Phase Transformation of a K2GeF6 Polymorph for Phosphors Driven by a Simple Precipitation-Dissolution Equilibrium and Ion Exchange.

Tuning crystal phase transformations is very important for obtaining polymorphs for phosphors with the ideal optical properties and stability. Mn4+-doped K2GeF6 (KGF) is a typical polymorphic phosphor, but the thermodynamic and kinetic mechanism of its phase transformation is still unclear. Herein, the phase transformation of polymorphs varying from P63mc KGF and trigonal KGF to P63mc Si4+-doped KGF is realized by introducing the synergistic action of an HF solution and Si4+ ions. The full structural refinements of KGF polymorphs at room temperature and the electronic band structure calculations were performed. The results show that the Si4+-doped hexagonal KGF polymorph with good photoluminescence properties is the most stable phase according to the calculated total energy landscape and relative formation energy. The morphologic changes were monitored in situ to clearly understand the rapid phase transformation mechanism, which proves that the phase transformation is driven by a simple precipitation-dissolution equilibrium and ionic exchange.

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification.

INTRODUCTION: An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study. METHODS: A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others). RESULTS: The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint Petersburg, Russia. CONCLUSION: The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.