Signal analysis of elastic waveguide-based techniques for monitoring bone fracture healing: application to structural state evolution in biological composites

Interfacial solute clustering is an essential step preceding grain boundary (GB) precipitation. Both states, i.e., clusters and precipitates, alter the mechanical, chemical, and corrosion properties of materials. Continuum models cannot capture the atomic details of these phenomena, specifically of the transition from clustering to precipitation. We thus use the structural phase-field crystal (XPFC) model to study the compositional and structural evolution during GB clustering in Al–Cu alloys. The results show that the compositional evolution is dominated by solute segregation to lattice defects at the very beginning and then by confined spinodal decomposition along the GBs. The latter leads to a steep increase in the concentration and then the formation of disordered clusters. This structure acts as a precursor for phase nucleation, just like the decomposed solid solution, and Guinier–Preston zones are the precursors of the thermodynamically stable Al2Cu phase in the interior of grains. Two modes of spinodal decomposition are found. (a) On low-angle tilt GBs, spinodal decomposition occurs at the dislocations that constitute the GB. (b) On high-angle tilt GBs, spinodal decomposition takes place inside the entire GB plane. In either case, the structural transition from the disordered low-dimensional precursor states to an ordered phase state takes place following the compositional enrichment. These results shed light on atomic-scale early-stage GB decomposition and precipitation processes in Al–Cu alloys and enrich our knowledge about the coupling effects between compositional and structural evolution during GB phase transformation phenomena.

Hydrocephalus includes a range of disorders characterized by clinical symptoms, abnormal brain imaging and altered cerebrospinal fluid (CSF) dynamics. Infusion tests can be used to study CSF circulation in patients with hydrocephalus. In them, intracranial pressure (ICP) is deliberately raised and CSF circulation disorders evaluated through measurements of the resulting ICP. In this study, we analyzed 77 ICP signals recorded during infusion tests using the spectral entropy (SE). Each signal was divided into four artifact-free epochs. The mean SE, <SE>, and the standard deviation of SE, SD[SE], were calculated for each epoch. Statistically significant differences were found between phases of the infusion test using <SE> and SD[SE] (p<1.7 · 10(-3), Bonferroni-corrected Wilcoxon tests). Furthermore, we found significantly lower <SE> and SD[SE] values in the plateau phase than in the basal phase. These findings suggest that the increase in ICP during infusion studies is associated with a significant decrease in irregularity and variability of the spectral content of ICP signals, measured in terms of SE. We conclude that the spectral analysis of ICP signals could be useful for understanding CSF dynamics in hydrocephalus.

With outstanding material removal ability and high finish quality, robotic belt grinding has great advantages in processing difficult-to-machine materials like nickel-based superalloys. Tool wear is a severe problem in such grinding processes; thus, detection of tool wear is critical to precision finishing of a surface profile. This work proposes a novel acoustic signal-based detection method that combines a random forest (RF) classifier and a multiple linear regression (MLR) model to detect different wear periods and evaluate the remaining grinding ability for robotic belt grinding of nickel-based superalloys. The correlation between grinding sound and belt conditions is established through experimental studies and signal analysis. Through mapping the acoustic features of grinding sound and conditions of grinding belts, the RF classifier and the MLR model are trained and applied in prediction of grinding belt conditions. The total prediction accuracy of RF classifier for distinguishing different wear periods is over 94%, and the mean absolute percentage error of MLR model for evaluating the grinding ability in accelerated wear period is less than 9%. The online detection method can be used as a basis for adaptive control of grinding parameters to achieve precision profile finishing.

Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.

The purpose of the present Ph.D. thesis is to develop and apply advanced algorithms for EEG/ERP signal analysis in order to study neurophysiological alterations associated with dyslexia. The used methods aim at a reliable analysis of synchronization, causal connectivity and complexity of EEG/ERP signals and are evaluated on both synthetic and real EEG/ERP signals of dyslexics and controls, acquired during Wechsler auditory test. First, the conventional components of ERP waveforms (peak amplitudes, latencies) are studied. Statistical analysis points out that dyslexics’ signals present significantly lower N100 amplitudes which are known to be associated with memory performance. An important parameter in dyslexia is the pre-attentive reaction time to auditory stimuli which is reflected through P50 latency and is found to be significantly prolonged at specific electrodes. Energy differentiations in time-frequency between the two groups (dyslexics and controls) are examined, enabling study of the temporal changes of ERP content. Various second order and adaptive time-frequency methods are comparatively assessed in terms of their accuracy in representing temporally changing spectra. Matching pursuit is proved to be quite effective in cross terms suppression and representation of energy peaks. Significant energy differentiations at delta (0-4 Hz), theta (5-7 Hz), alpha (8-13 Hz) and beta (14-30 Hz) frequency bands are detected, through a methodology of statistical evaluation based on normalization and multiple comparisons correction methods. The presence of significant energy differentiations may be the result of differing functional connectivity patterns between the two groups (controls, dyslexics). In order to study causal connectivity patterns, the multivariate autoregressive model is estimated using the Yule-Walker, Burg and Least Squares methods, with Burg and Least Squares proved to provide superior performance in terms of prediction error. A new measure for the estimation of direct causal interactions is proposed, which is based on the combination of the full frequency directed transfer function and the partial directed coherence, exhibiting spectral properties similar with those of the involved signals, and increased efficiency in suppressing false and non direct flows. Study of rest EEG connectivity patterns, by means of the new connectivity measure, revealed differentiations in specific activity flows between the two groups under study (controls and dyslexics). In order to calculate coupling measures of non-stationary signals, like ERP, the dynamic autoregressive model is used and its ability to accurately represent rapid changes of causal interactions is assessed using short window and adaptive Kalman filter approaches. The superiority of the Kalman filter approach in terms of the accuracy provided in the estimation of the model’s autoregressive parameters is demonstrated on both synthetic and real EEG/ERP signals. Furthermore, the predictability/complexity of EEG/ERP time-series of dyslexics versus controls was studied, using measures of spectral and approximate entropy. Spectral entropy and its modifications quantify the spectral complexity of time-series and are related with synchronization and dominance of specific frequency bands. In order to study the temporal evolution of signals’ spectral complexity, wavelet transform and optimal kernel approaches were used, and the superiority of the latter concerning its ability to discriminate the two groups was demonstrated. The representation through optimal kernel permits the adjustment to each analyzed signal, a property that is quite important in analyzing data characterized by intense variability. Finally, through approximate entropy, the presence of differentiations in predictability of EEG time series related with single electrodes or pairs of electrodes is studied, demonstrating that dyslexics’ signals are characterized by more predictable patterns.

This paper presents a new approach for damage detection in thin plates by fusing variational mode decomposition and spectral entropy (VMD-SE). In this method, after the received signal is decomposed into some intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the spectral entropy ratio of the first and last IMFs is calculated for optimizing the VMD’s parameters and improving its decomposition performance. Moreover, the cross-correlation coefficient between the decomposed IMFs and the reference signal is computed to separate the desired IMF, which contains more damage information. Finally, the spectral entropy of the obtained IMF is calculated as an indicator for assessing the damage’s severity. The comparative analysis of the simulated signal clearly shows that only the proposed method can successfully separate the damage-related and reference signals. To verify the VMD-SE method, damage detection of two different types of damage on aluminum and composite fiber-reinforced polymer (CFRP) plates is conducted by using this new approach. The experimental results demonstrate that the parameters of VMD affect greatly its decomposition performance, and the best parameters are selected. The results also indicate that the normalized spectral entropy monotonically increases when the diameter of the through-hole or the length of the scratch increases. In addition, the correlation coefficients of the fitting lines of the plates are larger than 0.998. The experimental results of aluminum specimens demonstrate that the damage’s location has an influence on the normalized spectral entropy. At last, based on the linear relationship, the severity of damage in the fourth specimen is identified. The identification results demonstrate that the relative error of the aluminum and CFRP plates is less than 7.34%, which indicates that this new algorithm by fusing VMD and spectral entropy can detect the damage size in thin plates accurately and efficiently.

Several statistical tests are available for testing the Poisson hypothesis and/or the equidispersion of a point process. The capability to discriminate between the Poissonian behaviour and more complex processes is fundamental in many areas of research including earthquake analysis, hydrology, ecology, biology, signal analysis and sociology. This study investigates the relationship between two indices often used for detecting departures from equidispersion, namely, the index of dispersion (ID) and the Allan factor (AF). Since an approximation of the sampling distribution of AF for Poisson data has been recently proposed in the literature, we perform a detailed analysis of its properties and its relationship with the asymptotic sampling distribution of ID. Moreover, the statistical power of the AF for testing the Poisson hypothesis is assessed by using an extensive Monte Carlo simulation, and the performances of AF and ID are compared. We propose a simplified version of the AF sampling distribution that does not depend on the rate of occurrence keeping the maximum errors of extreme percentiles always smaller than 2–3 %. The power study highlights that ID systematically outperforms AF for discriminating between equidispersed and under/over dispersed data. Both indices show the same lack of power for distinguishing between data drawn from equidispersed non-Poissonian distribution functions. Therefore, even though AF is a useful statistical tool for detecting the possible fractal behaviour of point processes, ID should be preferred when the analysis aims at assessing equidispersion. The lack of power for a small sample size confirms the difficulty of identifying the true nature of the occurrence process of rare events.

Boron nitride (BN) was prepared by polymer-derived method using precursor poly [(alkylamino) borazin (PABZ). The evolution of composition and structure of precursor PABZ into BN ceramic during curing and pyrolysis process was investigated by FTIR, TG, XRD techniques and chemical analysis. The results showed that PABZ could fulfill curing heated at 80°C for 2hrs in NH3 atmosphere. During the curing process the both transamination reaction and condensation reaction occurred, resulting in cross-link between monomer molecular. With the proceeding of curing process, the new B-N bonds were formed and continued to expand in three dimensions. After cured treatment the thermal pyrolysis of PABZ in ammonia atmosphere was took place, and inorganic degree and crystallinity of products were better, which was more suitable for preparation of high purity hexagonal BN (h-BN) ceramic.

Anti-solvent polarity engineering for structure, morphology and composition control of cesium copper (I) halide with efficient, stable and adjustable photoluminescence

Structural evolution in Ti–Si alloy synthesized by mechanical alloying

Structure evolution and optimization in the fabrication of PVA-based activated carbon fibers

A-phases consist of transient cortical events that normally occur during NREM sleep and can be observed directly in the EEG signals. One particular kind of A-phases, namely, A3-phases are related to arousals from sleep during which increased activity in other systems (such as the cardiovascular and respiratory systems) can also be observed. This study aims to characterize disruptions in the oscillations of the airflow signal during A3-phases of sleep. Spectral entropy was used to quantify the bandwidth of the airflow signal, which under baseline conditions (prior to an A3-phase) resembles a sinusoidal wave with a frequency of about 0.25 Hz and has low spectral entropy values. It was found that during most A3-phases the spectral entropy increases significantly in 70% of the test subjects. These changes occur with higher probability during A3-phases that are longer than 10[Formula: see text]s, suggesting a delay between the onset of an A3-phase and the effect it has on the respiratory system.

Background and ObjectivesThere are increasing concerns about mild traumatic brain injury (mTBI) because of its effects in later life. However, objective markers that help physicians quantify the injury are still understudied. We aim to investigate the underlying neuroplasticity reflected by working memory processing after mTBI.MethodsWe used cognitive brain challenges to explore neuroplasticity in acute mTBI. Brain activities during the N□back working memory (WM) test was investigated using quantitative electroencephalography (qEEG) in an acute and longitudinal mild traumatic brain injury study. mTBI patients (n=22) and controls (n=9) (trauma patients without head injury), 18–50 years of age, were recruited from the emergency department of Huntington Memorial Hospital in Pasadena, CA. Brain challenges were administered using E‐prime software. Data were collected from 21 recording head sensors at four visits: within 1 week, 14 days, 30 days, and 6–12 months after injury (with some missing visits). Behavioral performance as well as spectral power were analyzed to compare the two groups. Brain WM processing were also evaluated by event‐related potentials (ERPs) P300, and corresponding EEG signal (ir)regularity or “noise” level measured by spectral entropy (SE). High SE means signal is more irregular and “noisier”.Results &amp; DiscussionBehavioral performance during 0‐back challenge was similar between the two groups, though mTBI patients had significantly lower accuracy than controls during 2‐back at the first visit. qEEG analysis revealed altered brain activities in mTBI group during 0‐back: alpha and beta power were lower in mTBI patients than controls at the second and third visit. Further, theta power during 2‐back at second visit were significantly higher (P=0.0099 and 0.0018) in controls (0.73+/−0.37 and 0.96+/−0.46) compared to mTBI (0.21+/−0.41 and 0.09+/−0.55) patients at the left and right temporal regions. When comparing regional theta power from first visit to second visit, controls were increased (p&lt;0.005), indicating a learning mechanism, while mTBIs were not changed (p&gt;0.10). SE analysis of the EEG signals during the P300 time window demonstrated more irregular or noisier brain signals in mTBI patients. These changes indicate neuroplasticity acutely and longitudinally after mTBI, consistent with learning impairment and “noisier” brain after mTBI. The spectral power and SE under WM challenge are sensitive measures of neuroplasticity after injury, and could be potential objective mTBI markers to help diagnosis, prognosis, or treatment management.Support or Funding InformationThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Bulk nanomaterials with an open porosity offer exciting prospects for creating new functional materials for various applications in photonics, IR-THz optics, metamaterials, heterogeneous photocatalysis, monitoring and cleaning toxic impurities in the environment. However, their availability is limited by the complexity of controlling the process of synthesis of bulk 3D nanostructures with desired physicochemical and functional properties. In this paper, we performed a detailed analysis of influence of a silica monolayer chemically deposited on the surface of a monolithic ultraporous nanostructure, consisting of a 3D nanofibril network of aluminum oxyhydroxide, on the evolution of structure and morphology, chemical composition and phase transformations after heat treatment in the temperature range of 20−1700 °C. The experimental results are interpreted in the framework of a physical model taking into account surface and volume mass transport and sintering kinetics of nanofibrils, which made it possible to estimate activation energies of the surface diffusion and sintering processes. It is shown that the presence of a surface silica monolayer on the surface affects the kinetics of aluminum oxyhydroxide dehydration and inhibits diffusion mass transfer and structural phase transformations. As a result, the overall evolution of the 3D nanostructure significantly differs from that of nanomaterials without surface chemical modification.

Epilepsy is a neurological condition that affects the central nervous system. While its effects are different for each person, they mostly include abnormal behaviour, periods of loss of awareness and seizures. There are various traditional methods used to analyse EEG signals for epilepsy detection, which concludes to be time-consuming. Recently, several automated seizure detection frameworks using machine learning algorithms have been proposed to replace conventional methods. In this paper, more emphasis has been given to develop SPPCA and SUBXPCA dimensionality reduction algorithms to increase the classification accuracy of various machine learning models. Firstly, Discrete Wavelet Transform (DWT) is applied to EEG signals for extracting the time-frequency domain features of epileptic seizures such as the energy of each sub-pattern, spike rhythmicity, Relative Spike Amplitude (RSA), Dominant Frequency (DF) and Spectral Entropy (SE). The features obtained after performing DWT on an EEG signal are extensive in number, to select the prominent features and to retain their properties, correlation feature sub-pattern-based PCA (SPPCA), and cross sub-pattern correlation-based PCA (SUBXPCA) are used as a dimensionality reduction techniques. To validate the proposed work, performance evaluation parameter such as the accuracy of the time-frequency domain features from different combinations of the dataset has been compared with the latest state-of-the-art works. Simulation results show that the best accuracy of 97% is achieved for SPPCA algorithm by CatBoost classifier. And the best accuracy of 98% for SUBXPCA is achieved by random forest classifier, which clearly outperformed the other related works both in terms of accuracy and computational complexity.