Search results for: computer- supported collaborative learning

Authors: Shamini Dhana, G. Subrahmanya VRK Rao

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The fashion industry is advancing towards a mindset of zero waste, personalization, creativity, and circularity. The trend of upcycling clothing and materials into personalized fashion is being demanded by the next generation. There is a need for a digital tool to accelerate the process towards mass customization. Dhana’s D/Sphere fashion technology platform uses digital tools to accelerate upcycling. In essence, advanced fashion garments can be designed and developed via reuse, repurposing, recreating activities, and using existing fabric and circulating materials. The D/Sphere platform has the following objectives: to provide (1) An opportunity to develop modern fashion using existing, finished materials and clothing without chemicals or water consumption; (2) The potential for an everyday customer and designer to use the medium of fashion for creative expression; (3) A solution to address the global textile waste generated by pre- and post-consumer fashion; (4) A solution to reduce carbon emissions, water, and energy consumption with the participation of all stakeholders; (5) An opportunity for brands, manufacturers, retailers to work towards zero-waste designs and as an alternative revenue stream. Other benefits of this alternative approach include sustainability metrics, trend prediction, facilitation of disassembly and remanufacture deep learning, and hyperheuristics for high accuracy. A design tool for mass personalization and customization utilizing existing circulating materials and deadstock, targeted to fashion stakeholders will lower environmental costs, increase revenues through up to date upcycled apparel, produce less textile waste during the cut-sew-stitch process, and provide a real design solution for the end customer to be part of circular fashion. The broader impact of this technology will result in a different mindset to circular fashion, increase the value of the product through multiple life cycles, find alternatives towards zero waste, and reduce the textile waste that ends up in landfills. This technology platform will be of interest to brands and companies that have the responsibility to reduce their environmental impact and contribution to climate change as it pertains to the fashion and apparel industry. Today, over 70% of the $3 trillion fashion and apparel industry ends up in landfills. To this extent, the industry needs such alternative techniques to both address global textile waste as well as provide an opportunity to include all stakeholders and drive circular fashion with new personalized products. This type of modern systems thinking is currently being explored around the world by the private sector, organizations, research institutions, and governments. This technological innovation using digital tools has the potential to revolutionize the way we look at communication, capabilities, and collaborative opportunities amongst stakeholders in the development of new personalized and customized products, as well as its positive impacts on society, our environment, and global climate change.

Keywords: circular fashion, deep learning, digital technology platform, personalization

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Authors: Rodrigo C. Belleza, Jr., Roselyn A. Maaño, Karl Patrick E. Camota, Darwin Kim Q. Bulawan

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Eyes are an essential and conspicuous organ of the human body. Human eyes are outward and inward portals of the body that allows to see the outside world and provides glimpses into ones inner thoughts and feelings. Inevitable blindness and visual impairments may result from eye-related disease, trauma, or congenital or degenerative conditions that cannot be corrected by conventional means. The study emphasizes innovative tools that will serve as an aid to the blind and visually impaired (VI) individuals. The researchers fabricated a prototype that utilizes the Microsoft Kinect for Windows and Arduino microcontroller board. The prototype facilitates advanced gesture recognition, voice recognition, obstacle detection and indoor environment navigation. Open Computer Vision (OpenCV) performs image analysis, and gesture tracking to transform Kinect data to the desired output. A computer vision technology device provides greater accessibility for those with vision impairments.

Keywords: algorithms, blind, computer vision, embedded systems, image analysis

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Authors: Rodlyn Remina Hines

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Africans who migrate to the United States (U.S.) go through an acculturation period. When they join the U.S. workforce during the period they are still acquainting to the new geographic area and culture, they may experience work and family conflict in addition to the stressors of acculturation. This study investigated the impact of acculturation stress and work-family conflict on the health and wellbeing of African immigrants in the U.S. using a growing immigrant population. Ghanaian immigrants (n = 100, males= 43%; females= 56%) residing in New York and Massachusetts, United States (U.S.), were recruited via purposive sampling to investigate the role acculturation stress and work-family conflict play on the health and wellbeing of African immigrants in the U.S. Using the Sociocultural theory, three hypotheses were proposed: (1) High acculturation stress will lead to high work-family conflict, (2) High work-family conflict will result in poor health and wellbeing, and (3) Work-family conflict will mediate the relationship between acculturation stress and health and wellbeing. The results fully supported the first hypothesis and partially supported the second and third. High acculturation stress led to high work-family conflict. Although high work-family conflict resulted in poorer health and wellbeing, high family support mediated work-family conflict and health and wellbeing. Participants who reported poor health also reported a lack of family or other support and those who reported strong family or other support also reported excellent health and wellbeing even with high work-family conflict. The latter group did not expect their health and wellbeing to get worse. I draw on these findings to conclude that African immigrants in the U.S. experience significant acculturation stress and work-family conflict resulting in poor health and wellbeing during their acculturation period if there is a lack of family or other support. These findings have implications for practitioners and policymakers.

Keywords: acculturation stress, work-family conflict, Ghanaian immigrants, health and wellbeing

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Authors: Miramar Damanhouri

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This study examines the relationship between language use in computer-mediated communication and the social identity of the user. The data were collected by surveying 298 Saudi bilingual speakers who are familiar with Arabizi, a blend of Latin characters and Arabic numerals to transliterate Arabic sounds, and then analyzed quantitatively by running tests for statistical confidence in order to determine differences in perceptions between young adults (ages 15-25 years) and middle-aged adults (ages 26-50 years). According to the findings of this study, English is the dominant language among most of the young adults surveyed, and when they do use Arabic, they use Arabizi because of its flexibility, compatibility with modern technology, and its acceptance among people of their age and sociocultural backgrounds. On the other hand, most middle-aged adults surveyed here tend to use Arabic, as they believe that they should show their loyalty to their origin. The results of the study demonstrate a mutual relationship between language use in computer-mediated communication and the user’s social identity, as language is used both to reflect and construct that identity.

Keywords: Arabizi, computer mediated communication, digital communication, language use

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Ngong Eugene Ekia

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Education is the foundation for the success of any society as its impact transcends across all sectors, including economics, politics, and social welfare. It is through education that individuals acquire the necessary knowledge and skills to succeed in life and contribute meaningfully to society. However, the world is changing rapidly, and it is vital for education systems to adapt to these changes to remain relevant. In this paper, we will discuss the current trends and challenges in education and management and propose solutions that can enable individuals to thrive in an ever-evolving world.

Keywords: access to education, effective teaching and learning, strong management practices, and empowering and personal development

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

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This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

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Authors: Deepa Kapoor, Rajshree Singh

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This paper appreciates the emergence and growing importance as a new production factor makes the development of technologies, methodologies and strategies for measurement, creation, and diffusion into one of the main priorities of the organizations in the knowledge society. There are many models for creation and management of knowledge and diverse and varied perspectives for study, analysis, and understanding. In this article, we will conduct a theoretical approach to the type of models for the creation and management of knowledge; we will discuss some of them and see some of the difficulties and the key factors that determine the success of the processes for the creation and management of knowledge.

Keywords: knowledge creation, knowledge management, organizational development, organization learning

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Authors: Maha Wiss, Wael Khreich

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Animals, objects, foods, plants, and other non-human terms are commonly used as a source of metaphors to describe females in formal and slang language. Comparing women to non-human items not only reflects cultural views that might conceptualize women as subordinates or in a lower position than humans, yet it conveys this degradation to the listeners. Moreover, the dehumanizing representation of females in the language normalizes the derogation and even encourages sexism and aggressiveness against women. Although dehumanization has been a popular research topic for decades, according to our knowledge, no studies have linked women's dehumanizing language to the machine learning field. Therefore, we introduce our research work as one of the first attempts to create a tool for the automated detection of the dehumanizing depiction of females in English texts. We also present the first labeled dataset on the charted topic, which is used for training supervised machine learning algorithms to build an accurate classification model. The importance of this work is that it accomplishes the first step toward mitigating dehumanizing language against females.

Keywords: gender bias, machine learning, NLP, women dehumanization

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Authors: Carlos Theran, Yohn Parra Bautista, Victor Adankai, Richard Alo, Jimwi Liu, Clement G. Yedjou

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Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. AD has gained relevant attention in the last decade. An estimated 24 million people worldwide suffered from this disease by 2011. In 2016 an estimated 40 million were diagnosed with AD, and for 2050 is expected to reach 131 million people affected by AD. Therefore, detecting and confirming AD at its different stages is a priority for medical practices to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to study AD's stages handling missing values in multiclass, focusing on the delineation of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and normal cognitive (CN). But, to our best knowledge, robust performance information of these models and the missing data analysis has not been presented in the literature. In this paper, we propose studying the performance of five different machine learning models for AD's stages multiclass prediction in terms of accuracy, precision, and F1-score. Also, the analysis of three imputation methods to handle the missing value problem is presented. A framework that integrates ML model for AD's stages multiclass prediction is proposed, performing an average accuracy of 84%.

Keywords: alzheimer's disease, missing value, machine learning, performance evaluation

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Authors: Eda Ozkara San

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Nurse educators have been charged by several nursing organizations and accrediting bodies to provide innovative and evidence-based educational experiences, both didactic and clinical, to help students to develop the knowledge, skills, and attitudes needed to provide culturally competent nursing care to patients. Clinical simulation, which offers the opportunity for students to practice nursing skills in a risk-free, controlled environment and helps develop self-efficacy (confidence) within the nursing role. As one simulation method, the standardized patients (SPs) simulation helps educators to teach nursing students variety of skills in nursing, medicine, and other health professions. It can be a helpful tool for nurse educators to enhance cultural competence of nursing students. An alarming gap exists within the literature concerning the effectiveness of SP strategy to enhance cultural competence development of diverse student groups, who must work with patients from various backgrounds. This grant-supported, longitudinal, one-group, pretest and post-test educational intervention study aimed to examine the effect of the Diverse Standardized Patient Simulation (DSPS) cultural competence education strategy on students’ (n = 53) transcultural self-efficacy (TSE). The researcher-developed multidimensional DSPS strategy involved careful integration of transcultural nursing skills guided by the Cultural Competence and Confidence (CCC) model. As a carefully orchestrated teaching and learning strategy by specifically utilizing the SP pedagogy, the DSPS also followed international guidelines and standards for the design, implementation, evaluation, and SP training; and had content validity review. The DSPS strategy involved two simulation scenarios targeting underrepresented patient populations (Muslim immigrant woman with limited English proficiency and Irish-Italian American gay man with his partner (Puerto Rican) to be utilized in a second-semester, nine-credit, 15-week medical-surgical nursing course at an urban public US university. Five doctorally prepared content experts reviewed the DSPS strategy for content validity. The item-level content validity index (I-CVI) score was calculated between .80-1.0 on the evaluation forms. Jeffreys’ Transcultural Self-Efficacy Tool (TSET) was administered as a pretest and post-test to assess students’ changes in cognitive, practical, and affective dimensions of TSE. Results gained from this study support that the DSPS cultural competence education strategy assisted students to develop cultural competence and caused statistically significant changes (increase) in students’ TSE perceptions. Results also supported that all students, regardless of their background, benefit (and require) well designed cultural competence education strategies. The multidimensional DSPS strategy is found to be an effective way to foster nursing students’ cultural competence development. Step-by-step description of the DSPS provides an easy adaptation of this strategy with different student populations and settings.

Keywords: cultural competence development, the cultural competence and confidence model, CCC model, educational intervention, transcultural self-efficacy, TSE, transcultural self-efficacy tool, TSET

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Authors: Linnea Stenliden

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The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors by Latour. The learning conditions are found to be distinguished by broad complexity characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data.

Keywords: analytical reasoning, complexity, data use, problem space, visual analytics, visual storytelling, translation

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Authors: Mamani Acurio Alex, Aguilar Alonso Igor

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The lack of interest and the lack of motivation are related. The lack of both in school generates serious problems such as school dropout or a low level of learning. Augmented reality has been very useful in different areas, and in this research, it was implemented in the area of education. Information necessary for the correct development of this mobile application with augmented reality was searched using six different research repositories. It was concluded that the application must be immersive, attractive, and fun for students, and the necessary technologies for its construction were defined.

Keywords: augmented reality, Vuforia, school interest, learning

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Authors: Aiydh Aljeddani, Fran Martin

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Sustainable human development is an essential component of a sustainable economic, social and environmental development. Addressing sustainable learning only through the addition of new teaching methods, or embedding certain approaches, is not sufficient on its own to support the goals of sustainable human development. This research project seeks to explore how the process of redesigning the current principles of curriculum based on the concept of sustainable learning could contribute to preparing a citizen who could later contribute towards sustainable human development. Multiple qualitative methodologies were employed in order to achieve the aim of this study. The main research methods were teachers’ field notes, artefacts, informal interviews (unstructured interview), a passive participant observation, a mini nominal group technique (NGT), a weekly diary, and weekly meeting. The study revealed that the integration of a curriculum for sustainable development, in addition to the use of innovative teaching approaches, highly valued by students and teachers in social studies’ sessions. This was due to the fact that it created a positive atmosphere for interaction and aroused both teachers and students’ interest. The content of the new curriculum also contributed to increasing students’ sense of shared responsibility through involving them in thinking about solutions for some global issues. This was carried out through addressing these issues through the concept of sustainable development and the theory of Thinking Activity in a Social Context (TASC). Students had interacted with sustainable development sessions intellectually and they also practically applied it through designing projects and cut-outs. Ongoing meetings and workshops to develop work between both the researcher and the teachers, and by the teachers themselves, played a vital role in implementing the new curriculum. The participation of teachers in the development of the project through working papers, exchanging experiences and introducing amendments to the students' environment was also critical in the process of implementing the new curriculum. Finally, the concept of sustainable learning can contribute to the learning outcomes much better than the current curriculum and it can better develop the learning objectives in educational institutions.

Keywords: redesigning, social studies and citizenship education curriculum, sustainable learning, thinking activity in a social context

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Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

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Authors: Yuh-Jen Wu, Chun-Min Lin

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Homophonic words are common in Mandarin Chinese which belongs to the tonal language family. Using homophonic cues to study foreign languages is one of the learning techniques of mnemonics that can aid the retention and retrieval of information in the human memory. When learning difficult foreign words, some learners transpose them with words in a language they are familiar with to build an association and strengthen working memory. These phonological clues are beneficial means for novice language learners. In the classroom, if mnemonic skills are used at the appropriate time in the instructional sequence, it may achieve their maximum effectiveness. For Chinese-speaking students, proper use of Chinese-English homophonic word pairs may help them learn difficult vocabulary. In this study, a database program is developed by employing Visual Basic. The database contains two corpora, one with Chinese lexical items and the other with English ones. The Chinese corpus contains 59,053 Chinese words that were collected by a web crawler. The pronunciations of this group of words are compared with words in an English corpus based on WordNet, a lexical database for the English language. Words in both databases with similar pronunciation chunks and batches are detected. A total of approximately 1,000 Chinese lexical items are located in the preliminary comparison. These homophonic word pairs can serve as a valuable tool to assist Chinese-speaking students in learning and memorizing new English vocabulary.

Keywords: Chinese, corpus, English, homophonic words, vocabulary

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Authors: Paulomi Polly Burey, Mark Lynch

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It has been shown that laboratory activities can help cement understanding of theoretical concepts, but it is difficult to deliver such an activity to an online cohort and issues such as occupational health and safety in the students’ learning environment need to be considered. Chemistry, in particular, is one of the sciences where practical experience is beneficial for learning, however typical university experiments may not be suitable for the learning environment of a distance learner. Food provides an ideal medium for demonstrating chemical concepts, and along with a few simple physical and virtual tools provided by educators, analytical chemistry can be experienced by distance learners. Food chemistry experiments were designed to be carried out in a home-based environment that 1) Had sufficient scientific rigour and skill-building to reinforce theoretical concepts; 2) Were safe for use at home by university students and 3) Had the potential to enhance student learning by linking simple hands-on laboratory activities with high-level virtual science. Two main components of the resources were developed, a home laboratory experiment component, and a virtual laboratory component. For the home laboratory component, students were provided with laboratory kits, as well as a list of supplementary inexpensive chemical items that they could purchase from hardware stores and supermarkets. The experiments used were typical proximate analyses of food, as well as experiments focused on techniques such as spectrophotometry and chromatography. Written instructions for each experiment coupled with video laboratory demonstrations were used to train students on appropriate laboratory technique. Data that students collected in their home laboratory environment was collated across the class through shared documents, so that the group could carry out statistical analysis and experience a full laboratory experience from their own home. For the virtual laboratory component, students were able to view a laboratory safety induction and advised on good characteristics of a home laboratory space prior to carrying out their experiments. Following on from this activity, students observed laboratory demonstrations of the experimental series they would carry out in their learning environment. Finally, students were embedded in a virtual laboratory environment to experience complex chemical analyses with equipment that would be too costly and sensitive to be housed in their learning environment. To investigate the impact of the intervention, students were surveyed before and after the laboratory series to evaluate engagement and satisfaction with the course. Students were also assessed on their understanding of theoretical chemical concepts before and after the laboratory series to determine the impact on their learning. At the end of the intervention, focus groups were run to determine which aspects helped and hindered learning. It was found that the physical experiments helped students to understand laboratory technique, as well as methodology interpretation, particularly if they had not been in such a laboratory environment before. The virtual learning environment aided learning as it could be utilized for longer than a typical physical laboratory class, thus allowing further time on understanding techniques.

Keywords: chemistry, food science, future pedagogy, STEM education

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Authors: Tianyu Cao, KIRA (Ruizhi Zhao)

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The purpose of our research is to study the possibility of people with physical disabilities manipulating mechanical prostheses through brain-computer interface (BCI) technology. The brain-machine interface (BCI) of the neural prosthesis records signals from neurons and uses mathematical modeling to decode them, converting desired movements into body movements. In order to improve the patient's neural control, the prosthesis is given a natural feeling. It records data from sensitive areas from the body to the prosthetic limb and encodes signals in the form of electrical stimulation to the brain. In our research, the brain-computer interface (BCI) is a bridge connecting patients’ cognition and the real world, allowing information to interact with each other. The efficient work between the two is achieved through external devices. The flow of information is controlled by BCI’s ability to record neuronal signals and decode signals, which are converted into device control. In this way, we could encode information and then send it to the brain through electrical stimulation, which has significant medical application.

Keywords: biomedical engineering, brain-computer interface, prosthesis, neural control

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Authors: Ya-Ting Carolyn Yang, Ping-Han Cheng, Shi-Hui Gilbert Chang, Terry Yuan-Fang Chen, Chih-Chieh Li

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Nanotechnology is widely applied in various areas so professionals in the related fields have to know more than nano knowledge. In the study, we focus on adopting ICT-assisted PBL in college general education to foster professionals who possess multiple abilities. The research adopted a pretest and posttest quasi-experimental design. The control group received traditional instruction, and the experimental group received ICT-assisted PBL instruction. Descriptive statistics will be used to describe the means, standard deviations, and adjusted means for the tests between the two groups. Next, analysis of covariance (ANCOVA) will be used to compare the final results of the two research groups after 6 weeks of instruction. Statistics gathered in the end of the research can be used to make contrasts. Therefore, we will see how different teaching strategies can improve students’ understanding about nanotechnology and learning skills.

Keywords: nanotechnology, science education, project-based learning, information and communication technology

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Authors: Junyu Chen, Peng Xu

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In the context of global efforts to enhance building energy efficiency, accurate thermal load forecasting is crucial for both device sizing and predictive control. Variable Refrigerant Flow (VRF) systems are widely used in buildings around the world, yet VRF zonal load prediction has received limited attention. Due to differences between VRF zones in building-level prediction methods, zone-level load forecasting could significantly enhance accuracy. Given that modern VRF systems generate high-quality data, this paper introduces transfer learning to leverage this data and further improve prediction performance. This framework also addresses the challenge of predicting load for building zones with no historical data, offering greater accuracy and usability compared to pure white-box models. The study first establishes an initial variable set of VRF zonal building loads and generates a foundational white-box database using EnergyPlus. Key variables for VRF zonal loads are identified using methods including SRRC, PRCC, and Random Forest. XGBoost and LSTM are employed to generate pre-trained black-box models based on the white-box database. Finally, real-world data is incorporated into the pre-trained model using transfer learning to enhance its performance in operational buildings. In this paper, zone-level load prediction was integrated with transfer learning, and a framework was proposed to improve the accuracy and applicability of VRF zonal load prediction.

Keywords: zonal load prediction, variable refrigerant flow (VRF) system, transfer learning, energyplus

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Authors: Kelechi Ezeji

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The use of Computer Aided Design (CAD) technologies has become pervasive in the Architecture, Engineering and Construction (AEC) industry. This has led to its inclusion as an important part of the training module in the curriculum for Architecture Schools in Nigeria. This paper examines the ethical questions that arise in the implementation of Computer Aided Design (CAD) Content of the curriculum for Architectural education. Using existing literature, it begins this scrutiny from the propriety of inclusion of CAD into the education of the architect and the obligations of the different stakeholders in the implementation process. It also examines the questions raised by the negative use of computing technologies as well as perceived negative influence of the use of CAD on design creativity. Survey methodology was employed to gather data from the Department of Architecture, Chukwuemeka Odumegwu Ojukwu University Uli, which has been used as a case study on how the issues raised are being addressed. The paper draws conclusions on what will make for successful ethical implementation.

Keywords: computer aided design, curriculum, education, ethics

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Authors: Karl Eddie T. Malabanan

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Due to the quick transmission and public health risk of coronavirus disease, schools and universities have shifted to distant learning. Teachers everywhere were forced to shift gears instantly in order to react to the needs of students and families using synchronous and asynchronous virtual teaching. This study aims to explore the lived experiences of physical education teachers who are currently experiencing remote learning in teaching during the time of the COVID-19 pandemic. Specifically, the challenges that the physical education teachers encounter during remote learning and teaching. The participants include 12 physical education teachers who have taught in higher education institutions for at least five years. The researcher utilized qualitative research; specifically, the researcher used Consensual Qualitative Research (CQR). The results of this study showed that there are five categories for the Lived Experiences of Physical Education Teachers with thirty-one subcategories. This study revealed that physical education teachers experienced very challenging situations during the time of the pandemic. It also found that students had challenges in the abrupt transition from traditional to virtual learning classes, but it also showed that students are tenacious and willing to face any adversity. The researcher also finds that teachers are mentally drained during this time. Furthermore, one of the main focuses for the teachers should be on improving their well-being. And lastly, to cope with the challenges, teachers employ socializing to relieve tension and anxiety.

Keywords: lived experiences, consensual qualitative research, pandemic, education

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Authors: Vladimir Messerle, Alexandr Ustimenko, Oleg Lavrichshev

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This work presents plasma technology for solid fuel ignition and combustion. Plasma activation promotes more effective and environmentally friendly low-rank coal ignition and combustion. To realise this technology at coal fired power plants plasma-fuel systems (PFS) were developed. PFS improve efficiency of power coals combustion and decrease harmful emission. PFS is pulverized coal burner equipped with arc plasma torch. Plasma torch is the main element of the PFS. Plasma forming gas is air. It is blown through the electrodes forming plasma flame. Temperature of this flame is varied from 5000 to 6000 K. Plasma torch power is varied from 100 to 350 kW and geometrical sizes are the following: the height is 0.4-0.5 m and diameter is 0.2-0.25 m. The base of the PFS technology is plasma thermochemical preparation of coal for burning. It consists of heating of the pulverized coal and air mixture by arc plasma up to temperature of coal volatiles release and char carbon partial gasification. In the PFS coal-air mixture is deficient in oxygen and carbon is oxidised mainly to carbon monoxide. As a result, at the PFS exit a highly reactive mixture is formed of combustible gases and partially burned char particles, together with products of combustion, while the temperature of the gaseous mixture is around 1300 K. Further mixing with the air promotes intensive ignition and complete combustion of the prepared fuel. PFS have been tested for boilers start up and pulverized coal flame stabilization in different countries at power boilers of 75 to 950 t/h steam productivity. They were equipped with different types of pulverized coal burners (direct flow, muffle and swirl burners). At PFS testing power coals of all ranks (lignite, bituminous, anthracite and their mixtures) were incinerated. Volatile content of them was from 4 to 50%, ash varied from 15 to 48% and heat of combustion was from 1600 to 6000 kcal/kg. To show the advantages of the plasma technology before conventional technologies of coal combustion numerical investigation of plasma ignition, gasification and thermochemical preparation of a pulverized coal for incineration in an experimental furnace with heat capacity of 3 MW was fulfilled. Two computer-codes were used for the research. The computer simulation experiments were conducted for low-rank bituminous coal of 44% ash content. The boiler operation has been studied at the conventional mode of combustion and with arc plasma activation of coal combustion. The experiments and computer simulation showed ecological efficiency of the plasma technology. When a plasma torch operates in the regime of plasma stabilization of pulverized coal flame, NOX emission is reduced twice and amount of unburned carbon is reduced four times. Acknowledgement: This work was supported by Ministry of Education and Science of the Republic of Kazakhstan and Ministry of Education and Science of the Russian Federation (Agreement on grant No. 14.613.21.0005, project RFMEFI61314X0005).

Keywords: coal, ignition, plasma-fuel system, plasma torch, thermal power plant

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Authors: He Lingsong, Wang Junfeng, Tan Qiong, Xu Jiang

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The paper describes efforts on reconstruction methods of engineering testing and measurement technology course by applying new techniques and applications. Firstly, flipped classroom was introduced. In-class time was used for in-depth discussions and interactions while theory concept teaching was done by self-study course outside of class. Secondly, two hands-on practices of technique applications, including the program design of MATLAB Signal Analysis and the measurement application of Arduino sensor, have been covered in class. Class was transformed from an instructor-centered teaching process into an active student-centered learning process, consisting of the pre-class massive open online course (MOOC), in-class discussion and after-class practice. The third is to change sole written homework to the research-oriented application practice assignments, so as to enhance the breadth and depth of the course.

Keywords: testing and measurement, flipped classroom, MOOC, research-oriented learning, practice-oriented learning

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Authors: Edgardo V. Gubatanga Jr., Mark Joshua Salvacion

Abstract:

Most aerial LiDAR systems have accompanying aerial cameras in order to capture not only the terrain of the surveyed area but also its true-color appearance. However, the presence of atmospheric clouds, poor lighting conditions, and aerial camera problems during an aerial survey may cause absence of aerial photographs. These leave areas having terrain information but lacking aerial photographs. Intensity images can be derived from LiDAR data but they are only grayscale images. A deep learning model is developed to create a complex function in a form of a deep neural network relating the pixel values of LiDAR-derived intensity images and true-color images. This complex function can then be used to predict the true-color images of a certain area using intensity images from LiDAR data. The predicted true-color images do not necessarily need to be accurate compared to the real world. They are only intended to look realistic so that they can be used as base maps.

Keywords: aerial LiDAR, colorization, deep learning, intensity images

Procedia PDF Downloads 169

Authors: James Purnama, Riri Fitri Sari

Abstract:

We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

Procedia PDF Downloads 541

Authors: Yuta Chishima, Tatsuya Murakami, Michael McKay

Abstract:

Temporal focus is described as one component of an individual’s time perspective and defined as the attention individuals devote to thinking about the past, present, and future. It affects how people incorporate perceptions about past experiences, current situations, and future expectations into their attitudes, cognitions, and behavior. The 12-item Temporal Focus Scale (TFS) is comprised of three-factors (past, current and future focus). The purpose of this study was to examine the reliability and validity of TFS scores in Japanese adolescents and young adults. The TFS was translated into Japanese by a professional translator, and the original author confirmed the back translated items. Study 1 involved 979 Japanese university students aged 18-25 years old in a questionnaire-based study. The hypothesized three-factor structure (with reliability) was confirmed, although there were problems with item 10. Internal consistency estimates for scores without item 10 were over .70, and test-retest reliability was also adequate. To verify the concurrent and convergent validity, we tested the relationship between TFS scores and life satisfaction, time perspective, self-esteem, and career efficacy. Results of correlational analyses supported our hypotheses. Specifically, future focus was strongly correlated to career efficacy, while past and current focus was not. Study 2 involved 1030 Japanese junior and junior high school students aged 12-18 years old in a questionnaire-based study, and results of multigroup analyses supported the age invariance of the TFS.

Keywords: Japanese, reliability, scale, temporal focus, validity

Procedia PDF Downloads 358

Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

Abstract:

This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

Procedia PDF Downloads 57

Authors: Gianluca Castelnuovo, Gian Mauro Manzoni, Giada Pietrabissa, Stefania Corti, Emanuele Giusti, Roberto Cattivelli, Enrico Molinari, Susan Simpson

Abstract:

Obesity is currently an important public health problem of epidemic proportions (globesity). Moreover Binge Eating Disorder (BED) is typically connected with obesity, even if not occurring exclusively in conjunction with overweight conditions. Typically obesity with BED requires a longer term treatment in comparison with simple obesity. Rehabilitation interventions that aim at improving weight-loss, reducing obesity-related complications and changing dysfunctional behaviors, should ideally be carried out in a multidisciplinary context with a clinical team composed of psychologists, dieticians, psychiatrists, endocrinologists, nutritionists, physiotherapists, etc. Long-term outpatient multidisciplinary treatments are likely to constitute an essential aspect of rehabilitation, due to the growing costs of a limited inpatient approach. Internet-based technologies can improve long-term obesity rehabilitation within a collaborative approach. The new m health (m-health, mobile health) paradigm, defined as clinical practices supported by up to date mobile communication devices, could increase compliance- engagement and contribute to a significant cost reduction in BED and obesity rehabilitation. Five psychological components need to be considered for successful m Health-based obesity rehabilitation in order to facilitate weight-loss.1) Self-monitoring. Portable body monitors, pedometers and smartphones are mobile and, therefore, can be easily used, resulting in continuous self-monitoring. 2) Counselor feedback and communication. A functional approach is to provide online weight-loss interventions with brief weekly or monthly counselor or psychologist visits. 3) Social support. A group treatment format is typically preferred for behavioral weight-loss interventions. 4) Structured program. Technology-based weight-loss programs incorporate principles of behavior therapy and change with structured weekly protocolos including nutrition, exercise, stimulus control, self-regulation strategies, goal-setting. 5) Individually tailored program. Interventions specifically designed around individual’s goals typically record higher rates of adherence and weight loss. Opportunities and limitations of m health approach in clinical psychology for obesity and BED are discussed, taking into account future research directions in this promising area.

Keywords: obesity, rehabilitation, out-patient, new technologies, tele medicine, tele care, m health, clinical psychology, psychotherapy, chronic care management

Procedia PDF Downloads 477