Heavy metals in benthic foraminifera from the highly polluted sediments of the Naples harbour (Southern Tyrrhenian Sea, Italy)

none6openRumolo, P.; SALVAGIO MANTA, D.; Sprovieri, M.; Coccioni, Rodolfo; Ferraro, L.; Marsella, E.P., Rumolo; D., SALVAGIO MANTA; M., Sprovieri; Coccioni, Rodolfo; L., Ferraro; E., Marsell

Epidemiology of canine atopic dermatitis

Canine atopic dermatitis (CAD) is a chronic, allergic skin disease associated with IgE-mediated reactions to environmental allergens. Atopic dermatitis/eczema in humans shares many similarities with CAD and is an increasing problem in industrialized countries. This increase has been attributed to lifestyle and environment factors. The current knowledge about the epidemiology of CAD is limited. The aim of this thesis was therefore to investigate the incidence of and potential risk factors for the development of CAD. Three of the included studies involve the use of a large animal-insurance database. The database includes information about a large number of healthy and diseased individuals over time, but it was not collected for research purposes and data-quality issues needed to be addressed. A validation of the diagnosis CAD in the insurance database showed that although the vast majority of the recorded cases could be considered allergic, the important differential diagnosis cutaneous adverse food reactions had not been ruled out for many patients. The overall incidence rate of CAD was 1.7 cases per 1000 dog years at risk. Several factors were found to be associated with an increased risk of CAD in the insured population; living in an urban area or in the south of Sweden, being born in the autumn and belonging to a high-risk breed. Furthermore, a spatial analysis showed that the incidence of CAD increased by increasing human population density and increasing annual rainfall, and was decreased in the north of Sweden and if there was no veterinary dermatologist present in the county. Finally, a case-control study was performed where 12 veterinarians collected CAD cases from the three identified high-risk breeds; boxer, bullterrier and West Highland white terrier. The main finding was that feeding a diet containing home-made/ non-commercial ingredients to the bitch during lactation protected her offspring from developing CAD. In conclusion, a strong breed predisposition for CAD was seen. Evidence of an increased incidence of CAD in densely populated areas exists but might be biased by the locations of veterinary dermatologists. The potential of using diet for primary prevention of CAD is interesting but randomized controlled clinical trials are required to support this finding

Terror threat perception and its consequences in contemporary Britain

The terrorist attacks of 9/11, and subsequent terrorist acts around the world, have alerted social psychologists to the need to examine the antecedents and consequences of terrorist threat perception. In these two studies we examined the predictive power of demographic factors (age, sex, location), individual values and normative influences on threat perception and the consequences of this perception for behavioural change and close relationships. In study 1 (N = 100) gender, benevolence values and normative influences were all correlates of threat perception, whilst sense of personal threat was correlated with increased contact with friends and family. In study 2 (N = 240) age, sex, location, and the values of Openness to Change and Hedonism, all predicted threat perception, which in turn predicted behavioural change and relationship contact. Such findings point to the important role social psychologists should play in understanding responses to these new terrorist threats

Affect and believability in game characters:a review of the use of affective computing in games

Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions

Program Agenda - Symposium for the Advancement of Women in Business

A symposium devoted to creating a forum for discussing the expanding opportunities for women in business today

Dynamic Facial Expression of Emotion Made Easy

Facial emotion expression for virtual characters is used in a wide variety of areas. Often, the primary reason to use emotion expression is not to study emotion expression generation per se, but to use emotion expression in an application or research project. What is then needed is an easy to use and flexible, but also validated mechanism to do so. In this report we present such a mechanism. It enables developers to build virtual characters with dynamic affective facial expressions. The mechanism is based on Facial Action Coding. It is easy to implement, and code is available for download. To show the validity of the expressions generated with the mechanism we tested the recognition accuracy for 6 basic emotions (joy, anger, sadness, surprise, disgust, fear) and 4 blend emotions (enthusiastic, furious, frustrated, and evil). Additionally we investigated the effect of VC distance (z-coordinate), the effect of the VC's face morphology (male vs. female), the effect of a lateral versus a frontal presentation of the expression, and the effect of intensity of the expression. Participants (n=19, Western and Asian subjects) rated the intensity of each expression for each condition (within subject setup) in a non forced choice manner. All of the basic emotions were uniquely perceived as such. Further, the blends and confusion details of basic emotions are compatible with findings in psychology

Stochastic Digital Backpropagation with Residual Memory Compensation

Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to non-optimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.Comment: 7 pages, accepted to publication in 'Journal of Lightwave Technology (JLT)

Speech-driven Animation with Meaningful Behaviors

Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table