What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

scholarly journals What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

scholarly journals What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

scholarly journals Tribology of 2D Nanomaterials: A Review

2020 ◽  
Author(s):  
Paul С. Uzoma ◽  
Huan Hu ◽  
Mahdi Khadem ◽  
Oleksiy V. Penkov
Keyword(s):  
High Performance ◽  
2D Materials ◽  
Chemical Properties ◽  
Solid Lubricants ◽  
Friction Behavior ◽  
Comprehensive Overview ◽  
Wear Rates ◽  
Recent Developments ◽  
Frictional Coefficients ◽  
Physical And Chemical

The exfoliation of graphene has opened a new frontier in material science with a focus on 2D materials. The unique thermal, physical and chemical properties of these materials have made them one of the choicest candidates in novel mechanical and nano-electronic devices. Notably, 2D materials such as graphene, MoS2, WS2, h-BN, and Black Phosphorus have shown outstanding lowest frictional coefficients and wear rates, making them attractive materials for high-performance nano-lubricants and lubricating applications. The objective of this work is to provide a comprehensive overview of the most recent developments in the tribological potentials of 2D materials. At first, the essential physical, wear, and frictional characteristics of the 2D materials including their production techniques are discussed. Subsequently, the experimental explorations and theoretical simulations of the most common 2D materials are reviewed in regards to their tribological applications such as their use as solid lubricants and surface lubricant nano-additives. The effects of micro/nano textures on friction behavior are also reviewed. Finally, the current challenges in tribological applications of 2D materials and their prospects are discussed.

scholarly journals Tribology of 2D Nanomaterials: A Review

Coatings ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 897
Author(s):  
Paul C. Uzoma ◽  
Huan Hu ◽  
Mahdi Khadem ◽  
Oleksiy V. Penkov
Keyword(s):  
High Performance ◽  
2D Materials ◽  
Chemical Properties ◽  
Solid Lubricants ◽  
Friction Behavior ◽  
Comprehensive Overview ◽  
Wear Rates ◽  
Recent Developments ◽  
Frictional Coefficients ◽  
Physical And Chemical

The exfoliation of graphene has opened a new frontier in material science with a focus on 2D materials. The unique thermal, physical and chemical properties of these materials have made them one of the choicest candidates in novel mechanical and nano-electronic devices. Notably, 2D materials such as graphene, MoS2, WS2, h-BN and black phosphorus have shown outstanding lowest frictional coefficients and wear rates, making them attractive materials for high-performance nano-lubricants and lubricating applications. The objective of this work is to provide a comprehensive overview of the most recent developments in the tribological potentials of 2D materials. At first, the essential physical, wear and frictional characteristics of the 2D materials including their production techniques are discussed. Subsequently, the experimental explorations and theoretical simulations of the most common 2D materials are reviewed in regards to their tribological applications such as their use as solid lubricants and surface lubricant nano-additives. The effects of micro/nano textures on friction behavior are also reviewed. Finally, the current challenges in tribological applications of 2D materials and their prospects are discussed.

scholarly journals Preface to “Understanding volcanic processes through geophysical and volcanological data investigations: some case studies from Italian sites (EGU2019 GMPV5.11 session, COV10 S01.11 session)”

2021 ◽  
Vol 52 ◽  
pp. 153-158
Author(s):  
Paola Cusano ◽  
Enza De Lauro ◽  
Antonietta Esposito ◽  
Mariarosaria Falanga ◽  
Danilo Galluzzo ◽  
...  
Keyword(s):  
Field Experiments ◽  
Quality Data ◽  
High Quality Data ◽  
Starting Point ◽  
Rock Structure ◽  
Volcanic Processes ◽  
Flow Circulation ◽  
Physical And Chemical ◽  
Shed Light ◽  
Monitoring Activity

Abstract. Volcanic dynamics is driven by the complex interplay between fluid flow (circulation of magmatic and/or hydrothermal fluids) and rock structure (volcano conduits, dykes), the comprehension of which requires both multi-parametric monitoring and modelling of relevant physical and chemical processes of the system. Understanding the factors controlling the dynamics of the processes involved in these interactions is necessary to characterize the overall behaviour of a volcano and the eventual transition mechanisms among stationarity, unrest phases and eruptive styles. The starting point in this context is to have high-quality data of several parameters (seismological, geochemical, geodetic, volcanological), acquired both over years of monitoring activity and focused field experiments. Fundamental contributions come from the use of combined multi-parametric datasets and the adoption of innovative analysis techniques and multi-disciplinary approaches. This Special Issue is addressed to those researchers, who focus their investigations in the field of volcano dynamics. Its main purpose is to shed light on the processes occurring in active volcanic systems over different time scales, with relevant implications for the hazards and the modern monitoring, thus promoting future discussions on this topic. The Issue contains this introducing preface, which describes the Volume aims, and 14 papers, reflecting the main themes. The papers are devoted to the study of some Italian sites, but the proposed approaches are general and therefore applicable to any other volcanic/hydrothermal areas.

scholarly journals Method validation in pharmaceutical analysis: from theory to practical optimization

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Jaqueline Kaleian Eserian ◽  
Márcia Lombardo
Keyword(s):  
High Performance Liquid Chromatography ◽  
Method Validation ◽  
Pharmaceutical Analysis ◽  
High Performance ◽  
Hplc Analysis ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Validation Procedure ◽  
The One

The validation of analytical methods is required to obtain high-quality data. For the pharmaceutical industry, method validation is crucial to ensure the product quality as regards both therapeutic efficacy and patient safety. The most critical step in validating a method is to establish a protocol containing well-defined procedures and criteria. A well planned and organized protocol, such as the one proposed in this paper, results in a rapid and concise method validation procedure for quantitative high performance liquid chromatography (HPLC) analysis.   Type: Commentary

scholarly journals VIRTUALIZING SUPER-COMPUTATION ON-BOARD UAS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1291-1298 ◽  
Author(s):  
E. Salami ◽  
J. A. Soler ◽  
R. Cuadrado ◽  
C. Barrado ◽  
E. Pastor
Keyword(s):  
Real Time ◽  
High Performance ◽  
Unmanned Aerial Systems ◽  
Sensing Applications ◽  
Recent Developments ◽  
Level Information ◽  
Information Products ◽  
High Level ◽  
The Cost ◽  
Aerial Systems

Unmanned aerial systems (UAS, also known as UAV, RPAS or drones) have a great potential to support a wide variety of aerial remote sensing applications. Most UAS work by acquiring data using on-board sensors for later post-processing. Some require the data gathered to be downlinked to the ground in real-time. However, depending on the volume of data and the cost of the communications, this later option is not sustainable in the long term. This paper develops the concept of virtualizing super-computation on-board UAS, as a method to ease the operation by facilitating the downlink of high-level information products instead of raw data. Exploiting recent developments in miniaturized multi-core devices is the way to speed-up on-board computation. This hardware shall satisfy size, power and weight constraints. Several technologies are appearing with promising results for high performance computing on unmanned platforms, such as the 36 cores of the TILE-Gx36 by Tilera (now EZchip) or the 64 cores of the Epiphany-IV by Adapteva. The strategy for virtualizing super-computation on-board includes the benchmarking for hardware selection, the software architecture and the communications aware design. A parallelization strategy is given for the 36-core TILE-Gx36 for a UAS in a fire mission or in similar target-detection applications. The results are obtained for payload image processing algorithms and determine in real-time the data snapshot to gather and transfer to ground according to the needs of the mission, the processing time, and consumed watts.

scholarly journals Knowledge-infused Learning for Entity Prediction in Driving Scenes

2021 ◽  
Vol 4 ◽  
Author(s):  
Ruwan Wickramarachchi ◽  
Cory Henson ◽  
Amit Sheth
Keyword(s):  
Scene Understanding ◽  
Autonomous Driving ◽  
Semantic Knowledge ◽  
Quality Data ◽  
Knowledge Graph ◽  
Social Environments ◽  
High Quality Data ◽  
Knowledge Based ◽  
Standard Mapping ◽  
High Level

Scene understanding is a key technical challenge within the autonomous driving domain. It requires a deep semantic understanding of the entities and relations found within complex physical and social environments that is both accurate and complete. In practice, this can be accomplished by representing entities in a scene and their relations as a knowledge graph (KG). This scene knowledge graph may then be utilized for the task of entity prediction, leading to improved scene understanding. In this paper, we will define and formalize this problem as Knowledge-based Entity Prediction (KEP). KEP aims to improve scene understanding by predicting potentially unrecognized entities by leveraging heterogeneous, high-level semantic knowledge of driving scenes. An innovative neuro-symbolic solution for KEP is presented, based on knowledge-infused learning, which 1) introduces a dataset agnostic ontology to describe driving scenes, 2) uses an expressive, holistic representation of scenes with knowledge graphs, and 3) proposes an effective, non-standard mapping of the KEP problem to the problem of link prediction (LP) using knowledge-graph embeddings (KGE). Using real, complex and high-quality data from urban driving scenes, we demonstrate its effectiveness by showing that the missing entities may be predicted with high precision (0.87 Hits@1) while significantly outperforming the non-semantic/rule-based baselines.

scholarly journals Software for online reconstruction and filtering at the Belle II experiment

2020 ◽  
Vol 35 (33) ◽  
pp. 2043001 ◽  
Author(s):  
Nils Braun ◽  
Thomas Kuhr
Keyword(s):  
Quality Data ◽  
Software Components ◽  
Data Transport ◽  
High Quality Data ◽  
Processing Pipeline ◽  
Level Trigger ◽  
Belle Ii ◽  
High Level ◽  
Online Reconstruction ◽  
Monitoring Information

The Belle II experiment is designed to collect 50 times more data than its predecessor. For a smooth collection of high-quality data, a robust and automated data transport and processing pipeline has been established. We describe the basic software components employed by the high level trigger. It performs a reconstruction of all events using the same algorithms as offline, classifies the events according to physics criteria, and provides monitoring information. The improved system described in this paper has been deployed successfully since 2019.

Microstructure of sputter deposited Ni-Sb ohmic contacts on GaAs

1989 ◽  
Vol 47 ◽  
pp. 450-451
Author(s):  
S. Yegnasubramanian ◽  
V.C. Kannan ◽  
R. Dutto ◽  
P.J. Sakach
Keyword(s):  
High Performance ◽  
Ohmic Contacts ◽  
Surface Defects ◽  
Pure Metal ◽  
Device Fabrication ◽  
Native Oxide ◽  
Metal Thin Films ◽  
Recent Developments ◽  
Different Temperatures ◽  
Sputter Deposited

Recent developments in the fabrication of high performance GaAs devices impose crucial requirements of low resistance ohmic contacts with excellent contact properties such as, thermal stability, contact resistivity, contact depth, Schottky barrier height etc. The nature of the interface plays an important role in the stability of the contacts due to problems associated with interdiffusion and compound formation at the interface during device fabrication. Contacts of pure metal thin films on GaAs are not desirable due to the presence of the native oxide and surface defects at the interface. Nickel has been used as a contact metal on GaAs and has been found to be reactive at low temperatures. Formation Of Ni2 GaAs at 200 - 350C is reported and is found to grow epitaxially on (001) and on (111) GaAs, but is shown to be unstable at 450C. This paper reports the investigations carried out to understand the microstructure, nature of the interface and composition of sputter deposited and annealed (at different temperatures) Ni-Sb ohmic contacts on GaAs by TEM. Attempts were made to correlate the electrical properties of the films such as the sheet resistance and contact resistance, with the microstructure. The observations are corroborated by Scanning Auger Microprobe (SAM) investigations.

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