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Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
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Soil compaction is one of the most harmful elements affecting soil structure, limiting plant growth and agricultural productivity. It is crucial to assess the degree of soil penetration resistance to discover solutions to the harmful consequences of compaction. In order to obtain the appropriate value, using soil cone penetration requires time and labor-intensive measurements. Currently, satellite technologies, electronic measurement control systems, and computer software help to measure soil penetration resistance quickly and easily within the precision agriculture applications approach. The quantitative relationships between soil properties and the factors affecting their diversity contribute to digital soil mapping. Digital soil maps use machine learning algorithms to determine the above relationship. Algorithms include multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), cubist, random forest (RF), and artificial neural networks (ANN). Machine learning made it possible to predict soil penetration resistance from huge sets of environmental data obtained from onboard sensors on satellites and other sources to produce digital soil maps based on classification and slope, but whose output must be verified if they are to be trusted. This review presents soil penetration resistance measurement systems, new technological developments in measurement systems, and the contribution of precision agriculture techniques and machine learning algorithms to soil penetration resistance measurement and prediction.

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Publication Date
Wed Feb 01 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Bitcoin Prediction with a hybrid model
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In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

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Publication Date
Fri Jan 01 2021
Journal Name
Advances In Intelligent Systems And Computing
Optimal Prediction Using Artificial Intelligence Application
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Publication Date
Thu Sep 01 2022
Journal Name
Computers And Electrical Engineering
Automatic illness prediction system through speech
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Publication Date
Tue Feb 28 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Bitcoin Prediction with a hybrid model
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. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Behavior of Partially Saturated Cohesive Soil under Strip Footing
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In this paper, a shallow foundation (strip footing), 1 m in width is assumed to be constructed on fully saturated and partially saturated Iraqi soils, and analyzed by finite element method. A procedure is proposed to define the H – modulus function from the soil water characteristic curve which is measured by the filter paper method. Fitting methods are applied through the program (SoilVision). Then, the soil water characteristic curve is converted to relation correlating the void ratio and matric suction. The slope of the latter relation can be used to define the H – modulus function. The finite element programs SIGMA/W and SEEP/W are then used in the analysis. Eight nodded isoparametric quadrilateral elements are used for modeling

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Publication Date
Tue Oct 01 2024
Journal Name
Infrastructures
Enhancing Moisture Damage Resistance in Asphalt Concrete: The Role of Mix Variables, Hydrated Lime and Nanomaterials
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Moisture-induced damage is a serious problem that severely impairs asphaltic pavement and affects road serviceability. This study examined numerous variables in asphalt concrete mixtures to assess their impact on moisture damage resistance. Mix design parameters such as the asphalt content (AC) and aggregate passing sieve No. 4 (PNo. 4) were considered as variables during this study. Additionally, hydrated lime (HL) was utilized as a partial substitute for limestone dust (LS) filler at 1.5% by weight of the aggregate in asphalt concrete mixtures for the surface layer. This study also investigated the potential enhancement of traditional asphalt binders and mixtures by adding nano-additives, specifically nano-silica oxide (NS) and na

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Publication Date
Mon Oct 29 2018
Journal Name
International Journal Of Women's Health And Reproduction Sciences
Prediction of Placenta Accreta Using Hyperglycosylated Human Chorionic Gonadotropin
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Objectives: Hyperglycosylated human chorionic gonadotropin (hCG) is a variant of hCG. In addition, it has a different oligosaccharide structure compared to the regular hCG and promotes the invasion and differentiation of peripheral cytotrophoblast. This study aimed to measure hyperglycosylated hCG as a predictor in the diagnosis of placenta accreta. Materials and Methods: In general, 90 pregnant women were involved in this case-control study among which, 30 ladies (control group) were pregnant within the gestational age of ≥36 weeks with at least one previous caesarean section and a normal sited placenta in transabdominal ultrasound (TAU). The other 60 pregnant women (case

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Publication Date
Mon Jan 01 2024
Journal Name
Iraqi Journal Of Veterinary Sciences
Risk factors assessment and antimicrobial resistance of Salmonella isolates from apparently healthy and diarrheal dogs in Baghdad, Iraq
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Publication Date
Fri Jun 14 2024
Journal Name
The Ukrainian Biochemical Journal
PREX proteins level correlation with insulin resistance markers and lipid profile in obese and overweight non-diabetic patients
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Metabolic dysregulation and obesity are associated with many metabolic alterations, including impairment of insulin sensitivity and dyslipidemia. Recent studies highlight the key role of phosphatidylinositol 3,4,5-triphosphate-dependent Rac exchange proteins (PREX proteins) in the pathogenesis of obesity, advocating further elucidation of their potential therapeutic implications. The present study aimed to estimate the serum level of PREX proteins and its potential association with insulin resistance markers and plasma lipids level in obese and overweight non-diabetic patients. The study included 30 persons classified as obese, 30 as overweight, and 30 healthy individuals of similar age and gender. The levels of PREX1 and PREX2 were

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Publication Date
Mon May 31 2021
Journal Name
Journal Of Research In Medical And Dental Science
A Stereomicroscopic Evaluation of Four Endodontic Sealers Penetration into Artificial Lateral Canals Using Gutta-Percha Single Cone Obturation Technique
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A Stereomicroscopic Evaluation of Four Endodontic Sealers Penetration into Artificial Lateral Canals Using Gutta-Percha Single Cone Obturation Technique, Omar Jihad Banawi*, Raghad

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