Universal Scaling and Real-Time Monitoring of the Production of Liquid Exfoliated Graphene

<div>Shear-assisted liquid exfoliation is a primary candidate for producing defect-free two-dimensional materials from labs to industry. Diverse hydrodynamic conditions exist across production methods, and combined with low-throughput, high-cost characterization techniques, strongly contribute to the wide variability in performance and material quality. Through investigations on strikingly different flow regimes, and using graphene as the prototypical two-dimensional material, we find that scaling of production depends on local stress fi eld distributions and precursor residence time. We report a novel indirect diffuse reflectance method to measure graphene concentration in real-time, using low-cost optoelectronics and without the need to remove the precursor material from the heterogeneous dispersions. We show that this high-throughput, <i>in situ</i> approach has broad applicability by controlling the number of atomic layers on the fly, rapidly optimising green solvent design for maximum yield, and viewing live production rates. Combining insights on the hydrodynamics of exfoliation with this scalable monitoring technique, targeted process intensi fication, quality control, batch traceability and individually customisable materials on-demand are possible.</div>

Universal Scaling and Real-Time Monitoring of the Production of Liquid Exfoliated Graphene

<div>Shear-assisted liquid exfoliation is a primary candidate for producing defect-free two-dimensional materials from labs to industry. Diverse hydrodynamic conditions exist across production methods, and combined with low-throughput, high-cost characterization techniques, strongly contribute to the wide variability in performance and material quality. Through investigations on strikingly different flow regimes, and using graphene as the prototypical two-dimensional material, we find that scaling of production depends on local stress fi eld distributions and precursor residence time. We report a novel indirect diffuse reflectance method to measure graphene concentration in real-time, using low-cost optoelectronics and without the need to remove the precursor material from the heterogeneous dispersions. We show that this high-throughput, <i>in situ</i> approach has broad applicability by controlling the number of atomic layers on the fly, rapidly optimising green solvent design for maximum yield, and viewing live production rates. Combining insights on the hydrodynamics of exfoliation with this scalable monitoring technique, targeted process intensi fication, quality control, batch traceability and individually customisable materials on-demand are possible.</div>

Scalable monitoring data processing for the LHCb software trigger

The LHCb High Level Trigger (HLT) is split in two stages. HLT1 is synchronous with collisions delivered by the LHC and writes its output to a local disk buffer, which is asynchronously processed by HLT2. Efficient monitoring of the data being processed by the application is crucial to promptly diagnose detector or software problems. HLT2 consists of approximately 50000 processes and 4000 histograms are produced by each process. This results in 200 million histograms that need to be aggregated for each of up to a hundred data taking intervals that are being processed simultaneously. This paper presents the multi-level hierarchical architecture of the monitoring infrastructure put in place to achieve this. Network bandwidth is minimised by sending histogram increments and only exchanging metadata when necessary, using a custom lightweight protocol based on boost::serialize. The transport layer is implemented with ZeroMQ, which supports IPC and TCP communication, queue handling, asynchronous request/response and multipart messages. The persistent storage to ROOT is parallelized in order to cope with data arriving from a hundred of data taking intervals being processed simultaneously by HLT2. The performance and the scalability of the current system are presented. We demonstrate the feasibility of such an approach for the HLT1 use case, where real-time feedback and reliability of the infrastructure are crucial. In addition, a prototype of a high-level transport layer based on the stream-processing platform Apache Kafka is shown, which has several advantages over the lower-level ZeroMQ solution.

Smart Tyres: An Environmental-Friendly Solution to Road Accidents

The absence of the tyres monitoring system on vehicle has caused difficulty for driver to check the pressure and temperature of the tyres in real time. Besides that, due to the large geographical area of rural area where the distribution of petrol station with air pump might not be equally distributed, certain area is hard to access air pump. The abnormal pressure and increases in temperature on tyre lead to longer braking distance, tyre blowouts and related issues. The paper describes the deployment of IoT sensors for monitoring application in tyres and data is accessible on mobile app. This monitoring system consists of two sensors to measure the temperature and pressure of the tyre using ESP32 microcontroller board and uploaded into the cloud platform using Wi-Fi technology. While Blynk the mobile app is designed to collect the informative data from the cloud platform and the data is represented in graphical representation using open source Cloud platform. It is made available for real-time monitoring data. Apart from that, this system also incorporates alert system to provide a scalable monitoring system as well as alerting the user for any abnormal reading of the tyre.

Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters

This work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform’s compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.

Anomaly Detection Using Autoencoders in High Performance Computing Systems

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states).We propose a novel approach for anomaly detection in HighPerformance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with).We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).

SFCMon: An Efficient and Scalable Monitoring System for Network Flows in SFC-enabled Domains

A comprehensive monitoring system is essential to assist solutions for most of SFC problems. Therefore, in this work, we propose SFCMon, an efficient and scalable monitoring solution to keep track network flows in SFC environments. To achieve the desired goals, SFCMon works with a pipeline of probabilistic data structures to detect and store large flows as well as perflow counters. For evaluation purposes, based on the SFC reference architecture defined by RFC 7665, we implement a Proof-of-Concept (PoC) framework, which provides a P4-based SFC switch and Python-based SFC Controller. Presented initial experiments demonstrate that SFCMon introduces a negligible performance penalty while providing significant scalability gains.