X-ray scaling relations from a complete sample of the richest maxBCG clusters

We use a complete sample of 38 richest maxBCG clusters to study the ICM-galaxy scaling relations and the halo mass selection properties of the maxBCG algorithm, based on X-ray and optical observations. The clusters are selected from the two largest bins of optical richness in the Planck stacking work with the maxBCG richness $N_{200} \geq 78$. We analyze their Chandra and XMM-Newton data to derive the X-ray properties of the ICM. We then use the distribution of $P(X|N)$, $X=T_X,\ L_X,\ Y_X$, to study the mass selection $P(M|N)$ of maxBCG. Compared with previous works based on the whole richness sample, a significant fraction of blended systems with boosted richness is skewed into this richest sample. Parts of the blended halos are picked apart by the redMaPPer, an updated red-sequence cluster finding algorithm with lower mass scatter. Moreover, all the optical blended halos are resolved as individual X-ray halos, following the established $L_X-T_X$ and $L_X-Y_X$ relations. We further discuss that the discrepancy between ICM-galaxy scaling relations, especially for future blind stacking, can come from several factors, including miscentering, projection, contamination of low mass systems, mass bias and covariance bias. We also evaluate the fractions of relaxed and cool core clusters in our sample. Both are smaller than those from SZ or X-ray selected samples. Moreover, disturbed clusters show a higher level of mass bias than relaxed clusters.Comment: 28 pages, 12 figures, MNRAS in pres

How do musicians evaluate their musical performances? The impact of positive and negative information from normative, ipsative, and expectation standards

The purpose of the research reported in this article was to test two hypotheses about how musicians evaluate their musical performances. The first hypothesis was that musicians’ self-evaluations would be more influenced by their expectations and their past performances than by comparisons to the performances of other musicians. The second hypothesis was that musicians would exhibit an ‘adaptive evaluational style’ by showing more sensitivity to positive feedback than to negative feedback. We used the Experimental Evaluational Styles Questionnaire (Goolsby & Chaplin, 1988) in a sample of 78 music performance students (43 men and 35 women) to test these hypotheses, and both were supported. These results represent one of the first examples where the dominant theory of evaluation in psychology, Festinger’s (1954) social comparison theory, did not have the greatest influence on people’s performance evaluations. However, we did find individual differences in the influence of the different evaluative standards. Understanding the causes and consequences of these individual differences should be a fruitful target for future research

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Symposium on mutual funds and monetary aggregates - commentary

Money supply ; Mutual funds

Design of a Graphene Nitrene Two-Dimensional Catalyst Heterostructure Providing a Well-Defined Site Accommodating 1 to 3 Metals, with Application to CO₂ Reduction Electrocatalysis for the 2 Metal Case

Recently, the reduction of CO₂ to fuels has been the subject of numerous studies, but the selectivity and activity remain inadequate. Progress has been made on single-site two-dimensional catalysts based on graphene coupled to a metal and nitrogen for the CO₂ reduction reaction (CO₂RR); however, the product is usually CO, and the metal–N environment remains ambiguous. We report a novel two-dimensional graphene nitrene heterostructure (grafiN₆) providing well-defined active sites (N₆) that can bind one to three metals for the CO₂RR. We find that homobimetallic FeFe–grafiN₆ could reduce CO₂ to CH₄ at −0.61 V and to CH₃CH₂OH at −0.68 V versus reversible hydrogen electrode, with high product selectivity. Moreover, the heteronuclear FeCu–grafiN₆ system may be significantly less affected by hydrogen evolution reaction, while maintaining a low limiting potential (−0.68 V) for C1 and C2 mechanisms. Binding metals to one N₆ site but not the other could promote efficient electron transport facilitating some reaction steps. This framework for single or multiple metal sites might also provide unique catalytic sites for other catalytic processes

On a triangulation of the 3-ball and the solid torus

AbstractWe show that neither the 3-ball nor the solid torus admits a triangulation in which (i) every vertex is on the boundary, and (ii) every tetrahedron has exactly one triangle on the boundary. Such triangulations are relevant to an unresolved conjecture of Perles

Psychosocial vulnerabilities to depression after acute coronary syndrome: the pivotal role of rumination in predicting and maintaining depression

Psychosocial vulnerabilities may predispose individuals to develop depression after a significant life stressor, such as an acute coronary syndrome (ACS). The aims are (1) to examine the interrelations among vulnerabilities, and their relation with changes in depressive symptoms 3 months after ACS, (2) to prospectively assess whether rumination interacts with other vulnerabilities as a predictor of later depressive symptoms, and (3) to examine how these relations differ between post-ACS patients who meet diagnostic criteria for depression at baseline versus patients who do not. Within 1 week after hospitalization for ACS, and again after 3 months, 387 patients (41% female, 79.6% white, mean age 61) completed the Beck Depression Inventory (BDI) and measures of vulnerabilities (lack of pleasant events, dysfunctional attitudes, role transitions, poor dyadic adjustment). Exclusion criteria were a BDI score of 5–9, terminal illness, active substance abuse, cognitive impairment, and unavailability for follow-up visits. We used hierarchical regression modeling cross-sectionally and longitudinally. Controlling for baseline (in-hospital) depression and cardiovascular disease severity, vulnerabilities significantly predicted 3 month depression severity. Rumination independently predicted increased depression severity, above other vulnerabilities (β = 0.75, p < 0.001), and also interacted with poor dyadic adjustment (β = 0.32, p < 0.001) to amplify depression severity. Among initially non-depressed patients, the effects of vulnerabilities were amplified by rumination. In contrast, in patients who were already depressed at baseline, there was a direct effect of rumination above vulnerabilities on depression severity. Although all vulnerabilities predict depression 3 months after an ACS event has occurred rumination plays a key role to amplify the impact of vulnerabilities on depression among the initially non-depressed, and maintains depression among those who are already depressed

Reconstruction of tokamak plasma safety factor profile using deep learning

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep learning-based surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system