In the present study, the exploitation of the dam reservoir to meet irrigation needs has been investigated based on the new evolutionary algorithm of the King Butterfly with the aim of minimizing irrigation deficiencies. Genetic and particle swarm algorithms were used as the most widely used and successful algorithms for comparison with the King Butterfly algorithm and a multi-criteria decision model to select the superior method. The results showed that the King Butterfly algorithm with the first rank based on the multi-criteria decision model and various indicators such as reliability, vulnerability, reversibility and objective function has a better performance than the particle swarm and genetic algorithm. In addition, the amount of irrigation water supply shortages based on the King Butterfly algorithm is lower than the other two algorithms during the seven years of study. Therefore, the study showed that the King Butterfly algorithm has a good performance for use in water resources management issues.

Much studied have addressed Path planning as one of the main topics in unmanned aerial vehicles; which has yielded different results due to the existing conditions and limitations. To this end, the present study proposed an efficient algorithm underpinned by the propeller optimization algorithm, which had a utility function and could optimize multiple responses simultaneously. BOA is different from other meta-heuristic algorithms as each propeller produces its own unique fit in the path by combining information extracted from different sensory receptors. Accordingly, BOA can solve multi-objective problems. A 3D objective function was used in this study to estimate the length of the shortest path and the intensity of collisions with obstacles, avoid collisions and enhance UAVs’ operational capacity as a function of consumed energy. Furthermore, A smart launcher factor was also included in this algorithm to prevent trapping in local optimizations and promote network coverage in the routing process at the same time. The launcher prevents collision with obstacles in UAVs by adopting geometric techniques and the contour line. The performance of the proposed algorithm was compared with that of the most practical meta-heuristic algorithms (namely ACO and PSO methods). The findings revealed that the BOA algorithm compared to the other two algorithms had the lowest cost and the second lowest cost under the best and worst conditions, respectively. The findings also confirmed the better performance of BOA than the other two algorithms regarding execution time and the optimal value of the fit function.

Velocity analysis is one of the most important step in seismic data processing. It affects not only many processing steps directly and indirectly, but also is known as a primary interpretation of the data. However, it can also be assumed as one of the most time consuming processing step. The conventional velocity analysis method measures the energy amplitude along hyperbolic trajectories within a velocity interval and creates a velocity model. In this procedure, the data from time-offset domain is mapped to time-velocity or time-slowness domain. For a number of Nr, Nh and Nv time, offset and velocity samples respectively Nr× Nh× Nv computations is necessary to obtain a velocity model. However, in the presence of large size data and model parameters, computing the velocity spectrum using conventional method would be a time consuming task. On the other hand, in order to improve the initial velocity model obtained in the processing steps, usually velocity analysis is conducted several times during the processing of the seismic data. Hence, there should be a better way to compute the velocity model in a much less time computation. In this paper, we introduce the Butterfly algorithm for fast computation of hyperbolic Radon transform (HRT), as a kind of time variant operator, with an application in seismic velocity analysis. In seismic data processing, Radon transforms map the overlapping data in seismic gathers to another domain which they can be separated.Among different types of Radon transforms, the HRT has the most similarity to the seismic events and hence, produce the most accurate approximation in the velocity spectrum. However, its time-variant kernel prohibits its fast computation especially for large size data. Unlike time-invariant operators which use the convolution theorem in the Fourier domain to compute the velocity domain for each frequency separately and therefore efficiently, Fourier transform of time-variant operators is a function of both frequency and time and using the convolution theorem is not applicable. The Butterfly algorithm can be used as a fast solver for the Fourier Integral Operators (FIO), so reformulating the HRT integral in the Fourier domain as FIO makes it possible to use this algorithm to overcome the problem of the time-variant kernel. The basis of this solution is the existence of low-rank approximations of the kernel when it is restricted to subdomains in data and model spaces. Subdividing the model and data domain properly to smaller subdomains admits low-rank approximations of the kernel. These low-rank approximations enable us to obtain functions of only one variable, time or frequency, which approximate the kernel. This decoupling of time and frequency variables allows fast computation of the HRT integral. In order to do the subdivision properly, a pair of quad trees, one for each data and model domains, is used to restrict the domains in a level-base structure in which the size of data domain subsets are increasing while the size of model domain subsets are decreasing in each level. The Butterfly algorithm is used to compute the kernel equivalent functions in each level of these quad trees for each subdomain. Finally, at the last level, the Radon panel or velocity model is obtained.The complexity of this method for two dimensional data is O (N2 log N) in which N depends on data and model variables range. As it was demonstrated in the synthetic and the real numerical examples, O (N2 log N) complexity results in reduction of computation time in several orders relative to the conventional method.

Cloud Computing, employed in various applications and services, refers to using computational resources as a service depending on customer needs via the Internet. The computing paradigm is built on data outsourcing to third-party-controlled data centers. Despite the significant developments in Cloud Services and Applications, various security vulnerabilities remain. This research proposes the EBBKG Model for Efficient Data Sharing in Cloud. For secure data sharing in the cloud, the approach combines BBKG with ABS. The method offers good data management that efficiently specifies the subsequent processing processes. The paradigm imposes encrypted access control, along with specific enhanced access capabilities. Secondly, the user's privacy may be adequately protected with a secure authentication paradigm that employs ABS to safeguard the user's private data. The key is optimized using BOA to enhance security and cloud providers and limit dangerous user threats using these implementations. Criteria like security, time complexity, and accountability govern the suggested method's effectiveness.

Purpose: To report a case of butterfly-shaped macular dystrophy.Patient and Findings: A 34-year-old woman presented with metamorphopsia in both eyes from 2 years ago. Visual acuity was 9/10 in right eye and 10/10 in left eye. There was no pathologic finding on slit lamp biomicroscopy and funduscopy other than mild macular retinal pigment epithelium mottling. Visual fields and electroretinogram were normal but electrooculogram showed sub-normal results. Fluoresce in angiography showed black, non-fluorescent butterfly-shaped macular structures.Conclusion: Butterfly-shaped macular dystrophy occurs in middle aged subjects and presents with mild visual loss and metamorphopsia. It is slowly progressive and leads to marked visual acuity reduction in older ages. Considering the paucity of clinical findings, fluoresce in angiography and/or electrophysiological tests are recommended for making a correct diagnosis.

In recent few years fractals have been employed in conjunction with antennas to develop new applications. In this work, novel fractal geometry is introduced as a miniaturized microstrip patch antenna. Compared to a square patch antenna, the antenna shows an improvement of 68% size reduction. Furthermore, by applying of a new feeding method, refereed to here as the sleeve feeding, up to 27% impedance bandwidth is achieved as shown in the experiments.

Today, artificial intelligence techniques and machine learning technologies have made it easy to identify and classify plant diseases. In this research, in order to diagnose and classify some diseases of grapevine leaves with the names of black measles, black rot, and leaf blight, after removing the background from the image of the leaves and extracting the characteristics of texture and color and from the images, a combination of support vector machine classification and butterfly optimization algorithm was used to select the most important features in the diagnosis of grape plant leaf disease. The results of classification accuracy for black measles, black rot, leaf blight, and healthy leaf diseases are 100, 100, 100 and 95% respectively, and the classification accuracy for the diagnosis of all diseased and healthy groups is 98.75%. It was achieved. The classification results showed that image processing and machine learning are excellent in diagnosing and classifying some plant diseases of grape leaves. In this research, 15 features of texture, color and shape have been introduced to the researchers of plant pathology and data science with the help of the butterfly optimization feature selection algorithm.