title: Machine learning-based prediction of young’s modulus in Ti-Alloys

authors: Dinibutun, Seza

abstract: This study explores the use of machine learning to predict the experimental Young’s modulus of titanium alloys based on their mechanical and microstructural properties. Several regression models were developed and compared, including Random Forest, XGBoost, CatBoost, Multi-Layer Perceptron, and a Stacking Regressor. Among these, Random Forest, XGBoost and CatBoost achieved the most accurate results with R<sup>2</sup> values above 0.85. To improve interpretability, SHapley Additive exPlanations were applied to examine which input features most strongly influenced the predictions. The results showed that yield strength, hardness, and the molybdenum equivalent parameter (moe) were among the most influential descriptors. While yield strength and hardness were positively associated with the predicted values, higher moe values corresponded to lower predicted Young’s modulus. This study focuses on the prediction of Young’s modulus, a comparatively less explored elastic property in Ti-alloy machine learning studies and combines systematic model comparison with SHAP-based interpretability to provide physically consistent insights into feature–property relationships.

text: Machine learning-based prediction of young’s modulus in Ti-Alloys This study explores the use of machine learning to predict the experimental Young’s modulus of titanium alloys based on their mechanical and microstructural properties. Several regression models were developed and compared, including Random Forest, XGBoost, CatBoost, Multi-Layer Perceptron, and a Stacking Regressor. Among these, Random Forest, XGBoost and CatBoost achieved the most accurate results with R<sup>2</sup> values above 0.85. To improve interpretability, SHapley Additive exPlanations were applied to examine which input features most strongly influenced the predictions. The results showed that yield strength, hardness, and the molybdenum equivalent parameter (moe) were among the most influential descriptors. While yield strength and hardness were positively associated with the predicted values, higher moe values corresponded to lower predicted Young’s modulus. This study focuses on the prediction of Young’s modulus, a comparatively less explored elastic property in Ti-alloy machine learning studies and combines systematic model comparison with SHAP-based interpretability to provide physically consistent insights into feature–property relationships.

year: 2026-02-19

title: Testing the stability of the benefit transfer function for discrete choice contingent valuation data

authors: Matthews, David I.

subjects: benefit function transfer

abstract: We examine the stability of the benefit transfer function across 42 recreational forests in the British Isles. A working definition of reliable function transfer is put forward, and a suitable statistical test is provided. The test is based on the sensitivity of the model log-likelihood to removal of individual forest recreation sites. We apply the proposed methodology on discrete choice contingent valuation data and find that a stable function improves our measure of transfer reliability, but not by much. We conclude that, in empirical studies on transferability, function stability considerations are secondary to the availability and quality of site attribute data. Modellers’ can study the advantages of transfer function stability vis-à-vis the value of additional information on recreation site attributes.

text: Testing the stability of the benefit transfer function for discrete choice contingent valuation data We examine the stability of the benefit transfer function across 42 recreational forests in the British Isles. A working definition of reliable function transfer is put forward, and a suitable statistical test is provided. The test is based on the sensitivity of the model log-likelihood to removal of individual forest recreation sites. We apply the proposed methodology on discrete choice contingent valuation data and find that a stable function improves our measure of transfer reliability, but not by much. We conclude that, in empirical studies on transferability, function stability considerations are secondary to the availability and quality of site attribute data. Modellers’ can study the advantages of transfer function stability vis-à-vis the value of additional information on recreation site attributes.

year: 2007-05

title: Intellectual Property, Mātauranga Māori

authors: Sterling, Rogena

abstract: This literature review has been conducted to consider the various national legislation and international agreements that comprise New Zealand’s Intellectual Property Rights (IP) regime. It will evaluate if and how such legislation and agreements protect and enable Māori IP rights and interests with respect to Māori data, genomic data and mātauranga Māori. The review also identifies some mechanisms that might also enhance Māori control of these types of data. The Westminster approach of legislation in New Zealand and its approach to IP protection based on Copyrights, Patents and Trade Marks are juxtaposed against traditional Maori approaches of communally held ancestral knowledge (mātauranga) passed down through generations (whanaungatanga) based on guardianship and protection (kaitiakitanga) and the self-determination of use of such knowledge (rangatiratanga). Attempting to align tikanga concepts to the Westmionster model of law is challenging as the two share completely different notions of ownership and responsibility. Expectations of protection, to prevent misappropriation and commercialisation by non- Māori of mātauranga Māori and Māori data, extend beyond the parameters of existing IP law, creates a similar disjunct. Genomic Research generates data, some of which can be protected by IP, however researchers working with genetic/genomic data from taonga species have often failed to acknowledge the non-IP interests of Maori. As a result, Maori have taken it upon themselves to advocate for their rights to data through Māori data sovereignty discourse as well as create guidelines for culturally appropriate genomic research with explicit references to data security and management (e.g. Te Mata Ira, Te Nohonga Kaitiaki). Other extra-legal options, such as Biocultural Labelling to alert users where particular data has Māori rights and/or interests, are emerging to maintain create durable provenance data and connect next users of data with the responsible Indigenous communities. Though the intellectual property regime in New Zealand may provide some protections, there are still significant areas where the legal system does not provide sufficient protections for Māori data, taonga species and mātauranga. The UN Declaration on the Rights of Indigenous Peoples provides a framework of international support for Indigenous rights, but local government and other home-grown mechanisms are important to enable Māori governance of data. Local approaches Māori Data Sovereignty and Māori Data Governance afford Maori the opportunity to be directly involved as kaitiaki of their mātauranga and assert rangatiratanga over data and its use.

text: Intellectual Property, Mātauranga Māori This literature review has been conducted to consider the various national legislation and international agreements that comprise New Zealand’s Intellectual Property Rights (IP) regime. It will evaluate if and how such legislation and agreements protect and enable Māori IP rights and interests with respect to Māori data, genomic data and mātauranga Māori. The review also identifies some mechanisms that might also enhance Māori control of these types of data. The Westminster approach of legislation in New Zealand and its approach to IP protection based on Copyrights, Patents and Trade Marks are juxtaposed against traditional Maori approaches of communally held ancestral knowledge (mātauranga) passed down through generations (whanaungatanga) based on guardianship and protection (kaitiakitanga) and the self-determination of use of such knowledge (rangatiratanga). Attempting to align tikanga concepts to the Westmionster model of law is challenging as the two share completely different notions of ownership and responsibility. Expectations of protection, to prevent misappropriation and commercialisation by non- Māori of mātauranga Māori and Māori data, extend beyond the parameters of existing IP law, creates a similar disjunct. Genomic Research generates data, some of which can be protected by IP, however researchers working with genetic/genomic data from taonga species have often failed to acknowledge the non-IP interests of Maori. As a result, Maori have taken it upon themselves to advocate for their rights to data through Māori data sovereignty discourse as well as create guidelines for culturally appropriate genomic research with explicit references to data security and management (e.g. Te Mata Ira, Te Nohonga Kaitiaki). Other extra-legal options, such as Biocultural Labelling to alert users where particular data has Māori rights and/or interests, are emerging to maintain create durable provenance data and connect next users of data with the responsible Indigenous communities. Though the intellectual property regime in New Zealand may provide some protections, there are still significant areas where the legal system does not provide sufficient protections for Māori data, taonga species and mātauranga. The UN Declaration on the Rights of Indigenous Peoples provides a framework of international support for Indigenous rights, but local government and other home-grown mechanisms are important to enable Māori governance of data. Local approaches Māori Data Sovereignty and Māori Data Governance afford Maori the opportunity to be directly involved as kaitiaki of their mātauranga and assert rangatiratanga over data and its use.

year: 2021