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This report presents an experimental study on two types of heat treatable aluminium alloys to determine the best process for achieving ultra-high-strength materials by means of severe plastic deformation (SPD) and heat treatment. It is well-known that age hardening and grain refinement are two mechanisms that contribute to strengthening. Accordingly, high pressure torsion (HPT), which induces an ultra-fine grain (UFG) structure, combined with artificial ageing were performed on an Al-Cu-Li and an Al-Zn-Mg-Cu alloy. A series of hardness measurements against various ageing times and temperatures were investigated to provide an insight into strengthening. A strong increase in hardness of Al-Cu-Li alloys was achieved through the combination of age hardening and HPT. Specifically, following solution treatment, materials were processed through five turns of HPT at room temperature (RT), followed by low temperature ageing (i.e. T4-HPTAA). For the Al-Cu-Li alloy, a micro-hardness up to ~240 Hv for ageing 110 oC/60h with the maximum percentage increase of 8.5% after as-HPT condition was achieved. However, the low temperature ageing after HPT does not improve the hardness of AlZn-Mg-Cu alloy. Besides, a further improvement in the hardness to ~260 Hv for AlCu-Li was accomplished by a pre-ageing 110 oC/24h before HPT in combination with a post-HPT ageing process at 110 oC for approximately 180h (i.e. T6-HPT-AA). These novel multi-stage processes give rise to an increase in hardness by a factor of 2 as compared to the T4 condition (~120 Hv). Under the same process sequence using similar ageing temperatures, the Al-Zn-Mg-Cu alloy shows post-HPT age-softening. However, a reduced age-softening rate was obtained compared with the sample that had undergone solution treatment followed by HPT and ageing process, which connotes the stability of the material increased due to pre-HPT ageing. As both T4-HPT-AA and T6-HPT-AA processes do not enhance the hard

Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features. This paper proposes an end-to-end deep learning framework for facial AU detection with graph convolutional network (GCN) for AU relation modeling, which has not been explored before. In particular, AU related regions are extracted firstly, latent representations full of AU information are learned through an auto-encoder. Moreover, each latent representation vector is feed into GCN as a node, the connection mode of GCN is determined based on the relationships of AUs. Finally, the assembled features updated through GCN are concatenated for AU detection. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for facial AU detection. The proposed framework is also validated through a series of ablation studies., Comment: Accepted by MMM2020

CD, a new reagent with SH and OH functional groups, was developed as a depressant for chalcopyrite in bulk sulphides using the principles of electrochemistry and surface chemistry. Comparative tests have shown that the new reagent differs from sodium sulphide in its surface adsorbability on to chalcopyrite. This is because the new reagent can form oxides on the surface of chalcopyrite whereas sodium sulphide forms HS-, which is readily oxidised and therefore easily loses its depressing effect., CD, a new reagent with SH and OH functional groups, was developed as a depressant for chalcopyrite in bulk sulphides using the principles of electrochemistry and surface chemistry. Comparative tests have shown that the new reagent differs from sodium sulphide in its surface adsorbability on to chalcopyrite. This is because the new reagent can form oxides on the surface of chalcopyrite whereas sodium sulphide forms HS-, which is readily oxidised and therefore easily loses its depressing effect.