Dry Powder Microfeeding System for Solid Freeform Fabrication

Second generation SFF techniques allow both composition and shape to be downloaded directly from a computer file so that 3D functionally graded materials (FGM) can be assembled. Methods for multi-material deposition are also needed in combinatorial research, colour management and pharmaceutical dosing. In this work, computer-controlled microfeeding systems using ultrasonic vibration of a capillary were built. A wide range of stable flow rate control and switching control were achieved in the acoustic vibration system, and uniform powder doses were obtained in the ultrasonic system. The experimental results show that the nozzle diameter, transmission fluid depth, waveforms, voltage amplitude, frequency and oscillation duration all influence the dose mass. Among these factors, the nozzle diameter, voltage amplitude and oscillation duration can be used to control the dose mass. Raster printing of patterns with various resolution and dot size are demonstrated.Mechanical Engineerin

Random sampling and generation over data streams and graphs

Ph.DDOCTOR OF PHILOSOPH

Magnetoelectric plasma preparation of silicon-carbon nanocomposite as anode material for lithium ion batteries

A high-performance silicon-carbon nanocomposite facilely prepared by one-step magnetoelectric plasma pyrolysis of the mixture of methane, silane, and hydrogen is proposed for lithium-ion batteries. The ratio of silane, methane, and hydrogen was studied to optimize the properties of the composite. When the ratio of hydrogen/silane/methane is 1:1:3, the composite is composed of spherical Si nanoparticles that uniformly attach to the surface of the tremelliform carbon nanosheets framework, in which the tremelliform carbon nanosheets can effectively resist the volumetric change of the Si nanoparticles during the cycles and serve as electronic channels. The silicon-carbon nanocomposite exhibits a high reversible capacity (1007 mAh g−1 after 50 cycles), a low charge transfer resistance, and an excellent rate performance. In addition, the proposed process for synthesizing silicon-carbon nanocomposite without expensive materials or toxic reagents is an environmentally friendly and cost-effective method for mass production

The Development of Biomimetic Spherical Hydroxyapatite/Polyamide 66 Biocomposites as Bone Repair Materials

A novel biomedical material composed of spherical hydroxyapatite (s-HA) and polyamide 66 (PA) biocomposite (s-HA/PA) was prepared, and its composition, mechanical properties, and cytocompatibility were characterized and evaluated. The results showed that HA distributed uniformly in the s-HA/PA matrix. Strong molecule interactions and chemical bonds were presented between the s-HA and PA in the composites confirmed by IR and XRD. The composite had excellent compressive strength in the range between 95 and 132 MPa, close to that of natural bone. In vitro experiments showed the s-HA/PA composite could improve cell growth, proliferation, and differentiation. Therefore, the developed s-HA/PA composites in this study might be used for tissue engineering and bone repair

CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure

Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However, these methods fail to consider the inherent characteristics of codes. In this paper, to address the problem, we propose a novel probing method CAT-probing to quantitatively interpret how CodePTMs attend code structure. We first denoise the input code sequences based on the token types pre-defined by the compilers to filter those tokens whose attention scores are too small. After that, we define a new metric CAT-score to measure the commonality between the token-level attention scores generated in CodePTMs and the pair-wise distances between corresponding AST nodes. The higher the CAT-score, the stronger the ability of CodePTMs to capture code structure. We conduct extensive experiments to integrate CAT-probing with representative CodePTMs for different programming languages. Experimental results show the effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are publicly available at https://github.com/nchen909/CodeAttention.Comment: Accepted by EMNLP 202

A Mixed-Bouncing Based Non-Stationarity and Consistency 6G V2V Channel Model with Continuously Arbitrary Trajectory

In this paper, a novel three-dimensional (3D) irregularshaped geometry-based stochastic model (IS-GBSM) is proposedfor sixth-generation (6G) millimeter wave (mmWave) massivemultiple-input multiple-output (MIMO) vehicle-to-vehicle(V2V) channels. To investigate the impact of vehicular trafficdensity (VTD) on channel statistics, clusters are divided into staticclusters and dynamic clusters, which are further distinguishedinto static/dynamic single/twin-clusters to capture the mixed bouncingpropagation. A new method, which integrates thevisibility region and birth-death process methods, is developedto model space-time-frequency (S-T-F) non-stationarity of V2Vchannels with time-space (T-S) consistency. The continuouslyarbitrary vehicular movement trajectory (VMT) and soft clusterpower handover are modeled to further ensure channel T-Sconsistency. From the proposed model, key channel statistics arederived. Simulation results show that S-T-F non-stationarity ofchannels with T-S consistency is modeled and the impacts of VTDand VMT on channel statistics are analyzed. The generality ofthe proposed model is validated by comparing simulation resultsand measurement/ray-tracing (RT)-based results

Deep knowledge tracing with learning curves

Knowledge tracing (KT) models students' mastery level of knowledge concepts based on their responses to the questions in the past and predicts the probability that they correctly answer subsequent questions in the future. Recent KT models are mostly developed with deep neural networks and have demonstrated superior performance over traditional approaches. However, they ignore the explicit modeling of the learning curve theory, which generally says that more practices on the same knowledge concept enhance one's mastery level of the concept. Based on this theory, we propose a Convolution-Augmented Knowledge Tracing (CAKT) model and a Capsule-Enhanced CAKT (CECAKT) model to enable learning curve modeling. In particular, when predicting a student's response to the next question associated with a specific knowledge concept, CAKT uses a module built with three-dimensional convolutional neural networks to learn the student's recent experience on that concept, and CECAKT improves CAKT by replacing the global average pooling layer with capsule networks to prevent information loss. Moreover, the two models employ LSTM networks to learn the overall knowledge state, which is fused with the feature learned by the convolutional/capsule module. As such, the two models can learn the student's overall knowledge state as well as the knowledge state of the concept in the next question. Experimental results on four real-life datasets show that CAKT and CECAKT both achieve better performance compared to existing deep KT models