Achieving high ductility and strength in magnesium alloy through cryogenic-hot forming

Magnesium alloys are the lightest structural alloys and have attracted substantial research attention in the past two decades. However, their mechanical properties, including ductility and strength, are limited after forming due to the formation of coarse grains and strong texture. This study proposes and proves a new cryogenic-hot forming process concept. Cryogenic deformation is imposed before the hot deformation. The effect of the cryogenic step has been compared with a conventional direct hot deformation process. The mechanical properties, microstructure,and texture of both the novel and conventional process routes have been compared. The cryogenic-hot deformed sample exhibits the highest ductility and fracture strength(ultimate tensile strength: 321 MPa, ductility: 21%) due to effective grain refinement and texture weakening by cryogenically formed twin-twin interaction induced recrystallisation. The proposed cryogenic-hot forming process can be a potential innovative manufacturing method for producing high-performance magnesium components.

Predicting Big-Five Personality for Micro-blog Based on Robust Multi-task Learning

Personality prediction on social network has become a hot topic.At present,most studies are using single-task classification/regression machine learning.However,this method ignores the potential association between multiple tasks.Also an ideal prediction result is difficult to achieve based on the small scale training data,since it is not easy to get a lot of social network data with personality label samples.In this paper,a robust multi-task learning method(RMTL)is proposed to predict Big-Five personality on Micro-blog.We aim to learn five tasks simultaneously by extracting and utilizing appropriate shared information among multiple tasks as well as identifying irrelevant tasks.For a set of Sina Micro-blog users’information and personality labeled data retrieved by questionnaire,we validate the RMTL method by comparing it with 4 single-task learning methods and the mere multi-task learning.Our experiment demonstrates that the proposed RMTL can improve the precision rate,recall rate of the prediction and F value.

Effects of Low-pressure Annealing on the Performance of 3D Printed CF/PEEK Composites

The interlayer bonding properties are normally unsatisfying for 3D printed composites owing to the layer-by-layer formation process.In this study,low-pressure annealing was performed on 3D printed carbon fiber reinforced polyether ether ketone(CF/PEEK)to improve the interlayer bonding strength.The effects of annealing parameters on the mechanical properties and microstructure were studied.The results showed that the interlaminar shear strength(ILSS)of CF/PEEK improved by up to 55.4%after annealing.SEM and𝜇-CT were also applied to reveal the reinforcing mechanism.This improvement could mainly be attributed to the increased crystallinity of the CF/PEEK after annealing.Additionally,annealing reduced the porosity of the printed CF/PEEK and improved the fiber-resin interface.This resulted in a reduction in the stress concentration areas during loading,thereby enhancing the interlayer bonding strength of CF/PEEK.

Data Transmission Using HDD as Microphone

The technology of Covert Channel is often used for communications between computers and the Internet with high sensitivity or security levels.Presently,some research were carried out on covert channel using computer screen light radiation,speakers,electromagnetic leakage,etc.In this paper,the technology of SoundHammer is studied.It is a technical bridgeware that use acoustic waves to transmit data from sound device into an air-gapped network.Firstly,an idea was proposed for data transmission by covert channel through acoustic waves.Then,this method was validated by experiment and the risks of the air-gapped network were confirmed.Finally,some countermeasures for detecting and eliminating such covert channels were listed.