Introduction Social jetlag, characterized by later bedtimes and waketimes on off days compared with work days, is associated with adverse mental and physical health outcomes. Given the common use of ...three 12-hour shifts in nursing schedules, we hypothesized that day shift nurses with a later chronotype would exhibit greater social jetlag and associated sleep disruption compared with day shift nurses with an earlier chronotype. Methods Full-time nurses who work day shift (n=140) completed the Biological Clocks Questionnaire for 8 consecutive days and the Standard Shiftwork Index. Hierarchical cluster analysis was used to examine sleep schedules on workdays and off days to identify distinct clusters of sleep schedules. Results Three distinct clusters were identified: early bedtime, variable social jetlag (Group A; n=43); later bedtime, no social jetlag (Group B; n=48); and very late bedtime, high social jetlag (Group C; n=49). Nurses with very late bedtimes and high social jetlag (Group C) were younger (p < 0.01), had lower scores on the morningness scale (p < 0.01), and reported being less adapted to their work schedule (p < 0.05). All three groups reported greater sleep disturbance on work days compared with off days (p < 0.001); however, this difference was potentiated for Group C (p < 0.01). Conclusion Chronotype should be considered when assigning individuals to working shifts. Additionally, shift workers should be educated on strategies to minimize social jetlag in order and resulting sleep disruption. Support (If Any) This study was funded by the UAB Center for Clinical and Translational Science (CCTS) Grant Number ULITR000165.
Abstract In this paper, a data mining based valve flow curve optimization technique is proposed. Firstly, the data mining technique is described. Then, a valve flow curve optimization method based on ...improved cluster analysis is proposed. Finally, the effectiveness of these methods was verified through experiments. The results show that the valve flow curve optimization technique based on data mining can effectively improve the performance and reliability of the valve.
Clustering biological sequences into similar groups is an increasingly important task as the number of available sequences continues to grow exponentially. Search-based approaches to clustering scale ...super-linearly with the number of input sequences, making it impractical to cluster very large sets of sequences. Approaches to clustering sequences in linear time currently lack the accuracy of super-linear approaches. Here, I set out to develop and characterize a strategy for clustering with linear time complexity that retains the accuracy of less scalable approaches. The resulting algorithm, named Clusterize, sorts sequences by relatedness to linearize the clustering problem. Clusterize produces clusters with accuracy rivaling popular programs (CD-HIT, MMseqs2, and UCLUST) but exhibits linear asymptotic scalability. Clusterize generates higher accuracy and oftentimes much larger clusters than Linclust, a fast linear time clustering algorithm. I demonstrate the utility of Clusterize by accurately solving different clustering problems involving millions of nucleotide or protein sequences.
Gaia Early Data Release 3 Torra, F; Castañeda, J; Fabricius, C ...
Astronomy and astrophysics (Berlin),
05/2021, Letnik:
649
Journal Article
Recenzirano
Context. The Gaia Early Data Release 3 (Gaia EDR3) contains results derived from 78 billion individual field-of-view transits of 2.5 billion sources collected by the European Space Agency’s Gaia ...mission during its first 34 months of continuous scanning of the sky. Aims. We describe the input data, which have the form of onboard detections, and the modeling and processing that is involved in cross-matching these detections to sources. For the cross-match, we formed clusters of detections that were all linked to the same physical light source on the sky. Methods. As a first step, onboard detections that were deemed spurious were discarded. The remaining detections were then preliminarily associated with one or more sources in the existing source list in an observation-to-source match. All candidate matches that directly or indirectly were associated with the same source form a match candidate group. The detections from the same group were then subject to a cluster analysis. Each cluster was assigned a source identifier that normally was the same as the identifiers from Gaia DR2. Because the number of individual detections is very high, we also describe the efficient organising of the processing. Results. We present results and statistics for the final cross-match with particular emphasis on the more complicated cases that are relevant for the users of the Gaia catalogue. We describe the improvements over the earlier Gaia data releases, in particular for stars of high proper motion, for the brightest sources, for variable sources, and for close source pairs.
•(Multi)modal travel groups are identified by applying latent class cluster analysis.•Attitudes are included in addition to structural variables to explain group membership.•In four of the five ...groups, attitudes are congruent with travel mode use.•The group who uses public transport most often has only average PT attitudes.•Challenges for sustainable policies are formulated for each of the identified groups.
For developing sustainable travel policies, it may be helpful to identify multimodal travelers, that is, travelers who make use of more than one mode of transport within a given period of time. Of special interest is identifying car drivers who also use public transport and/or bicycle, as this group is more likely to respond to policies that stimulate the use of those modes. It is suggested in the literature that this group may have less biased perceptions and different attitudes towards those modes. This supposition is examined in this paper by conducting a latent class cluster analysis, which identifies (multi)modal travel groups based on the self-reported frequency of mode use. Simultaneously, a membership function is estimated to predict the probability of belonging to each of the five identified (multi)modal travel groups, as a function of attitudinal variables in addition to structural variables. The results indicate that the (near) solo car drivers indeed have more negative attitudes towards public transport and bicycle, while frequent car drivers who also use public transport have less negative public transport attitudes. Although the results suggest that in four of the five identified travel groups, attitudes are congruent with travel mode use, this is not the case for the group who uses public transport most often. This group has relatively favorable car attitudes, and given that many young, low-income travelers belong to this group, it may be expected that at least part of this group will start using car more often once they can afford it. Based on the results, challenges for sustainable policies are formulated for each of the identified (multi)modal travel groups.
•Differentiable reformulation of the k-Means problem in a learned embedding space.•Proposition of an alternative to pretraining based on deterministic annealing.•Straightforward training algorithm ...based on stochastic gradient descent.•Careful comparison against k-Means-related and deep clustering approaches.
We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for k-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.
Animals have evolved a wide variety of signals that punctuate social interactions, thus optimizing communication systems. In the study of communicative strategies, real and play fighting are good ...models, as they are associated with risks and injuries. Therefore, within these two domains, clear ‘statement’ signals should be recruited to disambiguate messages. We gathered video data (135h) from 38 wolves from three mixed-age captive groups (Canis lupus arctos, C. l. lupus, C. l. occidentalis) and analysed all the facial expressions in aggressive and playful domains. The analyses revealed the presence of three different threatening faces (Light-, Medium- and High-TF), mainly performed during aggressive encounters, which differed in the degree of mouth opening and lip stretching. We also identified two different relaxed open mouth facial expressions (Full- and Half-ROM) exclusively performed during play and possibly signalling different levels of playful arousal. Interestingly, facial expressions did not differ between groups thus suggesting a hard-wired facial communication system at least in these two domains. The next step will be to test hypotheses on the efficacy of such facial displays in eliciting an appropriate response in the receivers, potentially translating into a fine modulation of behavioural patterns in both play and real fighting.
•Wolf facial displays differ strongly in playful and aggressive contexts.•During aggression three different threatening faces can be present.•Wolves display two different playful facial displays.•Playful and aggressive facial displays always differ in the same key elements.•Play and aggressive facial displays were consistent across three wolf groups.
A hierarchical-agglomerative clustering workflow is proposed to investigate complex, tectonically controlled regions on planetary surfaces. This algorithm comprises two primary steps: (i) calculating ...the dissimilarity between each object and (ii) grouping objects using a hierarchical clustering method. The efficiency of the workflow hinges on two critical parameters that require careful selection: the attributes for grouping objects and the number of clusters for interpretation. We applied this approach to tectonic lineaments in the Claritas Fossae (CF) region on Mars, a complex area significantly shaped by tectonic activity. Our analysis considered three attributes of these lineaments: (i) azimuthal direction, (ii) length, and (iii) centroid position. The optimal number of clusters was determined using the Silhouette index (S), which assesses the robustness of the clustering results. Our objectives were twofold: (i) to replicate the distribution of lineament sets obtained from classical geostatistical analysis of their azimuthal orientation and (ii) to refine the subdivision by varying combinations of the three attributes. Our results provide crucial insights into the geo-tectonic evolution of CF, supporting the hypothesis of a tectonic evolution characterised by a polyphase history. In addition, we demonstrate how the method is capable of effectively identified areas with varying intensities of brittle deformation.
•The presented hierarchical-agglomerative clustering workflow is able to unravel tectonics on planetary surfaces•Tectonic lineaments of the Claritas Fossae (Mars) are clustered based on specific characteristics•The capability of the method is confirmed by replicating results obtained with classical geostatistical analysis•Results show clusters unidentified with classical methods thus constraining the tectonic settingof the Claritas Fossae.
PDF
