title: An assessment of the corrosion resistance of powder sprayed titanium coatings

authors: Probert, Evan Guy Gray

subjects: Titanium

abstract: Aluminium and stainless steels are susceptible to pitting corrosion in sea water. Mild steel is susceptible to corrosion in general. Titanium however, has good corrosion resistance in chloride solutions and thus is not susceptible to pitting in sea water. The following is an assessment of the corrosion resistance of Titanium powder coatings. Potentiostatic electrochemical experiments were carried out on three substrates (stainless steel, mild steel and Aluminium) coated using two spray methods (plasma and HVOF). A discussion of the results was given with reference to the polarisation curves that were created, SEM images, XRD results, estimates of porosity, and Pourbaix diagrams. It was concluded that the plasma coating provides the best corrosion resistance due to the fact that it had less porosity than HVOF and that it is made up of Titanium oxides. Also, the corrosion mechanism for the coatings is pitting of the substrates at the end of pores. The extent of this is far greater for the HVOF than the plasma coating. It was found that features found in the polarisation curves for the substrates are present in the curves for coatings. This is more evident in the HVOF polarisation curves. Mild steel benefited the most from the Titanium coatings compared to the substrate (more so for the plasma coating). Finally, the plasma coating improves the substrate based on corrosion rate and thermodynamics, except for Aluminium which remains the same thermodynamically.

text: An assessment of the corrosion resistance of powder sprayed titanium coatings Aluminium and stainless steels are susceptible to pitting corrosion in sea water. Mild steel is susceptible to corrosion in general. Titanium however, has good corrosion resistance in chloride solutions and thus is not susceptible to pitting in sea water. The following is an assessment of the corrosion resistance of Titanium powder coatings. Potentiostatic electrochemical experiments were carried out on three substrates (stainless steel, mild steel and Aluminium) coated using two spray methods (plasma and HVOF). A discussion of the results was given with reference to the polarisation curves that were created, SEM images, XRD results, estimates of porosity, and Pourbaix diagrams. It was concluded that the plasma coating provides the best corrosion resistance due to the fact that it had less porosity than HVOF and that it is made up of Titanium oxides. Also, the corrosion mechanism for the coatings is pitting of the substrates at the end of pores. The extent of this is far greater for the HVOF than the plasma coating. It was found that features found in the polarisation curves for the substrates are present in the curves for coatings. This is more evident in the HVOF polarisation curves. Mild steel benefited the most from the Titanium coatings compared to the substrate (more so for the plasma coating). Finally, the plasma coating improves the substrate based on corrosion rate and thermodynamics, except for Aluminium which remains the same thermodynamically.

year: 2010

title: Recognition of foot strike pattern in Asian recreational runners.

authors: Patoz, Aurélien

subjects: asymmetry

abstract: Close to 90% of recreational runners rearfoot strike in a long-distance road race. This prevalence has been obtained from North American cohorts of runners. The prevalence of rearfoot strikers has not been extensively examined in an Asian population of recreational runners. Therefore, the aim of this study was to determine the prevalence of rearfoot, midfoot, and forefoot strikers during a long-distance road race in Asian recreational runners and compare this prevalence to reported values in the scientific literature. To do so, we classified the foot strike pattern of 950 recreational runners at the 10 km mark of the Singapore marathon (77% Asian field). We observed 71.1%, 16.6%, 1.7%, and 10.6% of rearfoot, midfoot, forefoot, and asymmetric strikers, respectively. Chi-squared tests revealed significant differences between our foot strike pattern distribution and those reported from North American cohorts (P < 0.001). Our foot strike pattern distribution was similar to one reported from elite half-marathon runners racing in Japan (Fisher exact test, P = 0.168). We conclude that the prevalence of rearfoot strikers is lower in Asian than North American recreational runners. Running research should consider and report ethnicity of participants given that ethnicity can potentially explain biomechanical differences in running patterns.

text: Recognition of foot strike pattern in Asian recreational runners. Close to 90% of recreational runners rearfoot strike in a long-distance road race. This prevalence has been obtained from North American cohorts of runners. The prevalence of rearfoot strikers has not been extensively examined in an Asian population of recreational runners. Therefore, the aim of this study was to determine the prevalence of rearfoot, midfoot, and forefoot strikers during a long-distance road race in Asian recreational runners and compare this prevalence to reported values in the scientific literature. To do so, we classified the foot strike pattern of 950 recreational runners at the 10 km mark of the Singapore marathon (77% Asian field). We observed 71.1%, 16.6%, 1.7%, and 10.6% of rearfoot, midfoot, forefoot, and asymmetric strikers, respectively. Chi-squared tests revealed significant differences between our foot strike pattern distribution and those reported from North American cohorts (P < 0.001). Our foot strike pattern distribution was similar to one reported from elite half-marathon runners racing in Japan (Fisher exact test, P = 0.168). We conclude that the prevalence of rearfoot strikers is lower in Asian than North American recreational runners. Running research should consider and report ethnicity of participants given that ethnicity can potentially explain biomechanical differences in running patterns.

year: 2019

title: Machine translation for te reo Māori

authors: Keegan, Te Taka Adrian Gregory

abstract: What is Machine Translation? Machine Translation (MT) is a process where computer software is used to translate texts from one natural (or spoken) language to another. Early research centred on two distinct approaches: Rules Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). In simple terms RBMT makes use of large sets of linguistic rules that define languages whereas SMT uses statistical techniques to build language models from large language corpora. Increases in computing power and the amount of language corpora available has meant that SMT had become the preferred option with recent advances in neural networks also being applied to improve the accuracy of SMT. For commercial reasons, this is an area of research that has generated a lot of interest and funding support from some major international computer companies, including Google¹,Microsoft² and Facebook³.
Why is Machine Translation important for te reo Māori? A te reo Māori purist may argue that it is not important to focus activities on having a machine undertake translations for te reo Māori; if people want to understand te reo Māori then they should put in the effort to learn the language. This line of reasoning is difficult to argue with. But, from a Māori language activist perspective the value of MT is not so much in the translation of te reo Māori to (say) English, but rather the translation of English to te reo Māori. If this translation can be done efficiently, with low costs, it will assist in the proliferation of te reo Māori into new contexts, new environments and will assist its normalisation in New Zealand's society.
At this time, two of the major international companies, Google and Microsoft, have invested significantly in MT for te reo Māori. This paper summarises their endeavours and reports on the quality of translations they have been able to generate.

text: Machine translation for te reo Māori What is Machine Translation? Machine Translation (MT) is a process where computer software is used to translate texts from one natural (or spoken) language to another. Early research centred on two distinct approaches: Rules Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). In simple terms RBMT makes use of large sets of linguistic rules that define languages whereas SMT uses statistical techniques to build language models from large language corpora. Increases in computing power and the amount of language corpora available has meant that SMT had become the preferred option with recent advances in neural networks also being applied to improve the accuracy of SMT. For commercial reasons, this is an area of research that has generated a lot of interest and funding support from some major international computer companies, including Google¹,Microsoft² and Facebook³.
Why is Machine Translation important for te reo Māori? A te reo Māori purist may argue that it is not important to focus activities on having a machine undertake translations for te reo Māori; if people want to understand te reo Māori then they should put in the effort to learn the language. This line of reasoning is difficult to argue with. But, from a Māori language activist perspective the value of MT is not so much in the translation of te reo Māori to (say) English, but rather the translation of English to te reo Māori. If this translation can be done efficiently, with low costs, it will assist in the proliferation of te reo Māori into new contexts, new environments and will assist its normalisation in New Zealand's society.
At this time, two of the major international companies, Google and Microsoft, have invested significantly in MT for te reo Māori. This paper summarises their endeavours and reports on the quality of translations they have been able to generate.

year: 2017