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

Statistical Modeling of Single Target Cell Encapsulation

High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems.Wallace H. Coulter Foundation (Young Investigator in Bioengineering Award)National Institutes of Health (U.S.) (Grant R01AI081534)National Institutes of Health (U.S.) (Grant R21AI087107

Portable Microfluidic Integrated Plasmonic Platform for Pathogen Detection

Timely detection of infectious agents is critical in early diagnosis and treatment of infectious diseases. Conventional pathogen detection methods, such as enzyme linked immunosorbent assay (ELISA), culturing or polymerase chain reaction (PCR) require long assay times, and complex and expensive instruments, which are not adaptable to point-of-care (POC) needs at resource-constrained as well as primary care settings. Therefore, there is an unmet need to develop simple, rapid, and accurate methods for detection of pathogens at the POC. Here, we present a portable, multiplex, inexpensive microfluidic-integrated surface plasmon resonance (SPR) platform that detects and quantifies bacteria, i.e., Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) rapidly. The platform presented reliable capture and detection of E. coli at concentrations ranging from ∼105 to 3.2 × 107 CFUs/mL in phosphate buffered saline (PBS) and peritoneal dialysis (PD) fluid. The multiplexing and specificity capability of the platform was also tested with S. aureus samples. The presented platform technology could potentially be applicable to capture and detect other pathogens at the POC and primary care settings. © 2015, Nature Publishing Group. All rights reserved

Coherent diffraction of single Rice Dwarf virus particles using hard X-rays at the Linac Coherent Light Source

Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a wellcharacterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 mu m diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 angstrom ngstrom were recorded. The diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.11Ysciescopu

Screening of non-alkaloid acetylcholinesterase inhibitors from extracts and essential oils of Anthriscus nemorosa (M.Bieb.) Spreng. (Apiaceae)

WOS: 000488195200034This screening of biologically active compounds, cholinesterase inhibition and antioxidant potentials of extracts and essential oils from different plant parts such as fruits, aerial parts, roots and flowers of Anthriscus nemorosa was performed. GC analytical results of essential oil compositions have been detailed described. It was found that EtOAc fraction of root and root essential oil had the highest total phenolics content and antioxidant activity (DPPH test). the essential oil of roots showed the highest butyrylcholinesterase inhibition (88.51%). alpha-Pinene as the major component of root essential oil also indicated strong butyrylcholinesterase inhibitory activity (72.09%) and antioxidant effect. the GC-FID and GC-MS analysis assessed that major monoterpene of roots and aerial parts were alpha-pinene (25.5.%), myristicin (10.4%), p-cymene (8.2%), limonene (6.0%) and fatty alcohol 1-heptadecanol (7.5%). the root and aerial part canals revealed the smaller number of secretory canals which contains mainly monoterpene and oxygenated monoterpenes. the secretory canals of fruits and flowers were characterised by the largest shape and contain a high amount of sesquiterpene hydrocarbon bicyclogermacrene. the high content of sesquiterpene spathulenol (49.6%) was estimated in the extracts of the aerial part. These presented findings represented that the roots essential oil of A. nemorosa may be a novel alternative source of natural antioxidant and anticholinesterase. (C) 2019 SAAB. Published by Elsevier B.V. All rights reserved.Research Fund of the Ataturk UniversityThis work was supported by Research Fund of the Ataturk University