The basis of the Ohmic Heating process is the transmission of alternating electric current through multi-phase solutions that is followed by heat generation due to particle resistance to the transmitted electric current. Throughout the present study, simultaneous transfer of heat and electricity was modeled in a Two-phase system of solid-liquid food to investigate the critical factors affecting the process. A three-dimensional simulation was employed in the modeling to investigate the effect of particle distribution, salt diffusion as well as electrical conductivity. The results revealed that there existed a good agreement between the results of the modeling with the experimental results. It was also revealed that with increase in the concentration of salts and electrical conductivity, heating rate increased. In total, it can be concluded that heat and electricity diffusion throughout the product is faster than that in conventional heating methods and proceed similarly and almost with equal speed in both liquid and solid phases.

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.

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.

Hospitals are one of the most important centers of medical service. At the time of the occurrence of natural and abnormal disasters, the demand for these centers may increase and some of these hospitals will be damaged and lose their functionality. On the other hand, in order to protect the lives of the injured, the distance and time of their transfer to hospitals is of particular importance. The proposed problem addressed simultaneously the crisis and normal conditions and tried to determine the location of hospitals in such a way that with limited resources in hand and the normal capacity and extra that can be used in times of crisis, the minimum distances Have Taking into account the decisions of the hardening of existing hospitals and the location of new hospitals simultaneously, post-abandonment distances are reduced compared to when the location is taken regardless of disability and rehabilitation, and this can be an e, ective step in crisis management. The capacity of hospitals in normal conditions and in the event of a crisis is limited and considering this in the model gives more realistic results. In this research, the problem of locating new hospitals and hardening a number of existing hospitals with limited available budget has been discussed. First, the potential locations to build a hospital are speci, ed. Then, using the proposed mathematical model, the optimal locations for the construction of new hospitals were determined, as well as decisions regarding the hardening of a number of existing hospitals. In order to solve the model due to its bi-objective function, the Epsilonconstraint Method (II) are used. To evaluate the validity of the model, a case study was carried out based on demographic and geographical information of Yazd city. Regarding the possibility of applying the model to large-scale applications and operating issues as well as NP-hardness of the model, an innovative multi-objective Two-phase Algorithm is proposed and evaluated based on some numerical examples.

In this study, the thermal characteristics of an individual room in a building are measured by making use of environmental data. Subsequently, the target heat capacity consumption by the air-conditioning system can be calculated and controlled, depending on the environmental target. By establishing the relationship between a change in the room environment (environmental evaluation index) and the heat capacity consumption of the room (the amount of change of the enthalpy) by the air conditioning, we can calculate the target heat capacity consumption feasible as an environmental target. Relying on an environmental target in calculating the target heat capacity consumption enables setting suitable targets to avert the risk of heat exhaustion or even heatstroke to the residents of the building. In addition, a useful room heat capacity model is suggested that includes a management method using a heat capacity consumption target, with an administration table for evaluating the target.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant Algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control Algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.