The problem of a metal impurity in an oxide: ab-initio study of electronic and structural properties of Cd in Rutile TiO2

In this work we undertake the problem of a transition metal impurity in an oxide. We present an ab-initio study of the relaxations introduced in TiO2 when a Cd impurity replaces substitutionally a Ti atom. Using the Full-Potential Linearized-Augmented-Plane-Wave method we obtain relaxed structures for different charge states of the impurity and computed the electric-field gradients (EFGs) at the Cd site. We find that EFGs, and also relaxations, are dependent on the charge state of the impurity. This dependence is very remarkable in the case of the EFG and is explained analyzing the electronic structure of the studied system. We predict fairly anisotropic relaxations for the nearest oxygen neighbors of the Cd impurity. The experimental confirmation of this prediction and a brief report of these calculations have recently been presented [P.R.L. 89, 55503 (2002)]. Our results for relaxations and EFGs are in clear contradiction with previous studies of this system that assumed isotropic relaxations and point out that no simple model is viable to describe relaxations and the EFG at Cd in TiO2 even approximately.Comment: 11 pages, 8 figures, Revtex 4, published in Physical Review

Modeling and Simulation of Rotating Machine Windings Fed by High-Power Frequency Converters for Insulation Design

Modern power systems include a considerable amount of power electronic converters related to the introduction of renewable energy sources, high-voltage direct current (HVDC) systems, adjustable speed drives, and so on. These components introduce repetitive pulses generated by the commutation of semiconductor switches, resulting in overvoltages with very steep fronts and high dielectric stresses. This phenomenon is one of the main causes of accelerated insulation aging of motors in power electronic-based systems. This chapter presents state-of-the-art computational tools for the analysis of motor windings excited by fast-front pulses related to the use of frequency converters based on pulse-width modulation (PWM). These tools can be applied for the accurate prediction of overvoltages and dielectric stresses required to propose insulation design improvements. In the case of the stress-grading system used in medium-voltage (MV) motors, transient finite-element method (FEM) is used to study the effect of fast pulses. It is shown how, by controlling the material properties and the design of the stress-grading systems, solutions to reduce the adverse effects of fast pulses from PWM-type inverters can be proposed

Development and Application of Effective Stochastic Potential Method for Investigating Temperature-dependent Electronic Properties of Nanomaterials

Temperature plays an incredibly important role in determining what values a quantum mechanical property of a chemical system can assume. The mechanism by which temperature and the other features of a chemical system’s environment effects observable properties is through their effect on the population of thermally-accessible structures. As temperature changes, the population of these thermally-accessible structures shifts, and correspondingly so do the distributions of quantum mechanical properties. Prediction, calculation, and analysis of these distributions are fundamental to the study of statistical mechanics, and are integral to understanding what role the chemical environment has on any quantum mechanical property that may be of interest. One of the largest ongoing challenges concerning the determination of quantum mechanical distributions is the need for 105 to 106 conformational samples from the population of structures in order to obtain accurate and reliable distributions of properties. For large chemical systems consisting of many electrons, performing ab-initio calculations on such a large number of structures is computationally infeasible using traditional quantum chemistry methods. This problem is even further exacerbated when distributions of excited electronic state properties such as electronic spectra are desired, due to the increased computational cost of ab-initio excited-state techniques. To overcome this computational barrier, I have developed the Effective Stochastic Potential (ESP) method which addresses the challenge of conformational sampling. The ESP method is a first-principles technique which uses random matrix theory to treat noisy chemical environments of a system stochastically. In doing so, the computational cost of performing conformational sampling on the system can be drastically reduced. The accuracy of the ESP method has been confirmed by benchmarking against calculations of both ground and excited-state properties of H2O. I have applied the ESP method on various systems, including semiconductor nanoparticles to efficiently obtain temperature-dependent distributions of HOMO-LUMO gap energies, excitation energies, and exciton binding energies comprised of a million samples. For many of the systems studied, calculation of these distributions using traditional first-principle methods would be infeasible. Using the ESP method, it has been calculated that the distributions of excitation energies of PbS and CdSe nanoparticles have a pronounced red-shift as the temperature of the system increases. It has also been found that the excitation energy distributions in PbS nanoparticles exhibit sub-Gaussian characteristics at physically-relevant temperatures. These results highlight the ability of the ESP method to uncover unique temperature-dependent features of quantum mechanical distributions that may otherwise be impossible to obtain

Thermal Aware Design Automation of the Electronic Control System for Autonomous Vehicles

The autonomous vehicle (AV) technology, due to its tremendous social and economical benefits, is transforming the entire world in the coming decades. However, significant technical challenges still need to be overcome until AVs can be safely, reliably, and massively deployed. Temperature plays a key role in the safety and reliability of an AV, not only because a vehicle is subjected to extreme operating temperatures but also because the increasing computations demand more powerful IC chips, which can lead to higher operating temperature and large thermal gradient. In particular, as the underpinning technology for AV, artificial intelligence (AI) requires substantially increased computation and memory resources, which have been growing exponentially through recent years and further exacerbated the thermal problems. High operating temperature and large thermal gradient can reduce the performance, degrade the reliability, and even cause an IC to fail catastrophically. We believe that dealing with thermal issues must be coupled closely in the design phase of the AVs’ electronic control system (ECS). To this end, first, we study how to map vehicle applications to ECS with heterogeneous architecture to satisfy peak temperature constraints and optimize latency and system-level reliability. We present a mathematical programming model to bound the peak temperature for the ECS. We also develop an approach based on the genetic algorithm to bound the peak temperature under varying execution time scenarios and optimize the system-level reliability of the ECS. We present several computationally efficient techniques for system-level mean-time-to-failure (MTTF) computation, which show several orders-of-magnitude speed-up over the state-of-the-art method. Second, we focus on studying the thermal impacts of AI techniques. Specifically, we study how the thermal impacts for the memory bit flipping can affect the prediction accuracy of a deep neural network (DNN). We develop a neuron-level analytical sensitivity estimation framework to quantify this impact and study its effectiveness with popular DNN architectures. Third, we study the problem of incorporating thermal impacts into mapping the parameters for DNN neurons to memory banks to improve prediction accuracy. Based on our developed sensitivity metric, we develop a bin-packing-based approach to map DNN neuron parameters to memory banks with different temperature profiles. We also study the problem of identifying the optimal temperature profiles for memory systems that can minimize the thermal impacts. We show that the thermal aware mapping of DNN neuron parameters on memory banks can significantly improve the prediction accuracy at a high-temperature range than the thermal ignorant for state-of-the-art DNNs

Structure and Properties of Simple and Aggregate Systems by Circular Dichroism Spectroscopy

This thesis deals with the investigation of structural properties of many different systems via Electronic Circular Dichroism (ECD). The interpretation of experimental data has been carried out mainly with quantum-chemistry methods, such as Density Functional Theory (DFT), on both solution and solid-state systems. The analysis of solution systems is oriented towards applications on biologically active compounds, both natural or synthetic, and its objective is to underline the key role of these approaches in the determination of the absolute configuration and the difficulties that may be encountered in case of flexible molecules. Solid-state measurements represent an attractive alternative to these cases where a lot of conformations are present, but difficulties in the interpretation of the signals due to solid-state interactions which are not observable in solution may be faced. For a better understanding of spectral lineshapes, more detailed analyses have been performed taking into account vibronic effects, which may also assist in the determination of the conformational situation of the investigated substrate. The limitations of the vibronic treatment for coupled electronic states have been considered, leading to a general all-coordinate approach which allows simulating the electronic spectrum of “dimeric” molecules with weakly coupled electronic states through a time dependent approach

Комплексный подход к прогнозированию развития полного общего среднего образования

У статті висвітлено роль прогнозування на сучасному етапі реформування освіти України; обґрунтовано застосування комплексного підходу до прогнозування розвитку повної загальної середньої освіти, зокрема до визначення й відбору об’єктів, методів і способів прогнозування. Автор розглядає комплексність як одну з ключових умов розроблення якісних прогнозів розвитку повної загальної середньої освіти та окремих освітніх об’єктів прогнозування (тенденцій, явищ, процесів, суб’єктів освітнього (педагогічного) процесу, навчального закладу тощо).The article highlights the role of prediction at the present stage of reforming the education of Ukraine; the use of a complex approach to prediction of the development of complete general secondary education, in particular to the determination and selection of objects and methods of prediction, is substantiated. Qualitative and accessible education is one of the most important conditions for the development of the economy and society of the country, because the educational system forms and multiplies the most valuable resource - the human one, which provides intellectual and material capital for the development of the country. The current stage of reforming the Ukrainian educational system, in particular its component that is full secondary education, is marked by important changes in the content and structure of education in order to ensure the quality of education of students and their preparation for life. This confirms the urgency and expediency of scientific research of problems of prediction of the development of complete general secondary education, in particular in our study – the justification of the complex approach to the determination and selection of objects and methods of prediction. The complex approach to prediction of the development of complete secondary education provides: 1) a complex study of the state of educational objects and the predictive background (processes and phenomena of social and economic development); 2) determination based on the analysis of qualitative and quantitative indicators of education development in combination with socio-economic processes; 3) identification of a set of objective conditions, factors and trends in the development of complete general secondary education; 4) studying the process of development of complete general secondary education as a complex of objects for prediction; prediction of the development of each of them creates an integral picture of the state and development of the entire educational system, enabling the development of short, medium and long-term predictions; 5) identification and development of predicting tools – methods and means of prediction. The complex of methods and means of prediction of the development of complete general secondary education includes such methods as: general science methods (theoretical analysis, comparison, classification, systematization, generalization, survey, observation, experiment), which allow to investigate the real (or past) state of the object of research; classical methods of prediction: extrapolation, expert estimation method; prognostic modeling used in the development of prediction; statistical methods for determining the quantitative indicators of the development of educational objects; normative method for calculating the needs of subjects of education on the basis of established norms and technical-economic standards. We have identified such means of prediction as: organizational (measures and regime of the process of prediction of the development of general secondary education); information and communication (means of searching, processing and storing information, electronic databases, computer programs, web-tools); material and technical (scientific-methodical and reference literature, technical devices and equipment); communicative (methods and techniques of communication and interaction). A set of methods and means for prediction of the development of complete general secondary education is variable and may be supplemented by other methods and means of prediction, depending on the purpose, objects, conditions of prediction and expected results on the basis of scientific justification. Complexity is considered by us as a key condition for prediction of the development of complete general education, which ensures systemacy and variability in the determination and application of methods and means of prediction and controllability of predictive activity. Prospects for the further research are considered for the problems of teacher training for the complex application of methods and means of prediction to make prognosis of the development of innovative processes in the educational system, educational institutions and other educational objects.В статье освещена роль прогнозирования образовательных процессов на современном этапе реформирования образования Украины; обосновано применение комплексного подхода к прогнозированию развития полного общего среднего образования, в частности в выявлении и отборе объектов, методов и способов прогнозирования. Автор рассматривает комплексность как одну из ключевых условий разработки качественных прогнозов развития полного общего среднего образования и отдельных образовательных объектов прогнозирования (тенденций, явлений, процессов, субъектов образовательного (педагогического) процесса, учебного заведения и т. д.)

Probabilistic Monte-Carlo method for modelling and prediction of electronics component life

Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated