Multi-scale analysis of the roughness effect on lubricated rough contact

Determining friction is as equally essential as determining the film thickness in the lubricated contact, and is an important research subject. Indeed, reduction of friction in the automotive industry is important for both the minimization of fuel consumption as well as the decrease in the emissions of greenhouse gases. However, the progress in friction reduction has been limited by the difficulty in understanding the mechanism of roughness effects on friction. It was observed that micro-surface geometry or roughness was one of the major factors that affected the friction coefficient. In the present study, a new methodology coupling the multi-scale decomposition of the surface and the prediction of the friction coefficient by numerical simulation was developed to understand the influence of the scale of roughness in the friction coefficient. In particular, the real surface decomposed in different roughness scale by multi-scale decomposition, based on ridgelets transform was used as input into the model. This model predicts the effect of scale on mixed elastohydroynamic point contact. The results indicate a good influence of the fine scale of surface roughness on the friction coefficient for full-film lubrication as well as a beginning of improvement for mixed lubrication

The Digital Transformation of the Valuation Sector in the World of Algorithms

Over the years, the concept of digitalisation has rapidly integrated into many sectors. This Research Paper will discuss the valuation sector’s digital transformation, predominantly investigating the automated valuation models (AVMs) and their integration in valuation. Real estate is one of the oldest and the largest asset class in cities (Kok et al., 2017: 202). As explained by (Gilbertson and Preston,2005: 123), in mature economies, a large proportion of financial decision-making relates to property. Therefore, if the assets are not correctly valued then an extensive range of stakeholders are exposed. The 1970s property crash prompted RICS to publish the Red Book, setting out standards of valuation and professional conduct expected of valuers (Gilbertson and Preston, 2005: 124). However, the fluctuation and the relationship between value, worth and risk remain unchanged. The recent paradigm shift to the concept of digitalisation requires a discussion of economic development in relation to social development. This necessitates considering political (the role of governmental bodies concerning smart governance), social (individuals\dwellers in regards to raising the quality of life) and economic (such as real estate markets together with its stakeholders, including government, banks, building societies, insurance companies, and investment firms in regards to the coordination and collaboration) factors

Automated Valuation Models (AVMs): Machine Learning, namely Mass (Advanced) Valuation Methods and Algorithms

Digitalisation is becoming increasingly common within the valuation sector. Thus, it is vital to understand how traditional valuation methods are being replaced by machine learning technology, namely mass (advanced) valuation methods. According to Soni and Sadiq (2015: 100), real estate markets are popular with investors, who are keen to identify a fast way to play the market or to hedge against existing volatile portfolios. Therefore, an accurate prediction of house price is essential to prospective home owners, developers, investors, valuers, tax assessors, mortgage lenders and insurers. Demirci, O (2021) stated that the fluctuation and the relationship between value, worth, and risk remain unchanged in the current market. This means that the increased use of Automated Valuation Models (AVMs) requires a discussion of the machine learning technology, namely mass (advanced) valuation methods, which are the fundamental basis of the algorithms used within the valuation sector. As defined by Erdem (2017), valuation can be categorised into traditional, statistical and modern methods. This Research Paper will investigate both the statistical and modern methods of valuation and their application to the real estate valuation. In particular, it will look at the main limitations of the traditional valuation methods in respect to their accuracy, consistency and speed (Jahanshiri, 2011; Wang & Wolverton, 2012; Adetiloye & Eke, 2014). Moreover, these methods will be compared against mass (advanced) valuation methods, when there is a need to value a group of properties. Indeed, with the increasing volume of transactions and changing marketplace of real estate, mass (advanced) valuation has been widely adopted in many countries for different purposes, including assessment of property tax (Osborn, 2014). https://doi.org/10.13140/RG.2.2.12649.4208