In vitro and in vivo antifungal properties of silver nanoparticles against Rhizoctonia solani,a common agent of rice sheath blight disease

Sheath blight disease in rice has caused major crop losses worldwide. Managing the causal agent of disease Rhizoctonia solani Kühn is difficult because of its broad host range and formation of sclerotia which can survive in harsh environmental conditions; therefore developing innovative disease management methods without application of hazardous chemicals has been considered as the main concern to maintain sustainable agriculture. This presented research has revealed the negative impact of silver nanoparticles (SNPs) on R. solani and disease progress both in vitro and in vivo. The adverse effects of the SNPs on R. solani are significantly dependent on the quantity of SNPs, sprayed at different concentrations in vitro. The highest inhibition level against sclerotia formation and mycelia growth are 92 and 85%, respectively, at a SNPs concentration of 50 ppm. In vivo glasshouse experiments also showed that SNPs at the same concentration favourably affects both the fresh and dry weight of rice plants with a remarkable suppressive effect on the lesion development in leaves.Inspec keywords: antibacterial activity, silver, nanoparticles, nanomedicine, plant diseases, cropsOther keywords: in vitro antifungal properties, in vivo antifungal properties, silver nanoparticles, rice sheath blight disease, major crop losses, causal agent, Rhizoctonia solani Kühn, sclerotia, harsh environmental conditions, disease management methods, hazardous chemicals, sustainable agriculture, negative impact, inhibition level, mycelia growth, in vivo glasshouse experiments, rice plants, suppressive effect, lesion development, leaves     

A high performing meta-heuristic for training support vector regression in performance forecasting of supply chain

The prevalence of the use of third-party logistics (3PL) providers is noticeable. The complexity of the relationships pertinent to 3PL is greater than that of any traditional logistics supplier relationships. Moreover, they can be considered as truly strategic alliances. The use of the mentioned relationships to increase the flexibility of the organization to address the rapid changes occurring in market conditions has become popular while these relationships concentrate on the core competencies as well as the development of long-term growth strategies. A good number of studies have examined the selection of service providers. With respect to the selection of the service providers, the most recent studies approved the better performance of neural networks in comparison with the conventional methods to provide a solution for the real-world engineering problems, one of the sociopolitically inspired optimization strategies named imperialist competitive algorithm (ICA) is used. In order to select the 3PL, integration of the support vector regression (SVR) and self-adaptive ICA (SAICA) has offered a novel model, in which SAICA is utilized to adjust the parameters of the SVR. The suggested model is applied for cosmetics production. Moreover, the comparison of the suggested model and back-propagation neural networks, pure SVR, and ICA–SVR is presented. Higher estimation accuracy is achieved as the results of the proposed model reveal, which leads to the effective prediction.     

Study on Soybean Oil Plant Wastewater Treatment Using the Electro‐Fenton Technique

Due to its low viscosity, soybean oil is commonly used as edible oil. Its production is accompanied by a large amount of wastewater containing hazardous materials like phenolic compounds. In this study, the electro‐Fenton process with response surface methodology was applied for oil removal from wastewater of soybean oil plants. Five independent variables, including reaction time, pH, current density, molar ratio of H₂O₂ to Fe²⁺, and volume fraction of H₂O₂ to wastewater, were investigated for their effects on oil removal. The significant independent variables were optimized by the Box‐Behnken design. The optimum conditions were statistically obtained, and the results were confirmed by experiment. 92.66 % oil removal were experimentally found for the optimum conditions.     

Modeling and analysis of MEMS disk resonators

Microsystem Technologies - In this work a very accurate process for modeling a microdisk resonator is presented and the dynamic behavior of the resonator is investigated. Using the minimization of...     

Effects and Mechanism of Combinational Chemical Agents on Solution‐Derived K0.5Na0.5NbO3 Piezoelectric Thin Films

Sodium potassium niobate (KNN) piezoelectric ceramic thin films were prepared by a chemical solution deposition method, in which the introduction of appropriately combinational organic stabilizing agents, including monoethanolamine (MEA), diethanolamine (DEA), and ethylenediaminetetraacetic acid (EDTA), dramatically suppressed the volatile loss of the alkali ions, improved the crystallinity, and promoted crystal orientation of the perovskite phase in the resulting KNN thin films. Theoretical analyses including ab initio calculation based on molecular model indicated that stronger binding structure could form comprising the alkali ions and the combinational EDTA‐(DEA‐MEA) chemical agents than the cases of using individual binding agents separately. Outstandingly large effective piezoelectric strain coefficient d₃₃ under the substrate clamping condition was achieved. The theoretical and experimental results provide the insight on the underlying interaction mechanism between the multiple stabilizing chemical agents and metal ions, and the guidance for establishing the methodology for producing high quality oxide thin films from chemical solutions with dedicatedly designed combinational stabilizing agents.     

ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels

In the present study, an artificial neural networks-based model was developed to predict the ferrite fraction of microalloyed steels during continuous cooling. Fourteen parameters affecting the ferrite fraction were considered as inputs, including the cooling rate, initial austenite grain size, and different chemical compositions. The network was then trained to predict the ferrite fraction amounts as outputs. A multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the ferrite fraction.     

Safety analysis of discrete event systems using a simplified Petri net controller

This paper deals with the problem of forbidden states in discrete event systems based on Petri net models. So, a method is presented to prevent the system from entering these states by constructing a small number of generalized mutual exclusion constraints. This goal is achieved by solving three types of Integer Linear Programming problems. The problems are designed to verify the constraints that some of them are related to verifying authorized states and the others are related to avoiding forbidden states. The obtained constraints can be enforced on the system using a small number of control places. Moreover, the number of arcs related to these places is small, and the controller after connecting them is maximally permissive.