This paper explores the use of chemical symbolism in works by the new media artist Sonya Rapoport, with a focus on the pivotal Cobalt series from the late 1970s. These works, drawings on computer printouts generated by research at the Lawrence Berkeley Laboratory, respond to experiments in nuclear chemistry. They mark the beginning of three productive decades in which Rapoport produced a variety of images related to chemistry in her work. She states, “I looked for authentic research projects (...) that were interesting to me, preferably with captivating pictorial subject matter. Then came the creative chaotic process of resolving a cohesive product that combined scientific research with art concept.” Rapoport had an unusual degree of access to scientific materials through her husband, organic chemist Henry Rapoport, a faculty member at the University of California, Berkeley. At the time of production, these works were outside mainstream art world interests and they have received little critical attention. This paper examines the development of Rapoport’s images and places her use of chemical references in context in her lifetime of work. (shrink)

The evolution of Intelligent Tutoring System (ITS) is the result of the amount of research in the field of education and artificial intelligence in recent years. English is the third most common languages in the world and also is the internationally dominant in the telecommunications, science and trade, aviation, entertainment, radio and diplomatic language as most of the areas of work now taught in English. Therefore, the demand for learning English has increased. In this paper, we describe the design of (...) an Intelligent Tutoring System for teaching English language grammar to help students learn English grammar easily and smoothly. The system provides all topics of English grammar and generates a series of questions automatically for each topic for the students to solve. The system adapts with all the individual differences of students and begins gradually with students from easier to harder level. The intelligent tutoring system was given to a group of students of all age groups to try it and to see the impact of the system on students. The results showed a good satisfaction of the students toward the system. (shrink)

Vizantiĭsʹkyĭ neoplatonizm i︠a︡k predmet relihii︠e︡znavchoho ta istoryko-filosofsʹkoho doslidz︠h︡enni︠a︡ -- Formuvanni︠a︡ vizantiĭsʹkoho neoplatonizmu u tvorakh Ĭoana Filopona (Dionisii︠a︡ Areopahita) -- Filosofii︠a︡ butti︠a︡ Maksyma Spovidnyka--kulʹminat︠s︡ii︠a︡ rozvytku vizantiĭsʹkoho neoplatonizmu -- Vizantiĭsʹkyĭ neoplatonichnyĭ esent︠s︡ializm kint︠s︡i︠a︡ VII-pochatku XIV stolitʹ -- Palamizm i︠a︡k osoblyva forma vizantiĭsʹkoho neoplatonizmu.

S. I. Benn; VI*—Freedom, Autonomy and the Concept of a Person, Proceedings of the Aristotelian Society, Volume 76, Issue 1, 1 June 1976, Pages 109–130, https://.

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key property characteristic (...) of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector.. It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one. (shrink)