In this paper, we present a modified density-dependent Drucker-Prager Cap (DPC) model to simulate the compaction behaviour of pharmaceutical powders. In particular, a nonlinear elasticity law is proposed to describe the observed nonlinear unloading behaviour following compaction. To extract the material parameters for the modified DPC model, a novel experimental calibration procedure is used, based on uniaxial single-ended compaction tests using an instrumented cylindrical die. The model is implemented in ABAQUS by writing a user subroutine, and a calibration process on microcrystalline cellulose (MCC) Avicel PH101 powders is detailed. The calibrated parameters are used for the manufacturing process simulation of two kinds of typical pharmaceutical tablets: the flat-face tablet and the concave tablet with single or double radius curvatures. The model developed can describe not only the compression and decompression phases, but also the ejection phase. The model is validated by comparing finite element simulations with experimental loading-unloading curves during the manufacture of 8 and 11 mm round tablets with flat-face (FF), single radius concave (SRC) and double radius concave (DRC) profiles. Moreover, the density and stress distributions during tabletting are used to analyse and explain the failure mechanism of tablets. The results show that the proposed model can quantitatively reproduce the compaction behaviour of pharmaceutical powders and can be used to obtain the stress and density distributions during compression, decompression and ejection.

Physically realistic simulations for large breast deformation are of great interest for many medical applications such as cancer diagnosis, image registration, surgical planning and image-guided surgery. To support fast, large deformation simulations of breasts in clinical settings, we proposed a patient-specific biomechanical modelling framework for breasts, based on an open-source graphics processing unit-based, explicit, dynamic, nonlinear finite element (FE) solver. A semi-automatic segmentation method for tissue classification, integrated with a fully automated FE mesh generation approach, was implemented for quick patient-specific FE model generation. To solve the difficulty in determining material parameters of soft tissues in vivo for FE simulations, a novel method for breast modelling, with a simultaneous material model parameter optimization for soft tissues in vivo, was also proposed. The optimized deformation prediction was obtained through iteratively updating material model parameters to maximize the image similarity between the FE-predicted MR image and the experimentally acquired MR image of a breast. The proposed method was validated and tested by simulating and analysing breast deformation experiments under plate compression. Its prediction accuracy was evaluated by calculating landmark displacement errors. The results showed that both the heterogeneity and the anisotropy of soft tissues were essential in predicting large breast deformations under plate compression. As a generalized method, the proposed process can be used for fast deformation analyses of soft tissues in medical image analyses and surgical simulations.

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

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