A multi-orbital iterated perturbation theory for model Hamiltonians and real material-specific calculations of correlated systems

Perturbative schemes utilizing a spectral moment expansion are well known and extensively used for investigating the physics of model Hamiltonians and real material systems. The advantages they offer, in terms of being computationally inexpensive, with real frequency output at zero and finite temperatures, compensate for their deficiencies and offer a quick, qualitative analysis of the system behavior. In this work, we have developed a method, that can be classified as a multi-orbital iterative perturbation theory (MO-IPT) to study N-fold degenerate and non degenerate Anderson impurity models. As applications of the solver, we have combined the method with dynamical mean field theory to explore lattice models like the single orbital Hubbard model, covalent band insulator and the multi-orbital Hubbard model for density-density type interactions in different parameter regimes. The Hund's coupling effects in case of multiple orbitals is also studied. The limitations and quality of results are gauged through extensive comparison with data from the numerically exact continuous time quantum Monte Carlo method (hybridization expansion CTQMC). In general we observe that the agreement with CTQMC results gets better as we move away from particle-hole symmetry. We have integrated MO-IPT with density functional theory based electronic structure methods to study real material systems. As a test case, we have studied the classic, strongly correlated electronic material, SrVO$_3$. A comparison of density of states and photo emission spectrum (PES) with results obtained from different impurity solvers and experiments yields good agreement.Comment: 20 pages, 20 figure

A first principles investigation of cubic BaRuO3_3: A Hund's metal

A first-principles investigation of cubic-BaRuO$_3$, by combining density functional theory with dynamical mean-field theory and a hybridization expansion continuous time quantum Monte-Carlo solver, has been carried out. Non-magnetic calculations with appropriately chosen on-site Coulomb repulsion, $U$ and Hund's exchange, $J$, for single-particle dynamics and static susceptibility show that cubic-BaRuO$_3$ is in a spin-frozen state at temperatures above the ferromagnetic transition point. A strong red shift with increasing $J$ of the peak in the real frequency dynamical susceptibility indicates a dramatic suppression of the Fermi liquid coherence scale as compared to the bare parameters in cubic-BaRuO$_3$. The self-energy also shows clear deviation from Fermi liquid behaviour that manifests in the single-particle spectrum. Such a clean separation of energy scales in this system provides scope for an incoherent spin-frozen (SF) phase, that extends over a wide temperature range, to manifest in non-Fermi liquid behaviour and to be the precursor for the magnetically ordered ground state.Comment: 10 pages, 12 figures, 1 tabl

A FRAMEWORK TO DETECT AND CORRECT ERRORS IN CIRCUITS

The fundamental structure of BFs has additionally been extended through the years. For instance, counting BFs (CBFs) were brought to allow elimination of components from the BF. They are utilized in lots of networking programs too in computer architectures. Reliability has become challenging for advanced electronic circuits as the amount of errors because of manufacturing versions, radiation, and reduced noise margins increase as technology scales. There's also ongoing research to increase and enhance BFs and for their services in new situations. Blossom filters (BFs) give a fast and efficient method to check whether confirmed element goes to some set. The BFs are utilized in several programs, for instance, in communications as well as networking. Within this brief, it's proven that BFs may be used to identify and proper errors within their connected data set. This enables a synergetic reuse of existing BFs also to identify and proper errors. This really is highlighted through one particular counting BF employed for IP traffic classification. The outcomes reveal that the suggested plan can effectively correct single errors within the connected set. The suggested plan could be of great interest in practical designs to effectively mitigate errors having a reduced overhead when it comes to circuit area and power

Knowledge on prevention of mother to child transmission of HIV among women of reproductive age group

Introduction: Mother to child transmission (MTCT) of HIV continues to be a significant public health concern. MTCT has a deleterious effect on child survival, hence its knowledge is of utmost importance for prevention of transmission of HIV from mother to child. Materials and Methods: A cross sectional descriptive study was conducted during October –December 2013 among women attending antiretroviral therapy (ART) clinic. A semistr u c t u r e d q u e sti o n n a ir e c o n sist e d o f sociodemographic characteristics, clinical characteristics and knowledge about MTCT was administered to 80 women. Results: The mean age of the respondents was 24.89 ± 3.62 years and 30% were illiterate. The majority (62.5%) of them were belonged to an upper lower class. Atotal 47.5% of the respondents were aware of their HIV status since 1-5 years. The majority (82.5%) perceived that HIV can be transmitted from mother to child. Many of the respondents felt that MTCT of HIV may occur during breastfeeding 49 (74.24%), pregnancy 48 (72.72%) and labour 42 (63.63%). The majority 62 (93.93%) of the respondents were aware that MTCT of HIV can be prevented. Avoidance of breastfeeding was the most frequently mentioned strategy by 40 (64.51%) respondents, followed by maternal ARV prophylaxis by 35 (56.45%) and caesarean section by 25 (40.32%) of the respondents. Conclusion: The study demonstrates a relatively good level of knowledge on MTCT of HIV, but some knowledge gaps on certain specific aspects still remained which needs to be addressed for effective prevention of mother to child transmission of HI

Tailoring on-surface molecular reactions and assembly through hydrogen-modified synthesis: From triarylamine monomer to 2D covalent organic framework

Relative to conventional wet-chemical synthesis techniques, on-surface synthesis of organic networks in ultrahigh vacuum has few control parameters. The molecular deposition rate and substrate temperature are typically the only synthesis variables to be adjusted dynamically. Here we demonstrate that reducing conditions in the vacuum environment can be created and controlled without dedicated sources -- relying only on backfilled hydrogen gas and ion gauge filaments -- and can dramatically influence the Ullmann-like on-surface reaction used for synthesizing two-dimensional covalent organic frameworks (2D COFs). Using tribromo dimethylmethylene-bridged triphenylamine ((Br$_3$)DTPA) as monomer precursors, we find that atomic hydrogen blocks aryl-aryl bond formation. Control of the relative monomer and hydrogen fluxes is used to produce large islands of self-assembled monomers, dimers, or macrocycle hexamers. On-surface synthesis of these oligomers, from a single precursor, circumvents potential challenges with protracted wet-chemical synthesis or low precursor volatility for large molecules. Using scanning tunneling microscopy and spectroscopy (STM/STS), we show that changes in the electronic states through this oligomer sequence provide an insightful view of the 2D-COF (synthesized in the absence of atomic hydrogen) as the endpoint in an evolution of electronic structures from the monomer

Supervised machine learning algorithms used to predict post-surgical outcomes following anterior surgical fixation of odontoid fractures

Background: Odontoid fractures have a high mortality rate, and numerous classification systems have previously predicted surgical outcomes with mixed consensus. We generated a machine learning (ML) construct to predict post-operative adverse events following anterior (ORIF) of odontoid fractures. Methods: 266 patients from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) with anterior ORIF (CPT 22318) of odontoid fractures from 2008-2018 were analyzed using ML algorithms random forest classifier (RF), gradient boosting classifier (GB), support vector machine classifier (SVM), Gaussian Naive Bayes classifier (GNB), and multi-layer perceptron classifier (MLP), and were compared to logistic regression classifier (LR). Algorithms predicted increased length of stay (LOS), need for transfusion (Transf), non-home discharge (NHD), and any adverse event (AAE). Permutation feature importance (PFI) identified risk factors. Results: ML algorithms outperformed LR. The average AUC for predicting Transf was 0.635 (accuracy=77.4%), extended LOS=0.652 (accuracy 59.6%), NHD 0.788 (accuracy=71.9%) and AAE 0.649 (accuracy 68.1%). GB performed highest for Transf (AUC=0.861), identifying operative time (PFI 0.253, p=0.016). GB and RF performed equally for NHD (AUC=0.819), highlighting preoperative hematocrit (PFI=0.157, p<0.001). GB predicted AAE (AUC=0.720) also identifying preoperative hematocrit (PFI=0.112, p<0.001). RF predicted extended LOS (AUC=0.669) highlighting preoperative hematocrit (PFI=0.049, p<0.001). Conclusions: ML outperformed LR, successfully predicting Transf, extended LOS, NHD, and AAE for anterior ORIF of odontoid fractures. Our construct may complement conventional risk stratification to reduce adverse outcomes and excess cost