Stationarity Index Research Articles

ОБЗОР ЭПИЗООТИЧЕСКОЙ СИТУАЦИИ ПО ЧУМЕ МЕЛКИХ ЖВАЧНЫХ ЖИВОТНЫХ В МИРЕ С ПРИМЕНЕНИЕМ КАРТОГРАФИЧЕСКИХ ИНСТРУМЕНТОВ

Plague of small ruminants (PPR) is a highly contagious, transboundary disease. This emerging disease, along with bluetongue, is most frequently reported among small ruminants. The PPR pathogen can infect up to 100% of susceptible animals. The disease occurs most often in hyperacute, acute, and subacute forms, while from 30 to 70% of patients die. In this regard, the purpose of this work was to analyze the current epizootic situation of PPR in the world from 2018 to 2022. Primary data on epizootic outbreaks of plague of small ruminants for the period 2018-2022 in the world were taken from the international FAO Empress database. Statistical data processing was carried out using the tools of the Microsoft Excel 2016 program. Cartographic analysis of the PPR epizootic situation was carried out using ArcGis 10.4 GIS software. The authors of this article carried out a cartographic analysis of outbreaks of the disease under study, from which it follows that most of the PPR outbreaks are concentrated in Africa and Asia. The most affected regions are in the zone of North Africa (Algeria, Morocco), as well as Asia (Mongolia and the People's Republic of China). The results of data analysis showed that more than half (59%) of all PPR outbreaks in the world were registered in 2019. 15% in 2018, 13% in 2021, 7% in 2020. The PPR stationarity index for the period under review was 0.6 in Algeria, Israel, China, Morocco; in Bulgaria, Libya, Mongolia - 0.4; in the rest of the above territories by 0.2, respectively.

Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning

Deep learning is not the most accurate way for recognizing time series signals, and it is unable to identify non-stationary time series signals with numerous chaotic classes. Moreover, the signal detection benefits from data preprocessing have gone unnoticed. Therefore, this paper investigates the detection and analysis of non-stationary time series signals using deep learning and data preprocessing. The fitting model of the historical stationarity index is built based on the Gaussian mixture model of single Gaussian models, and the change point of the non-stationary time series signal is detected. To further increase the signal's recognition rate, the non-stationary time series signal is preprocessed using the truncated migration algorithm. The main classification task and the auxiliary classification tasks are constructed to identify non-stationary time series signals characterized by huge chaotic classes through multi-task learning. The efficiency of the suggested method and model is validated by experimental data.

What drives spatially varying ecological relationships in a wide‐ranging species?

AbstractAimDecades of research on species distributions has revealed geographic variation in species‐environment relationships for a given species. That is, the way a species uses the local environment varies across geographic space. However, the drivers underlying this variation are contested and still largely unexplored. Niche traits that are conserved should reflect the evolutionary history of a species whereas more flexible ecological traits could vary at finer scales, reflecting local adaptation.LocationNorth America.MethodsWe used mammal observations during a 5‐year period from the iNaturalist biodiversity database and a local ensemble modelling approach to explore spatial variation in American black bear (Ursus americanus) relationships with eight ecological correlates. We tested four biologically driven hypotheses to explain the patterns of local adaptation. We evaluated non‐stationarity in ecological relationships using a Stationarity Index and tested predictive performance using an independent, national‐level animal occurrence data set.ResultsWe documented considerable spatial non‐stationarity in all eight environmental relationships, with the greatest spatial variation occurring in bear's relationship to climatic factors. Notably, the greatest variation in environmental relationships tended to occur along the current boundaries of the species' range, potentially representing the ecological limits to the species geographic range. We additionally documented that spatial variation in relationships with land cover and anthropogenic factors were best explained by niche conservatism at the subspecies level, whereas climatic relationships were better explained by local adaptation.Main ConclusionsBased on these results, we propose that the current distribution of American black bear is determined by an evolutionary legacy of habitat relationships unique to each subspecies combined with more fine‐scale local adaptation to climatic conditions. This result suggests that black bears should be adaptable to climatic changes over the 21st century and that management of habitat and human‐bear relationships could be considered at the subspecies level.

Influence of visual feedback, hand dominance and sex on individuated finger movements

The ability to perform individual finger movements, highly important in daily activities, involves visual monitoring and proprioception. We investigated the influence of vision on the spatial and temporal control of independent finger movements, for the dominant and non-dominant hand and in relation to sex. Twenty-six healthy middle-aged to old adults (M age = 61 years; range 46–79 years; females n = 13) participated. Participants performed cyclic flexion–extension movements at the metacarpophalangeal joint of one finger at a time while keeping the other fingers as still as possible. Movements were recorded using 3D optoelectronic motion technique (120 Hz). The movement trajectory distance; speed peaks (movement smoothness); Individuation Index (II; the degree a finger can move in isolation from the other fingers) and Stationarity Index (SI; how still a finger remains while the other fingers move) were extracted. The main findings were: (1) vision only improved the II and SI marginally; (2) longer trajectories were evident in the no-vision condition for the fingers of the dominant hand in the female group; (3) longer trajectories were specifically evident for the middle and ring fingers within the female group; (4) females had marginally higher II and SI compared with males; and (5) females had fewer speed peaks than males, particularly for the ring finger. Our results suggest that visual monitoring of finger movements marginally improves performance of our non-manipulative finger movement task. A consistent finding was that females showed greater independent finger control compared with males.

Audio coding via EMD

In this paper an audio coding scheme based on the empirical mode decomposition in association with a psychoacoustic model is presented. The principle of the method consists in breaking down adaptively the audio signal into intrinsic oscillatory components, called Intrinsic Mode Functions (IMFs), that are fully described by their local extrema. These extrema are encoded. The coding is carried out frame by frame and no assumption is made upon the signal to be coded. The number of allocated bits varies from mode to mode and obeys to the coding error inaudibility constraint. Due to the symmetry of an IMF, only the extrema (maxima or minima) of one of its interpolating envelopes are perceptually coded. In addition, to deal with rapidly changing audio signals, a stationarity index is used and when a transient is detected, the frame is split into two overlapping sub-frames. At the decoder side, the IMFs are recovered using the associated coded maxima, and the original signal is reconstructed by IMFs summation. Performance of the proposed coding is analyzed and compared to that of MP3 and AAC codecs, and the wavelet-based coding approach. Based on the analyzed mono audio signals, the obtained results show that the proposed coding scheme outperforms the MP3 and the wavelet-based coding methods and performs slightly better than the AAC codec, showing thus the potential of the EMD for data-driven audio coding.

Wrist Posture Does Not Influence Finger Interdependence.

A task involving an instructed finger movement causes involuntary movements in the noninstructed fingers of the hand, also known as finger interdependence. It is associated with both mechanical and neural mechanisms. The current experiment investigated the effect of finger interdependence due to systematic changes of the wrist posture, close to neutral. Eight right-handed healthy human participants performed submaximal cyclic flexion and extension at the metacarpophalangeal joint at 0° neutral, 30° extension, and 30° flexion wrist postures, respectively. The experiment comprised of an instruction to move one of the 4 fingers-index, middle, ring, and little. Movements of the instructed and noninstructed fingers were recorded. Finger interdependence was quantified using enslavement matrix, individuation index, and stationarity index, and it was compared across wrist postures. The authors found that the finger interdependence does not change with changes in wrist posture. Further analysis showed that individuation and stationarity indices were mostly equivalent across wrist postures, and their effects were much smaller than the average differences present among the fingers. The authors conclude that at wrist postures close to neutral, the finger interdependence is not affected by wrist posture.

Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.

A training image optimization method in multiple-point geostatistics and its application in geological modeling

Abstract Based on the analysis of the high-order compatibility optimization method proposed by predecessors, a new training image optimization method based on data event repetition probability is proposed. The basic idea is to extract the data event contained in the condition data and calculate the number of repetitions of the extracted data events and their repetition probability in the training image to obtain two statistical indicators, unmatched ratio and repeated probability variance of data events. The two statistical indicators are used to characterize the diversity and stability of the sedimentary model in the training image and evaluate the matching of the geological volume spatial structure contained in data of the well block to be modeled. The unmatched ratio reflects the completeness of geological model in training image, which is the first choice index. The repeated probability variance reflects the stationarity index of geological model of each training image, and is an auxiliary index. Then, we can integrate the above two indexes to achieve the optimization of training image. Multiple sets of theoretical model tests show that the training image with small variance and low no-matching ratio is the optimal training image. The method is used to optimize the training image of turbidite channel in Plutonio oilfield in Angola. The geological model established by this method is in good agreement with the seismic attributes and can better reproduce the morphological characteristics of the channels and distribution pattern of sands.

Assessment of the peste des petits ruminants world epizootic situation and estimate its spreading to Russia.

Aim:This study focuses on the spatial dynamic associated with the spreading of the peste des petits ruminants (PPR) disease for the past decade (from the year 2007 to 2017), assesses the resulting situation in the world, and has an emphasis on Russian advantages been a PPR host.Materials and Methods:Outbreaks were confirmed and reported officially by the World Organization for Animal Health (enzyme-linked immunosorbent assay and polymerase chain reaction were used). Data contain the account number of infected, dead, and all susceptible animals in focus of infection in the period of 2007-2017. Once conventional statistical population was defined, a model was installed. Geo-information system QuickMAP was used to clear up the map disease, and through the @Risk program, we got our forecasting value of future situations (by Monte Carlo method).Results:The spatial study of PPR’s occurrence and its spread was mapping according to the incidence of cases and outbreaks. Clusters demonstrated risk levels in the world in the period from 2007 to 2017 year. Based on the epizootological analysis, an assessment of PPR risk and the probability movement of infection in Russia from nearby disadvantaged countries had been carried out. A statistically significant impact of the socioeconomic system on the stationarity index was found equal to 0.63. The PPR risk of spreading could not be ignored. Nevertheless, conducting effective large-scale vaccine companies in a complex of antiepizootic activities against PPR could reduce the risk of spread of the disease up to 91.8%.Conclusion:Despite all mentioned facts above, the PPR probability can only be reduced by coordinating work of border veterinary services, as in disadvantaged as in free from this disease country, that is, what makes an effective and complete eradication of the disease could be quite realistic.

Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

Car sharing is a type of car rental service, by which consumers rent cars for short periods of time, often charged by hours. The analysis of urban traffic big data is full of importance and significance to determine locations of depots for car-sharing system. Taxi OD (Origin-Destination) is a typical dataset of urban traffic. The volume of the data is extremely large so that traditional data processing applications do not work well. In this paper, an optimization method to determine the depot locations by clustering taxi OD points with AP (Affinity Propagation) clustering algorithm has been presented. By analyzing the characteristics of AP clustering algorithm, AP clustering has been optimized hierarchically based on administrative region segmentation. Considering sparse similarity matrix of taxi OD points, the input parameters of AP clustering have been adapted. In the case study, we choose the OD pairs information from Beijing’s taxi GPS trajectory data. The number and locations of depots are determined by clustering the OD points based on the optimization AP clustering. We describe experimental results of our approach and compare it with standard K-means method using quantitative and stationarity index. Experiments on the real datasets show that the proposed method for determining car-sharing depots has a superior performance.

Characterization of the spatial variability of soil available zinc at various sampling densities using grouped soil type information.

The influence of anthropogenic activities and natural processes involved high uncertainties to the spatial variation modeling of soil available zinc (AZn) in plain river network regions. Four datasets with different sampling densities were split over the Qiaocheng district of Bozhou City, China. The difference of AZn concentrations regarding soil types was analyzed by the principal component analysis (PCA). Since the stationarity was not indicated and effective ranges of four datasets were larger than the sampling extent (about 400m), two investigation tools, namely F3 test and stationarity index (SI), were employed to test the local non-stationarity. Geographically weighted regression (GWR) technique was performed to describe the spatial heterogeneity of AZn concentrations under the non-stationarity assumption. GWR based on grouped soil type information (GWRG for short) was proposed so as to benefit the local modeling of soil AZn within each soil-landscape unit. For reference, the multiple linear regression (MLR) model, a global regression technique, was also employed and incorporated the same predictors as in the GWR models. Validation results based on 100 times realization demonstrated that GWRG outperformed MLR and can produce similar or better accuracy than the GWR approach. Nevertheless, GWRG can generate better soil maps than GWR for limit soil data. Two-sample t test of produced soil maps also confirmed significantly different means. Variogram analysis of the model residuals exhibited weak spatial correlation, rejecting the use of hybrid kriging techniques. As a heuristically statistical method, the GWRG was beneficial in this study and potentially for other soil properties.

Voiced/unvoiced speech classification‐based adaptive filtering of decomposed empirical modes for speech enhancement

This study presents a speech filtering method exploiting the combined effects of the empirical mode decomposition (EMD) and the local statistics of the speech signal using the adaptive centre weighted average (ACWA) filter. The novelty lies in incorporating the frame class (voiced/unvoiced) in the conventional filtering using the EMD and the ACWA filter. The speech signal is segmented into frames and each one is broken down by the EMD into a finite number of intrinsic mode functions (IMFs). The number of filtered IMFs depends on whether the frame is voiced or unvoiced. An energy criterion is used to identify voiced frames while a stationarity index distinguishes between unvoiced and transient sequences. Reported results obtained on signals corrupted by additive noise (white, F16, factory) show that the proposed filtering in line with the frame class is very effective in removing noise components from noisy speech signal. Compared with filtering results of the wavelet, the ACWA, and the EMD-ACWA methods, the proposed technique gives much better results in terms of average segmental signal-to-noise ratio and listening quality based on perceptual evaluation speech quality score.

Introducing ‘presence’ and ‘stationarity index’ to study partial migration patterns: an application of a spatio-temporal clustering technique

Partial migration is a crucial mobility pattern in animal ecology. Unlike complete migrations that take place when all the individuals in a population migrate with a clear separation of ranges, partial migrations are migrations that often follow modalities and times that vary from animal to animal. As a result the distinction between migratory and nonmigratory behavior becomes less defined. In this paper, we present an interdisciplinary effort geared to evaluate whether a recent time-aware, density-based clustering technique, called SeqScan, relying on the novel concept of object’s presence can be effectively applied to the study of partial migrations. To that end, we propose an extended framework centered on SeqScan, comprising a noise model for the detection of fine-grained movement patterns, i.e. excursions and inter-cluster transitions, and an internal time-aware validity index, for clustering evaluation. Furthermore, we contrast SeqScan with a recent technique developed in the context of animal ecology and grounded on statistical methods. For the study, we use real trajectories from two large herbivorous species located in Bavaria. We argue that the classification capabilities of SeqScan are comparable to those of the reference method. Moreover, the SeqScan framework overcomes important limitations of more conventional techniques, offering, in particular, the opportunity of quantifying the mobility behavior of individuals.

Data mining techniques for performance analysis of onshore wind farms

Abstract Wind turbines are an energy conversion system having a low density on the territory, and therefore needing accurate condition monitoring in the operative phase. Supervisory Control And Data Acquisition (SCADA) control systems have become ubiquitous in wind energy technology and they pose the challenge of extracting from them simple and explanatory information on goodness of operation and performance. In the present work, post processing methods are applied on the SCADA measurements of two onshore wind farms sited in southern Italy. Innovative and meaningful indicators of goodness of performance are formulated. The philosophy is a climax in the granularity of the analysis: first, Malfunctioning Indexes are proposed, which quantify goodness of merely operational behavior of the machine, irrespective of the quality of output. Subsequently the focus is shifted to the analysis of the farms in the productive phase: dependency of farm efficiency on wind direction is investigated through the polar plot, which is revisited in a novel way in order to make it consistent for onshore wind farms. Finally, the inability of the nacelle to optimally follow meandering wind due to wakes is analysed through a Stationarity Index and a Misalignment Index, which are shown to capture the relation between mechanical behavior of the turbine and degradation of the power output.

Combined stationarity index for the estimation of turbulent fluxes of scalars and particles in the atmospheric surface layer

In this work the performances of three different tests widely used in scientific community for assessing turbulent flow stationarity have been tested and compared. The stationarity tests have been applied to turbulent fluxes of different scalars (temperature, carbon dioxide and water vapour concentration, ultrafine particle number concentration), collected over a wide range of surface roughness conditions, from almost smooth surfaces to surfaces with different degrees of complexity: suburban, urban and forested canopies. The application of the selected tests to the scalar and particle turbulent fluxes measured above the different experimental sites has frequently shown an ambiguous identification of non-stationarity in turbulent flux records, regardless of the surface conditions and of the transported quantity. The potential impact of surface characteristics, transported scalar, atmospheric stratification and micrometeorological conditions on test performances has been investigated. Finally a new operative stationarity index obtained by the combination of two tests has been proposed to assess the second-order stationarity of turbulence time series.

On extraction of time-varying mean wind speed from wind record based on stationarity index

We have proposed in a previous study a non-stationary wind model to represent the typhoon record as a summation of a time-varying mean wind speed (TVM) and a stationary turbulence. This note further suggests a quantitative scheme, rather than the previous qualitative method, to find the best TVM for any given wind record. Trial TVMs are first extracted from the wind record by a data-processing technique named empirical mode decomposition. For each TVM, its corresponding turbulent component is computed by removing the TVM from the original wind record, and the degree of stationarity of the turbulence component is checked. The best TVM is taken as the one that leads to the maximum degree of stationarity. The degree of stationarity of turbulence is quantified by two indicators: β the ratio of horizontal wind variability and wind speed; and γ the ratio of friction velocity at different Reynolds averaging periods. The applicability of the suggested scheme is validated with 550 typhoon and 3300 monsoon records of 10 minute duration and at different measurement heights. Threshold values for the two stationary indicators β and γ are determined using field measurements and their sensitivities to the Reynolds averaging periods are discussed. Observations in this study demonstrate that the suggested scheme is proper for finding the TVM of a wind record. For a stationarity quantification of 10 minute duration record, the γ indicator with 30 second Reynolds averaging period is recommended.

Spatial nonstationarity and the scale of species–environment relationships in the Mojave Desert, California, USA

Species distribution models (SDM) have become a fertile area of research interest at the confluence of spatial ecology and GIScience and have been used to study a wide range of biogeographical phenomena, including invasive species, vector-borne diseases, and biological diversity. Scale is one of the most important considerations in any spatial analysis study because different spatial patterns emerge at different scales. An issue related to the ‘extent’ concept of scale that has more recently been recognized as important is spatial nonstationarity, which exists when processes or models of processes vary across space. This research examined the scale of species–environment relationships by a relatively new (in SDM) statistical method, geographically weighted regression (GWR). We tested four different types of species and 10 different types of environmental (climate and topography) variables in univariate GWR models to explore how stationarity and explanatory power varied with scale (as a function of GWR bandwidth size). The results suggest that the scale of species–environment relationships varies for both different types of species and different types of environmental variables. The two metrics used here – stationarity index and explained variance – did not show congruity in terms of a ‘characteristic scale.’ Species' relationships with climate and elevation variables became stationary at broader scales, and in some cases the models did not become stationary at the largest bandwidth tested. The complex topographic variables used here operate at finer scales and were often stationary across all scales or became stationary at small bandwidths. In addition to being instrumental for examining the effects of scale on spatial nonstationarity and a model's explanatory ability, GWR can also be used to explore potential geographical factors that result in nonstationarity.

Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature ( LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology.

Patterns of Impairment in Digit Independence After Subcortical Stroke

The nature of impairment in hand motor control after stroke and its relationship to hand function are still not well understood. In this study, we investigated digit independence in patients with subcortical stroke (n = 8) and moderate hand impairment, defined by wrist and hand Fugl-Meyer scale scores < or =25/33, and age-matched controls (n = 8). Subjects made cyclical flexion-extension movements of an instructed digit while keeping the other digits as still as possible. Movements of the metacarpo-phalangeal (MCP) joints of the five digits were measured using an instrumented glove. The ability to move an instructed digit individually (individuation index), and the ability to keep a noninstructed digit as still as possible (stationarity index) were determined for each digit. Contrary to the finding of normal thumb individuation in a recent study of patients with variable hand motor impairment after stroke, we found that independent movement for all digits was significantly impaired, although individuation and stationarity were differentially affected for each digit. All the digits, including the thumb, showed a similar impairment in individuation. In contrast, stationarity was affected in a digit-dependent pattern: the thumb was affected least, and the middle finger was most impaired. Stroke subjects did not extend their digits fully to the baseline position, and the angular displacement at maximum digit extension correlated significantly with digit individuation. Contrary to expectation, digit independence correlated weakly with clinical tests of hand function, which emphasize grasp. This suggests that corticospinal projections might be separated with respect to function rather than finger topography.

Stationarity index for abrupt changes detection in the time-frequency plane

This paper presents a new method based on time frequency representations (TFRs) for detecting abrupt changes in non-stationary noisy signals, Stationarity indices (SIs), based on different distance measures between TFRs, are defined and a comparison of the performances is performed using ROC curves and statistical properties. Two sets of signals are specially studied here: signals presenting very short segments and nonstrictly stepwise stationary signals. For these signals, classical methods based on autoregressive (AR) spectral estimators are often unsuccessful.