Experimental Investigations on Microcracks in Vibrational and Conventional Drilling of Cortical Bone

Bone drilling is widely used in orthopedic surgery. Microcracks will be generated in bone drilling, which may cause fatigue damages and stress fractures. Fresh bovine cortical bones were drilled via vibrational and conventional ways. Drilling operations were performed by a dynamic material testing machine, which can provide the vibration while maintaining uniform feed motion. The drill site and bone debris were observed through scanning electron microscope (SEM). The experimental results showed that fewer and shorter micro-cracks were formed in vibrational drilling than those formed in conventional way. And the surface morphology of bone debris from two different drilling ways was also quite different. It is expected that vibrational drilling in orthopedic surgery operation could decrease the microdamage to the bone, which could lower the incidence of stress fracture and contribute to the postoperative recovery

Energy-Efficient Joint Resource Allocation Algorithms for MEC-Enabled Emotional Computing in Urban Communities

This paper considers a mobile edge computing (MEC) system, where the MEC server first collects data from emotion sensors and then computes the emotion of each user. We give the formula of the emotional prediction accuracy. In order to improve the energy efficiency of the system, we propose resources allocation algorithms. We aim to minimize the total energy consumption of the MEC server and sensors by jointly optimizing the computing resources allocation and the data transmitting time. The formulated problem is a non-convex problem, which is very difficult to solve in general. However, we transform it into convex problems and apply convex optimization techniques to address it. The optimal solution is given in closed form. Simulation results show that the total energy consumption of our system can be effectively reduced by the proposed scheme compared with the benchmark

Surface-potential-based compact model for the gate current of p-GaN Gate HEMTs

The gate leakage current of p-GaN gate HEMTs is modeled based on surface potential calculations. The model accurately describes the bias and temperature dependence of the gate leakage. Thermionic emission is the main mechanism of the gate current in forward bias operation while hopping transport component is the main mechanism of gate current in reverse bias operation. This newly developed gate current model was implemented in Verilog-A. A good agreement between the simulations and experimental data demonstrates the accuracy of the model

Preparation and Characteristic of PC/PLA/TPU Blends by Reactive Extrusion

To overcome the poor toughness of PC/PLA blends due to the intrinsic properties of materials and poor compatibility, thermoplastic urethane (TPU) was added to PC/PLA blends as a toughener; meantime, catalyst di-n-butyltin oxide (DBTO) was also added for catalyzing transesterification of components in order to modify the compatibility of blends. The mechanical, thermal, and rheological properties of blends were investigated systematically. The results showed that the addition of TPU improves the toughness of PC/PLA blends significantly, with the increase of TPU, the elongation at break increases considerably, and the impact strength increases firstly and then falls, while the tensile strength decreases significantly and the blends exhibit a typical plastic fracture behavior. Meantime, TPU is conducive to the crystallinity of PLA in blends which is inhibited seriously by PC and damages the thermal stability of blends slightly. Moreover, the increased TPU makes the apparent viscosity of blends melt decrease due to the well melt fluidity of TPU; the melt is closer to the pseudoplasticity melt. Remarkably, the transesterification between the components improves the compatibility of blends significantly, and more uniform structure results in a higher crystallinity and better mechanical properties

Spatial organization of synaptic plasticity in cerebellar parallel fiber : Purkinje cell synapses

Not much is known about spatial patterns of Parallel fiber-Purkinje cell long-term depression (PF-PC LTD). For this reason, I visualized the distribution of PF-PC LTD in relation to the area of conjunctive stimulation by using voltage-sensitive dye imaging. During stimulation, the basic optical response was observed to have an early “spiky” component and a slower trailing component. Following a 1 Hz train of stimuli, LTD was induced and could be visualized as a decrease in the response elicited by subsequent stimuli. Such LTD was restricted to the molecular layer of the cerebellum, consistent with a decrease in transmission at the PF-PC synapse. Ratio imaging allowed me to further characterize the spatial range of LTD. Line scan analysis revealed that the amount of LTD generally decreased with distance from the stimulation site, demonstrating a gradient limited by the distribution of activated PF synapses. In addition, LTD extended for 80 μm further than the peak area of PF activation, consistent with previously indications of the spread of LTD beyond active PF synapses. I conclude that LTD spread is likely to be limited by 2 things: (1) the distribution of activated PF synapses; and (2) the spread of chemical signals generated during LTD.Bachelor of Science in Biological Science

Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks

Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative adversarial network. Then, long short-term memory with autoencoder and attention-based models, the temporal convolutional network and the generative adversarial model are proposed and applied to time series classification and forecasting. Gaussian sliding window weights are proposed to speed the training process up. Finally, the performances of the proposed methods are assessed using five optimizers and loss functions with the public benchmark datasets, and comparisons between the proposed temporal convolutional network and several classical models are conducted. Experiments show the proposed models’ effectiveness and confirm that the temporal convolutional network is superior to long short-term memory models in sequence modeling. We conclude that the proposed temporal convolutional network reduces time consumption to around 80% compared to others while retaining the same accuracy. The unstable training process for generative adversarial network is circumvented by tuning hyperparameters and carefully choosing the appropriate optimizer of “Adam”. The proposed generative adversarial network also achieves comparable forecasting accuracy with traditional methods

Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection

Anomaly detection research was conducted traditionally using mathematical and statistical methods. This topic has been widely applied in many fields. Recently reinforcement learning has achieved exceptional successes in many areas such as the AlphaGo chess playing and video gaming etc. However, there were scarce researches applying reinforcement learning to the field of anomaly detection. This paper therefore aimed at proposing an adaptable asynchronous advantage actor-critic model of reinforcement learning to this field. The performances were evaluated and compared among classical machine learning and the generative adversarial model with variants. Basic principles of the related models were introduced firstly. Then problem definitions, modelling processes and testing were detailed. The proposed model differentiated the sequence and image from other anomalies by proposing appropriate neural networks of attention mechanism and convolutional network for the two kinds of anomalies, respectively. Finally, performances with classical models using public benchmark datasets (NSL-KDD, AWID and CICIDS-2017, DoHBrw-2020) were evaluated and compared. Experiments confirmed the effectiveness of the proposed model with the results indicating higher rewards and lower loss rates on the datasets during training and testing. The metrics of precision, recall rate and F1 score were higher than or at least comparable to the state-of-the-art models. We concluded the proposed model could outperform or at least achieve comparable results with the existing anomaly detection models

Immunoproteomic to analysis the pathogenicity factors in leukopenia caused by Klebsiella pneumonia bacteremia.

Incidences of leukopenia caused by bacteremia have increased significantly and it is associated with prolonged hospital stay and increased cost. Immunoproteomic is a promising method to identify pathogenicity factors of different diseases. In the present study, we used immunoproteomic to analysis the pathogenicity factors in leukopenia caused by Klebsiella Pneumonia bacteremia. Approximately 40 protein spots localized in the 4 to 7 pI range were detected on two-dimensional electrophoresis gels, and 6 differentially expressed protein spots between 10 and 170 kDa were identified. Pathogenicity factors including S-adenosylmethionine synthetase, pyruvate dehydrogenase, glutathione synthetase, UDP-galactose-4-epimerase, acetate kinase A and elongation factor tu (EF-Tu). In validation of the pathogenicity factor, we used western blotting to show that Klebsiella pneumonia had higher (EF-Tu) expression when they accompanied by leukopenia rather than leukocytosis. Thus, we report 6 pathogenicity factors of leukopenia caused by Klebsiella pneumonia bacteremia, including 5 housekeeping enzymes and EF-Tu. We suggest EF-Tu could be a potential pathogenicity factor for leukopenia caused by Klebsiella pneumonia