Impact of atmospheric O3 and NO2 on the secondary sulfates in real atmosphere.

As an important component of secondary aerosols, sulfate plays a crucial role in regulating atmospheric radiative balance and influencing the secondary formation of ozone (O3). In real atmosphere, atmospheric oxidants NO2 and O3 can promote the oxidation of SO2 to form sulfate (SO42-) through multiphase chemistry that occur at different time scales. Due to the combined impact of meteorology, pollution sources, atmospheric chemistry, etc., time-scale dependence of SO2-SO42- conversion makes the impact of NO2/O3 on it more complex. In this study, based on long-term time series (2013-2020) of air pollution variables from seven stations in Hong Kong, the Multifractal Detrended Cross-Correlation Analysis (MFDCCA) method has been employed to quantify the cross-correlations between SO2 and SO42- in real atmosphere at different time scales, for examining the time-scale dependence of SO2-SO42- conversion efficiency. Furthermore, the Pearson correlation analysis has been used to study the influence of NO2/O3 on SO2-SO42- conversion, and the regional and seasonal differences have been analyzed by considering factors such as meteorology, pollution sources, and regional transport. Changes in the main components of secondary aerosols are closely linked with the co-control of regional PM2.5 and O3. Therefore, the exploration of the impact of co-existing NO2/O3 gases on the secondary formation of sulfates in real atmosphere is significant.

Computational exploration of compounds in Xylocarpus granatum as a potential inhibitor of Plasmodium berghei using docking, molecular dynamics, and DFT studies.

Malaria remains a global health concern, with the emergence of resistance to the antimalarial drug atovaquone through cytochrome b (cyt b) being well-documented. This study was prompted by the presence of this mutation in cyt b to enable new drug candidates capable of overcoming drug resistance. Our objective was to identify potential drug candidates from compounds of Xylocarpus granatum by computationally assessing their interactions with Plasmodium berghei cyt b. Using computational methods, we modeled cyt b (GenBank: AF146076.1), identified the binding cavity, and analyzed the Ramachandran plot against cyt b. Additionally, we conducted drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, along with density functional theory (DFT) analysis of the compounds. Molecular docking and molecular dynamics simulation (MDS) were used to evaluate the binding energy and stability of the cyt b-ligand complex. Notably, our investigation highlighted kaempferol as a promising compound due to its high binding energy of 7.67 kcal/mol among all X. granatum compounds, coupled with favorable pharmacological properties (ADMET) and antiprotozoal properties at Pa 0.345 > Pi 0.009 (PASS value). DFT analysis showed that kaempferol has an energy gap of 4.514 eV. MDS indicated that all tested ligands caused changes in bonding and affected the structural conformation of cyt b, as observed before MDS (0 ns) and after MDS (100 ns). The most notable differences were observed in the types of hydrogen bonds between 0 and 100 ns. Nevertheles, MDS results from a 100 ns simulation revealed consistent behavior for kaempferol across various parameters including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), and hydrogen bonds. The cyt b-kaempferol complex demonstrated favorable energy stability, as supported by the internal energy distribution values observed in principal component analysis (PCA), which closely resembled those of the atovaquone control. Additionally, trajectory stability analysis indicated structural stability, with a cumulative eigenvalue of 24.7 %. Dynamic cross-correlation matrix (DCCM) analysis revealed a positive correlation among catalytic cytochrome residues within the amino acid residues range 119-268. The results of our research indicate that the structure of kaempferol holds promise as a potential candidate against Plasmodium.

In situ Synchrotron X-ray Metrology Boosted by Automated Data Analysis for Real-time Monitoring of Cathode Calcination.

Synchrotron X-ray-based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on-site, on-the-fly analysis needed for real-time process monitoring. Here, a weighted lagged cross-correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X-ray diffraction metrology to monitor the calcination process of the archetypal nickel-based cathode, LiNiO2. The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO2, providing information for agile on-site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early-stage phase nucleation and growth. Expanding this data-powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

Newest Methods and Approaches to Enhance the Performance of Optical Frequency-Domain Reflectometers.

In this review, we summarize the latest advances in the design of optical frequency-domain reflectometers (OFDRs), digital signal processing, and sensors based on special optical fibers. We discuss state-of-the-art approaches to improving metrological characteristics, such as spatial resolution, SNR, dynamic range, and the accuracy of determining back reflection coefficients. We also analyze the latest achievements in the OFDR-based sensors: the accuracy of spatial localization of the impact, the error in detecting temperatures, deformation, and other quantities, and the features of separate measurement of various physical quantities. We also pay attention to the trend of mutual integration of frequency-domain optical reflectometry methods with time-domain optical reflectometry, which provides completely new sensing possibilities. We believe that this review may be useful to engineers and scientists focused on developing a lab setup, complete measurement instrument, or sensing system with specific requirements.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-ß Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent ß (SCOT-ß) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-ß, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

Visual analysis of pulmonary blood flow in pulmonary circulation assessment: differences between two variant algorithms for processing dynamic images.

Background: In the quantitative assessment of pulmonary blood flow, two different processing algorithms [cross-correlation calculation processing (CCC-pro) and reference frame subtraction processing (RFS-pro)] within dynamic imaging systems have been reported to exhibit high correlations with conventional measurement methods. However, reports still need to evaluate these two processing algorithms regarding the different aspects of pulmonary blood flow. This study aimed to analyze the differences in pulmonary circulation. Methods: We conducted a cross-sectional study to evaluate patients with lung cancer who underwent radical surgery, simultaneous dynamic chest radiography (DCR), and pulmonary perfusion scintigraphy (PPS). We assessed the correlation between PPS and two algorithms (CCC-pro and RFS-pro) regarding calculated blood flow ratio (BFR) using Pearson's correlation and linear regression analysis. Additionally, we evaluated consistency using the Bland-Altman analysis. We compared the pulmonary blood flow distributions across six-division lung fields and evaluated each method's blood flow images and histograms of pixel values. Results: From May 2018 to December 2020, we consecutively enrolled 46 patients with lung cancer who met the inclusion criteria (40 male patients, with a mean age of 72.91 years). In these patients, CCC-pro and RFS-pro were correlated (R=0.718, P<0.01); however, CCC-pro was more strongly correlated with PPS than RFS-pro (R=0.859, P<0.01 vs. R=0.549, P<0.01). The Bland-Altman analysis showed high agreement, although systematic errors were observed in relationships other than RFS-pro to PPS. CCC-pro and RFS-pro showed similar blood flow distributions in the upper and lower lung fields, with RFS-pro being dominant in the middle. RFS-pro showed higher pixel values in the hilar region and a histogram shape similar to PPS; however, posture affected the right upper lung field gradient. RFS-pro showed no difference in the BFR when the pulmonary artery region was symmetric; however, potential inaccuracies existed when it overlapped with the cardiovascular shadow. Conclusions: The CCC-pro algorithm was useful for quantifying BFRs, whereas the RFS-pro algorithm accurately evaluated blood flow distribution in lung fields. Further algorithm development is required to enable versatile pulmonary blood flow analysis.

Inference of monosynaptic connections from parallel spike trains: A review.

This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of "neuronal connectivity" in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity. Among these, we focus on the methods used to infer the monosynaptic connectivity from spike data. We then summarize the inference methods based on two main approaches, i.e., correlation-based and model-based approaches. Finally, we describe available source codes for connectivity inference and future challenges. Although inference will never be perfect, the accuracy of identifying the monosynaptic connections has improved dramatically in recent years due to continuous efforts.

Combined interpretation of objective firearm evidence comparison algorithms using Bayesian networks.

Traditionally, firearm and toolmark examiners manually evaluate the similarity of features on two bullets using comparison microscopy. Advances in microscopy have made it possible to collect 3D topographic data, and several automated comparison algorithms have been introduced for the comparison of bullet striae using these data. In this study, open-source approaches for cross-correlation, congruent matching profile segments, consecutive matching striations, and a random forest model were evaluated. A statistical characterization of these automated approaches was performed using four datasets of consecutively manufactured firearms to provide a challenging comparison scenario. Each automated approach was applied to all samples in a pairwise fashion, and classification performance was compared. Based on these findings, a Bayesian network was empirically learned and constructed to leverage the strengths of each individual approach, model the relationship between the automated results, and combine them into a posterior probability for the given comparison. The network was evaluated similarly to the automated approaches, and the results were compared. The developed Bayesian network classified 99.6% of the samples correctly, and the resultant probability distributions were significantly separated more so than the automated approaches when used in isolation.

A Low-Complexity Beamformer for Ultrasound Imaging Based on Sub-Beamformer and Multi-Apodization With Cross-Correlation.

OBJECTIVE: This paper proposes an ultrasound imaging algorithm based on sub-beamformer and multi-apodization with cross-correlation (SUB-MAX), aiming to achieve high resolution close to the minimum variance (MV) beamforming with low complexity and to enhance image contrast while maintaining background quality. METHODS: The output of two (N/2)-element DAS beamformers with asymmetric phase centers is subtracted, resulting in a large drop in the main-lobe amplitude, while the sidelobe maintains a relatively high amplitude level. Inspired by this characteristic, the coefficients with opposite trends compared with the subtracted output are obtained and fused with the normalized cross-correlation (NCC) weighting matrix acquired by using multi-pair received apodization, the proposed SUB-MAX obtains a new weighting matrix to weight the output of the DAS beamformer. RESULTS: For ats_wire point targets, the average full-width at half-maximum (FWHM) of SUB-MAX compared with DAS, DMAS, CF, and MAX decreases by 52.7%, 43.5%, 33.3%, and 52.7%, respectively. For geabr_0 cysts, the average contrast ratio (CR) of SUB-MAX compared with DAS, MV, DMAS, and CF increases by 57.7%, 86.8%, 2.5%, and 14.4%, respectively. Experiments on rat_tumor dataset also indicate that SUB-MAX has a superior comprehensive imaging performance. CONCLUSION: The experimental results indicate that the superior comprehensive imaging performance of the proposed SUB-MAX is expected to be suitable for real-time imaging systems due to its non-reliance on covariance matrix inversion.

Cross-correlation adjustment full-waveform inversion with source encoding in ultrasound computed tomography.

Full-waveform inversion (FWI) is one of the leading-edge techniques in ultrasound computed tomography (USCT). FWI reconstructs the images of sound speed by iteratively minimizing the difference between the predicted and measured signals. The challenges of FWI are to improve its stability and reduce its computational cost. In this paper, a new USCT algorithm based on cross-correlation adjustment FWI with source encoding (CCAFWI-SE) is proposed. In this algorithm, the gradient is adjusted using the intermediate signals as the inversion target rather than the measured signals during iteration. The intermediate signals are generated using the travel time difference calculated by cross-correlation. In the case of conventional FWI failure, using the proposed algorithm, the estimated sound speed can converge toward the ground truth. To reduce the computational cost, an intermittent update strategy is implemented. This strategy only requires one time for the calculation of the travel time difference per stage, so that the source encoding can be used. Simulation and laboratory experiments are implemented to validate this approach. The experiment results show it has successfully recovered the sound speed model, while conventional FWI failed when the initial model greatly differed from the ground truth. This verifies that our approach improves the stability of the reconstruction in USCT. In practice, additional computational costs can be reduced by combining our approach with existing methods. The proposed approach increases the robustness of the FWI and expands its application.

Relationships among soil moisture at various depths under diverse climate, land cover and soil texture.

Soil moisture is an important component of the hydrological cycle and a key mediator between land surface and atmospheric interactions. Although substantial progress has been made in remote sensing of soil moisture at different spatial scales, the shallow penetration depth of remote sensors greatly limits their utility for applications in meteorological modelling and hydrological studies where the critical variable of interest is the root-zone soil moisture content. Therefore, this study assesses the relationship between soil moisture at the surface (10 cm) and in lower soil layers (20, 40, 60, 80, 100, and 120 cm) under varying climates, soils, and crop types. Cross-correlation analysis is applied to daily in-situ soil moisture measurements from 4712 locations in agricultural lands across the contiguous United States. Our analysis demonstrates that zero-day lag always produced the highest correlation between 10 cm soil moisture and soil moisture in the lower layers. In addition, a positive and strong relationship between 10 and 20 cm soil moisture (r = 0.84) was observed, while the relationships between 10 and 40 cm soil moisture were moderate (r = 0.52). The decline in cross-correlation continued to the deeper soil layers, which indicated that, on a daily timescale, the surface soil moisture gradually becomes decoupled with soil moisture at greater depths. Therefore, our research suggests that the estimation of soil moisture in the soil layers up to 40 cm based on surface soil moisture is most promising. However, the influence of climate, crop type, and soil texture on the strength of relationships between surface and lower layers makes the prediction difficult. The comparatively weak relationship between precipitation and soil moisture (0.09-0.32), as well as the relationship between reference evapotranspiration (ETo) and soil moisture (-0.19-0.18), in this study can be attributed to scale mismatching from different data sources.

Disrupting EGFR-HER2 Transactivation by Pertuzumab in HER2-Positive Cancer: Quantitative Analysis Reveals EGFR Signal Input as Potential Predictor of Therapeutic Outcome.

Pertuzumab (Perjeta®), a humanized antibody binding to the dimerization arm of HER2 (Human epidermal growth factor receptor-2), has failed as a monotherapy agent in HER2 overexpressing malignancies. Since the molecular interaction of HER2 with ligand-bound EGFR (epidermal growth factor receptor) has been implied in mitogenic signaling and malignant proliferation, we hypothesized that this interaction, rather than HER2 expression and oligomerization alone, could be a potential molecular target and predictor of the efficacy of pertuzumab treatment. Therefore, we investigated static and dynamic interactions between HER2 and EGFR molecules upon EGF stimulus in the presence and absence of pertuzumab in HER2+ EGFR+ SK-BR-3 breast tumor cells using Förster resonance energy transfer (FRET) microscopy and fluorescence correlation and cross-correlation spectroscopy (FCS/FCCS). The consequential activation of signaling and changes in cell proliferation were measured by Western blotting and MTT assay. The autocorrelation functions of HER2 diffusion were best fitted by a three-component model corrected for triplet formation, and among these components the slowly diffusing membrane component revealed aggregation induced by EGFR ligand binding, as evidenced by photon-counting histograms and co-diffusing fractions. This aggregation has efficiently been prevented by pertuzumab treatment, which also inhibited the post-stimulus interaction of EGFR and HER2, as monitored by changes in FRET efficiency. Overall, the data demonstrated that pertuzumab, by hindering post-stimulus interaction between EGFR and HER2, inhibits EGFR-evoked HER2 aggregation and phosphorylation and leads to a dose-dependent decrease in cell proliferation, particularly when higher amounts of EGF are present. Consequently, we propose that EGFR expression on HER2-positive tumors could be taken into consideration as a potential biomarker when predicting the outcome of pertuzumab treatment.

Crystal structure via fluctuation scattering.

Crystallography is a quintessential method for determining the atomic structure of crystals. The most common implementation of crystallography uses single crystals that must be of sufficient size, typically tens of micrometres or larger, depending on the complexity of the crystal structure. The emergence of serial data-collection methods in crystallography, particularly for time-resolved experiments, opens up opportunities to develop new routes to structure determination for nanocrystals and ensembles of crystals. Fluctuation X-ray scattering is a correlation-based approach for single-particle imaging from ensembles of identical particles, but has yet to be applied to crystal structure determination. Here, an iterative algorithm is presented that recovers crystal structure-factor intensities from fluctuation X-ray scattering correlations. The capabilities of this algorithm are demonstrated by recovering the structure of three small-molecule crystals and a protein crystal from simulated fluctuation X-ray scattering correlations. This method could facilitate the recovery of structure-factor intensities from crystals in serial crystallography experiments and relax sample requirements for crystallography experiments.

Photoacoustic method for measuring the elasticity of polydimethylsiloxane at various mixing ratios.

Measuring elasticity without physical contact is challenging, as current methods often require deconstruction of the test sample. This study addresses this challenge by proposing and testing a photoacoustic effect-based method for measuring the elasticity of polydimethylsiloxane (PDMS) at various mixing ratios, which may be applied on the wide range of applications such as biomedical and optical fields. A dual-light laser source of the photoacoustic (PA) system is designed, employing cross-correlation signal processing techniques. The platform systems and a mathematical model for performing PDMS elasticity measurements are constructed. During elasticity detection, photoacoustic signal features, influenced by hardness and shapes, are analyzed using cross-correlation calculations and phase difference detection. Results from phantom tests demonstrate the potential of predicting Young's modulus using the cross-correlation method, aligning with the American Society for Testing and Materials (ASTM) standard samples. However, accuracy may be affected by mixed materials and short tubes. Normalization or calibration of signals is suggested for aligning with Young's coefficient.

Ultrasound-based bone tracking using cross-correlation enables dynamic measurements of knee kinematics during clinical assessments.

Purpose: Measuring joint kinematics in the clinic is important for diagnosing injuries, tracking healing and guiding treatments; however, current methods are limited by accuracy and/or feasibility of widespread clinical adoption. Therefore, the purpose of this study was to develop and validate an ultrasound (US)-based method for measuring knee kinematics during clinical assessments. Methods: We mimicked four clinical laxity assessments (i.e., anterior, posterior, varus, valgus) on five human cadaver knees using our robotic testing system. We simultaneously collected B-mode cine loops with an US transducer. We computed the errors in kinematics between those measured using our bone-tracking algorithm, which cross-correlated regions of interest across frames of the cine loops, and those measured using optical motion capture with bone pins. Additionally, we conducted studies to determine the effects of loading rate and transducer placement on kinematics measured using our US-based bone tracking. Results: Pooling the trials at experimental speeds and those downsampled to replicate clinical laxity assessments, the maximum root-mean-square errors of knee kinematics using our bone-tracking algorithm were 2.2 mm and 1.3° for the anterior-posterior and varus-valgus laxity assessments, respectively. Repeated laxity assessments proved to have good-to-excellent repeatability (intraclass correlation coefficients [ICCs] of 0.81-0.99), but ICCs from repositioning the transducer varied more widely, ranging from poor-to-good reproducibility (0.19-0.89). Conclusion: Our results demonstrate that US is capable of tracking knee kinematics during dynamic movement. Because US is a safe and commonly used imaging modality, when paired with our bone-tracking algorithm, US has the potential to assess dynamic knee kinematics across a wide variety of applications in the clinic. Level of Evidence: Not applicable.

The pypadf package: computing the pair angle distribution function from fluctuation scattering data.

The pair angle distribution function (PADF) is a three- and four-atom correlation function that characterizes the local angular structure of disordered materials, particles or nanocrystalline materials. The PADF can be measured using X-ray or electron fluctuation diffraction data, which can be collected by scanning or flowing a structurally disordered sample through a focused beam. It is a natural generalization of established pair distribution methods, which do not provide angular information. The software package pypadf provides tools to calculate the PADF from fluctuation diffraction data. The package includes tools for calculating the intensity correlation function, which is a necessary step in the PADF calculation and also the basis for other fluctuation scattering analysis techniques.

Toroidal orders and related phenomena in nonmagnetic and magnetic materials.

In this short article, we overview a concept of electronic toroidal multipoles, and their ordering with associated physical properties in non-magnetic and magnetic materials. The toroidal multipoles are introduced as microscopic electronic variables in view of symmetry and connection to Dirac theory. They are classified according to crystallographic and magnetic point groups, which allows us to discuss various possible cross correlations in a transparent and unified manner. The representative examples of toroidal orders and related phenomena, and the mutual relationship between these orders are given, with focusing on monopoles and dipoles. The concept of toroidal multipoles would promote future studies toward observations and identifications of unknown electronic phases and their related physical phenomena. .

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach.

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

Affect and satisfaction time dependency: An experience sampling study.

The aim of this study was to test the time dependency between affect and satisfaction on a momentary level. Ninety-eight students participated in the study, using the experience sampling method. Affect and satisfaction scales were administered five times a day for 7 days via handheld devices, sampling the whole awake period with ratings approximately 3-4 h apart. The aim of this study was to examine the cross-correlation between affect and satisfaction at the intra-individual level and to test their temporal consistency via lagged cross-correlations. On average, satisfaction was robustly associated with positive affect (PA; mean correlation 0.50) and negative affect (NA; mean correlation -0.38). The correlation of satisfaction with affect factors showed a consistent temporal dependency. Lag (i.e., the shift of one time series with respect to another) significantly affected the magnitude of the correlation coefficients of satisfaction with PA and NA (explaining almost half of the correlation variance). A significant affect-satisfaction cross-correlation can be found when no lag is present. The introduction of a lag reduces the affect-satisfaction cross-correlation to virtually zero. Research suggests that affect and satisfaction overlap at the momentary level, and the results of this study imply that they are also time-dependent. These findings corroborate the idea that momentary satisfaction judgments are partially based on available emotional information, both in terms of intensity and temporal consistency.