In this research, we studied isolation of the cellulolitic fungi from the persimmon tree, grapevine, pomegranate tree and walnut soils. Among of them, persimmon soil was selected because of maximum cellulolitic fungi. Seven fungi werw isolated from persimmon soil, which three of them had suitable cellulose activity and they were identificated by 18S rRNA and named: Aspergillus niger MZM 89-a2, Penicillium decumbens ZHE 89-p3, Penicillium decumbens MMH 89-p1. Cellulase activities of these fungi were respectively (U/d): (FPA) 3.1671, 3.5740, 3.1812 and Avicelase 1.6605, 0.3869, 1.1451 and CMCase 2.950, 0.2644, 0.4604. Response surface activity (RSM) was studied to evaluate the effects of temperature, moisture content and particle size for (FPA) by Aspergillus niger MZM 89-a2. The optimum (FPA) was in temperature, moisture content and particle size respectively: 28.49oC, 66.62٪ and 1.5-2 (mm). The maximum predicted (FPA) was 4.35 (U/g) and obtained (FPA) under this condition was 4.42 (U/g), which indicates the efficacy of the model for prediction of (FPA) activity under different conditions of the medium.

In recent years, the use of renewable energy systems has grown significantly, among which photovoltaic systems have received much attention. Solar cells are known as the building blocks of a photovoltaic system. Because of the nonlinear nature of solar cells and the continuous changes in atmospheric conditions, maximum power point tracking (MPPT) is essential to extract the maximum power of a photovoltaic system. In this study, in order to achieve the maximum power, it was proposed to apply flower pollination Algorithm ((FPA)) combined with a comprehensive selection Algorithm, named as improved (FPA). In addition, to evaluate the proposed Algorithm, its performance was compared with genetic Algorithm (GA) and standard (FPA) under rapid changes in atmospheric conditions. The calculated results showed that the improved (FPA) has a better accuracy than GA; moreover it has a higher convergence rate as compared with other applied Algorithms.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space.Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature space without FS increases the computational cost which is a function of the length of the vector, and also, it helps to remove irrelevant attributes. The general approach in this paper combines the hybrid of Flower Pollination Algorithm ((FPA)) with Ada-Boost Algorithm. The (FPA) is used for FS and the Ada-Boost is used for classification of text documents. Tests were conducted on Reuters-21578, WEBKB and CADE 12 datasets. The results show that the hybrid model has higher detection accuracy in FS compared with Ada-Boost Algorithm with model. And comparisons are indicative of higher detection accuracy of the proposed model compared with KNN-K-Means, NB-K-Means and learning models.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly Algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA Algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA Algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

Summary In this study, for inversion of surface wave dispersion curves, a new modified flower pollination Algorithm (M(FPA)) is introduced. The goal of the proposed Algorithm is to find the unknown parameters of the problem, i. e., the thicknesses and shear wave velocities of the soil layers. The performance of the Algorithm has been evaluated by synthetic models and also actual dataset. The results, in both of synthetic models and experimental data, represent the acceptable performance of the proposed Algorithm. The M(FPA) inversion method is a suitable technique for reducing the non-uniqueness of the surface wave inversion task. Introduction The inversion of the surface wave dispersion curves is one of the practical issues in identifying the subsurface layers and shear wave velocities structures. Shear wave velocity is one of the most important parameters in geotechnical studies that is used to evaluate soil properties, including site effects and seismic microzonation. Typically, surface waves are used to estimate the shear wave velocity. Linear inversion methods are not very reliable due to the nonlinear nature of the problem. With the development of computer sciences and the development of intelligent optimization methods, rapid and easy techniques for inversion of surface waves could be used. In this paper, a new modified flower pollination Algorithm (M(FPA)) for inversion of Rayleigh wave dispersion curves is introduced. In the proposed Algorithm in comparison to standard flower pollination Algorithm ((FPA)), the exploration ability of the Algorithm is improved. Methodology and Approaches In order to process surface waves and find an adequate shear wave velocity structure, a new hybrid metaheuristic Algorithm that adds a dynamic factor to mutation operator of the standard (FPA), called M(FPA), is applied. In this study, the mutation rate increases gradually from 𝑊 𝑚 𝑖 𝑛 (1/number of flowers) to 𝑊 𝑚 𝑎 𝑥 (1/ number of subsurface layers) as the number of iterations is increased. The M(FPA) approach could accelerate the convergence speed in comparison to the standard FBA. The code of the M(FPA) inversion method has been written in MATLAB environment. Then, the proposed technique has been tested on a synthetic dataset. To more explore the reliability of the applied method, 10 percent noise has also been added to the synthetic dataset. The results of synthetic dataset show the capability of the M(FPA) technique in the absence and presence of noise. For further evaluation of the proposed method, the M(FPA) has been applied on an actual dataset for geotechnical assessment in an area in the city of Tabriz, northwest of Iran. The results of the experimental data indicate a three-layer model that is in a good agreement with the geological evidence of the study area. Results and Conclusions In this study, a new surface wave inversion Algorithm, i. e. M(FPA) is proposed. Then, capability of this technique is tested by synthetic and actual datasets. The results show that the applied method is a fast and powerful technique in the inversion of surface wave dispersion curves. Moreover, the performance of M(FPA) has been compared with standard (FPA). Because of strong exploration ability of M(FPA), this Algorithm in estimation of the model parameters has higher convergence and accuracy than (FPA).