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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
Sun Jan 01 2017
Journal Name
International Journal Of Advanced Computer Science And Applications
Fast Hybrid String Matching Algorithm based on the Quick-Skip and Tuned Boyer-Moore Algorithms
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Publication Date
Sat Feb 27 2021
Journal Name
Journal Of Engineering
Shaft Resistance of Long (Flexible) Piles Considering Strength Degradation
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Soil-structure frictional resistance is an important parameter in the design of many foundation systems. The soil-structure interface area is responsible for load transferring from the structure to the surrounding soil. The mobilized shaft resistance of axially loaded, long slender pile embedded in dense, dry sand is experimentally and numerically analyzed when subjected to pullout force. Experimental setup including an instrumented model pile while the finite element method is used as a numerical analysis tool. The hypoplasticity model is used to model the soil adjacent to and surrounding the pile by using ABAQUS FEA (6.17.1). The soil-structure interface behavior depends on many factors, but mainly on the interface soi

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Publication Date
Tue Apr 02 2024
Journal Name
Iraqi Journal Of Applied Physics
Evaluation of Corrosion Resistance of Laser-Treated and PEKK-Coated Titanium
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This research focuses on improvement of the corrosion behaviour of commercial pure titanium (Ti) grade II when exposed to Hank’s solution through different surface treatments. The disc shape of titanium samples were constructed to be divided according to their surface treatment. The first experimental group the Ti sample was exposed to computer numerical control (CNC) fiber laser machine. Whereas, the other experimental group the Ti sample was only coated with Polyetherketon keton (PEKK) by using carbon dioxide (CO2) laser technique while the last experimental group the Ti sample was treated with CNC fiber laser followed by PEKK coating by using CO2 laser technique. All were compared with the untreated control group. The electrochemical a

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Publication Date
Mon May 06 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

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Publication Date
Fri Jan 01 2021
Journal Name
Conference: 4th International Conference On Innovative Studies Of Contemporary Sciences
An anatomical and chemical comparison study of Epipremnum aureum cultivated in soil and soilless
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The present study took up the different ways to cultivate the species Epipremnum aureum by two habitat water and soil and comber the anatomical features of the root, stem, and leaf. The results showed amazing significant anatomical features to the ecosystem. The root and stem anatomy showing decrease in all characters that studied but the leaf anatomy showing increase of palisade, spongy tissue thickness, midrib thickness, number of vessels in the xylem also the long and width of stomata of the soilless plants than soil ones. The upper epidermis empty from the stomata for the two treatment and the stoma diffuse in the lower epidermis, the type of it paracytic type. Also the total of flavonoids in the plant that were growth in soil reached 1

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Publication Date
Tue Jan 01 2019
Journal Name
Energy Procedia
Humidity effect on diffusion and length coefficient of radon in soil and building materials
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Publication Date
Thu Oct 10 2019
Journal Name
Plant Archives
A study of qualitative, classification soil algae in some areas from Baghdad, Iraq
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A study of taxonomic quality of soil algae was conducted with some environmental variables in three sites of local gardens (Kadhimiya, Adhamiya and Dora) within the governorate of Baghdad for the period from October 2016 to March 2017. The study identified 28 species belonging to 16 species in which the predominance of blue green algae (18 species) Followed by Bacillarophyta algae (7 species) and three types of Chlorophyta. The study showed an increase in species of Oscillatoria. The results showed no significant differences between sites in temperature, pH and relative humidity, while there were clear differences between sites for salinity and nutrient The study showed a difference of irrigation water quality and use of different fertilize

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Publication Date
Sun Sep 01 2019
Journal Name
Baghdad Science Journal
Measuring Uranium Concentrations in Soil Samples of Midland Refineries Company - Doura - Baghdad – Iraq
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      In this study, the CR-39 detector technique was used, to estimate the uranium concentration from the soil in midland refineries Company (Doura refine (, Baghdad, Iraq. Uranium concentrations in soil samples have been measured using solid state nuclear track detector type CR-39.  Nine soil samples were collected from different areas within the Doura refinery and other soil samples were collected form Abu Tayara Street and ALshortaa District outside the refinery for comparison. The results showed variable values for uranium concentrations. The average value of uranium concentration was found to be 0.37 ppm in doura refinery. For areas outside the refinery, the concentration of uranium was 0.008 ppm. Thes

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Publication Date
Sun Sep 01 2013
Journal Name
Baghdad Science Journal
Determination of Testosterone level as predictor for insulin resistance in young men with family history of type2 diabetes and hypertension.
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Background: Insulin resistance is associated with metabolic syndrome , type 2 diabetes and representing a risk factor for cardiovascular disease . This relationship may be modulated to some extent by age related changes in sex hormone status.. In particular, reduced total testosterone (TT) levels have been associated with insulin resistance and subsequent risk for developing type 2 diabetes. Aim of study: we examined whether low total testosterone level were associated with insulin resistance in young adult men. Methods: a total of 83 men (young adult men) divided into 2 group : (group1 ) 49 men with a risk factor for insu

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
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ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

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