Time resolution below 100 ps for the SciTil detector of PANDA employing SiPM

The barrel time-of-flight (TOF) detector for the PANDA experiment at FAIR in Darmstadt is planned as a scintillator tile hodoscope (SciTil) using 8000 small scintillator tiles. It will provide fast event timing for a software trigger in the otherwise trigger-less data acquisition scheme of PANDA, relative timing in a multiple track event topology as well as additional particle identification in the low momentum region. The goal is to achieve a time resolution of sigma ~ 100 ps. We have conducted measurements using organic scintillators coupled to Silicon Photomultipliers (SiPM). The results are encouraging such that we are confident to reach the required time resolution.Comment: 10 pages, 7 figure

Studies on the maize cold tolerance tests in the Martonvásár phytotron

The climatic conditions in Hungary and in the countries to which seed is exported makes the study of maize cold tolerance and constant improvements in the cold tolerance of Martonvásár hybrids especially important. An improvement in the early spring cold tolerance of maize would allow it to be grown in more northern areas with a cooler climate, while on traditional maize-growing areas the profitability of maize production could be improved by earlier sowing, leading to a reduction in transportation and drying costs and in diseases caused by Fusarium sp. The recognition of this fact led Martonvásár researchers to start investigating this subject nearly four decades ago. The phytotron has proved an excellent tool for studying and improving the cold tolerance of maize. The review will give a brief summary of the results achieved in the field of maize cold tolerance in the Martonvásár institute in recent decades

Spontaneous Participation in Secondary Prevention Programs : The Role of Psychosocial Predictors

Disease prevention is a multifaceted construct that has been widely studied. Nevertheless, in spite of its importance, it is still not sufficiently considered by the general population. Since the reasons for this lack of consideration are not yet fully understood, we created an Online Prevention Survey (OPS) to investigate the role of both sociodemographic and psychological factors in predicting individuals\u2019 spontaneous participation in secondary prevention programs. The results revealed that younger people, men, manual workers, unemployed people, and those who do not regularly practise physical activity were less likely to spontaneously participate in such programs. Furthermore, an analysis of the psychological determinants of the willingness to participate in secondary prevention programs showed that depressive symptoms negatively predict it, while an individual\u2019s perception of receiving high social support acts as a positive predictor. Based on these results, we suggest the need for implementing new tailored approaches to promote prevention initiatives to those segments of the population which are more reluctant to spontaneously undertake prevention paths

Identification of Young Stellar Object candidates in the GaiaGaia DR2 x AllWISE catalogue with machine learning methods

The second $Gaia$ Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean $Gaia$ $G$ magnitude $<$20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the $Gaia$ DR2 database with WISE and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match table. We classified each object into four main classes: YSOs, extragalactic objects, main-sequence stars and evolved stars. At a 90% probability threshold we identified 1,129,295 YSO candidates. To demonstrate the quality and potential of our YSO catalogue, here we present two applications of it. (1) We explore the 3D structure of the Orion A star forming complex and show that the spatial distribution of the YSOs classified by our procedure is in agreement with recent results from the literature. (2) We use our catalogue to classify published $Gaia$ Science Alerts. As $Gaia$ measures the sources at multiple epochs, it can efficiently discover transient events, including sudden brightness changes of YSOs caused by dynamic processes of their circumstellar disk. However, in many cases the physical nature of the published alert sources are not known. A cross-check with our new catalogue shows that about 30% more of the published $Gaia$ alerts can most likely be attributed to YSO activity. The catalogue can be also useful to identify YSOs among future $Gaia$ alerts.Comment: 19 pages, 12 figures, 3 table

Emotional tone, analytical thinking, and somatosensory processes of a sample of Italian Tweets during the First Phases of the COVID-19 Pandemic: Observational Study

Background: The COVID-19 pandemic is a traumatic individual and collective chronic experience, with tremendous consequences on mental and psychological health that can also be reflected in people's use of words. Psycholinguistic analysis of tweets from Twitter allows obtaining information about people's emotional expression, analytical thinking, and somatosensory processes, which are particularly important in traumatic events contexts. Objective: We aimed to analyze the influence of official Italian COVID-19 daily data (new cases, deaths, and hospital discharges) and the phase of managing the pandemic on how people expressed emotions and their analytical thinking and somatosensory processes in Italian tweets written during the first phases of the COVID-19 pandemic in Italy. Methods: We retrieved 1,697,490 Italian COVID-19-related tweets written from February 24, 2020 to June 14, 2020 and analyzed them using LIWC2015 to calculate 3 summary psycholinguistic variables: emotional tone, analytical thinking, and somatosensory processes. Official daily data about new COVID-19 cases, deaths, and hospital discharges were retrieved from the Italian Prime Minister's Office and Civil Protection Department GitHub page. We considered 3 phases of managing the COVID-19 pandemic in Italy. We performed 3 general models, 1 for each summary variable as the dependent variable and with daily data and phase of managing the pandemic as independent variables. Results: General linear models to assess differences in daily scores of emotional tone, analytical thinking, and somatosensory processes were significant (F6,104=21.53, P&lt;.001, R2= .55; F5,105=9.20, P&lt;.001, R2= .30; F6,104=6.15, P&lt;.001, R2=.26, respectively). Conclusions: The COVID-19 pandemic affects how people express emotions, analytical thinking, and somatosensory processes in tweets. Our study contributes to the investigation of pandemic psychological consequences through psycholinguistic analysis of social media textual data

When in doubt, Google it: distress-related information seeking in Italy during the COVID-19 pandemic

Background: Psychological health has been one of the aspects affected by the recent COVID-19 pandemic. We aim to evaluate the patterns of Google search for mental distress symptoms of Italian citizens during the various phases of the COVID-19 pandemic. Methods: We assessed Google searches for psychological-health related words. We gathered and analyzed data on daily search queries on depression, anxiety, and insomnia from Google Trends, in a time ranging from the Pre-COVID phase (beginning 25th January 2020) up to the second wave phase (ending 17th October 2020). We performed three general linear models on search trends of the three words and tested whether and to what extent official data about new cases of COVID-19, information searching on new cases, and the government health measures impacted on these trends. Results: Average daily search queries were higher for anxiety, followed by depression and insomnia. General linear models performed to assess differences in daily search queries for anxiety, depression and insomnia were significant, respectively [F(13, 253) = 6.80, P &lt;.001]; [F(13, 253) = 10.25, P &lt;.001]; [F(13, 253) = 6.61, P &lt;.001]. Specifically, daily search queries differed among different phases of managing the COVID-19 outbreak: anxiety [F(5, 253) = 10.35, P &lt;.001, np2 =.17]; depression [F(5, 253) = 13.59, P &lt;.001, np2 =.21]; insomnia [F(5, 253) = 3.52, P =.004, np2 =.07]. Conclusions: Our study contributed to the investigation of online information-seeking behaviors of Italians regarding mental health throughout the entire phase of the pandemic and provides insights on the possible future trends of mental distress during upcoming pandemic phases