Silicon carbide waste as a source of mixture materials for cement mortar

This paper presents an investigation of the feasibility of recycling silicon carbide waste （SCW） as a source of mixture materials in the production of cement mortar. Mortars with SCW were prepared by replacing different amounts of cement with SCW, and the properties of the resulting mortars, such as the fluidity, strength and shrinkage, were studied in this work. Thermogravimetry-differential scanning calorimet;＇y and scanning electron microscopy were employed to understand the reasons for the property changes of the mortars. The results indicate that SCW decreases the initial and l-h fluidity of fresh mortar but improves the loss of fluidity. The mortar with SCW exhibits a lower strength at 3 d and7 d but a higher strength at 28 d and 56 d compared to the control. The shrinkage rate of cement mortar with SCW shows an obvious decrease as the replacement ratio increases. In addition, the content of calcium hydroxide in hardened paste also shows that SCW has some impact on the hydration of the cement-SCW system. The microstructures of the hardened paste also show evidence for a later strength change of mortar containing SCW. This work provides a strategic reference for possibly applying SCW as a mixture material in the production of cement mortar.

Weakly supervised action anticipation without object annotations

Anticipating future actions without observing any partial videos of future actions plays an important role in action prediction and is also a challenging task.To obtain abundant information for action anticipation,some methods integrate multimodal contexts,including scene object labels.However,extensively labelling each frame in video datasets requires considerable effort.In this paper,we develop a weakly supervised method that integrates global motion and local finegrained features from current action videos to predict next action label without the need for specific scene context labels.Specifically,we extract diverse types of local features with weakly supervised learning,including object appearance and human pose representations without ground truth.Moreover,we construct a graph convolutional network for exploiting the inherent relationships of humans and objects under present incidents.We evaluate the proposed model on two datasets,the MPII-Cooking dataset and the EPIC-Kitchens dataset,and we demonstrate the generalizability and effectiveness of our approach for action anticipation.

High-tenacity in-situ Ti/Zr-based bulk metallic glasses composites fabricated by industrial high-pressure die casting

The glass-forming ability and mechanical properties of metallic glasses and their composites are well known to be sensitive to the preparation conditions and are highly deteriorated by industrial preparing conditions such as low-purity raw materials and low vacuum.Here,we showed that a series of in-situ bulk metallic glass composites(BMGCs)which exhibit excellent ductility and segmental work hardening were successfully developed utilizing a high vacuum high-pressure die casting(HV-HPDC)technology along with industrial-grade raw materials.The tensile properties of these BMGCs are systematically investigated and correlated with the alloy microstructure.As compared with the copper mold suction casting method,the volume fraction difference of the dendrite phase for the BMGCs with the same composition is not significant when fabricated by the HV-HPDC,whereas the size of theβ-phase is generally larger.Insitu BMGCs with the composition of Ti_(48)Zr_(20)(V_(12/17)Cu_(5/17))19 Be 13 obtained by the HV-HPDC process show ductility up to 11.3%under tension at room temperature and exhibit a certain amount of work hardening.Two conditions need to be met to enable the BMGCs,which are prepared by vacuum die-casting to retain favorable ductility:(1)The volume fraction ofβphase stays below 62%±2%;(2)The equiaxed crystals with a more uniform size in the range of 5-10μm.Meanwhile,the results of the present study provided guidance for developing BMGCs with good ductile properties under industrial conditions.