Linear discontinuous ground deformation detection based on coherence analysis of pre and post event radar image pairs

The article presents information on how to use satellite interferometry to detect linear discontinuous ground deformation [LDGD] caused by underground mining. Assumptions were made based on the properties of the SAR signal correlation coefficient (coherence). Places of LDGD have been identified based on these assumptions. Changes taking place on the surface between two acquisitions lead to worse correlation between two radar images. This results in lower values of the SAR signal correlation coefficient in the coherence maps. Therefore, it was assumed that the formation of LDGD could reduce the coherence value compared to the previous state. The second assumption was an increase in the standard deviation of coherence, which is a classic measurement of variability. Therefore any changes in the surface should lead to increasing standard deviation of coherence compared to the previous state. Images from the Sentinel-1 satellite and provided by the ESA were used for analysis. The research is presented on the basis of two research areas located in the Upper Silesian Coal Basin in the south of Poland. The area in which LDGD could occur was limited to 6 % of the total area in case 1 and 36 % in case 2 by applying an appropriate methodology of satellite image coherence analysis. This paper is an introduction to the development of a method of detecting LDGDs caused by underground mining and to study these issues further.

Visual speech is processed differently in auditory and visual cortex: evidence from MEG and partial coherence analysis

Speech perception in noisy environments is enhanced by seeing facial movements of communication partners. However, the neural mechanisms by which audio and visual speech are combined are not fully understood. We explore MEG phase locking to auditory and visual signals in MEG recordings from 14 human participants (6 female) that reported words from single spoken sentences. We manipulated the acoustic clarity and visual speech signals such that critical speech information is present in auditory, visual or both modalities. MEG coherence analysis revealed that both auditory and visual speech envelopes (auditory amplitude modulations and lip aperture changes) were phase-locked to 2-6Hz brain responses in auditory and visual cortex, consistent with entrainment to syllable-rate components. Partial coherence analysis was used to separate neural responses to correlated audio-visual signals and showed non-zero phase locking to auditory envelope in occipital cortex during audio-visual (AV) speech. Furthermore, phase-locking to auditory signals in visual cortex was enhanced for AV speech compared to audio-only (AO) speech that was matched for intelligibility. Conversely, auditory regions of the superior temporal gyrus (STG) did not show above-chance partial coherence with visual speech signals during AV conditions, but did show partial coherence in VO conditions. Hence, visual speech enabled stronger phase locking to auditory signals in visual areas, whereas phase-locking of visual speech in auditory regions only occurred during silent lip-reading. Differences in these cross-modal interactions between auditory and visual speech signals are interpreted in line with cross-modal predictive mechanisms during speech perception.

Composite Quantile Probability Predictions: Performance and Coherence Analysis of US COVID-19 Confirmed Infection Cases

An analytical framework is presented for the evaluation of composite probability forecasts using empirical quantiles. The framework is demonstrated via the examination of forecasts of the changes in the number of US COVID-19 confirmed infection cases, applying 18 two-week ahead quantile forecasts from four forecasting organisations. The forecasts are analysed individually for each organisation and in combinations of organisational forecasts to ascertain the highest level of performance. It is shown that the relative error reduction achieved by combining forecasts depends on the extent to which the component forecasts contain independent information. The implications of the study are discussed, suggestions are offered for future research and potential limitations are considered.

Examining non-technical skills for ad hoc resuscitation teams: a scoping review and taxonomy of team-related concepts

Background Non-technical skills (NTS) concepts from high-risk industries such as aviation have been enthusiastically applied to medical teams for decades. Yet it remains unclear whether—and how—these concepts impact resuscitation team performance. In the context of ad hoc teams in prehospital, emergency department, and trauma domains, even less is known about their relevance and impact. Methods This scoping review, guided by PRISMA-ScR and Arksey & O’Malley’s framework, included a systematic search across five databases, followed by article selection and extracting and synthesizing data. Articles were eligible for inclusion if they pertained to NTS for resuscitation teams performing in prehospital, emergency department, or trauma settings. Articles were subjected to descriptive analysis, coherence analysis, and citation network analysis. Results Sixty-one articles were included. Descriptive analysis identified fourteen unique non-technical skills. Coherence analysis revealed inconsistencies in both definition and measurement of various NTS constructs, while citation network analysis suggests parallel, disconnected scholarly conversations that foster discordance in their operationalization across domains. To reconcile these inconsistencies, we offer a taxonomy of non-technical skills for ad hoc resuscitation teams. Conclusion This scoping review presents a vigorous investigation into the literature pertaining to how NTS influence optimal resuscitation performance for ad hoc prehospital, emergency department, and trauma teams. Our proposed taxonomy offers a coherent foundation and shared vocabulary for future research and education efforts. Finally, we identify important limitations regarding the traditional measurement of NTS, which constrain our understanding of how and why these concepts support optimal performance in team resuscitation. Graphical abstract

Multiscale coherence analysis of reference evapotranspiration of north-western Iran using wavelet transform

This study applies different wavelet coherence formulations for investigating the multiscale associations of reference Evapotranspiration (ET0) of Tabriz and Urmia stations in North West Iran with five climatic variables, mean temperature (T), pressure (P), relative humidity (RH), wind speed (U) and Solar Radiation (SR). The relationships between different variables are quantified using the Average Wavelet Coherence (AWC) and the Percentage of Significant Coherence (PoSC). The Bivariate Wavelet Coherence (BWC) analysis showed that mean temperature (AWC = 0.73, PoSC = 59.18%) and wind speed (AWC = 0.63, PoSC = 49.55%) are the dominant predictors at Tabriz and Urmia stations. On considering the Multiple Wavelet Coherence (MWC) analysis, it is noticed that among the two-factor combinations, the T-P and P-RH combinations resulted in the highest coherence values for Tabriz and Urmia stations. T-U-SR combination produced the highest multiple wavelet coherence values among the three-factor cases for both the stations. The Partial Wavelet Coherence (PWC) analysis indicated a drastic reduction in coherence from the values of respective BWC analysis, indicating a strong interrelationship between different variables and ET0. The interrelationship between meteorological variables and ET0 is more apparent at Tabriz, while it is controlled more by the local-scale meteorology at Urmia.

A Discourse Coherence Analysis Method Combining Sentence Embedding and Dimension Grid

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. However, existing coherence models focus on measuring individual aspects of coherence, such as lexical overlap, entity centralization, rhetorical structure, etc., lacking measurement of the semantics of text. In this paper, we propose a discourse coherence analysis method combining sentence embedding and the dimension grid, we obtain sentence-level vector representation by deep learning, and we introduce a coherence model that captures the fine-grained semantic transitions in text. Our work is based on the hypothesis that each dimension in the embedding vector is exactly assigned a stated certainty and specific semantic. We take every dimension as an equal grid and compute its transition probabilities. The document feature vector is also enriched to model the coherence. Finally, the experimental results demonstrate that our method achieves excellent performance on two coherence-related tasks.