amin noori

The scientific modeling of cellular growth and proliferation can significantly help to physicians to offer an appropriate treatment. In this paper the modeling of the body's immune system function for a patient with melanoma cancer has been studied. The main advantage of this model is considering more variables to describe dynamics of the melanoma patient’s body, which will make our model more realistic. For estimating the coefficients of the mathematical model, a multi-objective optimization method, "Non-Dominated Sorting Genetic Algorithm", is used. One of the major advantages of such method is considering multiple objectives and simultaneously constraining them. Simulation results reveal that our mathematical model can successfully simulate the function of the body defense system of the melanoma patient. In order to analyze the sensitivity and determine the correlation of the mathematical model output to changing in some parameters, Partial Rank Correlation Coefficient (PRCC) method is employed. Finally, it is demonstrated that Interleukin-2 (IL-2) generation rate change has the highest PRCC value and changing this parameter will have a high impact on the model outputs.

This paper mainly aims to determine the optimal drug dosage for the purpose of reducing the population of cancer cells in melanoma patients. To do so, Reinforcement Learning method and the eligibility traces algorithm are employed, giving us the advantage of creating a compromise between the two algorithms of the reinforcement learning, being Monte-Carlo and Temporal Difference. Furthermore, it can be said that using this approach, there was no need to employ a mathematical model in the whole process. However, as its implementation on the real system was not possible, a delayed nonlinear mathematical model is used to investigate the performance of the proposed controller and simulate the behavior of the environment. It should be noted this mathematical model made use of no control method. This is the first time that population control of cancer cells is applied and tested on this model. To know of the optimal dosage of the drug, it should be mentioned that the drug is required to prevent the side effects on healthy/normal cells as much as possible. According to the obtained results, the eligibility traces algorithm is able to control and reduce the population of cancer cells through injecting the sub-optimal drug dose. This will increase the level of immunity in our body. Finally, to demonstrate the advantage of a selective method of increasing the rate of cancer cell death, this method is compared with the Q-learning algorithm and optimal control. By applying the fault to the sensor, the performance of the proposed controller to reduce cancer cells was investigated. The adaptability of the proposed method with the environment changes is checked afterwards. To this end, uncertainty in the system parameters and initial conditions are applied and the population of cancer cells are controlled in five melanoma patients. Moreover, having added noise to the system, it was shown that the eligibility traces algorithm is able to control the population of cancer cells and make it reach zero. Additionally, the convergence speed of both eligibility traces algorithm and Q learning algorithm in reducing the number of cancer cells for different learning rates was investigated.

One of major parameters in determining shear capacity of beams is the height of beam. Researches show that with increase in beam’s height, normalized shear strength decreases which this phenomena is called size effect. In recent years due to advances in construction methods, the idea of using lightweight concrete deep beams has been proposed, this should be done with a full understanding of the behavior of lightweight concrete. Moreover, truss models are recently used for analysis and design of deep beams in codes which their validity for lightweight concrete should be investigated.In this research to investigating size effect in lightweight concrete deep beams and comparison with normal concrete, two series of beams including 8 deep beam with shear span to height ratio of 0.5 were built in lab. First series included 4 beams with height of 30, 45, 60 and 90 cm using lightweight concrete in their construction, specimens of second series were similar to first but normal concrete was used in there construction. Results show that normalized shear strength in both groups of beams decreases with increase in height but the intensity of this decrease in lightweight concrete deep beams is more than normal concrete which shows that size effect in lightweight concrete is more than normal concrete. Results of Experiment were compared to truss methods in codes and some of proposed models in codes. Results indicate that all methods are conservative in low height beams and with increase in height, safety margin decreases.

Melanoma is one of the most dangerous types of cancers and every year, many people suffer from this cancer. Hence, quick diagnosis and treatment are significantly important for the physicians. In the recent decade, intelligent methods have attracted considerable attention for diagnosing and treating the melanoma. The main objective of this paper is determining the optimal dosage of the drug for the elimination of the cancer cells while preventing from the side effect of the drug on the normal cells. To this aim, the Q-learning algorithm is employed. In order to select the actions, a Case-Based Reasoning (CBR) policy is used, which is an accelerated heuristic policy. The considered policy has increased the learning speed and reduced the overall time, to reach the optimal policy. The half-life effect of the drug is also considered to obtain the side effect of the drug on the patientchr('39')s body, at each time step. In order to demonstrate Q-learning algorithm performance in cancer cells control and optimal dosage determination purposes, Q-learning is compared with two methods, including fix dosage injection method and Hamiltonian method, which is one of the most important optimal control methods. Finally, it is revealed that the total injected dosage by using Reinforcement Learning method (Q-learning) is significantly reduced within the whole period of time in comparison with employing the optimal control and a fixed dosage injection cases. The number of cancer cells is controlled, as well. It should be noted that by applying the noise and uncertainty to the system parameters and the initial conditions, the proposed method can successfully control the cancer cells.

In this paper, the main focus is on blood glucose level control and the possible sensor and actuator faults which can be observed in a given system. To this aim, the eligibility traces algorithm (a Reinforcement Learning method) and its combination with sliding mode controllers is used to determine the injection dosage. Through this method, the optimal dosage will be determined to be injected to the patient in order to decrease the side effects of the drug. To detect the fault in the system, residual calculation techniques are utilized. To calculate the residual, it is required to predict states of the normal system at each time step, for which, the Radial Basis Function neural network is used. The proposed method is compared with another reinforcement learning method (Actor-Critic method) with its combination with the sliding mode controller. Finally, both RL-based methods are compared with a combinatory method, Neural network and sliding mode control. Simulation results have revealed that the eligibility traces algorithm and actor-critic method can control the blood glucose concentration and the desired value can be reached, in the presence of the fault. However, in addition to the reduced injected dosage, the eligibility traces algorithm can provide lower variations about the desired value. The reduced injected dosage will result in the mitigated side effects, which will have considerable advantages for diabetic patients.

Solid particles transport in fluid flow is a common phenomenon in nature, and its typical example in hydraulic science is sediment transport in rivers. In a channel with a bed of sedimentary materials, there are no moving particles in the streams with a low discharge and remain steady in their place. As the particle velocity increases, the bed particles begin to move, which is called the threshold of motion of the sediment particles. In this study, using a numerical model based on OpenFOAM software, a two-dimensional simulation of the threshold of group motion of sediment particles in turbulent flow has been investigated. The approach used in the simulation is Eulerian-Lagrangian, in which the continuous phase is emulated in a continuous manner in the form of an Eulerian with finite volume method and sediment particles are simulated in a Lagrangian and particle tracing method. In the current model, the effect of fluid on the particle, the particle on the fluid, and the forces that the particles of the sediment enter to each other are considered. In order to consider the effect of sediment particles inner interacting, a soft contact approach and a friction damper spring impact model were used. The results of the numerical model have been compared with other researchers in the form of critical velocity method and a relation has been proposed for the particle motion threshold criterion. Also, Incipient motion of sediment particles with different criteria has been studied. The results show that the present model can simulate the phenomenon well with proper accuracy.

The anxiety disorders are the most common mental health condition. Through modulation of stress responses, the anxiety can be controlled. Stress clearly affects the autonomic nervous system and leads to excessive sweating and tachycardia. New methods to identify and manage stress are still needed.
In this paper, anxiety is controlled by reinforcement learning (RL). For stress level measurement, galvanic skin resistance (GSR) and heart rate were measured. Skin electrical resistance and heart rates are measured in 15 subjects (8 females and 7 males), ages between 22 to 30 years, during exposure to melodies and pictures. By connecting bioelectric sensors to the hand, GSR signal can be tested. Anxiety level was measured by biological signals (biofeedback) through playing melodies and showing pictures. First, GSR and heart rate signals were measured (states of RL). Then, the application plays a melody (agent) and shows a picture (actions). After a period of time, GSR and heart rate were measured again. Based on the differences between initial and secondary values, the reward, indicating stress variations were calculated. Based on the received reward, the agent learns to control the stress level.
The intelligent agent of RL, learns which picture to show and what melody to play in order to manage the stress. For each person, different melodies and pictures reduce the stress. This learning procedure should be repeated for each subject.
A novel method is proposed and investigated based on RL for anxiety control. The accuracy of diagnosis is improved in a simple, rapid, and low-cost way.

Restructuring in the power industry is followed by splitting different parts and creating a competition between purchasing and selling sections. As a consequence, through an active participation in the energy market, the service provider companies and large consumers create a context for overcoming the problems resulted from lack of demand side participation in the market. The most prominent challenge for customers on demand side, is bidding strategy selection manner for attending in the competitive market. In this regard, they attempt to pay the least expense for purchasing the energy, while tolerating the least risk. In this paper, bidding strategy of service provider companies and the large consumers in the power market is proposed under the eligibility traces algorithm. In this algorithm, the demand side customers are considered as agents of Reinforcement Learning (RL). These agents learn through interaction with environment to bid such that earn the highest benefit.

Spur dike is a structure that extends transversely from the river bank toward its thalweg and deflects the flows away from the bank toward the center. One common type of this structure is the T-shaped spur dike, and the wings present in this dike can have major effects on the proximate flow pattern. In this study, the turbulent flow around three attracting T-shaped spur dikes placed in series was simulated using the FLUENT, the finite volume method and the k-? turbulence model. The results of numerical modeling show that increasing the dike length and the spacing between dikes both alter the flow pattern around the structure, but flow pattern is more affected by the increase in the dike length than by the increase in the dike spacing.

The foreign direct investment is one of the factors affecting capital markets in different countries. Improvements in capital market and facilitate in the investment situation are one of the factors affecting countries’ economic growth path. In the recent economic literature, transparency is regarded as an important issue that influences foreign capital inflow into countries. For this reason and because the Middle East is formed by developing countries and so FDI has a great importance in this area. This paper studies the influence of transparency on FDI in these countries. Transparency involves a country's situation in terms of property right, the size of corruption and efficiency of the judicial system. The data that are examined within this study covers the period 2000-2015. The results indicate that transparency has a positive impact on the foreign direct investment in these countries, meaning that rising transparency increases FDI in these countries.

The growing IT and electronic devices as well as limitations in traditional education have led to the use of virtual education as an alternative. In order to use e-learning methods in medical science, localization and research studies to evaluate its effectiveness is essential. The purpose of this study was to review the studies related to virtual education in Medical Sciences in Iran.Key words such as virtual education, e-learning, computerized education issued by Google scholar databases were used to find articles in Persian and for Latin articles, PUBMED and Scopus databases were used. As the result, a limited number of 35 articles in the areas of educational software, multimedia tools, web-based training, studies using e-learning system, using email and mobile phones, electronic testing and continuing education via electronic methods were found and 27 of them were selected. The majority of the studies suggested that the effectiveness of virtual education is equal to or more than that of traditional education. Moreover, students and faculty members have a positive outlook on using virtual education in most of the mentioned studies and are highly satisfied by them. However, since the effect of virtual education on learning and the satisfaction of the learners can differ depending on the method employed, the different majors of the medical sciences require more extensive studies in Iran.

Mercury is one of the three heavy metals which are listed on top of the US EPA,s list for toxic and hazardous material with priority for human health. The aim of this study was to evaluate the situation of environmental and occupational human contamination with mercury in Iran. All studies conducted in Iran which had measured mercury levels in humans in Iran, were searched by electronic databases including Iranmedex, SID, Magiran, Google Scholar, Web of Knowledge, Science Direct and PubMed. Due to the heterogeneity of the study results, meta analysis was not performed on the results. After searching the databases, fourty seven articles were found. From these, 44 were chosen and evaluated. Although contamination with mercury is not high in Iran, but occupational exposure is considerable and contamination in specific groups such as dentists is high. Considering the high exposure to mercury in some occupational groups, using personal protective equipment and proper ventilation at workplaces should be emphasized. Also periodic monitoring of mercury exposure and medical examination of related workers is necessary. In order to decrease environmental exposure, it is suggested that legislations for routine monitoring of mercury in food and using composites without mercury be practiced.

Virtual learning is a type of electronic learning system based on the web. It models traditional in- person learning by providing virtual access to classes, tests, homework, feedbacks and etc. Students and teachers can interact through chat rooms or other virtual environments. Web 2.0 services are usually used for this method. Internet audio-visual tools, multimedia systems, a disco CD-ROMs, videotapes, animation, video conferencing, and interactive phones can all be used to deliver data to the students. E-learning can occur in or out of the classroom. It is time saving with lower costs compared to traditional methods. It can be self-paced, it is suitable for distance learning and it is flexible. It is a great learning style for continuing education and students can independently solve their problems but it has its disadvantages too. Thereby, blended learning (combination of conventional and virtual education) is being used worldwide and has improved knowledge, skills and confidence of pharmacy students.The aim of this study is to review, discuss and introduce different methods of virtual learning for pharmacy students.Google scholar, Pubmed and Scupus databases were searched for topics related to virtual, electronic and blended learning and different styles like computer simulators, virtual practice environment technology, virtual mentor, virtual patient, 3D simulators, etc. are discussed in this article.Our review on different studies on these areas shows that the students are highly satisfied with virtual and blended types of learning.