Mechanics of binary crushable granular assembly through discrete element method

The mechanical response of a granular system is not only influenced by the bulk material properties but also on various factors due to it’s discrete nature. The factors like topology, packing fraction, friction between particles, particle size distribution etc. influence the behavior of granular systems. For a reliable design of such systems like fusion breeder units comprising of pebble beds, it is essential to understand the various factors influencing the response of the system. Mechanical response of a binary assembly consisting of crushable spherical pebbles is studied using Discrete Element Method (DEM) which is based on particle–particle interactions. The influence of above mentioned factors on the macroscopic stress–strain response is investigated using an in-house DEM code. Furthermore, the effect of these factors on the damage in the assembly is investigated. This present investigation helps in understanding the macroscopic response and damage in terms of microscopic factors paving way to develop a unified prediction tool for a binary crushable granular assembly

Ability for a Stateful Path Computation Element (PCE) to Request and Obtain Control of a Label Switched Path (LSP)

Stateful Path Computation Element (PCE) retains information about the placement of Multiprotocol Label Switching (MPLS) Traffic Engineering Label Switched Paths (TE LSPs). When a PCE has stateful control over LSPs it may send indications to LSP head-ends to modify the attributes (especially the paths) of the LSPs. A Path Computation Client (PCC) that has set up LSPs under local configuration may delegate control of those LSPs to a stateful PCE. There are use-cases in which a stateful PCE may wish to obtain control of locally configured LSPs of which it is aware but that have not been delegated to the PCE. This document describes an extension to the Path Computation Element communication Protocol (PCEP) to enable a PCE to make requests for such control

Clinical profile and outcomes of semi-permanent pacing in a tertiary care institute in southern India

Background: Semi-permanent pacing (SPP) includes the placement of a permanent lead through the internal jugular vein and connection to a pulse generator on the skin outside the venous access site. Aim: To evaluate the clinical profile and outcomes of semi-permanent pacing in a tertiary care institute in Southern India. Methods: This is a retrospective observational study. All patients admitted and requiring management with semi-permanent pacing from January 2017 to June 2020 were included. Results: From January 2017 to June 2020, 20 patients underwent semi-permanent pacing (SPP) with a median age of 54 (21–74) years. Males comprised a majority of the patients (55%). Hypertension was noted in 50% of patients and 30% were diabetic. The right internal jugular vein was the most common access in 95% of patients. The most common indication for semi-permanent pacing was pocket site infection in 30% of patients. There were no procedural complications. The median duration on SPP was 7 (5–14) days and the median duration of hospital stay was 13 (8–21) days. Permanent pacemaker implantation was done in 55% of patients. Mortality in our study group was 15% with 10% dying due to cardiogenic shock (post resuscitated cardiac arrest) and 5% dying due to non-cardiac cause (Epidural hematoma). Conclusion: In our study, semi-permanent pacing was noted to be a safe procedure and was more commonly indicated in emergent conditions with complete heart block secondary to underlying reversible causes and in the management of pocket site infection

Mechanical properties of Austenitic Stainless Steel 304L and 316L at elevated temperatures

Austenitic Stainless Steel grade 304L and 316L are very important alloys used in various high temperature applications, which make it important to study their mechanical properties at elevated temperatures. In this work, the mechanical properties such as ultimate tensile strength (UTS), yield strength (YS), % elongation, strain hardening exponent (n) and strength coefficient (K) are evaluated based on the experimental data obtained from the uniaxial isothermal tensile tests performed at an interval of 50 °C from 50 °C to 650 °C and at three different strain rates (0.0001, 0.001 and 0.01 s−1). Artificial Neural Networks (ANN) are trained to predict these mechanical properties. The trained ANN model gives an excellent correlation coefficient and the error values are also significantly low, which represents a good accuracy of the model. The accuracy of the developed ANN model also conforms to the results of mean paired t-test, F-test and Levene's test