Nanoindentation at elevated temperatures

Relating the creep response observed with high temperature instrumented indentation experiments to macroscopic uniaxial creep response is of great practical value. In this review, we present an overview of various methods currently being used to measure creep at small scales with instrumented indentation, with a focus on geometrically self-similar indenters, and their relative merits and demerits from an experimental perspective. A comparison of the various methods to use those instrumented indentation results to predict the uniaxial power law creep response of a wide range of materials (stress exponent of 1 to 8), will be presented to assess their validity. The interplay of size dependent hardness effects, strain rate effects and temperature effects will also be discussed. The extension of rapid testing and mapping techniques to high temperatures will also be demonstrated. Figure 1 shows a map of hardness vs position in a carbide containing steel at 300 degrees C. These techniques are extended to stress exponent and pre-exponential maps determined at high temperatures. Please click Additional Files below to see the full abstract

Discovery of an old nova remnant in the Galactic globular cluster M 22

A nova is a cataclysmic event on the surface of a white dwarf in a binary system that increases the overall brightness by several orders of magnitude. Although binary systems with a white dwarf are expected to be overabundant in globular clusters (GCs) compared to the Galaxy, only two novae from Galactic globular clusters have been observed. We present the discovery of an emission nebula in the Galactic globular cluster M 22 (NGC 6656) in observations made with the integral-field spectrograph MUSE. We extract the spectrum of the nebula and use the radial velocity determined from the emission lines to confirm that the nebula is part of NGC 6656. Emission-line ratios are used to determine the electron temperature and density. It is estimated to have a mass of 1 to $17 \times 10^{-5}$ solar masses. This mass and the emission-line ratios indicate that the nebula is a nova remnant. Its position coincides with the reported location of a 'guest star', an ancient Chinese term for transients, observed in May 48 BCE. With this discovery, this nova may be one of the oldest confirmed extrasolar events recorded in human history.Comment: 7 pages, 3 figures; accepted for publication in Astronomy & Astrophysic

An empirical, Bayesian approach to modelling crop yield: Maize in USA

We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18 °C–19 °C, corresponding to a daily maximum temperature of 24 °C–25 °C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of 2 °C, relative to 1981–2014, results in the mean yield decreasing by 8%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate

Cloud manufacturing as a sustainable process manufacturing route

Cloud Manufacturing (CM) is a service oriented business model to share manufacturing capabilities and resources on a cloud platform. Manufacturing is under pressure to achieve cost and environmental impact reductions, as manufacturing becomes more integrated and complex. Cloud manufacturing offers a solution, as it is capable of making intelligent decisions to provide the most sustainable and robust manufacturing route available. Although CM research has progressed, a consensus is still lacking on the concepts within CM as well as applications and scope beyond discrete manufacturing. The aim of this paper is to demonstrate how CM offers a more sustainable manufacturing future to the industry as a whole, before focusing specifically on the application to process manufacturing (e.g. food, pharmaceuticals and chemicals). This paper details the definitions, characteristics, architectures and previous case studies on CM. From this, the fundamental aspects of the CM concept are identified, along with an analysis of how the concept has progressed. A new, comprehensive CM definition is formulated by combining key concepts drawn from previous definitions and emphasizes CM potential for sustainable manufacturing. Four key methods of how CM increases sustainability are identified: (1) collaborative design; (2) greater automation; (3) improved process resilience and (4) enhanced waste reduction, reuse and recovery. The first two key methods are common to both discrete and process manufacturing, however key methods (3) and (4) are more process manufacturing specific and application of CM for these has yet to be fully realised. Examples of how CM’s characteristics may be utilised to solve various process manufacturing problems are presented to demonstrate the applications of CM to process manufacturing. Waste is an important consideration in manufacturing, with strong sustainability implications. The current focus has been on using CM for waste minimisation; however, process manufacturing offers waste as a resource (valorisation opportunities from diversifying co-products, reuse, recycle and energy recovery). Exploring CM’s potential to characterise and evaluate alternative process routes for the valorisation of process manufacturing waste is considered for the first time. The specific limitations preventing CM adoption by process manufacturers are discussed. Finally, CM’s place in the future of manufacturing is explored, including how it will interact with, and complement other emerging manufacturing technologies to deliver a circular economy and personalised products

Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess

Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilised within data-driven models (DDM). However, data volume and variability complications make developing models that captures the underlying biological nature of the bioprocesses challenging. In this study, a framework for developing data-driven models of bioprocesses is proposed and evaluated by modelling an industrial bioprocess, which treats industrial or agrifood wastewaters whilst simultaneously generating bioenergy. Six models were developed to predict the reduction in chemical oxygen demand from the wastewater by the bioprocess and statistically evaluated using both testing data (randomly partitioned data from the model development) and unseen data (new data not used during the model development). The statistical error metrics employed were the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The stacked neural network model was best able to model the bioprocess, having the highest accuracy on the testing data (R2: 0.98; RMSE: 1.29; MAE: 2.27; MAPE: 4.08) and the unseen data (R2: 0.82; RMSE: 2.57; MAE: 1.75; MAPE: 3.68). Data visualisation is used to observe (or confirm) whether new data points are within the model boundaries, helping to increase confidence in the model’s predictions on future data

Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study

Process manufacturing industries constantly strive to make their processes increasingly sustainable from an environmental and economic perspective. A manufacturing system model is a powerful tool to holistically evaluate various manufacturing configurations to determine the most sustainable one. Previously models of process manufacturing systems are typically single target models, trained to fit and/or predict data for a single output variable. However, process manufacturing systems produce a variety of outputs with multiple, sometimes contradictory, sustainability implications. These systems require multiple target models to find the most sustainable manufacturing configuration which considers all outputs. A novel bioprocess that treats process wastewaters to reduce pollutant load for reuse, while simultaneously generating energy in the form of biogas was studied. Multiple target models were developed to predict the percentage removal of chemical oxygen demand and total suspended solids, in addition to the biogas (as volume of methane) produced. Predictions from the models were able to reduce wastewater treatment costs by 17.0%. Eight models were developed and statistically evaluated by the coefficient of determination (R2), normalised root mean square error (nRMSE) and mean absolute percentage error (MAPE). An artificial neural network model built following the ensemble of regressor chains demonstrated the best multi target model performance, averaged across all the bioprocess’s outputs (R2 of 0.99, nRMSE of 0.02, MAPE of 1.74). The model is able to react to new regulations and legislation and/or variations in company, sector, world circumstances to provide the most up to date sustainable manufacturing configuration

Identifying priority questions regarding rapid systematic reviews’ methods: protocol for an eDelphi study

Introduction: Rapid systematic reviews (RRs) have the potential to provide timely information to decision-makers, thus directly impacting healthcare. However, consensus regarding the most efficient approaches to performing RRs and the presence of several unaddressed methodological issues pose challenges. With such a large potential research agenda for RRs, it is unclear what should be prioritised.// Objective: To elicit a consensus from RR experts and interested parties on what are the most important methodological questions (from the generation of the question to the writing of the report) for the field to address in order to guide the effective and efficient development of RRs.// Methods and analysis: An eDelphi study will be conducted. Researchers with experience in evidence synthesis and other interested parties (eg, knowledge users, patients, community members, policymaker, industry, journal editors and healthcare providers) will be invited to participate. The following steps will be taken: (1) a core group of experts in evidence synthesis will generate the first list of items based on the available literature; (2) using LimeSurvey, participants will be invited to rate and rank the importance of suggested RR methodological questions. Questions with open format responses will allow for modifications to the wording of items or the addition of new items; (3) three survey rounds will be performed asking participants to re-rate items, with items deemed of low importance being removed at each round; (4) a list of items will be generated with items believed to be of high importance by ≥75% of participants being included and (5) this list will be discussed at an online consensus meeting that will generate a summary document containing the final priority list. Data analysis will be performed using raw numbers, means and frequencies.// Ethics and dissemination: This study was approved by the Concordia University Human Research Ethics Committee (#30015229). Both traditional, for example, scientific conference presentations and publication in scientific journals, and non-traditional, for example, lay summaries and infographics, knowledge translation products will be created