A Review of Machine Learning Approaches for Real Estate Valuation

Real estate managers must identify the value for properties in their current market. Traditionally, this involved simple data analysis with adjustments made based on manager’s experience. Given the amount of money currently involved in these decisions, and the complexity and speed at which valuation decisions must be made, machine learning technologies provide a newer alternative for property valuation that could improve upon traditional methods. This study utilizes a systematic literature review methodology to identify published studies from the past two decades where specific machine learning technologies have been applied to the property valuation task. We develop a data, reasoning, usefulness (DRU) framework that provides a set of theoretical and practice-based criteria for a multi-faceted performance assessment for each system. This assessment provides the basis for identifying the current state of research in this domain as well as theoretical and practical implications and directions for future research

Using a Mark-to-Market Valuation Technique to Objectively Measure IT Portfolio Value Creation

Enterprise executives frequently face the challenge of making decisions under conditions of significant uncertainty when dealing with IT investments, IT project management and realization of intangible organizational benefits enabled by IT. A suitable methodology for accurately estimating the current financial standing of each project in a portfolio of IT projects over the full project lifecycle is useful for IT managers to understand IT value creation and manage the IT projects across the portfolio. In line with this perspective, we propose a Mark-to-Market valuation technique that enables a standardized approach across diverse IT projects that comprise the IT portfolio. Three main contributions may be drawn from this study: 1) the Mark-to-Market approach is a useful valuation technique in the context of IT projects; 2) practitioners may leverage the technique to assess project value and the performance of the IT portfolio over the lifecycle of such projects; and 3) the consistent treatment of each project allows aggregation and applications of standard portfolio management techniques to the practice of IT portfolio management

Machine Learning Stock Market Prediction Studies: Review and Research Directions

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine studies, studies using genetic algorithms combined with other techniques, and studies using hybrid or other artificial intelligence approaches. Studies in each category are reviewed to identify common findings, unique findings, limitations, and areas that need further investigation. The final section provides overall conclusions and directions for future research

) The Influence of Low-powered Family LED Lighting on Eyes in Mice Experimental Model

Abstract: Ocular tissue damage because of exposure to visible light has been demonstrated by the results of human and animal studies. The short-wavelength visible light between 430 nm to 500 nm (blue light) is especially associated with retina damage. Recently, new powerful sources and relatively inexpensive blue energy of LED (light emitting diodes) family lamps in home illumination are available. The aim of this study is to investigate the effects of illumination source from the low-powered and the conscious spectrum source of LED family lamps on retina tissues. The illumination source of LED family lamps was analyzed from 300 nm to 800 nm using an UVvisible spectrophotometer. In animal experiments, young adult mice were assigned to expose to family LED light for 2h every day ranging 2 to 4 weeks or light environment using LED family lamps for 39 weeks. After LED light treatment, sections of eyes were stained with hematoxylin and examined using histopathology. The data clearly demonstrated irradiation of the white LED is above 400 nm and is not within the ultraviolet light region. However, the analysis of spectrum distribution demonstrated that the family LED lighting exhibited power-peak at 450 nm is within the blue light region. Histological results showed that the photoreceptor layer is significantly reduced in thickness after 4 weeks of LED exposure 2h every day or LED illuminated environment. This study provides important data regarding the efficacy and safety of LED light in family illumination. It is impossible to consider these degenerative changes are related unavoidably part of their mechanism of action or an avoidable toxic effect

Optimizing Nervous System-Specific Gene Targeting with Cre Driver Lines: Prevalence of Germline Recombination and Influencing Factors.

The Cre-loxP system is invaluable for spatial and temporal control of gene knockout, knockin, and reporter expression in the mouse nervous system. However, we report varying probabilities of unexpected germline recombination in distinct Cre driver lines designed for nervous system-specific recombination. Selective maternal or paternal germline recombination is showcased with sample Cre lines. Collated data reveal germline recombination in over half of 64 commonly used Cre driver lines, in most cases with a parental sex bias related to Cre expression in sperm or oocytes. Slight differences among Cre driver lines utilizing common transcriptional control elements affect germline recombination rates. Specific target loci demonstrated differential recombination; thus, reporters are not reliable proxies for another locus of interest. Similar principles apply to other recombinase systems and other genetically targeted organisms. We hereby draw attention to the prevalence of germline recombination and provide guidelines to inform future research for the neuroscience and broader molecular genetics communities

The Compton Spectrometer and Imager

The Compton Spectrometer and Imager (COSI) is a NASA Small Explorer (SMEX) satellite mission in development with a planned launch in 2027. COSI is a wide-field gamma-ray telescope designed to survey the entire sky at 0.2-5 MeV. It provides imaging, spectroscopy, and polarimetry of astrophysical sources, and its germanium detectors provide excellent energy resolution for emission line measurements. Science goals for COSI include studies of 0.511 MeV emission from antimatter annihilation in the Galaxy, mapping radioactive elements from nucleosynthesis, determining emission mechanisms and source geometries with polarization measurements, and detecting and localizing multimessenger sources. The instantaneous field of view for the germanium detectors is >25% of the sky, and they are surrounded on the sides and bottom by active shields, providing background rejection as well as allowing for detection of gamma-ray bursts and other gamma-ray flares over most of the sky. In the following, we provide an overview of the COSI mission, including the science, the technical design, and the project status.Comment: 8 page

The cosipy library: COSI's high-level analysis software

The Compton Spectrometer and Imager (COSI) is a selected Small Explorer (SMEX) mission launching in 2027. It consists of a large field-of-view Compton telescope that will probe with increased sensitivity the under-explored MeV gamma-ray sky (0.2-5 MeV). We will present the current status of cosipy, a Python library that will perform spectral and polarization fits, image deconvolution, and all high-level analysis tasks required by COSI's broad science goals: uncovering the origin of the Galactic positrons, mapping the sites of Galactic nucleosynthesis, improving our models of the jet and emission mechanism of gamma-ray bursts (GRBs) and active galactic nuclei (AGNs), and detecting and localizing gravitational wave and neutrino sources. The cosipy library builds on the experience gained during the COSI balloon campaigns and will bring the analysis of data in the Compton regime to a modern open-source likelihood-based code, capable of performing coherent joint fits with other instruments using the Multi-Mission Maximum Likelihood framework (3ML). In this contribution, we will also discuss our plans to receive feedback from the community by having yearly software releases accompanied by publicly-available data challenges

Вихретоковый анизотропный термоэлектрический первичный преобразователь лучистого потока

Представлена оригинальная конструкция первичного преобразователя лучистого потока, который может служить основой для создания приемника неселективного излучения с повышенной чувствительностью

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe