neural networks are employed as regional models for sub-daily rainfall extremes. The models aim to learn a robust relation to estimate locally the parameters of Gumbel distributions of extreme rainfall depths for any sub-daily duration (1-24h). The predictions depend on twenty morphoclimatic descriptors. A large study area in north-central Italy is adopted, where 2238 annual maximum series are available. Validation is performed over an independent set of 100 gauges. Our results show that multivariate ANNs may remarkably improve the estimation of percentiles relative to the benchmark approach from the literature, where Gumbel parameters depend on mean annual precipitation. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across space and time-aggregation intervals. In the second Part, decision trees are used to combine a selected blend of input geomorphic descriptors for predicting FH. Relative to existing DEM-based approaches, this method is innovative, as it relies on the combination of three characteristics: (1) simple multivariate models, (2) a set of exclusively DEM-based descriptors as input, and (3) an existing FH map as reference information. First, the methods are applied to northern Italy, represented with the MERIT DEM (∼90m resolution), and second, to the whole of Italy, represented with the EU-DEM (25m resolution). The results show that multivariate approaches may (a) significantly enhance flood-prone areas delineation relative to a selected univariate one, (b) provide accurate predictions of expected inundation depths, (c) produce encouraging results in extrapolation, (d) complete the information of imperfect reference maps, and (e) conveniently convert binary maps into continuous representation of FH.

A note on Chambers's "long memory and aggregation in macroeconomic time series"

Chambers (1998) explores the interaction between long memory and aggregation. For continuous-time processes, he takes the aliasing effect into account when studying temporal aggregation. For discrete-time processes, however, he seems to fail to do so. This note gives the spectral density function of temporally aggregated long memory discrete-time processes in light of the aliasing effect. The results are different from those in Chambers (1998) and are supported by a small simulation exercise. As a result, the order of aggregation may not be invariant to temporal aggregation, specifically if d is negative and the aggregation is of the stock type.

Perioperative administration of vedaprofen, tramadol or their combination does not interfere with platelet aggregation, bleeding time and biochemical variables in cats

A randomized double blind and placebo controlled design was used to investigate the hemostatic, biochemical, gastrointestinal and behavioral effects of pre- and postoperative administration of vedaprofen 0.5 mg/kg PO (V), tramadol 2 mg/kg SC (T), their association (VT) or placebo (P) in 40 adult female cats (3.0 +/- 0.32 kg; 1.8 +/- 0.7 years) distributed in groups of 10. Platelet aggregation and bleeding time were measured before and 52 11 after ovariohysterectomy. Serum urea, creatinine, alanine aminotransferase, alkaline phosphatase and gamma-glutamyl transferase concentrations were measured before and 7 days postoperatively. The occurrence of vomiting, frequency and consistency of feces, and behavior were observed for 7 days postoperatively. Morphine (0.5 mg/kg, IM) was used as rescue analgesic. Laboratory variables did not change. Vomiting was observed only after morphine administration. Mild euphoria was observed in T and VT. The perioperative use of vedaprofen and/or tramadol did not modify the hemostatic, biochemical and gastrointestinal function in cats. (C) 2009 ESFM and AAFP. published by Elsevier Ltd. All rights reserved.

Spatial aggregation of time-variant stream water ages in urbanizing catchments

Date of Acceptance: 25/03/2015

Mitigating congestion by integrating time forecasting and realtime information aggregation in cellular networks

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

Cyclic Guanosine Monophosphate Signaling Cascade Mediates Pigment Aggregation in Freshwater Shrimp Chromatophores

The cell signaling cascades that mediate pigment movements in crustacean chromatophores are not yet well established, although Ca(2+) and cyclic nucleotide second messengers are involved. Here, we examine the participation of cyclic guanosine monophosphate (cGMP) in pigment aggregation triggered by red pigment concentrating hormone (RPCH) in the red ovarian chromatophores of freshwater shrimp. In Ca(2+)-containing (5.5 mmol l(-1)) saline, 10 mu mol l(-1) dibutyryl cGMP alone produced complete pigment aggregation with the same time course (approximate to 20 min) and peak velocity (approximate to 17 mu m/min) as 10(-8) mol l(-1) RPCH; however, in Ca(2+)-free saline (9 X 10(-11) mol l(-1) Ca(2+)), db-cGMP was without effect. The soluble guanylyl cyclase (GC-S) activators sodium nitroprusside (SNP, 0.5 mu mol l(-1)) and 3-morpholinosydnonimine (SIN-1, 100 mu mol l(-1)) induced moderate aggregation by themselves (approximate to 35%-40%) but did not affect RPCH-triggered aggregation. The GC-S inhibitors zinc protoporphyrin IX (ZnPP-XI, 30 mu mol l(-1)) and 6-anilino-5,8-quinolinedione (LY83583, 10 mu mol l(-1)) partially inhibited RPCH-triggered aggregation by approximate to 35%. Escherichia coli heat-stable enterotoxin (STa, 1 mu mol l(-1)), a membrane-receptor guanylyl cyclase stimulator, did not induce or affect RPCH-triggered aggregation. We propose that the binding of RPCH to an unknown membrane-receptor type activates a Ca(2+)-dependent signaling cascade coupled via cytosolic guanylyl cyclase and cGMP to protein kinase G-phosphorylated proteins that regulate aggregation-associated, cytoskeletal molecular motor activity. This is a further example of a cGMP signaling cascade mediating the effect of a crustacean X-organ neurosecretory peptide.

A tick salivary protein targets cathepsin G and chymase and inhibits host inflammation and platelet aggregation

Platelet aggregation and acute inflammation are key processes in vertebrate defense to a skin injury. Recent studies uncovered the mediation of 2 serine proteases, cathepsin G and chymase, in both mechanisms. Working with a mouse model of acute inflammation, we revealed that an exogenous salivary protein of Ixodes ricinus, the vector of Lyme disease pathogens in Europe, extensively inhibits edema formation and influx of neutrophils in the inflamed tissue. We named this tick salivary gland secreted effector as I ricinus serpin-2 (IRS-2), and we show that it primarily inhibits cathepsin G and chymase, while in higher molar excess, it affects thrombin activity as well. The inhibitory specificity was explained using the crystal structure, determined at a resolution of 1.8 angstrom. Moreover, we disclosed the ability of IRS-2 to inhibit cathepsin G-induced and thrombin-induced platelet aggregation. For the first time, an ectoparasite protein is shown to exhibit such pharmacological effects and target specificity. The stringent specificity and biological activities of IRS-2 combined with the knowledge of its structure can be the basis for the development of future pharmaceutical applications. (Blood. 2011;117(2):736-744)

The effect of resource aggregation at different scales: Optimal foraging behavior of Cotesia rubecula

Resources can be aggregated both within and between patches. In this article, we examine how aggregation at these different scales influences the behavior and performance of foragers. We developed an optimal foraging model of the foraging behavior of the parasitoid wasp Cotesia rubecula parasitizing the larvae of the cabbage butterfly Pieris rapae. The optimal behavior was found using stochastic dynamic programming. The most interesting and novel result is that the effect of resource aggregation within and between patches depends on the degree of aggregation both within and between patches as well as on the local host density in the occupied patch, but lifetime reproductive success depends only on aggregation within patches. Our findings have profound implications for the way in which we measure heterogeneity at different scales and model the response of organisms to spatial heterogeneity.