Cross-correlation Analysis Research Articles

Neuromuscular Control in Postural Stability: Insights into Myoelectric Activity Involved in Postural Sway During Bipedal Balance Tasks

Examining the dynamic interplay of muscle contributions to postural stability enhances our understanding of the neuromuscular mechanisms underlying balance control. This study examined the similarity in shape (using cross-correlation analysis) between seven individual lower limb electromyographic (EMG) signals and center-of-pressure (COP) displacements (i.e., EMG–COP correlation) in 20 young adults (25.2 ± 4.0 years) performing bipedal balance tasks on both stable and multi-axially unstable surfaces, testing the effects of four factors—leg dominance, surface stability, sway direction, and foot position—on individual EMG–COP correlations. The results revealed significant effects of leg dominance (p = 0.004), surface stability (p ≤ 0.001), and sway direction (p ≤ 0.001) on specific muscles. Notably, balancing on the non-dominant leg resulted in a stronger correlation between tibialis anterior activity and postural sway compared to the dominant leg. On a stable surface, postural sway showed stronger correlations with the rectus femoris, semitendinosus, biceps femoris, gastrocnemius medialis, and soleus muscles than on an unstable surface. Additionally, anteroposterior postural sway exhibited a greater correlation with semitendinosus and tibialis anterior activity compared to mediolateral sway. These findings underscore the importance of specific muscles in maintaining bipedal balance, with implications for improving balance performance across various populations.

Ultrasonic guided wave detection of point rail damage of high-speed railway turnout

The precise and efficient identification of damage in point rails of turnouts represents a critical and challenging technological issue that requires immediate attention. Guided wave inspection, as a non-destructive testing method, holds significant potential for practical applications in the research field focused on the identification of damage in point rails of high-speed railway turnouts. This study integrates rigorous theoretical analysis, advanced numerical simulation techniques, and meticulous experimental investigations to comprehensively investigate the excitation, propagation, and reception processes of ultrasonic-guided waves within the point rail. By strategically applying excitation signals at optimized positions specifically tailored for point rail detection, employing a frequency of 30 kHz, appropriate excitation modes are carefully selected. Defect identification within the extended point rail is performed from a dual perspective, encompassing both cross-correlation analysis and time-frequency analysis. The obtained results substantiate that when employing the cross-correlation coefficient to detect cracks in the elongated point rail, the sensitivity of crack detection is significantly higher in the rail bottom region as opposed to the rail head region. Furthermore, through the analysis of variations in the cross-correlation coefficient and the application of wavelet transformations to the guided wave signals, it is not only feasible to ascertain the presence and location of damages within the rail base region of the extended point rail but also to evaluate the relative size of the cracks. The findings of this research contribute a robust theoretical foundation for the subsequent analysis and evaluation of damage detection in high-speed turnout point rails.

Gamma-ray flares from the jet of the blazar CTA 102 in 2016–2018

CTA 102 is a γ-ray bright blazar that exhibited multiple flares in observations by the Large Area Telescope on board the Fermi Gamma-Ray Space Telescope during the period of 2016--2018. We present results from the analysis of multi-wavelength light curves with the aim of revealing the nature of γ-ray flares from the relativistic jet in the blazar. We analysed radio, optical, X-ray, and γ-ray data obtained in a period from 2012 September 29 to 2018 October 8. We identified six flares in the γ-ray light curve, showing a harder-when-brighter trend in the γ-ray spectra. We performed a cross-correlation analysis of the multi-wavelength light curves. We found nearly zero time lags between the γ-ray and optical and X-ray light curves, implying a common spatial origin for the emission in these bands. We found significant correlations between the γ-ray and radio light curves as well as negative or positive time lags with the γ-ray emission lagging or leading the radio during different flaring periods. The time lags between the γ-ray and radio emission propose the presence of multiple γ-ray emission sites in the source. As seen in 43 GHz images from the Very Long Baseline Array, two moving disturbances (or shocks) were newly ejected from the radio core. The γ-ray flares from 2016 to 2017 are temporally coincident with the interaction between a travelling shock and a quasi-stationary one at ∼0.1 mas from the core. The other shock was found to have emerged from the core nearly simultaneously with the γ-ray flare in 2018. Our results suggest that the γ-ray flares originated from shock-shock interactions.

Temporal associations between microclimate, adult Aedes mosquito indices, and dengue cases at the residence level in Malaysia: Implications for targeted interventions.

Dengue continues to be a major public health concern in Malaysia, as evidenced by the significant surge in cumulative dengue case numbers and deaths in 2023 compared to the previous year. While previous studies have explored the interplay of abiotic and biotic factors of mosquito density and dengue cases on a local scale in Malaysia, there is a notable gap in the research focusing on adult Aedes mosquito populations. This study aims to contribute to the existing knowledge by investigating the association and time lags (TLs) between daily microclimate (DM), mosquito indices (MIs), and dengue cases at the residence level. In this longitudinal study, field data were collected over 26 weeks using data loggers, gravid oviposit sticky (GOS) traps, and non-structural 1 (NS1) test kits in both non-dengue hotspot (NDH) and dengue hotspots (DH). The collected data encompassed DM variables, vegetation cover (VC), MIs, and number of dengue cases. An autocorrelation analysis was conducted to determine the TLs between MIs and their preceding values, while a cross-correlation analysis revealed the TLs between MIs and DM variables. The study indicated there are positive correlations between the adult index (AI) of Ae. albopictus, their preceding values and rainfall at an NDH. Conversely, the AIs of total Aedes at the DH exhibited positive correlations with their preceding values, temperature, rainfall, and maximum relative humidity (RH), but negative correlations with the mean and maximum RH. The dengue-positive trap index (DPTI) of total Aedes at DHs demonstrated positive associations with their preceding values, mean temperature, minimum temperature, maximum RH, and rainfall, with negative correlations observed for the maximum temperature, mean RH, and minimum RH. Similar trends were identified for the Ae. aegypti and Ae. albopictus at DHs. The association between dengue cases, DM, and MIs was inconclusive due to underreported cases. This study highlighted the DM and TLs of dengue virus-infected and non-infected adult female Aedes mosquitoes using onsite data collection. Furthermore, this study presents a replicable methodology that can be adopted by researchers worldwide for investigating the dynamics of dengue transmission in similar settings. The findings offer valuable insights for decision-makers, providing them with evidence-based information to implement targeted interventions and strategies aimed at controlling Aedes mosquito populations and mitigating the spread of dengue virus infections.

A Network Analysis of the Impact of the Coronavirus Pandemic on the US Economy: A Comparison of the Return and the Momentum Picture

This study examines a cross-correlation analysis of companies included in the S&P 500 Index at three different intervals: before, during, and after the pandemic’s onset. The aim is to evaluate how the pandemic and related governmental actions have affected market structures and economic conditions. This paper introduces the notion of momentum time series, integrating return and volume data. We show that these momentum time series provide unique insights that differ from return time series, suggesting their potential utility in economic analysis. Our analysis employs the Manhattan and Mantegna distances to construct a threshold-based network, which we subsequently scrutinize. Lastly, we evaluate how the pandemic has influenced these outcomes.

Impact of infusion conditions and anesthesia on CSF tracer dynamics in mouse brain.

Tracer imaging has been instrumental in mapping the brain's solute transport pathways facilitated by cerebrospinal fluid (CSF) flow. However, the impact of tracer infusion parameters on CSF flow remains incompletely understood. This study evaluated the influence of infusion location, rate, and anesthetic regimens on tracer transport using dynamic contrast-enhanced MRI with Gd-DTPA as a CSF tracer. Infusion rate effects were assessed by administering Gd-DTPA into the cisterna magna (ICM) at two rates under isoflurane anesthesia. Anesthetic effects were evaluated by comparing transport patterns between isoflurane and ketamine/xylazine (K/X) anesthesia at the slower rate. Gd-DTPA transport was also examined after lateral ventricle (ICV) infusion, the primary site of CSF production. The results demonstrate that, besides anesthesia, both the location and rate of infusion substantially affected solute transport within the brain. ICV infusion led to rapid, extensive transport into deep brain regions, while slower ICM infusion resulted in more pronounced transport to dorsal brain regions. Cross-correlation and hierarchical clustering analyses of region-specific Gd-DTPA signal time courses revealed that ICM infusion facilitated transport along periarterial spaces, while ICV infusion favored transport across the ventricular-parenchymal interface. These findings underscore the importance of experimental conditions in influencing tracer kinetics and spatial distribution in the brain.

Global cellular proteo-lipidomic profiling of diverse lysosomal storage disease mutants using nMOST.

Lysosomal storage diseases (LSDs) comprise ~50 monogenic disorders marked by the buildup of cellular material in lysosomes, yet systematic global molecular phenotyping of proteins and lipids is lacking. We present a nanoflow-based multiomic single-shot technology (nMOST) workflow that quantifies HeLa cell proteomes and lipidomes from over two dozen LSD mutants. Global cross-correlation analysis between lipids and proteins identified autophagy defects, notably the accumulation of ferritinophagy substrates and receptors, especially in NPC1-/- and NPC2-/- mutants, where lysosomes accumulate cholesterol. Autophagic and endocytic cargo delivery failures correlated with elevated lysophosphatidylcholine species and multilamellar structures visualized by cryo-electron tomography. Loss of mitochondrial cristae, MICOS complex components, and OXPHOS components rich in iron-sulfur cluster proteins in NPC2-/- cells was largely alleviated when iron was provided through the transferrin system. This study reveals how lysosomal dysfunction affects mitochondrial homeostasis and underscores nMOST as a valuable discovery tool for identifying molecular phenotypes across LSDs.

Identifying fracture-controlled resonance modes for structural health monitoring: insights from Hunter Canyon Arch (Utah, USA)

Abstract. Progressive fracturing contributes to structural degradation of natural rock arches and other freestanding rock landforms. However, methods to detect structural changes arising from fracturing are limited, particularly at sites with difficult access and high cultural value, where non-invasive approaches are essential. This study aims to determine how fractures affect the dynamic properties of rock arches, focusing on resonance modes as indicators of structural health conditions. We hypothesize that damage resulting from fracture propagation may influence specific resonance modes that can be identified through ambient vibration modal analysis. We characterized the dynamic properties (i.e., resonance frequencies, damping ratios, and mode shapes) of Hunter Canyon Arch, Utah (USA), using spectral and cross-correlation analyses of data generated from an array of nodal geophones. Results revealed properties of nine resonance modes with frequencies between 1 and 12 Hz. Experimental data were then compared to numerical models with homogeneous and heterogeneous compositions, the latter implementing weak mechanical zones in areas of mapped fractures. All numerical solutions replicated the first two resonance modes of the arch, indicating these modes are insensitive to structural complexity derived from fractures. Meanwhile, heterogenous models with discrete fracture zones succeeded in matching the frequency and shape of one additional higher mode, indicating this mode is sensitive to the presence of fractures and thus most likely to respond to structural change from fracture propagation. An evolutionary crack damage model was then applied to simulate fracture propagation, confirming that only this higher mode is sensitive to structural damage resulting from fracture growth. While examination of fundamental modes is common practice in structural health monitoring studies, our results suggest that analysis of higher-order resonance modes can be more informative for characterizing fracture-driven structural damage.

Diversity and seasonality of ectoparasite burden on two species of Madagascar fruit bat, Eidolon dupreanum and Rousettus madagascariensis.

Bats are important reservoir hosts for a variety of microparasites, some of which are transmitted by ectoparasite vectors that include mites, fleas, lice, ticks, and bat flies (families Nycteribiidae and Streblidae). All of these ectoparasite taxa are known to parasitize two endemic fruit bats of Madagascar, Eidolon dupreanum and Rousettus madagascariensis. We aimed to describe the diversity of ectoparasite infestation for both bat species through morphological observation and DNA barcoding and elucidate ecological and climatic correlates of seasonal nycteribiid parasitism of these hosts. Live E. dupreanum and R. madagascariensis fruit bats were captured monthly in northern and central-eastern Madagascar from 2013-2020. Ectoparasites on all captured bats were counted and identified in the field, then collected into ethanol. Field identification of a subset of samples were confirmed via microscopy and DNA barcoding of the cytochrome C oxidase subunit 1 (COI) and 18S genes. The seasonal abundance of nycteribiid bat flies on both host bats was analyzed using generalized additive models, and the role of climate in driving this seasonality was assessed via cross-correlation analysis combined with generalized linear models. Phylogenetic trees were generated to compare COIand 18S sequences of Madagascar nycteribiid and streblid bat flies with available reference sequences from GenBank. Ectoparasites corresponding to four broad taxa (mites, ticks, fleas, and bat flies) were recovered from 628 of 873 E. dupreanum and 831 of 862 R. madagascariensis . E. dupreanum were most commonly parasitized by Cyclopodia dubia nycteribiids and R. madagascariensis by Eucampsipoda madagascariensis nycteribiids or Megastrebla wenzeli streblids. We observed significant seasonality in nycteribiid abundance on both bat hosts, which varied by bat sex and was positively correlated with lagged temperature, precipitation, and humidity variables. Barcoding sequences recovered for all three bat fly species grouped with previously reported sequences, confirming morphological species identification. Our study contributes the first DNA barcodes of any kind reported for M. wenzeli and the first 18S barcodes for C. dubia . This study explores the diversity and abundance of ectoparasite burdens in two Malagasy fruit bat species, highlighting the importance of seasonal ecology and the influence of climate variables on parasitism, which correlates with resource availability.

Are carbon trading markets efficient? A longitudinal investigation

ABSTRACT We examine the evolution of price efficiency in the European Union Carbon Emissions Market and the net cross-correlations between the carbon and commodities markets using Multifractal Detrended Fluctuation Analysis (MFDFA), and Detrended Partial Cross-Correlation Analysis (DPCCA), methods based on dynamic systems derived from econophysics. Our analysis is based on daily values of the S&P GSCI Carbon Emission Allowances (EUA) EUR index and the S&P GSCI index for the period between January 2010 and June 2024 and uses seven-year rolling windows. Our results are supportive of the adaptive market hypothesis and indicate that the efficiency of the EU ETS markets has been improving steadily and that its correlation with the broader commodities market is increasing over time.

High-resolution awake mouse fMRI at 14 tesla.

High-resolution awake mouse functional magnetic resonance imaging (fMRI) remains challenging despite extensive efforts to address motion-induced artifacts and stress. This study introduces an implantable radio frequency (RF) surface coil design that minimizes image distortion caused by the air/tissue interface of mouse brains while simultaneously serving as a headpost for fixation during scanning. Furthermore, this study provides a thorough acclimation method used to accustom animals to the MRI environment minimizing motion-induced artifacts. Using a 14 T scanner, high-resolution fMRI enabled brain-wide functional mapping of visual and vibrissa stimulation at 100 µm×100 µm×200 µm resolution with a 2 s per frame sampling rate. Besides activated ascending visual and vibrissa pathways, robust blood oxygen level-dependent (BOLD) responses were detected in the anterior cingulate cortex upon visual stimulation and spread through the ventral retrosplenial area (VRA) with vibrissa air-puff stimulation, demonstrating higher-order sensory processing in association cortices of awake mice. In particular, the rapid hemodynamic responses in VRA upon vibrissa stimulation showed a strong correlation with the hippocampus, thalamus, and prefrontal cortical areas. Cross-correlation analysis with designated VRA responses revealed early positive BOLD signals at the contralateral barrel cortex (BC) occurring 2 s prior to the air-puff in awake mice with repetitive stimulation, which was not detected using a randomized stimulation paradigm. This early BC activation indicated a learned anticipation through the vibrissa system and association cortices in awake mice under continuous exposure of repetitive air-puff stimulation. This work establishes a high-resolution awake mouse fMRI platform, enabling brain-wide functional mapping of sensory signal processing in higher association cortical areas.

Multifractal detrended cross-correlation analysis of Islamic stock markets in the pacific Asia region

This study aims to uncover the multifractal characteristics and complex interactions among six Islamic stock markets in the pacific Asia region, enhancing understanding of their dynamics. Employing Multifractal Detrended Cross-Correlation Analysis (MF-DCCA), this research analyzes a dataset covering the period from January 1, 2011, to August 1, 2024, with approximately 3315 observations. In the preliminary analysis, the application of the DCCA Cross-Correlation Coefficient method revealed that the cross-correlations are persistent and exhibit long-term stability across most index pairs. Utilizing core components of the MF-DCCA method, such as Generalized Hurst Exponents, Rényi Exponents, and the Hölder Singularity Spectrum, further confirmed that the index pairs exhibit long-range persistent cross-correlations and multifractal behavior. Surrogate and shuffling transformations showed that the observed multifractality is influenced by both long-term cross-correlations and heavy-tailed distributions. The findings have important implications for investors, policymakers, and financial analysts concerned with portfolio diversification, risk management, and the efficiency of Islamic financial markets in this economically vital region. Understanding the interconnectedness of these markets can aid in developing more effective investment strategies and regulatory frameworks. This study contributes original insights into the dynamics of Islamic stock markets in the Pacific Asia region, offering a nuanced perspective on their complex behaviors and interactions through the lens of multifractality.

Calibration Method for Inverse Heat Conduction Problems Under Sensor Delay and Attenuation

Resolving inverse heat conduction problems (IHCPs) based on interior measured temperatures is a reliable approach for aerodynamic heat prediction. In most IHCP studies, it is generally assumed that the data obtained by temperature sensors are the positional temperature, whereas in actual measurements, the thermocouple undergoes a transient heat transfer process, leading to a slight bias in its readings compared to the desired temperature at the target location. In this paper, a thermocouple model is established to simulate the sensor dynamic response effect. Then, a traditional space marching method (SMM) and the calibration integral equation method (CIEM) are applied to investigate the sensor dynamic response on surface heat flux predictions. The Gauss filtering method and future time regularization method are used as regularization schemes for SMM and CIEM, respectively. For achieving optimal predictions, phase-plane and cross-correlation analyses are applied to extract the optimal regularization parameter. The results indicate that delay and attenuation will occur in surface heat flux prediction using SMM, and this negative phenomenon can be eliminated by using CIEM. In general, CIEM has great merits for resolving IHCPs compared to traditional approaches, which eliminate the impact of dynamic response and improve the accuracy of aerodynamic heat predictions. The results of this study can provide a reference for transient IHCPs that should take the influence of sensor dynamic response on inverse prediction into consideration.

Investigation of combustion-induced vibration sources in a diesel engine in the time-frequency domain using the wavelet analysis method and wavelet cross-correlation analysis method

Investigation of combustion-induced vibration sources in a diesel engine in the time-frequency domain using the wavelet analysis method and wavelet cross-correlation analysis method

Autocorrelation and Cross-Correlation of Climatological Time Series: A Bibliometric Analysis

Objective: This article aims to contribute to the investigation of the evolution of scientific knowledge related to autocorrelation and cross-correlation analysis techniques in time series of climatological variables, wind speed and solar radiation, in order to understand what is discussed on the subject and, in addition, to support future productions. Theoretical Framework: Conducting a study to investigate trends in the evolution of publications related to autocorrelation and cross-correlation measures, used to analyze climatological time series, is relevant to consolidate and deepen knowledge in this scientific field. Understanding the behavior of climatic elements is relevant in several areas of application: the spread of respiratory diseases; agricultural and urban planning tools; the relationship between climatological factors and pollutants; and renewable energy sources. Method: To achieve this objective, the bibliometric review technique was used to map the literature on the research topic, assessing the impact of production by country, identifying the authors, institutions and journals that stand out, trends in the number of publications and microanalysis. Results and Discussion: A total of 173 scientific articles were recorded during the analysis period, with the highest productivity peaks during the years 2013 and 2019 with 12 and 13 publications, respectively, representing together 18.25% of the total publications and from these years onwards a significant growth in the number of publications was evidenced. The countries that stood out the most in number of publications were China (21.96%), followed by the United States (4.05%) and Japan (4.05%). The most relevant authors were Li, Y. in the lead with 5 articles and Dinpashoh, Y. with 4, the same authors maintained productions on the subject over time. Conclusion: Our findings highlighted the relevance of mapping scientific advances related to autocorrelation and cross-correlation of climatological time series. It is noted that China is the country with the highest number of citations received and its significant number of articles and citations may indicate that this country has relevant scientific publications on the subject. These results will be useful as a constructive reference for future research on the subject.

The impact of Helicobacter pylori on gastric cancer formation and early warning signal identification.

Gastric cancer is highly prevalent in Asia and is characterized by poor prognosis post-surgery and a high recurrence rate within five years. Research has highlighted the role of Helicobacter pylori in initiating or accelerating gastric cancer development. However, quantitative analysis of its impact on gastric cancer carcinogenesis is still lacking. This study employs gene regulatory networks and landscape and flux theory, integrating genetic and epigenetic factors, to quantitatively elucidate how Helicobacter pylori influences gastric cancer progression. Varied Helicobacter pylori infection concentrations lead to significant shifts in system thermodynamic and dynamic driving forces, altering gene expression levels. Quantitative analysis of entropy production rate and mean-flux in the gastric cancer system reveals the global changes in thermodynamic and dynamic driving forces. Coupled with autocorrelation, cross correlation, and variance analysis, we pinpoint critical stages of Helicobacter pylori infection, serving as early warning signals for gastric cancer. This approach bridges theoretical and clinical realms, dynamically assessing Helicobacter pylori's impact on gastric cancer and identifying crucial early warning signals, with significant clinical and translational implications.

Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy.

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants. The analysis revealed significant differences in fractal features across five emotional states (neutral, amused, bored, relaxed, and scared), particularly those derived from wavelet entropy. A cross-correlation analysis showed robust correlations between fractal features and both the arousal and valence dimensions of emotion, challenging the conventional view of EDA as a predominantly arousal-indicating measure. The application of machine learning for emotion classification using fractal features achieved a leave-one-subject-out accuracy of 84.3% and an F1 score of 0.802, surpassing the performance of previous methods on the same dataset. This study demonstrates the potential of fractal analysis in capturing the intricate, multi-scale dynamics of EDA signals for emotion recognition, opening new avenues for advancing emotion-aware systems and affective computing applications.

The nature of low-luminosity AGNs discovered by JWST based on clustering analysis: Progenitors of low-z quasars?

Abstract James Webb Space Telescope (JWST) has discovered many faint AGNs at high-z by detecting their broad Balmer lines. However, their high number density, lack of X-ray emission, and overly high black hole masses with respect to their host stellar masses suggest that they are a distinct population from general type-1 quasars. Here, we present clustering analysis of 27 low-luminosity broad-line AGNs found by JWST (JWST AGNs) at 5 < z < 6 based on cross-correlation analysis with 679 photometrically-selected galaxies to characterize their host dark matter halo (DMH) masses. From the angular and projected cross-correlation functions, we find that their typical DMH mass is $\log (M_{\mathrm{halo}}/h^{-1}\, \mathrm{M_\odot }) = 11.46_{-0.25}^{+0.19},$ and $11.53_{-0.20}^{+0.15}$, respectively. This result implies that the host DMHs of these AGNs are ∼1 dex smaller than those of luminous quasars. The DMHs of the JWST AGNs at 5 < z < 6 are predicted to grow to 1012 − 13 h−1 M⊙ at z ≲ 3, which is comparable to that of a more luminous quasar at the same epoch. Applying the empirical stellar-to-halo mass ratio to the measured DMH mass, we evaluate their host stellar mass as $\log (M_*/\mathrm{M_\odot })=9.48_{-0.41}^{+0.31},$ and $9.60_{-0.33}^{+0.24}$, which are higher than some of those estimated by the SED fitting. We also evaluate their duty cycle as $f_{\mathrm{duty}}=0.37_{-0.15}^{+0.19}$ per cent, corresponding to ∼4 × 106 yr as the lifetime of the JWST AGNs. While we cannot exclude the possibility that the JWST AGNs are simply low-mass type-1 quasars, these results suggest that the JWST AGNs are a different population from type-1 quasars and the progenitors of quasars at z ≲ 3.

Can a human sing with an unseen artificial partner? Coordination dynamics when singing with an unseen human or artificial partner

This study investigated whether a singer’s coordination patterns differ when singing with an unseen human partner versus an unseen artificial partner (VOCALOID 6 voice synthesis software). We used cross-correlation analysis to compare the correlation of the amplitude envelope time series between the partner’s and the participant’s singing voices. We also conducted a Granger causality test to determine whether the past amplitude envelope of the partner helps predict the future amplitude envelope of the participants, or if the reverse is true. We found more pronounced characteristics of anticipatory synchronization and increased similarity in the unfolding dynamics of the amplitude envelopes in the human-partner condition compared to the artificial-partner condition, despite the tempo fluctuations in the human-partner condition. The results suggested that subtle qualities of the human singing voice, possibly stemming from intrinsic dynamics of the human body, may contain information that enables human agents to align their singing behavior dynamics with a human partner.

Determining the groundwater movement velocity using cross-correlation analysis: Velika Morava alluvium case study

Monitoring of water levels and flow rates of the Velika Morava River, due to its significance for the Republic of Serbia, was established over 100 years ago at the profiles of Ljubičevski Most and Ćuprija. This was followed a year later by the activation of the Varvarin monitoring station, then in 1935 by the Žabarski Most, and in 1952 by the Bagrdan station. Groundwater monitoring started in 1977 with 12 piezometers and the network was gradually expanded to include 92 piezometers in 2002. This study presents a comparative analysis of the water level elevations of the Velika Morava River and associated piezometers arranged along and normal to the river flow. Cross-correlation analysis was applied to evaluate the existing hydraulic connectivity between the Velika Morava River and the groundwater, with results used to calculate the average groundwater flow velocities within this section of the Velika Morava basin.