Fast fourier analysis of structural organization in decellularized cartilage-on-bone laminates

Denaturation of tissues can provide a unique biological environment for regenerative medicine application only if minimal disruption of their microarchitecture is achieved during the decellularization process. The goal is to keep the structural integrity of such a construct as functional as the tissues from which they were derived. In this work, cartilage-on-bone laminates were decellularized through enzymatic, non-ionic and ionic protocols. This work investigated the effects of decellularization process on the microarchitecture of cartiligous extracellular matrix; determining the extent of how each process deteriorated the structural organization of the network. High resolution microscopy was used to capture cross-sectional images of samples prior to and after treatment. The variation of the microarchitecture was then analysed using a well defined fast Fourier image processing algorithm. Statistical analysis of the results revealed how significant the alternations among aforementioned protocols were (p < 0.05). Ranking the treatments by their effectiveness in disrupting the ECM integrity, they were ordered as: Trypsin> SDS> Triton X-100.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Estimating the probability of an invalid match for matching using the rank transform

A fundamental problem faced by stereo matching algorithms is the matching or correspondence problem. A wide range of algorithms have been proposed for the correspondence problem. For all matching algorithms, it would be useful to be able to compute a measure of the probability of correctness, or reliability of a match. This paper focuses in particular on one class for matching algorithms, which are based on the rank transform. The interest in these algorithms for stereo matching stems from their invariance to radiometric distortion, and their amenability to fast hardware implementation. This work differs from previous work in that it derives, from first principles, an expression for the probability of a correct match. This method was based on an enumeration of all possible symbols for matching. The theoretical results for disparity error prediction, obtained using this method, were found to agree well with experimental results. However, disadvantages of the technique developed in this chapter are that it is not easily applicable to real images, and also that it is too computationally expensive for practical window sizes. Nevertheless, the exercise provides an interesting and novel analysis of match reliability.

Rapid quantification of methamphetamine: using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Chemometrics

In Australia and increasingly worldwide, methamphetamine is one of the most commonly seized drugs analysed by forensic chemists. The current well-established GC/MS methods used to identify and quantify methamphetamine are lengthy, expensive processes, but often rapid analysis is requested by undercover police leading to an interest in developing this new analytical technique. Ninety six illicit drug seizures containing methamphetamine (0.1% - 78.6%) were analysed using Fourier Transform Infrared Spectroscopy with an Attenuated Total Reflectance attachment and Chemometrics. Two Partial Least Squares models were developed, one using the principal Infrared Spectroscopy peaks of methamphetamine and the other a Hierarchical Partial Least Squares model. Both of these models were refined to choose the variables that were most closely associated with the methamphetamine % vector. Both of the models were excellent, with the principal peaks in the Partial Least Squares model having Root Mean Square Error of Prediction 3.8, R2 0.9779 and lower limit of quantification 7% methamphetamine. The Hierarchical Partial Least Squares model had lower limit of quantification 0.3% methamphetamine, Root Mean Square Error of Prediction 5.2 and R2 0.9637. Such models offer rapid and effective methods for screening illicit drug samples to determine the percentage of methamphetamine they contain.

Possible, inevitable or fait accompli? An analysis of streaming video acquisition, acceptance and use in higher education

This paper explores the potential for online video as a mechanism to transform the ways students learn, as measured by research, user experience and usage following surveys and trials of patron-driven acquisition collaboratively undertaken by Queensland University of Technology, La Trobe University and Kanopy.

Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Background Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data. Methods Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series. Results The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors. Conclusions The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.

Insight into the thermal decomposition of kaolinite intercalated with potassium acetate : an evolved gas analysis

The thermal decomposition process of kaolinite–potassium acetate intercalation complex has been studied using simultaneous thermogravimetry coupled with Fourier-transform infrared spectroscopy and mass spectrometry (TG-FTIR-MS). The results showed that the thermal decomposition of the complex took place in four temperature ranges, namely 50–100, 260–320, 320–550, and 650–780 °C. The maximal mass losses rate for the thermal decomposition of the kaolinite–potassium acetate intercalation complex was observed at 81, 296, 378, 411, 486, and 733 °C, which was attributed to (a) loss of the adsorbed water, (b) thermal decomposition of surface-adsorbed potassium acetate (KAc), (c) the loss of the water coordinated to potassium acetate in the intercalated kaolinite, (d) the thermal decomposition of intercalated KAc in the interlayer of kaolinite and the removal of inner surface hydroxyls, (e) the loss of the inner hydroxyls, and (f) the thermal decomposition of carbonate derived from the decomposition of KAc. The thermal decomposition of intercalated potassium acetate started in the range 320–550 °C accompanied by the release of water, acetone, carbon dioxide, and acetic acid. The identification of pyrolysis fragment ions provided insight into the thermal decomposition mechanism. The results showed that the main decomposition fragment ions of the kaolinite–KAc intercalation complex were water, acetone, carbon dioxide, and acetic acid. TG-FTIR-MS was demonstrated to be a powerful tool for the investigation of kaolinite intercalation complexes. It delivers a detailed insight into the thermal decomposition processes of the kaolinite intercalation complexes characterized by mass loss and the evolved gases.

Identifying weak linear features with the "coalescing shortest path image transform"

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Computational analysis of epigenetic information in human DNA sequences

Over the last few years, investigations of human epigenetic profiles have identified key elements of change to be Histone Modifications, stable and heritable DNA methylation and Chromatin remodeling. These factors determine gene expression levels and characterise conditions leading to disease. In order to extract information embedded in long DNA sequences, data mining and pattern recognition tools are widely used, but efforts have been limited to date with respect to analyzing epigenetic changes, and their role as catalysts in disease onset. Useful insight, however, can be gained by investigation of associated dinucleotide distributions. The focus of this paper is to explore specific dinucleotides frequencies across defined regions within the human genome, and to identify new patterns between epigenetic mechanisms and DNA content. Signal processing methods, including Fourier and Wavelet Transformations, are employed and principal results are reported.

Evolved gas analysis of coal-derived pyrite/marcasite

The products evolved during the thermal decomposition of the coal-derived pyrite/marcasite were studied using simultaneous thermogravimetry coupled with Fourier-transform infrared spectroscopy and mass spectrometry (TG-FTIR–MS) technique. The main gases and volatile products released during the thermal decomposition of the coal-derived pyrite/marcasite are water (H2O), carbon dioxide (CO2), and sulfur dioxide (SO2). The results showed that the evolved products obtained were mainly divided into two processes: (1) the main evolved product H2O is mainly released at below 300 °C; (2) under the temperature of 450–650 °C, the main evolved products are SO2 and small amount of CO2. It is worth mentioning that SO3 was not observed as a product as no peak was observed in the m/z = 80 curve. The chemical substance SO2 is present as the main gaseous product in the thermal decomposition for the sample. The coal-derived pyrite/marcasite is different from mineral pyrite in thermal decomposition temperature. The mass spectrometric analysis results are in good agreement with the infrared spectroscopic analysis of the evolved gases. These results give the evidence on the thermal decomposition products and make all explanations have the sufficient evidence. Therefore, TG–MS–IR is a powerful tool for the investigation of gas evolution from the thermal decomposition of materials.

Wave propagation in multi-walled carbon nanotube

In this paper, wave propagation in multi-walled carbon nanotubes (MWNTs) are studied by modeling them as continuum multiple shell coupled through van der Waals force of interaction. The displacements, namely, axial, radial and circumferential displacements vary along the circumferential direction. The wave propagation are simulated using the wavelet based spectral finite element (WSFE) method. This technique involves Daubechies scaling function approximation in time and spectral element approach. The WSFE Method allows the study of wave properties in both time and frequency domains. This is in contrast to the conventional Fourier transform based analysis which are restricted to frequency domain analysis. Here, first, the wavenumbers and wave speeds of carbon nanotubes (CNTs) are Studied to obtain the characteristics of the waves. These group speeds have been compared with those reported in literature. Next, the natural frequencies of a single-walled carbon nanotube (SWNT) are studied for different values of the radius. The frequencies of the first five modes vary linearly with the radius of the SWNT. Finally, the time domain responses are simulated for SWNT and three-walled carbon nanotubes.