Structure and microstructure of the high pressure synthesised misfit layer compound [Sr2O2][CrO2]1.85

The strontium chromium oxide [Sr2O2][CrO2]1.85 misfit layer compound has been synthesised at high pressure and high-temperature conditions. Electron diffraction patterns and high-resolution transmission electron microscopy images along [001] show the misfit character of the different layers composing the structure with a supercell along the incommensurate parameter b=7b1=13b2. The modulated crystal structure has been refined within the superspace formalism against single-crystal X-ray diffraction data, employing the (3+1)-dimensional superspace group Cnmb(0s20)0 0 s. The compound has a composite structure with lattice parameters a1=5.182(1)A˚ , b1=5.411(1)A˚ , c1=18.194(3)A˚ for the first, SrO, subsystem and the same a and c, but with b2=2.925(1)A˚ for the second, CrO2, subsystem. The layer stacking is similar to that of orthorhombic PbS(TiS2)1.18, but with a much stronger intersubsytem bonding in the case of the oxide. The intersubsystem lattice mismatch is mainly handled by displacement modulations of the Sr atoms, correlated with modulations of the valence, the coordination and the anisotropic displacement parameters

Hybrid Local Search for Constrained Financial Portfolio Selection Problems

Abstract. Portfolio selection is a relevant problem arising in finance and economics. While its basic formulations can be efficiently solved through linear or quadratic programming, its more practical and realistic variants, which include various kinds of constraints and objectives, have in many cases to be tackled by approximate algorithms. In this work, we present a hybrid technique that combines a local search, as master solver, with a quadratic programming procedure, as slave solver. Experimental results show that the approach is very promising and achieves results comparable with, or superior to, the state of the art solvers.

Heterogeneous multiple bank financing: does it reduce inefficient credit-renegotiation incidences?

Relationship lending, Asymmetric information, Financial distress, Hold-up, Coordination failure, D82, G21, L14,

Biobanking in everyday clinical practice in psychiatry—the Munich Mental Health Biobank

Translational research on complex, multifactorial mental health disorders, such as bipolar disorder, major depressive disorder, schizophrenia, and substance use disorders requires databases with large-scale, harmonized, and integrated real-world and research data. The Munich Mental Health Biobank (MMHB) is a mental health-specific biobank that was established in 2019 to collect, store, connect, and supply such high-quality phenotypic data and biosamples from patients and study participants, including healthy controls, recruited at the Department of Psychiatry and Psychotherapy (DPP) and the Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital of the Ludwig-Maximilians-University (LMU), Munich, Germany. Participants are asked to complete a questionnaire that assesses sociodemographic and cross-diagnostic clinical information, provide blood samples, and grant access to their existing medical records. The generated data and biosamples are available to both academic and industry researchers. In this manuscript, we outline the workflow and infrastructure of the MMHB, describe the clinical characteristics and representativeness of the sample collected so far, and reveal future plans for expansion and application. As of 31 October 2021, the MMHB contains a continuously growing set of data from 578 patients and 104 healthy controls (46.37% women; median age, 38.31 years). The five most common mental health diagnoses in the MMHB are recurrent depressive disorder (38.78%; ICD-10: F33), alcohol-related disorders (19.88%; ICD-10: F10), schizophrenia (19.69%; ICD-10: F20), depressive episode (15.94%; ICD-10: F32), and personality disorders (13.78%; ICD-10: F60). Compared with the average patient treated at the recruiting hospitals, MMHB participants have significantly more mental health-related contacts, less severe symptoms, and a higher level of functioning. The distribution of diagnoses is also markedly different in MMHB participants compared with individuals who did not participate in the biobank. After establishing the necessary infrastructure and initiating recruitment, the major tasks for the next phase of the MMHB project are to improve the pace of participant enrollment, diversify the sociodemographic and diagnostic characteristics of the sample, and improve the utilization of real-world data generated in routine clinical practice