Modeling Main and Interactional Effects of Some Physiochemical Properties of Egyptian Soils on Cation Exchange Capacity Via Artificial Neural Networks

Ibrahim, Omar M.; El-Gamal, Eman H.; Darwish, Kh. M.; Kianfar, Nima

doi:10.1134/s1064229322080051

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…In the present experiment, a three-layer feed-forward multilayer perceptron neural network (MLP) was used using a back propagation algorithm to model the relationship between yield components and seed yield/plant. The relative importance of yield components to seed yield/plant using three different methods Figure 12 ( Ibrahim et al., 2022 ). The seed index or weight of 100 seeds was the most important yield component to seed yield followed by the number of seeds per pod (NSP), while the number of pods/plant was the least important yield component to seed yield/plant.…”

Section: Resultsmentioning

confidence: 99%

Under cadmium stress, silicon has a defensive effect on the morphology, physiology, and anatomy of pea (Pisum sativum L.) plants

et al. 2022

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Soil pollution with cadmium (Cd) is a serious threat to plant growth and development. On the other hand, silicon (Si) can support plants to cope with Cd stress. However, the Cd stress mitigating impact of Si reduction in pea (Pisum sativum L.) is not known. The objective of this study is to see if and how Si can reduce Cd toxicity. To the end, a greenhouse pot experiment was performed twice during the 2018/2019 and 2019/2020 seasons to investigate the effect of Si on the growth, anatomy, and biochemistry of Cd stressed peas plants. Cd exposure increased the contents of Cd ions in the root and shoot of pea plants. Consequentially, Cd accumulation in pea tissue significantly reduced plant growth i.e., plant height, leaf area, and shoot and root dry weights. The effect of Cd was concentration-dependent, where at low concentration (50 mg/kg soil), the plant height was 94.33 and 97.33cm and at high concentration (100 mg/kg soil), it was 89.0 and 91.0 cm in the two seasons, respectively. This growth reduction can be explained by the decrease in plants’ photosynthesis, whereas plants exposed to Cd toxicity had lower chlorophyll levels. At the anatomy level, high Cd concentrations resulted in anatomical abnormalities such as an unusual vascular system, abnormal lignification in the pith parenchyma, and enlarged cortical cells. Moreover, all Cd concentrations resulted in a highly significant decrease in stomatal area and stomatal density (the number of stomata per mm2). In addition to growth inhibition, Cd-induced oxidative damage to pea plants as indicated by increased hydrogen peroxide (H2O2) and Malondialdehyde (MDA) levels. To reduce stress toxicity, plants treated with Cd at 50 and 100 (mg/kg) showed a significant increase in antioxidant capacity. Peroxidase (POD) enzyme activity was significantly increased by 41.26%, 28.64%, 77.05%, and 60.77% in both seasons, respectively. Si at 300 ppm under Cd (100 mg/kg) stress conductions considerably reduced (MDA) contents by 29.02% and 29.12%, in the two seasons, respectively. The findings pointed out that Si’s ability to protect pea against the oxidative stress caused by Cd toxicity.

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Section: Resultsmentioning

confidence: 99%

Under cadmium stress, silicon has a defensive effect on the morphology, physiology, and anatomy of pea (Pisum sativum L.) plants

et al. 2022

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“…They consist of layers, which in turn consist of neurons; each layer is connected to another layer through the neurons by connection weights, but neurons in the same layer are not connected to each other. The strength of the final connection weights after training the network is used to estimate the relative importance of inputs to the output [40]. The network used in the present work is illustrated in Figure 7.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

The Role of Carbon Nanotubes in Improving Drought Tolerance via Upregulation of the Physiological Processes of Peanut Plants Grown in Sandy Soils

Bakry,

Sadak,

Al Ashkar

et al. 2024

Agronomy

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Drought stress is an important challenge to global food security and agricultural output, and dramatic and rapid climate change has made the problem worse, causing unexpected impacts on the growth, development, and yield of different plants. Understanding the biochemical, ecological, and physiological reactions to these pressures is essential for improved management. Carbon materials’ impacts on plants subjected to different stresses are still poorly studied. Thus, this study was carried out investigate the feasibility of applying carbon nanotubes (CNTs) (0, 20, and 40 mg/L) as a foliar treatment for mitigating the effect of water stress (100%, 75%, and 50% irrigation water, IW) on peanut plants growing in sandy soil through assessments of growth and productivity and some physiological and biochemical measurements. Exposure of peanuts to decreased irrigation water led to significant decreases in growth, yield, photosynthetic pigments, indole acetic acid (IAA), and some nutritional components in peanut seeds, but increased levels of osmolytes such as total soluble carbohydrates (TSS) and proline, in addition to free amino acids and phenolics. However, foliar spraying with CNTs could ameliorate the impacts of decreased irrigation water on growth and production via enhancing the studied physiological parameters, such as photosynthetic pigments, IAA, osmolytes, and phenolics. Furthermore, the application of carbon nanotubes improved the nutrient contents, as expressed by the oil yield, protein yield, total carbohydrates, antioxidant activities (DPPH), B-carotene, lycopene, and flavonoids in peanut seeds, either under normal or water stress conditions. The higher level of CNTs (40 mg/L) was more effective than the lower one (20 mg/L) at increasing the above-mentioned parameters. In conclusion, foliar treatment with carbon nanotubes has the ability to enhance peanut drought tolerance and increase its growth and productivity under sandy soil conditions.

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“…To train the network, 80% of the data set was used and the other 20% was used for testing its performance. After training and testing the network, the connection weights algorithm [50,51] was used to calculate the relative importance of the inputs. It is worth mentioning that the output layer's activation function was linear.…”

Section: Ann Modellingmentioning

confidence: 99%

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

et al. 2023

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This paper is devoted to the determination of the coefficient of friction (COF) in the drawbead region in metal forming processes. As the test material, AW-5251 aluminium alloys sheets fabricated under various hardening conditions (AW-5251-O, AW-5251-H14, AW-5251-H16 and AW-5251H22) were used. The sheets were tested using a drawbead simulator with different countersample roughness and different orientations of the specimens in relation to the sheet rolling direction. A drawbead simulator was designed to model the friction conditions when the sheet metal passed through the drawbead in sheet metal forming. The experimental tests were carried out under conditions of dry friction and lubrication of the sheet metal surfaces with three lubricants: machine oil, hydraulic oil, and engine oil. Based on the results of the experimental tests, the value of the COF was determined. The Random Forest (RF) machine learning algorithm and artificial neural networks (ANNs) were used to identify the parameters affecting the COF. The R statistical package software version 4.1.0 was used for running the RF model and neural network. The relative importance of the inputs was analysed using 12 different activation functions in ANNs and nine different loss functions in the RF. Based on the experimental tests, it was concluded that the COF for samples cut along the sheet rolling direction was greater than for samples cut in the transverse direction. However, the COF’s most relevant input was oil viscosity (0.59), followed by the average counter sample roughness Ra (0.30) and the yield stress Rp0.2 and strength coefficient K (0.05 and 0.06, respectively). The hard sigmoid activation function had the poorest R2 (0.25) and nRMSE (0.30). The ideal run was found after training and testing the RF model (R2 = 0.90 ± 0.028). Ra values greater than 1.1 and Rp0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9–35 for viscosity and 105–190 for Rp0.2, with a gap between 110 and 130 when the oil viscosity was added. The COF was low when the oil viscosity was 9–35, and the Ra was 0.95–1.25. The interaction between K and the other inputs, which produces a relatively limited range of reduced COF values, was the least relevant. The COF was reduced by setting the Rp0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.

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Modeling Main and Interactional Effects of Some Physiochemical Properties of Egyptian Soils on Cation Exchange Capacity Via Artificial Neural Networks

Cited by 8 publications

References 44 publications

Under cadmium stress, silicon has a defensive effect on the morphology, physiology, and anatomy of pea (Pisum sativum L.) plants

Under cadmium stress, silicon has a defensive effect on the morphology, physiology, and anatomy of pea (Pisum sativum L.) plants

The Role of Carbon Nanotubes in Improving Drought Tolerance via Upregulation of the Physiological Processes of Peanut Plants Grown in Sandy Soils

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

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