Climate change vulnerability, water resources and social implications in North Africa

North Africa is considered a climate change hot spot. Existing studies either focus on the physical aspects of climate change or discuss the social ones. The present article aims to address this divide by assessing and comparing the climate change vulnerability of Algeria, Egypt, Libya, Morocco, and Tunisia and linking it to its social implications. The vulnerability assessment focuses on climate change exposure, water resources, sensitivity, and adaptive capacity. The results suggest that all countries are exposed to strong temperature increases and a high drought risk under climate change. Algeria is most vulnerable to climate change, mainly due to the country’s high sensitivity. Across North Africa, the combination of climate change and strong population growth is very likely to further aggravate the already scarce water situation. The so-called Arab Spring has shown that social unrest is partly caused by unmet basic needs of the population for food and water. Thus, climate change may become an indirect driver of social instability in North Africa. To mitigate the impact of climate change, it is important to reduce economic and livelihood dependence on rain-fed agriculture, strengthen sustainable land use practices, and increase the adaptive capacity. Further, increased regional cooperation and sub-national vulnerability assessments are needed.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659National Geographic Society http://dx.doi.org/10.13039/10000636

Climate change vulnerability, water resources and social implications in North Africa

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Schulische Entwicklung und Berufsvorbereitung: der Einfluß von Familie und Schule

Klein-Allermann E, Kracke B. Schulische Entwicklung und Berufsvorbereitung: der Einfluß von Familie und Schule. In: Klug H-P, Schilling H, Hundsalz A, eds. Jugend und Erziehungsberatung. Weinheim: Juventa; 1995: 249-266

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark

Background: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field. Methods: An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC). Results: Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538-0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613-0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark. Conclusions: We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification. (c) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)