Measurement of depth-dependence and anisotropy of ultrasound speed of bovine articular cartilage in vitro

Author name used in this publication: S. G. PatilAuthor name used in this publication: Y. P. ZhengAuthor name used in this publication: J. Y. WuAuthor name used in this publication: J. Shi2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

How to involve, motivate and sustain students in service learning programs

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The Growth of Azotobacter vinelandii on p-Hydroxybenzoic Acid from Soil Medium

The purpose of this study was to search for the substrates utilized by Azotobacter vinelandii in dialysed soil media. Also, we sought to determine the relationship between these substrates and the growth and morphological variations of A. vinelandii. p-Hydroxybenzoic acid was shown to be used as the carbon and energy source by A. vinelandii in dialysed soil medium. The amount of this compound in the soil dialysed soil medium ranged from 14 to 21 micrograms per gram of soil. In a dialysed soil medium, p-hydroxybenzoic acid induced A. vinelandii to form minute bodies, similar to the filtrable forms reported by Gonzalez and Vela, although no growth of minute bodies was detected

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

Exponential energy decay of solutions for a system of viscoelastic wave equations of Kirchhoff type with strong damping

The initial boundary value problem for a system of viscoelastic wave equations of Kirchhoff type with strong damping is considered. We prove that, under suitable assumptions on relaxation functions and certain initial data, the decay rate of the solutions energy is exponential

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC

LOQUAT: an open-source GPU-accelerated SPH solver for geotechnical modeling

Smoothed particle hydrodynamics (SPH) is a meshless method gaining popularity recently in geotechnical modeling. It is suitable to solve problems involving large deformation, free-surface, cracking and fragmentation. To promote the research and application of SPH in geotechnical engineering, we present LOQUAT, an open-source three-dimensional GPU accelerated SPH solver. LOQUAT employs the standard SPH formulations for solids with two geomechnical constitutive models which are the Drucker–Prager model and a hypoplastic model. Three stabilization techniques, namely, artificial viscosity, artificial pressure and stress regularization are included. A generalized boundary particle method is presented to model static and moving boundaries with arbitrary geometry. LOQUAT employs GPU acceleration technique to greatly increase the computational efficiency. Numerical examples show that the solver is convergent, stable and highly efficient. With a mainstream GPU, it can simulate large scale problems with tens of millions of particles, and easily performs more than one thousand times faster than serial CPU code

Individually Frequency Tunable Dual- and Triple-band Filters in a Single Cavity

© 2013 IEEE. This paper presents a new class of second-order individually and continuously tunable dual- and triple-band bandpass filters in a single metal cavity. Each passband is realized by two identical metal posts. These dual- and triple-band tunable filters are achieved by putting two or three identical sets of metal-post pair in a single metal cavity. Metal screws are co-designed as a part of the metal posts to control their insertion depth inside the cavity. In this way, the resonant frequencies can be continuously controlled and designed at the desired frequency bands. Moreover, the distance between the two metal posts in a post pair can be freely tuned. Thus, the external quality factor (Qe) and coupling coefficient (k) between the adjacent modes can be easily adjusted to meet the specified requirement in synthesis design. At the bottom of the cavity, some grooves are used to extend the tunable frequency range and make the resonant frequency linearly varied with the height of the metal post. The center frequency of each passband can be independently tuned with a frequency range of 0.8-3.2 GHz and tunable ratio of 4. Finally, the continuously tunable dual- and triple-band bandpass filters prototypes with second order response are designed and fabricated, of which each passband can be individually tuned with a large tuning range

Plant Homeo Domain Finger Protein 8 Regulates Mesodermal and Cardiac Differentiation of Embryonic Stem Cells Through Mediating the Histone Demethylation of pmaip1

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