Accuracy of generalized gradient approximation functionals for density functional perturbation theory calculations

We assess the validity of various exchange-correlation functionals for computing the structural, vibrational, dielectric, and thermodynamical properties of materials in the framework of density-functional perturbation theory (DFPT). We consider five generalized-gradient approximation (GGA) functionals (PBE, PBEsol, WC, AM05, and HTBS) as well as the local density approximation (LDA) functional. We investigate a wide variety of materials including a semiconductor (silicon), a metal (copper), and various insulators (SiO$_2$ $\alpha$-quartz and stishovite, ZrSiO$_4$ zircon, and MgO periclase). For the structural properties, we find that PBEsol and WC are the closest to the experiments and AM05 performs only slightly worse. All three functionals actually improve over LDA and PBE in contrast with HTBS, which is shown to fail dramatically for $\alpha$-quartz. For the vibrational and thermodynamical properties, LDA performs surprisingly very good. In the majority of the test cases, it outperforms PBE significantly and also the WC, PBEsol and AM05 functionals though by a smaller margin (and to the detriment of structural parameters). On the other hand, HTBS performs also poorly for vibrational quantities. For the dielectric properties, none of the functionals can be put forward. They all (i) fail to reproduce the electronic dielectric constant due to the well-known band gap problem and (ii) tend to overestimate the oscillator strengths (and hence the static dielectric constant)

Evaluation of a Novel Biphasic Culture Medium for Recovery of Mycobacteria: A Multi-Center Study

on L-J slants. Automated liquid culture systems are expensive. A low-cost culturing medium capable of rapidly indicating the presence of mycobacteria is needed. The aim of this study was to develop and evaluate a novel biphasic culture medium for the recovery of mycobacteria from clinical sputum specimens from suspected pulmonary tuberculosis patients.<0.001).

The Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020

Digital PCR (dPCR) has developed considerably since the publication of the Minimum Information for Publication of Digital PCR Experiments (dMIQE) guidelines in 2013, with advances in instrumentation, software, applications, and our understanding of its technological potential. Yet these developments also have associated challenges; data analysis steps, including threshold setting, can be difficult and preanalytical steps required to purify, concentrate, and modify nucleic acids can lead to measurement error. To assist independent corroboration of conclusions, comprehensive disclosure of all relevant experimental details is required. To support the community and reflect the growing use of dPCR, we present an update to dMIQE, dMIQE2020, including a simplified dMIQE table format to assist researchers in providing key experimental information and understanding of the associated experimental process. Adoption of dMIQE2020 by the scientific community will assist in standardizing experimental protocols, maximize efficient utilization of resources, and further enhance the impact of this powerful technology

Analytic solution of fair share scheduling in layered queueing networks

Fair share scheduling has been widely used in many distributed systems. Layered Queueing Networks (LQN) are a widely used performance evaluation technique for distributed systems. Therefore, being able to evaluate performance of systems using fair share scheduling is essential. However, Fair share scheduling in a LQN model could only be solved using simulation previously. A main concern of simulation is long execution times. This paper uses a method called ‘Dynamic Parameter substitutions’ (DPS) to solve the Fair share scheduling analytically. DPS is an iterative method to calculate state-based parameters using performance results that are found using Mean Value Analysis (MVA). The paper shows how DPS is integrated into the LQNS solver (LQNS-DPS), which makes solutions of models with fair scheduling both fast and scalable. LQNS-DPS was verified using two sets of models, both with cap and guarantee shares. Over 150 randomly parameterized models, throughput found using LQNS-DPS was on average no worse than 6% of the result found from simulation

Improved chain calculation for sub-chain dependencies in layered queueing networks

Often, many software systems fail to meet requirements because of a lack of performance. A proven method for preventing or for diagnosing performance problems is through modeling. Layered Queueing Networks (LQN) are one popular technique for solving performance models. However, if a LQN is solved through decomposition and Mean Value Analysis (MVA), erroneous results can arise because of traffic dependencies in the decomposed models. This paper addresses one traffic dependency, called sub-chains, where customers from one chain "bleed into" another chain causing "extraneous" queueing delays. The new approach described here changes approximate MVA by adjusting the population in a routing chain depending on the originating sub-chain. This new approach substantially reduces, or even eliminates, the extraneous queueing delay caused by the sub-chain dependent traffic. The approach was applied to a substantial model of an on-line bookstore, and reduced the overall error in queueing time by a factor of 20 times, when compared to simulation. The more accurate queueing estimates yield better results for the other outputs of the LQN solver

Efficiency improvements for solving layered queueing networks

Layered Queueing Networks (LQN) have been used success-fully by numerous researchers to solve performance models of multi-tier client server systems. A common approach for solving a LQN is to split the model up into a set of submodels, then employ approximate mean value analysis (AMVA) on each of these submodels in an interactive fashion and using the results from the solution of one submodel as inputs to the others. This paper addresses the performance of the layered queueing network solver, LQNS, in terms of submodel construction and in terms of changes to Bard-Schweitzer and Linearizer AMVA, in order to improve performance. In some of the models described in this paper, there is a difference in four orders of magnitude between the fastest and slowest approaches. Copyright 2012 ACM

Hybrid solution of layered qeueing networks

The layered queueing network model has been very useful for solving performance models of distributed systems with client-server interactions. Fast analytic solutions exist for a broad range of models. However, models which contain elements which cannot be solved using approximate mean value analysis (MVA) must instead be solved using simulation. In this work, a hybrid solution strategy is used where MVA is used for part of the solution, and another solver, in this case simulation, is used for the remainder. This differs from other work where the analytic LQN solver was used as part of a solution because these other approaches have always solved a complete layered model as part of the o