title: Gaining understanding of the effect of alloy composition on the properties of Ti–Cu–Mn–Al alloys

authors: Al-Hajiri, M

abstract: High performance cheaper Ti alloys can be developed based on the addition of several alloying elements, amongst which are Cu, Mn and Al. Studies are available in literature about binary and some ternary alloys based on the addition of these elements; however, no quaternary Ti–Cu–Mn–Al alloys were developed. Therefore, in this study quaternary Ti–Cu–Mn–Al alloys were manufactured via powder metallurgy to gain a better understanding of the effect of the alloy composition and the achievable mechanical performance. It is found that the selected quaternary Ti–Cu–Mn–Al alloys are characterised by a lamellar structure, which is progressively refined as the amount of alloying elements increases, and precipitation of the Ti2Cu intermetallic occurs if the Cu content is high enough. The amount of residual porosity increases and changes morphology as more thermal energy is invested in the diffusion of the alloying elements. In terms of mechanical behaviour, the quaternary Ti–Cu–Mn–Al alloys undergo both elastic and plastic deformation upon tensile loading. Strength and hardness linearly increase and the ductility monotonically decreases, which is the result of the compromise between the amount of residual porosity and the several strengthening mechanisms brought about by the actual total amount of alloying elements added.

text: Gaining understanding of the effect of alloy composition on the properties of Ti–Cu–Mn–Al alloys High performance cheaper Ti alloys can be developed based on the addition of several alloying elements, amongst which are Cu, Mn and Al. Studies are available in literature about binary and some ternary alloys based on the addition of these elements; however, no quaternary Ti–Cu–Mn–Al alloys were developed. Therefore, in this study quaternary Ti–Cu–Mn–Al alloys were manufactured via powder metallurgy to gain a better understanding of the effect of the alloy composition and the achievable mechanical performance. It is found that the selected quaternary Ti–Cu–Mn–Al alloys are characterised by a lamellar structure, which is progressively refined as the amount of alloying elements increases, and precipitation of the Ti2Cu intermetallic occurs if the Cu content is high enough. The amount of residual porosity increases and changes morphology as more thermal energy is invested in the diffusion of the alloying elements. In terms of mechanical behaviour, the quaternary Ti–Cu–Mn–Al alloys undergo both elastic and plastic deformation upon tensile loading. Strength and hardness linearly increase and the ductility monotonically decreases, which is the result of the compromise between the amount of residual porosity and the several strengthening mechanisms brought about by the actual total amount of alloying elements added.

year: 2024-08-01

title: A teaching and support tool for building formal models of graphical user-interfaces

authors: Reeves, Steve

subjects: computer science

abstract: In this paper we propose the design of a tool that will allow the construction of a formal, textual description of a software system even if it has a graphical user-interface as a component. An important aspect of this design is that it can be used for two purposes—the teaching of predicate calculus and the formal specification of graphical user-interfaces. The design has been suggested by considering a system that has already been very successful for teaching predicate logic, namely Tarski's World.

text: A teaching and support tool for building formal models of graphical user-interfaces In this paper we propose the design of a tool that will allow the construction of a formal, textual description of a software system even if it has a graphical user-interface as a component. An important aspect of this design is that it can be used for two purposes—the teaching of predicate calculus and the formal specification of graphical user-interfaces. The design has been suggested by considering a system that has already been very successful for teaching predicate logic, namely Tarski's World.

year: 1995-08

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22