To achieve a reasonable level of precision in tractor-based field operations, a tractor operator has to guide accurately, monitor and control both the tractor and the attached implement. Since guidance is the most time consuming task among the others, researchers have attempted to automate the guidance task. However, the use of automatic guidance and control in agricultural applications is not always appropriate. Transportation of the vehicle on a public road is an example of this. Some researchers therefore have focused on Vision-Aided methods to give some guidance aid to the driver rather than on eliminating the driver. To investigate the accuracy of such methods, a Vision-Aided tractor guidance belt-type simulator was developed. An experimental prototype of the simulator was constructed. To evaluate the prototype, a completely randomized factorial experiment was conducted with forward speed, heading angle, and camera tilt angle being the major factors under investigation. The simulator performed satisfactorily at 5 and 7km/h and mean deviations of 1.14 and 2.31cm were obtained respectively.

In a real electricity market, complete information of rivals’ behavior is not available to market participants. Therefore, they make their bidding strategies based on the historical information of the market clearing price. In this paper, a new market simulator is introduced for a joint energy and spinning reserve market, in which market participants’ learning process is modeled using Q-learning algorithm.The main feature of this simulator is simulating a real market, in which market participants make decisions based on incomplete information of the market. Using the proposed simulator, the clearing price for each submarket is computed considering the participants’ behavior, under different load levels and/or contingency conditions. The results show that Q-learning approach can modify the agent’s strategy under different market situations.

Virtualization is a technique to make the cloud computations secure. This technique can reduce the costs and increase the reliability of systems by means of sharing the physical resources between several VMs (Virtual Machines). However, virtualized environments are vulnerable to some security issues such as lack of performance isolation. These days, using the virtualization as a technique is growing slowly in large organizations. Despite these advantages, virtualizations have created several security challenges. So, the main concern of virtualization service providers and their customers is how to detect and encounter these security challenges. In this research, the resource freeing attack is selected among all available virtualization attacks because of its simplicity and its drastic effects. Resource freeing attack causes an unfair resource distribution among VMs. We use Cloudsim as a powerful cloud simulation toolkit to implement resource freeing attack. After simulating the attack, the CPU's efficiency, response time diagrams, and the obtained bandwidth are measured. The simulation results show the significant changes in VM behavior after occurring the attack in the virtualized environment. These changes help cloud providers to detect resource freeing attack in the cloud.

Rain simulation is a method widely used in detecting hydrological and erosional processes. Most portable samples have inconvenient and challenging transport, high water consumption, and energy supply. The objective of this study was to design and test a rainfall simulator characterized by the following innovative features: 1) The simulator is easy to be transported and assembled in the field, thereby allowing the necessary experimental replicates, 2) Applicability on different slopes. The first calibration step regarded the spatial distribution of rainfall, the stability of the rainfall intensity, and the reproducibility of the rainfall intensities over time (among successive experiments). Next, the drop size distribution (DSD) and the related rainfall characteristics (median volumetric drop diameter D50 and mean kinetic energy per unit area and unit depth) were evaluated by the flour pellet method. A fluorescent tracer method is used to measure the velocity of falling drops. According to the findings, the Christiansen uniformity coefficient (Cu) of this rainfall simulator varies from 77-87% for rainfall intensities of 35-75 mmh-1. The best rainfall distribution has been achieved for rainfall intensities of 55 and 75 mmh-1, with rain droplet sizes ranging from 0.6 to 3.8 mm. The raindrop velocity was also measured by photo-shooting and revealed a velocity rate of 2.7-5.7 ms-1. The system allows rainfall simulation on the fields and under laboratory conditions. Moreover, erosion, runoff, and sediment production under natural and intact soil conditions can also be examined with the highest possible accuracy

Rainfall simulator is one of the most effective instruments for using in soil erosion studies. Thus, many researches have been conducted in order to design, manufacture and calibrate various rainfall simulators commensurate with aim of study and climatic conditions all around the world. However, using the imported nozzles in Iran is not feasible due to high cost, and different climatic conditions. Therefore, the current study has tried to evaluate some various nozzles’ characteristics made by the authors in order to simulating natural rainfall happened in Mazandaran Province climate. For this purpose, a minute evaluation was made for a number of rainfall characteristics produced by nozzles used in other rainfall simulators as well as those made by the authors. The results of the study revealed that the nozzle designed and manufactured by the authors and coded as Apd4 could successfully simulate the natural rain under consideration. The medium drop size, velocity and fall angle of the simulated rain were respectively recorded by high speed imaging of 1.4 mm, 6.4 m s-1 and 89o.

An Intelligent Tutoring System (ITS) can be effectively employed for a power plant simulator so that the need for instructor is minimized. In this paper using the above concept as well as object oriented programming and SIMULINK Toolbox of MATLAB, an intelligent tutoring power plant simulator is proposed. Its successful application on a typical 11 MW power plant is demonstrated.

Purpose: To assess various aspects of integrating a virtual reality (VR) simulator in the training of ophthalmology residents.Methods: In this qualitative study, some aspects of the VR EYESI simulator as a health technology were assessed. Data was collected using a multiple-choice questionnaire and several deep interviews addressed to users and administrators of simulator training. Face and construct validity were measured as a score out of 100. Scores were given by 19 novice residents and 4 experienced fellowship candidates.Results: Residents gave a score of 85.08 for face validity and a 70.87 for the construct validity of the EYESIsimulator. Fellowship candidates scored its face and construct validity at 60 and 80.66, respectively. 63.2% of residents believed that the simulator is a very useful training tool and 75% of fellowships viewed it as useful. Eighty percent of interviewed attending physicians believed that the simulator reduced the time and improved the quality of training. They also stated that residents who were trained with the simulator were adequately prepared for entering the operating room and showed better eye-hand coordination. All the stakeholders strongly believed that simulation is a necessary method of education.Conclusion: The EYESI simulator is an up-to-date virtual reality medium that enjoys good face and content and face validity. It was also shown to be an effective method of training since it decreased the time and improved the quality of training.