A Lagrangian analysis of ice-supersaturated air over the North Atlantic

Understanding the nature of air parcels that exhibit ice-supersaturation is important because they are the regions of potential formation of both cirrus and aircraft contrails, which affect the radiation balance. Ice-supersaturated air parcels in the upper troposphere and lower stratosphere over the North Atlantic are investigated using Lagrangian trajectories. The trajectory calculations use ERA-Interim data for three winter and three summer seasons, resulting in approximately 200,000 trajectories with ice-supersaturation for each season. For both summer and winter, the median duration of ice-supersaturation along a trajectory is less than 6 hours. 5% of air which becomes ice-supersaturated in the troposphere, and 23% of air which becomes ice-supersaturated in the stratosphere will remain ice-supersaturated for at least 24 hours. Weighting the ice-supersaturation duration with the observed frequency indicates the likely overall importance of the longer duration ice-supersaturated trajectories. Ice-supersaturated air parcels typically experience a decrease in moisture content while ice-supersaturated, suggesting that cirrus clouds eventually form in the majority of such air. A comparison is made between short-lived (less than 24 h) and long-lived (greater than 24 h) ice-supersaturated air flows. For both air flows, ice-supersaturation occurs around the northernmost part of the trajectory. Short-lived ice-supersaturated air flows show no significant differences in speed or direction of movement to subsaturated air parcels. However, long-lived ice-supersaturated air occurs in slower moving air flows, which implies that they are not associated with the fastest moving air through a jet stream.

Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

The analysis step of the (ensemble) Kalman filter is optimal when (1) the distribution of the background is Gaussian, (2) state variables and observations are related via a linear operator, and (3) the observational error is of additive nature and has Gaussian distribution. When these conditions are largely violated, a pre-processing step known as Gaussian anamorphosis (GA) can be applied. The objective of this procedure is to obtain state variables and observations that better fulfil the Gaussianity conditions in some sense. In this work we analyse GA from a joint perspective, paying attention to the effects of transformations in the joint state variable/observation space. First, we study transformations for state variables and observations that are independent from each other. Then, we introduce a targeted joint transformation with the objective to obtain joint Gaussianity in the transformed space. We focus primarily in the univariate case, and briefly comment on the multivariate one. A key point of this paper is that, when (1)-(3) are violated, using the analysis step of the EnKF will not recover the exact posterior density in spite of any transformations one may perform. These transformations, however, provide approximations of different quality to the Bayesian solution of the problem. Using an example in which the Bayesian posterior can be analytically computed, we assess the quality of the analysis distributions generated after applying the EnKF analysis step in conjunction with different GA options. The value of the targeted joint transformation is particularly clear for the case when the prior is Gaussian, the marginal density for the observations is close to Gaussian, and the likelihood is a Gaussian mixture.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles

Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

New-on-line method for water isotope analysis of speleothem fluid inclusions using laser absorption spectroscopy (WS-CRDS)

A new online method to analyse water isotopes of speleothem fluid inclusions using a wavelength scanned cavity ring down spectroscopy (WS-CRDS) instrument is presented. This novel technique allows us simultaneously to measure hydrogen and oxygen isotopes for a released aliquot of water. To do so, we designed a new simple line that allows the online water extraction and isotope analysis of speleothem samples. The specificity of the method lies in the fact that fluid inclusions release is made on a standard water background, which mainly improves the δ D robustness. To saturate the line, a peristaltic pump continuously injects standard water into the line that is permanently heated to 140 °C and flushed with dry nitrogen gas. This permits instantaneous and complete vaporisation of the standard water, resulting in an artificial water background with well-known δ D and δ18O values. The speleothem sample is placed in a copper tube, attached to the line, and after system stabilisation it is crushed using a simple hydraulic device to liberate speleothem fluid inclusions water. The released water is carried by the nitrogen/standard water gas stream directly to a Picarro L1102-i for isotope determination. To test the accuracy and reproducibility of the line and to measure standard water during speleothem measurements, a syringe injection unit was added to the line. Peak evaluation is done similarly as in gas chromatography to obtain &delta D; and δ18O isotopic compositions of measured water aliquots. Precision is better than 1.5 ‰ for δ D and 0.4 ‰ for δ18O for water measurements for an extended range (−210 to 0 ‰ for δ D and −27 to 0 ‰ for δ18O) primarily dependent on the amount of water released from speleothem fluid inclusions and secondarily on the isotopic composition of the sample. The results show that WS-CRDS technology is suitable for speleothem fluid inclusion measurements and gives results that are comparable to the isotope ratio mass spectrometry (IRMS) technique.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Process analysis of trauma-focused cognitive behavioural therapy for individuals with schizophrenia

Objective. Therapeutic alliance, modality, and ability to engage with the process of therapy have been the main focus of research into what makes psychotherapy successful. Individuals with complex trauma histories or schizophrenia are suggested to be more difficult to engage and may be less likely to benefit from therapy. This study aimed to track the in-session ‘process’ of working alliance and emotional processing of trauma memories for individuals with schizophrenia. Design. The study utilized session recordings from the treatment arm of an open randomized clinical trial investigating trauma-focused cognitive behavioural therapy (TF-CBT) for individuals with schizophrenia (N = 26). Method. Observer measures of working alliance, emotional processing, and affect arousal were rated at early and late phases of therapy. Correlation analysis was undertaken for process measures. Temporal analysis of expressed emotions was also reported. Results. Working alliance was established and maintained throughout the therapy; however, agreement on goals reduced at the late phase. The participants appeared to be able to engage in emotional processing, but not to the required level for successful cognitive restructuring. Conclusion. This study undertook novel exploration of process variables not usually explored in CBT. It is also the first study of process for TF-CBT with individuals with schizophrenia. This complex clinical sample showed no difficulty in engagement; however, they may not be able to fully undertake the cognitive–emotional demands of this type of therapy. Clinical and research implications and potential limitations of these methods are considered.

A psychometric analysis of reliability and validity of the index of learning styles (ILS)

Prior literature showed that Felder and Silverman learning styles model (FSLSM) was widely adopted to cater to individual styles of learners whether in traditional or Technology Enhanced Learning (TEL). In order to infer this model, the Index of Learning Styles (ILS) instrument was proposed. This research aims to analyse the soundness of this instrument in an Arabic sample. Data were integrated from different courses and years. A total of 259 engineering students participated voluntarily in the study. The reliability was analysed by applying internal construct reliability, inter-scale correlation, and total item correlation. The construct validity was also considered by running factor analysis. The overall results indicated that the reliability and validity of perception and input dimensions were moderately supported, whereas processing and understanding dimensions showed low internal-construct consistency and their items were weakly loaded in the associated constructs. Generally, the instrument needs further effort to improve its soundness. However, considering the consistency of the produced results of engineering students irrespective of cross-cultural differences, it can be adopted to diagnose learning styles.

Big social data and political sentiment: the tweet stream during the UK General Election 2015 campaign

The General Election for the 56th United Kingdom Parliament was held on 7 May 2015. Tweets related to UK politics, not only those with the specific hashtag ”#GE2015”, have been collected in the period between March 1 and May 31, 2015. The resulting dataset contains over 28 million tweets for a total of 118 GB in uncompressed format or 15 GB in compressed format. This study describes the method that was used to collect the tweets and presents some analysis, including a political sentiment index, and outlines interesting research directions on Big Social Data based on Twitter microblogging.

Are visual threats prioritized without awareness? A critical review and meta analysis involving 3 behavioral paradigms and 2696 observers

Given capacity limits, only a subset of stimuli 1 give rise to a conscious percept. Neurocognitive models suggest that humans have evolved mechanisms that operate without awareness and prioritize threatening stimuli over neutral stimuli in subsequent perception. In this meta analysis, we review evidence for this ‘standard hypothesis’ emanating from three widely used, but rather different experimental paradigms that have been used to manipulate awareness. We found a small pooled threat-bias effect in the masked visual probe paradigm, a medium effect in the binocular rivalry paradigm and highly inconsistent effects in the breaking continuous flash suppression paradigm. Substantial heterogeneity was explained by the stimulus type: the only threat stimuli that were robustly prioritized across all three paradigms were fearful faces. Meta regression revealed that anxiety may modulate threat biases, but only under specific presentation conditions. We also found that insufficiently rigorous awareness measures, inadequate control of response biases and low level confounds may undermine claims of genuine unconscious threat processing. Considering the data together, we suggest that uncritical acceptance of the standard hypothesis is premature: current behavioral evidence for threat-sensitive visual processing that operates without awareness is weak.