The several heuristic algorithms have been proposed for portfolio selection. One of these algorithms is based on spin glasses that have local searching and parallel Processing properties. Because of the spin glass algorithms are actually based on Monte Carlo simulation such as simulated annealing (SA) and have low convergence speed against other method, yet composing with other methods such as Learning Automata (LA) and genetic algorithms have been considered. In this paper, one of the composing methods based on SA and Exteremal Optimization (EO) has been proposed, this algorithm select and change the low order spins with higher probability and take the state of all spins into the better situation. After a sufficient number of steps, the system reaches a highly correlated that almost all species have reached fitness above a certain threshold. This co-evolutionary activity gives rise to chain reactions and every fluctuation that rearrange major parts of the system, potentially making any configuration accessible. Therefore any fluctuations allow escaping from local minima and efficiently explore the configuration space. The experimental results show this method is powerful paradigm for finding ground state of spin glass and better than other methods such as SA and LA for solving portfolio selection problem.

In this article, the design and implementation of Distributed systems for big data Processing using artificial intelligence algorithms have been thoroughly investigated. Due to the rapid and continuous growth of data volume in today's digital world, the utilization of these systems and AI-driven algorithms for efficient data Processing has gained increasing importance. The results obtained from this study indicate that these methods can significantly enhance the performance of big data Processing, offering substantial advantages for organizations and companies. This paper demonstrates that leveraging Distributed systems in combination with artificial intelligence algorithms can lead to remarkable improvements in the performance, scalability, and overall efficiency of various systems, applications, and computational frameworks.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image Processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

In traditional speech Processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech Processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech Processing where these filters have proven effective.

Plastic hinge properties play a crucial role in predicting the nonlinear response of structural elements. The plastic hinge region of reinforced concrete normal beams has been previously studied experimentally and analytically. The main objective of this research is to evaluate the behavior of the plastic hinge region of reinforced concrete deep beams and its comparison with normal beams through finite element simulation. To do so, ten beams contain six deep beams, and four normal beams, under concentrated and uniformly Distributed loading, are investigated. Lengths in the plastic hinge region involving curvature localization, rebar yielding, and concrete crushing zones are studied. The results indicate that the curvature localization zone is not suitable for the prediction of plastic hinge length in reinforced concrete deep beams. Based on the results it can be stated that in simply supported normal beams the concrete crushing zone is focused on the middle span, but in simply supported deep beams by creating a compression strut between loading place and support, the concrete crushing zone spreads along the compression trajectory. The rebar yielding zone of simply supported beams increases as the loading type is changed from the concentrated load at the middle to the uniformly Distributed load.

BACKGROUND AND OBJECTIVE: Varicocele is one of the causes of male infertility. It changes with temperature and drainage of testicular veins that due to spermatogenesis disturbance and decrease of fertility in these patients. However varicocelectomy is a treatment for varicocele, but there are other methods of treating this disease including assisted reproductive technology (ART), Sperm Processing and swim up. METHODS: This interventional study was performed on 155 cases in fertility and infertility Center of Babol University of medical sciences in 2004. Sperm parameters in varicocele patients was assessed before and after sperm Processing. FINDINGS: The mean age of patients was 31.8 years. Sperm motility before sperm Processing in grade III and IV was 53.9% and 12.9%, respectively but after sperm Processing in grade III and IV changed to 78.7% and 56.8%, respectively. Total motility before sperm Processing was 40 % and changed after Processing to 80%. CONCLUSION: The results show that with sperm Processing and collection of appropriate sperms, we are able to increase fertility.

in this paper, for the first time in the literature, we integrated production scheduling decisions and WIPs planning decisions in a Distributed environment. We study the Distributed and flexible job shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a Distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. It is also assumed that the work-in-process (WIP) parts can be bought from the market instead of manufacturing them in-house, and they also can be sold in the market instead of Processing their remaining operations and selling the end products. Moreover, the Processing times of the operations can be decreased by paying a cost. However, there are a lower limit and an upper limit for the Processing time of each operation. We formulate this general problem as a mixed integer linear programming (MILP) model. A fast heuristic algorithm is also developed to obtain good solutions in very short time. The algorithm is tested on some problem instances in order to evaluate its performance. Computational results show that the proposed heuristic is a computationally efficient and practical approach.