Search results for: practice learning

Authors: Parima Kotanut

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It is commonly known that Le Corbusier, a pivotal figure in modern architecture, developed a diverse range of architectural languages. Due to his emblematic work of the International Style has become a central focus in architectural education and historiography, many scholars have redirected their attention to the architect's formative years and early career. One of the significant contributions to this is the treatise titled Le Corbusier before Le Corbusier: Applied Arts, Architecture, Painting, Photography, 1907-1922, published in conjunction with an exhibition in Switzerland in 2002, aiming to provide an extensive catalog of Le Corbusier's personal life, the societal contexts he engaged with, and his artistic pursuits. While the publication offers profound insights into the experiences and ideologies that shaped his early works, it only partially addresses the influence of those formative ideas on his later practice. This raises a question regarding the long-term impact of his early experiences as Charles-Edouard Jeanneret upon his later practice as Le Corbusier. The research aims to re-examine theoretical frameworks proposed by Le Corbusier and other scholars in juxtaposition to his built works, correspondences, and artistic persona. Although there are several milestones shaping Le Corbusier's works, through historical research, the paper is divided into three categories: 1911 "Voyage d'Orient", the immersion in the technology of reinforced concrete, and the participation in the Parisian art world. These three experiences serve as a foundation for his lifetime theories concerning function, construction, and form of architecture, demonstrating how experiences beyond the architectural domain are critical to the design of buildings. Furthermore, this study intends to illuminate the contributions of lesser-known figures who consistently influenced Le Corbusier's passionate journey toward his architectural identity.

Keywords: architecture, Le Corbusier, 20th century architecture, modern architecture

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Authors: Mehmet Akif Bütüner, İlhan Koşalay

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Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. While the control systems operating in these power plants ensure that the system operates at the desired operating point, it is also responsible for stopping the relevant unit safely in case of any malfunction. While these control systems are expected not to miss signals that require stopping, on the other hand, it is desired not to cause unnecessary stops. In traditional control systems including modern systems with SCADA infrastructure, alarm conditions to create warnings or trip conditions to put relevant unit out of service automatically are usually generated with predefined limits regardless of different operating conditions. This approach results in alarm/trip conditions to be less likely to detect minimal changes which may result in serious malfunction scenarios in near future. With the methods proposed in this research, routine behavior of the oil circulation of hydraulic governor of a HEPP will be modeled with machine learning methods using historical data obtained from SCADA system. Using the created model and recently gathered data from control system, oil pressure of hydraulic accumulators will be estimated. Comparison of this estimation with the measurements made and recorded instantly by the SCADA system will help to foresee failure before becoming worse and determine remaining useful life. By using model outputs, maintenance works will be made more planned, so that undesired stops are prevented, and in case of any malfunction, the system will be stopped or several alarms are triggered before the problem grows.

Keywords: hydroelectric, governor, anomaly detection, machine learning, regression

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Authors: Dipti Parida, Atasi Mohanty

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The focus of this research is to examine the interaction effect of flipped teaching and traditional teaching mode across two different medium (English and Odia) of instructional groups. Both Science and History subjects were taken to be taught in the Class- VIII in two different instructional mode/s. In total, 180 students of Class-VIII of both Odia and English medium schools were taken as the samples of this study; 90 participants (each group) were from both English and Odia medium schools ; 45 participants of each of these two groups were again assigned either to flip or traditional teaching method. We have two independent variables and each independent variable with two levels. Medium and mode of instruction are the two independent variables. Medium of instruction has two levels of Odia medium and English medium groups. The mode of instruction has also two levels of flip and traditional teaching method. Here we get 4 different groups, such as Odia medium students with traditional mode of teaching (O.M.T), Odia medium students with flipped mode of teaching (O.M.F), English medium students with traditional mode of teaching (E.M.T) and English medium students with flipped mode of teaching (E.M.F). Before the instructional administration, these four groups were given a test on the concerned topic to be taught. Based on this result, a one-way ANOVA was computed and the obtained result showed that these four groups don’t differ significantly from each other at the beginning. Then they were taught the concerned topic either in traditional or flip mode of teaching method. After that a 2×2×2 repeated measures ANOVA was done to analyze the group differences as well as the learning outcome before and after the teaching. The result table also shows that in post-test the learning outcome is highest in case of English medium students with flip mode of instruction. From the statistical analysis it is clear that the flipped mode of teaching is as effective for Odia medium students as it is for English medium students.

Keywords: medium of instruction, mode of instruction, test mode, vernacular medium

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Authors: Yasin I. Tayem, Jamil Ahmed, Mahmood Bahzad, Abdullah Alnama, Fahad Al Asfoor, Mahmood A. Jalil, Mohammed Radhi, Ahmed Alenezi, Khalid A. J. Al-Khaja

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Drug-drug interactions (DDIs) represent a significant cause of patient’s morbidity and mortality. The rate of DDIs is rapidly increasing worldwide with the increasing proportion of ageing population and frequent requirement of polypharmacy-prescription of multiple drugs to treat comorbidities. Prescribing physicians are responsible for checking their prescriptions for the presence and severity of DDIs. However, since a large number of new drugs are approved and marketed every year, new interactions between medications are increasingly reported. Consequently, it is no longer practical for physicians to rely only upon their previous knowledge of medicine to avoid potential DDIs. The aim of this study was to explore the perceptions of physicians working at primary healthcare centers in Bahrain towards DDIs and how they manage them during their practice. Methodology: In this cross-sectional study, physicians working at all governmental primary healthcare centers in Bahrain were invited to voluntarily, privately and anonymously respond to a self-administered questionnaire. The questionnaire aims to assess their self-reported knowledge of DDIs and how they check for them in their practice. The participants were requested to provide socio demographic data and information related to their attitudes towards DDIs including strategies they employ for detecting and managing them, and their awareness of drugs which commonly cause DDIs. At the end of the questionnaire, an open-ended item was added to allow participants to further add any comment. Findings and Conclusions: The study is going on currently, and the results and conclusions will be presented at the conference.

Keywords: awareness, drug interactions, health centres, physicians

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Evelyn Fabian

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The focus of this discussion centres on the emerging issues in Early Childhood Care and development in Nigeria. Early childhood care is the bedrock of Nigeria’s educational system. However, there are critical issues that had not been addressed and it is frustrating the entire educational process. Thus, this paper will show the inter-connectedness between these issues such as poor funding, trained skillful teachers that would supervise the learning process of the kids, unconducive learning environment and lack of relevant facilities. For a clear grasp of these issues, the researcher visited 36 early childhood centres distributed across the 36 spates of Nigeria. The findings which were expressed in simple percentages revealed a near total absence or government neglect of these critical areas. The findings equally showed a misplaced priority in the government allocation of funds to early child care education and development. The study concludes that this mismatch in the training of these categories of pupils, government should expedite action in addressing these emerging issues in early childhood care and development in Nigeria.

Keywords: early childhood, ECCE, education, emerging issues

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Authors: Julia Baker

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Development plays a major role in stopping biodiversity loss. But the ‘silo species’ protection of legislation (where certain species are protected while many are not) means that development can be ‘legally compliant’ and result in biodiversity loss. ‘Net Gain’ (NG) policies can help overcome this by making it an absolute requirement that development causes no overall loss of biodiversity and brings a benefit. However, offsetting biodiversity losses in one location with gains elsewhere is controversial because people suspect ‘offsetting’ to be an easy way for developers to buy their way out of conservation requirements. Yet the good practice principles (GPP) of offsetting provide several advantages over existing legislation for protecting biodiversity from development. This presentation describes the learning from implementing NG approaches based on GPP. It regards major upgrades of the UK’s transport networks, which involved removing vegetation in order to construct and safely operate new infrastructure. While low-lying habitats were retained, trees and other habitats disrupting the running or safety of transport networks could not. Consequently, achieving NG within the transport corridor was not possible and offsetting was required. The first ‘lessons learnt’ were on obtaining a commitment from business leaders to go beyond legislative requirements and deliver NG, and on the institutional change necessary to embed GPP within daily operations. These issues can only be addressed when the challenges that biodiversity poses for business are overcome. These challenges included: biodiversity cannot be measured easily unlike other sustainability factors like carbon and water that have metrics for target-setting and measuring progress; and, the mindset that biodiversity costs money and does not generate cash in return, which is the opposite of carbon or waste for example, where people can see how ‘sustainability’ actions save money. The challenges were overcome by presenting the GPP of NG as a cost-efficient solution to specific, critical risks facing the business that also boost industry recognition, and by using government-issued NG metrics to develop business-specific toolkits charting their NG progress whilst ensuring that NG decision-making was based on rich ecological data. An institutional change was best achieved by supporting, mentoring and training sustainability/environmental managers for these ‘frontline’ staff to embed GPP within the business. The second learning was from implementing the GPP where business partnered with local governments, wildlife groups and land owners to support their priorities for nature conservation, and where these partners had a say in decisions about where and how best to achieve NG. From this inclusive approach, offsetting contributed towards conservation priorities when all collaborated to manage trade-offs between: -Delivering ecologically equivalent offsets or compensating for losses of one type of biodiversity by providing another. -Achieving NG locally to the development whilst contributing towards national conservation priorities through landscape-level planning. -Not just protecting the extent and condition of existing biodiversity but ‘doing more’. -The multi-sector collaborations identified practical, workable solutions to ‘in perpetuity’. But key was strengthening linkages between biodiversity measures implemented for development and conservation work undertaken by local organizations so that developers support NG initiatives that really count.

Keywords: biodiversity offsetting, development, nature conservation planning, net gain

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Authors: Jarek Krajewski, Andre Wittenborn, Martin Sauerland

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This abstract describes a promising approach for estimating post-traumatic stress disorder (PTSD) based on prosodic speech characteristics. It illustrates the validity of this method by briefly discussing results from an Arabic refugee sample (N= 47, 32 m, 15 f). A well-established standardized self-report scale “Reaction of Adolescents to Traumatic Stress” (RATS) was used to determine the ground truth level of PTSD. The speech material was prompted by telling about autobiographical related sadness inducing experiences (sampling rate 16 kHz, 8 bit resolution). In order to investigate PTSD-induced speech changes, a self-developed set of 136 prosodic speech features was extracted from the .wav files. This set was adapted to capture traumatization related speech phenomena. An artificial neural network (ANN) machine learning model was applied to determine the PTSD level and reached a correlation of r = .37. These results indicate that our classifiers can achieve similar results to those seen in speech-based stress research.

Keywords: speech prosody, PTSD, machine learning, feature extraction

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: Emily Simone Doffing, Danielle Kohfeldt

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Disabled students are underrepresented in graduate-level degree enrollment and completion. There is limited research on disabled students' progression during the pandemic. Disabled graduate students (DGS) face unique interpersonal and institutional barriers, yet, limited research explores these barriers, buffering facilitators, and aids to academic persistence. This study adopts an asset-based, embodied disability approach using the critical pedagogy theoretical framework instead of the deficit research approach. The Participatory Action Research (PAR) paradigm, the critical pedagogy theoretical framework, and emancipatory disability research share the same purpose -creating a socially just world through reciprocal learning. This study is one of few, if not the first, to center solely on DGS’ lived understanding using a Participatory Action Research (PAR) epistemology. With a PAR paradigm, participants and investigators work as a research team democratically at every stage of the research process. PAR has individual and systemic outcomes. PAR lessens the researcher-participant power gap and elevates a marginalized community’s knowledge as expertise for local change. PAR and critical pedagogy work toward enriching everyone involved with empowerment, civic engagement, knowledge proliferation, socio-cultural reflection, skills development, and active meaning-making. The PAR process unveils the tensions between disability and graduate school in policy and practice during the pandemic. Likewise, institutional and ideological tensions influence the PAR process. This project is recruiting 10 DGS until September through purposive and snowball sampling. DGS will collectively practice praxis during four monthly focus groups in the fall 2023 semester. Participant researchers can attend a focus group or an interview, both with field notes. September will be our orientation and first monthly meeting. It will include access needs check-ins, ice breakers, consent form review, a group agreement, PAR introduction, research ethics discussion, research goals, and potential research topics. October and November will be available for meetings for dialogues about lived experiences during our collaborative data collection. Our sessions can be semi-structured with “framing questions,” which would be revised together. Field notes include observations that cannot be captured through audio. December will focus on local social action planning and dissemination. Finally, in January, there will be a post-study focus group for students' reflections on their experiences of PAR. Iterative analysis methods include transcribed audio, reflexivity, memos, thematic coding, analytic triangulation, and member checking. This research follows qualitative rigor and quality criteria: credibility, transferability, confirmability, and psychopolitical validity. Results include potential tension points, social action, individual outcomes, and recommendations for conducting PAR. Tension points have three components: dubious practices, contestable knowledge, and conflict. The dissemination of PAR recommendations will aid and encourage researchers to conduct future PAR projects with the disabled community. Identified stakeholders will be informed of DGS’ insider knowledge to drive social sustainability.

Keywords: participatory action research, graduate school, disability, higher education

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: S. Chee Choy, Pauline Swee Choo Goh, Yow Lin Liew

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The purpose of this study is to evaluate the English version and a Malay translation of the 21-item Learner Awareness Questionnaire for its application to assess student learning in higher education. The Learner Awareness Questionnaire, originally written in English, is a quantitative measure of how and why students learn. The questionnaire gives an indication of the process and motives to learn using four scales: survival, establishing stability, approval, and loving to learn. Data in the present study came from 680 university students enrolled in various programs in Malaysia. The Malay version of the questionnaire supported a similar four-factor structure and internal consistency to the English version. The four factors of the Malay version also showed moderate to strong correlations with those of the English versions. The results suggest that the Malay version of the questionnaire is similar to the English version. However, further refinement for the questions is needed to strengthen the correlations between the two questionnaires.

Keywords: student learning, learner awareness, questionnaire development, instrument validation

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Authors: Taksina Bunbut

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Personality and grooming are vital for creating professionalism and safety image for all staffs in the airline industry. Airline Business Program also has an aim to educate students through the subject Personality Development and Grooming in order to elevate the quality of students to meet standard requirements of the airline industry. However, students agree that there are many difficulties that cause unsuccessful learning experience in this subject. The research is to study problems that can afflict students from getting good results in the classroom. Furthermore, exploring possible solutions to overcome challenges are also included in this study. The research sample consists of 140 students who attended the class of Personality Development and Grooming. The employed research instrument is a questionnaire. Statistic for data analysis is t-test and Multiple Regression Analysis. The result found that although students are satisfied with teaching and learning of this subject, they considered that teaching in English and teaching topics in social etiquette in different cultures are difficult for them to understand.

Keywords: personality development, grooming, Airline Business Program, soft skill

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Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

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Authors: Justin D. Olmanson, Fitsum Abebe, Valerie Jones, Eric Kyle, Xianquan Liu, Katherine Robbins, Guieswende Rouamba

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The history of education technology--and designing, adapting, and adopting technologies for use in educational spaces--is nuanced, complex, and dynamic. Yet, despite a range of continually emerging technologies, the design and development process often yields results that appear quite similar in terms of affordances and interactions. Through this study we (1) verify the extent to which designs have been constrained, (2) consider what might account for it, and (3) offer a way forward in terms of how we might identify and strategically sidestep these influences--thereby increasing the diversity of our designs with a given technology or within a particular learning domain. We begin our inquiry from the perspective that a host of co-influencing elements, fields, and meta narratives converge on the education technology design process to exert a tangible, often homogenizing effect on the resultant designs. We identify several elements that influence design in often implicit or unquestioned ways (e.g. curriculum, learning theory, economics, learning context, pedagogy), we describe our methodology for identifying the elemental positionality embedded in a design, we direct our analysis to a particular subset of technologies in the field of literacy, and unpack our findings. Our early analysis suggests that the majority of education technologies designed for use/used in US public schools are heavily influenced by a handful of mainstream theories and meta narratives. These findings have implications for how we approach the education technology design process--which we use to suggest alternative methods for designing/ developing with emerging technologies. Our analytical process and re conceptualized design process hold the potential to diversify the ways emerging and established technologies get incorporated into our designs.

Keywords: curriculum, design, innovation, meta narratives

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Authors: Sofia Horjales, Florencia Palma

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Our society is currently experiencing a profound transformation, demanding a proactive response from governmental bodies and higher education institutions to empower the next generation as catalysts for change. Environmental sustainability is rooted in the critical need to maintain the equilibrium and integrity of natural ecosystems, ensuring the preservation of precious natural resources and biodiversity for the benefit of both present and future generations. It is an essential cornerstone of sustainable development, complementing social and economic sustainability. In this evolving landscape, active methodologies take a central role, aligning perfectly with the principles of the 2030 Agenda for Sustainable Development and emerging as a pivotal element of teacher education. The emphasis on active learning methods has been driven by the urgent need to nurture sustainability and instill social responsibility in our future leaders. The Universidad Tecnológica of Uruguay (UTEC) is a public, technologically-oriented institution established in 2012. UTEC is dedicated to decentralization, expanding access to higher education throughout Uruguay, and promoting inclusive social development. Operating through Regional Technological Institutes (ITRs) and associated centers spread across the country, UTEC faces the challenge of remote student populations. To address this, UTEC utilizes e-learning for equal opportunities, self-regulated learning, and digital skills development, enhancing communication among students, teachers, and peers through virtual classrooms. The Interdisciplinary Continuing Education Program is part of the Innovation and Entrepreneurship Department of UTEC. The main goal is to strengthen innovation skills through a transversal and multidisciplinary approach. Within this Program, we have developed a Case of Study and Project-Based Learning Virtual Course designed for university students and open to the broader UTEC community. The primary aim of this course is to establish a strong foundation for comprehending and addressing environmental sustainability issues from an interdisciplinary perspective. Upon completing the course, we expect students not only to understand the intricate interactions between social and ecosystem environments but also to utilize their knowledge and innovation skills to develop projects that offer enhancements or solutions to real-world challenges. Our course design centers on innovative learning experiences, rooted in active methodologies. We explore the intersection of these methods with sustainability and social responsibility in the education of university students. A paramount focus lies in gathering student feedback, empowering them to autonomously generate ideas with guidance from instructors, and even defining their own project topics. This approach underscores that when students are genuinely engaged in subjects of their choice, they not only acquire the necessary knowledge and skills but also develop essential attributes like effective communication, critical thinking, and problem-solving abilities. These qualities will benefit them throughout their lifelong learning journey. We are convinced that education serves as the conduit to merge knowledge and cultivate interdisciplinary collaboration, igniting awareness and instigating action for environmental sustainability. While systemic changes are undoubtedly essential for society and the economy, we are making significant progress by shaping perspectives and sparking small, everyday actions within the UTEC community. This approach empowers our students to become engaged global citizens, actively contributing to the creation of a more sustainable future.

Keywords: active learning, environmental education, project-based learning, soft skills development

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Authors: Michael Brazley

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Virtual Reality (VR) and 3D videos are the most current generation of learning technology today. Virtual Reality and 3D videos are being used in professional offices and Schools now for marketing and education. Technology in the field of design has progress from two dimensional drawings to 3D models, using computers and sophisticated software. Virtual Reality is being used as collaborative means to allow designers and others to meet and communicate inside models or VR platforms using avatars. This research proposes to teach students from different backgrounds how to take a digital model into a 3D video, then into VR, and finally VR with multiple avatars communicating with each other in real time. The next step would be to develop the model where people from three or more different locations can meet as avatars in real time, in the same model and talk to each other. This research is longitudinal, studying the use of 3D videos in graduate design and Virtual Reality in XR (Extended Reality) courses. The research methodology is a combination of quantitative and qualitative methods. The qualitative methods begin with the literature review and case studies. The quantitative methods come by way of student’s 3D videos, survey, and Extended Reality (XR) course work. The end product is to develop a VR platform with multiple avatars being able to communicate in real time. This research is important because it will allow multiple users to remotely enter your model or VR platform from any location in the world and effectively communicate in real time. This research will lead to improved learning and training using Virtual Reality and Avatars; and is generalizable because most Colleges, Universities, and many citizens own VR equipment and computer labs. This research did produce a VR platform with multiple avatars having the ability to move and speak to each other in real time. Major implications of the research include but not limited to improved: learning, teaching, communication, marketing, designing, planning, etc. Both hardware and software played a major role in project success.

Keywords: virtual reality, avatars, education, XR

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Authors: Carla Aramouny

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Digital fabrication, between advancements in software and machinery, is pushing practice today towards more complexity in design, allowing for unparalleled explorations. It is giving designers the immediate capacity to apply their imagined objects into physical results. Yet at no time have questions of material knowledge become more relevant and crucial, as technological advancements approach a radical re-invention of the design process. As more and more designers look towards tactile crafts for material know-how, an interest in natural behaviors has also emerged trying to embed intelligence from nature into the designed objects. Concerned with enhancing their immediate environment, designers today are pushing the boundaries of design by bringing in natural systems, materiality, and advanced fabrication as essential processes to produce active designs. New Territories, a yearly architecture and design course on digital design and materiality, allows students to explore processes of digital fabrication in intersection with natural systems and hands-on experiments. This paper will highlight the importance of learning from nature and from physical materiality in a digital design process, and how the simultaneous move between the digital and physical realms has become an essential design method. It will detail the work done over the course of three years, on themes of natural systems, crafts, concrete plasticity, and active composite materials. The aim throughout the course is to explore the design of products and active systems, be it modular facades, intelligent cladding, or adaptable seating, by embedding current digital technologies with an understanding of natural systems and a physical know-how of material behavior. From this aim, three main themes of inquiry have emerged through the varied explorations across the three years, each one approaching materiality and digital technologies through a different lens. The first theme involves crossing the study of naturals systems as precedents for intelligent formal assemblies with traditional crafts methods. The students worked on designing performative facade systems, starting from the study of relevant natural systems and a specific craft, and then using parametric modeling to develop their modular facades. The second theme looks at the cross of craft and digital technologies through form-finding techniques and elastic material properties, bringing in flexible formwork into the digital fabrication process. Students explored concrete plasticity and behaviors with natural references, as they worked on the design of an exterior seating installation using lightweight concrete composites and complex casting methods. The third theme brings in bio-composite material properties with additive fabrication and environmental concerns to create performative cladding systems. Students experimented in concrete composites materials, biomaterials and clay 3D printing to produce different cladding and tiling prototypes that actively enhance their immediate environment. This paper thus will detail the work process done by the students under these three themes of inquiry, describing their material experimentation, digital and analog design methodologies, and their final results. It aims to shed light on the persisting importance of material knowledge as it intersects with advanced digital fabrication and the significance of learning from natural systems and biological properties to embed an active performance in today’s design process.

Keywords: digital fabrication, design and craft, materiality, natural systems

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Authors: Thelma Mort

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This empirical, qualitative research uses interviews of four intermediate phase English language student teachers at one university in South Africa and is an exploration of student teacher learning on their teaching practicum in their penultimate year of the initial teacher education course. The country’s post-apartheid language in education policy provides a context to this study in that children move from mother tongue language of instruction in foundation phase to English as a language of instruction in Intermediate phase. There is another layer of context informing this study which is the school context; the student teachers’ reflections are from their teaching practicum in resource constrained schools, which make up more than 75% of schools in South Africa. The findings were that in these schools, deep biases existed to local languages, that language was being used as a proxy for social class, and that conditions necessary for language acquisition were absent. The student teachers’ attitudes were in contrast to those found in the schools, namely that they had various pragmatic approaches to overcoming obstacles and that they saw language as enabling interdisciplinary work. This study describes language issues, tensions created by policy in South African schools and also supplies a regional account of learning to teach in resource constrained schools in Cape Town, where such language tensions are more inflated. The central findings in this research illuminate attitudes to language and language education in these teaching practicum schools and the complexity of learning to be a language teacher in these contexts. This study is one of the few local empirical studies regarding language teaching in the classroom and language teacher education; as such it offers some background to the country’s poor performance in both international and national literacy assessments.

Keywords: language teaching, narrative, post apartheid, South Africa, student teacher

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Gorazd Karer

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When performing a diagnostic procedure or surgery in general anesthesia (GA), a proper introduction and dosing of anesthetic agents are one of the main tasks of the anesthesiologist. However, depth of anesthesia (DoA) also seems to be a suitable process for closed-loop control implementation. To implement such a system, one must be able to acquire the relevant signals online and in real-time, as well as stream the calculated control signal to the infusion pump. However, during a procedure, patient monitors and infusion pumps are purposely unable to connect to an external (possibly medically unapproved) device for safety reasons, thus preventing closed-loop control. The paper proposes a conceptual solution to the aforementioned problem. First, it presents some important aspects of contemporary clinical practice. Next, it introduces the closed-loop-control-system structure and the relevant information flow. Focusing on transferring the data from the patient to the computer, it presents a non-invasive image-based system for signal acquisition from a patient monitor for online depth-of-anesthesia assessment. Furthermore, it introduces a UDP-based communication method that can be used for transmitting the calculated anesthetic inflow to the infusion pump. The proposed system is independent of a medical device manufacturer and is implemented in Matlab-Simulink, which can be conveniently used for DoA control implementation. The proposed scheme has been tested in a simulated GA setting and is ready to be evaluated in an operating theatre. However, the proposed system is only a step towards a proper closed-loop control system for DoA, which could routinely be used in clinical practice.

Keywords: closed-loop control, depth of anesthesia (DoA), modeling, optical signal acquisition, patient state index (PSi), UDP communication protocol

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Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

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Authors: Narendra Babu Bommenahalli Veerabhadrappa

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Teaching sustainability concepts along with profit maximizing philosophy of business in management education is a challenge. This paper explores and evaluates various learning models to inculcate sustainability concepts in management education. The paper explains about a new pedagogy that was tested in a business management school (Indus Business Academy, Bangalore, India) to teach sustainability. The pedagogy was designed by intertwining concepts related to sustainability with digital marketing concepts. As part of this experimental method, students (in groups) were assigned with various topics of sustainability and were asked to work with concepts of digital marketing and thus market the concepts of sustainability. The paper explains as a case study as to how sustainability was integrated with digital marketing tools and how learning towards sustainability was facilitated. It also explains the outcomes of this pedagogical method, in terms of inculcating sustainability concepts amongst management students as well as marketing and proliferation of sustainability concepts to bring about the behavioral changes amongst target audience towards sustainability.

Keywords: management-education, pedagogy, sustainability, behavior

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Authors: Haftu Hailu Berhe, Hailekiros Sibhato Gebremichael, Kinfe Tsegay Beyene, Haileselassie Mehari

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The purpose of the study is to develop and validate integrated literature-based JIT, TQM, TPM, SCM and LSS framework through a combination of the PDCA cycle and DMAIC methodology. The study adopted a mixed research approach. Accordingly, the qualitative study employed to develop the framework is based on identifying the uniqueness and common practices of JIT, TQM, TPM, SCM and LSS initiatives, the existing practice of the integration, identifying the existing gaps in the framework and practices, developing new integrated JIT, TQM, TPM, SCM and LSS practice framework. Previous very few studies of the uniqueness and common practices of the five initiatives are preserved. Whereas the quantitative study working to validate the framework is based on empirical analysis of the self-administered questionnaire using a statistical package for social science. A combination of the PDCA cycle and DMAIC methodology stand integrated CI framework is developed. The proposed framework is constructed as a project-based framework with five detailed implementation phases. Besides, the empirical analysis demonstrated that the proposed framework is valuable if adopted and implemented correctly. So far, there is no study proposed & validated the integrated CI framework within the scope of the study. Therefore, this is the earliest study that proposed and validated the framework for manufacturing industries. The proposed framework is applicable to manufacturing industries and can assist in achieving competitive advantages when the manufacturing industries, institutions and government offer unconditional efforts in implementing the full contents of the framework.

Keywords: integrated continuous improvement framework, just in time, total quality management, total productive maintenance, supply chain management, lean six sigma

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: Jacopo Baboni Schilingi

Abstract:

We here describe the theoretical and philosophical understanding of a long term use and development of algorithmic computer-based tools applied to music composition. The findings of our research lead us to interrogate some specific processes and systems of communication engaged in the discovery of specific cultural artworks: artistic creation in the sono-musical domain. Our hypothesis is that the patterns of auditory learning cannot be only understood in terms of social transmission but would gain to be questioned in the way they rely on various ranges of acoustic stimuli modes of consciousness and how the different types of memories engaged in the percept-action expressive systems of our cultural communities also relies on these shadowy conscious entities we named “Reduced Descriptive Structures”.

Keywords: algorithmic sonic computation, corrected and self-correcting learning patterns in acoustic perception, morphological derivations in sensorial patterns, social unconscious modes of communication

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Authors: Oumaima Lahmar

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This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.

Keywords: finance literature, textual analysis, topic modeling, perplexity

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Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

Abstract:

Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: Bharatendra Rai

Abstract:

The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Salman Amur Alhajri

Abstract:

Purpose: This paper explores the art of contemporary Arabic calligraphy in Oman. It explains the aesthetic features of Arabic calligraphy as a unique icon of Islamic art. This paper also explores the profile of one Omani artist, Salman Alhajri, as an example of Omani artists who have developed unique styles in this art stream. Methodology and approach: The paper is based on a theoretical study using a descriptive and case-study approach. Omani artists are fascinated by the art forms of Arabic calligraphy, which combine both spiritual meaning and aesthetic beauty. Artist Salman Alhajri is an example of a contemporary Arabic artist who uses Arabic calligraphy as the main theme in his art. Dr. Alhajri is trying to introduce the beauty of Arabic letters from a new aesthetic point of view. He also aims to create unusual visual effects that viewers can easily interact with. Even though words and phrases appear in Alhajri’s artwork, they are not conveying direct meanings: viewers can create their own meaning or expressions from them by appreciating the compositions of the artwork. Results: Arabic writing is directly related to the identity of Omani artists and their cultural background. This paper shows how the beauty of Arabic letters comes from its indefinite possibilities in designing calligraphic expressions, even within a single word, because letters can be stretched and transformed in various ways to create different compositions. Omani artists are interested in employing new media applications in this kind of practice to find new techniques for creating artwork based on Arabic writing. It is really important for all Omani artists to practice this art style because Arabic calligraphy and its flexibility introduce infinite possibilities that involve further exploration and investigation.

Keywords: Islamic art, contemporary Arabic calligraphy, new techniques, Omani artist

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