In the present study, the influence of moisture content, temperature and time during heat treatment of wheat flour was investigated. Heat treatment was carried out on laboratory scale in a water bath at 50-90 degrees C for times up to 3 h. Flour functionality was evaluated by analysing protein solubility in acetic acid as well as by the formation of bread-like doughs, which were then analysed with dynamic oscillatory and rotational rheometry. Effects during heat treatment were explained on a molecular level using a simplified physical model describing wheat dough as a continuous gluten matrix with starch as filler particles. Heat treatment causes the formation of gluten aggregates resulting in decreased protein solubility and lower network strength of dough. Rheological data also indicate the formation of starch aggregates and modified interactions between gluten and starch. The effects were more pronounced in heat-treated flours with increased moisture content due to a higher mobility of the molecules.

During dough development, a number of different changes occur on different time and length scales. We present here a simplified physical model able to explain the main rheological changes occurring during this process. It is based on the view of dough as a continuous gluten polymer matrix, into which starch granules are embedded as filler particles. The overall viscoelastic properties of this system are governed not only by those of its matrix, but also by the interplay of its components' interactions, each dominating at different length scales. They can be separated by performing rheological measurements at different deformation levels. Their relative importance can also be probed via the use of starch/gluten model systems in which the volume fraction of filler is varied. Dynamic oscillatory measurements in the linear and nonlinear viscoelastic range were performed on model dough as well as on natural wheat flour dough at different stages of development.

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Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

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