Harnessing the Potential of Volatility: Advancing GDP Prediction

This paper presents a novel machine learning approach to GDP prediction that incorporates volatility as a model weight. The proposed method is specifically designed to identify and select the most relevant macroeconomic variables for accurate GDP prediction, while taking into account unexpected shocks or events that may impact the economy. The proposed method's effectiveness is tested on real-world data and compared to previous techniques used for GDP forecasting, such as Lasso and Adaptive Lasso. The findings show that the Volatility-weighted Lasso method outperforms other methods in terms of accuracy and robustness, providing policymakers and analysts with a valuable tool for making informed decisions in a rapidly changing economic environment. This study demonstrates how data-driven approaches can help us better understand economic fluctuations and support more effective economic policymaking. Keywords: GDP prediction, Lasso, Volatility, Regularization, Macroeconomics Variable Selection, Machine Learning JEL codes: C22, C53, E37.Comment: Pennsylvania Economic Association (PEA

Psychoacoustic evaluation of a garden tractor noise

In addition to achieving sustainable development of agricultural mechanization, it has causing problems on occupational health and safety for people working in different fields of agriculture. Noise is considered as one of the most debilitating conditions in farming and a comprehensive investigation of this relationship is required. In this study, some factors affecting the noise generated by a Goldoni garden tractor were evaluated. Research factors were including engine speed, gear ratios and type of operation. Accordingly, factorial experiments in completely randomized design with three replicates were performed. According to variance analysis with LAeq, PA and UBA, operation type, gear ratio and engine speed were found to be significant (P< 0.01). The results of this study indicate that PA and UBA correlated strongly with LAeq analysis (R2=0.97). The results also show that LAeq, PA and UBA for rural road are higher than tillage condition. Also, results indicated that the highest mean of LAeq, PA and UBA were 77.76 dBA, 9.83 and 21.16, respectively and occurred in the case of rural road and 2100 rpm engine speed

Forecasting the Performance of US Stock Market Indices During COVID-19: RF vs LSTM

The US stock market experienced instability following the recession (2007-2009). COVID-19 poses a significant challenge to US stock traders and investors. Traders and investors should keep up with the stock market. This is to mitigate risks and improve profits by using forecasting models that account for the effects of the pandemic. With consideration of the COVID-19 pandemic after the recession, two machine learning models, including Random Forest and LSTM are used to forecast two major US stock market indices. Data on historical prices after the big recession is used for developing machine learning models and forecasting index returns. To evaluate the model performance during training, cross-validation is used. Additionally, hyperparameter optimizing, regularization, such as dropouts and weight decays, and preprocessing improve the performances of Machine Learning techniques. Using high-accuracy machine learning techniques, traders and investors can forecast stock market behavior, stay ahead of their competition, and improve profitability. Keywords: COVID-19, LSTM, S&P500, Random Forest, Russell 2000, Forecasting, Machine Learning, Time Series JEL Code: C6, C8, G4.Comment: Pennsylvania Economic Association (PEA)- June 202

The Effect of Short-Term Treadmill Exercise on the Expression Level of TFAM in the Heart of Nicotine-Sensitized Rats

Introduction: TFAM (mitochondrial transcription factor A) is involved in mitochondrial biogenesis and induces anti-oxidant and anti-apoptotic effects. Nicotine can also alter the function of cardiovascular system and induce heart failure and other heart diseases. Interestingly, it has been reported that exercise can interfere with the effects of nicotine, and change the expression pattern of different genes. The goal of the present study was to investigate the effect of short-term treadmill exercise on the expression level of TFAM in the heart of nicotine-sensitized rats.Materials and Methods: Nicotine was administered intraperitoneally at the dose of 0.21 mg/kg. Treadmill exercise was performed during 14 days, according to the study’s protocol.Results: The results revealed that nicotine reduced the expression of TFAM. The treadmill (Fourteen-day training) increased the expression of TFAM in the heart of the control rats. Furthermore, 14-day training with treadmill restored the effect of nicotine on the expression of TFAM in nicotine-sensitized rats.Conclusion: Nicotine induced pro-apoptotic and anti-oxidative stress effects via down-regulating the expression of TFAM. Fourteen -day training with treadmill induced a protective effect against nicotine-induced cardiac apoptosis and oxidative stress, via restoring the effect of nicotine on TFAM. The results are indicative of the fact that short-term treadmill exercise may decrease the risk of heart failure and other cardiac diseases. 

BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets

The free flow of information has been accelerated by the rapid development of social media technology. There has been a significant social and psychological impact on the population due to the outbreak of Coronavirus disease (COVID-19). The COVID-19 pandemic is one of the current events being discussed on social media platforms. In order to safeguard societies from this pandemic, studying people's emotions on social media is crucial. As a result of their particular characteristics, sentiment analysis of texts like tweets remains challenging. Sentiment analysis is a powerful text analysis tool. It automatically detects and analyzes opinions and emotions from unstructured data. Texts from a wide range of sources are examined by a sentiment analysis tool, which extracts meaning from them, including emails, surveys, reviews, social media posts, and web articles. To evaluate sentiments, natural language processing (NLP) and machine learning techniques are used, which assign weights to entities, topics, themes, and categories in sentences or phrases. Machine learning tools learn how to detect sentiment without human intervention by examining examples of emotions in text. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful in gaining a better understanding of society's needs and predicting future trends. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models. The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.Comment: 20 pages, 5 figure

Estimating capital and operational costs of backhoe shovels

Material loading is one of the most critical operations in earthmoving projects. A number of different equipment is available for loading operations. Project managers should consider different technical and economic issues at the feasibility study stage and try to select the optimum type and size of equipment fleet, regarding the production needs and project specifications. The backhoe shovel is very popular for digging, loading and flattening tasks. Adequate cost estimation is one of the most critical tasks in feasibility studies of equipment fleet selection. This paper presents two different cost models for the preliminary and detailed feasibility study stages. These models estimate the capital and operating cost of backhoe shovels using uni-variable exponential regression (UVER) as well as multi-variable linear regression (MVLR), based on principal component analysis. The UVER cost model is suitable for quick cost estimation at the early stages of project evaluation, while the MVLR cost function, which is more detailed, can be useful for the feasibility study stage. Independent variables of MVLR include bucket size, digging depth, dump height, weight and power. Model evaluations show that these functions could be a credible tool for cost estimations in prefeasibility and feasibility studies of mining and construction projects

Golden Search Optimization Algorithm

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CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works

Wood chip sound absorbers: Measurements and models

Normal incidence absorption coefficient spectra of samples made from glued wood chips have been measured for various mesh sizes, bulk densities, thicknesses, and air gaps. Increasing thickness introduces additional layer resonance peaks and shifts the initial peak towards lower frequencies. The wood chip samples composed of the smallest mesh sizes were found to offer the highest sound absorption, comparable with that of the same thickness of materials made from synthetic fibers. Measured absorption spectra are compared with predictions of four models for the acoustical properties of rigid porous media. These include a model for slanted parallel identical uniform slits (SS), the Johnson-Champoux-Allard (JCA) and Johnson-Champoux-Allard-Lafarge (JCAL) models for arbitrary pore structures, and model for a non-uniform pore size distribution (NUPSD). Porosity and flow resistivity values have been determined non-acoustically. However, the tortuosity and characteristic lengths required for the JCA model have been obtained by fitting the measured absorption spectra. The thermal permeability required for the JCAL model has been deduced indirectly from the fitted tortuosity through a relationship with standard deviation of the pore size distribution due to the NUPSD model. JCAL and JCA models give the best agreement overall, but predictions of the SS and NUPSD models that use only the fitted tortuosity in addition to measured porosity and flow resistivity are found to give comparable agreement with data for many samples. SS and NUPSD predictions are improved by increasing the tortuosity values compared with those obtained by fitting the JCA model. The study should encourage the creation of sustainable sound-absorbing materials from wood chip wastes