Time Series Of Coefficients Research Articles

Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing-based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infra-red and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (Júcar) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities’ own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.

A High-Resolution Distribution Dataset of Paddy Rice in India Based on Satellite Data

India, as the world’s second-largest rice producer, accounting for 21.7% of global rice production, plays a crucial role in ensuring global food supply stability. However, creating high-resolution rice maps for India, such as those at 10 to 30 m, poses significant challenges due to frequent cloudy weather conditions and the complexities of its agricultural systems. This study used a sample-independent mapping method for rice in India using the synthetic aperture radar (SAR)-based Rice Index (SPRI). We produced 10 m spatial resolution rice distribution maps for three years (i.e., 2018, 2020, and 2022) for 23 states in India, covering 98% of Indian rice production. The method effectively utilized the unique characteristics of rice in the vertical–horizontal (VH) backscatter coefficient time series of Sentinel-1, from ttransplantation to the maturity stage, combined with cloud-free Sentinel-2 imagery. By calculating the SPRI values for each agricultural field object using adaptive parameters, the planting locations of rice were accurately identified. On average, the user, producer, and overall accuracy over all investigated states and union territories was 84.72%, 82.31%, and 84.40%, respectively. Additionally, the regional-scale validation based on the statistical area at the district level showed that the coefficient of determination (R2) ranged from 0.53 to 0.95 for each state, indicating that the spatial distribution of the statistical planted area at the district level was reproduced well.

Innovative Decision Fusion for Accurate Crop/Vegetation Classification with Multiple Classifiers and Multisource Remote Sensing Data

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.

Geographical proximity and technological similarity

From a time-space perspective, we assess the effects of geographical proximity on technological convergence over time identifying proximity dimensions associated with countries’ technological similarities. We compare a time series of input-output coefficients for 66 different countries extracted from the 2021 edition of OECD Inter-Country Input-Output to verify whether nearby countries are more likely to share similar technologies. Our results reveal that geographical technological spillovers are important since closer economies tend to be more similar than distant ones. This is particularly evident for the European economies in the sample, suggesting that institutional proximity also matters for technological convergence. Over time, closer economies are becoming structurally more similar; however, this trend seems to have slowed down after the 2008–9 financial crisis. Conjectures on how informational gaps are filled in the consolidation of the databases – encountered in an environment of limited information – based on known practices of using regional and global average structures may add a layer of uncertainty to our results.

Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model.

Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.

An automatic arrival time picking algorithm of P-wave based on adaptive characteristic function

The accuracy of arrival time and picking speed are significant issues in terms of micro-seismic identification, location, travel-time tomography, as well as source mechanism. In order to detect small changes in signal amplitude and frequency with different signal-to-noise ratios (SNRs), the Allen's characteristic function (Allen's CF) was modified to an adaptive characteristic function (ACF) by relative time series and relative energy coefficients which was compared with the Allen's CF, Baer's CF, and Sedlak's CF. Then, an algorithm for S/L-AIC picker based on ACF for automatic P-wave arrival time picking was presented by the short-term-average to long-term-average ratios (STA/LTA) picker and the Akaike Information Criterion (AIC). In addition, the algorithm was applied to a dataset acquired by a local micro-seismic network at the Jing-Gong coal mine in Shanxi, China, which was compared with a two-step AIC picker, using the original manual selection as a reference to verify the reliability and robustness of the algorithm. Furthermore, the influence of five basic parameters on the accuracy of the S/L-AIC picker based on ACF was discussed by orthogonal tests. The results highlighted that: 81 % of the automatic picks processed by the S/L-AIC picker based on ACF were within +4 ms of deviation, while 66.3 % of the automatic picks processed by the two-step AIC picker were within the same error range; Compared with the other CFs, the ACF applied to the S/L-AIC algorithm performs better for low-SNR data and confirms more accurate and stable results. This study highlights that accurate automatic P-wave arrival time determination is feasible when using the S/L-AIC picker based on ACF.

The dynamics of demographic situation and state of reproductive health of population of circumpolar region of the Russian Federation

The retrospective ecological epidemiological study was carried out using reports of the Federal statistical observation. To evaluate demographic processes the values of main demographic parameters were calculated. To analyze dynamics of demographic processes time series of demographic coefficients were constructed and values of growth rate indicators were calculated. The number of male and female population of the Komi Republic in 1990-2023 decreased by 46.5% and 37.1%. The specific weight of people older than able-bodied age increased in males from 4.9% to 14.7% and in females from 15.7% to 31.6%. The number of females of fertile age (16-49 years) in the Komi Republic decreased by 152.602 people (-47.5%) and their general fertility rate decreased by 33.3%. The birth rate in the group of young women aged 15-19 years decreased in 1990-2021 by 80.1%; and of women aged 20-24 years decreased by 60.4%. The frequency of abortions tends to decrease, but significantly exceeds the national average values. The replacement-level fertility is not achieved in the Komi Republic. Taking into account the peculiarities of demographic, social economic processes, it is necessary to develop and to implement regional program of reproductive health improvement.

A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)

Abstract. This paper describes a simple method for characterizing global-scale waves in the mesosphere and lower thermosphere (MLT), such as tides and traveling planetary waves, using uniformly gridded two-dimensional longitude–time data. The technique involves two steps. In the first step, the Fourier transform is performed in space (longitude), and then the time series of the space Fourier coefficients are derived. In the second step, the wavelet transform is performed on these time series, and wavelet coefficients are derived. A Fourier–wavelet spectrum can be obtained from these wavelet coefficients, which gives the amplitude and phase of the wave as a function of time and wave period. It can be used to identify wave activity that is localized in time, similar to a wavelet spectrum, but the Fourier–wavelet spectrum can be obtained separately for eastward- and westward-propagating components and for different zonal wavenumbers. The Fourier–wavelet analysis can be easily implemented using existing Fourier and wavelet software. MATLAB and Python scripts are created and made available at https://igit.iap-kborn.de/yamazaki/fourierwavelet (last access: 18 August 2023) that compute Fourier–wavelet spectra using the wavelet software provided by Torrence and Compo (1998). Some application examples are presented using MLT data from atmospheric models.

Development of Nottingham Arc Model for DC Series Arc Modeling in Photovoltaic Panels

DC series arc faults are known as a great threat to photovoltaic (PV) systems. Due to component aging and the high level of dc voltage, the occurrence of dc series arc fault is a serious concern in PV generation units. This article deals with driving effective dc series arc models from the original Nottingham arc model using the recorded practical data in a PV system. The original Nottingham arc model has three constant parameters and suffers significant inaccuracy for modeling the series arc faults. To avoid the inaccuracy in the modeling, at first, a Nottingham model is developed that is relevant to voltage and current of the fault with three parameters including one constant model order for exponents of the current and two time-series coefficients. Also, to further improvement in the model's accuracy, a two-fold Nottingham model is proposed that is relevant to voltage and current of the fault with five parameters including two constant model orders for exponents of the current and three time-series coefficients. Finally, a full-time-variant Nottingham model is proposed that mathematically contains three time-series parameters including one time-variant model order of the current exponent. In the full-time-variant Nottingham model, the optimal parameters corresponding to each sliding window of data are utilized. In all the proposed models, the arc coefficients are estimated using a new formulation using the least-squares error technique. Verifying the effectiveness of the proposed models through actual data, the accuracy of the proposed models is compared with the original Nottingham model.

Signatures of Eurasian heat waves in global Rossby wave spectra

Abstract. This paper investigates systematic changes in the global atmospheric circulation statistics during Eurasian heat waves (HWs). The investigation of Rossby wave energy anomalies during HWs is based on the time series of Hough expansion coefficients representing Rossby waves with the troposphere–barotropic structures through the extended boreal summer in the European ERA5, ERA-Interim, Japanese 55-year Reanalysis (JRA-55) and Modern-Era Retrospective analysis for Research and Applications (MERRA). The climatological Rossby wave energy distribution is shown to follow a χ2 distribution with skewness dependent on the zonal scale. The applied multivariate decomposition reveals signatures of the Eurasian HWs in the probability density functions (PDFs) of the Rossby wave energy across scales. Changes in the PDFs are consistent with changes in the intramonthly variance during HWs. For the zonal-mean state (the zonal wavenumber k=0), a decrease in skewness is found, although it is not statistically significant. A reduction in skewness hints to an increase in the number of active degrees of freedom, indicating more independent modes involved in the circulation. A shift in the spectral distribution of the k=0 intramonthly variance is shown to describe a weakening of the mean westerlies near their core at 45∘ N and their strengthening at high latitudes. At planetary scales (k= 1–3), the skewness in the troposphere–barotropic Rossby wave energy significantly increases during HWs. This coincides with a reduction of intramonthly variance, in particular at k=3, and persistent large-scale circulation anomalies. Based on the χ2 skewness, we estimate a reduction of the active degrees of freedom for the planetary-scale Rossby waves of about 25 % compared to climatology. At synoptic scales (k= 4–10), no change in skewness is detected for the Eurasian HWs. However, synoptic waves k= 7–8 are characterised by a statistically significant increase in intramonthly variance of about 5 % with respect to the climatology. In addition, a shift of the entire Rossby wave energy distribution at synoptic scales, along with amplification, is observed during HWs.

Driving factors of spatial-temporal difference in China's transportation sector carbon productivity: an empirical analysis based on Geodetector method.

Carbon productivity is the core index to measure the performance of carbon emission reduction. Exploring the driving factors of the spatial-temporal differences in China's transportation sector, carbon productivity (TSCP) is conducive to the low-carbon sustainable development of the transportation sector. Based on the calculation of TSCP in 30 provinces in China from 2000 to 2019, we use time series, spatial visualization, and Dagum Gini coefficient to reveal the characteristics of spatial-temporal evolution and regional differences of TSCP, and uses Geodetector to identify the driving factors that affecting the spatial-temporal differences of TSCP. The results are as follows: (1) from 2000 to 2019, China's TSCP shows a U-shaped change trend of "decline to rise," and shows a spatial pattern of "high in the eastern and central, low in the western". (2) There are obvious regional differences in China's TSCP. The differences within each region show the trend of "eastern > central > western," while the differences between regions show the trend of "central-western > eastern-western > eastern-central," and the differences between regions are the main reason for the overall differences. (3) The spatial-temporal differences in China's TSCP are affected by many factors, such as social economy and self-endowment. Overall, energy intensity, foreign trade, technological innovation level, energy structure, and industrial structure are the dominant factors. Additionally, the interaction between the driving factors enhances the impact on the spatial-temporal differences of TSCP. Finally, according to the analysis results, some policy suggestions are put forward to improve TSCP.

Predicting aerodynamic pressure on a square cylinder from wake velocity field by masked gated recurrent unit model

A masked gated recurrent unit (GRU) model is proposed to establish the mapping relationship between surface pressures on a square cylinder and wake velocities, which can be used to predict statistical and instantaneous aerodynamic pressure fields on a square cylinder from its wakefield. A novel mask net is proposed to figure out one or two wake points where the velocities contribute dominantly to the surface pressure field. A three-dimensional unsteady large-eddy simulation of flow around a square cylinder is performed at Re = 22 000 to generate data for training and validating the proposed models. Results show that local mean pressure coefficients can be well predicted from velocities at even one wake point, but the accuracies of predicting fluctuating pressure coefficients and time-series of local pressure coefficients depend on both the model and the surface pressure location, with more satisfactory predictions achieved in the cross-flow direction. High correlation coefficients of pressure coefficient distributions around a square cylinder between predicted and real distributions are achieved except for the masked GRU model with one wake point. Meanwhile, in terms of the temporal correlation coefficient, all models exhibit good prediction of time-series of pressure coefficients on the side and back surfaces where they are strongly affected by vortex shedding and lower accuracy on the front surface where the pressure coefficients deviate somewhat randomly around the mean value. Large prediction error occurs at the corners of the square cylinder. This study has potential application to risk analysis of structures subject to flow-induced loads.

Assessment of environment of an area’s social vulnerability: ecological aspect

Purpose. The purpose of the current research is identification and integral estimation of ecological determinants of forming environment of an area’s social vulnerability (on the example of the oblasts of the Carpathian region of Ukraine).
 Methodology / approach. The empirics of environment of the social vulnerability based on the identification of ecological components is carried out by construction of commensurate series of indicators’ values through the normalizing for each group of parameters, in particular, within the selected set of areas (oblasts of the Carpathian region). The construction of time series of significance coefficients (calculation of weight coefficients) stipulates the use of the theory of sensitivity, while the calculation of the integral coefficient of the social vulnerability natural environment requires the multiplicative approach. 
 Results. The calculated integral coefficients of the natural environment of social vulnerability contributed to determining the living standards of the population of the natural resource sector and showed a significant socio-ecological divergence among the oblasts of the Carpathian region and Ukraine. In 2019, the highest values of integral coefficients of social vulnerability natural environment in the Carpathian region of Ukraine were observed in Ivano-Frankivsk (0.574) and Chernivtsi (0.391) oblasts. Ivano-Frankivsk oblast was the leader in that regard throughout the studied period. 
 Originality / scientific novelty. The methodological approach for analyzing the social vulnerability of the area has been improved, which, unlike the traditional ones based on determinants of the social, economic, and political environment, provides for a comprehensive analysis of the sensitivity of changes in the social vulnerability of the territory regarding the changes in the natural environment determinants.
 Practical value / implications. The paper offers and substantiates a methodological approach to the estimation of ecological determinants as the indicators of an area’s vulnerability growth, which serves as the information-analytical base for the development of the initiative-taking social vulnerability reduction policy.

On the use of time coefficients of proper orthogonal decomposition for describing quasi-periodic flow processes in the highly turbulent flow of a fish pass

Flow velocity measurements using particle tracking velocimetry carried out in a scale model of a vertical-slot fish pass are analysed using proper orthogonal decomposition. Based on the analysis, the oscillating main stream and related time-varying processes are identified as dominant repeating flow processes. In particular, the time coefficients of the modes are examined in detail. Firstly, the cross-correlation functions of the time coefficients are used to identify the modes best representing this process. Secondly, the time series of coefficients themselves are used to identify the temporal occurrences of the repeating process even when the occurrences are neither identical nor periodical. The presented methodology reduces the examination of the full measured velocity dataset to an analysis of a limited number of coefficient time series, which can be used to detect the occurrences of flow processes repeating at irregular time intervals, and hence to describe their temporal evolution.

Time Series and Mel-frequency Cepstrum Coefficient Analyses of Venous Pulsatile Tinnitus Vascular Sound and Flow Velocity Sensed by Transcranial/Retroauricular Doppler Ultrasound Approaches

Time Series and Mel-frequency Cepstrum Coefficient Analyses of Venous Pulsatile Tinnitus Vascular Sound and Flow Velocity Sensed by Transcranial/Retroauricular Doppler Ultrasound Approaches

Stem girth changes in response to soil water potential in lowland dipterocarp forest in Borneo: An individualistic time-series analysis.

Time-series data offer a way of investigating the causes driving ecological processes as phenomena. To test for possible differences in water relations between species of different forest structural guilds at Danum (Sabah, NE Borneo), daily stem girth increments (gthi), of 18 trees across six species were regressed individually on soil moisture potential (SMP) and temperature (TEMP), accounting for temporal autocorrelation (in GLS-arima models), and compared between a wet and a dry period. The best-fitting significant variables were SMP the day before and TEMP the same day. The first resulted in a mix of positive and negative coefficients, the second largely positive ones. An adjustment for dry-period showers was applied. Interactions were stronger in dry than wet period. Negative relationships for overstorey trees can be interpreted in a reversed causal sense: fast transporting stems depleted soil water and lowered SMP. Positive relationships for understorey trees meant they took up most water at high SMP. The unexpected negative relationships for these small trees may have been due to their roots accessing deeper water supplies (if SMP was inversely related to that of the surface layer), and this was influenced by competition with larger neighbour trees. A tree-soil flux dynamics manifold may have been operating. Patterns of mean diurnal girth variation were more consistent among species, and time-series coefficients were negatively related to their maxima. Expected differences in response to SMP in the wet and dry periods did not clearly support a previous hypothesis differentiating drought and non-drought tolerant understorey guilds. Trees within species showed highly individual responses when tree size was standardized. Data on individual root systems and SMP at several depths are needed to get closer to the mechanisms that underlie the tree-soil water phenomena in these tropical forests. Neighborhood stochasticity importantly creates varying local environments experienced by individual trees.

Temporal and Spatial Taylor's Law: Application to Japanese Subnational Mortality Rates

AbstractTaylor's law is a widely observed empirical pattern that relates the variances to the means of population densities. We present four extensions of the classical Taylor's law (TL): (1) a cubic extension of the linear TL describes the mean–variance relationship of human mortality at subnational levels well; (2) in a time series, long-run variance measures not only variance but also autocovariance, and it is a more suitable measure than variance alone to capture temporal/spatial correlation; (3) an extension of the classical equally weighted spatial variance takes account of synchrony and proximity; (4) robust linear regression estimators of TL parameters reduce vulnerability to outliers. Applying the proposed methods to age-specific Japanese subnational death rates from 1975 to 2018, we study temporal and spatial variations, compare different coefficient estimators, and interpret the implications. We apply a clustering algorithm to the estimated TL coefficients and find that cluster memberships are strongly related to prefectural gross domestic product. The time series of spatial TL coefficients has a decreasing trend that confirms the narrowing gap between rural and urban mortality in Japan.

Real‐Time 3‐D Modeling of the Ground Electric Field Due To Space Weather Events. A Concept and Its Validation

AbstractWe present a methodology that allows researchers to simulate in real time the spatiotemporal dynamics of the ground electric field (GEF) in a given 3‐D conductivity model of the Earth based on continuously augmented data on the spatiotemporal evolution of the inducing source. The formalism relies on the factorization of the source by spatial modes (SM) and time series of respective expansion coefficients and exploits precomputed GEF kernels generated by corresponding SM. To validate the formalism, we invoke a high‐resolution 3‐D conductivity model of Fennoscandia and consider a realistic source built using the Spherical Elementary Current Systems (SECS) method as applied to magnetic field data from the International Monitor for Auroral Geomagnetic Effect network of observations. The factorization of the SECS‐recovered source is then performed using the principal component analysis. Eventually, we show that the GEF computation at a given time instant on a 512 × 512 grid requires less than 0.025 s provided that GEF kernels due to pre‐selected SM are computed in advance. Taking the 7–8 September 2017 geomagnetic storm as a space weather event, we show that real‐time high‐resolution 3‐D modeling of the GEF is feasible. This opens a practical opportunity for GEF (and eventually geomagnetically induced currents) nowcasting and forecasting.

Slope with Predetermined Shear Plane Stability Predictions under Cyclic Loading with Innovative Time Series Analysis by Mechanical Learning Approach.

We propose a mechanical learning method that can be used to predict stability coefficients for slopes where slopes with predetermined shear planes are subjected to cyclic seismic loads under undrained conditions. Firstly, shear tests with cyclic loading of different parameters were simulated on designated slip zone soil specimens, in which the strain softening process leading to landslide occurrence was closely observed. At the same time, based on the limit equilibrium analysis of the Sarma method, the variation of slope stability coefficients under different cyclic loads was investigated. Finally, a Box–Jenkins’ modeling approach is used to predict the data from the time series of slope stability coefficients using a mechanical learning approach. The simulation results show that (1) reduction in coordination number can be an accurate indicator of the level of strain softening and evolutionary processes; (2) the gradual reduction of shear stress facilitates the soil strain softening process, while different cyclic loading stress amplitudes will result in rapid penetration or non-penetration of the fracture zone by means of particulate flow. Although the confining pressure of the slip zone soil can inhibit the increase of fractures, it has a limited inhibitory effect on strain softening; (3) based on field observations of the slope stability factor and stress field, two possible landslide triggering mechanisms are described. (4) Mechanical learning of time series can accurately predict the changing pattern of stability coefficients of slopes without loading. This study establishes a potential bridge between the geological investigation of landslides and the theoretical background of landslide stability coefficient prediction.

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network.

Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.