In this paper, we improve some previous definitions of fuzzy-type Turing machines to obtain degrees of accepting and rejecting in a computational manner. We apply a BFS-based search method and some level’ s upper bounds to propose a computational process in calculating degrees of accepting and rejecting. Next, we introduce the class of Extended Fuzzy Turing machines equipped with indeterminacy states. These states are used to characterize the loops of classical Turing machines in a mathematical sense. In the sequel, as well as the notions of acceptable and decidable languages, we define the new notion of indeterminable language. An indeterminable language corresponds to non-halting runs of a machine. Afterwards, we show that there is not any universal extended machine; which concludes that these machines cannot solve the halting problem. Also, we show that our extended machines and classical Turing machines have the same computational power. Then, we define the new notion of semi-universality and prove that there exists a semi-universal extended machine. This machine can indeterminate the complement of classical halting problem. Moreover, to each r. e or co-r. e language, we correspond a language that is related to some extended fuzzy Turing machines.

Putnam's 1960 article, "Minds and machines", sketched a novel approach to the mind based on the analogy between mental states and logical-computational state of Turing machine, which later acknowledged as functionalism. Based on his interpretation of the phenomenology of Merleau-Ponty and Heidegger, Dreyfus criticized Putnam’s functionalist-computation a list approach to the mind in an article directly addressing to him. In both physical and psychological-phenomenological level, Dreyfus proposes that mind can’t be understood as Turing machine which works with formal rules and in which inputs and outputs are context-free and independent of environment. About three decades after his article "minds and machines", according to Carnap’s failure to formulate induction and Quine’s holism and also based on his semantic externalism, Putnam rejected his functionalist-computation a list approach and accepted an externalist-contextualist approach. Although Putnam does not mention the name of Dreyfus, his criticism to reject functionalism was obviously resembling the one Dreyfus stated previously contrary to the functionalism. In this paper, in order to reconstruct the critical dialogue between Dreyfus and Putnam, first, functionalism is formulated in the works of Putnam. After that, the phenomenological critique of Dreyfus to Putnam is stated. Then, it is shown that the causes of Putnam’s turning from functionalism to contextual point of view were thoroughly similar to Dreyfus’s critics to him. Last, it is shown that based on this turning from functionalism to contextualism, physicalism as a theory of mind is challenged and instead “subject in the world” provides a better model of the mind.

Patterns are found everywhere and the past fifty years studies have advanced our understanding of the mechanisms. In this paper, we study those systems that develop temporary patterns. Special emphasis is made on Turing instabilities as one of the most common sources of pattern formation. Gierer-Meinhardt model acts as one of prototypical reaction diffusion systems describing pattern formation phenomena in natural events. Bifurcation analysis, including theoretical and numerical analysis, is carried out on the Gierer-Meinhardt activator-substrate model. The effects of diffusion on the stability of equilibrium points is investigated. It shows that under some conditions, diffusion-driven instability, i. e, the Turing instability, about the equilibrium point will occur, which is stable without diffusion. These diffusion-driven instabilities will lead to the occurrence of spatially nonhomogeneous solutions. Consequently, some pattern formations, like stripe and spots solutions, will appear. To illustrate theoretical analysis, we carry out numerical simulations. These diffusion-driven instabilities will lead to the occurrence of spatially nonhomogeneous solutions. Consequently, some pattern formations, like stripe and spots solutions, will appear. To illustrate theoretical analysis, we carry out numerical simulations.

At this study, after designing and manufacTuring a rotary tool, its machining parameters and their effects on surface roughness of aluminum alloy of Al99 which is one of the most consumable alloys in aerospace industry, is studied and optimized. First, the rotary tool is designed and manufactred. Then according to Taguchi method, the experiments have been designed to record surface roughness, machining temperature and exerted forces on the tool. In order to evaluate optimum condition of the experiment results, MINITAB and Genetic Programming (GP) have been implemented to obtain the best regression; then for obtaining the optimum condition of machining, the results of running Genetic Programming has been used. In the end optimum machining parameters to achieve the best surface quality is presented.

 In recent years, artificial intelligence has created significant developments in various artistic fields, including architecture, cinema, music and other artistic fields, by providing innovative facilities, tools and techniques. Especially in the field of visual arts, it has caused the creation of significant works of art, whose nature requires a more detailed evaluation and understanding. Therefore, like any other transformative technology, the integration of artificial intelligence in the world of art and its undeniable role in the creation of works of art raises many questions and considerations that may not have been carefully examined until now. Among the most important of these questions comes back to the performance of artificial intelligence in creating an original work of art. Specifically, the question of whether artificial intelligence can create works equal to human works of art? Since AI is algorithm-based and often trained on existing art, can its output be considered a creative and authentic work of art?Therefore, the main goal of this article is to investigate the performance of artificial intelligence in the production of original works of art. Artificial intelligence is closely related to behaviorism, as one of the most important theories of philosophy and psychology. The emphasis of this theory is on performance or "observable behavior" as the main focus of understanding and analysis. Therefore, in this research, the performance of artificial intelligence is evaluated based on this approach at the level of behavior. For this purpose, the approach of behaviorism and its connection with artificial intelligence will be briefly discussed at first. Then the Turing test is described as a central test in the field of artificial intelligence. This test provides a behavioral criterion for measuring the intelligence and creativity of the machine. In the next section, two categories of works of art will be evaluated according to the behavioral criteria of the Turing test and considering the main elements of original works of art. These two categories include works produced by artificial intelligence and works created by humans. In this way, by using different sources, data related to art works from artificial intelligence are collected and described in a qualitative way. Then these data are compared with the elements found in human works of art and are analyzed based on the main criteria of the Turing test. This review provides a good basis for evaluating the machine's performance compared to humans.As mentioned, in the present research, in order to investigate the performance of artificial intelligence in creating an original work of art, firstly, the approach of behaviorism and its relationship with artificial intelligence is explained. Behaviorism is a school of thought in philosophy and psychology that claims that mental states such as thoughts, feelings, and beliefs can only be understood in terms of their relationship with "observable behavior". According to behaviorists, behavior can be explained based on causal relationships between environmental stimuli and behavioral responses. The focus of this research is on philosophical behaviorism. Philosophical behaviorism, also known as logical behaviorism, is a theory about the nature of the mind. Behaviorists in the field of philosophy also believe that mental states can only be explained in terms of behavior. In other words, mental states are only descriptions of certain types of behavior. This approach originated from the works of philosophers such as Gilbert Ryle and Ludwig Wittgenstein.From the 1950s until now, the connection between behaviorism and artificial intelligence was formed. Although these two may seem different from each other, they both pay special attention to "observable behavior". The initial research of artificial intelligence was influenced by the principles of behaviorism. For example, the Turing test, which is one of the most popular artificial intelligence tests, is basically a behavioral evaluation. Just as behaviorists consider the only valid data for studying the mind to be "observable behaviors," the Turing test also shows that a machine can be considered "intelligent" based on its "observable behavior" regardless of its internal processes or understanding. Therefore, this test provides a suitable criterion for measuring intelligence based on performance and "observable behavior".The Turing test is conducted through a question and answer process. A human evaluator or judge asks an unseen audience, either a human or a computer, through a text and with a computer, and based on the answers, he must determine whether he is talking to a human or a machine. If the judge cannot distinguish the machine from the human in the answers he receives, it is said that the machine has passed the test successfully. The results of this test depend on how much the machine's answers to the questions are indistinguishable from the answers given by a human or how similar they are to human answers. In this section, the performance stages of the Turing test are formulated as follows:Input behavior: the evaluator provides data to the machine and asks for an output from it.Output behavior: Then the machine responds to the input. This answer is "observable behavior" in this context.At the end of this section, in order to take advantage of the main criteria of the Turing test in order to evaluate the performance of artificial intelligence in the creation of artwork, a new formulation of this test in the field of art is presented, which is as follows:Input behavior: The programmer provides data related to the creation of the artwork to the machine and asks it for an artwork.Output behavior: Then the device produces the artwork in response to the input data. The output of the machine is "observable behavior" in the context of art.In the next section, first, the main elements of the works of art created by humans are listed as "observable behaviors". These elements provide criteria for measuring the artistic performance of artificial intelligence and include: form, content, style, context, intention, impact, innovation and novelty, complexity and depth, adaptation and evolution, individuality and uniqueness. These elements can interact in complex ways to create a rich and multifaceted work of art that engages the viewer's senses, emotions, and intellect. Therefore, they can be evaluated as "observable behaviors" in the works produced by artificial intelligence and compared with human art.In the following, the main components of a number of works produced by artificial intelligence are compared with the components in similar works of art created by humans. In this section, according to the main criteria in the theory of behaviorism and the Turing test, which only focus on "observable behaviors" rather than mental processes, it will be evaluated that the elements of the works of artificial intelligence as "observable behaviors" to what extent and in what ways are similar to the elements in human works of art. In addition, to what extent artificial intelligence has been successful in creating a human-like behavior in order to create a work of art.By comparing the work of Tom White and Wassily Kandinsky's machine, it can be seen that both of them turned to abstractionism in the creation of works of art, and with this method, in their works, they revealed new concepts in relation to visible reality. By taking a creative approach, Gene Kogan and Jackson Pollack have abandoned the usual traditions to create original works of art; They have found art in many things that are not in the art scene or do not even call themselves art. Obvious’s machine and Caspar Netscher consider realism in their works. Light is considered one of the basic elements of their images, as its intensity and angle of radiation emphasize the inner power of the portrait in their paintings. paintings by Robbie Barratt’s machine and Claude Monet are both in impressionism style. The elements in their works have vague and often abstract lines. The colors have been combined with each other in such a way that they have created a soft surface and cover, and the way of using natural light in their paintings has made them attractive.To pass the Turing test, the only thing that was considered was how close the answers of the machine are to the answers of a human. In this research, just like the hypothesis that was raised in the Turing test, by evaluating the similarities between the works of artificial intelligence and humans, it is shown that machines have the same "observable behaviors" as human artists in creating works of art. to be Therefore, they pass the Turing test successfully. Therefore, they can be considered intelligent. In the present research, the result is that artificial intelligence can express itself effectively and with a distinctive style in terms of "observable behavior" in the creation of artwork and create original and creative works, just like human works.

In recent years, one of the main topics of interest in the security of outsource computations is checking the integrity of the results received from the outsourced computations. Outsourced computations can be run on data received from single or multiple data sources. There are a few methods proposed for system models with distributed data sources. The main solutions provided in this area to verify the correctness of the execution of any or some special functions such as linear, polynomial or aggregate functions are categorised to: (1) verifiable computations, (2) homomorphic authenticators, and (3) methods proposed for specific applications such as outsourced databases, wireless sensor networks and data stream management systems. In this paper, these methods, especially the methods proposed for outsourced computations in data stream management systems, have been reviewed and compared.

Most of the Iranian oil and gas wells in the Persian Gulf region are producing through their natural productivity and, in the near future, the use of stimulation methods will be undoubtedly necessary. Hydraulic fracTuring as a popular technique can be a stimulation candidate. Due to the absence of adequate research in this field, numerical simulation can be an appropriate method to investigate the effectiveness of hydraulic fracTuring. In the current study, the hydraulic fracTuring process is simulated for a wellbore in the Persian Gulf region with Abaqus software. The main parameters that are necessary for the simulation are collected through wellbore logs and core tests. FracTuring process is studied with more emphasis on the pressure of fracTuring fluid and fracture opening. Finally, several 3D fluid-solid coupling finite element models are generated and the main obtained results are compared.

First, in the light of Feferman’s views, we will examine Gödel’s dichotomy that either the capabilities of the human mind are beyond any finite machine, or there are Diophantine-type mathematical equations that are absolutely unsolvable. Then we examine Putnam’s argument that if scientific competence of the mind can be simulated by a Turing machine with the ability to prepare a list of scientific propositions, this machine will not print out the sentence that expresses this ability. In an effort to better understand this proof, we restate it in the language of modal logic. Then, we discuss the possibility of supertask computations to perform infinite basic operations in finite time. This is a possibility that has recently been proposed based on new physical theories. We argue that, assuming that such a possibility is realized, arithmetic will be determinate, meaning that the truth or falsity of each arithmetic sentence will be explainable.

Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. There are a lot of well log data related with this parameter. In this study, permeability is predicted using them and a supervised committee machine neural network (SCMNN) which is combined of 30 estimators. All of data were divided in two low and high permeability populations using statistical study. Each estimator of SCMNN was combined of two simple networks to predict permeability in both low and high classes and one gating network, considered as a classifier, classified data to these two classes. Thus, each low and/or high input data would predict in related network. This SCMNN was used to predict permeability on the data of one of petroleum reservoirs of south-west of Iran. 210 samples of this reservoir were available. Because of the fewness of data 80% of them were used as training data and 20% of them were used as validation and testing data. The overall fitting between predicted permeability versus measured ones was qualified through R2 (R=correlation coefficient) to be 97.72% which is considered appropriate. Whereas, R2 in the simple network in the best stat was 84.14%. The high power and efficiency of SCMNN are indicated by lower bias and better R2 in results.