Periodically grown quantum nanostructures with arbitrary geometries : periodicity effects on the induced electro-elastic fields

Quantum nanostructures ( QNSs), due to their widespread and attractive physical, optical, and electronic properties, have been at the center of attention of many nanoscience and nanotechnology researches. In order to predict the electro-mechanical behavior of QNSs, accurate determination of the electro-elastic fields induced by quantum wells ( QWs), quantum wires ( QWRs), and quantum dots ( QDs) in such nanostructures would be of great importance and particular interest. In this study, by utilization of the electro-mechanical eigenfield concept in conjunction with the Fourier series technique, an analytical solution is presented which gives the electro-elastic fields induced by one-, two-, and three-dimensional periodic distribution of QWs, QWRs, and QDs, respectively. This methodology takes into account the electro-mechanical couplings of elastic and electric fields within the piezoelectric barrier as well as the interaction between periodically grown QWRs and QDs. The latter would be so important since the density of the periodically grown QNSs will have significant effects on the induced electro-elastic fields within both the QNSs and the surrounding barrier; this issue is addressed precisely in the present study by measuring the induced electro-elastic fields due to different periodicities of pyramidal QDs. Furthermore, the current formulation is capable of treating arbitrary geometries of QWRs and QDs which makes the solution more interesting and powerful. (C) 2015 Published by Elsevier Ltd

Financial evaluation of Sungun Copper Project using DCF method

The Sungun copper mine that operated by National Iranian Copper Industries Company (NICICO) is a world class project of great magnitude and complexity. A detail financial model of the Sungun Copper Project was constructed. The Internal Rate of Return (IRR) of the base case is 18%. At a discount rate of 6.5% the Net Present Value (NPV) of the Project is $1,554M at a copper price of$4,500/t. The breakeven copper price at the 6.5% discount rate is $2,460/t. The most sensitive factors, as is usual in projects of this nature are copper price and discount rate. Because of the contractual mining system, OPEX is slightly more influential than CAPEX

Interacting functionally graded quantum wires/quantum dots with arbitrary shapes and general anisotropy within a distinct piezoelectric matrix

An accurate determination of the two- and three-dimensional electro-elastic fields of periodically as well as arbitrarily distributed interacting quantum wires (QWRs) and interacting quantum dots (QDs) of arbitrary shapes within a piezoelectric matrix is of particular interest. Both the QWR/QD and the barrier may be made of materials with distinct general rectilinear anisotropy in elastic, piezoelectric, and dielectric constants. The lattice mismatch between the QWR/QD and the barrier is accounted by prescribing an initial misfit strain field within the QWR/QD. Previous analytical treatments have neglected the distinction between the electro-mechanical properties of the QWR/QD and those of the barrier. This simplifying assumption is circumvented in the present work by using a novel electro-mechanical equivalent inclusion method in Fourier space (FEMEIM). Moreover, the theory can readily treat cases where the QWRs/QDs are multiphase or functionally graded (FG). It was proven that for two-dimensional problems of either a periodic or an arbitrary distribution of FG QWRs in a transversely isotropic piezoelectric barrier, the elastic and electric fields are electrically and elastically impotent, respectively, and no electric field would be induced in the medium provided that the rotational symmetry and polarization axes coincide. Some numerical examples of more frequent shapes and different distributions of indium nitride QDs/QWRs within transversely isotropic aluminum nitride barrier are solved

Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning

We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient data coverage. Our algorithm combines the marginalized importance sampling framework with the actor-critic paradigm, where the critic returns evaluations of the actor (policy) that are pessimistic relative to the offline data and have a small average (importance-weighted) Bellman error. Compared to existing methods, our algorithm simultaneously offers a number of advantages: (1) It achieves the optimal statistical rate of $1/\sqrt{N}$ -- where $N$ is the size of offline dataset -- in converging to the best policy covered in the offline dataset, even when combined with general function approximators. (2) It relies on a weaker average notion of policy coverage (compared to the $\ell_\infty$ single-policy concentrability) that exploits the structure of policy visitations. (3) It outperforms the data-collection behavior policy over a wide range of specific hyperparameters. We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.Comment: 24 pages, 3 figure

Impact of an Antimicrobial Stewardship Program on the Frequency of Drug resistant Bacteria in an Intensive Care Unit

Background: One of the major health problems in the intensive care unit (ICU) is the nosocomial infection caused by multidrug-resistant (MDR) pathogens. The antimicrobial stewardship program (ASP) is a solution to prevent antibiotic resistance. This study aimed to determine the impact of an antimicrobial stewardship program on the frequency of drug-resistant bacteria in an ICU.Materials and Methods: This quasi-experimental study was conducted between 2019 and 2021 in Labbafinejad Hospital, Tehran, Iran. This study consisted of two time periods: 1) one year with no restriction of antibiotic prescription (before ASP), and 2) one year with restriction of antibiotic prescription based on the stewardship program (after ASP). We obtained demographic and clinical characteristics of patients from their medical records. Standard disk diffusion and broth microdilution were used to determine the antibiotic susceptibility of bacterial pathogens isolated from the patients.Results: A total of 300 ICU-admitted patients were included in the study (150 for each period). We found out that the total length of hospitalization, length of hospitalization in ICU, and treatment duration were lower after ASP (P=0.022, P=0.383, and P<0.001, respectively). Also, the frequency of antibiotic resistance, including MDR and Vancomycin-Resistant Enterococci (VRE) strains, decreased significantly after performing ASP (P=0.013). However, in terms of mortality, there was no significant difference between the two periods (P=0.236).Conclusion: The results of our study highlight the implementation of the antibiotic Stewardship program and the rational use of antibiotics in the ICU setting to inhibit the spread of antibiotic-resistant bacteria

Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism

Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from a fixed dataset without active data collection. Based on the composition of the offline dataset, two main categories of methods are used: imitation learning which is suitable for expert datasets and vanilla offline RL which often requires uniform coverage datasets. From a practical standpoint, datasets often deviate from these two extremes and the exact data composition is usually unknown a priori. To bridge this gap, we present a new offline RL framework that smoothly interpolates between the two extremes of data composition, hence unifying imitation learning and vanilla offline RL. The new framework is centered around a weak version of the concentrability coefficient that measures the deviation from the behavior policy to the expert policy alone. Under this new framework, we further investigate the question on algorithm design: can one develop an algorithm that achieves a minimax optimal rate and also adapts to unknown data composition? To address this question, we consider a lower confidence bound (LCB) algorithm developed based on pessimism in the face of uncertainty in offline RL. We study finite-sample properties of LCB as well as information-theoretic limits in multi-armed bandits, contextual bandits, and Markov decision processes (MDPs). Our analysis reveals surprising facts about optimality rates. In particular, in all three settings, LCB achieves a faster rate of $1/N$ for nearly-expert datasets compared to the usual rate of $1/\sqrt{N}$ in offline RL, where $N$ is the number of samples in the batch dataset. In the case of contextual bandits with at least two contexts, we prove that LCB is adaptively optimal for the entire data composition range, achieving a smooth transition from imitation learning to offline RL. We further show that LCB is almost adaptively optimal in MDPs.Comment: 84 pages, 6 figure