The Influence of Loading System Stiffness on Empirical Correlations for Determination of Tensile Characteristics from the Results of SP Tests

The present chapter describes the influence of the loading system stiffness on empirical correlations for determination of yield and tensile strengths at laboratory temperature from the results of small punch (SP) tests. The results obtained proved that measuring of test specimen deflection during SP test eliminates the significant effect of the loading system stiffness on the above-mentioned correlations

Comparative studies on the multi-component pharmacokinetics of Aristolochiae Fructus and honey-fried Aristolochiae Fructus extracts after oral administration in rats

PK parameters of 7-OH AA I in rats after oral administration of AF and HAF. (DOC 37 kb

Congestion control using in-network telemetry for lossless datacenters

In the Ethernet lossless Data Center Networks (DCNs) deployed with Priority-based Flow Control (PFC), the head-of-line blocking problem is still difficult to prevent due to PFC triggering under burst traffic scenarios even with the existing congestion control solutions. To address the head-of-line blocking problem of PFC, we propose a new congestion control mechanism. The key point of Congestion Control Using In-Network Telemetry for Lossless Datacenters (ICC) is to use In-Network Telemetry (INT) technology to obtain comprehensive congestion information, which is then fed back to the sender to adjust the sending rate timely and accurately. It is possible to control congestion in time, converge to the target rate quickly, and maintain a near-zero queue length at the switch when using ICC. We conducted Network Simulator-3 (NS-3) simulation experiments to test the ICC’s performance. When compared to Congestion Control for Large-Scale RDMA Deployments (DCQCN), TIMELY: RTT-based Congestion Control for the Datacenter (TIMELY), and Re-architecting Congestion Management in Lossless Ethernet (PCN), ICC effectively reduces PFC pause messages and Flow Completion Time (FCT) by 47%, 56%, 34%, and 15.3×, 14.8×, and 11.2×, respectively

Characterization of domain distributions by second harmonic generation in ferroelectrics

Domain orientations and their volume ratios in ferroelectrics are recognized as a compelling topic recently for domain switching dynamics and domain stability in devices application. Here, an optimized second harmonic generation method has been explored for ferroelectric domain characterization. Combing a unique theoretical model with azimuth-polarization-dependent second harmonic generation response, the complex domain components and their distributions can be rigidly determined in ferroelectric thin films. Using the proposed model, the domain structures of rhombohedral BiFeO3 films with 71° and 109° domain wall, and, tetragonal BiFeO3, Pb(Zr0.2Ti0.8)O3, and BaTiO3 ferroelectric thin films are analyzed and the corresponding polarization variants are determined. This work could provide a powerful and all-optical method to track and evaluate the evolution of ferroelectric domains in the ferroelectric-based devices

An HPLC Method for the Determination of Auraptene in Dog Plasma: Application to Pharmacokinetic Study

A simple and sensitive high-performance liquid chromatography (HPLC) method was developed and validated for the determination of auraptene (AUR) in dog plasma. The chromatographic separation of AUR was achieved on a C18 column using isocratic elution with acetonitrile-0.1 % formic acid (volume ratio 84.5:15.5). AUR was detected at 322 nm. Sample extraction with ethyl acetate resulted in high recoveries of AUR. A linear curve over the concentration range 10.2-408.8 ng/mL (r2 = 0.9995) was obtained. Satisfactory intra-day and inter-day precisions were achieved with RSDs less than 9.5 % and the average recovery factors were in the range of 99.1-106.2 %. The method was used to determine the plasma concentration–time profiles for AUR after oral doses of 50, 100 and 200 mg/kg in dogs. A nonlinear pharmacokinetics was found in dogs at doses from 50 to 200 mg/kg. No significant accumulation of AUR in dogs following multiple doses was observed.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Chlorimuronethyl Resistance Selectable Marker Unsuited for the transformation of rice blast fungus (Magnaporthe grisea)

Abstract: Chlorimuronethyl resistance gene is increasingly used as a selectable marker for transformation, especially fungal transformation. Magnaporthe grisea is an important model organism for investigating fungal pathogenicity, and Agrobacterium tumefaciens-mediated transformation (ATMT) is used for functional mutagenesis of the fungus. However, our results showed that rice blast strains collected from infectious rice fields have highly conserved resistance to chlorimuronethyl, even comparable to transformants which carrying chlorimuronethyl resistance genes as selectable marker in laboratory conditions. PCR results showed that all tested field strains presented the amplified products of the same size as the selectable marker amplified from plasmid carrying chlorimuronethyl gene. Sequence analysis of PCR products amplified from field strains confirmed that field strains harbored the highly identity homolog of chlorimuronethyl resistance gene. Blast search in GenBank suggested that the fragment is presenting in reference genome sequence of 70-15, but it is not a wide-spread gene in other organisms, excepted for Herpetosiphon aurantiacus. Although the origin and reason of the conserved chlorimuronethyl resistance gene in field isolates of blast fungus is unclear, the ecological function of the gene is noteworthy

Full connected neural-network for simulation of extantion in self-stressed monolitic slabs on ground

Желткович А. Е., Молош В. В., Пархоц K. Г., Савейко Н. Г., Юань Цзиньбинь, Чжэньхао Цзян, Чжэн Хаоюань. Моделирование перемещений в самонапряженных монолитных плитах на основании при помощи полносвязной нейронной сетиIn this article the strategy of interdisciplinary convergence of mechanics and artificial intelligence is illustrated. The article presents the results of calculating displacements in self-stressed monolithic slabs on ground obtained using a trained fully connected neural network. The empirical results of displacements in slabs on ground, displacements calculated according to the physicomechanical model, and obtained using a neural network are represented. The inspiration brought us to study neural networks modeling biological neural networks are follow: neural networks can autonomously detect patterns hidden in phenomena and can identify parameters on complex behavioral tracks of different physical systems. The authors describe in detail the developed and trained fully connected neural network