A general multivariate latent growth model with applications in student careers Data warehouses

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context the analysis of student performance and capabilities plays a fundamental role. In this work we propose a multivariate latent growth model for studying the performances of a cohort of students of the University of Bologna. The model proposed is innovative since it is composed by: (1) multivariate growth models that allow to capture the different dynamics of student performance indicators over time and (2) a factor model that allows to measure the general latent student capability. The flexibility of the model proposed allows its applications in several fields such as socio-economic settings in which personal behaviours are studied by using panel data.Comment: 20 page

A framework for power analysis using a structural equation modelling procedure

BACKGROUND: This paper demonstrates how structural equation modelling (SEM) can be used as a tool to aid in carrying out power analyses. For many complex multivariate designs that are increasingly being employed, power analyses can be difficult to carry out, because the software available lacks sufficient flexibility. Satorra and Saris developed a method for estimating the power of the likelihood ratio test for structural equation models. Whilst the Satorra and Saris approach is familiar to researchers who use the structural equation modelling approach, it is less well known amongst other researchers. The SEM approach can be equivalent to other multivariate statistical tests, and therefore the Satorra and Saris approach to power analysis can be used. METHODS: The covariance matrix, along with a vector of means, relating to the alternative hypothesis is generated. This represents the hypothesised population effects. A model (representing the null hypothesis) is then tested in a structural equation model, using the population parameters as input. An analysis based on the chi-square of this model can provide estimates of the sample size required for different levels of power to reject the null hypothesis. CONCLUSIONS: The SEM based power analysis approach may prove useful for researchers designing research in the health and medical spheres

The effects of acute inflammation on cognitive functioning and emotional processing in humans: A systematic review of experimental studies

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Objective The cognitive neuropsychological model of depression proposes that negative biases in the processing of emotionally salient information have a central role in the development and maintenance of depression. We have conducted a systematic review to determine whether acute experimental inflammation is associated with changes to cognitive and emotional processing that are thought to cause and maintain depression. Methods We identified experimental studies in which healthy individuals were administered an acute inflammatory challenge (bacterial endotoxin/vaccination) and standardised tests of cognitive function were performed. Results Fourteen references were identified, reporting findings from 12 independent studies on 345 participants. Methodological quality was rated strong or moderate for 11 studies. Acute experimental inflammation was triggered using a variety of agents (including endotoxin from E. coli, S. typhi, S. abortus Equi and Hepatitis B vaccine) and cognition was assessed over hours to months, using cognitive tests of i) attention/executive functioning, ii) memory and iii) social/emotional processing. Studies found mixed evidence that acute experimental inflammation caused changes to attention/executive functioning (2 of 6 studies showed improvements in attention executive function compared to control), changes in memory (3 of 5 studies; improved reaction time: reduced memory for object proximity: poorer immediate and delayed memory) and changes to social/emotional processing (4 of 5 studies; reduced perception of emotions, increased avoidance of punishment/loss experiences, and increased social disconnectedness). Conclusions Acute experimental inflammation causes negative biases in social and emotional processing that could explain observed associations between inflammation and depression.National Institute for Health Research (NIHR

A linear radiofrequency ion trap for accumulation, bunching, and emittance improvement of radioactive ion beams

An ion beam cooler and buncher has been developed for the manipulation of radioactive ion beams. The gas-filled linear radiofrequency ion trap system is installed at the Penning trap mass spectrometer ISOLTRAP at ISOLDE/CERN. Its purpose is to accumulate the 60-keV continuous ISOLDE ion beam with high efficiency and to convert it into low-energy low-emittance ion pulses. The efficiency was found to exceed 10% in agreement with simulations. A more than 10-fold reduction of the ISOLDE beam emittance can be achieved. The system has been used successfully for first on-line experiments. Its principle, setup and performance will be discussed

Position-sensitive ion detection in precision Penning trap mass spectrometry

A commercial, position-sensitive ion detector was used for the first time for the time-of-flight ion-cyclotron resonance detection technique in Penning trap mass spectrometry. In this work, the characteristics of the detector and its implementation in a Penning trap mass spectrometer will be presented. In addition, simulations and experimental studies concerning the observation of ions ejected from a Penning trap are described. This will allow for a precise monitoring of the state of ion motion in the trap.Comment: 20 pages, 13 figure

Multi-wavelength VLTI study of the puffed-up inner rim of a circumbinary disc

The presence of stable, compact circumbinary discs of gas and dust around post-asymptotic giant branch (post-AGB) binary systems has been well established. We focus on one such system: IRAS 08544-4431. We present an interferometric multi-wavelength analysis of the circumstellar environment of IRAS 08544-4431. The aim is to constrain different contributions to the total flux in the H, K, L, and N-bands in the radial direction. The data from VLTI/PIONIER, VLTI/GRAVITY, and VLTI/MATISSE range from the near-infrared, where the post-AGB star dominates, to the mid-infrared, where the disc dominates. We fitted two geometric models to the visibility data to reproduce the circumbinary disc: a ring with a Gaussian width and a flat disc model with a temperature gradient. The flux contributions from the disc, the primary star (modelled as a point-source), and an over-resolved component are recovered along with the radial size of the emission, the temperature of the disc as a function of radius, and the spectral dependencies of the different components. The trends of all visibility data were well reproduced with the geometric models. The near-infrared data were best fitted with a Gaussian ring model while the mid-infrared data favoured a temperature gradient model. This implies that a vertical structure is present at the disc inner rim, which we attribute to a rounded puffed-up inner rim. The N-to-K size ratio is 2.8, referring to a continuous flat source, analogues to young stellar objects. By combining optical interferometric instruments operating at different wavelengths we can resolve the complex structure of circumstellar discs and study the wavelength-dependent opacity profile. A detailed radial, vertical, and azimuthal structural analysis awaits a radiative transfer treatment in 3D to capture all non-radial complexity.Comment: 8 pages, 5 figures, accepted for publication in A&A Letter

Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding

First Direct Double-Beta Decay Q-value Measurement of 82Se in Support of Understanding the Nature of the Neutrino

In anticipation of results from current and future double-beta decay studies, we report a measurement resulting in a 82Se double-beta decay Q-value of 2997.9(3) keV, an order of magnitude more precise than the currently accepted value. We also present preliminary results of a calculation of the 82Se neutrinoless double-beta decay nuclear matrix element that corrects in part for the small size of the shell model single-particle space. The results of this work are important for designing next generation double-beta decay experiments and for the theoretical interpretations of their observations.Comment: 5 pages, 2 figures, accepted for publication in Physical Review Letter

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201