3D coupled thermomechanical finite element analysis of ultrasonic consolidation

A 3-D coupled temperature-displacement finite element analysis is performed to study an ultrasonic consolidation process. Results show that ultrasonic wave is effective in causing deformation in aluminum foils. Ultrasonic vibration leads to an oscillating stress field. The oscillation of stress in substrate lags behind the ultrasonic vibration by about 0.1 cycle of ultrasonic wave. The upper foil, which is in contact with the substrate, has the most severe deformation. The substrate undergoes little deformation. Apparent material softening by ultrasonic wave, which is of great concern for decades, is successfully simulated. The higher the friction coefficient, the more obvious the apparent material softening effect.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.

3D Efflux Velocity Characteristics of Marine Propeller Jets

The determination of the efflux velocity is key to the process of calculating the subsequent value of velocity at any other location within a propeller jet. This paper reports on the findings of an experimental investigation into the magnitude of the efflux velocities within the jets produced by four differing propellers. Measurements of velocity have been made using a 3D LDA system with the test propellers operating at a range of rotational speeds which bound typical operational values. Comparisons are made with existing predictive theories and to aid design engineers, methods are presented by which the 3D efflux velocity components, as well as the resultant efflux value, can be determined.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

Indoor and Outdoor monitoring of photocatalytic activity using a mobile phone app. and a photocatalytic activity indicator ink (paii)

A resazurin (Rz) based photocatalyst activity indicator ink (paii) is used to test the activity of commercial self-cleaning materials. The semiconductor photocatalyst driven colour change of the ink is monitored indoors and outside using a simple mobile phone application that measures the RGB colour components of the digital image of the paii-covered, irradiated sample in real time. The results correlate directly with those generated using a traditional, lab-bound method of analysis (UV–vis spectrophotometry).

In situ preparation of 3D graphene aerogels@hierarchical Fe3O4 nanoclusters as high rate and long cycle anode materials for lithium ion batteries

We describe a novel strategy for in situ fabrication of hierarchical Fe₃O₄ nanoclusters-GAs. Fe₃O₄ NCs-GAs deliver excellent rate capability (the reversible capacities obtained were 1442, 392 and 118 mA h g^-1 at 0.1C, 12C and 35C rates), and a high reversible capacity of 577 mA h g^-1 over 300 cycles at the current density of 5.2 A g^-1 (6C).