Attitudes toward sex roles among A and B psychotherapists.

81 male practicing psychotherapists rated themselves and an unknown male and unknown female adult on 82 bipolar sex-role relevant adjectives. Results show that both A and B therapists considered themselves equally masculine, but that A therapists were more likely to ascribe to themselves traditional feminine characteristics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Conjugate heat transfer through nano scale porous media to optimize vacuum insulation panels with lattice Boltzmann methods

Due to reduced thermal conductivity, vacuum insulation panels (VIPs) provide significant thermal insulation performance. Our novel vacuum panels operate at reduced pressure and are filled with a powder of precipitated silicic acid to further hinder convection and provide static stability against atmospheric pressure. To obtain an in depth understanding of heat transfer mechanisms, their interactions and their dependencies inside VIPs, detailed microscale simulations are conducted.Particle characteristics for silica are used with a discrete element method (DEM) simulation, using open source software Yade-DEM, to generate a periodic compressed packing of precipitated silicic acid particles. This aggregate packing is then imported into OpenLB (openlb.net) as a fully resolved geometry, and used to study the effects on heat transfer at the microscale. A three dimensional Lattice Boltzmann method (LBM) for conjugated heat transfer is implemented with open source software OpenLB, which is extended to include radiative heat transport. The infrared intensity distribution is solved and coupled with the temperature through the emissivity, absorption and scattering of the studied media using the radiative transfer equation by means of LBM. This new holistic approach provides a distinct advantage over similar porous media approaches by providing direct control and tuning of particle packing characteristics such as aggregate size, shape and pore size distributions and studying their influence directly on conduction and radiation independently. Our aim is to generate one holistic tool which can be used to generate silica geometry and then simulate automatically the thermal conductivity through the generated geometry.     

The Open International Soccer Database for machine learning

How well can machine learning predict the outcome of a soccer game, given the most commonly and freely available match data? To help answer this question and to facilitate machine learning research in soccer, we have developed the Open International Soccer Database. Version v1.0 of the Database contains essential information from 216,743 league soccer matches from 52 leagues in 35 countries. The earliest entries in the Database are from the year 2000, which is when football leagues generally adopted the “three points for a win” rule. To demonstrate the use of the Database for machine learning research, we organized the 2017 Soccer Prediction Challenge. One of the goals of the Challenge was to estimate where the limits of predictability lie, given the type of match data contained in the Database. Another goal of the Challenge was to pose a real-world machine learning problem with a fixed time line and a genuine prediction task: to develop a predictive model from the Database and then to predict the outcome of the 206 future soccer matches taking place from 31 March 2017 to the end of the regular season. The Open International Soccer Database is released as an open science project, providing a valuable resource for soccer analysts and a unique benchmark for advanced machine learning methods. Here, we describe the Database and the 2017 Soccer Prediction Challenge and its results.

     

Interstitial Mo‐Assisted Photovoltaic Effect in Multilayer MoSe2 Phototransistors

Thin‐film transistors (TFTs) based on multilayer molybdenum diselenide (MoSe₂) synthesized by modified atmospheric pressure chemical vapor deposition (APCVD) exhibit outstanding photoresponsivity (103.1 A W^?1), while it is generally believed that optical response of multilayer transition metal dichalcogenides (TMDs) is significantly limited due to their indirect bandgap and inefficient photoexcitation process. Here, the fundamental origin of such a high photoresponsivity in the synthesized multilayer MoSe₂ TFTs is sought. A unique structural characteristic of the APCVD‐grown MoSe₂ is observed, in which interstitial Mo atoms exist between basal planes, unlike usual 2H phase TMDs. Density functional theory calculations and photoinduced transfer characteristics reveal that such interstitial Mo atoms form photoreactive electronic states in the bandgap. Models indicate that huge photoamplification is attributed to trapped holes in subgap states, resulting in a significant photovoltaic effect. In this study, the fundamental origin of high responsivity with synthetic MoSe₂ phototransistors is identified, suggesting a novel route to high‐performance, multifunctional 2D material devices for future wearable sensor applications.     

Adaptive random forests for evolving data stream classification

Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.     

Unravelling the Carbohydrate‐Binding Preferences of the Carbohydrate‐Binding Modules of AMP‐Activated Protein Kinase

The β subunit of adenosine monophosphate (AMP)‐activated protein kinase (AMPK), which exists as two isoforms (β1 and β2) in humans, has a carbohydrate‐binding module (CBM) that interacts with glycogen. Although the β1‐ and β2‐CBMs are structurally similar, with strictly conserved ligand‐contact residues, they show different carbohydrate affinities. β2‐CBM shows the strongest affinity for both branched and unbranched oligosaccharides and it has recently been shown that a Thr insertion into β2‐CBM (Thr101) forms a pocket to accommodate branches. This insertion does not explain why β2‐CBM binds all carbohydrates with stronger affinity. Herein, it is shown that residue 134 (Val for β2 and Thr for β1), which does not come into contact with a carbohydrate, appears to account for the affinity difference. Characterisation by NMR spectroscopy, however, suggests that mutant β2‐Thr101Δ/Val134Thr differs from that of β1‐CBM, and mutant β1‐Thr101ins/Thr134Val differs from that of β2‐CBM. Furthermore, these mutants are less stable to chemical denaturation, relative to that of wild‐type β‐CBMs, which confounds the affinity analyses. To support the importance of Thr101 and Val134, the ancestral CBM has been constructed. This CBM retains Thr101 and Val134, which suggests that the extant β1‐CBM has a modest loss of function in carbohydrate binding. Because the ancestor bound carbohydrate with equal affinity to that of β2‐CBM, it is concluded that residue 134 plays an indirect role in carbohydrate binding.     

A computational study of the effects of multiphase dynamics in catalytic upgrading of biomass pyrolysis vapor

A recurring challenge among the variety of existing biomass‐to‐biofuel conversion technologies is the need to ensure optimal and homogeneous contact between the various phases involved. The formulation of robust design rules from an empirical standpoint alone remains difficult due to the wide range of granular flow regimes coexisting within a given reactor. In this work, a volume‐filtered Eulerian‐Lagrangian framework is employed that solves chemically reacting flows in the presence of catalytic particles. The simulation strategy is used to quantify the role of the particle clustering on catalytic upgrading of biomass pyrolysis vapor in risers. It is shown that particle clustering can reduce the catalytic conversion rate of biomass pyrolysis vapors by up to about 50%. The simulation results are also compared with an engineering model based on continuously stirred tank reactor (CSTR). A one‐dimensional Reynolds‐averaged transport equation is derived, and the unclosed terms that account for the heterogeneity caused by clusters are evaluated. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3341–3353, 2018