To accelerate the learning process in high-dimensional learning problems, the combination of TD techniques, such as Q-learning or SARSA, is usually used with the mechanism of Eligibility Traces. In the newly introduced DQN algorithm, it has been attempted to using deep neural networks in Q learning, to enable reinforcement learning algorithms to reach a greater understanding of the visual world and to address issues Spread in the past that was considered unbreakable. DQN, which is called a deep reinforcement learning algorithm, has a low learning speed. In this paper, we try to use the mechanism of Eligibility Traces, which is one of the basic methods in reinforcement learning, in combination with deep neural networks to improve the learning process speed. Also, for comparing the efficiency with the DQN algorithm, a number of Atari 2600 games were tested and the experimental results obtained showed that the proposed method significantly reduced learning time compared to the DQN algorithm and converges faster to the optimal model.

Some people suffering from diabetes use insulin injection pumps to control the blood glucose level. Sometimes, the fault may occur in the sensor or actuator of these pumps. The main objective of this paper is controlling the blood glucose level at the desired level and fault-tolerant control of these injection pumps. To this end, the Eligibility Traces algorithm is combined with the sliding mode control. The Eligibility Traces algorithm is one of the newest solving methods of the Reinforcement Learning approach. The major disadvantage of the sliding mode control method is the chattering phenomenon. In this paper, the novel idea is the combination of these methods to remove the chattering phenomena in simulation results. To demonstrate the superiority of the proposed method, it is compared with another combinatory method that is the sliding mode control and Artificial Neural Networks. Simulation results reveal that the combination of the Eligibility Traces algorithm and the sliding mode control can control the blood glucose level and insulin with a higher speed and bring them to the desired level, even in the case the sensor and actuator faults are present in the system. When the proposed hybrid method is used, the injected dosage of the drug is lower, which will result in reduced side effects. Finally, the noise, as well as the uncertainty in system parameters and initial conditions are applied to the system to investigate the performance of the proposed controller, under the faulty condition.

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.

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.

Shahīd-i ‘Awwal is one of the great jurists of 14th century AD. His works are still part of the Islamic seminaries’ curriculum. One of his most famous book is Ghāyat al-Murād, which includes several topics on Islamic law. One of these topics is the problem of the daily prayers which have not been done at they proper time and ought to be done again. The solution of that problem needs some knowledge of mathematics, especially combinatorics.It is necessary prayers exactly in the same sequence as the original prayers.Shahīd-i ‘Awwal has solved the problem by inventing a mathematical method, the function that is called today Factorial. Other great jurist, Shahīd-i Thānī, also has solved the problem, but not exactly in the same way. He wrote a commentary on Shahīd-i ‘Awwal’s Lom’a. There is no doubt that the concept of this function is well understood.

Undoubtedly, one of the dangerous blights which has afflicted the narrative interpretations and the interpretive narrations are the exaggerative ideas and beliefs of the narrators which have sometimes led to hadith fabrication or semantic forgery and incorrect ta'vil of the Quranic verses. Developing cognizance of this phenomenon especially in the field of interpretive narrations involves some problems: the precise meaning of exaggeration and its bordering line with the real virtues of the infallible leaders of the religion (a.s.), the required criteria for the recognition of exaggerators and their exaggerative beliefs as well as the exaggerating narrators or narrators accused of exaggeration are not that much clear. Some of the questions dealt with in this article are: What kind of role and influence have the exaggerators had in fabrication of interpretive narrations and/or false ta'vils? What is the number of the narrators accused of exaggeration and the exaggerated narrations?

Mathnawi and Shams Divan are not only the greatest heritages of Iranian mysticirm but they should also be considered as the source of many philosophical theories and ideas in the form of poetry. For instance, one can find the solipsismic ideas of Proust in Rumi's ideas on love. One could also see Traces of phenomenological philosophy as well as deconstructive ideas of Derrida in Mathnawi. In this article, without paying attention to the overall general structure of Rumi's though, it is claimed that the gist of many western philosophies can be found in Rumi's writings.

The research addresses the factors which have given the Islamic Persian Architecture its extra-temporal and extra-spatial characteristics, such as: structural  focus; physical storylines; disposition according to spatial significance; perfectionism; patterns; proportions; prudence; predilection; thriftiness; and finally strong links with nature. The contrasting features of contemporary architecture of Iran are then reviewed: individualism, nonconformity; and westernization. This leads to recommendations for channeling the contemporary architecture towards a more valid architecture.