The KMC-3 XPP beamline at BESSY II

The KMC-3 beamline is installed at teh bending magnet of the BESSY II synchrotron light source. It provides focused beam of monochromatic X-ray light at energies between 2.2 and 14 keV. It is dedicated to two experiments: X-ray Pump Probe (XPP) and CryoEXAFS

Major challenges ahead for Hungarian healthcare

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively

Development of an e-supported illness management and recovery programme for consumers with severe mental illness using intervention mapping, and design of an early cluster randomized controlled trial

Background: E-mental health is a promising medium to keep mental health affordable and accessible. For consumers with severe mental illness the evidence of the effectiveness of e-health is limited. A number of difficulties and barriers have to be addressed concerning e-health for consumers with severe mental illness. One possible solution might be to blend e-health with face-to-face delivery of a recovery-oriented treatment, like the Illness Management & Recovery (IMR) programme. This paper describes the development of an e-health application for the IMR programme and the design of an early clustered randomized controlled trial. Method/Design: We developed the e-IMR intervention according to the six-step protocol of Intervention Mapping. Consumers joined the development group to address important and relevant issues for the target group. Decisions during the six-step development process were based on qualitative evaluations of the Illness Management & Recovery programme, structured interviews, discussion in the development group, and literature reviews on qualitative papers concerning consumers with severe mental illness, theoretical models, behavioural change techniques, and telemedicine for consumers with severe mental illness. The aim of the e-IMR intervention is to help consumers with severe mental illness to involve others, manage achieving goals, and prevent relapse. The e-IMR intervention consists of face-to-face delivery of the Illness Management & Recovery programme and an e-health application containing peer-testimonials on videos, follow up on goals and coping strategies, monitoring symptoms, solving problems, and communication opportunities. We designed an early cluster randomized controlled trial that will evaluate the e-IMR intervention. In the control condition the Illness Management & Recovery programme is provided. The main effect-study parameters are: illness management, recovery, psychiatric symptoms severity, self-management, quality of life, and general health. The process of the IMR program will be evaluated on fidelity and feasibility in semi-structured interviews with participants and trainers. Discussion: Intervention Mapping provided a systematic procedure for the development of this e-health intervention for consumers with severe mental illness and the preparation of an early randomized controlled trial

Cavity-Magnon-Polariton spectroscopy of strongly hybridized electro-nuclear spin excitations in LiHoF4

We first present a formalism that incorporates the input-output formalism and the linear response theory to employ cavity-magnon-polariton coupling as a spectroscopic tool for investigating strongly hybridized electro-nuclear spin excitations. A microscopic relation between the generalized susceptibility and the scattering parameter |S11| in strongly hybridized cavity-magnon-polariton systems has been derived without resorting to semi-classical approximations. The formalism is then applied to both analyze and simulate a specific systems comprising a model quantum Ising magnet (LiHoF4) and a high-finesse 3D re-entrant cavity resonator. Quantitative information on the electro-nuclear spin states in LiHoF4 is extracted, and the experimental observations across a broad parameter range were numerically reproduced, including an external magnetic field titraversing a quantum critical point. The method potentially opens a new avenue not only for further studies on the quantum phase transition in LiHoF4 but also for a wide range of complex magnetic systems.Comment: 16 pages, 8 figure

Self-stabilization of the equilibrium state in ferroelectric thin films

(K,Na)NbO3 is a lead-free and sustainable ferroelectric material with electromechanical parameters comparable to Pb(Zr,Ti)O3 (PZT) and other lead-based solid solutions. It is therefore a promising candidate for caloric cooling and energy harvesting applications. Specifically, the structural transition from the low-temperature Mc- to the high-temperature c-phase displays a rich hierarchical order of domains and superdomains, that forms at specific strain conditions. The relevant length scales are few tens of nanometers for the domain and few micrometers for the superdomain size, respectively. Phase-field calculations show that this hierarchical order adds to the total free energy of the solid. Thus, domains and their formation has a strong impact on the functional properties relevant for electrocaloric cooling or energy harvesting applications. However, monitoring the formation of domains and superdomains is difficult and requires both, high spatial and high temporal resolution of the experiment. Synchrotron-based time-resolved X-ray diffraction methods in combination with scanning imaging X-ray microscopy is applied to resolve the local dynamics of the domain morphology with sub-micrometer spatial and nanosecond temporal resolution. In this regime, the material displays a novel self-stabilization mechanism of the domain morphology, which may be a general property of first-order phase transitions

What is the speed limit of martensitic transformations?

Structural martensitic transformations enable various applications, which range from high stroke actuation and sensing to energy efficient magnetocaloric refrigeration and thermomagnetic energy harvesting. All these emerging applications benefit from a fast transformation, but up to now their speed limit has not been explored. Here, we demonstrate that a thermoelastic martensite to austenite transformation can be completed within 10 ns. We heat epitaxial Ni-Mn-Ga films with a nanosecond laser pulse and use synchrotron diffraction to probe the influence of initial temperature and overheating on transformation rate and ratio. We demonstrate that an increase in thermal energy drives this transformation faster. Though the observed speed limit of 2.5 × 1027 (Js)1 per unit cell leaves plenty of room for further acceleration of applications, our analysis reveals that the practical limit will be the energy required for switching. Thus, martensitic transformations obey similar speed limits as in microelectronics, as expressed by the Margolus–Levitin theorem

Spatio-temporal coherent control of thermal excitations in solids

X-ray reflectivity (XRR) measurements of femtosecond laser-induced transient gratings are applied to demonstrate the spatio-temporal coherent control of thermally induced surface deformations on ultrafast timescales. Using gracing incidence X-ray diffraction we unambiguously measure the amplitude of transient surface deformations with sub-\AA{} resolution. Understanding the dynamics of femtosecond TG excitations in terms of superposition of acoustic and thermal gratings makes it possible to develop new ways of coherent control in X-ray diffraction experiments. Being the dominant source of TG signal, the long-living thermal grating with spatial period $\Lambda$ can be canceled by a second, time-delayed TG excitation shifted by $\Lambda/2$. The ultimate speed limits of such an ultrafast X-ray shutter are inferred from the detailed analysis of thermal and acoustic dynamics in TG experiments

Vanishing point detection for visual surveillance systems in railway platform environments

© 2018 Elsevier B.V. Visual surveillance is of paramount importance in public spaces and especially in train and metro platforms which are particularly susceptible to many types of crime from petty theft to terrorist activity. Image resolution of visual surveillance systems is limited by a trade-off between several requirements such as sensor and lens cost, transmission bandwidth and storage space. When image quality cannot be improved using high-resolution sensors, high-end lenses or IR illumination, the visual surveillance system may need to increase the resolving power of the images by software to provide accurate outputs such as, in our case, vanishing points (VPs). Despite having numerous applications in camera calibration, 3D reconstruction and threat detection, a general method for VP detection has remained elusive. Rather than attempting the infeasible task of VP detection in general scenes, this paper presents a novel method that is fine-tuned to work for railway station environments and is shown to outperform the state-of-the-art for that particular case. In this paper, we propose a three-stage approach to accurately detect the main lines and vanishing points in low-resolution images acquired by visual surveillance systems in indoor and outdoor railway platform environments. First, several frames are used to increase the resolving power through a multi-frame image enhancer. Second, an adaptive edge detection is performed and a novel line clustering algorithm is then applied to determine the parameters of the lines that converge at VPs; this is based on statistics of the detected lines and heuristics about the type of scene. Finally, vanishing points are computed via a voting system to optimize detection in an attempt to omit spurious lines. The proposed approach is very robust since it is not affected by ever-changing illumination and weather conditions of the scene, and it is immune to vibrations. Accurate and reliable vanishing point detection provides very valuable information, which can be used to aid camera calibration, automatic scene understanding, scene segmentation, semantic classification or augmented reality in platform environments