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A hotspot at a position compatible with the BL. Lac object 1ES 2322-409 was serendipitously detected with H.E.S.S. during observations performed in 2004 and 2006 on the blazar PKS 2316-423. Additional data on 1ES 2322-409 were taken in 2011 and 2012, leading to a total live-time of 22.3 h. Point-like very-high-energy (VHE; E > 100 GeV) gamma-ray emission is detected from a source centred on the IFS 2322-409 position, with an excess of 116.7 events at a significance of 6.0 sigma. The average VHE gamma-ray spectrum is well described with a power law with a photon index Gamma = 3.40 +/- 0.66(stat) +/- 0.20(sys) and an integral flux Phi(E > 200 GeV) = (3.11 +/- 0.71(stat) 0.62(sys)) x 10(-2)cm(-2)s(-1), which corresponds to 1.1 per cent of the Crab nebula flux above 200 GeV. Multiwavelength data obtained with Fermi LAT, Swift XRT and UVOT, RXTE PCA, ATOM, and additional data from WISE, GROND, and Catalina are also used to characterize the broad-band non-thermal emission of lES 2322-409. The multiwavelength behaviour indicates day-scale variability. Swift UVOT and XRT data show strong variability at longer scales. A spectral energy distribution (SED) is built from contemporaneous observations obtained around a high state identified in Swift data. A modelling of the SED is performed with a stationary homogeneous one-zone synchrotronself-Compton leptonic model. The redshift of the source being unknown, two plausible values were tested for the modelling. A systematic scan of the model parameters space is performed, resulting in a well-constrained combination of values providing a good description of the broad-band behaviour of 1ES 2322-409.
Self-leadership and volition are conceptually similar concepts. Both propose self-influence strategies that aim to improve the motivation and self-direction necessary to perform well. The present study assesses whether self- leadership strategies maintain construct-specific variance when compared with the similar strategies of volition. Results from a questionnaire study (N=320) indicate that self-leadership and volitional strategies are distinguishable and only moderately (r=.33) correlated. Self-leadership, therefore, supplements volition during goal attainment. Findings are discussed in light of the Rubicon model of action phases.
Dwarf spheroidal galaxies are among the most promising targets for detecting signals of Dark Matter (DM) annihilations. The H.E.S.S. experiment has observed five of these systems for a total of about 130 hours. The data are re-analyzed here, and, in the absence of any detected signals, are interpreted in terms of limits on the DM annihilation cross section. Two scenarios are considered: i) DM annihilation into mono-energetic gamma-rays and ii) DM in the form of pure WIMP multiplets that, annihilating into all electroweak bosons, produce a distinctive gamma-ray spectral shape with a high-energy peak at the DM mass and a lower-energy continuum. For case i), upper limits at 95% confidence level of about <sigma upsilon > less than or similar to 3 x 10(-25) cm(3) s(-1) are obtained in the mass range of 400 GeV to 1TeV. For case ii), the full spectral shape of the models is used and several excluded regions are identified, but the thermal masses of the candidates are not robustly ruled out.
The inner region of the Milky Way halo harbors a large amount of dark matter (DM). Given its proximity, it is one of the most promising targets to look for DM. We report on a search for the annihilations of DM particles using gamma-ray observations towards the inner 300 pc of the Milky Way, with the H.E.S.S. array of ground-based Cherenkov telescopes. The analysis is based on a 2D maximum likelihood method using Galactic Center (GC) data accumulated by H.E.S.S. over the last 10 years (2004-2014), and does not show any significant gamma-ray signal above background. Assuming Einasto and Navarro-Frenk-White DM density profiles at the GC, we derive upper limits on the annihilation cross section <sigma nu >. These constraints are the strongest obtained so far in the TeV DM mass range and improve upon previous limits by a factor 5. For the Einasto profile, the constraints reach <sigma nu > values of 6 x 10(-26) cm(3) s(-1) in the W+W- channel for a DM particle mass of 1.5 TeV, and 2 x 10(-26) cm(3) s(-1) in the tau(+)tau(-) channel for a 1 TeV mass. For the first time, ground-based gamma-ray observations have reached sufficient sensitivity to probe <sigma nu > values expected from the thermal relic density for TeV DM particles.
The demanding working and training conditions of psychotherapists in training (PiA) are discussed more and more frequently from social and research perspectives. The complementary focus on job and training pressure and its consequences have received little attention so far.
The nationwide survey (N = 464) contains information on essential person-related and training-related risk factors and resources. Moreover, the study provides comparisons concerning different professional categories with high psychological and social demands as well as with the training conditions of psychotherapists in 2009.
The study shows the difficulties and demanding situation of financing the training. The PiAs showed significantly higher stress levels than other groups of persons and comparably low resilience combined with a high working engagement. Only a few PiAs had a healthy behavioral and perceptional work pattern.
The results underline the necessity for an examination of the training and working conditions of PiAs. Accordingly, options for interventions are presented.
Background: Healthy university students have been shown to use psychoactive substances, expecting them to be functional means for enhancing their cognitive capacity, sometimes over and above an essentially proficient level. This behavior called Neuroenhancement (NE) has not yet been integrated into a behavioral theory that is able to predict performance. Job Demands Resources (JD-R) Theory for example assumes that strain (e.g. burnout) will occur and influence performance when job demands are high and job resources are limited at the same time. The aim of this study is to investigate whether or not university students’ self-reported NE can be integrated into JD-R Theory’s comprehensive approach to psychological health and performance.
Methods: 1,007 students (23.56 ± 3.83 years old, 637 female) participated in an online survey. Lifestyle drug, prescription drug, and illicit substance NE together with the complete set of JD-R variables (demands, burnout, resources, motivation, and performance) were measured. Path models were used in order to test our data’s fit to hypothesized main effects and interactions.
Results: JD-R Theory could successfully be applied to describe the situation of university students. NE was mainly associated with the JD-R Theory’s health impairment process: Lifestyle drug NE (p < .05) as well as prescription drug NE (p < .001) is associated with higher burnout scores, and lifestyle drug NE aggravates the study demands-burnout interaction. In addition, prescription drug NE mitigates the protective influence of resources on burnout and on motivation.
Conclusion: According to our results, the uninformed trying of NE (i.e., without medical supervision) might result in strain. Increased strain is related to decreased performance. From a public health perspective, intervention strategies should address these costs of non-supervised NE. With regard to future research we propose to model NE as a means to reach an end (i.e. performance enhancement) rather than a target behavior itself. This is necessary to provide a deeper understanding of the behavioral roots and consequences of the phenomenon.
Objective: Questionnaires are often applied in sports psychology to measure a person's trait or state. However, the extent to which the questionnaire captures differences because of trait or state influences is often unclear. The latent state-trait (LST) theory offers the opportunity to separate both variance sources. This separation allows estimating specific reliability coefficients. Design: The present paper gives a theoretical introduction to LST and its basic ideas. Using a real data set with N = 156 athletes we exemplify the steps necessary to derive the LST coefficients. All athletes filled out a comprehensive inventory assessing competitive anxiety on two occasions. Method: The data are analysed with structural equation models based on LST principles. Results: The results confirm the questionnaire's trait saturation. Conclusion: Finally, results are discussed in light of practical and theoretical implications.
Method: Following a known-group differences validation strategy, the doping attitudes of 43 athletes from bodybuilding (representative for a highly doping prone sport) and handball (as a contrast group) were compared using the picture-based doping-BIAT. The Performance Enhancement Attitude Scale (PEAS) was employed as a corresponding direct measure in order to additionally validate the results.
Results: As expected, in the group of bodybuilders, indirectly measured doping attitudes as tested with the picture-based doping-BIAT were significantly less negative (eta(2) = .11). The doping-BIAT and PEAS scores correlated significantly at r = .50 for bodybuilders, and not significantly at r = .36 for handball players. There was a low error rate (7%) and a satisfactory internal consistency (r(dagger dagger) = .66) for the picture-based doping-BIAT.
Conclusions: The picture-based doping-BIAT constitutes a psychometrically tested method, ready to be adopted by the international research community. The test can be administered via the internet. All test material is available "open source". The test might be implemented, for example, as a new effect-measure in the evaluation of prevention programs.
Background: Doping attitude is a key variable in predicting athletes' intention to use forbidden performance enhancing drugs. Indirect reaction-time based attitude tests, such as the implicit association test, conceal the ultimate goal of measurement from the participant better than questionnaires. Indirect tests are especially useful when socially sensitive constructs such as attitudes towards doping need to be described. The present study serves the development and validation of a novel picture-based brief implicit association test (BIAT) for testing athletes' attitudes towards doping in sport. It shall provide the basis for a transnationally compatible research instrument able to harmonize anti-doping research efforts.
Method: Following a known-group differences validation strategy, the doping attitudes of 43 athletes from bodybuilding (representative for a highly doping prone sport) and handball (as a contrast group) were compared using the picture-based doping-BIAT. The Performance Enhancement Attitude Scale (PEAS) was employed as a corresponding direct measure in order to additionally validate the results.
Results: As expected, in the group of bodybuilders, indirectly measured doping attitudes as tested with the picture-based doping-BIAT were significantly less negative (eta(2) = .11). The doping-BIAT and PEAS scores correlated significantly at r = .50 for bodybuilders, and not significantly at r = .36 for handball players. There was a low error rate (7%) and a satisfactory internal consistency (r(dagger dagger) = .66) for the picture-based doping-BIAT.
Conclusions: The picture-based doping-BIAT constitutes a psychometrically tested method, ready to be adopted by the international research community. The test can be administered via the internet. All test material is available "open source". The test might be implemented, for example, as a new effect-measure in the evaluation of prevention programs.
The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners.
The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies.
This technical report presents results of research projects executed in 2018. Selected projects have presented their results on April 17th and November 14th 2017 at the Future SOC Lab Day events.