Influences on Music Preference Formation

Music preference is a multifaceted topic that addresses questions which continuously elude musicologists, music researchers and social psychologists. How does something so pervasive in our lives, such as music, remain a mystery to us? Music preference has been studied on many levels and the factors that influence the types of music we prefer are numerous, including genres, exposure, personality, and musical characteristics. However, our understanding of how and why music preferences are formed is still fragmented. We can narrow down music preferences into two broad categories: intrinsic and extrinsic qualities. In attempt to explore these characteristics, three commonly emerging theories concerning musical preference formation will serve as the foundation: repeated exposure, social learning, and inherent musical qualities. The current paper aims to draw on these theories in relation to the development and reasoning behind our musical preferences

Performance of nominal and ultimate LHC beams in the CERN PS Booster

The requirements for nominal and ultimate LHC beams in the CERN PS-Booster were specified in 1994 and served as input for the definition of the "PS conversion for LHC" project. Already during the upgrade project and also after its completion in 2000, the beam intensities to be provided from the PS Booster were increased in order to compensate for changes on the LHC machine, the beam production scheme in the PS and for nonanticipated beam losses along the injector chain. In order to improve the beam brightness, to be compatible with the increased requirements, extensive machine studies have taken place on the PS-Booster. The working point was changed to reduce the influence of systematic resonances and the injection line optics was re-matched to improve the injection efficiency. The paper summarizes briefly the evolution of the performance requirements. The various measures undertaken to improve the LHC beam quality are outlined and the present performance achieved in the PS Booster is presented

Profiling SO2 air pollution patterns in 9 EU Aphekom cities: The Aphekom Project

A detailed analysis of hourly pollutant concentrations mainly focusing on SO2 data obtained from 9 centres involved in the Aphekom project was conducted. This involved the generation of individual diurnal SO2 profiles in order to: (i) identify city specific patterns including source apportionment and quantification, (ii) track changes over time, (iii) analyse the changes in SO2 concentrations from different emission sources, i.e. traffic, heating, shipping and industrial sources, overtime

The assessment of the implementation of fuel related legislations and their impact on air quality and public health

The main focus of Work Package 6 of the Aphekom project was: to develop innovative methods to analyse the decrease in air pollution levels following implementation of an European regulation to reduce the sulphur content in liquid fuels; to follow the evolution of health risks over time; to track related effect modifiers; and to quantify the monetary costs of health impacts of the implemented regulation

Mortality impacts of sulphur concetrations in 20 European cities in the APHEKOM Project

The implementation of three EU directives to reduce sulphur content in fuel was assessed for mortality impacts in 20 European cities, between 1990 and 2007 in the APHEKOM project. This specific study aimed to examine whether different lag structures apply to the relationships between cardiovascular and respiratory events and SO2 concentrations, which will therefore result in differences in mortality impacts from regulation implementation. Prior evidence has shown that cardiovascular mortality is more likely to be affected by SO2 concentrations on the same or the previous day of the event, while respiratory mortality more likely to show a delayed effect of exposure to the same pollutant

The APHEKOM Project: A literature review of air pollution interventions and their impact of public health

Intervention studies play an important role in supporting and complementing scientific validation of results of epidemiological non-intervention studies linking air pollution and health. In this paper a collection of existing published intervention studies is reviewed with the aim to give a summarized overview spanning a variety of approaches regarding the type of the intervention and findings with the main focus on studies that assessed interventions that improved air quality and the associated positive impact on public health. Air pollution interventions were defined as events aimed at reducing air pollution and also events where air pollution reductions occurred as a side effect

High Intensity Beams from the CERN PS Booster

The CERN Proton Synchrotron Booster (PSB) has been running for more than 30 years. Originally designed to accelerate particles from 50 to 800 MeV, later upgradedto an energy of 1 GeV and finally 1.4 GeV, it is steadily being pushed to its operational limits. One challenge is the permanent demand for intensity increase, in particular for CNGS and ISOLDE, but also in view of Linac4. As it is an accelerator working with very high space charge during the low energy part of its cycle, its operational conditions have to be precisely tuned. Amongst other things resonances must be avoided, stop band crossings optimised and the machine impedance minimised. Recently, an operational intensity record was achieved with >4.25×1013 protons accelerated. An orbit correction campaign performed during the 2007/2008 shutdown was a major contributing factor to achieving this intensity. As the PSB presently has very few orbit correctors available,the orbit correction has to be achieved by displacing and/or tilting some of the defocusing quadrupoles common to all 4 PSB rings. The contributing factors used to optimise performance will be reviewed

Neuromotor Changes in Participants with a Concussion History can be Detected with a Custom Smartphone App

Neuromotor dysfunction after a concussion is common, but balance tests used to assess neuromotor dysfunction are typically subjective. Current objective balance tests are either cost- or space-prohibitive, or utilize a static balance protocol, which may mask neuromotor dysfunction due to the simplicity of the task. To address this gap, our team developed an Android-based smartphone app (portable and cost-effective) that uses the sensors in the device (objective) to record movement profiles during a stepping-in-place task (dynamic movement). The purpose of this study was to examine the extent to which our custom smartphone app and protocol could discriminate neuromotor behavior between concussed and non-concussed participants. Data were collected at two university laboratories and two military sites. Participants included civilians and Service Members (N = 216) with and without a clinically diagnosed concussion. Kinematic and variability metrics were derived from a thigh angle time series while the participants completed a series of stepping-in-place tasks in three conditions: eyes open, eyes closed, and head shake. We observed that the standard deviation of the mean maximum angular velocity of the thigh was higher in the participants with a concussion history in the eyes closed and head shake conditions of the stepping-in-place task. Consistent with the optimal movement variability hypothesis, we showed that increased movement variability occurs in participants with a concussion history, for which our smartphone app and protocol were sensitive enough to capture

Feature extraction based on bio-inspired model for robust emotion recognition

Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin