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R, Bhuvaneswari, Cynthiya Rose J S, and Maria Baptist S. "Editorial: Indian Literature: Past, Present and Future." Studies in Media and Communication 11, no. 2 (February 22, 2023): 1. http://dx.doi.org/10.11114/smc.v11i2.5932.
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IntroductionIndian Literature with its multiplicity of languages and the plurality of cultures dates back to 3000 years ago, comprising Vedas, Upanishads, Puranas and Epics like Ramayana and Mahabharata. India has a strong literary tradition in various Indian regional languages like Sanskrit, Prakrit, Pali, Hindi, Marathi, Bengali, Oriya, Tamil, Kannada, Telugu, Malayalam and so on. Indian writers share oral tradition, indigenous experiences and reflect on the history, culture and society in regional languages as well as in English. The first Indian novel in English is Bankim Chandra Chatterjee’s Rajmohan’s Wife (1864). Indian Writing in English can be viewed in three phases - Imitative, First and Second poets’ phases. The 20th century marks the matrix of indigenous novels. The novels such as Mulk Raj Anand’s Untouchable (1935), Anita Nair’s Ladies Coupé (2001), and Khuswant Singh’s Memories of Madness: Stories of 1947 (2002) depict social issues, vices and crises (discrimination, injustice, violence against women) in India. Indian writers, and their contribution to world literature, are popular in India and abroad.Researchers are keen on analysing the works of Indian writers from historical, cultural, social perspectives and on literary theories (Post-Colonialism, Postmodernity, Cultural Studies). The enormity of the cultural diversity in India is reflected in Indian novels, plays, dramas, short stories and poems. This collection of articles attempts to capture the diversity of the Indian land/culture/landscape. It focuses on the history of India, partition, women’s voices, culture and society, and science and technology in Indian narratives, documentaries and movies.Special Issue: An Overview“Whatever has happened, has happened for goodWhatever is happening, is also for goodWhatever will happen, shall also be good.”- The Bhagavad-Gita.In the Mahabharata’s Kurukshetra battlefield, Lord Krishna counsels Arjuna on how everything that happens, regardless of whether it is good or bad, happens for a reason.Indian Literature: Past, Present and Future portrays the glorious/not-so-glorious times in history, the ever-changing crisis/peace of contemporary and hope for an unpredictable future through India’s literary and visual narratives. It focuses on comparison across cultures, technological advancements and diverse perspectives or approaches through the work of art produced in/on India. It projects India’s flora, fauna, historical monuments and rich cultural heritage. It illustrates how certain beliefs and practices come into existence – origin, evolution and present structure from a historical perspective. Indian Literature: Past, Present and Future gives a moment to recall, rectify and raise to make a promising future. This collection attempts to interpret various literary and visual narratives which are relevant at present.The Epics Reinterpreted: Highlighting Feminist Issues While Sustaining Deep Motif, examines the Women characters in the Epics – Ramayana and Mahabharata. It links the present setting to the violence against women described in the Epics Carl Jung’s archetypes are highlighted in a few chosen characters (Sita, Amba, Draupati). On one note, it emphasises the need for women to rise and fight for their rights.Fictive Testimony and Genre Tension: A Study of ‘Functionality’ of Genre in Manto’s Toba Tek Singh, analyses the story as a testimony and Manto as a witness. It discusses the ‘Testimony and Fictive Testimony’ in Literature. It explains how the works are segregated into a particular genre. The authors conclude that the testimony is to be used to understand or identify with the terror.Tangible Heritage and Intangible Memory: (Coping) Precarity in the select Partition writings by Muslim Women, explores the predicament of women during the Partition of India through Mumtaz Shah Nawaz’s The Heart Divided (1990) and Attia Hosain’s Sunlight on a Broken Column (2009). It addresses ‘Feminist Geography’ to escape precarity. It depicts a woman who is cut off from her own ethnic or religious group and tries to conjure up her memories as a means of coping with loneliness and insecurity.Nation Building Media Narratives and its Anti-Ecological Roots: An Eco-Aesthetic Analysis of Khushwant Singh’s Train to Pakistan, analyses the post-Partition trauma in the fictional village, Mano Majra. It illustrates the cultural and spiritual bond between Mano Majrans — the inhabitants of Mano Majra — and nature (the land and river). It demonstrates how the media constructs broad myths about culture, religion, and nation. According to the authors, Mano Majrans place a high value on the environment, whilst the other boundaries are more concerned with nationalism and religion.Pain and Hopelessness among Indian Farmers: An Analysis of Deepa Bhatia’s Nero’s Guests documents the farmers’ suicides in India as a result of debt and decreased crop yield. The travels of Sainath and his encounters with the relatives of missing farmers have been chronicled in the documentary Nero’s Guests. It uses the Three Step Theory developed by David Klonsky and Alexis May and discusses suicide as a significant social issue. The authors conclude that farmers are the foundation of the Indian economy and that without them, India’s economy would collapse. It is therefore everyone’s responsibility—the people and the government—to give farmers hope so that they can overcome suicidal thoughts.The link between animals and children in various cultures is discussed in The New Sociology of Childhood: Animal Representations in Leslie Marmon Silko’s Garden in the Dunes, Amazon’s Oh My Dog, and Netflix’s Mughizh: A Cross-Cultural Analysis. It examines the chosen works from the perspectives of cross-cultural psychology and the New Sociology of Childhood. It emphasises kids as self-sufficient, engaged, and future members of society. It emphasises universal traits that apply to all people, regardless of culture. It acknowledges anthropomorphized cartoons create a bond between kids and animals.Life in Hiding: Censorship Challenges faced by Salman Rushdie and Perumal Murugan, explores the issues sparked by their writings. It draws attention to the aggression and concerns that were forced on them by the particular sect of society. It explains the writers’ experiences with the fatwa, court case, exile, and trauma.Female Body as the ‘Other’: Rituals and Biotechnical Approach using Perumal Murugan’s One Part Woman and Matrubhoomi: A Nation Without Women, questions the society that limits female bodies for procreation and objectification. It talks about how men and women are regarded differently, as well as the cultural ideals that apply to women. It explains infertility, which is attributed to women, as well as people’s ignorance and refusal to seek medical help in favour of adhering to traditional customs and engaging in numerous rituals for procreation.Life and (non) Living: Technological and Human Conglomeration in Android Kunjappan Version 5.25, explores how cyborgs and people will inevitably interact in the Malayalam film Android Kunjappan Version 5.25. It demonstrates the advantages, adaptability, and drawbacks of cyborgs in daily life. It emphasises how the cyborg absorbs cultural and religious notions. The authors argue that cyborgs are an inevitable development in the world and that until the flaws are fixed, humans must approach cyborgs with caution. The Challenges of Using Machine Translation While Translating Polysemous Words, discusses the difficulty of using machine translation to translate polysemous words from French to English (Google Translate). It serves as an example of how the machine chooses the formal or often-used meaning rather than the pragmatic meaning and applies it in every situation. It demonstrates how Machine Translation is unable to understand the pragmatic meaning of Polysemous terms because it is ignorant of the cultures of the source and target languages. It implies that Machine Translation will become extremely beneficial and user-friendly if the flaws are fixed.This collection of articles progresses through the literary and visual narratives of India that range from historical events to contemporary situations. It aims to record the stories that are silenced and untold through writing, film, and other forms of art. India’s artistic output was influenced by factors such as independence, partition, the Kashmir crisis, the Northeast Insurgency, marginalisation, religious disputes, environmental awareness, technical breakthroughs, Bollywood, and the Indian film industry. India now reflects a multitude of cultures and customs as a result of these occurrences. As we examine the Indian narratives produced to date, we can draw the conclusion that India has a vast array of tales to share with the rest of the world.Guest Editorial BoardGuest Editor-in-ChiefDr. Bhuvaneswari R, Associate Professor, School of Social Sciences and Languages, Vellore Institute of Technology, Chennai. She has pursued her master’s at the University of Madras, Chennai and doctoral research at HNB Central University, Srinagar. Her research areas of interest are ELT, Children/Young Adult Literature, Canadian writings, Indian literature, and Contemporary Fiction. She is passionate about environmental humanities. She has authored and co-authored articles in National and International Journals.Guest EditorsCynthiya Rose J S, Assistant Professor (Jr.), School of Social Sciences and Languages, Vellore Institute of Technology, Chennai. Her research interests are Children’s Literature, Indian Literature and Graphic Novels.Maria Baptist S, Assistant Professor (Jr.), School of Social Sciences and Languages, Vellore Institute of Technology, Chennai. His research interests include Crime/Detective fiction and Indian Literature.MembersDr. Sufina K, School of Science and Humanities, Sathyabama Institute of Science and Technology, Chennai, IndiaDr. Narendiran S, Department of Science and Humanities, St. Joseph’s Institute of Technology, Chennai, India
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Monin, Monica. "Unconventional Classifiers and Anti-social Machine Intelligences." Digital Culture & Society 4, no. 1 (March 1, 2018): 227–38. http://dx.doi.org/10.14361/dcs-2018-0114.
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Abstract Artificial intelligence technologies and data structures required for training have become more accessible in recent years and this has enabled artists to incorporate these technologies into their works to various ends. This paper is concerned with the ways in which present day artists are engaging with artificial intelligence, specifically material practices that endeavour to use these technologies and their potential non-human agencies as collaborators with differential objectives to commercial fields. The intentions behind artists’ use of artificial intelligence is varied. Many works, with the accelerating assimilation of artificial intelligence technologies into everyday life, follow a critical path. Such as attempting to unveil how artificial intelligence materially works and is embodied, or to critically work through the potential future adoptions of artificial intelligence technologies into everyday life. However, I diverge from unpacking the criticality of these works and instead follow the suggestion of Bruno Latour to consider their composition. As for Latour, critique implies the capacity to discover a ‘truer’ understanding of reality, whereas composition addresses immanence, how things come together and the emergence of experience. Central to this paper are works that seek to collaborate with artificial intelligence, and to use it to drift out of rather than to affirm or mimic human agency. This goes beyond techniques such as ‘style transfer’ which is seen to support and encode existing human biases or patterns in data. Collaboration with signifies a recognition of a wider field of what constitutes the activity of artistic composition beyond being a singularly human, or AI, act, where composition can be situated in a system. This paper will look at how this approach allows an artist to consider the emerging materiality of a system which they are composing, its resistances and potentials, and the possibilities afforded by the exchange between human and machine intentions in co-composition.
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Chalmers, David. "Thinking Just Happens." Stance: an international undergraduate philosophy journal 11, no. 1 (April 23, 2018): 132–51. http://dx.doi.org/10.33043/s.11.1.132-151.
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David Chalmers is a professor of philosophy and neural science at New York University. He received his PhD in philosophy and cognitive science from Indiana University in 1993 and has held positions at University of California, Santa Cruz, University of Arizona, and Australian National University. He helped to found the Association for the Scientific Study of Consciousness and co-directs the PhilPapers Foundation. Chalmers has written extensively on a wide range of topics, including philosophy of mind, philosophy of language, metaphysics, epistemology, and metaphilosophy. He is a prolific author. His well-known works include The Conscious Mind, The Character of Consciousness, and Constructing the World. For more information about Chalmers and his current projects, visit http://consc.net/.
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Zhao, X. M., Y. Z. Bi, and Hai Long Wang. "Collision and Determination of Workpiece Location in Parallel Mechanism Type Machine Tools." Materials Science Forum 471-472 (December 2004): 855–59. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.855.
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This paper uses HexaM made by Toyoda Machine Works Co. as an analytical model of parallel mechanism type machine tools. Much collision may be generated for its complicated structure and working space, using this machine. For example, those collisions are generated between the moving components of the parallel mechanism, between the moving components and a workpiece and between the moving components and the tool storage for automatic changing. In this paper, an algorithm for detecting and avoiding those collisions is proposed. The method for avoiding the collisions is conducted by readjusting the location of the workpiece without amending the tool path. It is confirmed through experimental works that the proposed algorithm is useful for detecting the collisions and determining the location of the workpiece prior to the actual machining.
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Rai, Jitendra Kumar, Atul Negi, and Rajeev Wankar. "Using Machine Learning Techniques for Performance Prediction on Multi-Cores." International Journal of Grid and High Performance Computing 3, no. 4 (October 2011): 14–28. http://dx.doi.org/10.4018/jghpc.2011100102.
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Sharing of resources by the cores of multi-core processors brings performance issues for the system. Majority of the shared resources belong to memory hierarchy sub-system of the processors such as last level caches, prefetchers and memory buses. Programs co-running on the cores of a multi-core processor may interfere with each other due to usage of such shared resources. Such interference causes co-running programs to suffer with performance degradation. Previous research works include efforts to characterize and classify the memory behaviors of programs to predict the performance. Such knowledge could be useful to create workloads to perform performance studies on multi-core processors. It could also be utilized to form policies at system level to mitigate the interference between co-running programs due to use of shared resources. In this work, machine learning techniques are used to predict the performance on multi-core processors. The main contribution of the study is enumeration of solo-run program attributes, which can be used to predict concurrent-run performance despite change in the number of co-running programs sharing the resources. The concurrent-run involves the interference between co-running programs due to use of shared resources.
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Acchar, Wilson, and Harim Revoredo de Macedo. "Influence of NbC-Addition on Mechanical Properties of WC-Co." Materials Science Forum 498-499 (November 2005): 363–68. http://dx.doi.org/10.4028/www.scientific.net/msf.498-499.363.
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Cemented carbides have been intensively used as cutting tool through their high hardness, high fracture toughness and high wear resistance. A considerable amount of works has been developed in order to improve the mechanical properties of alternate cemented carbide systems. This work has the purpose to reports the first results obtained to WC-Co reinforced with 5 wt.% NbC. The mixture of powders was hot-pressed at 1250 °C in a inert atmosphere. Hardness and fracture toughness were carried out in a Vickers hardness testing machine. The results have showed that the addition of niobium carbide improves the hardness of tungsten carbide and inhibits the WCgrain growth.
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Peng, Yun, Byron Choi, and Jianliang Xu. "Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art." Data Science and Engineering 6, no. 2 (April 28, 2021): 119–41. http://dx.doi.org/10.1007/s41019-021-00155-3.
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AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.
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Bidgoli, Hassan Sharifi, and Ramezan Ali Mahdavinejad. "Vibration Effects on Tailstock of a Turning Machine." Advanced Materials Research 118-120 (June 2010): 876–80. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.876.
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Machine tools as a main device in manufacturing and production lines are widely used nearly in all branches of industries. Precision continuation of these systems with times and against inspected shocks is necessary and very important. Turning as a main kind and high usage machine tool is used in many industrial parts production, especially those with rounded sections. Spindle, tailstock and bed in machine tools play engraving role in dimensional accuracy. In this paper the vibration effects on the tailstock of a turning machine during its operation, is analyzed. For this purpose, a LZ330 VS machine from Weiler Co. in Germany is modeled with Solid Works software. This model with IGES format is transferred to ANSYS 5.4 analytical software. In this area, the model is analyzed via modal and transient dynamic analysis. The results show that, the more tailstock’s sleeve length, the less critical frequency amount.
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Koyano, Hitoshi, Morihiro Hayashida, and Tatsuya Akutsu. "Maximum margin classifier working in a set of strings." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2187 (March 2016): 20150551. http://dx.doi.org/10.1098/rspa.2015.0551.
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Numbers and numerical vectors account for a large portion of data. However, recently, the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem for a consensus sequence of strings demonstrated by one of the authors and co-workers in a previous study. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein–protein interactions using amino acid sequences and classifying RNAs by the secondary structure using nucleotide sequences.
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Rogers, Emily Buhrow. "Exhibiting Moments: Qualla Arts and Crafts Mutual at the Mathers Museum of World Cultures." Museum Anthropology Review 13, no. 1 (March 29, 2019): 32–46. http://dx.doi.org/10.14434/mar.v13i1.26472.
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In 1973, Indiana University’s Mathers Museum of World Cultures purchased a selection of works from the Eastern Band of Cherokee Indians’ Qualla Arts and Crafts Mutual, one of the oldest Native American-owned art and craft cooperatives in the United States. In this paper, I discuss, from my perspective as co-curator, the development of the museum’s 2015 exhibition of that collection, Cherokee Craft, 1973. Through this project, the curatorial team sought to creatively evoke the Qualla cooperative at the dynamic historical moment these works represented, while also contending with significant resource limitations. What resulted was an exhibit organized around the concept of a moment in time. This alternative presentation strategy gave us an opportunity to explore a variety of important topics and ongoing processes specific to the institution in the early 1970s. In this paper, I discuss how this approach allowed us to present a plurality of voices, while also showcasing many of the cooperative’s most renowned makers. I also position Cherokee Craft, 1973 as an exhibit curated by graduate students for a university audience: it was a site of innovation and representational experimentation for its creators, unique to its institutional type and its own particular moment in time.
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Doucet, J. P., and A. Doucet-Panaye. "Dibenzoylhydrazines as Insect Growth Modulators: Topology-Based QSAR Modelling." Advances in Sciences and Engineering 12, no. 1 (June 25, 2020): 28–40. http://dx.doi.org/10.32732/ase.2020.12.1.28.
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Dibenzoylhydrazines Xa-(C6H5)a-CO-N-(t-Bu)-NH-CO-(C6H5)b-Yb are efficient insect growth regulators with high activity and selectivity toward lepidopteran and coleopteran pests. For 123 congeneric molecules, a quantitative structure activity relationship model was built in the framework of the QSARINS package using 2D, Topology-based, PaDEL descriptors. Variable selection by GA-MLR allows building an efficient multilinear regression linking pEC50 values to nine structural variables. Robustness and quality of the model were carefully examined at various levels: data-fitting (recall), leave-one (or some) - out, internal and external validation (including random splitting), points not in depth investigated in previous works. Various Machine Learning approaches (Partial Least Squares Regression, Projection Pursuit Regression, Linear Support Vector Machine or Three Layer Perceptron Artificial Neural Network) confirm the validity of the analysis, giving highly consistent results of comparable quality, with only a slight advantage for the three-layer perceptron.
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Muliaee, Maryam. "Media-as-things: The Intensified Materiality of Degenerated Images." Metacritic Journal for Comparative Studies and Theory 7, no. 1 (July 8, 2021): 40–57. http://dx.doi.org/10.24193/mjcst.2021.11.03.
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This paper adopts an art-based research model to investigate how media objects, as entangled material agencies, can become co-creators with artists and condition the viewers' memory and imagination. My work Recycled Series among other artists’ work are the subjects of this analysis. All these works involve images that are degenerated with a copy machine. The degenerated images lose coherence and become forms of ruins that the copier builds. Drawing from theories of things (Brown; Harman; Shaviro), I examine these works as the examples of “media-as-things” to show when media is misused, the potential of media is revealed. I place these works in the context of “broken-tech art” (Boym) and “haptic visuality” (Marks). I argue that these images determine a different object-subject relationship for their audience and their “thingness,” which is intensified through degeneration effects, becomes a major factor in their aesthetic reception.
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Dimitrova, Zornitsa. "Robotic Performance: An Ecology of Response." Performance Philosophy 3, no. 1 (June 25, 2017): 162. http://dx.doi.org/10.21476/pp.2017.3135.
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This article looks into regions of human-robotic and inter-robotic relations in performance. Artwork presenting inter-machine and human-machine entanglements in robotic performance requires attention: it addresses recent concerns with the crisis of �the obsolete body�, yet also calls for acritique of what constitutes a response. The present article works toward a concept of response that simultaneously reinstates the status of automata as counterparts to �humans� and invites biological bodies to reassess their place in a world. Herein notions of interaction, empathetic immersion, and machine personhood intertwine to shape a new ecology of bodies. Robotic performance thus drafts out an immersive ontology of bodiesto raise questions that are mostly ecological. It allows us to look into practices that put on display the border between an organism and an environment, prompting us to think toward the dissolution of that border into a network of co-determinative systems.
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Chang, Teng-Wen, Hsin-Yi Huang, Cheng-Chun Hong, Sambit Datta, and Walaiporn Nakapan. "SENS+: A Co-Existing Fabrication System for a Smart DFA Environment Based on Energy Fusion Information." Sensors 23, no. 6 (March 7, 2023): 2890. http://dx.doi.org/10.3390/s23062890.
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In factories, energy conservation is a crucial issue. The co-fabrication space is a modern-day equivalent of a new factory type, and it makes use of Internet of Things (IoT) devices, such as sensors, software, and online connectivity, to keep track of various building features, analyze data, and produce reports on usage patterns and trends that can be used to improve building operations and the environment. The co-fabrication user requires dynamic and flexible space, which is different from the conventional user’s usage. Because the user composition in a co-fabrication space is dynamic and unstable, we cannot use the conventional approach to assess their usage and rentals. Prototyping necessitates a specifically designed energy-saving strategy. The research uses a “seeing–moving–seeing” design thinking framework, which enables designers to more easily convey their ideas to others through direct observation of the outcomes of their intuitive designs and the representation of their works through design media. The three components of human behavior, physical manufacture, and digital interaction are primarily the focus of this work. The computing system that connects the physical machine is created through communication between the designer and the digital interface, giving the designer control over the physical machine. It is an interactive fabrication process formed by behavior. The Sensible Energy System+ is an interactive fabrication process of virtual and real coexistence created by combining the already-existing technology, the prototype fabrication machine, and SENS. This process analyzes each step of the fabrication process and energy, fits it into the computing system mode to control the prototype fabrication machine, and reduces the problem between virtual and physical fabrication and energy consumption.
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Kumar, A. Kiran. "Analysis and Fabrication of Control Surface of a Missile." Journal for Manufacturing Science and Production 14, no. 1 (April 30, 2014): 31–38. http://dx.doi.org/10.1515/jmsp-2013-0020.
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AbstractThe aim and objective of the project work is the modeling of control surface which has complex three dimensional aerodynamic profiles using solid works software, performing analysis of the model with the imposed loads and boundary conditions using ANSYS to estimate the maximum stress and deflection. The material used for this component is titanium alloy (Ti-6Al-4V) which has good properties like resistance to corrosion, high strength to weight ratio, which are mostly needed for aerospace vehicles. The actual dimensions of control surface are 350×225×20. The model of the control surface is scaled to 1:2 for manufacturing using Proe/Manufacturing module, the part programmes are generated and are transferred to the specific machine through DNC system.Control surface is probably the most commonly used form of missile control, particularly for long range airto-air missiles and surface-to-air missiles. The primary reason for this application is because tail provides excellent maneuverability at high angles of attack.Deckel E3LV CNC milling machine has been used for manufacturing the control surface. Development of 3D model using solid works then it is transferred to PROE software for generating error-free tool path and checking the tool path using simulation. Post processing of CL (cutter location) Data file to generate CNC part program, transferring the same to the respective machine tool via DNC system from CAM workstation and manufacturing of the control surface. Co-ordinate measuring machine (CMM) is used for the inspection of features, as the control surface is having complex three-dimensional aerodynamic profile. On this machine, every dimension of the control surface has been inspected and found that the dimensions are within the design considerations of the given design.
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Carek, Andreja, Ljerka Slokar Benić, Vatroslav Bubalo, and Nika Kosović. "Microscopic and Mechanical Characterization of Co-Cr Dental Alloys Joined by the TIG Welding Process." Materials 16, no. 10 (May 22, 2023): 3890. http://dx.doi.org/10.3390/ma16103890.
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Due to their good mechanical and other properties, cobalt-chromium alloys (Co-Cr) are often used in prosthetic therapy. The metal structures of prosthetic works can be damaged and break, and depending on the extent of the damage, they can be re-joined. Tungsten inert gas welding (TIG) produces a high-quality weld with a composition very close to that of the base material. Therefore, in this work, six commercially available Co-Cr dental alloys were joined by TIG welding, and their mechanical properties were evaluated to determine the quality of the TIG process as a technology for joining metallic dental materials and the suitability of the Co-Cr alloys used for TIG welding. Microscopic observations were made for this purpose. Microhardness was measured using the Vickers method. The flexural strength was determined on a mechanical testing machine. The dynamic tests were carried out on a universal testing machine. The mechanical properties were determined for welded and non-welded specimens, and the results were statistically evaluated. The results show the correlation between the investigated mechanical properties and the process TIG. Indeed, characteristics of the welds have an effect on the measured properties. Considering all the results obtained, the TIG—welded I—BOND NF and Wisil M alloys showed the cleanest and most uniform weld and, accordingly, satisfactory mechanical properties, highlighting that they withstood the maximum number of cycles under dynamic load.
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Muthuraman, Veluswamy, Raju Ramakrishnan, Ponnusamy Sengottuvel, and C. Karthikeyan. "Experimental Study and Optimization of Wire-Electrical Discharge Machined WC-15%Co Metal Matrix Composites." Advanced Materials Research 984-985 (July 2014): 227–32. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.227.
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Wire Electrical discharge Machining plays an important role in the field of hard, difficult to machine materials like metal matrix composites. Machining process must keep pace with material development. For electrically conductive materials wire electro discharge machining is a viable option due to high accuracy, precision, and ability to achieve complex, intricate shaped profiles on even thin works. Due to thermo-electric nature it is a stochastic process in nature. To simplify the difficulty in determining parameters for the improvement of cutting performance and optimization, analysis of variance and regression analysis were made use of. Tungsten carbide cobalt metal matrix composites finds increasing applications in conventional application like tools and dies as well as in developing fields like bio-medical instruments and aero-space industries. In this present work, the problem of parameter selection, optimization for wire electro discharge machining on tungsten carbide-15% cobalt metal matrix composites, a less worked composition has been undertaken. Sodick AQ-427L wire-edm machine was used with a 0.25 diameter zinc coated brass wire electrode, to cut the material. Each experiment done under different cutting conditions of inputs like pulse on time, pulse off time, wire speed and peak current and repeated for three observations and the average was selected. Optimum machining parameter combination for material removal rate was obtained by using the desirability response optimizer function. Analysis of variance, Confirmation experiment was carried and good improvements were obtained.
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McCarthy, Andrew, Essam Ghadafi, Panagiotis Andriotis, and Phil Legg. "Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey." Journal of Cybersecurity and Privacy 2, no. 1 (March 17, 2022): 154–90. http://dx.doi.org/10.3390/jcp2010010.
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Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, we included some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this was mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink, and Google Scholar. The literature search was conducted up to January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd.
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Demir, Servet. "Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches." Journal of Research in Didactical Sciences 2, no. 1 (December 28, 2023): 1–26. http://dx.doi.org/10.51853/jorids/14202.
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Machine learning (ML) techniques hold promise for innovating teacher preparation and development programs. However, the current state of research leveraging AI in teacher-focused contexts remains unclear. This study undertook a systematic bibliometric analysis to characterize the emerging domain investigating ML applications for enhancing teacher effectiveness. Using the bibliographic R tool Bibliometrix, metadata of 740 English-language articles published during 2019–2023 extracted from Web of Science educational databases were examined to determine performance metrics, science mapping, citation networks, and research trends situating at the intersection of machine learning and teacher education. Document growth averaged 39.57% annually, with collaborations involving 87% of publications and 21.62% engaging international co-authorships. The USA led productivity metrics, though opportunities exist to expand geographical diversity. Analyses revealed research activity presently concentrates around employing ML for student analytics, assessment frameworks, and online learning environments. Highly cited works dealt with ML systems for evaluation and competency modeling of teachers rather than directly supporting pedagogical practice. Significant gaps persist exploring intelligent recommendation engines and affective computing chatbots tailored to teachers’ dynamic training needs and emotional responses. This bibliometric review synthesizes the contours and trends in investigating ML applications for augmenting teachers’ capabilities. Findings inform stakeholders to mobilize efforts strategically advancing this domain for enriching classrooms.
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Patel, Ashish, Richa .., and Aditi Sharma. "Maize Plant Leaf Disease Classification Using Supervised Machine Learning Algorithms." Fusion: Practice and Applications 13, no. 2 (2023): 08–21. http://dx.doi.org/10.54216/fpa.130201.
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Maize is an important staple crop all over the world, and its health is very important for food security. It is important for crop management and yield to find diseases that affect maize plants as soon as possible. In this study, we suggest a new way to classify diseases on maize plant leaves by using supervised machine learning algorithms. Our method uses the power of texture analysis with Gray-Level Co-occurrence Matrix (GLCM) and Gabor feature extraction techniques on the Plant-Village dataset, which has images of both healthy and unhealthy maize leaves. This method uses four supervised machine learning algorithms, called Decision Tree, Gradient Boosting, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), to sort the extracted features into healthy and diseased groups. By doing a lot of tests, we show that our way of finding maize leaf diseases works well. The results show that these techniques have the potential to quickly and non-invasively diagnose diseases, giving farmers important information for acting quickly. We talk about the pros and cons of each algorithm and suggest ways to make them even better. This research contributes to the advancement of automated plant disease detection systems, fostering sustainable agriculture practices and aiding in crop management decisions. The proposed approach holds promise for real-world application, enabling farmers to mitigate disease-related losses and secure global food supplies.
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M. Varun, M. Varun, and C. Annadurai M. Varun. "Intelligent Spectrum Sensing Using Optimized Machine Learning Algorithms for Cognitive Radio in 5G Communication." 網際網路技術學刊 23, no. 4 (July 2022): 827–36. http://dx.doi.org/10.53106/160792642022072304017.
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<p>Spectrum Sensing plays an important role in cognitive radio which is used to resolve the co-existence issue and to optimize the available spectrum efficiency. However, the upcoming 5G communication involves the diversified scenarios with distinguished characteristics which makes spectrum sensing more difficult to serve different application in terms of high performance and flexible implementation. Also motivated by this challenge, the paper proposes the new algorithm which implemented the novel bat optimized multi-layer extreme learning machine and works on different input vectors such as received signal strength, distance, Energy and channel ID and classifies the users for the better classification and sensing. Moreover, to prove the suitability of the proposed algorithm in terms of performance under 5G scenario for health care applications, we have compared the other spectrum sensing techniques and parameters such as sensitivity, selectivity and channel detection probability. Ultimately the results demonstrate that the proposed spectrum sensing has outperformed the other algorithms and shows its capability to adopt for various 5G scenario.</p> <p> </p>
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Ding, Zhili. "Between Man and Machine: A Posthumanist Analysis of I, Robot." Journal of Education, Humanities and Social Sciences 31 (April 27, 2024): 194–203. http://dx.doi.org/10.54097/b1p5ar74.
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In Isaac Asimov’s groundbreaking anthology, I, Robot, the intricacies of human and robotic interactions take center stage, delving deep into questions of consciousness, right, morality. Characterized by Asimov’s unique blend of science fiction and philosophical pondering, the stories establish a framework to reflect on the evolving dynamics of an advanced technological society in space. Robots are capable to interact with human, to interpret complicate orders and act automatically, to help human with dull or dangerous works such as calculating and mining, even replacing the workers. Asimov also raised Three Laws of Robotics to make sure that all robots function in order. Through the lens of posthumanism, the anthology is examined for its portrayal of the blurred boundaries between human and artificial intelligences. Robots and human co-exist in the society. However, due to the limitation of “Three Laws of Robotics”, some logical contradictions inevitably appears and lead to the dysfunction of robots. As a result, I, Robot emerges as a poignant critique of humanity’s ethical and existential challenges in the face of rapid technological advancements. Building on existing research, this article attempts to forge a new perspective by reflecting on the broader implications of the artificial intelligence in Asimov’s works through the lens of post-humanism. It considers the existential questions of AI on a consciousness level, explores the egalitarian relationship between humans and machines from a rights perspective, and analyzes the concepts of “humanity” and “human-like” from a moral and ethical standpoint. It encourages readers to recognize that robots are not mere slaves to humans; instead, humans should view AI with equality and reverence towards the advancements of the era’s technology. Humanity should step away from anthropocentrism, not solely viewing humans as the measure of all things in this rapidly evolving era of AI, and properly handle the relationship between humans and non-humans. Currently, with rapid advancements in science and technology, the world of human-machine coexistence depicted by Isaac Asimov is increasingly becoming a reality. The emergence of artificial intelligences like AlphaGo and ChatGPT constantly reminds us of the advent of a post-human era. This article aims to examine the connections between AI and humans, discussing the dynamic interaction and mutual shaping between robots and human society. This article hopes to provide new thoughts and strategies for understanding and addressing the challenges brought by the age of artificial intelligence.
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Liang, Zhihui, Yerlan Assembaiuly, and Sholpan Akbayeva. "Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm." Wireless Communications and Mobile Computing 2022 (August 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/7081645.
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College education has also widely adopted new media technology, which has changed the previous single teaching method and enriched the teaching content. Art education plays an important role in higher education and is an indispensable part of our country’s quality education. The content, methods, and concepts of art education in colleges and universities are also changing with the advent of the new media era. New courses such as multimedia technology, network animation technology, and network advertising design have been added to the teaching curriculum system of colleges and universities for stimulating students’ interest in learning. The online network platform represented by WeChat has had a profound impact in the field of art education. Based on the technical characteristics of art works, this paper briefly classifies the characteristics of art works; the works perform the extraction of local data (picture elements); secondly, the texture feature parameters of each picture element are extracted by using the image histogram processing technology and the gray level co-occurrence matrix statistical analysis method; finally, a relatively mature machine learning model, BP, is introduced. The neural network algorithm learns the technical style characteristics of each part of the ink and wash work, so that on the whole, it can initially achieve the artistic style learning of the entire ink and wash painting.
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Huszár, Viktor. "Application Possibilities of Decentralization and Blockchain Technology Using Computer Vision and Artificial Intelligence in Defense Management, Military and Police Organizations." Honvédségi Szemle 148, no. 1 (September 7, 2020): 4–14. http://dx.doi.org/10.35926/hdr.2020.1.1.
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Military science has faced new challenges at the end of the 20th century with the emergence of the Internet. Challenges and threats to traditional security got a new interpretation with a new concept of cybersecurity, which led to an organic transformation of military engineering and IT. As the Internet has fundamentally changed the way the world works, new technologies have emerged on the network that can revolutionize the multitude of industries. Such innovation is Distributed Ledger Technology (DLT) and Blockchain Technology supplemented with Artificial Intelligence and machine vision. The potential uses of the block chain represent a multitude of military technical scientific challenges. The technology makes it possible to co-operate freely with cryptographic procedures on distributed networks without state control, but it can also serve military and defense management purposes.
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Grander, Gustavo, Luciano Ferreira da Silva, Alan Tadeu Moraes Moraes, and Paulo Sergio Gonçalves de Oliveira. "RELATIONSHIP BETWEEN BIG DATA AND DECISION SUPPORT SYSTEMS." Journal on Innovation and Sustainability RISUS 11, no. 1 (July 14, 2020): 82–93. http://dx.doi.org/10.23925/2179-3565.2020v11i1p82-93.
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This article aimed to identify relationships between Big Data and Decision Support Systems. For this, we conducted a search in the Scopus database and as a result, we identified a report according to the increased frequency of publications, frequency of publications in journals and, using the VOSviewer software, we performed an analysis of words co-citation. We identified 5 groups of keywords that suggest different areas of study (e.g., logistics, health and social media), as well as a more recent focus on studies aimed at sustainable development, machine learning, analytical techniques and decision-making processes decision. An important contribution that should also be highlighted was the strong relationship between the keywords Big Data, artificial intelligence and decision making, suggesting studies involving the three terms in a large number of works.
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Sámano-Robles, Ramiro. "Stability and Delay of NDMA-MPR Protocol in Rice-Correlated Channels with Co-Channel Interference." Technologies 7, no. 1 (February 13, 2019): 22. http://dx.doi.org/10.3390/technologies7010022.
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This paper investigates backlog retransmission strategies for a class of random access protocols with retransmission diversity (i.e., network diversity multiple access or NDMA) combined with multiple-antenna-based multi-packet reception (MPR). This paper proposes NDMA-MPR as a candidate for 5G contention-based and ultra-low latency multiple access. This proposal is based on the following known features of NDMA-MPR: (1) near collision-free performance, (2) very low latency values, and (3) reduced feedback complexity (binary feedback). These features match the machine-type traffic, real-time, and dense object connectivity requirements in 5G. This work is an extension of previous works using a multiple antenna receiver with correlated Rice channels and co-channel interference modelled as a Rayleigh fading variable. Two backlog retransmission strategies are implemented: persistent and randomized. Boundaries and extended analysis of the system are here obtained for different network and channel conditions. Average delay is evaluated using the M/G/1 queue model with statistically independent vacations. The results suggest that NDMA-MPR can achieve very low values of latency that can guarantee real- or near-real-time performance for multiple access in 5G, even in scenarios with high correlation and moderate co-channel interference.
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Bonalumi, Davide, Antonio Giuffrida, and Federico Sicali. "Thermo-economic analysis of a supercritical CO2-based waste heat recovery system." E3S Web of Conferences 312 (2021): 08022. http://dx.doi.org/10.1051/e3sconf/202131208022.
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This work investigates the performance of a supercritical CO2 cycle as the bottoming cycle of a commercial gas turbine with 4.7 MW of electric power output. In detail, the partial heating cycle is the layout chosen for the interesting trade-off between heat recovery and cycle efficiency with a limited number of components. Single-stage radial turbomachines are selected according to the theory of similitude. In particular, the compressor is a troublesome turbomachine as it works near the critical point where significant variations of the CO2 properties occur. Efficiency values for turbomachinery are not fixed at first glance but result from actual size and running conditions, based on flow rates, enthalpy variations as well as rotational speeds. In addition, a limit is set for the machine Mach numbers in order to avoid heavily loaded turbomachinery. The thermodynamic study of the bottoming cycle is carried out by means of the mass and energy balance equations. A parametric analysis is carried out with particular attention to a number of specific parameters. Considering the power output calculated for the supercritical CO2 cycle, economic calculations are also carried out and the related costs compared to those specific of organic Rankine cycles with similar power output.
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Singh, Prashant Kumar, Amit Ranjan Hota, and S. B. Mishra. "Finite Element Modelling of Erosion Parameters in Boiler Components." Asian Journal of Engineering and Applied Technology 7, no. 2 (October 5, 2018): 12–16. http://dx.doi.org/10.51983/ajeat-2018.7.2.964.
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Erosion wear of boiler components in power plant industry is a critical factor in predicting the life and durability of such components. In the aggressive environment, failure of components is accelerated by erosion wear. Various erosion resistant coatings have been developed in the recent past to improve the life of such components subjected to erosive wear. Among the various types of coatings, the development of WC-Co ceramic coatings for the protection against erosion wear require understanding of their complex failure mechanisms occurring during solid particle impact. Many experimental works have been done to find the effect of different parameters on the erosion wear of the WC-Co coatings however such data is insufficient as newer composition and processing methods are being developed every day. Further, experimentation requires a lot of human effort, machine hours, sophisticated equipment and is time consuming. The simulation of the erosion process parameters in available finite element modelling software enables the prediction of erosion behavior of different combination of materials. The factors affecting the erosion wear of WC-Co coating such as the particle size, the velocity of impacting particles, the coating thickness, angle of impact and the coating composition are considers in this work. The results for erosion wear are obtained and analysed using the Hashish model for erosion wear.
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Hassan, Muhammad Kabir, Fahmi Ali Hudaefi, and Ahmad Agung. "EVALUATING INDONESIAN ISLAMIC BANKING SCHOLARLY PUBLICATIONS: A DATA ANALYTICS." Journal of Islamic Monetary Economics and Finance 8, no. 3 (August 30, 2022): 341–70. http://dx.doi.org/10.21098/jimf.v8i3.1560.
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This study employs bibliometric analysis to evaluate 443 scholarly works on the topics of Indonesian Islamic banking published on 194 academic platforms, and authored by 1049 scholars. The machine learning tools i.e., R Studio and VOSviewer were employed to analyse the Scopus’ bibliographical data automatically harvested from the database. We developed four research questions based on the theories that are fundamental to bibliometric study, i.e., performance analysis, citation and co-citation analyses, bibliographic coupling and social network analysis, to identify the most impactful manuscripts, scholarly journals, authors, and institutions of affiliation. We further established the discussion of the current issues in Indonesian Islamic banking topics from the keyword analysis and the bibliographic coupling. These findings derive some recommendations for future research. This study provides a supply of scholarly novelty in the assessment of Indonesian Islamic banking publications which are both practically and theoretically importance to regulators, academia and industry professionals.
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Mishra, Harsh Vats, Vaishnavi Singh, and Krishna Murari. "Machine Learning and Image Processing based Segmentation and Classification of Brain Tumor." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 2430–35. http://dx.doi.org/10.22214/ijraset.2022.41092.
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Abstract: In the field of Medicine, especially in diagnostic applications, finding the right one is a daunting task requires the attention of a radiologist. Error detection early it is necessary to avoid further complications. Many the developing field in the latest technology is MRI scanning. The size of the tumor in the brain may vary different patients and minute details of the tumor, It is a complex and tedious task for radiologists to diagnose and split the tumor into a large number of images. Occasionally, cerebral fluid also appears as a mass tissue on MRI image. The project aims to be automated a system that plays an important role in assessing whether a lump (mass of tissue) in the brain can be healthy (clump thickening) or bad (sticking to the edges) in stages. The proposed model uses machine learning algorithms in sequence to improve the accuracy of the separation. The program is loaded come out with four steps that include pre-noise removal processing using a flexible median filter, segregation using Gaussian Mixture Model (GMM) for finding the place of interest, extraction element using the Gray Level Co-occurrence Matrix GLCM to extract features of different types of tumors and segregation using Neural Networks (NN) to determine and classify the plant as harmful or harmful. The test results of the proposed model show that 93.33% accuracy, 96.6% accuracy, 93.33% sensitivity as well 94.44% accuracy. For these results the proposed model it works better compared to a classical machine learning algorithms like Adaboost (Adaptive Boosting) which is divides image into different classes (Normal, OK, Dangerous) with 89.90% accuracy. Keywords: K-means, Adaptive Median Filter, GMMSegmentation, Neural Networks, GLCM.
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Woliński, Marek. "Industry 4.0 and explosion prevention." MATEC Web of Conferences 247 (2018): 00037. http://dx.doi.org/10.1051/matecconf/201824700037.
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Industry 4.0 creates so called “smart factory”, what involves cyber - physical systems, the Internet of things, cloud computing and cognitive computing. One of design principles in Industry 4.0 is technical assistance, understood as the ability of cyber - physical systems to support humans physically by conducting a range of tasks that are unpleasant, too exhausting or unsafe for their human co - workers. Such new approach to production will affect, among others, area of workplace safety and machine safety - also, at the field of explosion safety for works performed in areas with potentially explosive atmospheres. At present, this important issue is regulated in EU by Directive 1999/92/EC (ATEX Worker Directive), Directive 2014/34/EU (ATEX Directive), harmonized national law and technical standards. However, one of important challenges in implementation of idea of Industry 4.0 is lack of regulations, standards and forms of certification. Presented paper discusses problem if existing regulations concerning explosion safety are adequate for such new conditions, sometimes referred to as the fourth industrial revolution.
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Mahmood, Omar Ahmed, Ahmed Sabeeh Yousif, and Afzan Adam. "Brief Review for Multi-Class Brain Tumor Diseases Schemes Using Machine Learning Techniques." Al-Kitab Journal for Pure Sciences 8, no. 02 (July 7, 2024): 94–108. http://dx.doi.org/10.32441/kjps.08.02.p8.
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Brain tumor diseases have had a considerable impact worldwide, affecting millions of individuals of different age groups, including both children and adults above 20 years old. Due to they are more needed in people’s lives, using the method based classifying brain tumors by machine learning schemes has become necessary. However, healthcare applications face challenges in identifying the most suitable classification-based metric, such as accuracy, due to the utilization of recent datasets. This study paper aims to provide a thorough evaluation of computational intelligence strategies used in tumor diagnosis. Several successful data mining techniques have been implemented, including wavelet analysis and spatial pixel modulation techniques. Furthermore, feature extraction and reduction techniques, such as the Grey Level Co-occurrence Matrix (GLCM), have been used to prepare the features for classification. Magnetic resonance imaging scan (MRI) is frequently utilized for the diagnosis of brain tumor diseases which is highly applied for classification-based machine learning, The review paper was focused on gliomas, meningiomas, and pituitary adenoma diseases. Technically, the usage of kernel principal component KPCA analysis with the proposed adaptive back propagation neural network scheme produced better performance-based classification metrics, (i.e:99.84%) for the accuracy metric. The aforementioned review articles have demonstrated that usage of the machine learning-based health care applications (brain diseases) classification widely assists the patient’s outcome and operations inside the hospitals. In summary, the paper has highlighted the importance of machine learning schemes for brain tumor detection and classification, and it also provided a comprehensive analysis and comparison of the state-of-the-art to show the methods such as ;(feature extraction, feature reduction), pros, cons, and the contributions for each of them. The paper's results are considered an advantageous starting point for future works.
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Ohala, John J., and Gösta Bruce. "In Memoriam: Ralph L. Vanderslice and Gunnar Fant." Journal of the International Phonetic Association 39, no. 3 (November 12, 2009): 387–89. http://dx.doi.org/10.1017/s002510030999020x.
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RALPH L. VANDERSLICE, who contributed to many areas of phonetics, died on 24 August 2008, aged 78, in Portland, Oregon. He was born on 2 January 1930 in South Bend, Indiana. He received his B.A. and his M.Sc. in speech and theatre from Michigan State College (now Michigan State University) in 1951 and 1954, respectively. He subsequently taught at the Universities of Vermont and Hawai'i. He moved to UCLA where, in 1968, he obtained his Ph.D. His dissertation, Synthetic Elocution (http://repositories.cdlib.org/uclaling/wpp/No8/), was an exploration of the character and some of the specifics of the rules that would be necessary to implement the prosodic aspects of synthesized speech produced by a reading machine. Peter Ladefoged was the chair of his dissertation committee and Victoria Fromkin one of the other members. While still a student at UCLA Ralph Vanderslice was an expert witness testifying against the validity of ‘voiceprints’ (the use of spectrographic displays of speech to identify suspects in criminal trials). He was very much a ‘hands-on’ person, skilled in many of the mechanical and electronic arts, one manifestation of this being his invention of the ‘crico-thyrometer’, a device that could track vertical larynx movement in connected speech. He subsequently taught at City University of New York and Yale University. He published many papers on speech prosody, notably ‘Binary suprasegmental features and transformational word-accentuation rules’ (1972, Language, with Peter Ladefoged). His colleagues, including this author, his office-mate in the UCLA Phonetics Lab, remember him for his erudition, wit, and generosity in helping others with everything from providing references to the seventeenth- and eighteenth-century works on phonetics (which he was steeped in) to repairing a shorted circuit in an amplifier. His salutary influence continues.
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Amelides, Panos. "Acousmatic Storytelling." Organised Sound 21, no. 3 (November 11, 2016): 213–21. http://dx.doi.org/10.1017/s1355771816000182.
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The purpose of this article is to explore the idea of relating storytelling with acousmatic music in the creation of a hybrid vehicle for transmitting stories. The concept of acousmatic storytelling is introduced, illustrated by the example of one of my own works which was created with the elements and techniques of storytelling as its conceptual basis. The article continues to investigate concepts of acousmatic storytelling in works from the repertoire of electroacoustic music, with composers such as Ferrari, Westerkamp, Derbyshire, Cousins and Young providing especially pertinent examples. Acousmatic storytelling integrates interviews, archival recordings, soundscape recordings, sonic icons and music quotations; the microphone becomes a time machine, ‘thought capturer’ and a conduit for conveying cultural information, elements which, combined with the sonic world composed in the studio, create a hybrid form. The concepts introduced in this article are useful for all those working with recorded sound, offering an approach to sonic creativity based on storytelling techniques and the way we experience past events through memory and sound recording. Acousmatic storytelling transmits a unique version of a story to the mind of the listener, who participates in the creation of the story and acts as co-creator of that story as experienced. In applying the methodology of interviews as well as researching past events and ‘writing’ about them, acousmatic storytelling composers can also be seen as historians and journalists.
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Zhang, Xinyi, Yawen Wu, Peipei Zhou, Xulong Tang, and Jingtong Hu. "Algorithm-hardware Co-design of Attention Mechanism on FPGA Devices." ACM Transactions on Embedded Computing Systems 20, no. 5s (October 31, 2021): 1–24. http://dx.doi.org/10.1145/3477002.
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Multi-head self-attention (attention mechanism) has been employed in a variety of fields such as machine translation, language modeling, and image processing due to its superiority in feature extraction and sequential data analysis. This is benefited from a large number of parameters and sophisticated model architecture behind the attention mechanism. To efficiently deploy attention mechanism on resource-constrained devices, existing works propose to reduce the model size by building a customized smaller model or compressing a big standard model. A customized smaller model is usually optimized for the specific task and needs effort in model parameters exploration. Model compression reduces model size without hurting the model architecture robustness, which can be efficiently applied to different tasks. The compressed weights in the model are usually regularly shaped (e.g. rectangle) but the dimension sizes vary (e.g. differs in rectangle height and width). Such compressed attention mechanism can be efficiently deployed on CPU/GPU platforms as their memory and computing resources can be flexibly assigned with demand. However, for Field Programmable Gate Arrays (FPGAs), the data buffer allocation and computing kernel are fixed at run time to achieve maximum energy efficiency. After compression, weights are much smaller and different in size, which leads to inefficient utilization of FPGA on-chip buffer. Moreover, the different weight heights and widths may lead to inefficient FPGA computing kernel execution. Due to the large number of weights in the attention mechanism, building a unique buffer and computing kernel for each compressed weight on FPGA is not feasible. In this work, we jointly consider the compression impact on buffer allocation and the required computing kernel during the attention mechanism compressing. A novel structural pruning method with memory footprint awareness is proposed and the associated accelerator on FPGA is designed. The experimental results show that our work can compress Transformer (an attention mechanism based model) by 95x. The developed accelerator can fully utilize the FPGA resource, processing the sparse attention mechanism with the run-time throughput performance of 1.87 Tops in ZCU102 FPGA.
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Artamonov, E. V., V. V. Kireev, and V. A. Zyryanov. "Modeling of process of gear milling for definition of the intense deformed state in replaceable many-sided plates of a worm mill." MATEC Web of Conferences 298 (2019): 00024. http://dx.doi.org/10.1051/matecconf/201929800024.
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The tool equipment plays an important role in work of machine-building enterprises. One of the most important elements of equipment is the metal-cutting tool [1]. Replacement of the tool from HSS of steel on the cutting tool with use of WC-Co [2-7] sintered hard alloys gives increase in productivity and overall effectiveness of processing [8-11]. When the cutting ability of material of the tool increases, the universality of its application decreases therefore the most rational is comprehensive research approach which considers design of the tool the used WC-Co hard alloy. Design, production and testing of the toothed cutting tools, working off of the modes of cutting have to be carried out concerning a specific work piece and its material. When developing this kind of the tool it is necessary to consider features of WC-Co alloy as the cutting material, the cutting modes, destruction of the tool and other moments connected with its use. Influence of an initial contour on distribution of tension in the replaceable cutting many-sided plate. Results of calculation of the intense deformed state in a replaceable plate from the material WC-Co. Zones of stretching and compression are observed on the main cutting edge. Concentration of the dangerous stretching tension is located in a transitional zone between the direct side cutting edge to top as in this zone there is the greatest constraint of cutting. Respectively, the greatest destruction will happen in this zone. Important, the fact that in this part of the cutting edge there will be the biggest wear on a back surface. The analysis of isolines of distribution of tension showed that the stretching tension along a front surface of the replaceable cutting plate decreases because each replaceable cutting plate cuts off the part of material. First, work is entered by the made narrower cutting plate which cuts off metal an upper part of the cutting plate, and then the underestimated cutting plate which works with the side cutting edges works. As a result of the conducted researches it was established that with the progressive scheme of cutting in comparison with the standard scheme of cutting at which the initial profile of the making rail is made on DIN3972 at gear milling, dangerous tensile stresses in the cutting hard-alloy plates significantly decrease.
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Arlazarov, V. V., E. I. Andreeva, K. B. Bulatov, D. P. Nikolaev, O. O. Petrova, B. I. Savelev, and O. A. Slavin. "Document image analysis and recognition: a survey." Computer Optics 46, no. 4 (August 2022): 567–89. http://dx.doi.org/10.18287/2412-6179-co-1020.
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This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.
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Parolin, Laura Lucia. "Working and knowing in technology-mediated environments." Communication & Language at Work 7, no. 1 (December 7, 2020): 123–35. http://dx.doi.org/10.7146/claw.v7i1.123265.
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Comprehensions of how technology-mediated environments work are marked by a lack of theoretical sophistication about how expertise emerges and develops. Using a practice-based approach to workplace learning and knowing, a case of telecardiological consultation is discussed, where a dedicated call center works by connecting general practitioners (GPs) with remote cardiologists. The service allows GPsto send electrocardiogram traces (ECGs) and discuss the needs of patients with a cardiologist. The role of materials (ECG traces, the recording machine, infrastructure), and communication (synchronous communication in the form of a phone call) are considered in the practical application of the service. It is argued that being an expert telecardiologist entails the ability to align heterogeneous elements, and co-construct, a reliable interpretation of the patient’s situation alongside a doctor. To do that when the situation is not immediately clear, necessitates both doctors discursively mobilize different bio-physiological, chemical, material, social and psychological aspects of a patient’s condition, in order to arrive at a reliable interpretation. Thanks to a practice-based sensitivity, I analyze the case study reading these interactions as competent material-discursive practices.
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Ji, Fujiao, Zhongying Zhao, Hui Zhou, Heng Chi, and Chao Li. "A comparative study on heterogeneous information network embeddings." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3463–73. http://dx.doi.org/10.3233/jifs-191796.
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Heterogeneous information networks are widely used to represent real world applications in forms of social networks, word co-occurrence networks, and communication networks, etc. However, It is difficult for traditional machine learning methods to analyze these networks effectively. Heterogeneous information network embedding aims to convert the network into low dimensional vectors, which facilitates the following tasks. Thus it is receiving tremendous attention from the research community due to its effectiveness and efficiency. Although numerous methods have been present and applied successfully, there are few works to make a comparative study on heterogeneous information network embedding, which is very important for developers and researchers to select an appropriate method. To address the above problem, we make a comparative study on the heterogeneous information network embeddings. Specifically, we first give the problem definition of heterogeneous information network embedding. Then the heterogeneous information networks are classified into four categories from the perspective of network type. The state-of-the-art methods for each category are also compared and reviewed. Finally, we make a conclusion and suggest some potential future research directions.
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Clerico, Eugenia M., Wenli Meng, Alexandra Pozhidaeva, Karishma Bhasne, Constantine Petridis, and Lila M. Gierasch. "Hsp70 molecular chaperones: multifunctional allosteric holding and unfolding machines." Biochemical Journal 476, no. 11 (June 14, 2019): 1653–77. http://dx.doi.org/10.1042/bcj20170380.
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AbstractThe Hsp70 family of chaperones works with its co-chaperones, the nucleotide exchange factors and J-domain proteins, to facilitate a multitude of cellular functions. Central players in protein homeostasis, these jacks-of-many-trades are utilized in a variety of ways because of their ability to bind with selective promiscuity to regions of their client proteins that are exposed when the client is unfolded, either fully or partially, or visits a conformational state that exposes the binding region in a regulated manner. The key to Hsp70 functions is that their substrate binding is transient and allosterically cycles in a nucleotide-dependent fashion between high- and low-affinity states. In the past few years, structural insights into the molecular mechanism of this allosterically regulated binding have emerged and provided deep insight into the deceptively simple Hsp70 molecular machine that is so widely harnessed by nature for diverse cellular functions. In this review, these structural insights are discussed to give a picture of the current understanding of how Hsp70 chaperones work.
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Raj, Alex Noel Joseph, Ruban Nersisson, Vijayalakshmi G. V. Mahesh, and Zhemin Zhuang. "Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning." Ultrasonic Imaging 43, no. 1 (December 23, 2020): 29–45. http://dx.doi.org/10.1177/0161734620974273.
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Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.
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Gao, Shu Hua, Rui Fan, and Dan Wang. "Dynamic Modeling and Model-Based Force Control of a 3-DOF Translational Parallel Robot." Advanced Materials Research 1006-1007 (August 2014): 609–17. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.609.
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A 3-axis parallel loading mechanism, which works as a multi-axis load simulator, is proposed for reliability test of multi-axis CNC machine tools by exerting specific load spectrums on the spindle. To achieve efficient loading force control, dynamic model of the 3-DOF translational parallel robot is derived via the virtual work principle and is embedded into the control strategy to build a model-based control scheme. A mass distribution factor is introduced and the rotating inertia of the limbs is neglected to simplify the dynamics equations for better real-time control performance. This simplification method is verified by comparison with the complete dynamics model. Then the simplified dynamic model is integrated with a PI (proportional–integral) controller with feedforward to control the moving platform’s output force in the task space and this control strategy is verified through co-simulations with MATLAB/Simulink and ADAMS. Simulation results show that the proposed model-based PI controller is effective to control the three-dimensional output force of the 3-DOF translational parallel robot.
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Borgovan, Theo, Chibuikem Chukwudalu Nwizu, Laura R. Goldberg, Mark S. Dooner, Sicheng Wen, Mike Deltatto, Lorin Crawford, and Peter J. Quesenberry. "Extracellular Vesicles (EVs) Shape the Leukemic Microenvironment." Blood 132, Supplement 1 (November 29, 2018): 5428. http://dx.doi.org/10.1182/blood-2018-99-118955.
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Abstract Background Extracellular Vesicles (EVs) compose a naturally occurring, heterogeneous group of membrane-bound, nano-sized particles shed by all cells. Depending on cellular type, physiological state, and mode of secretion some harbor potent regenerative properties while others have the propensity to induce disease. Human bone marrow mesenchymal stem cell (MSC)-derived EVs harbor regenerative potential. Our own studies have shown MSC-EVs are able to mitigate radiation damage to bone marrow, and to reverse the malignant phenotype in prostate and colorectal cancer. On the contrary, EVs isolated from neoplastic cells induce a neoplastic phenotype in non-cancerous cells. Leukemic EVs also potentiate the phenotypic change of healthy MSCs into cancer associated fibroblasts. The role of EVs within the leukemic microenvironment may provide insight for therapeutic advances. We hypothesize that EVs derived from normal MSCs inhibit the growth of nascent acute myelogenous leukemia (AML), and that the predominant EV population changes during leukemia progression. Neural network machine learning will allow us to capture these changes in order to build predictive models. Methods Kasumi AML cells lines were seeded with various concentrations of MSC-derived EVs. Vesicles were isolated using an established differential centrifugation technique, and co-cultured with Kasumi. To study cellular proliferation we employed a fluorescence-based method for quantifying viable cells (CyQuant). We also investigated the modes of death (apoptosis vs necrosis) EVs may induce on AML via a three die fluorescent system. Fluorescence intensities were normalized to control wells containing non-EV treated cells alone. Our neural network achieved 90.16% classification accuracy with cell culture data, and was tasked to classify the similarity of patient samples to the AML-derived EVs. Results Proliferation of AML cells after one day of co-culture with 2.6E8 &1.3E10 MSC-EVs respectively was inhibited in a dose dependent manner: with 2.6E8 EVs leading to ~ 15% reduction in growth, and 1.3E10 EVs leading to ~60% reduction when normalized to non-EV treated controls. 3 days of co-culture with similar doses resulted in ~40% and ~80% reduction in proliferation when normalized to control. At day 6 of co-culture growth was inhibited by ~80% at both EV concentrations. At multiple time points hMSC-EV treated AML cells showed a significantly higher proportion of apoptosis. Cellular necrosis was negligible. There was no statistically significant change in proliferation of MSC exposed to MSC-derived EVs when compared to non-EV treated controls. There was also no statistically significant change in proliferation of AML cells exposed to AML-derived EVs. Samples of AML, Chronic Myelomonocytic Leukemia, and Multiple Myeloma entered into our machine alogrithm were calculated to be 100%, 100%, and 66%, respectively, similar to the AML-derived EVs. Summary/Conclusion MSC-derived EVs inhibits the proliferation of the AML cell line in vitro via an apoptotic mechanism. This effect is seen as early as one day of co-culture and persists out to three, and six days, implicating an miRNA-mediated mechanism that has been discussed in previous works. We have also shown that when cells are exposed to their own EV there is no change or (in the case of leukemia) a statistically insignificant increase in proliferation. We feel this is perhaps a model of how a normal marrow works to suppress early cancer. As leukemia develops the cross-talk between AML and its microenvironment, via direct EV mediated effects, alters the MSCs to promote a survival signal favoring AML growth. This restructuring is EV mediated. Future work involves the capacity of AML-derived EVs to alter the phenotype of normal marrow towards a pro-leuekmic phenotype. This also includes the use of AML mouse models to further investigate the therapeutic potential MSC-derived EVs may have as single or adjunct therapy; as well as to study potential cellular mediators that may be involved in EV-direceted AML progression. Lastly we endeavor to employ machine learning networks to characterize and predict the dynamic restructuring the EV milieu undergoes as leukemia progresses. Capturing these alteration will allow the creation of reliable predictive models that will have direct inferences on clinical diagnosis and prognosis. Disclosures No relevant conflicts of interest to declare.
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Gaber, Ahmed, Hassan A. Youness, Alaa Hamdy, Hammam M. Abdelaal, and Ammar M. Hassan. "Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm." Applied Sciences 12, no. 1 (January 5, 2022): 521. http://dx.doi.org/10.3390/app12010521.
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Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultrasound image features and a voting-based classifier. Four main contributions are provided by our developed method: firstly, the classification of liver images is achieved as normal or fatty without a segmentation phase. Secondly, compared to our proposed work, the dataset in previous works was insufficient. A combination of 26 features is the third contribution. Based on the proposed methods, the extracted features are Gray-Level Co-Occurrence Matrix (GLCM) and First-Order Statistics (FOS). The fourth contribution is the voting classifier used to determine the liver tissue type. Several trials have been performed by examining the voting-based classifier and J48 algorithm on a dataset. The obtained TP, TN, FP, and FN were 94.28%, 97.14%, 5.71%, and 2.85%, respectively. The achieved precision, sensitivity, specificity, and F1-score were 94.28%, 97.05%, 94.44%, and 95.64%, respectively. The achieved classification accuracy using a voting-based classifier was 95.71% and in the case of using the J48 algorithm was 93.12%. The proposed work achieved a high performance compared with the research works.
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Kaur, Avneet, Munish Bhatia, and Giovanni Stea. "A Survey of Smart Classroom Literature." Education Sciences 12, no. 2 (January 26, 2022): 86. http://dx.doi.org/10.3390/educsci12020086.
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Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis.
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Shakil, Rashiduzzaman, Bonna Akter, F. M. Javed Mehedi Shamrat, and Sheak Rashed Haider Noori. "A novel automated feature selection based approach to recognize cauliflower disease." Bulletin of Electrical Engineering and Informatics 12, no. 6 (December 1, 2023): 3541–51. http://dx.doi.org/10.11591/eei.v12i6.5359.
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Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).
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Ahmed, Ibrahim Abdulrab, Ebrahim Mohammed Senan, Taha H. Rassem, Mohammed A. H. Ali, Hamzeh Salameh Ahmad Shatnawi, Salwa Mutahar Alwazer, and Mohammed Alshahrani. "Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques." Electronics 11, no. 4 (February 10, 2022): 530. http://dx.doi.org/10.3390/electronics11040530.
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Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks [feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively.
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DENIKIN, ANTON A. "CRITICISM OF CERTAIN PROVISIONS OF THE PERFORMANCE THEORY BY ERIKA FISCHER-LICHTE (on the example of participatory performances)." ART AND SCIENCE OF TELEVISION 17, no. 1 (2021): 139–70. http://dx.doi.org/10.30628/1994-9529-2021-17.1-139-170.
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The article attempts to critically analyze the performance theory created by the German scholar Erika Fischer-Lichte. The theory is applicable to some performative art practices, but nevertheless its key provisions do not fully meet the objectives, capabilities and the very specifics of participatory performance. Instead of such concepts as “strong presence”, “liveness”, “authenticity” and the idea of “energy exchange”, the author suggests to analyze participatory practices using the method of “choragraphic communication”, which is understood as individual and collective generation-test of the possible, constant reinvention of the action figurativeness, re-shaping of the participants’ physicality, co-joint transformation of meaning-making. Comparison of the performances by J. Ono and M. Abramovich allows us to distinguish the key differences between the two approaches to the analysis of participatory performance. It is anticipated that the effect of a performance is to provoke in every viewer an affective experience of the diversity of the undone, of the unmanifested, which, however, might become possible through the realization of one of its incarnations in a specific action. The peculiarity of performative involvement is that the processes, triggered by the actions of the performers and the responses of the participantsspectators, make accessible the affective test of the possible as being in potency. Using the works of the modern performance artist Tino Sehgal as an example, the author shows that spectator’s participation as a special communication instrument appeals to a different culture of knowledge transfer, which is based not on presentation, documentation and archiving, but on the situational self-configuration of autopoietic systems, on the living muscle “memory” of the participants, actualizing such phenomena as “bodily knowledge”, “morethan- human” perception and procedural assemblage. Participatory performances create conditions under which “random” and unexpected actions of participants turn out to be a condition for (self) recreation of the form of the assemblage machine through constant reincarnation of the invariants of the whole. Sehgal’s works reveal each participant-spectator as a kind of an “actant machine” capable of reconfiguring the entire system by their actions, and offer an interface for realizing the possibilities of “electracy communication”. At the same time, the processes of joint reconfiguration themselves become available to the affective experience of each of the participants. They make the assemblage machine generate its own “proto-subjectivity”; and, probably, it is in the individual perception of each participant and in the experience of the stages of its formation where the aesthetics of participatory action may lie.
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Hussain, Ayaz, Umar Draz, Tariq Ali, Saman Tariq, Muhammad Irfan, Adam Glowacz, Jose Alfonso Antonino Daviu, Sana Yasin, and Saifur Rahman. "Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach." Energies 13, no. 15 (August 1, 2020): 3930. http://dx.doi.org/10.3390/en13153930.
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Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.
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Pransky, Joanne. "The Pransky interview: Professor Jacob Rosen, Co-Founder of Applied Dexterity and ExoSense." Industrial Robot: An International Journal 43, no. 5 (August 15, 2016): 457–62. http://dx.doi.org/10.1108/ir-06-2016-0162.
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Purpose The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned-entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Jacob Rosen, a Professor of Medical Robotics at the Department of Mechanical and Aerospace Engineering, University of California, Los Angeles (UCLA), where he directs the Bionics Lab. Professor Rosen is also the Director of Surgical Robotics Engineering at the UCLA School of Medicine’s Center for Advanced Surgical and Interventional Technology and has joint appointments at UCLA’s Department of Surgery and UCLA’s Department of Bioengineering. Professor Rosen is the co-founder of the companies Applied Dexterity, ExoSense and SPI. As a pioneer in medical robotics devices and technologies, Professor Rosen describes his unique approaches and philosophies. Findings Dr Rosen received his BSc degree in Mechanical Engineering, MSc and PhD degrees in Biomedical Engineering from Tel-Aviv University in 1987, 1993 and 1997, respectively. From 1987 to 1992, he served as an officer in the Israeli Defense Forces studying human–machine interfaces. From 1993 to 1997, he was a research associate at Tel-Aviv University, as well as held a position at a startup company developing innovative orthopedic spine/pelvis implants. From 2001-2013, he held faculty positions at the University of Washington and at University of California, Santa Cruz. Originality/value Dr Rosen developed several key systems in the field of medical robotics, such as the Blue and the Red Dragon, for minimally invasive surgical skill evaluation; RAVEN, a surgical robotic system for telesurgery; and several generations of upper and lower limb exoskeletons including the Exo-UL7 – a dual arm wearable robotic system. He is a co-author of 100 manuscripts in the field of medical robotics and a co-author and co-editor of two books entitled “Surgical Robotics – Systems, Applications, and Visions” and “Redundancy in Robot Manipulators and Multi-robot systems” published by Springer. Professor Rosen has filed eight different patent applications and also works as an expert witness and consultant on design, patent protection & litigation and malpractice regarding surgical robotics.