1,615 research outputs found
Software Engineering for Science
Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced, and which software engineering practices are effective for scientific software.
The book starts with a detailed overview of the Scientific Software Lifecycle, and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development, as well as solutions to these problems.
The second part of the book provides examples of the use of testing in scientific software development, including key issues and challenges. The chapters then describe solutions and case studies aimed at applying testing to scientific software development efforts.
The final part of the book provides examples of applying software engineering techniques to scientific software, including not only computational modeling, but also software for data management and analysis. The authors describe their experiences and lessons learned from developing complex scientific software in different domains
Coronavax : preparing community and government for COVID-19 vaccination:: a research protocol for a mixed methods social research project
Introduction Ahead of the implementation of a COVID-19 vaccination programme, the interdisciplinary Coronavax research team developed a multicomponent mixed methods project to support successful roll-out of the COVID-19 vaccine in Western Australia. This project seeks to analyse community attitudes about COVID-19 vaccination, vaccine access and information needs. We also study how government incorporates research findings into the vaccination programme. Methods and analysis The Coronavax protocol employs an analytical social media study, and a qualitative study using in-depth interviews with purposively selected community groups. Participant groups currently include healthcare workers, aged care workers, first responders, adults aged 65+ years, adults aged 30-64 years, young adults aged 18-29 years, education workers, parents/guardians of infants and young children (<5 years), parents/guardians of children aged 5-18 years with comorbidities and parents/guardians who are hesitant about routine childhood vaccines. The project also includes two studies that track how Australian state and Commonwealth (federal) governments use the study findings. These are functional dialogues (translation and discussion exercises that are recorded and analysed) and evidence mapping of networks within government (which track how study findings are used). Ethics and dissemination Ethics approval has been granted by the Child and Adolescent Health Service Human Research Ethics Committee (HREC) and the University of Western Australia HREC. Study findings will be disseminated by a series of journal articles, reports to funders and stakeholders, and invited and peer-reviewed presentations.</p
Design and Fabrication of Scalable Multifunctional Multimaterial Fibers and Textiles
Multimaterial fibers eschew the traditional mono-material structures typical of traditional optical fibers for novel internal architectures that combine disparate materials with distinct optical, mechanical, and electronic properties, thereby enabling novel optoelectronic functionalities delivered in the form factor of an extended fiber. This new class of fibers developed over the past two decades is attracting interest from researchers in such different fields as optics, textiles, and biomedicine. The juxtaposition of multiple materials integrated at micro- and nanoscales in complex geometries while ensuring intimate smooth interfaces extending continuously for kilometers facilitates unique applications such as non-invasive laser surgery, self-monitoring fibers, e-textiles, and extreme-environment tethers. In this work, I focus on the scalable manufacturing of novel multimaterial fibers that make possible the fabrication of hundreds of kilometers of optical micro-cables and producing fibers at volumes commensurate with the needs of the textile and apparel industry. Although a multiplicity of fabrication schemes exists, I have investigated thermal drawing and melt-extrusion for thermo-forming of multimaterial fibers. Such fibers can be readily integrated with a broad range of downstream processes and techniques, such as textile weaving, precision-winding of fiber micro-cables, and inline functional coating. Specifically, I have developed a hybrid fabrication approach to produce robust optical fibers for single-mode and multi-mode mid-infrared transmission with the added possibility of high-power-handling capability. Second, I describe an optoelectronic fiber in which an electrically conductive composite glass is thermally co-drawn in a transparent glass matrix with a crystalline semiconductor and metallic conductors, which is the first fully integrated thermally drawn optoelectronic fiber making use of a traditional semiconductor. Third, I appropriate the industry-proven system of multicomponent melt-extrusion traditionally utilized for the scalable production of textile yarns and non-woven fabrics to produce our multimaterial fiber structures previously fabricated via thermal drawing. This has enabled melt-spinning of user-controlled color-changing fibers that are subsequently woven into active color-changing fabrics. I additionally report the design and prototyping of structured capacitive fibers for potential integration into advanced functional e-textiles. Finally, I have produced a new class of optical scattering materials based on designer composite microspheres by exploiting a recently discovered capillary instability in multimaterial fibers produced by thermal drawing, multifilament yarn spinning, and melt-extruded non-woven fabrics
Design and Validation of Novel Potential High Entropy Alloys
The design approach and validation of single phase senary refractory high entropy alloys (HEAs) MoNbTaTiVW and HfNbTaTiVZr were presented in first part of this dissertation. The design approach was to combine phase diagram inspection of available binary and ternary systems and Calculation of Phase Diagrams (CALPHAD) prediction. Experiments using X-ray diffraction and scanning electron microscopy techniques verified single phase microstructure in body centered cubic lattice for both alloys. The observed elemental segregation agrees well with the solidification prediction using Scheil model. The lattice constant, density and microhardness were measured to be 0.3216 nm, 4.954 GPa and 11.70 g/cm3 for MoNbTaTiVW and 0.334 nm, 5.5 GPa and 9.36 g/cm3 for HfNbTaTiVZr.
To elaborate the single-phase stability of HEAs, CrxMoNbTaVW was examined over a certain range of Cr content in the second part of this dissertation. The change in composition led to different BCC structures with different microstructures and physical properties. Microstructure characterizations were performed using X-ray diffraction and scanning electron microscopy. Chemical micro-segregation during solidification predicted using the Scheil model generally agrees with the experimental results. The lattice constant, density, and Vickers\u27 micro-hardness of the high-entropy alloy samples in the as-cast state are measured and discussed. For CrxMoNbTaVW, x=2.0 case appears exceeding the upper limit of maintaining a single BCC phase HEA, determined by the XRD patterns. The elemental dependence of the mixing thermodynamic properties (entropy, enthalpy and Gibbs energy) in BCC phase in the senary system is analyzed. The calculated entropy of mixing and enthalpy of mixing for CrMoNbTaVW are 14.7 J/K/mol and −662.5 J/mol respectively.
Phase predictions and characterizations on as-solidified septenary refractory high-entropy alloy, CrMoNbReTaVW, are presented in the third part of the dissertation. The simulated solidification process predicts a single body-centered-cubic (BCC) crystal structure with the tendency of compositional segregation. X-ray diffraction results confirm the “single-phase-like” BCC structure, while further experimental characterizations reveal the existence of multiple grains with significantly different compositions yet the same crystal structure and similar lattice.
For better understanding of corrosion properties of high entropy alloys, the CALPHAD method was further used to simulate the Pourbaix diagram and the corrosion layer evolutions under equilibrium conditions for CoCrFeNi based HEAs in the last part of the dissertation. The oxidation layer pitting and forming potential were calculated and compared favorably with published experimental results on CoCrFeNi, CoCrFeNiCu and CoCrFeNiAl0.5 HEAs
Non-Brownian Particle Self-Assembly for Hierarchical Materials Development
Colloidal crystals have been explored in the literature for applications in molecular electronics, photonics, sensors, and drug delivery. Much of the research on colloidal crystals has been focused on nano-sized particles with limited attention directed towards building blocks with dimensions ranging from tens to hundreds of microns. This can be attributed, in part, to the fact that particles with greater than sub-micron dimensions do not naturally assemble in an organized fashion over reasonable time-scales due to the relatively small influence of thermalizing forces. Nevertheless, ordered arrays of large, micron-scale particles are of interest as a basis for the production of hierarchically structured materials with customizable pore sizes. Additionally, the ability to create materials from a bottom-up approach with these characteristics would allow for precise control over their pore structure (size and distribution) and surface properties (topography, functionalization and area), resulting in improved regulation of key characteristics such as mechanical strength, diffusive properties, and possibly even photonic properties. In this work, ultrasonic agitation is explored as a means of inducing large, non-Brownian microparticles (18-750 µm) to overcome kinetic barriers to rearrangement and, ultimately, to create close packed, highly ordered, crystals. Using ultrasonic agitation we have been able to create highly-ordered, two- and three-dimensional crystalline structures on a variety of length scales by adjusting external system properties. Additionally, by mixing particle populations, multicomponent crystals are created with complex organizational patterns, similar to those of stoichiometric chemical structures. By repurposing these crystalline materials as templates, a plethora of new hierarchical microarchitectures can be created for applications in fields such as biotechnology and energy. In this thesis we begin exploring applications in tissue (bone) engineering, catalysis, battery materials and the production of patchy particles via surface modification
Sustainable limestone and EAF aggregate concretes through particle packing models (PPMs) and life cycle assessment (LCA)
V.I. 226p.
V.II 131 p.In view of the current concern about environmental problems, the use of slags from the Electric Arc Furnace (EAF) as aggregates in the concrete has been proved to be successful for multiple applications avoiding the use of natural aggregates. Hence, the range of aggregates available for designing concretes is continuously growing.The main objective of this thesis is to design economic and environmentally sustainable concrete mixes made with natural limestone (NL) aggregates and electric arc furnace (EAF) aggregate through a particle packing density perspective without compromising their compressive strength and workability.In order to verify the potential of particle packing theories to design more economical and environmentally sustainable NL aggregate and EAF aggregate concrete mixes, two traditional optimal curves and two current discrete packing models were validated with experimental packing results to demonstrate its feasibility in the prediction of the most compacted structure. Several (17) NL and EAF aggregate concrete mixes were then designed by varying the aggregate proportion and the content of cement paste to analyse the effect of aggregate packing density on the fresh and hardened concrete properties. Finally, the economic and environmental impact of the different concrete mixes were assessed to evaluate the potential of the particle packing methods in the development of more sustainable concrete.It was concluded that the concrete mixtures designed by maximizing the coarse aggregates content in the range of the maximum packing density present the highest compressive strength and workability and the low environmental and economic impact. In addition, due to the higher compressive strength and the low contribution of aggregate in the concrete environmental impact, the EAF aggregate concrete contributes to a greater reduction of the environmental and economic impact.Tecnali
Machine learning models for the prediction of pharmaceutical powder properties
Error on title page – year of award is 2023.Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development.
The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction.
Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%.
Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%.
Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements.Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development.
The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction.
Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%.
Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%.
Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements
Characterisation of porous materials for the adsorption of volatile organic compounds
Volatile organic compounds (VOCs) are a broad class of chemicals which, at elevated levels, can contribute negatively to the health and quality-of-life of affected individuals. Adsorption using porous materials a common method of reducing the levels of VOCs in indoor spaces. This work investigates the VOC adsorption performance of industrial and synthetic porous materials in real-world conditions via a comprehensive experimental study.
Adsorbent hydrophobicity is a key parameter when considering application in environments with high relative humidity. To illustrate the humidity-dependant nature of a material’s adsorption hydrophobicity, single- and two-component gravimetric adsorption experiments were carried out. A new method of determining hydrophobicity indexes as a function of humidity was developed to illustrate the importance of the adsorption environment on selectivity, which correlated well with toluene adsorption following pre-exposure of humidity. To better understand the mechanism of competition occurring between water and toluene, a breakthrough analyser was designed and constructed. Fixed-bed measurements largely confirmed findings from gravimetric experiments, while revealing that displacement of toluene could occur in microporous adsorbents providing the critical pore filling pressure for water vapour was reached.
A single-step, modulator-based synthesis procedure was devised for ZIF-8/67 and MIL-100, producing millimetre-centimetre scale monoliths with enhanced density while still possessing the base MOF’s physicochemical and morphological characteristics. ZIF monoliths demonstrated gravimetric VOC capture performances up to 217 and 232% higher than their powder counterparts at 0% and 80% RH respectively. These monoliths may be a promising technology for applications where volume is a critical consideration, such as packed-bed columns and air filtration.
A comprehensive evaluation of VOC adsorption behaviour as a function of relative humidity for a range of adsorbents is reported. These results aim to highlight the interplay between pore size, aperture chemistry, and selectivity in determining the effectiveness of adsorbents in real world VOC capture.Open Acces
The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase
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