The Mediterranean diet (MD) is regarded as a healthy eating pattern with beneficial effects both for the decrease of the risk for non-communicable diseases and also for body weight reduction. In the current manuscript, we propose an automated smartphone application which monitors and evaluates the user’s adherence to MD using images of the food and drinks that they consume. We define a set of rules for automatic adherence estimation, which focuses on the main MD food groups. We use a combination of a convolutional neural network (CNN) and a graph convolutional network to detect the types of foods and quantities from the users’ food images and the defined set of rules to evaluate the adherence to MD. Our experiments show that our system outperforms a basic CNN in terms of recognizing food items and estimating quantity and yields comparable results as experienced dietitians when it comes to overall MD adherence estimation. As the system is novel, these results are promising; however, there is room for improvement of the accuracy by gathering and training with more data and certain refinements can be performed such as re-defining the set of rules to also be able to be used for sub-groups of MD (e.g., vegetarian type of MD).

We study the production of a photon pair in association with two bottom jets at the LHC. This process constitutes an important background to double Higgs production with the subsequent decay of the two Higgs bosons into a pair of photons and b-quarks respectively. We calculate this process at next-to-leading order accuracy in QCD and find that QCD corrections lead to a substantial increase of the production cross section due to new channels opening up at nextto-leading order and their inclusion is therefore inevitable for a reliable prediction. Furthermore, the approximation of massless b-quarks is scrutinized by calculating the process with both massless and massive b-quarks. We find that the massive bottom quark leads to a substantial reduction of the cross section where the biggest effect is, however, due to the use of a four-flavor PDF set and the corresponding smaller values for the strong coupling constant.

A film of sizing agents protects yarn during weaving. Its removal in a subsequent washing process causes 50% of the organic effluent load of textile finishing processes and requires large amounts of auxiliary chemicals (e.g., surfactants). Microbial desizing is a new bioprocess that uses the acidifying culture of a two-phase anaerobic digestion plant for the removal and partial degradation (acidification) of the sizing agent. Soluble starch is used in this study to characterize the enzymatic properties in the supernatant of the desizing culture and to link them to desizing efficiencies. The supernatant of the culture (grown at 37 degrees C, pH 5.5) displayed the highest enzymatic activity between pH 4 and 5 and in a broad temperature range (20-80 degrees C). Highest metabolization rates were determined with the substrate amylose. Short chain dextrins (average of 5 and 10 glucose units) and amylopectin were converted significantly more slowly. At 37 degrees C the half-life time of the enzymatic activity in the supernatant was 45 h. In a desizing test a decisive reduction of the chain length was found already after 1 h (allowing starch solubilization). A microbial desizing experiment with dyed, native maize starch demonstrated the efficiency of the proposed bioprocess.

Washing machines should not only deliver good removal of stains, but also take care of the garments. Mechanical action produced by the washing machine has a twofold impact: It supports the removal of stains, but it also influences the structure of the textiles negatively and is, therefore, critical to textile care. Most washing machines are currently assessed by consumer organisations and political regulations, such as energy labelling, just for their washing properties. However, a long programme may provide a good washing performance, but might also damage the textiles more than a shorter programme. Test specimens assessing the mechanical impact are well known and published, for example, IEC PAS 62473:2007, however, they are rarely used. Reasons may be poor knowledge about their effectiveness in assessing the mechanical action and their reaction to different washing conditions, for example, load size, temperature and duration of the washing programme. It was the task of this study to verify this relationship and confirm that the thread removal fabric, as specified in IEC PAS 62473:2007, adds additional information to the assessment of a washing process. As a result of a wide variation of washing parameters, it could be shown that this test fabric is almost independent of the washing temperature, but shows a clear correlation with the load size and the length of the washing process. The thread removal specimens add valuable additional information concerning a relevant parameter of the washing process.

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