Today, one of the most basic strategies for a government's survival is to defend sensitive places and resources, and to achieve this goal, strategic and efficient approaches are required. The goal of this research is to determine the best way to defend sensitive systems in which the defender tries to minimize the damage caused by the attacker while taking into account budget constraints and the space needed for defense equipment, and the attack tries to maximize the defender's injury. In this case, the defense creates a number of artificial targets in order to deceive the attacker, all while attempting to decrease the risk of damaging sensitive systems by not attacking them definitively. The goal's economic, human, and symbolic worth must be considered by both the defender and the attacker. The defender attempts to minimize projected harm and defense costs. The attacker aims to do as much damage as possible while keeping the attack's cost low. The attacker will "maybe" identify the targets because they are unable to identify the virtual targets. For the first time, a program model was developed that considers the possibilities for identifying virtual targets, reliability structures with mixed strategies (active and cold standby), non-homogeneous components in parallel series systems, and the game theory approach to finding equilibrium. A nonlinear layout is provided to optimize defense subsystems. The results are modeled and the results are evaluated in this research.

BackgroundMetabolic syndrome can cause cardiovascular disease and diabetes in the affected subjects. With 20 to 30% prevalence rate among the adult population of most countries, it is considered a pandemic problem. The guidelines currently available on the management of the specific components of metabolic syndrome highlight some lifestyle changes such as enhanced physical activity and weight reduction. Adherence to Mediterranean-style diet has been shown to be associated with lower risk of metabolic syndrome in some clinical studies.ObjectivesThe aim of this study was the evaluation of the effect Razavi dietary pattern, on metabolic syndrome. This is the first study performed to address this issue.Patients and MethodsSeventy five eligible subjects with metabolic syndrome were recruited into a single-blind randomized controlled clinical trial to determine the effect of Razavi diet on metabolic syndrome. Intervention was carried out by educating the Razavi diet in the experimental group while giving no dietary recommendations to the control group. The level of physical activity was similar between the two groups. Features of the metabolic syndrome as defined by the criteria of the Iranian National Committee of Obesity were assessed after two months.ResultsThe net reduction in the waist circumference (-2.85), weight (-1.44) and BMI (-0.58) in test group was significantly (P < 0.001) higher than the control. Decreases in systolic and diastolic blood pressure, fasting blood sugar and triglycerides were observed but were not statistically significant.ConclusionsThe results suggest that Razavi diet can improve some components of metabolic syndrome leading to reduced risk of cardiovascular disease and diabetes.

In this paper, we introduce a new multi-objective model and solution method for the reliability-redundancy allocation problem (RRAP) in a series-parallel system to maximize system reliability and minimize total cost. Most studies on RRAP assume the components are homogeneous, the reliability of components is predefined, and redundancy strategies in each subsystem are considered cold-standby or active. Each of the above assumptions serves as a constraint that doesn’t broaden solution regions. In the proposed multi-objective model, the components are heterogeneous, and the reliability of components is uncertain. In addition, mixed strategies (cold-standby and active redundancies) can be used in each subsystem. The proposed optimization problem is an NP-hard problem and cannot be solved by exact algorithms. Therefore, it is necessary to use meta-heuristic algorithms to solve this problem. Since the proposed model is a multi-objective model, a multiple evolutionary algorithm called NSGA-II will be used to solve the problem. Lastly, the performance of the proposed mathematical model is assessed by a well-known problem-testing method. The optimization results show the effectiveness of the proposed model and prove that the proposed method outperforms the previous ones.