Orthogonal Factors Research Articles

A Study on the Effect of Gear Hobbing Process Parameters on the Residual Stress of the Tooth Root

The root residual stress during gear machining has a significant impact on the bending fatigue performance of the gear. The process parameters of gear hobbing (hob speed, axial feed speed, and radial cutting depth) directly affect the residual stress of the tooth root. To investigate the relationship between the process parameters of hobbing and the residual stress of the tooth root respectively, an analysis of an orthogonal and single factor was conducted in the hobbing experiment, taking into account the interactions among factors, which revealed the influence rule and primary–secondary relationship of the process parameters on the residual stress of the tooth root. The importance coefficients of the process parameters on the residual stress of the tooth root were calculated using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The results indicate that the residual tensile stress at the tooth root increases with an increase in the hob speed and axial feed speed within the selected range but decreases with an increase in the radial cutting depth. The influence of the process parameters on the residual stress of the tooth root can be ranked as follows: hob speed (importance coefficient 0.460), axial feed speed (importance coefficient 0.278), and radial cutting depth (importance coefficient 0.262). This research provides a basis for improving the residual stress of the tooth root and enhancing the anti-fatigue manufacturing of gears, thus holding significant research value.

Aesthetic valence: Psychophysical perspectives.

Comparisons of aesthetic valence and of sensory magnitude are subject to similar order effects, indicating an evolved mechanism that sharpens also aesthetic discrimination. As the foundation of pleasantness and aesthetic valence of an object, an optimal level of evoked arousal or, in more recent research, of information load, has been proposed. According to discrepancy theory, this evoked effect is modulated by the object's deviation from the current adaptation level (AL). The AL is built up and updated by pooling recent stimulation. A model based on these concepts is proposed here, and it is illustrated by results of empirical studies by the author's students. For everyday objects such as cars and ladies' clothes, rated beauty was related by a U-shaped function to rated modernity. Minimal beauty occurred for intermediate modernity. For ladies' clothes, this minimum was situated higher on the modernity scale for females and extraverts. As modernity can be seen as the amount of deviation from the AL which represents the usual, this shift could be explained by faster upward adjustment of the AL. In contrast, for paintings the relation between modernity and beauty was inversely U-shaped. This could be due to paintings intrinsically carrying more information than other objects, as indicated by ratings of hard-to-access, with which rated beauty had an inverse U-shaped relation. In a factor-analytic study of preference for 42 paintings four orthogonal factors were extracted, interpreted as High and Low modernity, and High and Low information content. This could yield a rudimentary empirical typology of art.

The translation and validation of the MES for an Austrian sample.

Empathy plays an important role in midwifery care, not only for the women but also for midwives. The Midwifery Empathy Scale (MES) was developed to assess the empathy levels of midwives and midwifery students. The purpose of this study was the translation and validation of the MES for an Austrian sample. A total of 277 midwives working in Austria completed the questionnaire of the MES. The psychometric measurements that were performed included explanatory factor analysis using a varimax rotation and principal components analysis. Moreover, the internal consistency of the MES was assessed with reliability coefficients. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and a Bartlett's test of sphericity were carried out. Principal components analysis showed seven orthogonal factors. KMO measure of sample adequacy = 0.724 and Bartlett's test of sphericity = 1058.904 (df=231, p<0.0001). The MES showed an acceptable overall internal consistency: Cronbach's alpha was found to be 0.721 and the Guttman split-half coefficient was 0.611. The findings of our study confirm the multidimensionality of MES, demonstrating a seven-factor structure which contained subscales reflecting empathy and emotional connection. The mean total score of Austrian midwives' responses to the MES was 44.80 with scores ranging from 24 to 81. This study shows that the German version of the Midwifery Empathy Scale is a reliable instrument for evaluating the empathy levels of midwives and midwifery students in Austria. The German MES could be used in the selection and education of future midwives as well as in connection with empathy trainings of midwives.

Preparation of rutin imprinted monolith (RIM) by using porogen containing ion liquid [BMIM]PF6 and its molecular recognition

A [BMIM]PF6 ion liquid (IL)-assisted synthesis of a rutin imprinted monolith (RIM) was carried out in an in-situ polymerization method. Bi-functional monomers and a ternary porogen containing IL was used for the RIM preparation and a L9(33) orthogonal factor design performed. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR) and N2 adsorption method was for structural characterization of the RIMs. The monolith was directly used as stationary phase in liquid chromatography to test the retention selectivity, adsorption capacity and extraction application. The optimized porogen consists of 900 μL DMF, 144 μL ACN and 216 μL IL. The monolith RIM-13 obtained under the optimized conditions possessed improved adsorption performance, with a dynamic adsorption capacity of 6.695 mg/g, an imprinting efficiency of 4.841 and a selectivity α value of 4.821. Additionally, this monolith had also higher specific surface area, pore volume and permeability than that obtained without IL and the homogeneity of the imprint sites could be improved by using IL. When the RIM-13 was applied to the separation and purification of rutin from tartary buckwheat, a rutin product with a purity higher than 92 % can be obtained by one cycle. This molecular imprint solid-phase extraction (MI-SPE) is of potency to be applied to preparative-scale separation of other natural products.

Anchor-graph regularized orthogonal concept factorization for document clustering

Concept factorization (CF) has attracted widespread attention for its promising performance in document clustering. Among various CF variants, graph-regularized CF is the most impressive type, which can improve clustering effectiveness by exploring structural information. Nevertheless, their clustering efficiency is restricted by the following considerations: (1) the introduction of the full-sample graph is accompanied by an increase in computational complexity; (2) most of them require intensive multiplications in optimization, which impair the optimization efficiency. To address these issues, in this work, we propose an anchor-graph regularized orthogonal concept factorization (AROCF) method to enhance the clustering efficiency and effectiveness in document clustering tasks. Firstly, AROCF approximates the full-sample graph with a small-scale anchor graph to reduce the complexity of graph construction from quadratic to linear. Then, one of the factor matrices is constrained as the cluster indicator matrix in our method, which can avoid extra efficiency loss in K-means after optimization. Finally, an orthogonal constraint is employed to restrict the freedom of factorization to increase the clustering effectiveness. To optimize the AROCF model, we develop a fast optimization strategy by combining the trace and orthogonality of matrices. Extensive experiments on various document datasets demonstrate the effectiveness and efficiency of AROCF.

Performance of Principal Components Estimation Method on the Quality of Factor Analysis without and with Varimax Rotation

The objective of this work was to evaluate the performance of the principal components estimation method on the quality of estimates of some parameters of exploratory factor analysis (EFA), without and with varimax rotation. To this end, 18 parametric correlation matrices (r) were imposed. These matrices were obtained from combinations between six different values of parametric communalities of four normally distributed random variables with three different proportions of distances between the parametric factorial loadings of the first two orthogonal factors. For each matrix r, the following parameters were defined: the first two eigenvalues (l1 and l2), the matrix of factor loadings (G), the four communalities () and the matrix of the specific factors (Y). After the 36 factor analyses, the respective estimates of these parameters were obtained, and their respective absolute deviations between the estimates obtained a posteriori and the parameters known a priori were evaluated. With the results of the Student's t test at 5% significance applied to the response surface analysis, it was concluded that the principal components estimation method for estimating orthogonal factors was not adequate and the varimax rotation improved relatively little the quality of the SFA estimates.

Cross-scale environmental impacts across persistent and dynamic aggregations within a complex population: implications for fisheries management

Accounting for marine stocks spatiotemporal complexity has become one of the most pressing improvements that should be added to the new generation of stock assessment. Disentangling persistent and dynamic population subcomponents and understanding their main drivers of variation are still stock-specific challenges. Here, we hypothesized that the spatiotemporal variability of density in two adjacent fish stocks is associated with spatially structured environmental processes across multiple spatiotemporal scales. To test this, we applied a generalized empirical orthogonal function and dynamic factor analysis to fishery-independent and -dependent data of red mullet, a highly commercial species, in the Western Mediterranean Sea. Areas with persistent and dynamic high aggregations were detected for both stock units. A large-scale climatic index and local open-ocean convection were associated with both stocks, while other variables exhibited stock-specific effects. We also revealed spatially structured density dynamics within the examined management units. This suggests a metapopulation structure and supports the future implementation of a spatial stock assessment. Considering the common—generally unrealistic—assumptions of panmictic structure and absence of connectivity with neighbouring stock units, our methodology can be applied to other species and systems with putative spatial complexity to inform more accurate population dynamics and structure.

Systemic vascular risk, white matter injury, and relative cerebral blood flow independently contribute to cognitive decline beyond amyloid and tau burden

AbstractBackgroundAge, systemic vascular risk factors, white matter injury, cerebral blood flow, amyloid, and tau burden are well‐established contributors to late‐life cognitive decline. Given the strong relationship among these measures, we proposed a model to extract latent factors and isolate domain‐specific measures that underlie cognitive decline in preclinical AD.MethodData were available for 166 cognitively unimpaired older adults from Harvard Aging Brain Study (Table1). We used the following measures: 1) cortical amyloid burden from PiB‐PET; 2) inferior temporal cortex FTP‐PET; 3) relative cerebral blood flow (R1) from dynamically‐acquired PiB‐PET; 4) white matter hyperintensities (WMH) from FLAIR‐MRI; 5) Peak width of skeletonized mean diffusivity (PSMD) from DTI‐MRI; 6) gray matter volume (GMV) from MPRAGE‐MRI; 7) Framingham Heart Study cardiovascular disease risk (CVD); 8) Preclinical Alzheimer Cognitive Composite‐5 (PACC). We extracted slopes using linear mixed effect models for longitudinal PACC decline and GMV atrophy (follow‐up = 7.4±2.8 years). The remaining data were obtained from baseline. We used exploratory factor analysis (varimax rotations) to extract orthogonal factors. These factors were used in a regression model to explain PACC decline. We next examined whether GMV atrophy acts as a mediator of factors and PACC decline. Subsequently, we investigated whether factors interact to further explain PACC decline.ResultsOriginal variables were highly interrelated (Figure1A) and correlated with PACC decline (Figure1B). Scree test showed 6 factors were sufficient (cumulative variance:96%, Figure2A). All factors except Factor5 (age) independently explained PACC decline (Figure2B, r2 = 0.44). Including GMV atrophy removed effect of Factor1 (white matter injury, Figure2B, r2 = 0.51). Factor3 (amyloid) interacted with Factor1 and Factor4 (tau) in explaining PACC decline (Figure2C, r2 = 0.58).ConclusionLatent factors of systemic vascular risk, white matter injury, and relative cerebral blood flow (but not age) independently explain cognitive decline beyond amyloid and tau burden in this preclinical AD cohort. GMV atrophy mediates these associations with the strongest effect on white matter injury suggesting a strong link between white matter injury and neurodegeneration. These results suggest that current markers of cerebrovascular injury contribute to cognitive decline beyond systemic vascular risk (CVD), amyloid, and tau burden. The mechanism by which systemic vascular risk impacts cognition requires further explication.

Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition

Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition

Joint orthogonal symmetric non-negative matrix factorization for community detection in attribute network

Community detection is an important and challenging task in complex attribute network analysis. Symmetric non-negative matrix factorization-based methods have become promising because of their excellent ability to extract low-dimensional representations of attribute networks (which are characterized by their adjacency and attribute data). In this paper, we propose a novel community detector called joint symmetric non-negative matrix factorization model, in which the attribute homogeneity and topology similarities of an attribute network are characterized in a unified framework. An orthogonal constraint is imposed on the factor matrix to improve the accuracy of nodes’ affiliations to communities. Furthermore, a novel multi-order graph regularization was developed to preserve the intrinsic geometric structure, and an eigenvector centrality-based enhancement strategy was established to explore more comprehensive adjacency information. Extensive experiments on community detection tasks demonstrate that the proposed method performs significantly better than existing state-of-the-art methods in most benchmark complex networks.

Gait Domains May Be Used as an Auxiliary Diagnostic Index for Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction and behavioral impairment. We aimed to use principal components factor analysis to explore the association between gait domains and AD under single and dual-task gait assessments. A total of 41 AD participants and 41 healthy control (HC) participants were enrolled in our study. Gait parameters were measured using the JiBuEn® gait analysis system. The principal component method was used to conduct an orthogonal maximum variance rotation factor analysis of quantitative gait parameters. Multiple logistic regression was used to adjust for potential confounding or risk factors. Based on the factor analysis, three domains of gait performance were identified both in the free walk and counting backward assessments: "rhythm" domain, "pace" domain and "variability" domain. Compared with HC, we found that the pace factor was independently associated with AD in two gait assessments; the variability factor was independently associated with AD only in the counting backwards assessment; and a statistical difference still remained after adjusting for age, sex and education levels. Our findings indicate that gait domains may be used as an auxiliary diagnostic index for Alzheimer's disease.

Use of online sequential segmented heart cutting modulation and orthogonality approach for determination of isomeric impurity profile of a drug molecule using parallel 2-dimensional mass spectroscopy

Determination of impurity profile including chiral and achiral impurities is challenging as well as time consuming while analysis of the active moiety. With the recent evolution of 2-Dimensional separation modes, significant improvement can be made for simultaneous determination of both achiral and chiral impurities in a single set-up. Herein, the present work represents example of how both chiral and achiral impurities can be determined simultaneously. To represent the work, galantamine HBr was selected as the active moiety since it has achiral as well as many chiral impurities. Further, a 1D UPLC method with shorter runtime was developed to determine the achiral impurities and simultaneous heart cutting approach was used to cut the impurities and active compound and transfer to 2D chiral setup to separate out chiral impurities and perform its quantification. Both methods were proven to be orthogonal to each other with orthogonality factor of 0.241.

ECCA: Efficient Correntropy-Based Clustering Algorithm With Orthogonal Concept Factorization.

One of the hottest topics in unsupervised learning is how to efficiently and effectively cluster large amounts of unlabeled data. To address this issue, we propose an orthogonal conceptual factorization (OCF) model to increase clustering effectiveness by restricting the degree of freedom of matrix factorization. In addition, for the OCF model, a fast optimization algorithm containing only a few low-dimensional matrix operations is given to improve clustering efficiency, as opposed to the traditional CF optimization algorithm, which involves dense matrix multiplications. To further improve the clustering efficiency while suppressing the influence of the noises and outliers distributed in real-world data, an efficient correntropy-based clustering algorithm (ECCA) is proposed in this article. Compared with OCF, an anchor graph is constructed and then OCF is performed on the anchor graph instead of directly performing OCF on the original data, which can not only further improve the clustering efficiency but also inherit the advantages of the high performance of spectral clustering. In particular, the introduction of the anchor graph makes ECCA less sensitive to changes in data dimensions and still maintains high efficiency at higher data dimensions. Meanwhile, for various complex noises and outliers in real-world data, correntropy is introduced into ECCA to measure the similarity between the matrix before and after decomposition, which can greatly improve the clustering effectiveness and robustness. Subsequently, a novel and efficient half-quadratic optimization algorithm was proposed to quickly optimize the ECCA model. Finally, extensive experiments on different real-world datasets and noisy datasets show that ECCA can archive promising effectiveness and robustness while achieving tens to thousands of times the efficiency compared with other state-of-the-art baselines.

Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI.

The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current magnetic resonance based neuroimaging techniques. In this work, we used diffusion magnetic resonance imaging (dMRI) and a novel surface-based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the neurite orientation dispersion and density imaging (NODDI) model optimized for grey matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the cornu ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibres, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitectonic differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures were separable across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto- and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non-negative matrix factorization (OPNNMF) approach to capture co-varying regions of macro- and microstructure across the hippocampus. The clusters were highly variable along the medial-lateral (proximal-distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fibre pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo dMRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.

Expressed barcoding enables high resolution tracking of the evolution of drug tolerance.

For a majority of non-small cell lung cancer (NSCLC) patients with EGFR mutations, treatment with EGFR inhibitors (EGFRi) induces a clinical response. Despite this initial reduction in tumor size, residual disease persists that leads to disease relapse. Elucidating the pre-existing biological differences between sensitive cells and surviving drug-tolerant persister cells and deciphering how drug-tolerant cells evolve in response to treatment could help identify strategies to improve the efficacy of EGFRi. In this study, we tracked the origins and clonal evolution of drug tolerant cells at a high resolution by using an expressed barcoding system coupled with single cell RNA-sequencing. This platform enabled longitudinally profiling of gene expression and drug sensitivity in response to EGFRi across a large number of clones. Drug tolerant cells had higher expression of key survival pathways such as YAP and EMT at baseline and could also differentially adapt their gene expression following EGFRi treatment compared to sensitive cells. In addition, drug combinations targeting common downstream components (MAPK) or orthogonal factors (chemotherapy) showed greater efficacy than EGFRi alone, which is likely attributable to broader targeting of the multiple EGFRi tolerance mechanisms present in tumors. Overall, this approach facilitates thorough examination of clonal evolution in response to therapy that could inform the development of improved diagnostic approaches and treatment strategies for targeting drug tolerant cells.

Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals

Background and objective:Valvular heart disease (VHD) is associated with elevated mortality rates. Although transthoracic echocardiography (TTE) is the gold standard detection tool, phonocardiography (PCG) could be an alternative as it is a cost-effective and noninvasive method for cardiac auscultation. Many researchers have dedicated their efforts to improving the decision-making process and developing robust and precise approaches to assist physicians in providing reliable diagnoses of VHD. Methods:This research proposes a novel approach for the detection of anomalous valvular heart sounds from PCG signals. The proposed approach combines orthogonal non-negative matrix factorization (ONMF) and convolutional neural network (CNN) architectures in a three-stage cascade. The aim of the proposal is to improve the learning process by identifying the optimal ONMF temporal or spectral patterns for accurate detection. In the first stage, the time–frequency representation of the input PCG signal is computed. Next, band-pass filtering is performed to locate the spectral range that is most relevant for the presence of such cardiac abnormalities. In the second stage, the temporal and spectral cardiac structures are extracted using the ONMF approach. These structures are utilized in the third stage and fed into the CNN architecture to detect abnormal heart sounds. Results:Several state-of-the-art CNN architectures, such as LeNet5, AlexNet, ResNet50, VGG16 and GoogLeNet, have been evaluated to determine the effectiveness of using ONMF temporal features for VHD detection. The results reveal that the integration of ONMF temporal features with a CNN classifier significantly improve VHD detection. Specifically, the proposed approach achieves an accuracy improvement of approximately 45% when ONMF spectral features are used and 35% when time–frequency features from the short-time Fourier transform (STFT) spectrogram are used. Additionally, feeding ONMF temporal features into low-complexity CNN architectures yields competitive results comparable to those obtained with complex architectures. Conclusions:The temporal structure factorized by ONMF plays a critical role in distinguishing between normal heart sounds and abnormal heart sounds since the repeatability of normal heart cycles is disrupted by the presence of cardiac abnormalities. Consequently, the results highlight the importance of appropriate input data representation in the learning process of CNN models in the biomedical field of valvular heart sound detection.

Unilateral Orthogonal Nonnegative Matrix Factorization

Unilateral Orthogonal Nonnegative Matrix Factorization

Fundamental Fears in Greece (I)

Abstract: Introduction: The present study examined the psychometric properties of a Greek adaptation of the Anxiety Sensitivity Index-3 (ASI-3; Taylor et al., 2007 ). Method: We translated the ASI-3 following a forward-backward method and then, in addition to measures of anxiety and depression (DASS-21; Lovibond &amp; Lovibond, 1995 ; Lyrakos et al., 2011 ), we administered it to a nonclinical general population sample ( N = 611) recruited online. Results: Confirmatory factor analysis revealed that a bifactor model with three orthogonal group factors best fit the data, followed by a correlated three-factor model. An examination of the dimensionality of the ASI-3 and the reliability of its dimensions suggested the presence of a reliable, strong AS general factor and comparatively weaker group factors. The ASI-3 appears to measure AS invariantly across gender. We report preliminary evidence for its convergent, discriminant, and divergent validity. Conclusion: The Greek adaption of the ASI-3 revealed adequate psychometric properties. Future studies should explore its criterion-related validity by administering the Greek adaptation of the ASI-3 to clinical samples and explore its relationship to other key constructs of anxiety sensitivity’s nomological network.

Assessment of clinician’s knowledge and attitude toward health extension program in Ethiopia: An exploratory factor analysis

Health extension workers (HEWs) are the primary implementers of the health extension program (HEP) &amp;ndash; a government-led community-based health-care program, but clinicians are anticipated to play a critical part in providing extensive support, thus, this study aimed to assess clinicians&amp;rsquo; knowledge and perceptions of HEP, a cross-sectional study was conducted with 1239 clinicians. A set of 54-item questions was created based on HEP guidelines and relevant literature. Exploratory factor analysis (EFA) identified latent variables with Eigenvalues matrix &gt;1. Cluster variables were derived through orthogonal varimax factor rotation, and internal reliability was evaluated using Cronbach&amp;rsquo;s alpha coefficient. A composite score was generated for each constructed factor, and the associations between variables were determined using a one-way analysis of variance. Multiple linear regression analysis was conducted to eliminate confounders, with statistical significance set at P &lt; 0.05. EFA provides three factors explaining 91% of the total variance and labeled as &amp;ldquo;clinician-perceived attitude towards the skill of HEWs&amp;rdquo; (F1), &amp;ldquo;clinician knowledge on HEP activities&amp;rdquo; (F2), and &amp;ldquo;clinician-perceived attitude towards the impact of HEP&amp;rdquo; (F3). Internal reliability for the 54 items was 0.96, and it was 0.93, 0.90, and 0.89 for F1, F2, and F3, respectively. 75.5% and 76.2% of clinicians had favorable attitudes toward F1 and F3, respectively, and 70.2% had good knowledge of F2. F1 was positively correlated with participation in HEP review meetings, home visits, HEP outreach, and willingness to work in HEP but negatively correlated with degree holders. F2 was positively correlated with participation in HEP review meetings, home visits, being married, and non-medical doctors but negatively correlated with willingness to work in HEP, older age, female, and degree holder clinicians. Three factors, focusing on knowledge, skill, and the impact of HEP, were found and fall under the second-generation HEP framework. Therefore, strengthening HEP guidelines is essential to ensuring the delivery of sustainable and pro-poor HEP.

Separating the General and Content-Specific Components of Self-Reported Propensity for Mind-Wandering

Abstract: People differ in the propensity to mind-wander, that is, the tendency to slip into thinking about something else than the task at hand or the happenings in the immediate environment. Research indicates that mind-wandering tends to be associated with negative emotional states and that it declines with increasing age in adulthood. A question arises whether these relationships are true of mind-wandering in general, independently of its content and affective tone, or just particular kinds of off-task thinking. Participants ( N = 218) aged 18–84 years filled out the Task-Unrelated Thoughts Questionnaire (TUTQ), a multidimensional instrument for measuring individual tendencies in mind-wandering, and several scales related to affective functioning. A bifactor model for the TUTQ was employed, encompassing a general factor and three orthogonal specific factors that referred to affectively different modes of off-task thinking. The scores on the general factor decreased with increasing age of the respondents, dispositional positive affect, and satisfaction with life, whereas they increased with increasing negative affect and emotional reactivity. Age and affect-related variables, including individual tendencies in emotion regulation, also predicted, selectively, the specific factors. Although methodologically and theoretically challenging, the bifactor model seems to be a promising tool for representing and exploring individual variability in mind-wandering.