Search

Background: Various prognostic prediction models exist for evaluating the risk of nausea and vomiting in the postoperative period (PONV). So far, no systematic comparison of these prognostic scores is available. Method: A systematic literature search was carried out in seven medical databases to find publications on prognostic PONV models. Identified scores were assessed against prospectively defined quality criteria, including generalizability, validation and clinical relevance of the models. Results: The literature search revealed 62 relevant publications with a total of 81,834 patients which could be assigned to 8 prognostic models. The simplified Apfel score performed best, primarily because it was extensively validated. The Van den Bosch score and Sinclair score tied for second place. The simplified Koivuranta score was in third place. Conclusion: The qualitative analysis highlights the strengths and weaknesses of each prediction system based on predetermined standardized quality criteria. [ABSTRACT FROM AUTHOR]

The Rudolf‐Fettweis hydropower plant, owned and operated by EnBW Energie Baden‐Württemberg AG, is situated in the Murg valley in the Northern Black Forest. The construction of a new underground Lower Basin is planned to upgrade the existing hydropower station. The in‐situ rock mass consists of Forbach Granite with local weakness zones, as encountered in the boreholes. Considering the extent of the project area, a 3D geological prediction model was used to allow a better interpretation of the spatial relationships of weakness zones in the rock mass and to optimize the orientation of the underground caverns. Adverse effects for the public were counteracted at a very early stage, so that the project received a high level of public acceptance. The present paper describes the current state of the project. [ABSTRACT FROM AUTHOR]

The article explores how artificial intelligence and machine learning are important in the areas of vehicle development, traffic and mobility. Topics include how existing data, appropriate techniques and mathematical tools can be used to support and improve vehicle development; and how vehicle-independent environmental data derived from vehicle measurements with the help of suitable mathematical methods with digital map data, climate data, topographical data and also socio-economic information.

In this review article 78 evaluable globally accessible articles regarding the influence of heat, drought, heavy rain, flooding, black frost, early frost, late frost, extreme frost, storm, hail, radiation and wet snow have on significant plant pests - namely fungi, insects and viruses - infesting wheat, barley and maize are summarized and discussed. The influence of other extremes and/or plant pests wasn't backed up enough by research, hence it wasn't evaluated. Sufficient evaluable literature was available for the influence drought and heat have on fungal infections, especially Fusarium and Aspergillus species and various species of aphids and the damage caused by them, including the mycotoxin levels in the grains of wheat and maize. As statements obtained from literature are partly contradicting and overall lacking information, a generalization of results is impossible. Suggestions for further research, such as the obtainment of primary data concerning temperature extremes, increased night time temperatures and their implementation in existing simulation and prediction models are made. [ABSTRACT FROM AUTHOR]