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Desertification has been listed as one of the most critical global environmental issues, posing a significant threat to life, particularly in arid and semiarid regions. Therefore, gaining a comprehensive understanding of the present and future desertification trends becomes imperative. This study employs a feature space model, which effectively captures land surface changes related to desertification, enabling the extraction of pertinent information. Subsequently, time series models are used to determine the most accurate desertification simulation. Twenty-one ETM + sensor images were utilized to calculate the Topsoil Grain Size (TGSI) and Albedo remotely sensed indexes. Constructing the Albedo-TGSI feature space, the Desertification Degree Index (DDI) was extracted for each year. Different levels of desertification were identified by applying a natural break classification on the DDI values, and corresponding break values were obtained. The representative desertification degree for each year was determined by calculating the average of the minimum and maximum break values, resulting in the generation of five distinct time series for five desertification degrees. Different ARIMA models and wavelet transforms were selected to simulate the various desertification degrees based on the analysis of autocorrelation and partial autocorrelation functions and trial and error, respectively. The most suitable ARIMA models with the lowest errors were identified as follows: ARIMA (1,0,7) for severe desertification, ARIMA (0,1,6) for high desertification, ARIMA (0,0,7) for moderate desertification, and ARIMA (3,0,6) for non-desertification degrees. Among the various wavelet transform families tested, the Symlet family proved to be the most effective, except for the low desertification degree. The following wavelet transforms yielded the best results for each degree of desertification: Symlet3 for severe desertification, Symlet7 for high desertification, Symlet7 for moderate desertification, Daubechies 5 (db5) for low desertification, and Symlet7 for non-desertification degree simulations, all exhibiting the minimum error rates. • For 21 images (1994–2019 ETM + remotely sensed images): • Topsoil grain size and Albedo indexes were calculated and extracted; Then Desertification Degree Index was calculated each year. • A natural break classification was applied to divide DDI into five desertification degrees. • DDI time series were generated using the average break values of each degree. • The best ARIMA models based on autocorrelation and autocorrelation functions and the most suitable wavelet transforms were identified through trial and error. • The best ARIMA models and wavelet transforms were chosen for desertification simulation through the validation step. [ABSTRACT FROM AUTHOR]

Text versus non-text region classification is an essential but difficult step in scene-image analysis due to the considerable shape complexity of text and background patterns. There exists a high probability of confusion between background elements and letter parts. This paper proposes a feature-based classification of image blocks using the color autocorrelation histogram (CAH) and the scale-invariant feature transform (SIFT) algorithm, yielding a combined scale and color-invariant feature suitable for scene-text classification. For the evaluation, features were extracted from different color spaces, applying color-histogram autocorrelation. The color features are adjoined with a SIFT descriptor. Parameter tuning is performed and evaluated. For the classification, a standard nearest-neighbor (1NN) and a support-vector machine (SVM) were compared. The proposed method appears to perform robustly and is especially suitable for Asian scripts such as Kannada and Thai, where urban scene-text fonts are characterized by a high curvature and salient color variations. [ABSTRACT FROM AUTHOR]

Highlights • A novel bootstrap method is proposed to infer auto-correlation structure of a random field. • The method proposed in this study is non-parametric and data driven, and is applicable to non-stationary random field data. • Both simulated and real data are used to demonstrate and validate the proposed method. Abstract Bootstrapping is a simulation-based method to provide statistical inference, or to measure accuracy, on estimators of interest, such as random field parameters, i.e., mean and variance of a physical quantity at each location within a random field, and auto-correlation coefficient of the quantities among any two different locations of the field. Statistical inference may be performed using analytical methods together with multiple sets of complete measurement data. However, measurement data may be incomplete sometimes, due to, for examples, sensor failure, storage capacity in collecting measurement data, or cost/difficulty in increasing measurement density. In such a case, it is challenging to perform statistical inference of auto-correlation coefficients, particularly between locations where no data are measured directly. This study aims to develop an innovative and robust bootstrap method to perform statistical inference on auto-correlation coefficient in one dimensional random fields, e.g., time series or variations of a quantity along a spatial direction such as depth, from multiple sets of incomplete and sparse measurements. The proposed approach is based on a Bayesian compressive sampling (BCS)-Karhunen–Loève (KL) expansion random field generator, which has been used to generate random field samples from sparse measurement data. The proposed bootstrap method is illustrated and validated through both numerical examples and real measurement data. The results obtained from the proposed bootstrap method using multiples sets of incomplete measurement data are consistent with those obtained from analytical methods using multiple sets of complete measurement data. [ABSTRACT FROM AUTHOR]

Abstract This work derives new results on strong consistent estimation and prediction for autoregressive processes of order 1 in a separable Banach space B. The consistency results are obtained for the component-wise estimator of the autocorrelation operator in the norm of the space L (B) of bounded linear operators on B. The strong consistency of the associated plug-in predictor then follows in the B -norm. A Gelfand triple is defined through the Hilbert space constructed in Kuelbs' lemma (Kuelbs, 1970). A Hilbert–Schmidt embedding introduces the Reproducing Kernel Hilbert space (RKHS), generated by the autocovariance operator, into the Hilbert space conforming the Rigged Hilbert space structure. This paper extends the work of Bosq (2000) and Labbas and Mourid (2002). [ABSTRACT FROM AUTHOR]

Highlights • Temperature sensing is realized by using chaotic correlation fiber loop ring down system. • This method avoids the contradiction between the length of fiber loop and the width and frequency of pulse light. • The sensing system has simpler section of light source and shorter length of fiber loop. • The sensing system is characterized by the chaotic autocorrelation coefficient ring down time in the fiber loop. • The sensing system uses the fiber couplers to receive the temperature sensitivity of FBG in time domain. Abstract In this paper, we describe a novel temperature sensing system based on chaotic correlation fiber loop ring down technique. A fiber Bragg grating is introduced into the fiber loop cavity. The effect of temperature on the central wavelength of fiber Bragg grating is characterized by the ring down time of the autocorrelation coefficient of chaotic laser in the fiber loop cavity. The relationship between the autocorrelation coefficient ring down time of the chaotic laser and wavelength shifting of the fiber Bragg gratings induced by temperature is theoretically and experimentally analyzed. The sensitivity of 3.52 ns/°C is achieved in the proposed temperature sensing system with fiber cavity length of 6.05 m. We also study the relationship between temperature and central wavelength of fiber Bragg grating by chaotic correlation fiber loop ring down system and receive the temperature sensitivity of 0.01 nm/°C of the FBG. This sensing method is not only simple and low cost, but also has the great potential applications for various industry and agriculture. [ABSTRACT FROM AUTHOR]

Abstract Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We established that the phase dynamics of these models differed qualitatively due to an attractive coupling in the first and a repulsive coupling in the latter. Using empirical structural connectivity matrices, we determined that the two dynamics cover the functional connectivity observed in resting state activity. We further searched for two pivotal dynamical features that have been reported in many experimental studies: (1) a partial phase synchrony with a possibility of a transition towards either a desynchronized or a (fully) synchronized state; (2) long-term autocorrelations indicative of a scale-free temporal dynamics of phase synchronization. Only the Freeman phase model exhibited scale-free behavior. Its repulsive coupling, however, let the individual phases disperse and did not allow for a transition into a synchronized state. The Wilson-Cowan phase model, by contrast, could switch into a (partially) synchronized state, but it did not generate long-term correlations although being located close to the onset of synchronization, i.e. in its critical regime. That is, the phase-reduced models can display one of the two dynamical features, but not both. Highlights • Networks of neural masses are reduced to phase oscillator models. • Freeman- and Wilson-Cowan-based phase models can resemble empirically observed functional connectivity. • Neither a network of Freeman nor of Wilson-Cowan phase models can. • Capture both partial synchronization and scale-free temporal correlations. [ABSTRACT FROM AUTHOR]

Multivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that decodable information is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a multivariate decoding analysis and a spatially-unbiased searchlight approach. We then related brain activation patterns to behaviour by testing whether the classifier used for decoding can be used to predict behaviour. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, which were strongest in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim. [ABSTRACT FROM AUTHOR]

Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (15–30Hz) amplitude over frontocentral channels and with a suppression of alpha (8–13Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness. [ABSTRACT FROM AUTHOR]

Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (N EEG  = 45, N fMRI  = 11, N ECoG  = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing. [ABSTRACT FROM AUTHOR]

In this paper, the application and analysis of the asymptotic approximation method to a single degree-of-freedom has recently been produced. The original concepts are summarized, and the necessary probabilistic concepts are developed and applied to single degree-of-freedom systems. Then, these concepts are united, and the theoretical and computational models are developed. To determine the viability of the proposed method in a probabilistic context, numerical experiments are conducted, and consist of a frequency analysis, analysis of the effects of measurement noise, and a statistical analysis. In addition, two examples are presented and discussed. [ABSTRACT FROM AUTHOR]

Envelope analysis is one of the most advantageous methods for rolling element bearing diagnostics but finding a suitable frequency band for demodulation has been a substantial challenge for a long time. Introduction of the Spectral Kurtosis (SK) and Kurtogram mostly solved this problem but in situations where signal to noise ratio is very low or in presence of non-Gaussian noise these methods will fail. This major drawback may noticeably decrease their effectiveness and goal of this paper is to overcome this problem. Vibration signals from rolling element bearings exhibit high levels of second-order cyclostationarity, especially in the presence of localized faults. The autocovariance function of a 2nd order cyclostationary signal is periodic and the proposed method, named Autogram, takes advantage of this property to enhance the conventional Kurtogram. The method computes the kurtosis of the unbiased Autocorrelation (AC) of the squared envelope of the demodulated signal, rather than the kurtosis of the filtered time signal. Moreover, to take advantage of unique features of the lower and upper portions of the AC, two modified forms of kurtosis are introduced and the resulting colormaps are called Upper and Lower Autogram. In addition, a thresholding method is also proposed to enhance the quality of the frequency spectrum analysis. A new indicator, Combined Squared Envelope Spectrum, is employed to consider all the frequency bands with valuable diagnostic information and to improve the fault detectability of the Autogram. The proposed method is tested on experimental data and compared with literature results so to assess its performances in rolling element bearing diagnostics. [ABSTRACT FROM AUTHOR]

Four experiments tested how people learn cause-effect relations when there are many possible causes of an effect. When there are many cues, even if all the cues together strongly predict the effect, the bivariate relation between each individual cue and the effect can be weak, which can make it difficult to detect the influence of each cue. We hypothesized that when detecting the influence of a cue, in addition to learning from the states of the cues and effect (e.g., a cue is present and the effect is present), which is hypothesized by multiple existing theories of learning, participants would also learn from transitions – how the cues and effect change over time (e.g., a cue turns on and the effect turns on). We found that participants were better able to identify positive and negative cues in an environment in which only one cue changed from one trial to the next, compared to multiple cues changing (Experiments 1A, 1B). Within a single learning sequence, participants were also more likely to update their beliefs about causal strength when one cue changed at a time (‘one-change transitions’) than when multiple cues changed simultaneously (Experiment 2). Furthermore, learning was impaired when the trials were grouped by the state of the effect (Experiment 3) or when the trials were grouped by the state of a cue (Experiment 4), both of which reduce the number of one-change transitions. We developed a modification of the Rescorla-Wagner algorithm to model this ‘Informative Transitions’ learning processes. [ABSTRACT FROM AUTHOR]

Mangrove vegetation species respond to multiple environmental gradients, and an enhanced understanding of how mangrove species are distributed across these gradients will facilitate conservation and management. Many environmental gradients correlate with tidal inundation; however small-scale inundation patterns resulting from microtopographical changes are difficult to capture empirically. In contrast, surface elevation is often a suitable, measurable and cost-effective proxy for inundation. This study investigated the relationships between species distribution and surface elevation in a mangrove forest in northwest Singapore. Through high-resolution land surveying, we developed a digital elevation model (DEM) and conducted a comprehensive survey of 4380 trees with a stem diameter ≥ 5 cm. A total of 15 species were encountered, and elevation envelopes were generated for 12. Species envelopes were distributed along an elevation continuum, with most species overlapping within the continuum. Spatial autocorrelation (SAC) was present for nine of the 15 species, and when taken into account, species ordering was modified across the elevation continuum. The presence of SAC strongly reinforces the need for research to control for SAC: classical spatial description of mangrove species distribution should be revised to account for ecological factors. This study suggests that (1) surface elevation applies strong controls on species distribution and (2) most mangroves at our study site have similar physiological tolerances. [ABSTRACT FROM AUTHOR]

In complex colloidal systems, particle-poor regions can develop within particle-rich phases during sedimentation or creaming. These particle-poor regions are overlooked by 1D profiles, which are typically used to assess particle distributions in a sample. Alternative methods to visualise and quantify these regions are required to better understand phase separation, which is the focus of this paper. Magnetic resonance imaging has been used to monitor the development of compositional heterogeneity in a vesicle-polymer mixture undergoing creaming. T 2 relaxation time maps were used to identify the distribution of vesicles, with vesicle-poor regions exhibiting higher T 2 relaxation times than regions richer in vesicles. Phase separated structures displayed a range of different morphologies and a variety of image analysis methods, including first-order statistics, Fourier transformation, grey level co-occurrence matrices and Moran’s I spatial autocorrelation, were used to characterise these structures, and quantify their heterogeneity. Of the image analysis techniques used, Moran’s I was found to be the most effective at quantifying the degree and morphology of phase separation, providing a robust, quantitative measure by which comparisons can be made between a diverse range of systems undergoing phase separation. The sensitivity of Moran’s I can be enhanced by the choice of weight matrices used. [ABSTRACT FROM AUTHOR]

Larvae of coastal-marine fishes have been shown repeatedly to swim directionally in the pelagic environment. Yet, biophysical models of larval dispersal typically impose a Simple Random Walk (SRW) algorithm to simulate non-directional movement in the open ocean. Here we investigate the use of a Correlated Random Walk (CRW) algorithm; imposing auto-correlated directional swimming onto simulated larvae within a high-resolution 3D biophysical model of the Gulf of Aqaba, the Red Sea. Our findings demonstrate that implementation of auto-correlated directional swimming can result in an increase of up to ×2.7 in the estimated success rate of larval-settlement, as well as an increase in the extent of connectivity. With accumulating empirical support for the capacity for directional-swimming during the pelagic phase, we propose that CRW should be applied in biophysical models of dispersal by coastal marine fish-larvae. [ABSTRACT FROM AUTHOR]

The existence of a closed micro-crack in plates can be detected by using the nonlinear harmonic characteristics of the Lamb wave. However, its location identification is difficult. By considering the transient nonlinear Lamb under the noise interference, we proposed a location identification method for the closed crack based on the quantitative measurement of Duffing oscillator transient transfer in the phase space. The sliding short-time window was used to create a window truncation of to-be-detected signal. And then, the periodic extension processing for transient nonlinear Lamb wave was performed to ensure that the Duffing oscillator has adequate response time to reach a steady state. The transient autocorrelation method was used to reduce the occurrence of missed harmonic detection due to the random variable phase of nonlinear Lamb wave. Moreover, to overcome the deficiency in the quantitative analysis of Duffing system state by phase trajectory diagram and eliminate the misjudgment caused by harmonic frequency component contained in broadband noise, logic operation method of oscillator state transition function based on circular zone partition was adopted to establish the mapping relation between the oscillator transition state and the nonlinear harmonic time domain information. Final state transition discriminant function of Duffing oscillator was used as basis for identifying the reflected and transmitted harmonics from the crack. Chirplet time-frequency analysis was conducted to identify the mode of generated harmonics and determine the propagation speed. Through these steps, accurate position identification of the closed crack was achieved. [ABSTRACT FROM AUTHOR]

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences. [ABSTRACT FROM AUTHOR]

In this paper, an online and real-time system is presented for detecting partial broken rotor bar (BRB) of inverter-fed squirrel cage induction motors under light load condition. This system with minor modifications can detect any fault that affects the stator current. A fast and accurate spectral estimator based on the theory of Rayleigh quotient is proposed for detecting the spectral signature of BRB. The proposed spectral estimator can precisely determine the relative amplitude of fault sidebands and has low complexity compared to available high-resolution subspace-based spectral estimators. Detection of low-amplitude fault components has been improved by removing the high-amplitude fundamental frequency using an extended-Kalman based signal conditioner. Slip is estimated from the stator current spectrum for accurate localization of the fault component. Complexity and cost of sensors are minimal as only a single-phase stator current is required. The hardware implementation has been carried out on an Intel i7 based embedded target ported through the Simulink Real-Time. Evaluation of threshold and detectability of faults with different conditions of load and fault severity are carried out with empirical cumulative distribution function. [ABSTRACT FROM AUTHOR]

The Sahel of Africa is an eco-sensitive zone with complex relations emerging between vegetation productivity and rainfall. These relationships are spatially non-stationary, non-linear, scale dependant and often fail to be successfully modelled by conventional regression models. In response, we apply a local modelling technique, Geographically Weighted Regression (GWR), which allows for relationships to vary in space. We applied the GWR using climatic data (Normalized Vegetation Difference Index and rainfall) on an annual basis during the growing seasons (June–September) for 2002–2012. The operating scale of the Sahelian NDVI–rainfall relationship was found to stabilize around 160 km. With the selection of an appropriate scale, the spatial pattern of the NDVI-rainfall relationship was significantly better explained by the GWR than the traditional Ordinary Least Squares (OLS) regression. GWR performed better in terms of predictive power, accuracy and reduced residual autocorrelation. Moreover, GWR formed spatial clusters with local regression coefficients significantly higher or lower than those that the global OLS model resulted in, highlighting local variations. Areas near wetlands and irrigated lands displayed weak correlations while humid areas such as the Sudanian region at southern Sahel produced higher and more significant correlations. Finally, the spatial relationship of rainfall and NDVI displayed temporal variations as there were significant differences in the spatial trends throughout the study period. [ABSTRACT FROM AUTHOR]

Most methods for analyzing functional time series rely on the estimation of lagged autocovariance operators or surfaces. As in univariate time series analysis, testing whether or not such operators are zero is an important diagnostic step that is well understood when the data, or model residuals, form a strong white noise. When functional data are constructed from dense records of, for example, asset prices or returns, a weak white noise model allowing for conditional heteroscedasticity is often more realistic. Applying inferential procedures for the autocovariance based on a strong white noise to such data often leads to the erroneous conclusion that the data exhibit significant autocorrelation. We develop methods for performing inference for the lagged autocovariance operators of stationary functional time series that are valid under general conditional heteroscedasticity conditions. These include a portmanteau test to assess the cumulative significance of empirical autocovariance operators up to a user selected maximum lag, as well as methods for obtaining confidence bands for a functional version of the autocorrelation that are useful in model selection/validation. We analyze the efficacy of these methods through a simulation study, and apply them to functional time series derived from asset price data of several representative assets. In this application, we found that strong white noise tests often suggest that such series exhibit significant autocorrelation, whereas our tests, which account for functional conditional heteroscedasticity, show that these data are in fact uncorrelated in a function space. [ABSTRACT FROM AUTHOR]

We carefully characterized the fluorescence blinking of single colloidal CdSe-CdS/ZnS core-multishell quantum dots (QDs) with different surface modifications, including octadecylamine (ODA) coated QDs dispersed in chloroform, aqueous 3-mercaptopropionic acids (3MPA) coated QDs in HEPES solution treated by Ca 2 + ions and ethylene glycol tetraacetic acid (EGTA, Ca 2 + chelator), and aqueous 3MPA-QDs treated by glycerol. It was found that the on- and off-state probability density distributions displayed different rules. The off-state probability density distributions of all QDs complied well with the inverse power law, while the on-state probability density distributions bended upwards in log-log scale, and the degree of the upwards-bending correlated strongly with QD surface modification and fluorescence brightness of the single QD. Further autocorrelation analysis revealed that the fluorescence time series of a single QD was more random when the single QD showed a stronger fluorescence. Realistic numerical simulations with input parameters from quantum mechanical calculations showed that the QD exciton was first generated by an excitation photon; It radiatively recombined to give QD’s fluorescence response, i.e., the on-state, which displayed the upwards-bended on-state probability density distribution profile; The electron and/or the hole of the photoexcited exciton in the QD core, after tunneling to the QD surface, randomly walked through the two-dimensional network of the QD surface states, resulting in the off-state probability density distribution profile of the inverse power law. Surface modification modified the QD surface-state network, in turn modifying the on/off probability density distribution profiles. Our findings provide us with a novel highway of applying colloidal QDs to study microscopic physical, and chemical, processes in many fields including in vivo and in vitro imaging, sensing and labelling. [ABSTRACT FROM AUTHOR]

Lysine 27 to methionine (K27 M) mutation of the histone variant H3.3 drives the formation of an aggressive glioblastoma multiforme tumor in infants. Here we analyzed how the methionine substitution alters the stability of H3.3 nucleosomes in vitro and modifies its kinetic properties in live cells. We also determined whether the presence of mutant nucleosomes perturbed the mobility of the PRC2 subunit Ezh2 (enhancer-of-zeste homolog 2). We found that K27 M nucleosomes maintained the wild-type molecular architecture both at the level of bulk histones and single nucleosomes and followed similar diffusion kinetics to wild-type histones in live cells. Nevertheless, we observed a remarkable differential recovery of Ezh2 in response to transcriptional stress that was accompanied by a faster diffusion rate of the mobile fraction of Ezh2 and a significantly increased immobile fraction, suggesting tighter chromatin binding of Ezh2 upon transcription inhibition. The differential recovery of Ezh2 was dependent on transcription, however, it was independent from K27 M mutation status. These biophysical characteristics shed more light on the mechanism of histone H3.3 K27M in glioma genesis in relation to the kinetic properties of Ezh2. [ABSTRACT FROM AUTHOR]

Over 30 years ago, Kintigh and Ammerman (1982) outlined and applied a heuristic approach to spatial archaeology that balanced quantitative analyses and culturally and historically contextualized archaeology. The theoretical and methodological messages were that we need to do more than “eyeball” spatial patterns, we need to apply the proper analyses based on the characteristics of our datasets, and we need to ensure that our models, quantitative analyses, and resulting interpretations are based in the proper cultural and historical contexts. My goal in this paper is to examine how two of the concepts in this approach, significance and context, apply to a modern spatial archaeology that heavily utilizes geospatial computing tools. Although these tools help to solve several concerns that existed in the field 30 years ago, they can also cause others, such as mistaking autocorrelation for correlation or confusion about which of the multitude of available analytical tools is appropriate for particular questions and datasets. In this paper, I present a simplified version of the methodology I have used to address these concerns. I use archaeological, historical, and GIS-modeled data to compare the regional patterning of hierarchical and egalitarian societies in southeastern North America to examine why hierarchical sociopolitical organizations may have arose where they did. I end with a critical review of this approach and a discussion of how such research can be improved moving forward. [ABSTRACT FROM AUTHOR]

Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs. [ABSTRACT FROM AUTHOR]

This paper reports the employment of autocorrelation properties of Golay complementary codes (GCC) to enhance the performance of the time domain multiplexing fiber Bragg grating (TDM-FBG) sensing network. By encoding the light from laser with a stream of non-return-to-zero (NRZ) form of GCC and launching it into the sensing area that consists of the FBG sensors, we have found that the FBG signals can be decoded correctly with the autocorrelation calculations, confirming the successful demonstration of coded TDM-FBG sensor network. OptiGrating and OptiSystem simulators were used to design customized FBG sensors and perform the coded TDM-FBG sensor simulations, respectively. Results have substantiated the theoretical dependence of SNR enhancement on the code length of GCC, where the maximum SNR improvement of about 9 dB is achievable with the use of 256 bits of GCC compared to that of 4 bits case. Furthermore, the GCC has also extended the strain exposure up to 30% higher compared to the maximum of the conventional single pulse case. The employment of GCC in the TDM-FBG sensor system provides overall performance enhancement over the conventional single pulse case, under the same conditions. [ABSTRACT FROM AUTHOR]

Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. [ABSTRACT FROM AUTHOR]

We propose a variance components linear modeling framework to conduct statistical inference on functional connectivity networks that directly accounts for the temporal autocorrelation inherent in functional magnetic resonance imaging (fMRI) time series data and for the heterogeneity across subjects in the study. The novel method estimates the autocorrelation structure in a nonparametric and subject-specific manner, and estimates the variance due to the heterogeneity using iterative least squares. We apply the new model to a resting-state fMRI study to compare the functional connectivity networks in both typical and reading impaired young adults in order to characterize the resting state networks that are related to reading processes. We also compare the performance of our model to other methods of statistical inference on functional connectivity networks that do not account for the temporal autocorrelation or heterogeneity across the subjects using simulated data, and show that by accounting for these sources of variation and covariation results in more powerful tests for statistical inference. [ABSTRACT FROM AUTHOR]

This paper presents new results on the prediction of linear processes in function spaces. The autoregressive Hilbertian process framework of order one (ARH(1) framework) is adopted. A component-wise estimator of the autocorrelation operator is derived from the moment-based estimation of its diagonal coefficients with respect to the orthogonal eigenvectors of the autocovariance operator, which are assumed to be known. Mean-square convergence to the theoretical autocorrelation operator is proved in the space of Hilbert–Schmidt operators. Consistency then follows in that space. Mean absolute convergence, in the underlying Hilbert space, of the ARH(1) plug-in predictor to the conditional expectation is obtained as well. A simulation study is undertaken to illustrate the large-sample behavior of the formulated component-wise estimator and predictor. Additionally, alternative component-wise (with known and unknown eigenvectors), regularized, wavelet-based penalized, and nonparametric kernel estimators of the autocorrelation operator are compared with the one presented here, in terms of prediction. [ABSTRACT FROM AUTHOR]

The problem of constructing polynomials with all coefficients 1 or −1 and large merit factor (equivalently with small L 4 norm on the unit circle) arises naturally in complex analysis, condensed matter physics, and digital communications engineering. Most known constructions arise (sometimes in a subtle way) from difference sets, in particular from Paley and Singer difference sets. We consider the asymptotic merit factor of polynomials constructed from other difference sets, providing the first essentially new examples since 1991. In particular we prove a general theorem on the asymptotic merit factor of polynomials arising from cyclotomy, which includes results on Hall and Paley difference sets as special cases. In addition, we establish the asymptotic merit factor of polynomials derived from Gordon–Mills–Welch difference sets and Sidelnikov almost difference sets, proving two recent conjectures. [ABSTRACT FROM AUTHOR]

Blades are crucial components of turbomachinery with high failure risk due to harsh working conditions. Therefore, it is urgently necessary to monitor blade condition. Blade tip timing (BTT) is a potential technique for blade vibrating monitoring owing to its noncontact and efficiency. However, inherent undersampling is a brake on the application of BTT. How to identify the parameters characterizing blade condition from severe undersampled samples is the main hot topic in the BTT field. Here, we proposed coprime sampling-based BTT (CS-BTT) and nested sampling-based BTT (NS-BTT) methods for spectrum reconstruction and parameter identification. The proposed method focuses on reconstructing autocorrelation/covariance information rather than the signal waveform, which improves computational efficiency owing to the elimination of phase information in the autocorrelation function. The proposed method can reconstruct the spectrum without loss of frequency and amplitude information by generating dense lags to improve the decimated rate of autocorrelation function to satisfy the Nyquist criterion. In CS-BTT and NS-BTT, coprime and nested layouts are used to achieve sub-Nyquist sampling, respectively. Moreover, an extended nested sampling was introduced to expand spectrum sensing range. We listed the optimal layouts for coprime, nested, and extended nested samplings in the case that the probe number was given. Compared with the existing BTT methods, the proposed method has a wider applicability and lower computational complexity. The effectiveness and robustness of the proposed method were validated by both simulations and experiments. [ABSTRACT FROM AUTHOR]

Field boundaries are important habitat for bats in agricultural landscapes, serving as commuting and foraging areas for many species. The goal of our study was to better understand the drivers of bat activity in agricultural landscapes to inform conservation policy and make specific recommendations for habitat management. We placed sixty-four full spectrum bat detectors at random recording locations, weekly, along field boundaries in North Somerset between July and October 2020. We used an automated classifier to analyse recordings and performed error rate modelling to account for and remove the majority of error in automated classifications. We used generalised additive models to explore bat response to recording location metrics, controlling for spatial-autocorrelation and temporal differences in sampling. We validated our models with k-fold cross-validation and explored the utility of our models for predicting bat activity at new sites. We found that field boundary characteristics better describe bat activity than adjacent field characteristics or measures of local landscape for the majority of species studied. Bat activity was higher along tall, wide, vegetated field boundaries containing trees; there was lower activity at arable recording locations. Still, bat activity was highly variable and predictive error was high. We found a large spatial effect driving activity patterns, meaning models are not able to predict activity beyond the extent of the study area. We recommend management strategies that give incentives to farmers for replacing fences with hedgerows, planting hedgerow trees, and maintaining tall and outgrown field boundaries. • We investigated the drivers of bat activity in agricultural landscapes, aiming to make recommendations to improve management. • We found that field boundary characteristics better explained bat activity patterns than local landscape or adjacent fields. • We found bat activity was higher at tall, vegetated field boundaries containing trees. • We found a large spatial effect driving activity, partly explained by distance to known roosts in Rhinolophus ferrumequinum. • Our results highlight the value of field boundaries for biodiversity in agricultural landscapes. [ABSTRACT FROM AUTHOR]

Synchrosqueezing is a procedure for improving the frequency localization of a continuous wavelet transform. This research focuses on using a synchrosqueezed wavelet transform (SWT) to determine the damping ratios of a vibrating system using a free-response signal. While synchrosqueezing is advantageous due to its localisation in the frequency, damping identification with the original SWT is not sufficiently accurate. Here, the synchrosqueezing was researched in detail, and it was found that an error in the frequency occurs as a result of the numerical calculation of the preliminary frequencies. If this error were to be compensated, a better damping identification would be expected. To minimize the frequency-shift error, three different strategies are investigated: the scale-dependent coefficient method, the shifted-coefficient method and the autocorrelated-frequency method. Furthermore, to improve the SWT, two synchrosqueezing criteria are introduced: the average SWT and the proportional SWT. Finally, the proposed modifications are tested against close modes and the noise in the signals. It was numerically and experimentally confirmed that the SWT with the proportional criterion offers better frequency localization and performs better than the continuous wavelet transform when tested against noisy signals. [ABSTRACT FROM AUTHOR]

Functional panels are collections of functional time series, and arise often in the study of high frequency multivariate data. We develop a portmanteau style test to determine if the cross-sections of such a panel are independent and identically distributed. Our framework allows the number of functional projections and/or the number of time series to grow with the sample size. A large sample justification is based on a new central limit theorem for random vectors of increasing dimension. With a proper normalization, the limit is standard normal, potentially making this result easily applicable in other FDA context in which projections on a subspace of increasing dimension are used. The test is shown to have correct size and excellent power using simulated panels whose random structure mimics the realistic dependence encountered in real panel data. It is expected to find application in climatology, finance, ecology, economics, and geophysics. We apply it to Southern Pacific sea surface temperature data, precipitation patterns in the South-West United States, and temperature curves in Germany. [ABSTRACT FROM AUTHOR]

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicity library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation. [ABSTRACT FROM AUTHOR]

Context Childhood lead exposure has been associated with criminal behavior later in life. The current study aimed to analyze the association between elevated blood lead levels (n=59,645) and crime occurrence (n=90,433) across census tracts within St. Louis, Missouri. Design Longitudinal ecological study. Setting Saint Louis, Missouri. Exposure measure Blood lead levels. Main outcome measure Violent, Non-violent, and total crime at the census tract level. Results Spatial statistical models were used to account for the spatial autocorrelation of the data. Greater lead exposure at the census-tract level was associated with increased violent, non-violent, and total crime. In addition, we examined whether non-additive effects existed in the data by testing for an interaction between lead exposure and concentrated disadvantage. Some evidence of a negative interaction emerged, however, it failed to reach traditional levels of statistical significance (supplementary models, however, revealed a similar negative interaction that was significant). Conclusions More precise measurements of lead exposure in the aggregate, produced additional evidence that lead is a potent predictor of criminal outcomes. [ABSTRACT FROM AUTHOR]

Here we analyze how dispersal, genetic drift, and adaptation to the local environment affect the geographical differentiation of a quantitative character through natural selection using a spatial dynamic model for the evolution of the distribution of mean breeding values in space and time. The variation in optimal phenotype is described by local Ornstein–Uhlenbeck processes with a given spatial autocorrelation. Selection and drift are assumed to be governed by phenotypic variation within areas with a given mean breeding value and constant additive genetic variance. Between such neighboring areas there will be white noise variation in mean breeding values, while the variation at larger distances has a spatial structure and a spatial scale that we investigate. The model is analyzed by solving balance equations for the stationary distribution of mean breeding values. We also present scaling results for the spatial autocovariance function for mean breeding values as well as that for the covariance between mean breeding value and the optimal phenotype expressing local adaption. Our results show in particular how these spatial scales depend on population density. For large densities the spatial scale of fluctuations in mean breeding values have similarities with corresponding results in population dynamics, where the effect of migration on spatial scales may be large if the local strength of density regulation is small. In our evolutionary model strength of density regulation corresponds to strength of local selection so that weak local selection may produce large spatial scales of autocovariances. Genetic drift and stochastic migration are shown to act through the population size within a characteristic area with much smaller variation in optimal phenotypes than in the whole population. [ABSTRACT FROM AUTHOR]

Maps representing the distribution of chemical residues over anthropogenic floors are the main diagnostic tools used by archaeologists for addressing the identification of geochemical signatures of past actions. Geostatistics allows producing these maps from a sample of locations by modelling the spatial autocorrelation structure of these kind of phenomena. However, the homogeneity of the prediction regions is a strong assumption in the model. The presence of barriers, such as the inner walls of domestic units, introduces discontinuities in prediction areas. In this paper, we investigate how to incorporate information of a geographical nature into the process of geostatistical prediction. We propose the use of cost-based distances to quantify the correlation between locations, a solution which has proved to be a practical alternative approach for archaeological intrasite analysis. The cost-based approach produces more reliable results avoiding the unrealistic assumption of the homogeneity of the study area. As a working example, a case study of the distribution of two specific chemical signatures in domestic floors is presented within a controlled ethnographical context in Northern Gujarat (India). On a broad disciplinary scale, the benefits of using our approach include improved estimates in regions with complex geometry and lower uncertainty in the kriging predictions. [ABSTRACT FROM AUTHOR]

In this paper, smoothing localized directional cyclic autocorrelation (SLDCA) is proposed as a novel time–frequency distribution to analyze the vibration signal of rotor-bearing system in oil-film instability. Based on the cyclostationarity of real-valued signal, directional cyclic autocorrelation (DCA) is defined for the complex-valued signal. In order to suppress the cross-term interferences, the DCA is localized and filtered with two-dimensional time–frequency window which allows the smoothing kernel function to adapt to the signal time–frequency-varying characteristics. And then the localized DCA is smoothed by the localized optimal radially Gaussian kernel and cascaded to produce the SLDCA with high time–frequency resolution and less susceptibility to cross-term interferences. The application of SLDCA in the oil-film instability analysis verifies that the SLDCA can not only precisely detect the instantaneous frequency information in the time–frequency distribution with high resolution and robustness to the noises and cross-terms, but also provide the phase coupling information of rotor instantaneous planar motion by which the directivity and shape of the planar motion can be determined. [ABSTRACT FROM AUTHOR]

This paper studies the asymptotic properties of a plug-in predictor, based on the formulation of a componentwise estimator of the autocorrelation operator, for a special class of standard autoregressive Hilbertian processes of order one (ARH(1) processes). In the Gaussian case, double asymptotic functional plug-in prediction intervals are derived. Some numerical examples are considered for illustration. [ABSTRACT FROM AUTHOR]

We propose autocorrelation-based time synchronous averaging (ATSA) to cope with the challenges associated with the current practice of time synchronous averaging (TSA) for planet gears in planetary gearboxes of wind turbine (WT). An autocorrelation function that represents physical interactions between the ring, sun, and planet gears in the gearbox is utilized to define the optimal shape and range of the window function for TSA using actual kinetic responses. The proposed ATSA offers two distinctive features: (1) data-efficient TSA processing and (2) prevention of signal distortion during the TSA process. It is thus expected that an order analysis with the ATSA signals significantly improves the efficiency and accuracy in fault diagnostics of planet gears in planetary gearboxes. Two case studies are presented to demonstrate the effectiveness of the proposed method: an analytical signal from a simulation and a signal measured from a 2 kW WT testbed. It can be concluded from the results that the proposed method outperforms conventional TSA methods in condition monitoring of the planetary gearbox when the amount of available stationary data is limited. [ABSTRACT FROM AUTHOR]

Second order cyclostationary (CS2) components in vibration or acoustic emission signals are typical symptoms of a wide variety of faults in rotating and alternating mechanical systems. The square envelope spectrum (SES), obtained via Hilbert transform of the original signal, is at the basis of the most common indicators used for detection of CS2 components. It has been shown that the SES is equivalent to an autocorrelation of the signal׳s discrete Fourier transform, and that CS2 components are a cause of high correlations in the frequency domain of the signal, thus resulting in peaks in the SES. Statistical tests have been proposed to determine if peaks in the SES are likely to belong to a normal variability in the signal or if they are proper symptoms of CS2 components. Despite the need for automated fault recognition and the theoretical soundness of these tests, this approach to machine diagnostics has been mostly neglected in industrial applications. In fact, in a series of experimental applications, even with proper pre-whitening steps, it has been found that healthy machines might produce high spectral correlations and therefore result in a highly biased SES distribution which might cause a series of false positives. In this paper a new envelope spectrum is defined, with the theoretical intent of rendering the hypothesis test variance-free. This newly proposed indicator will prove unbiased in case of multiple CS2 sources of spectral correlation, thus reducing the risk of false alarms. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method׳s performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions. [ABSTRACT FROM AUTHOR]

For any spatially explicit management, determining the appropriate spatial scale of management decisions is critical to success at achieving a given management goal. Specifically, managers must decide how much to subdivide a given managed region: from implementing a uniform approach across the region to considering a unique approach in each of one hundred patches and everything in between. Spatially explicit approaches, such as the implementation of marine spatial planning and marine reserves, are increasingly used in fishery management. Using a spatially explicit bioeconomic model, we quantify how the management scale affects optimal fishery profit, biomass, fishery effort, and the fraction of habitat in marine reserves. We find that, if habitats are randomly distributed, the fishery profit increases almost linearly with the number of segments. However, if habitats are positively autocorrelated, then the fishery profit increases with diminishing returns. Therefore, the true optimum in management scale given cost to subdivision depends on the habitat distribution pattern. [ABSTRACT FROM AUTHOR]

The damage index based on the auto correlation function to detect the damage of the structure under white noise excitation is studied in detail in this paper. The maximum values of the auto correlation function of the vibration response signals (displacement, velocity and acceleration) from different measurement points of the structure are collected and formulated as a vector called Auto Correlation Function at Maximum Point Value Vector (AMV), which is expressed as a weighted combination of the Hadamard product of two mode shapes. AMV is normalized by its root mean square value so that the influence of the excitation can be eliminated. Sensitivity analysis for the different parts of the normalized AMV shows that the sensitivity of the normalized AMV to the local stiffness is dependent most on the sensitivity of the Hadamard product of the two lower order mode shapes to the local stiffness, which has a sudden change of the value around the local stiffness change position. The sensitivity of the normalized AMV has the similar shape and same trend that shows it is a very good damage indicator even for the very small damage. The relative change of the normalized AMV before and after damage occurs in the structure is adopted as the damage index to show the damage location. Several examples of the stiffness reduction detection of a 12-story shear frame structure are utilized to validate the results in sensitivity analysis, illustrate the effectiveness and anti-noise ability of the AMV-based damage detection method and compare the effect of the response type on the detectability of the normalized AMV. [ABSTRACT FROM AUTHOR]