Integral effects of brassinosteroids and timber waste biochar enhances the drought tolerance capacity of wheat plant

Drought stress is among the major threats that affect negatively crop productivity in arid and semi-arid regions. Probably, application of some additives such as biochar and/or brassinosteroids could mitigate this stress; however, the mechanism beyond the interaction of these two applications is not well inspected. Accordingly, a greenhouse experiment was conducted on wheat (a strategic crop) grown under deficit irrigation levels (factor A) i.e., 35% of the water holding capacity (WHC) versus 75% of WHC for 35 days while considering the following additives, i.e., (1) biochar [BC, factor B, 0, 2%] and (2) the foliar application of 24-epibrassinolide [BR, factor C, 0 (control treatment, C), 1 (BR1) or 3 (BR2) mu mol)]. All treatments were replicated trice and the obtained results were statistically analyzed via the analyses of variance. Also, heat-map conceits between measured variables were calculated using the Python software. Key results indicate that drought stress led to significant reductions in all studied vegetative growth parameters (root and shoot biomasses) and photosynthetic pigments (chlorophyll a, b and total contents) while raised the levels of oxidative stress indicators. However, with the application of BC and/or BR, significance increases occurred in the growth attributes of wheat plants, its photosynthetic pigments, especially the combined additions. They also upraised the levels of enzymatic and non-enzymatic antioxidants while decreased stress indicators. Furthermore, they increased calcium (Ca), phosphorus (P) and potassium (K) content within plants. It can therefore be deduced that the integral application of BR and BC is essential to mitigate drought stress in plants.Peer reviewe

Associative effects of activated carbon biochar and arbuscular mycorrhizal fungi on wheat for reducing nickel food chain bioavailability

Heavy metal stress and less nutrient availability are some of the major concerns in agriculture. Both abiotic stresses have potential to decrease the crops productivity. On the other hand, organic fertilizers i.e., activated carbon biochar (ACB) and arbuscular mycorrhizal fungi (AMF) increase nutritional and heavy metal like Nickel (Ni) stress tolerance and provide immunity to plants for their survival in unfavorable environments. Previous studies have only looked at single applications of either ACB or AMF thus far. There is limited evidence of their synergistic effects, especially in plants growing in soil contaminated with nickel (Ni). To cover the knowledge gap of combined use of AMF inoculation (Glomus intraradices) and/or wheat straw biochar amendments on wheat growth, antioxidant activities and osmolytes concentration, present study is conducted. The use of either the AMF inoculant or the ACB alone resulted in improved wheat growth and decreased Ni uptake. Furthermore, sole AMF or ACB also reduced Ni stress effectively, allowing wheat to grow faster and reducing soil Ni transfer into plant tissue. In comparison to a control, adding ACB with AMF inoculant considerably increased fungal populations. The most significant increase in wheat growth and decrease in tissue Ni contents came from amending soil with AMF inoculant and biochar. Inducing soil alkalinization and causing Ni immobilization, as well as decreasing Ni phyto-availability, the combination treatment had a synergistic impact. These findings imply that AMF inoculation in ACB treatment could be used not only for wheat production but also for Ni-contaminated soil phyto-stabilization. (C) 2022 The Author(s). Published by Elsevier B.V.Peer reviewe

Exogenously applied ZnO nanoparticles induced salt tolerance in potentially high yielding modern wheat (Triticum aestivum L.) cultivars

Salinity stress is one of the potential threats that adversely affect the productivity of many cereal crops worldwide. Spraying plants with nano-Zn particles may lessen effectively such negative impacts on plants; yet its mode of action is still not well explored. This study was performed to evaluate the effects of spraying nano-Zn particles with varying concentrations (0, 20, 50 and 80 mg L-1) on two wheat cultivars irrigated with saline water (EC = 6.3 dS m-1) versus a non-saline one. The key results revealed that root and shoot weights decreased significantly under salinity stress conditions, while improved considerably with nano-Zn-particles foliar application up to 50 mg nanoZn L-1; thereafter significant reductions occurred. Also, shoot and root lengths as well as plant leaf area index improved considerably owing to this foliar application. Clearly, roots and shoots weights of wheat plants sprayed with nano-Zn particles under salinity stress conditions exhibited higher values than the corresponding ones that was grown under non-saline conditions without nano-Zn-particles applications. Unexpectedly, this foliar spray led to significant reductions in plant pigments and also in enzymatic and non-enzymatic antioxidants in plants. Yet, this foliar spray enhanced formation of total soluble sugars and proline, and raised significantly Ca contents in wheat roots and shoots, and to some extent K contents. In conclusion, the foliar application of nano-Zn particles increased plant growth under salty stress conditions via two parallel processes, i.e., stimulating formation of osmolytes and stimulating nutrient uptake which may, in turn, increase plant metabolism. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CCPeer reviewe

Assessment of Using Artificial Neural Network and Support Vector Machine Techniques for Predicting Wave-Overtopping Discharges at Coastal Structures

Coastal defence structures play a crucial role in protecting coastal communities against extreme weather and flooding. This study investigates artificial neural network-based approaches, such as multilayer perceptron neural network (MPNN), cascade correlation neural network (CCNN), general regression neural network (GRNN), and support vector machine (SVM) with radial-bias function for estimating the wave-overtopping discharge at coastal structures featuring a straight slope ‘without a berm’. The newly developed EurOtop database was used for this study. Discriminant analysis was performed using the principal component analysis method, and Taylor diagram visualisation and other statistical analyses were performed to evaluate the models. For predicting wave-overtopping discharge, the GRNN yielded highly accurate results. As compared to the other models, the scatter index of the GRNN (0.353) was lower than that of the SVM (0.585), CCNN (0.791), and MPNN (1.068) models. In terms of the R-index, the GRNN (0.991) was superior to the SVM (0.981), CCNN (0.958), and MPNN (0.922). The GRNN results were compared with those of the previous models. An in-depth sensitivity analysis was conducted to determine the significance of each predictive variable. Furthermore, sensitivity analysis was conducted to select the optimal validation method for the GRNN model. The results revealed that both the validation methods were highly accurate, with the leave-one-out validation method outperforming the cross-validation method by only a small margin

Assessment of carbon neutrality in waste water treatment systems through deep learning algorithm

Around the world, it is growing harder to provide clean and safe drinking water. In wastewater treatment, sensors are employed, and the Internet of Things (IoT) is used to transmit data. Chemical oxygen demand (COD), biochemical demand (BOD), total nitrogen (T-N), total suspended solids (TSS), and phosphorous (T-P) components all contribute to eutrophication, which must be avoided. The wastewater sector has lately made efforts to become carbon neutral; however, the environmental impact and the road to carbon neutrality have received very little attention. The challenges are caused by poor prediction. This research proposes deep learning modified neural networks (DLMNN) with Binary Spotted Hyena Optimizer (BSHO) for modeling and calculations to address this challenge. All efforts for resource recovery, water reuse, and energy recovery partially attain this objective. In contrast to previous modeling techniques, the DLMNN-training BSHOs and validation demonstrated outstanding accuracy shown by the model's high coefficient (R2) for both training and testing. Also covered are recent developments and problems with nanomaterials made from sustainable carbon and graphene quantum dots, as well as their uses in the treatment and purification of wastewater. The proposed model DLMNN-BSHO achieved 95.936% precision, 95.326% recall, 93.747% F-score, and 99.637% accuracy. HIGHLIGHTS The carbon neutrality has received much attention for water treatment.; The deep learning modified neural networks (DLMNN) with Binary Spotted Hyena Optimizer (BSHO) for modeling were used.; The proposed model DLMNN-BSHO achieved 95.936% precision, 95.326% recall, 93.747% F-score, and 99.637% accuracy.

Study on the effect of hole size of Trombe wall in the presence of phase change material for different times of a day in winter and summer

In this article, a numerical study is performed on a Trobme wall in a tropical city for two seasons, summer and winter. A 1 × 1.5 m Trobme wall with a thickness of 15 cm is designed and analyzed. A 1-inch-diameter tube filled with PCM is used to enhance efficiency. The wall is analyzed at different times of the day for the two cold and hot seasons for different sizes of wall holes in the range of 70 to 17.5 cm when the wall height is 20 cm. A fluid simulation software is employed for the simulations. The problem variables include different hours of the day in the two cold and hot seasons, the presence or absence of PCM, as well as the size of the wall hole. The results of this simulation demonstrate that the maximum outlet temperature of the Trobme wall occurs at 2 P.M. Using PCM on the wall can allow the wall to operate for longer hours in the afternoon. However, the use of PCM reduces the outlet wall temperature in the morning. The smaller the size of the wall hole, the more air can be expelled from the wall.http://www.mdpi.com/journal/processespm2022Mechanical and Aeronautical Engineerin

Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures

Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach. HIGHLIGHTS Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants.; This research proposes a novel technique in the optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cells for water treatment.