Sozialberichterstattung im südlichen Afrika

"'Transformation' ist zum Schlüsselbegriff der neunziger Jahre geworden. Das Ende des kalten Krieges hat nicht nur in Europa, sondern in weiten Teilen der Welt einschneidende gesellschaftliche Umwälzungen ausgelöst. Im südlichen Afrika waren diese Umwälzungen in ihre Zuspitzung auf den Konflikt zwischen der schwarzen Bevölkerungsmehrheit und der weißen Bevölkerungsminderheit besonders dramatisch. Südafrika mit seinem charismatischen Staatschef Nelson Mandela, und Namibia, die einstige deutsche Kolonie, standen für einige Zeit im Zentrum des Medieninteresses. Inzwischen ist die 'heiße' Phase des Umbruchs vorbei. Die neuen Regierungen haben sich etabliert, ihre politischen Ziele formuliert, Programme verabschiedet und Reformen eingeleitet. In beiden Ländern gibt es Projekte der Sozialberichterstattung, die diesen Wandel begleiten und so dokumentieren, wie er sich auf die Lebensbedingungen, Lebensqualität und Lebenszufriedenheit der Menschen niederschlägt." (Autorenreferat

Does longer roster lead-time reduce temporary staff usage? A regression analysis of e-rostering data from 77 hospital units

Aims Utilisation of temporary nursing staff is contentious and expensive. Using e-rostering data from 77 hospital units, this research investigates whether longer roster lead-times reduce temporary staff usage. Background It is commonly assumed that longer roster approval lead-times, the time from when a roster is approved, to when it is worked, result in better, more cost-effective rosters. Consequently, many hospitals target lead-times of six weeks, a figure recommended for the UK National Health Service (NHS) in a recent governmental review. This contrasts with the minimum lead-time advocated by New South Wales Ministry of Health, which advises a shorter lead-time of two weeks. Using data from 77 hospital units, this paper explores this assumed relationship. Design Using data extracted from the e-rostering system of an NHS Acute Foundation Trust, this study uses linear regression analysis to explore the relationship between roster approval lead-time and temporary staff usage. The data were captured over a period of nine months from 15th February 2016 to 23rd October 2016, a total of 693 rosters. Results/Findings This research suggests that late roster approval may contribute to as much as 37% of temporary staff usage, while approval 4-6 weeks prior to the roster being worked reduces this to approximately 15%. However, this is only relevant under specific conditions. Importantly, this should be considered before mandating lead times across all units. Conclusions This research implies that the optimum approval lead-time lies between four to six weeks, however, given other challenges, achieving this in practice may prove difficult

The future of mental health nursing education in the United Kingdom: Reflections on the Australian and New Zealand experience

This paper provides a debate related to how proposed changes to preregistration nurse preparation in the United Kingdom (UK) may impact on the future of undergraduate mental health nursing workforce. In the first instance we set out the proposed changes and the underlying reasoning provided for these changes. We compare the proposals in relation to the present curricula and possible outcomes of mental health nursing education in the UK. Our discussion also considers if there are lessons to be learned from the Australian and New Zealand where nursing education underwent similar changes during the 1990s. We offer a critique of the underlying political, economic and ideological reasons for these radial changes to nursing education with due consideration of lessons learned by others

Semi-Automatic Generation of Training Samples for Detecting Renewable Energy Plants in High-Resolution Aerial Images

Deep learning (DL)—in particular convolutional neural networks (CNN)—methods are widely spread in object detection and recognition of remote sensing images. In the domain of DL, there is a need for large numbers of training samples. These samples are mostly generated based on manual identification. Identifying and labelling these objects is very time-consuming. The developed approach proposes a partially automated procedure for the sample creation and avoids manual labelling of rooftop photovoltaic (PV) systems. By combining address data of existing rooftop PV systems from the German Plant Register, the Georeferenced Address Data and the Official House Surroundings Germany, a partially automated generation of training samples is achieved. Using a selection of 100,000 automatically generated samples, a network using a RetinaNet-based architecture combining ResNet101, a feature pyramid network, a classification and a regression network is trained, applied on a large area and post-filtered by intersection with additional automatically identified locations of existing rooftop PV systems. Based on a proof-of-concept application, a second network is trained with the filtered selection of approximately 51,000 training samples. In two independent test applications using high-resolution aerial images of Saarland in Germany, buildings with PV systems are detected with a precision of at least 92.77 and a recall of 84.47

Dielectric relaxation of electrolyte solutions in acetonitrile

Complex permittivity spectra in the frequency range 0.95 ≤ ν (GHz) ≤ 89 for acetonitrile and its solns. of LiBr, NaI, NaClO4, and Bu4NBr at 25°C show one Debye equation for the neat solvent whereas the superposition of a Debye process for the solute and a Cole-Cole distribution for the solvent is necessary to account for the dielec. relaxation behavior of the solns. The reorientation of bulk acetonitrile is diffusive and only weakly coupled to viscosity. The no. of solvent mols. irrotationally bound to the electrolyte is in good agreement with conventional solvation no. for all electrolytes, when kinetic depolarization is assumed to be negligible. The solute relaxation process is dominated by the formation kinetics and reorientation of contact ion pairs. There is evidence for solvent-shared ion pairs in dil. NaClO4 solns

Semi-Automatic Generation of Training Samples for Detecting Renewable Energy Plants in High-Resolution Aerial Images

Deep learning (DL)—in particular convolutional neural networks (CNN)—methods are widely spread in object detection and recognition of remote sensing images. In the domain of DL, there is a need for large numbers of training samples. These samples are mostly generated based on manual identification. Identifying and labelling these objects is very time-consuming. The developed approach proposes a partially automated procedure for the sample creation and avoids manual labelling of rooftop photovoltaic (PV) systems. By combining address data of existing rooftop PV systems from the German Plant Register, the Georeferenced Address Data and the Official House Surroundings Germany, a partially automated generation of training samples is achieved. Using a selection of 100,000 automatically generated samples, a network using a RetinaNet-based architecture combining ResNet101, a feature pyramid network, a classification and a regression network is trained, applied on a large area and post-filtered by intersection with additional automatically identified locations of existing rooftop PV systems. Based on a proof-of-concept application, a second network is trained with the filtered selection of approximately 51,000 training samples. In two independent test applications using high-resolution aerial images of Saarland in Germany, buildings with PV systems are detected with a precision of at least 92.77 and a recall of 84.47

Verhinderung des Uebergangs von packgutverfaerbenden Stoffen aus Karton- und Pappeverpackungen

Available from TIB Hannover: RO 3209(1998,27) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman