Residual Time Series Research Articles

The article presents the results of assessing the reliability of a statistical model for developing a scenario of possible changes in the average winter air temperature over the territory of Azerbaijan. The model is based on the assumption of homogeneous soil cover in the horizontal direction, which allows the soil to be considered as a non-stationary onedimensional system with a vertical spatial coordinate and time. The statistical model used is grounded in the assumptions tha t the temporal structure of winter temperature consists of periodic and non-periodic changes over various time intervals, and the existence of a trend in the long-term dynamics of air temperature is a widely recognized and reliably established fact. Here, the random components of the time series are calculated using Schuster’s method and multiple cyclicities, and spectral analysis is conducted to identify short- and long-period fluctuations in these series. Furthermore, the expected values of air temperature residuals are computed. After this, the overall trend of the expected air temperature values was calculated as the sum of the values obtained from the linear trend and the computed residuals of air temperature. Given that the behavior of the climate system exhibits patterns of both short-period and long-period cyclicities, and their alternation within a certain time interval persists, we conducted numerical experiments based on the spectral analysis of time series of winter air temperature residuals. The calculations showed that the optimal approach is the use of a combination of cycles of 6, 9 -10, and 14-15 years. Only in the Nakhchivan Autonomous Republic was a combination of 6 and 9-10 year cycles used. To assess the quality of the proposed model, the correlation coefficient between the actual and calculated values of winter air temperature was utilized. To verify the reliability of the obtained research results using independent data (from 1998 to 2022), we compared the estimates of both current climatic changes and their expected magnitudes. The model was tested using both five -year averaged temperature changes and annual temperature changes, analyzed through graphical representations. It was found that across all physical-geographical zones and climatic periods, in 44 cases (73 %), the absolute calculation errors were ΔT ≤ 1.00C, and in 58 cases (97 %), ΔT ≤ 2.00C. These results, along with other data and the model's reliability measure, demonstrate that the proposed model adequately describes changes in winter temperature and can be used to develop a scenario for changes in average winter air temperature over the next 20-25 years.

This article is dedicated to the development and research of an advanced hybrid machine learning method for time series forecasting in decision support systems (DSS). The relevance of the work is driven by the rapid growth of data volumes in modern information systems, particularly in cloud infrastructures, and the need for accurate forecasting tools for effective resource management. The objective of the study is to increase the accuracy of computing resource load forecasting by developing a hybrid model that combines the advantages of statistical methods and deep learning architectures. A novel hybrid architecture is proposed, integrating the Autoregressive Integrated Moving Average (ARIMA) model for modeling linear components of a time series, and a Long Short-Term Memory (LSTM) recurrent neural network with a built-in Attention Mechanism for analyzing non-linear residuals. The ARIMA model is used to capture stationary dependencies and seasonality, while the LSTM network with an attention mechanism effectively models complex, non-linear, and long-term patterns in the data remaining after ARIMA processing. An experimental study was conducted on a real dataset of CPU utilization monitoring from virtual machines in the AWS (Amazon Web Services) cloud environment. The proposed hybrid ARIMA-LSTM model with an attention mechanism demonstrated a significant improvement in forecasting accuracy compared to baseline models: pure ARIMA, pure LSTM, and a standard hybrid ARIMA-LSTM model. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics for the developed model were 12-18% lower than those of the best-performing baseline model. Scientific novelty lies in the enhancement of existing hybrid approaches by integrating an attention mechanism into the LSTM architecture for analyzing time series residuals. Practical significance of the work consists in the potential for implementing the developed method in automated DSS for optimizing resource allocation, auto-scaling, preventing overloads, and reducing operational costs of cloud infrastructure.

Abstract. This study focuses on the classification of synoptic conditions leading to episodes of extreme high-frequency (HF) sea level oscillations in the Adriatic Sea (Mediterranean). Two types of extreme episodes were obtained from sea level time series measured at six tide gauge stations: (i) HF extremes, extracted from HF components (periods shorter than 2 h) of sea level time series and defined as periods in which the HF component was above a threshold value, and (ii) compound extremes, extracted from residual (de-tided) time series and defined as periods in which both HF and residual components were above their respective thresholds. Characteristic synoptic situations preceding both types of extremes were determined using the k-medoids clustering method applied on the ERA5 reanalysis data (mean sea level pressure, temperature at 850 hPa, and geopotential height of 500 hPa level). The structural similarity index measure (SSIM) was used as a distance metric. The data were divided into a training set (from the start of measurements to the beginning of 2018) and a testing set (from the beginning of 2018 to the end of 2020). For each station, the k-medoids method was used to obtain first two and then three clusters with characteristic synoptic patterns called “medoids”. Two distinct patterns related to HF and compound extremes were identified at all stations: (i) a “summer-type” pattern, characterised by a non-gradient mean sea level pressure, warm air advection from the south-southwest at 850 hPa, and the presence of a jet stream at the 500 hPa height, with all three conditions previously found to favour the development of meteorological tsunamis (i.e. the strongest of atmospherically triggered HF sea level oscillations); (ii) a “winter-type” pattern, characterised by pronounced mean sea level pressure gradients favouring winds that induce storm surges, a colder low troposphere, and the presence of a jet stream at the 500 hPa level. Including the third cluster in the analysis led to the extraction of either a novel “bora-type” pattern, involving strong northeast winds at the Bakar and Rovinj stations, or an additional cluster with a medoid that represents the refinement of summer- or winter-type patterns. The extracted medoids of clusters were used to label all days of the testing period. It was shown that HF or compound episodes recorded in the testing period mostly appeared during synoptic situations that highly resembled extracted medoids. The potential of using the k-medoids method for forecasting HF sea level oscillations is discussed.

Corrigendum to “A TG-AGD Anomaly Image Detection model based on residual bottleneck attention and time series prediction” [Appl. Soft Comput. 173 (2025) 112746

Abstract Climate change significantly influences vegetation growth, necessitating an in-depth understanding of the climate-driven dynamics of vegetation to formulate ecological and environmental policies. This study addresses the limitations of traditional correlation analysis methods by utilizing a combined approach of Residual Trend Analysis (RESTREND) and Time Series Segmentation Residual Trend Analysis (TSS-RESTREND), known as CTSS-RESTREND. By using this method, we examined the influence of near-surface air temperature, precipitation, humidity, and wind speed on vegetation growth in Guangdong Province from 2000 to 2020, using Normalized Difference Vegetation Index (NDVI) data and climatic variables. Using MOD13Q1 NDVI data and ERA5 downscaled climate reanalysis data, this research utilizes the CTSS-RESTREND algorithm to quantify the climate effects on vegetation. The analysis reveals that precipitation and humidity are the primary positive drivers of vegetation growth, temperature has a slightly higher positive than negative impact on vegetation, while wind speed generally has a negative impact on vegetation, but its effect is relatively slight. During the growing season, the growth of vegetation becomes more sensitive to the three climatic factors: temperature, precipitation, and humidity. This study provides a more accurate and detailed understanding of the spatiotemporal changes and climate driving factors affecting vegetation in Guangdong Province.

A TG-AGD Anomaly Image Detection model based on residual bottleneck attention and time series prediction

Tidal models that incorporate satellite altimeter data have historically shown discrepancies in accuracy between shallow and deep marine environments. A recent study suggests that these differences may partly stem from neglecting the nonlinear tide-surge interactions in tidal analyses. In this study, we introduce a novel method for estimating tidal constituents from satellite altimeter data in shallow waters, leveraging a 2D hydrodynamic model that accounts for these nonlinear interactions. This approach substantially reduces the variance of unaccounted water level variability, thereby benefiting the estimation. A distinctive feature of our method is the treatment of prior model tidal constituents as stochastic, which helps manage the low temporal resolution of altimeter data by ensuring that unresolved tidal constituents are not updated. We tested our method in the data-rich northwest European continental shelf region, using the high-resolution 2D Dutch Continental Shelf Model version 7 (DCSM). Results show a substantial reduction in the standard deviations of residual water level time series in the shallow waters around Great Britain and in the German Bight, from 11 cm to 5 cm. In deep waters (>200 m), the median standard deviation decreased from 6.8 cm to 6.2 cm. When compared to state-of-the-art ocean tide and surge corrections from publicly available models, our method outperformed them in shallow waters (median standard deviation of 6.0 cm versus 7.5 cm), though the alternative products performed better in deep waters (median standard deviation of 5.5 cm versus 6.2 cm). An estimate of the accuracy at satellite crossovers resulted in an estimated total tidal error of about 1.5 cm (RSS VD). We acknowledge that comparisons in shallow waters are complicated, as alternative products do not account for nonlinear tide-surge interactions. Overall, the demonstration along-track tidal product developed in this study shows potential for improving the tidal representation in the DCSM model. In data-poor regions, the number of tidal constituents that can be reliably estimated using the method may be limited, and alternative strategies might be needed to evaluate the model’s uncertainty in representing tides.

With the application and promotion of space geodesy, the popularization of remote sensing technology, and the development of artificial intelligence, a more accurate and stable Terrestrial Reference Frame (TRF) has become more urgent. For example, sea level change detection, crustal deformation monitoring, and driverless cars, among others, require the accuracy of the terrestrial reference frame to be better than 1 mm in positioning and 0.1 mm/a in velocity, respectively. However, the current frequently used ITRF2014 and ITRF2020 do not satisfy such requirements. Therefore, this paper analyzes the coordinate residual time series data of linear TRFs and finds there are still some unlabeled jumps and time-dependent periodic signals, especially in the GNSS coordinate residuals, which can lead to incorrect station epoch coordinates and velocities, further affecting the accuracy and stability of the TRF. The unlabeled jumps could be detected by the sequential t-test analysis of regime shifts (STARS) combined with the generalized extreme Studentized deviate (GESD) algorithms introduced in our earlier paper. These nonlinear time-dependent periodic signals could be modeled better by singular spectrum analysis (SSA) with respect to least squares fitting; the fitting period is no longer composed of semi-annual and annual items, as with ITRF2014. The periods of continuous coordinate residual time series data longer than 5 years are obtained by FFT. The results show that there are no period signals for individual SLR/VLBI sites, and there are still other period terms, such as 34 weeks, 20.8 weeks and 17.3 weeks, in addition to semi-annual and annual items for some GNSS sites. Moreover, after SSA corrections, the re-calculated TRF and the corresponding EOP could be obtained, based on data from the Chinese Earth Rotation and Reference System Service (CERS) TRF and the Earth Orientation Parameter (EOPs) multi-technique determination software package (CERS TRF&EOP V2.0) developed by the Shanghai Astronomical Observatory (SHAO). Their accuracy could be evaluated with respect to the ITRF2014 and the IERS 14 C04, respectively. The results show that the accuracy and stability of the newly established a nonlinear TRF and EOP based on SSA have been greatly improved and better than a linear TRF and EOP. SSA is better than least squares fitting, especially for those coordinate residual time series with varying amplitude and phase. For GPS, comparing with the ITRF2014, the station coordinate accuracy of 10.8% is better than 1 mm, and the station velocity accuracy of 4.4% is better than 0.1 mm/year. There are 3.1% VLBI stations, for which coordinate accuracy is better than 1 mm and velocity accuracy is better than 0.1 mm/year. However, there are no stations with coordinates and velocities better than 1 mm and 0.1 mm/year for the SLR and DORIS. The WRMS values of polar motion x, polar motion y, LOD, and UT1-UTC are reduced by 2.4%, 3.2%, 2.7%, and 0.96%, respectively. The EOP’s accuracy in SOL-B, in addition to LOD, is better than that of the JPL.

ABSTRACT Seismic events between 2021 and 2024 have made the Campi Flegrei area in southern Italy a prime site for investigating the interplay between fluids and moderate earthquakes. This phenomenon has been monitored by analysing land surface temperature-time series during the same period. The data sets were obtained using the NASA-JPL ECO System Spaceborne Thermal Radiometer Experiment (ECOSTRESS), removing noisy images. A residual temperature time series was analysed using an automatic, self-adaptive fitting method to detect thermal anomalies. To cross-check the retrieved anomalies, a study of the significance of wavelet analysis was performed of the same series, revealing oscillations around the time of the anomalies, thus suggesting their persistence. Then, a time comparison between cross-checked anomalies and seismic activity identified coincidences within 45 days, with 6 over 7 anomalies preceding earthquakes. The method identified precursors performing an accuracy of 71% with a precision of 67%. Notably, thermal anomalies preceded major seismic events by days, while temperature oscillations began to become significant weeks prior. Periods of oscillations between days 4 and 32 have been observed, including several thermal anomalies. The aforementioned suggests the utility of our ECOSTRESS image analysis in risk mitigation.

Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, making it challenging to separate these effects. This study utilized valuable long-term particle number and size distribution (PNSD) data from 2018 to 2023 to develop a tree-based machine learning model enhanced with an interpretable component, incorporating temporal markers to characterize background or time series residuals. Our results demonstrated that, differing from PM2.5, which is significantly shaped by planetary boundary layer height, wind speed plays a crucial role in determining the particle number concentration (PNC), showing strong regional specificity. Furthermore, we systematically identified and analyzed anthropogenically influenced periodic trends. Notably, while Aitken mode observations are initially linked to traffic-related peaks, both Aitken and nucleation modes contribute to concentration peaks during rush hour periods on short-term impacts after deweather adjustment. Pollutant baseline concentrations are largely driven by human activities, with meteorological factors modulating their variability, and the secondary formation of UFPs is likely reflected in temporal residuals. This study provides a flexible framework for isolating meteorological effects, allowing more accurate assessment of anthropogenic impacts and targeted management strategies for UFP and PNC.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

Residual head motion artifact in motion-corrected resting-state (rs-) functional MRI (fMRI) and fMRI datasets reduces the temporal signal-to-noise ratio and leaves non-neuronal signal components in the data, which can induce false findings in these studies. While various residual motion nuisance regressors have been proposed to regress out residual motion artifact after motion correction, these validations have typically been conducted empirically in in vivo data, since realistic head motion–corrupted MR data are not available. Here, we generated motion-corrupted MR data by altering imaging plane coordinates before each volume and slice acquisition from an ex vivo brain phantom using the simulated prospective acquisition correction (SIMPACE) sequence. Testing SIMPACE motion-corrupted data with various intervolume motion patterns, we first investigated the mechanism of the residual motion signal after motion correction and also proposed a voxel-wise motion nuisance regressor, called the partial volume (PV) regressor. We also modified the slice-oriented motion-correction method (SLOMOCO) pipeline with 6 volume-wise rigid intervolume motion parameters (Vol-mopa), 6 slice-wise rigid intravolume motion parameters (Sli-mopa), and the proposed PV motion nuisance regressor. We then compared the residual signal after application of the modified SLOMOCO (mSLOMOCO) pipeline with two other methods: intervolume motion-correction method (VOLMOCO), and the original SLOMOCO (oSLOMOCO). We found that mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors outperformed both VOLMOCO with 6 Vol-mopa and PV regressors and oSLOMOCO with 14 voxel-wise regressors. In tests of the 10 different motion patterns of SIMPACE datasets with 1× and 2× amplified intravolume motion, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline produced the average standard deviation (SD) of the residual time series signals in the gray matter (GM) smaller by 29% (1× amplified intravolume motion) and 45% (2× amplified intravolume motion) than VOLMOCO with 6 Vol-mopa and PV regressors pipeline. Also, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline outperformed oSLOMOCO with 14 voxelwise regressors pipeline, generating the average SD in GM smaller by 28% (1× amplified intravolume motion) and 31% (2× amplified intravolume motion) than oSLOMOCO with 14 voxel-wise regressors pipeline. The novel PV regressor also effectively reduced residual motion artifact as a motion nuisance regressor after both VOLMOCO and mSLOMOCO.

Context. The census of planets around M dwarfs in the solar neighbourhood meets two challenges: detecting the best targets for the future characterisation of planets with ELTs, and studying the statistics of planet occurrence that are crucial to formation scenarios. The radial velocity (RV) method remains the most appropriate for such a census as it is sensitive to the widest ranges of masses and periods. HARPS, mounted on the 3.6 m telescope at La Silla Observatory (ESO, Chile), has been obtaining velocity measurements since 2003, and can therefore be used to analyse a very large and homogeneous dataset. Aims. We performed a homogeneous analysis of the RV time series of 200 M dwarfs observed with HARPS from 2003 to 2019 (gathering more than 15 000 spectra), with the aim of understanding detectable signals such as stellar and planetary companions and activity signals. Methods. The RVs were computed with a template matching method before carrying out the time series analysis. First, we focused on the systematic analysis of the presence of a dominant long-term pattern in the RV time series (linear or quadratic trend and sine function). Then, we analysed higher-frequency perdiodic signals using periodograms of the residual time series and Keplerian function fitting. Results. We found long-term variability in 57 RV time series (28.5%). This led to the revision of the parameters of the massive planet (GJ 9482 b), as well as the detection of four substellar and stellar companions (around GJ 3307, GJ 4001, GJ 4254, and GJ 9588), for which we characterised inclinations and masses by combining RV and astrometry. The periodic analysis allowed us to recover 97% of the planetary systems already published in this sample, but also to propose three new planetary candidates orbiting GJ 300 (7.3 M⊕), GJ 654(5 M⊕), and GJ 739 (39 M⊕), which require additional measurements before they can be confirmed.

ABSTRACT A radio link directly probing the inner solar corona offers the possibility to characterize solar wind properties, including velocity, density, turbulence, and even the axial ratio. In this study, we leveraged radiometric data obtained during a joint superior solar conjunction of the ESA/JAXA BepiColombo mission and the JAXA Akatsuki mission. Our objective is to ascertain the solar wind velocity by analysing Doppler-shift timeseries of radio signals exchanged between the two spacecraft and two distinct ground stations. We conducted a cross-correlation analysis to determine the travel time of large-scale plasma density fluctuations as they intersect with the downlink signals of both spacecraft. This method is applied to the data collected on 2021 March 13 and 2021 March 14. The analysis of the March 13 data has shown that the two Doppler residuals timeseries present a clear correlation at a time-lag of 2910 s. Using the knowledge of the relative distance between the two probe-ground station lines of sight at the closest approach to the Sun, we estimated the solar wind velocity to be $421\pm 21$ km s−1. Following the same procedure for the second experiment, we estimated the solar wind speed velocity to be $336\pm 7$ km s−1. These results are compatible with the sampling of the slow solar wind at heliographic latitudes of $-22^\circ$ and $-26^\circ$, respectively.

Two fractional order cumulative residual time series measures based on Rényi entropy

The modern tectonic deformation of the Chinese mainland is dominated by landmass movements, and active tectonic block regions are geological units with a relatively uniform movement pattern. The removal of CMEs can provide more accurate GPS data for exploring the movement characteristics between active tectonic block regions. In order to improve the effect of CME extraction, we propose that the Crustal Movement Observation Network of China be divided into sub-regions based on the refined definition of active tectonic block regions of the Chinese mainland. In this paper, 247 stations in the CMONOC II network are used to form a large spatial scale GPS network and 6 sub-regions with small spatial scale GPS networks. For the large spatial scale GPS network, we compare and analyze the effects of PCA and ICA filtering, and the study shows that PCA is not suitable for CME extraction in this large spatial scale GPS network, while ICA filtering is better. Subsequently, the large spatial scale GPS network and six small spatial scale GPS networks were used to extract CME using ICA, with the results showing that the RMSE values of the residual time series of the large spatial scale GPS network were reduced by 9.60%, 17.08%, and 16.14% in the directions of E, N, and U, respectively, and that the subregions divided according to the refined and determined first-level active plots of the Chinese continent had their residual time series of the RMSE values were reduced by 26.19%, 26.95%, and 28.32% on average in the three respective directions of E, N, and U. The effect of extracting CMEs by dividing the subregions was 29.16%, 5.44%, and 39.84% higher than the effect of extracting them as a whole in the three directions of E, N, and U, respectively. The experimental results demonstrate that the CMONOC II observation network is an effective and feasible method to extract CMEs according to the finely defined active tectonic block region of the Chinese mainland at the first level.

Characterising continental shelf waves and their drivers for the southeast coast of Australia

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm, which seriously threatens the safety of people's production and life in the mining area. Therefore, it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining. Compared with traditional level survey and InSAR (Interferometric Synthetic Aperture Radar) technology, GNSS (Global Navigation Satellite System) online monitoring technology has the advantages of long-term monitoring, high precision and more flexible monitoring methods. The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation, which can not only describe the residual subsidence process from the mechanism, but also predict the residual subsidence. Therefore, based on GNSS online monitoring technology, combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics, this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area. Through the example, the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence. The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground, and the model prediction effect is good, which provides a new method for the prediction of residual subsidence in mountain mining.

BackgroundThe dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). MethodsAs primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21–50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. ResultsSensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). ConclusionGSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model's performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R2), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks.