Post-yield characterisation of metals with significant pile-up through spherical indentations

Finite element simulations of spherical indentations accounting for frictional contact provide validated load–indentation output for assessing and improving existing methods used to determine the stress–strain curve of materials with significant pile-up. The importance of friction to the proper assessment of the pile-up effect is established. Weaknesses in current characterisation relations and procedures are also identified. Existing correction formulae accounting for pile-up are modified so that the contact area radius is more accurately determined. This modification is implemented in the context of a characterisation process that relies on analysing unloading portions of load–indentation curves. Post-yield material behaviour predictions from such analysis are found to be in very good agreement with the initial finite element material input

Molecular markers relevant to myocardial injury following dental extraction in patients with or without coronary artery disease

Objectives The aim of this study was to characterize biological changes following dental extractions in patients with and without coronary artery disease (CAD). Materials and methods Forty-five patients (36 males and 9 females) referred for dental extraction underwent treatment and provided blood samples before, immediately after, and 24 h after the procedure. A broad array of biomarkers was employed to assess myocardial injury (highly sensitive troponin T, hs-TnT), bacterial burden (LPS endotoxin activity), and systemic inflammation (CRP, fibrinogen, IFN-γ, IL-1β, IL-6, IL-8, IL-10, IL-12, and TNF-α). Results Dental extraction in patients with and without CAD was associated with rises in hs-TnT (p = 0.013), hs-CRP (p < 0.001), fibrinogen (p = 0.005), endotoxin activity (p < 0.001), IFN-γ (p < 0.001), IL-6 (p < 0.001), IL-8 (p = 0.011), and IL-12 (p < 0.001) at 24 h compared with immediately post procedure. Changes in systemic inflammation and endotoxin activity were more evident in those with hs-TnT rise. Conclusions Simple dental extractions may cause mild increase in hs-TnT, indicating minor myocardial injury in both patients with and without CAD. Acute systemic inflammation and endotoxemia could represent a possible link between invasive dental treatment and increased risk of acute cardiovascular events. These findings indicate that invasive dental treatment (as simple as a single dental extraction) may impact negatively on clinical outcomes in dental patients, especially those with CAD

Socio-Economic Factors Influencing the Intent of Rural Youths to Migrate from Emery, San Juan, Kane, and Piute Counties of Utah

Rural communities in Utah continue to lose large numbers of their population. Counties with the highest birth rate are, in the main, those which have decreased most or remained relatively stationary in recent years. Rural migration presents a problem to the communities involved and to the individuals who move

Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Induction Machine Control

In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations.

Using machine learning to investigate the role of real estate in a mixed-asset portfolio

Investing in real estate offers significant benefits, such as diversification and potential long-term appreciation, making it an attractive option compared to stocks and bonds. However, direct investments in real estate often require substantial capital, which is a barrier for many individual investors. To overcome this, investors often use Real Estate Investment Trusts (REITs), which allow for indirect investment in real estate through shares in companies that own income-generating properties. This study examines the added value of including real estate in a diversified investment portfolio, utilising innovative methods to optimise asset allocation. Instead of relying on historical data, it employs machine learning algorithms (such as Linear Regression, Support Vector Regression, k-Nearest Neighbours, Extreme Gradient Boosting, and LSTM Neural Networks) to predict future asset prices. The study also incorporates Technical Analysis Indicators (TAIs) to further improve predictive accuracy. Furthermore, a Genetic Algorithm (GA) is used to determine optimal portfolio weightings, considering the expected returns and risks of each asset class. The study compares the performance of portfolios constructed using price predictions with those based on historical data, assessing diversification benefits and risk-adjusted returns. Overall, by integrating machine learning techniques, technical analysis, and optimisation algorithms, the study aims to demonstrate the potential advantages of including real estate investments in a diversified portfolio, enabling investors to make more informed decisions and improve their investment outcomes

Reduced-order observer for real-time implementation speed sensorless control of induction using RT-LAB software

In this paper, Reduced-Order Observer For Real-Time Implementation Speed Sensorless Control of Induction Using RT-LAB Softwareis presented. Speed estimation is performed through a reduced-order observer. The stability of the proposed observer is proved based on Lyapunov’s theorem. The model is initially built offline using Matlab/Simulink and implemented in real-time environment using RT-LAB package and an OP5600 digital simulator. RT-LAB configuration has two main subsystems master and console subsystems. These two subsystems were coordinated to achieve the real-time simulation. In order to verify the feasibility and effectiveness of proposed method, experimental results are presented over a wide speed range, including zero speed

Mapping Simulation optimization requirements for construction sites: A study in heavy-duty vehicles industry

The Construction and mining Industry comprises complex operations and interactions between various actors at different levels. Simulation has emerged as a valuable tool in this domain to better understand the site's behavior and optimize its operation. However, developing a simulation platform that can handle all the operations on the site is challenging due to the computational cost of the digital representation of reality along with the required accuracy level.This paper aims at extracting and mapping the optimization requirements of construction sites at three main levels: site level, operational level and dynamics level. More precisely, this work seeks to define and map the most important requirements between these levels that ensure simulation credibility and reliability.Based on interviews with experts in the domain, both from academia and industry, several key insights and recommendations emerged: at the site level, the layout and the key performance indicators, such as productivity, time, cost, number of machines and workers, need to be modeled and simulated. At the operational level, the simulation platform must include the main activities, such as loading, excavating, transporting and dumping. Moreover, the dynamics level should involve machine models and their interactions with the site's environment, such as earthmoving, drilling, excavating and blasting

Estimating Supply Response Function for Wheat: A Case Study

To increase wheat production, governments can subsidize wheat farmers by purchasing their produce at a price higher than the world price. This policy did not succeed in increasing wheat production in the Irbid Governorate of Jordan, our case study area. The agricultural sector in the study area was characterized by risk in production and prices. In our study, the supply response function based on the Nerlovian Model was estimated for wheat produced in Irbid Governorate. Wheat area, in the model, was the dependent variable in the supply response function. The independent variables were: wheat planted area in Dunums in the current and previous year respectively, the weighted price of wheat in the previous year deflated by the Consumer Price Index (CPI), the holding fragmentation coefficient in the previous year, the yield risk, and the amount of rain in millimeters during the early months of the season (October, November, and December). The study reached the following conclusions: Firstly, holdings fragmentation was the major factor that negatively affects wheat production. Since the heritage system is the main factor that affects holding fragmentation, the policy makers need to find a way that can decrease this effect. Secondly, lagged weighted prices were found more suitable than the current weighted prices from an economic and statical point of view. Thirdly, the partial adjustment coefficient was low (i.e. less than one), which means that the farmers need more than one year to change their producing habits. Finally, the farmers were found to be risk-neutral, because their decisions depend mainly on the level and distribution of rainfall during the rainy season

An in-depth investigation of five machine learning algorithms for optimizing mixed-asset portfolios including REITs

Real estate is a favored investment option as it allows investors to diversify their portfolios and minimize risk. Investors can invest in real estate directly by purchasing a property, or through real estate investment funds (REITs) where they can purchase shares in companies that own and manage real estate. Investing in REITs has become increasingly popular because it eliminates some of the disadvantages associated with direct real estate investment, such as the need for a large upfront payment. When investing in mixed asset portfolios, it is crucial to predict future prices accurately to ensure profitable and less risky asset allocation. However, literature on price prediction often focuses on only one or two algorithms, and there is no research that explores REITs’ price prediction in the context of portfolio optimization. To address this gap, we conducted a thorough evaluation of 5 machine learning algorithms (ML), including Ordinary Least Squares Linear Regression (LR), Support Vector Regression (SVR), k-Nearest Neighbors Regression (KNN), Extreme Gradient Boosting (XGBoost), and Long/Short-Term Memory Neural Networks (LSTM), as well as other financial benchmarks like Holt’s Exponential Smoothing (HES), Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and Auto-Regression Integrated Moving Average (ARIMA). We applied these algorithms to predict future prices for 30 REITs from the US, UK, and Australia, as well as 30 stocks and 30 bonds. The assets were then used as part of a portfolio, which we optimized using a genetic algorithm. Our results showed that using ML algorithms for price prediction provided at least three times the return over benchmark models and reduced risk by almost two-fold. For REITs, we observed that the use of ML algorithms led to a higher allocation to REITs diversified by country. In particular, our results showed that SVR was the best-performing algorithm in terms of risk-adjusted returns across different time horizons, as confirmed by our Friedman test results (Sharpe ratio). Overall, our study highlights the effectiveness of ML algorithms in predicting asset prices and optimizing portfolio allocation

Improving Real Estate Investment Trust (REITs) time-series prediction accuracy using Machine Learning and Technical Analysis indicators

The primary goal of investors who include Real Estate Investment Trusts (REITs) in their portfolios is to achieve better returns while reducing the overall risk of their investments. REITs are entities responsible for owning and managing real estate properties. To achieve greater returns while reducing risk, it is essential to accurately predict future REIT prices. This study explores the predictive capability of five different machine learning algorithms used to predict REIT prices. These algorithms include Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks. Additionally, historical REIT prices are supplemented with Technical Analysis indicators (TAIs) to aid in price predictions. While TA indicators are commonly used in stock market forecasting, their application in the context of REITs has remained relatively unexplored. The study applied these algorithms to predict future prices for 30 REITs from the United States, United Kingdom, and Australia, along with 30 stocks and 30 bonds. After obtaining our price predictions, we employ a Genetic Algorithm (GA) to optimise weights of a diversified portfolio. Our results reveal several key findings: (i) all machine learning algorithms demonstrated low average and standard deviation values in the error rate distributions, outperforming commonly used statistical benchmarks such as Holt’s Linear Trend Method (HLTM), Trigonometric Box-Cox Autoregressive Time Series (TBATS), and Autoregressive Integrated Moving Average (ARIMA); (ii) incorporating Technical Analysis indicators in the ML algorithms resulted in a significant reduction in prediction errors, up to 60% in some cases; and (iii) a multi-asset portfolio constructed using predictions that incorporated Technical Analysis indicators outperformed a portfolio based solely on predictions derived from past prices. Furthermore, this study employed Shapley Value-based techniques, specifically SHAP and SAGE, to analyse the importance of the features used in the analysis. These techniques provided additional evidence of the value added by Technical Analysis indicators in this context