Exact diagonalization of the S=1/2 Heisenberg antiferromagnet on finite bcc lattices to estimate properties on the infinite lattice

Here we generate finite bipartite body-centred cubic lattices up to 32 vertices. We have studied the spin one half Heisenberg antiferromagnet by diagonalizing its Hamiltonian on each of the finite lattices and hence computing its ground state properties. By extrapolation of these data we obtain estimates of the T = 0 properties on the infinite bcc lattice. Our estimate of the T = 0 energy agrees to five parts in ten thousand with third order spin wave and series expansion method estimates, while our estimate of the staggered magnetization agrees with the spin wave estimate to within a quarter of one percent.Comment: 16 pages, LaTeX, 1 ps figure, to appear in J.Phys.

Understanding the Mind or Predicting Signal-Dependent Action? Performance of Children With and Without Autism on Analogues of the False-Belief Task

To evaluate the claim that correct performance on unexpected transfer false-belief tasks specifically involves mental-state understanding, two experiments were carried out with children with autism, intellectual disabilities, and typical development. In both experiments, children were given a standard unexpected transfer false-belief task and a mental-state-free, mechanical analogue task in which participants had to predict the destination of a train based on true or false signal information. In both experiments, performance on the mechanical task was found to correlate with that on the false-belief task for all groups of children. Logistic regression showed that performance on the mechanical analogue significantly predicted performance on the false-belief task even after accounting for the effects of verbal mental age. The findings are discussed in relation to possible common mechanisms underlying correct performance on the two tasks

What guidance are researchers given on how to present network meta-analyses to end-users such as policymakers and clinicians? A systematic review

© 2014 Sullivan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: Network meta-analyses (NMAs) are complex methodological approaches that may be challenging for non-technical end-users, such as policymakers and clinicians, to understand. Consideration should be given to identifying optimal approaches to presenting NMAs that help clarify analyses. It is unclear what guidance researchers currently have on how to present and tailor NMAs to different end-users. Methods: A systematic review of NMA guidelines was conducted to identify guidance on how to present NMAs. Electronic databases and supplementary sources were searched for NMA guidelines. Presentation format details related to sample formats, target audiences, data sources, analysis methods and results were extracted and frequencies tabulated. Guideline quality was assessed following criteria developed for clinical practice guidelines. Results: Seven guidelines were included. Current guidelines focus on how to conduct NMAs but provide limited guidance to researchers on how to best present analyses to different end-users. None of the guidelines provided reporting templates. Few guidelines provided advice on tailoring presentations to different end-users, such as policymakers. Available guidance on presentation formats focused on evidence networks, characteristics of individual trials, comparisons between direct and indirect estimates and assumptions of heterogeneity and/or inconsistency. Some guidelines also provided examples of figures and tables that could be used to present information. Conclusions: Limited guidance exists for researchers on how best to present NMAs in an accessible format, especially for non-technical end-users such as policymakers and clinicians. NMA guidelines may require further integration with end-users' needs, when NMAs are used to support healthcare policy and practice decisions. Developing presentation formats that enhance understanding and accessibility of NMAs could also enhance the transparency and legitimacy of decisions informed by NMAs.The Canadian Institute of Health Research (CIHR) Drug Safety and Effectiveness Network (Funding reference number – 116573)

Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded six, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18/100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH as covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12, 95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.Comment: Submitted to JAMA Network Ope

Why are we not flooded by involuntary thoughts about the past and future? Testing the cognitive inhibition dependency hypothesis

© The Author(s) 2018In everyday life, involuntary thoughts about future plans and events occur as often as involuntary thoughts about the past. However, compared to involuntary autobiographical memories (IAMs), such episodic involuntary future thoughts (IFTs) have become a focus of study only recently. The aim of the present investigation was to examine why we are not constantly flooded by IFTs and IAMs given that they are often triggered by incidental cues while performing undemanding activities. One possibility is that activated thoughts are suppressed by the inhibitory control mechanism, and therefore depleting inhibitory control should enhance the frequency of both IFTs and IAMs. We report an experiment with a between-subjects design, in which participants in the depleted inhibition condition performed a 60-min high-conflict Stroop task before completing a laboratory vigilance task measuring the frequency of IFTs and IAMs. Participants in the intact inhibition condition performed a version of the Stroop task that did not deplete inhibitory control. To control for physical and mental fatigue resulting from performing the 60-min Stroop tasks in experimental conditions, participants in the control condition completed only the vigilance task. Contrary to predictions, the number of IFTs and IAMs reported during the vigilance task, using the probe-caught method, did not differ across conditions. However, manipulation checks showed that participants’ inhibitory resources were reduced in the depleted inhibition condition, and participants were more tired in the experimental than in the control conditions. These initial findings suggest that neither inhibitory control nor physical and mental fatigue affect the frequency of IFTs and IAMs.Peer reviewedFinal Published versio

Constraining the expansion rate of the Universe using low-redshift ellipticals as cosmic chronometers

We present a new methodology to determine the expansion history of the Universe analyzing the spectral properties of early type galaxies (ETG). We found that for these galaxies the 4000\AA break is a spectral feature that correlates with the relative ages of ETGs. In this paper we describe the method, explore its robustness using theoretical synthetic stellar population models, and apply it using a SDSS sample of $\sim$14 000 ETGs. Our motivation to look for a new technique has been to minimise the dependence of the cosmic chronometer method on systematic errors. In particular, as a test of our method, we derive the value of the Hubble constant $H_0 = 72.6 \pm 2.8$ (stat) $\pm2.3$ (syst) (68% confidence), which is not only fully compatible with the value derived from the Hubble key project, but also with a comparable error budget. Using the SDSS, we also derive, assuming w=constant, a value for the dark energy equation of state parameter $w = -1 \pm 0.2$ (stat) $\pm0.3$ (syst). Given the fact that the SDSS ETG sample only reaches $z \sim 0.3$, this result shows the potential of the method. In future papers we will present results using the high-redshift universe, to yield a determination of H(z) up to $z \sim 1$.Comment: 25 pages, 17 figures, JCAP accepte

Clustering and descendants of MUSYC galaxies at z<1.5

We measure the evolution of galaxy clustering out to a redshift of z~1.5 using data from two MUSYC fields, the Extended Hubble Deep Field South (EHDF-S) and the Extended Chandra Deep Field South (ECDF-S). We use photometric redshift information to calculate the projected-angular correlation function, omega(sigma), from which we infer the projected correlation function Xi(sigma). We demonstrate that this technique delivers accurate measurements of clustering even when large redshift measurement errors affect the data. To this aim we use two mock MUSYC fields extracted from a LambdaCDM simulation populated with GALFORM semi-analytic galaxies which allow us to assess the degree of accuracy of our estimates of Xi(sigma) and to identify and correct for systematic effects in our measurements. We study the evolution of clustering for volume limited subsamples of galaxies selected using their photometric redshifts and rest-frame r-band absolute magnitudes. We find that the real-space correlation length r_0 of bright galaxies, M_r<-21 (rest-frame) can be accurately recovered out to z~1.5, particularly for ECDF-S given its near-infrared photometric coverage. There is mild evidence for a luminosity dependent clustering in both fields at the low redshift samples (up to =0.57), where the correlation length is higher for brighter galaxies by up to 1Mpc/h between median rest-frame r-band absolute magnitudes of -18 to -21.5. As a result of the photometric redshift measurement, each galaxy is assigned a best-fit template; we restrict to E and E+20%Sbc types to construct subsamples of early type galaxies (ETGs). Our ETG samples show a strong increase in r_0 as the redshift increases, making it unlikely (95% level) that ETGs at median redshift z_med=1.15 are the direct progenitors of ETGs at z_med=0.37 with equivalent passively evolved luminosities. (ABRIDGED)Comment: 16 pages, 12 figures, 2 tables, accepted for publication in MNRA

Positive psychology of Malaysian students: impacts of engagement, motivation, self-compassion and wellbeing on mental health

Malaysia plays a key role in education of the Asia Pacific, expanding its scholarly output rapidly. However, mental health of Malaysian students is challenging, and their help-seeking is low because of stigma. This study explored the relationships between mental health and positive psychological constructs (academic engagement, motivation, self-compassion, and wellbeing), and evaluated the relative contribution of each positive psychological construct to mental health in Malaysian students. An opportunity sample of 153 students completed the measures regarding these constructs. Correlation, regression, and mediation analyses were conducted. Engagement, amotivation, self-compassion, and wellbeing were associated with, and predicted large variance in mental health. Self-compassion was the strongest independent predictor of mental health among all the positive psychological constructs. Findings can imply the strong links between mental health and positive psychology, especially selfcompassion. Moreover, intervention studies to examine the effects of self-compassion training on mental health of Malaysian students appear to be warranted.N/

Prevalence of chronic fatigue syndrome in metropolitan, urban, and rural Georgia

© 2007 Reeves et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable