Higher ethical objective (Maqasid al-Shari'ah) augmented framework for Islamic banks : assessing the ethical performance and exploring its determinants.

This study utilises higher objectives postulated in Islamic moral economy or the maqasid al-Shari’ah theoretical framework’s novel approach in evaluating the ethical, social, environmental and financial performance of Islamic banks. Maqasid al-Shari’ah is interpreted as achieving social good as a consequence in addition to well-being and, hence, it goes beyond traditional (voluntary) social responsibility. This study also explores the major determinants that affect maqasid performance as expressed through disclosure analysis. By expanding the traditional maqasid al-Shari’ah,, we develop a comprehensive evaluation framework in the form of a maqasid index, which is subjected to a rigorous disclosure analysis. Furthermore, in identifying the main determinants of the maqasid disclosure performance, panel data analysis is used by including several key variables alongside political and socio-economic environment, ownership structures, and corporate and Shari’ah governance-related factors. The sample includes 33 full-fledged Islamic banks from 12 countries for the period of 2008–2016. The findings show that although during the nine-year period the disclosure of maqasid performance of the sampled Islamic banks has improved, this is still short of ‘best practices’. Through panel data analysis, this study finds that the Muslim population indicator, CEO duality, Shari’ah governance, and leverage variables positively impact the disclosure of maqasid performance. However, the effect of GDP, financial development and human development index of the country, its political and civil rights, institutional ownership, and a higher share of independent directors have an overall negative impact on the maqasid performance. The findings reported in this study identify complex and multi-faceted relations between external market realities, corporate and Shari’ah governance mechanisms, and maqasid performance

Bi-Directional Effect of Cholecystokinin Receptor-2 Overexpression on Stress-Triggered Fear Memory and Anxiety in the Mouse

Fear, an emotional response of animals to environmental stress/threats, plays an important role in initiating and driving adaptive response, by which the homeostasis in the body is maintained. Overwhelming/uncontrollable fear, however, represents a core symptom of anxiety disorders, and may disturb the homeostasis. Because to recall or imagine certain cue(s) of stress/threats is a compulsory inducer for the expression of anxiety, it is generally believed that the pathogenesis of anxiety is associated with higher attention (acquisition) selectively to stress or mal-enhanced fear memory, despite that the actual relationship between fear memory and anxiety is not yet really established. In this study, inducible forebrain-specific cholecystokinin receptor-2 transgenic (IF-CCKR-2 tg) mice, different stress paradigms, batteries of behavioral tests, and biochemical assays were used to evaluate how different CCKergic activities drive fear behavior and hormonal reaction in response to stresses with different intensities. We found that in IF-CCKR-2 tg mice, contextual fear was impaired following 1 trial of footshock, while overall fear behavior was enhanced following 36 trials of footshock, compared to their littermate controls. In contrast to a standard Yerkes-Dodson (inverted-U shaped) stress-fear relationship in control mice, a linearized stress-fear curve was observed in CCKR-2 tg mice following gradient stresses. Moreover, compared to 1 trial, 36 trials of footshock in these transgenic mice enhanced anxiety-like behavior in other behavioral tests, impaired spatial and recognition memories, and prolonged the activation of adrenocorticotropic hormone (ACTH) and glucocorticoids (CORT) following new acute stress. Taken together, these results indicate that stress may trigger two distinctive neurobehavioral systems, depending on both of the intensity of stress and the CCKergic tone in the brain. A “threshold theory” for this two-behavior system has been suggested

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p