One of the important problems of the operator algebras theory is the geometric characterization of state spaces of operator algebras‎. ‎In this regard‎, ‎in mid-1980s‎, ‎a paper by Friedman and Russo introduced facially symmetric spaces‎. ‎The primary aim of this work was to provide the geometric characterization of predual spaces of $JBW^\ast$-triples that possess an algebraic structure‎. ‎Many of the properties required in these characterizations are natural assumptions for the state spaces of physical systems‎. ‎Such spaces are considered as a geometric model for the states of quantum mechanics‎. ‎In this paper, we show that if any indecomposable geometric tripotent‎ ‎of a neutral strongly facially symmetric space is a minimal geometric tripotent then any extreme point is a norm exposed point‎. ‎Moreover‎, ‎in an atomic neutral locally base normed strongly facially symmetric space any extreme point is a norm exposed point‎. ‎We also prove that every real neutral strongly facially symmetric space with unitary tripotents is finite‎.

The Artificial Bee Colony (ABC) Algorithm is a sophisticated optimization technique that is inspired by the intelligent behaviors of honey bee swarms. These behaviors, such as foraging and communication within complex social structures, serve as the foundation for the algorithm's effectiveness. In this paper, the ABC algorithm is utilized to optimize the design of reinforced cantilever concrete retaining walls, with the goal of minimizing both cost and weight. The results are compared to existing literature, demonstrating the success of the ABC algorithm in achieving the objectives. Furthermore, a comparison is conducted between the optimized design and a conventional manual design, revealing a significant reduction in cost and weight through optimization. Additionally, two types of reinforced concrete cantilever retaining walls-a T-shape wall with variable stem thickness and a standard T-shape wall-are presented and compared, considering their differing variables and constraints. These comparisons are made for two objective functions: the cost and weight of the wall. To further investigate the impact of initial parameters, such as the unit weight of soil and stem height, a sensitivity analysis is conducted. The robustness of the ABC algorithm in optimizing the cost and weight of reinforced concrete cantilever retaining walls is demonstrated by the results.

In most real world application and problems, a homogeneous product is carried from an origin to a destination by using different transportation modes (e. g., road, air, rail and water). This paper investigates a fixed charge transportation problem (FCTP), in which there are different routes with different capacities between suppliers and customers. To solve such a NP-hard problem, four meta-heuristic algorithms include Red Deer Algorithm (RDA), Stochastic Fractal Search (SFS), Genetic Algorithm (GA), and Simulated Annealing (SA) and two new hybrid meta-heuristics include hybrid RDA & GA (HRDGA) algorithm and Hybrid SFS & SA (HSFSA) algorithm are utilized. Regarding the literature, this is the first attempt to employ such optimizers to solve a FCTP. To tune up their parameters of algorithms, various problem sizes are generated at random and then a robust calibration is applied by using the Taguchi method. The final output shows that Simulated Annealing (SA) algorithm is the better than other algorithms for small-scale, medium-scale, and large-scale problems. As such, based on the Gap value of algorithms, the results of LINGO software shows that it reveals better outputs in comparison with meta-heuristic algorithms in small-scale and simulated annealing algorithm is better than other algorithms in large-scale and medium-scale problems. Finally, a set of computational results and conclusions are presented and analyzed.

Motor Imagery is a mental process that includes preparation for movement. The brain interface system intends to prepare direct connectivity between the brain and the computer to be aware of the requests of an individual and use them as a control signal for external devices. Motion imaging events occur in the three main frequency bands: beta, mu, and gamma. After preprocessing the EEG data, the next step is to apply various types of filters in order to reduce any residual noise present in the signal. Numerous functional imaging studies showed that motion-imaging results from the specific activation of neural circuits involved in the early stages of motor control. Studies have shown that the CSP algorithm performs better than other algorithms. Due to the lack of a suitable frequency band, the results of the frequency-dependent CSP method are not satisfactory, so the CSSP is similar to the FIR filter, but since this filter does not have all the coefficients of an FIR filter, the presence of noise in the EEG signal can lead to suboptimal definition of the frequency filter. The CSSSP algorithm was used to solve this problem. With using sequential feature selection for feature extraction, it was revealed that CSSSP performance has been better compared to the CSP and CSSP in most cases and the average accuracy was 92.55%.

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.

Researchers' attention has recently been focused to the measurement and tracking of prestressing force in the tendons of prestressed concrete (PC) constructions. Older structures need non-destructive testing techniques to evaluate these forces, even if modern structures are fitted with sensors to monitor prestress losses. This work presents a new approach that uses static displacement data under experimental loads to determine the real prestress force in the tendons of a prestressed concrete beam. This approach offers a more economical alternative by doing away with the requirement for destructive tests or pre-installed sensors. A genetic algorithm (GA) is created to precisely calculate the prestress force of tendons. Laboratory testing shows that the proposed method can detect prestress losses with excellent accuracy, even in the presence of intentional measurement mistakes of up to 10%.Researchers' attention has recently been focused to the measurement and tracking of prestressing force in the tendons of prestressed concrete (PC) constructions. Older structures need non-destructive testing techniques to evaluate these forces, even if modern structures are fitted with sensors to monitor prestress losses. This work presents a new approach that uses static displacement data under experimental loads to determine the real prestress force in the tendons of a prestressed concrete beam. This approach offers a more economical alternative by doing away with the requirement for destructive tests or pre-installed sensors. A genetic algorithm (GA) is created to precisely calculate the prestress force of tendons. Laboratory testing shows that the proposed method can detect prestress losses with excellent accuracy, even in the presence of intentional measurement mistakes of up to 10%.

The present study aimed at scheming a novel method of detecting target and non-target signals through selection of appropriate and timely chronic intervals of VEP signal leading to increasing the accuracy of data classification and decreasing the number of features. The suggested method was employed on the P300-Speller databases of the BCI2005 competitions and the data recorded by Hoffman et al. using effective and specified channels and SWLDA classifier. The methods available for determining the P300 signals are within a specified range of about 1 second after each stimulation. To this end, we first outlined the time range of the various components of visual Evoked potential including N20, P50, N100, N170, P300, N400 based on the results obtained from the physiologically-based articles. Then, the time intervals were scored by F-Score and the percentages of correct classifications. The most important and effective components of the VEP were selected by SFS Algorithm using the SWLDA classifier and the functions of the optimal combinations were compared with the total length of the signal utilizing two other classifiers namely Bayesand K_NN in order to confirm the functionality of the method. The findings, based on the results obtained from ten subjects, indicated that the most important components for detecting target and non-target signals include P300, N100, and N400 respectively. The method suggested here proved to improve the accuracy of output detection by 3. 95%.

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.

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.