Art Methodologies Research Articles

Ethnoaesthetics of the Ukrainian folk song tradition in the performance of the singer Oksana Mukha

The topicality is associated with the need for musicological understanding of the value categories of ethnoaesthetics in the creative activity of singers that represent the Ukrainian folk song tradition in modern variety vocal art.
 The purpose of the article is to identify ethno-aesthetic value components in the performance of O. Mukha as a personification of the uniqueness of the Ukrainian national expression.
 The methodology consists of scientific works of prominent Ukrainian scientists devoted to the study of ethno-aesthetic ideas and criteria in the traditional songs of Ukraine (Y. Harasym, T. Orlova, V. Lychkova), as well as genre-style and performance specifics of Ukrainian musical folklore (K. Kvitka, F. Kolessa, A. Ivanytskyi, S. Hryts).
 The results. The artistic imagery of the iconic folk songs of O. Mukha repertoire, which is a part of the ethno-aesthetic fund of the Ukrainian folk song tradition, is considered. The general features of ethnoaesthetics of the folk song performing tradition of Ukraine, including its vocal and technical component are outlined.
 The novelty. The article considers the performance of O. Mukha for the first time and reveals ethnoaesthetic value components in it.
 The practical significance. The study of the essence of folk singing as an art with its own artistic and value component, based on an established system of qualitative components of ethnoaesthetics, as well as the study of performing experience of prominent Ukrainian folk singers are the necessary practical material for creating a scientific basis and general methodology of folk vocal art.
 The conclusions. Musical ethnoaesthetics as a component of traditional culture covers the semantic field of songs, concentrating on the structure, rhythmics and size of a poem, the richness of national melos, its intonation and tonality basis. Ethnoaesthetics of the folk song tradition of Ukraine is manifested in the uniqueness of the national expression, namely: vocal-timbre color of sound, in the peculiarities of intonation, freedom of metro-rhythmic movement, variability and melismatic richness.
 In O. Mukha’s works, the creation of the sound matter of the modern performance version of a folk song is based on the perception of song intonation as sound emotion and understanding of the boundless ocean of ancient symbols of the national musical language, which during centuries has crystallized in the folk song tradition. Her works grow out from the reflection on the national song culture and traditions of her family. Work on each ancient relict of tradition is based on discovering the potential of the power of its aesthetic, spiritual and emotional impact on listeners, perception of the stock of expressive possibilities.

SEMIOTICS OF GESTURE: PERFORMATIVE CONCEPTUALISM

Problem setting. This article poses the problem of understanding and applying performance as a conceptual model of the functioning of an individual in a modern multi-layered reality. The study presents an analysis of popular actionist practices and the further development of the pure action ontology. The performance serves as an approbation of the dynamism technique and the principle of action in social, digital and artistic actions.
 Recent research and publications analysis. The assertion of the principle of dynamism and activity in the modern cultural space explains the popularity of performative topics in a parallel scientific and philosophical discourse. At the same time, the bulk of works on performance relates to the field of theory and methodology of art. These are the works of such authors: G. Elshevskaya, E. Krylova, E. Andreeva, J. Kostincova, D. Filippova, Andrey and Yaroslava Artemenko, H. Petrovsky, S. Levitt, H. Downey and J. Sherry, A. Eckersley and C. Duff.
 G Reingold, M. Cuellar-Moreno and J. Antonio Cubas-Delgado, A. Loskutov, O. Novoselova and E. Kurbanova and others have studied dynamic models of bodily expressiveness in social practices, including in the information space of culture and communication. In turn, the American philosophers J. Butler, J Dean and G. Standing develop the concept of performance in relation to political actionism. They view the gatherings of social rights agents as a performative practice of their relationship to power.
 Paper objective. The aim of the study is to review and theoretically analyze modern creative practices of the subject's bodily expression in the field of art, politics, law and digital communication, as well as to further substantiate the methodological role of the performative concept in modern culture.
 Paper main body. Today's popular installations, happenings, flash mobs and challenge explicates the very essence of modern human existence in the world. Art zones and other creative social spaces (workshops) use direct body language in their functioning and, at the same time, are highly attractive and effective in today's youth environment. Silent actions demonstrate the limited possibilities for a person to manifest themselves, despite the large number of rights, freedoms and technical means to improve life. Defective self-presentation expresses the one-sided and limited nature of the subject's existence in the social, legal, political and cultural space today.
 The natural change of technological structures, cultural eras and political models in the world irreversibly modifies a person, his environment and the possibilities of self-realization in it. The creative space of action allows to express the conceptual ontological principle of modernity – the break between the physical and the semantic, the act and the commentary, the action and the word, the body and corporeality. In a stratified reality, a person cannot maintain integrity. His verbal-symbolic being is separated from the physical (body); they express themselves and communicate autonomously and independently. Desocialization and the dominance of virtuality led to the dismemberment and impoverishment of many human functions, in particular the localization of his speech and actions. Therefore, performance is becoming an irreplaceable alternative way of self-realization of the silent body. While language remains a way of being a person as an agent of the information environment.
 Conclusions of the research. Thus, the study refers to the art experience of bodily subjects as a manifestation of their anthropological authenticity, social significance, legal usefulness, epistemological activity and political will.

The Junction Collection: A site-specific public fashion project as a (conceptual) magazine editorial

In the summer of 2018, the author was selected for the Junction Art Residency (JAR) programme, and for three months, he was integrated into the daily life of the Haines Junction village, a rural town in the Kluane region, Yukon. Like the rest of the Yukon, the Haines Junction village includes a sizable community of First Nation residents. To gain a practical understanding on how aboriginal principles and values shape life in Yukon, the author created a series of garments and accessories informed by the nature, histories and peoples of the Kluane region. This critical essay contextualizes the project and discusses the implications of using a magazine editorial as an alternative vehicle to disseminate it. With the direct participation of a diversity of Haines Junction’s residents, the author explored Yukon’s social and cultural complexities by means of integrating fashion design and public art methodologies. Titled The Junction Collection, the site-specific public fashion project incorporated locally available fabrics, traditionally harvested furs and upcycled materials. Throughout the design process, he consulted with indigenous makers, elders and knowledge keepers. Upon completing the series of garments, the author executed a photo shoot featuring Haines Junction’s residents wearing the outfits against the natural setting of the Kluane wilderness. Those images are the central content for the long-format magazine editorial presented and examined here. The critical essay argues that, as a visual narrative, this magazine editorial makes the project more accessible to the general public while effectively translating The Junction Collection’s theme of incorporating indigenous knowledge with contemporary aesthetics.

Using process drama to explore the causes of dental anxiety in primary-school children

BackgroundDrama and role play can be unlisted as methods to allow children to view problems from a range of different perspectives that may differ from their own experience. Application of drama technique to assess the cause of dental fear and anxiety in a school setting is novel.AimThe aim of this study was to engage primary school children in the core investigation via participatory arts methodologies, namely, process drama to gain understanding of the causes of dental anxiety.DesignSixty-three children, aged 7–10 years from three primary schools participated in this study. A 90-min drama workshop was carried in each school. The children were encouraged to identify the causes of dental anxiety using key concepts from process drama. The sessions were audio-recorded and transcribed.ResultsFour key concepts emerged: (1) fear of the unknown; (2) unpleasant sensory experience; (3) society’s perception and portrayal of the dentist; and (4) learnt negative associations with the dentist. Within each four key concepts, two sub-themes were identified.ConclusionsRole-playing and use of drama are a novel application and can reveal a considerable amount of information from the child’s perspective on the cause of dental fear and anxiety.

The possibilities of theatrical art in the devel-opment of the negotiation competence of the teacher-mediator

The purpose of the article is the theoretical substantiation of the didactic potential of the means of theatrical art in the development of the negotiation competence of the future teacher-mediator. The main idea of the research is to consider the problem of organizing the training of negotiators for the field of education at the interdisciplinary level, which ensures the complex nature of the educational activities of students-future teachers-mediators. More results consist in the presentation of an innovative approach to the development of the negotiation competence of the teacher-mediator, in particular, in development of the idea to Supplement the structure of the negotiation competence of acting component, the formulation of the scientific definition of acting competence of the teacher-mediator, development and justification of the conditions of realization of educational activity in the University by means of theatrical art, contributing to the successful professionalization of the future teacher-mediator. The theoretical significance of the research is to expand the understanding of the phenomenon of negotiation competence in relation to acting creativity, in particular, with acting technique, and to identify opportunities for developing the negotiation competence of a future teacher-mediator based on the methodology of theatrical art (on the example of theatricalization, forum theater and the use of acting and directing memoirs in professional training).

Design and value engineering of a high temperature high pressure multifunctional compression moulding unit to manufacture zero defect parts

Hot compression moulding is one of the better techniques employed to make zero defect products. This paper mainly discusses about the conversion of a conventional hydraulic press into a high pressure, high temperature compression moulding unit at a low cost using local state of the art methodology. A normal press used for blanking, drawing and various metal forming operations is modified here by addition of top and bottom platens with heating coils controlled by PID controllers to operate up to 500°c within which high Temperature Plastics, Aluminium, Tin, Lead, Magnesium etc. can be hot compacted with foil films and reinforcements or can be hot forged with matching dies. In this paper, hot compression moulding as a sound technique for manufacturing zero defect thermoplastic and advanced thermoplastic based composites is demonstrated and emphasized based on a defect evaluation of the moulded parts using an ultrasound scanning technique.

The performing principles of the Ukrainian pianist and teacher Viktor Syriatsky

Background. Viktor Syriatsky is a pianist, pedagogue, composer and scholar, a unique figure in the piano art of the second half of XX century. His talent as a pianist is incredibly multi-faceted, he brought up several generations of famous musician during 35 years, while working at Kharkiv I. P. Kotlyarevsky National University of Arts. The aim of the article is to reveal basic principles of the performing style of Viktor Syriatsky and essence of his artistic aspirations. The relevance of chosen facet of the research is caused by the fact that its results might be used in the work of performer on the interpretation of musical piece, and in practice of teaching, including such courses as history and methodology of performance art. Research methodology. In order to reveal the chosen theme, complex musicologically-analytical approach is used, which is founded on the works by such scholars as B. Asafev, S. Savshinskiy, S. Feynberg, Ya. Milshteyn, D. Rabinovich, N. Rastopchina, G. Neygauz, modern research – works by N. Drach (2006), I. Belonosova (2014), O. Fedorovich (2014) etc. These works allowed to regard performance style as an attribute of the personality of the performer himself, which might be dependent on his own physiological and psychological traits, from his mentality and worldview. Results for discussion. All his life V. Syriatsky consciously and unremittingly worked on his performer style. The play of the pianist draws attention from the very first sounds. His sound is full mellow, warm, resonant, equally noble even in mighty ff and in soft pp, always full and resonant. His sound is a tool, not the goal itself. The best sound is the one, the most fully. embodying artistic content. Mastership of V. Syriatsky is connected as well with rich usage of textural performer’s means and pedal. Understanding performance art as the one bearing artistic content, he regarded intonation to be the most important mean of musical expressiveness. Apart from performer’s intonation for him the was not truly valuable and significant recreation of the author’s idea. His play stood out apart from the play of other pianists by naturality of the performance: whatever he played, it became alive under his hands. The master was incredibly careful in his choosing of temporal organization in music such as tempo. He also underlined importance of the performer’s rubato. At the same time, the ideas of the composer were a law for him. Conclusion. In performance style of V. Syriatsky there was incredible calmness and sage of a man who went through diverse school of life.

A Survey on Low-Thrust Trajectory Optimization Approaches

In this paper, we provide a survey on available numerical approaches for solving low-thrust trajectory optimization problems. First, a general mathematical framework based on hybrid optimal control will be presented. This formulation and their elements, namely objective function, continuous and discrete state and controls, and discrete and continuous dynamics, will serve as a basis for discussion throughout the whole manuscript. Thereafter, solution approaches for classical continuous optimal control problems will be briefly introduced and their application to low-thrust trajectory optimization will be discussed. A special emphasis will be placed on the extension of the classical techniques to solve hybrid optimal control problems. Finally, an extensive review of traditional and state-of-the art methodologies and tools will be presented. They will be categorized regarding their solution approach, the objective function, the state variables, the dynamical model, and their application to planetocentric or interplanetary transfers.

High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales.

Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, models of root architecture that provide insight into resource utilization, and the elucidation of cell-to-cell communication mechanisms that are instrumental in plant development. Image segmentation and machine learning approaches for interpreting plant image data are among many of the computational methodologies that have evolved to address challenging agricultural and biological problems. These approaches have led to contributions such as the accelerated identification of gene that modulate stress responses in plants and automated high-throughput phenotyping for early detection of plant diseases. The continued acquisition of high throughput imaging across multiple biological scales provides opportunities to further push the boundaries of our understandings quicker than ever before. In this review, we explore the current state of the art methodologies in plant image segmentation and machine learning at the agricultural, organ, and cellular scales in plants. We show how the methodologies for segmentation and classification differ due to the diversity of physical characteristics found at these different scales. We also discuss the hardware technologies most commonly used at these different scales, the types of quantitative metrics that can be extracted from these images, and how the biological mechanisms by which plants respond to abiotic/biotic stresses or genotypic modifications can be extracted from these approaches.

Disrupting monopoly: homelessness, gamification and learned resourcefulness

ABSTRACT In 2019 a pilot project called ‘Homeless Monopoly’ evolved in Coventry to investigate how arts methodologies, dramatic scenarios and gamification could be used to raise awareness in young people regarding homelessness. This article investigates the origins and collaborative development of this project, but specifically focuses on how piloting the game in schools, colleges and universities has raised questions about the ways in which resourcefulness and resilience might be taught as adaptive and sustainable strategies through processes of rehearsing and discussing unpredictable challenges, vulnerabilities, support mechanisms and different life outcomes rooted in ‘real-world’ scenarios.

Evaluation of Reinforcement Learning Signalling Strategies on the large-scale network of Nicosia

Efficient utilization of urban road networks has been in the epicenter of researchers’ attention for many decades already. Nowadays, many cities rely on traffic-responsive strategies for the effective management of traffic flows. However, state of the art methodologies, based on Artificial Intelligence (AI), have started challenging the currently implemented solutions. Reinforcement Learning (RL) stands as one of the most promising AI-based methodologies aiming at the optimization of traffic signal controlling. Since the first appearance of relevant RL methodologies, numerous implementation strategies have been suggested. Nonetheless, these approaches are usually evaluated under ideal or simplistic conditions (e.g. toy networks, unrealistic demand patterns, etc.) thus lack the ability to assess the effectiveness of RL-based signaling optimization under realistic conditions. The currently presented study bridges this gap by evaluating a plethora of RL-based implementations under fully realistic urban demand conditions as manifested in the large-scale road network of Nicosia, Cyprus.

Design of low-power coupled chopper instrumentation amplifier using pin pong ripple reduction for biomedical applications

Low-control Capacitively Coupling Chopper Speaker (CCIA) suited for biomedical application such as EEG, ECG and Neural recoding. Thus in any signal generations, ripple noise will be presented. However, many states of art methodologies are focusing on self-biasing technique to suppress the noise in biomedical instruments. Due to this method, as noise increases suppression rate increases, it leads to change the properties of acoustic wave and increase the power in system. To overcome this drawbacks, in the proposed methodology instrumentation amplifier (IA) was introduced. Which not only mitigates the noise but also enhance the properties of wave for biomedical applications. The IA's information impedance is improved by the use of an optimistic test hover in the IA.CCIA is all expressed in a standard process of 18 nm CMOS. The reproduced result exhibits the IA uses a couple µA current at 1.8 V supply. The equivalent data uproar control extend thickness (PSD) is 54 nV/√Hz and the fuss capability factor (NEF) achieves 4.05 inside 1 kHz, while the corresponding information commotion PSD is 55.4 nV/√Hz and NEF is 4.15 inside 10 kHz. Likewise, the data impedance is about 100MΩ.

Spatio-temporal AI inference engine for estimating hard disk reliability

This paper focuses on building a spatio-temporal AI inference engine for estimating hard disk reliability. Most electronic systems such as hard disks routinely collect such reliability parameters in the field to monitor the health of the system. Changes in parameters as a function of time are monitored and any observed changes are compared with the known failure signatures. If the trajectory of the measured data matches that of a failure signature, operators are alerted to take corrective action. However, the interest of the operators lies in being able to identify the failures before they occur. The state of the art methodology including our prior work is to train machine learning models on temporal sequence data capturing the variations across multiple features and using it to predict the remaining useful life of the devices. However, as we show in this paper temporal prediction capability alone is not sufficient and can lead to low precision and the uncertainty around the prediction is very large. This is primarily due to the non-uniform progression of feature patterns over time. Our hypothesis is that the accuracy can be improved if we combine the temporal prediction methods with a spatial analysis that compares the value of key SMART features of the devices across similar model in a fixed time window (unlike the temporal method which uses the data from a single device and a much larger historical window). In this paper, we first describe both temporal and spatial approaches, describe the methods to select various hyperparameters, and then show a workflow to combine these two methodologies and provide comparative results. Our results illustrate that the average precision of temporal methods using long-short temporal memory networks to predict impending failures in the next ten days was 84 percent. To improve precision, we use the set of disks identified as potential failures and start applying spatial anomaly detection methods on those disks. This helps us remove the false positives from the temporal prediction results and provide a tighter bound on the set of disks with impending failure.

A new image restoration approach by combining empirical wavelet transform and total variation using chaotic squirrel search optimization

AbstractImage noise is the random variation of brightness or color information in the images. Noise can enter the picture during capturing or transmitting. Different linear and nonlinear methods have been implemented to remove the noise from an image, but transformation of wavelets is becoming increasingly important. We propose a fundamental concept for disintegrating the input bubble, sound, and movement signal of the object/camera into appropriate coefficients by transforming the empirical wavelet and making full changes. A new messy screen search strategy is used to assess the variability at each pixel of the decomposed picture. Empirical wavelet is a kind of wavelet tailored to the data being handled. This is an adaptive technique for decomposing the signal or picture to a set of sections recognized as phases. This wavelet transform is relevant for de‐noising, decompression, etc. Chaotic squirrel search optimizers imitate the vibrant feeding behavior and efficiency of northern flying squirrels, called gliding. Gliding is a powerful system used for lengthy ranges by tiny mammals. Compared to other wavelet transformations, the median peak signal for noise ratio (PSNR) enhancement obtained by EWT is 73%. Numerical tests indicate that our proposed method could be able to substantially enhance restored pictures' efficiency and achieve greater SNR and structural similarity index scores likened to the present state of the art methodologies.

Detection of Tuberculosis using Hybrid Features from Chest Radiographs

Tuberculosis is a contagious disease, but it’s diagnosis is still a difficult and challenging task as it is considered a big threat everywhere on the planet. Literature shows that underdeveloped countries widely use chest radiographs (X ray images) for the diagnosis of tuberculosis. Low accuracy of results and high cost are the two main reasons due to which most of the available methods are not useful for radiologists. In our research, we proposed a detection technique in which features extraction is performed on the basis of their texture, intensity and shape. For evaluating the performance of our proposed methodology, Montgomery Country (MC) dataset is used. It is a publically available data set which consists of 138 CXRs; among them, 80 CXRs are normal and 58 CXRs are malignant. The results of the proposed technique have outperformed state of the art methodologies on the MC dataset as it has shown 81.16% accuracy.

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

BackgroundNucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data.ResultsIn this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification.ConclusionsResults computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time.

Optimal Model for Energy Management Strategy in Smart Building with Energy Storage Systems and Electric Vehicles

The aim of this work was to develop an optimal model for an energy management strategy in a real micro-grid, which involves a smart building, a photovoltaic system with storage, and a plug-in full electric vehicle. A controller based on a mathematical algorithm was the core of each strategy, which directly acted on a relay board managing the interconnection between the different elements comprising the micro-grid. The development of an optimization model involving binary variables required an efficient code that achieved solutions in a short time. The analyzed case-study corresponded to the solar energy research center (CIESOL) smart building, a bioclimatic building, that is located at the University of Almería (Spain), designated to research in renewable energies. Using the methodologies described in this work, the total cost of the smart building energy consumption was minimized by decreasing the power supplied from the grid, especially at peak hours. Highlighting the use of a simple model that provided better performance than the current state of the art methodologies. The optimal model for energy management strategy demonstrated the advantages of using classical optimization techniques to solve this specific optimization problem, compared to a rule-based controller. The linear modeling was capable of producing a simple algorithm with less code development and a reduction in the computational effort.

The Afterimage of John Wayne and the Shape of Masculinity in the Landscape

Abstract In this article I consider how my performance work The Afterimage of John Wayne (2020) creates a framework drawn from Conceptual Art methodologies to critique and reconsider gendered masculinity and representations of the landscape in the Western genre of cinema. The legacies of Conceptual Art practice that emerged in the 1960s produced creative modes that enabled new ways to engage with contemporary culture across multiple disciplines. An example of these modes was the “Instructional Statement,” which forms the basis for The Afterimage of John Wayne. The Instructional Statement enabled performance to be understood as an art form that could both produce and critique culture. This article will consider how such performance art forms have the capacity to shift and loosen the mythologies that underpin the performance of masculinity within “The Western.”

Developing Theatre for the Young in Malaysia: Benefits and Challenges

This study investigates how young people in Malaysia enjoy theatre or find it relevant at all in the era of television, films and technology. The research was done using three approaches: A Naturalistic Inquiry methodology where the research was done at site which is in Kinabatangan, in East Malaysia with 25 young people to develop a script; a creative arts methodology was done in the studio where the script was explored and developed into a performance; the last phase of the research used a primary qualitative methodology to explore how young audiences watch theatre in which the research instruments used were questionnaires and open-ended interviews. There were 9 respondents from a Primary School and 23 respondents from a secondary School. This research that came out of the practice, enabled the researcher to investigate children’s life experiences and listen to themThe knowledge gathered is that the young in Malaysia are definitely excited about theatre which communicates to them and which has aesthetic, entertaining, imaginative and educational merits. The research connected the young in East Malaysia as participants who contributed to the script with the young in West Malaysia who performed it and young audiences who watched it.

An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges

In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.