FROM THE HISTORY OF LITERARY TRANSLATIONS OF K.L. KHETAGUROV’S WORKS: L.D. KIPIANI’S RUSSIAN INTERPRETATION OF THE POEM "SIDZÆRGÆS" ("MOTHER OF ORPHANS")

В статье впервые рассматривается переведенный на русский язык грузинским поэтом и прозаиком Л.Д. Кипиани текст стихотворения К.Л. Хетагурова «Сидзæргæс» (с осет. ‛Вдоваʼ, ‛Мать сиротʼ, в переводе звучит как «Мать»). Автором исследования представлен лексико-семантический анализ разноязычных текстов (оригинал, перевод), прослеживается степень воспроизведения переводчиком интенций автора. В ходе анализа установлено, что приоритетная для художественного текста функция эстетического воздействия на реципиента достигается Л.Д. Кипиани использованием лексических единиц с близким семантическим значением. Однако передача символики, заложенной К. Хетагуровым в художественных образах, не всегда находит выражение в переводе. Важные для раскрытия идейного замысла художественного текста образы-символы: «æргъæвст халон» (‛закоченелая воронаʼ), «сау айнæг» (‛черный утесʼ), «саударæг ус» (‛носящая траур женщинаʼ) и т.д. остаются вне поле зрения реципиента. Не всегда автор перевода преследует цель отразить национально-культурную специфику, заключенную в семантике выражений «зæрдæ къахын», «буц хъæбул», «дудгæ фæбадын», «къона», «гыцци», «лыстæн». В тексте перевода они находят свои русифицированные варианты или не передаются вовсе.Но там, где переводчик отходит от содержания подлинника, создается оригинальный текст в той же художественной манере, близкой к авторской. Основная идея произведения в переводе не теряется: переводчик и на русском языке демонстрирует мытарства матери сирот. Л. Кипиани в целом удалось изобразить картину жизни осетин конца XIX – начала ХХ в., представленную К.Л. Хетагуровым в стихотворении «Сидзæргæс». For the first time, the article considers the translation into Russian by L.D. Kipiani, the Georgian poet and prosaic, of the text of the poem by K.L. Khetagurova "Sidzærgæs" (‛Widowʼ, ‛Mother of Orphansʼ, translated as “Mother”). The author of the study presents a lexical and semantic analysis of multilingual texts (original and translation), traces the degree with which the translator reproduces the author's intentions. In the course of the analysis, it was found that the priority function of aesthetic impact on the recipient for a literary text is achieved by L.D. Kipiani by using lexical units with similar semantic meaning. However, the transmission of the symbolism laid down by K. Khetagurov in artistic images does not always find expression in translation. Symbolic images that are important for the disclosure of the ideological concept of a literary text: "ærgævst halon" (a freezing raven), "sau ainæg" (black rock), "saudaræg us" (a woman in mourning), etc. remain out of sight of the recipient. The author of the translation does not always pursue the goal of reflecting the national-cultural specifics contained in the semantics of the expressions "zarda kahyn", "buts kh'abul", "dudgk fabadyn", "qona", "gyzzi", "lystun". In the text of the translation, they find their Russified versions or are not transmitted at all. But where the translator deviates from the contents of the original, the created text is, nevertheless, in the artistic manner, close to the author's. The main idea of ​​the work is not lost in the translation: in the Russian translation the hardships and ordeals of the mother of the orphans are also vivdly conveyed. L. Kipiani as a whole managed to depict a picture of the life of the Ossetians of the late 19th - early 20th centuries, presented by K.L. Khetagurov in his poem "Sidzærgæs" ("Mother of Orphans").

Efficient Drill Sequence Optimization Using a Heuristic Priority Function

Drill sequence optimization is a common challenge faced in the oil and gas industry and yet it cannot be solved efficiently by existing optimization methods due to its unique features and constraints. For many fields, the drill queue is currently designed manually based on engineering heuristics. In this paper, a heuristic priority function is combined with traditional optimizers to boost the optimization efficiency at a lower computational cost to speed up the decision-making process. The heuristic priority function is constructed to map the individual well properties such as well index and inter-well distance to the well priority values. As the name indicates, wells with higher priority values will be drilled earlier in the queue. The heuristic priority function is a comprehensive metric of inter-well communication & displacement efficiency. For example, injectors with fast support to producers or producers with a better chance to drain the unswept region tend to have high scores. It contains components that weigh the different properties of a well. These components are then optimized during the optimization process to generate the beneficial drill sequences. Embedded with reservoir engineering heuristics, the priority function helps the optimizer focus on exploring scenarios with promising outcomes. The proposed heuristic priority function, combined with the Genetic Algorithm (GA), has been tested through drill sequence optimization problems for the Brugge field and Olympus field. Optimizations that are directly performed on the drill sequence are employed as reference cases. Different continu- ous/categorical parameterization schemes and various forms of heuristic priority functions are also investigated. Our exploration reveals that the heuristic priority function including well type, constraints, well index, distance to existing wells, and adjacent oil in place yields the best outcome. The proposed approach was able to achieve a better optimization starting point (∼5-18% improvement due to more reasonable drill sequence rather than random guess), a faster convergence rate (results stabilized at 12 vs. 30 iterations), and a lower computational cost (150-250 vs. 1,300 runs to achieve the same NPV) over the reference methods. Similar performance improvement was also observed in another application to a North Sea type reservoir. This demonstrated the general applicability of the proposed method. The employment of the heuristic priority function improves the efficiency and reliability of drill sequence optimization compared to the traditional methods that directly optimize the sequence. It can be easily embedded in either commercial or research simulators as an independent module. In addition, it is also an automatic process that fits well with iterative optimization algorithms.

Feature selection in evolving job shop dispatching rules with genetic programming

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

Feature selection in evolving job shop dispatching rules with genetic programming

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

Service Sector: Future System Challenges

The article is devoted to determining the system priorities of the strategic development of the service sector in the context of the formation of modern technological challenges. The study used the basics of a systematic approach and synergetic, theoretical generalizations of existing scientific developments, structural and functional analysis, and interpretation of objective trends. Special attention is paid to the turbulence of socio-economic processes in connection with the upcoming changes in the main parameters of employment for reasons of automation and digitalization, which cannot be strictly quantified at the present time. In order to maximize the use of real technological opportunities for the transition to a new level of labor productivity in the national economy, it is proposed to take into account the potential for using the principles of unconditional basic income in practice, which is considered as the main content of the upcoming transformation of the service sector. As a result, with sufficient reason, a new use of the created product is expected, which means the formation of new economic relations, primarily in the service sector. This consistently becomes a priority function and a trend of its immediate development, which is not yet paid due attention in economic science. Modern technological digitalization makes real the processes of consistent integration of the system components of social protection and social policy into the relations of unconditional basic income and becomes the basis for the synergy of the formation of new economic relations in specific sectors of the service sector and social security systems.

Formation of spiritual culture and environmentally responsible human behavior

The spiritual sphere of the society is a system of relations between people, reflecting the spiritual and moral life of a society, represented by such subsystems as culture, science, religion, morality, ideology, art. The significance of the spiritual sphere is determined by its most important, priority function of determining the value-normative system of a society, which, in its turn, reflects the level of development of public consciousness and the intellectual and moral potential of a society as a whole. The relevance of the research topic is determined by the sociocultural changes taking place in a society associated with the formation of a new type of sociality in Kyrgyzstan. In a society that has moved to a different stage of socio-historical development, the role of the spiritual sphere of society changes significantly. In the conditions of the existence of worldview pluralism and the complication of social reality, the need to reflect on society as a “field” for the implementation of various intentions of spiritual life is actualized. The formation of a new social reality of a society in the process of transition to market relations is associated with negative processes of spiritual impoverishment of citizens, gradually forgetting about their historical roots.

Hybrid evolutionary computation methods for quay crane scheduling problems

Quay crane scheduling is one of the most important operations in seaport terminals. The effectiveness of this operation can directly influence the overall performance as well as the competitive advantages of the terminal. This paper develops a new priority-based schedule construction procedure to generate quay crane schedules. From this procedure, two new hybrid evolutionary computation methods based on genetic algorithm (GA) and genetic programming (GP) are developed. The key difference between the two methods is their representations which decide how priorities of tasks are determined. While GA employs a permutation representation to decide the priorities of tasks, GP represents its individuals as a priority function which is used to calculate the priorities of tasks. A local search heuristic is also proposed to improve the quality of solutions obtained by GA and GP. The proposed hybrid evolutionary computation methods are tested on a large set of benchmark instances and the computational results show that they are competitive and efficient as compared to the existing methods. Many new best known solutions for the benchmark instances are discovered by using these methods. In addition, the proposed methods also show their flexibility when applied to generate robust solutions for quay crane scheduling problems under uncertainty. The results show that the obtained robust solutions are better than those obtained from the deterministic inputs. © 2013 Elsevier Ltd.

Hybrid evolutionary computation methods for quay crane scheduling problems

Quay crane scheduling is one of the most important operations in seaport terminals. The effectiveness of this operation can directly influence the overall performance as well as the competitive advantages of the terminal. This paper develops a new priority-based schedule construction procedure to generate quay crane schedules. From this procedure, two new hybrid evolutionary computation methods based on genetic algorithm (GA) and genetic programming (GP) are developed. The key difference between the two methods is their representations which decide how priorities of tasks are determined. While GA employs a permutation representation to decide the priorities of tasks, GP represents its individuals as a priority function which is used to calculate the priorities of tasks. A local search heuristic is also proposed to improve the quality of solutions obtained by GA and GP. The proposed hybrid evolutionary computation methods are tested on a large set of benchmark instances and the computational results show that they are competitive and efficient as compared to the existing methods. Many new best known solutions for the benchmark instances are discovered by using these methods. In addition, the proposed methods also show their flexibility when applied to generate robust solutions for quay crane scheduling problems under uncertainty. The results show that the obtained robust solutions are better than those obtained from the deterministic inputs. © 2013 Elsevier Ltd.