Search results for: deep learning methods

Authors: Aliya Kamilyevna Salahova

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This study examines the effectiveness of the Kids Science Labs intervention program, based on STEM, in fostering independent thinking among preschool and elementary school children and its influence on their academic achievement. Through a comprehensive methodology involving interviews, surveys, observations, case studies, and statistical tests, data were collected from various sources to accurately analyze the program's effects. The findings indicate a significant positive impact on children's independent thinking abilities, leading to improved academic performance in mathematics and science, enhanced learning motivation, and a propensity to critically evaluate problem-solving approaches. This research contributes to the theoretical understanding of how STEM activities can foster independent thinking and academic success in young children, providing valuable insights for the development of educational programs. Introduction: The goal of this study is to investigate the influence of the Kids Science Labs intervention program, grounded in STEM, on the development of independent thinking skills among preschool and elementary school children. By addressing this objective, we aim to explore the program's potential to enhance academic performance in mathematics and science. The study's findings have theoretical significance as they shed light on the ways in which STEM activities can foster independent thinking in young children, thus enabling educators to design effective learning programs that promote academic success. Methodology: This study employs a robust methodology that includes interviews, surveys, observations, case studies, and statistical tests. These methods were carefully selected to collect comprehensive data from multiple sources, such as documents and records, ensuring a thorough analysis of the program's effects. The use of diverse data collection and analysis procedures facilitated an in-depth exploration of the research questions and yielded reliable results. Results: The results indicate that children participating in the Kids Science Labs program experienced a sustained positive impact on their independent thinking abilities. Moreover, these children demonstrated improved academic performance in mathematics and science, displaying higher learning motivation and the capacity to critically evaluate problem-solving methods and seek optimal solutions. Theoretical Importance: This study contributes significantly to the existing theoretical knowledge by elucidating how STEM activities can foster independent thinking and enhance academic success in preschool and elementary school children. The findings have practical implications for educators, empowering them to develop learning programs that stimulate independent thinking, leading to improved academic performance in young children. Discussion: The findings of this research affirm that the Kids Science Labs intervention program is highly effective in fostering independent thinking among preschool and elementary school children. The program's positive impact extends to improved academic performance in mathematics and science, highlighting its potential to enhance learning outcomes. Educators can leverage these findings to develop educational programs that promote independent thinking and elevate academic achievement in young children. Conclusion: In conclusion, the Kids Science Labs intervention program has been found to be highly effective in fostering independent thinking among preschool and elementary school children. Furthermore, participation in the program correlates with improved academic performance in mathematics and science. The study's outcomes underscore the importance of developing educational initiatives that stimulate independent thinking in young children, thereby enhancing their academic success.

Keywords: STEM in preschool, STEM in elementary school, kids science labs, independent thinking, STEM activities in early childhood education

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Authors: M. P. Rajeeshkumar, K. V. Aneesh Kumar, J. L. Otero-Ferrer, A. Lombarte, M. Hashim, N. Saravanane, V. N.Sanjeevan, V. M. Tuset

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The anglerfishes are widely distributed from shallow to deep-water habitats and are highly diverse in morphology, behaviour, and niche occupancy patterns. To understand this interspecific variability and degree of niche overlap, we performed a functional analysis of five species inhabiting Indian waters where diversity of deep-sea anglerfishes is very high. The sensory capacities (otolith shape and eye size) were also studied to improve the understanding of coexistence of species. The analyses of fish body and otolith shape clustered species in two morphotypes related to phylogenetic lineages: i) Malthopsis lutea, Lophiodes lugubri and Halieutea coccinea were characterized by a dorso-ventrally flattened body with high swimming ability and relative small otoliths, and ii) Chaunax spp. were distinguished by their higher body depth, lower swimming efficiency, and relative big otoliths. The sensory organs did not show a pattern linked to depth distribution of species. However, the larger eye size in M. lutea suggested a nocturnal feeding activity, whereas Chaunax spp. had a large mouth and deeper body in response to different ecological niches. Therefore, the present study supports the hypothesis of spatial and temporal segregation of anglerfishes in the Indian waters, which can be explained from a functional approach and understanding from sensory capabilities.

Keywords: functional traits, otoliths, niche overlap, fishes, Indian waters

Procedia PDF Downloads 127

Authors: Meenalochana Inguva

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The context: The Center for Preparatory study is one of the strongest and most vibrant academic teaching units of the Sultan Qaboos University (SQU). The Foundation Programme English Language (FPEL) is part of a larger foundation programme which was implemented at SQU in fall 2010. The programme has been designed to prepare the students who have been accepted to study in the university in order to achieve the required educational goals (the learning outcomes) that have been designed according to Oman Academic Standards and published by the Omani Authority for Academic Accreditation (OAAA) for the English language component. The curriculum: At the CPS, the English language curriculum is based on the learning outcomes drafted for each level. These learning outcomes guide the students in meeting what is expected of them by the end of each level. These six levels are progressive in nature and are seen as a continuum. The study: A periodic evaluation of language programmes is necessary to improve the quality of the programmes and to meet the set goals of the programmes. An evaluation may be carried out internally or externally depending on the purpose and context. A self-study programme was initiated at the beginning of spring semester 2015 with a team comprising a total of 11 members who worked with-in the assigned course areas (level and programme specific). Only areas specific to FPEL have been included in the study. The study was divided into smaller tasks and members focused on their assigned courses. The self-study primarily focused on analyzing the programme LOs, curriculum planning, materials used and their relevance against the GFP exit standards. The review team also reflected on the assessment methods and procedures followed to reflect on student learning. The team has paid attention to having standard criteria for assessment and transparency in procedures. A special attention was paid to the staging of LOs across levels to determine students’ language and study skills ability to cope with higher level courses. Findings: The findings showed that most of the LOs are met through the materials used for teaching. Students score low on objective tests and high on subjective tests. Motivated students take advantage of academic support activities others do not utilize the student support activities to their advantage. Reading should get more hours. In listening, the format of the listening materials in CT 2 does not match the test format. Some of the course materials need revision. For e.g. APA citation, referencing etc. No specific time is allotted for teaching grammar Conclusion: The findings resulted in taking actions in bridging gaps. It will also help the center to be better prepared for the external review of its FPEL curriculum. It will also provide a useful base to prepare for the self-study portfolio for GFP standards assessment and future audit.

Keywords: curriculum planning, learning outcomes, reflections, self-evaluation

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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: Bipul Behari Saha, Sunil Kumar Singh, P. Padmaja, Kamlesh Vishwakarma

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Dissipation study of tebuconazole in alluvial, black and deep-black clayey soils collected from paddy, mango and peanut cropland of tropical agro-climatic zone of India at three concentration levels were carried out for monitoring the water contamination through persisted residual toxicity. The soil-slurry samples were analyzed by capillary GC-NPD methods followed by ultrasound-assisted extraction (UAE) technique and cleanup process. An excellent linear relationship between peak area and concentration obtained in the range 1 to 50 μgkg-1. The detection (S/N, 3 ± 0.5) and quantification (S/N, 7.5 ± 2.5) limits were 3 and 10 μgkg-1 respectively. Well spiked recoveries were achieved from 96.28 to 99.33 % at levels 5 and 20 μgkg-1 and method precision (% RSD) was ≤ 5%. The soils dissipation of tebuconazole was fitted in first order kinetic-model with half-life between 34.48 to 48.13 days. The soil organic-carbon (SOC) content correlated well with the dissipation rate constants (DRC) of the fungicide Tebuconazole. An increase in the SOC content resulted in faster dissipation. The results indicate that the soil organic carbon and tebuconazole concentrations plays dominant role in dissipation processes. The initial concentration illustrated that the degradation rate of tebuconazole in soils was concentration dependent.

Keywords: cropland soil, dissipation, laboratory incubation, tebuconazole

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Authors: Levent Taylan Ozgur Yildirim, Qihao Qian, John Yilin Wang

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A critical review of assessments of geological carbon dioxide (CO2) storage resources in Pennsylvania and the surrounding region was completed with a focus on the studies of Midwest Regional Carbon Sequestration Partnership (MRCSP), United States Department of Energy (US-DOE), and United States Geological Survey (USGS). Pennsylvania Geological Survey participated in the MRCSP Phase I research to characterize potential storage formations in Pennsylvania. The MRCSP’s volumetric method estimated ~89 gigatonnes (Gt) of total CO2 storage resources in deep saline formations, depleted oil and gas reservoirs, coals, and shales in Pennsylvania. Meanwhile, the US-DOE calculated storage efficiency factors using log-odds normal distribution and Monte Carlo sampling, revealing contingent storage resources of ~18 Gt to ~20 Gt in deep saline formations, depleted oil and gas reservoirs, and coals in Pennsylvania. Additionally, the USGS employed Beta-PERT distribution and Monte Carlo sampling to determine buoyant and residual storage efficiency factors, resulting in 20 Gt of contingent storage resources across four storage assessment units in Appalachian Basin. However, few studies have explored CO2 storage resources in shales in the region, yielding inconclusive findings. This article provides a critical and most up to date review and analysis of geological CO2 storage resources in Pennsylvania and the region.

Keywords: carbon capture and storage, geological CO2 storage, pennsylvania, appalachian basin

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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: Maja Ruzic-Baf, Mirjana Radetic-Paic

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The use of computers by children has many positive aspects, including the development of memory, learning methods, problem-solving skills and the feeling of one’s own competence and self-confidence. Playing on line video games can encourage hanging out with peers having similar interests as well as communication; it develops coordination, spatial relations and presentation. On the other hand, the Internet enables quick access to different information and the exchange of experiences. How kids use computers and what the negative effects of this can be depends on various factors. ICT has improved and become easy to get for everyone. In the past 12 years so many video games has been made even to that level that some of them are free to play. Young people, even some adults, had simply start to forget about the real outside world because in that other, digital world, they have found something that makes them feal more worthy as a man. This article present the use of ICT, forms of behavior and addictions to on line video games. The use of computers by children has many positive aspects, including the development of memory, learning methods, problem-solving skills and the feeling of one’s own competence and self-confidence. Playing on line video games can encourage hanging out with peers having similar interests as well as communication; it develops coordination, spatial relations and presentation. On the other hand, the Internet enables quick access to different information and the exchange of experiences. How kids use computers and what the negative effects of this can be depends on various factors. ICT has improved and become easy to get for everyone. In the past 12 years so many video games has been made even to that level that some of them are free to play. Young people, even some adults, had simply start to forget about the real outside world because in that other, digital world, they have found something that makes them feal more worthy as a man. This article present the use of ICT, forms of behavior and addictions to on line video games.

Keywords: addiction to video games, behaviour, ICT, young people

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Authors: Pranjali Avinash Yadav-Deshmukh

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Remarkable development of the Internet along with the new technological innovation, such as high-speed systems and affordable large storage space have led to a tremendous increase in the amount and accessibility to digital records. For any person, studying of all these data is tremendously time intensive, so there is a great need to access effective multi-document summarization (MDS) systems, which can successfully reduce details found in several records into a short, understandable summary or conclusion. For semantic representation of textual details in ontology area, as a theoretical design, our system provides a significant structure. The stability of using the ontology in fixing multi-document summarization problems in the sector of catastrophe control is finding its recommended design. Saliency ranking is usually allocated to each phrase and phrases are rated according to the ranking, then the top rated phrases are chosen as the conclusion. With regards to the conclusion quality, wide tests on a selection of media announcements are appropriate for “Jammu Kashmir Overflow in 2014” records. Ontology centered multi-document summarization methods using “NLP centered extraction” outshine other baselines. Our participation in recommended component is to implement the details removal methods (NLP) to enhance the results.

Keywords: disaster management, extraction technique, k-means, multi-document summarization, NLP, ontology, sentence extraction

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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

Procedia PDF Downloads 166

Authors: Harper Staples

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A necessary outcome of today’s contemporary globalised reality, current views of multilingualism hold that it no longer represents the exception, but rather the rule. As such, the simultaneous acquisition of multiple languages represents a common experience for many of today's students and therefore represents a key area of inquiry in the domain of foreign language learner identity. Second and multilingual language acquisition processes parallel each other in many ways; however, there are differences to be found in the ways in which a student may learn a third language. A multilingual repertoire will have to negotiate complex change as language competencies dynamically evolve; moreover, this process will vary according to the contextual factors attributed to a unique learner. A developing multilingual identity must, therefore, contend with an array of potential challenges specific to the individual in question. Despite an overarching recognition in the literature that pluri-language acquisition represents a unique field of inquiry within applied linguistic research, there is a paucity of empirical work which examines the ways in which individuals construct a sense of their own identity as multilingual speakers in such contexts of learning. This study explores this phenomenon via a mixed-methods, comparative case study approach at four school sites based in Finland, France, Wales, and England. It takes a strongly individual-in-context view, conceptualising each adolescent participant in dynamic terms in order to undertake a holistic exploration of the myriad factors that might impact upon, and indeed be impacted by, a learner's developing multilingual identity. Emerging themes of note thus far suggest that, beyond the expected divergences in the experience of multilinguality at the individual level, there are contradictions in the way in which adolescent students in each site 'claim' their plurilingualism. This can be argued to be linked to both meso and macro-level factors, including the foreign language curriculum and, more broadly, societal attitudes towards multilingualism. These diverse emergent identifications have implications not only for attainment in the foreign language but also for student well-being more generally.

Keywords: foreign language learning, student identity, multilingualism, educational psychology

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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: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

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Authors: Lin Po-Hsi, Sheu Ming-Thau

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Urea cycle disorders (UCD) are rare genetic metabolic disorders that compromise the body's urea cycle. Sodium phenylbutyrate (SPB) is a medication commonly administered in tablet or powder form to lower ammonia levels. Nonetheless, its high sodium content poses risks to sodium-sensitive UCD patients. This necessitates the creation of an alternative drug formulation to mitigate sodium load and optimize drug delivery for UCD patients. This study focused on crafting a novel oral drug formulation for UCD, leveraging choline bicarbonate and phenylbutyric acid. The active pharmaceutical ingredient-ionic liquids (API-ILs) and therapeutic deep eutectic systems (THEDES) were formed by combining these with choline chloride. These systems display characteristics like maintaining a liquid state at room temperature and exhibiting enhanced solubility. This in turn amplifies drug dissolution rate, permeability, and ultimately oral bioavailability. Incorporating choline-based phenylbutyric acid as a substitute for traditional SPB can effectively curtail the sodium load in UCD patients. Our in vitro dissolution experiments revealed that the ILs and DESs, synthesized using choline bicarbonate and choline chloride with phenylbutyric acid, surpassed commercial tablets in dissolution speed. Pharmacokinetic evaluations in SD rats indicated a notable uptick in the oral bioavailability of phenylbutyric acid, underscoring the efficacy of choline salt ILs in augmenting its bioavailability. Additional in vitro intestinal permeability tests on SD rats authenticated that the ILs, formulated with choline bicarbonate and phenylbutyric acid, demonstrate superior permeability compared to their sodium and acid counterparts. To conclude, choline salt ILs developed from choline bicarbonate and phenylbutyric acid present a promising avenue for UCD treatment, with the added benefit of reduced sodium load. They also hold merit in formulation engineering. The sustained-release capabilities of DESs position them favorably for drug delivery, while the low toxicity and cost-effectiveness of choline chloride signal potential in formulation engineering. Overall, this drug formulation heralds a prospective therapeutic avenue for UCD patients.

Keywords: phenylbutyric acid, sodium phenylbutyrate, choline salt, ionic liquids, deep eutectic systems, oral bioavailability

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Authors: Robert Jesiolowski, Monique Jesiolowski

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Today more than ever before, we have access to new technologies which provide unforeseen opportunities for educators to pursue in online education. It starts with an idea, but that needs to be coupled with the right team of experts willing to take big risks and put in the hard work to build something different. An instructional design team was empowered to reimagine an Introduction to Sociology university course as a Game-Based Learning (GBL) experience utilizing cutting edge Virtual Reality (VR) technology. The result was a collaborative process that resulted in a type of learning based in Game theory, Method of Loci, and VR Immersion Simulations to promote deeper retention of core concepts. The team deconstructed the way that university courses operated, in order to rebuild the educational process in a whole learner-centric manner. In addition to a review of the build process, this paper will explore the results of in-course surveys completed by student participants.

Keywords: higher education, innovation, virtual reality, game-based learning, loci method

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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: João P. E. De Souza, Rob Dekkers

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Whereas lean product development aims at maximising customer value whilst optimising product and process design, the question arises whether this approach includes sustainability. A systematic literature review reveals that methods associated with this conceptualisation of product development are suitable for including sustainability, but that the criteria for the triple-bottom line need to be included when using these methods; this is particularly the case for social aspects. Thus, the main finding is that not new methods should be developed, but that existing methods should be more inclusive towards all aspects of sustainability and product life-cycle thinking.

Keywords: lean product development, product life-cycle, sustainability, systematic literature review, triple bottom-line

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Authors: Padmalochan Panda, R. Ramaseshan

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Aluminum nitride (AlN) is a potential candidate for semiconductor industry due to its wide band gap (6.2 eV), high thermal conductivity and low thermal coefficient of expansion. A-plane oriented AlN film finds an important role in deep UV-LED with higher isotropic light extraction efficiency. Also, Cr-doped AlN films exhibit dilute magnetic semiconductor property with high Curie temperature (300 K), and thus compatible with modern day microelectronics. In this work, highly a-axis oriented wurtzite AlN and Al1-xMxN (M = Cr, Ti) films have synthesized by reactive co-sputtering technique at different concentration. Crystal structure of these films is studied by Grazing incidence X-ray diffraction (GIXRD) and Transmission electron microscopy (TEM). Identification of binding energy and concentration (x) in these films is carried out by X-ray photoelectron spectroscopy (XPS). Local crystal structure around the Cr and Ti atom of these films are investigated by X-ray absorption spectroscopy (XAS). It is found that Cr and Ti replace the Al atom in AlN lattice and the bond lengths in first and second coordination sphere with N and Al, respectively, decrease concerning doping concentration due to strong p-d hybridization. The nano-indentation hardness of Cr and Ti-doped AlN films seems to increase from 17.5 GPa (AlN) to around 23 and 27.5 GPa, respectively. An-isotropic optical properties of these films are studied by the Spectroscopic Ellipsometry technique. Refractive index and extinction coefficient of these films are enhanced in normal dispersion region as compared to the parent AlN film. The optical band gap energies also seem to vary between deep UV to UV regions with the addition of Cr, thus by bringing out the usefulness of these films in the area of optoelectronic device applications.

Keywords: ellipsometry, GIXRD, hardness, XAS

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Authors: Brenda Ravenscroft, Ulemu Luhanga, Bev King

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A comprehensive evaluation of online learning needs to include a blend of educational design, technology use, and online instructional practices that integrate technology appropriately for developing and delivering quality online courses. Research shows that classroom-based evaluation tools do not adequately capture the dynamic relationships between content, pedagogy, and technology in online courses. Furthermore, studies suggest that using classroom evaluations for online courses yields lower than normal scores for instructors, and may affect faculty negatively in terms of administrative decisions. In 2014, the Faculty of Arts and Science at Queen’s University responded to this evidence by seeking an alternative to the university-mandated evaluation tool, which is designed for classroom learning. The Faculty is deeply engaged in e-learning, offering large variety of online courses and programs in the sciences, social sciences, humanities and arts. This paper describes the process by which a new student survey instrument for online courses was developed and piloted, the methods used to analyze the data, and the ways in which the instrument was subsequently adapted based on the results. It concludes with a critical reflection on the challenges of evaluating e-learning. The Student Evaluation of Online Teaching Effectiveness (SEOTE), developed by Arthur W. Bangert in 2004 to assess constructivist-compatible online teaching practices, provided the starting point. Modifications were made in order to allow the instrument to serve the two functions required by the university: student survey results provide the instructor with feedback to enhance their teaching, and also provide the institution with evidence of teaching quality in personnel processes. Changes were therefore made to the SEOTE to distinguish more clearly between evaluation of the instructor’s teaching and evaluation of the course design, since, in the online environment, the instructor is not necessarily the course designer. After the first pilot phase, involving 35 courses, the results were analyzed using Stobart's validity framework as a guide. This process included statistical analyses of the data to test for reliability and validity, student and instructor focus groups to ascertain the tool’s usefulness in terms of the feedback it provided, and an assessment of the utility of the results by the Faculty’s e-learning unit responsible for supporting online course design. A set of recommendations led to further modifications to the survey instrument prior to a second pilot phase involving 19 courses. Following the second pilot, statistical analyses were repeated, and more focus groups were used, this time involving deans and other decision makers to determine the usefulness of the survey results in personnel processes. As a result of this inclusive process and robust analysis, the modified SEOTE instrument is currently being considered for adoption as the standard evaluation tool for all online courses at the university. Audience members at this presentation will be stimulated to consider factors that differentiate effective evaluation of online courses from classroom-based teaching. They will gain insight into strategies for introducing a new evaluation tool in a unionized institutional environment, and methodologies for evaluating the tool itself.

Keywords: evaluation, online courses, student survey, teaching effectiveness

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Authors: Ann Devitt, Mairead Brady, Markus Lamest, Stephen Gomez

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This study set out to explore the use of an online collaborative serious game for student learning in a postgraduate introductory marketing module. The simulation game aimed to bridge the theory-practice divide in marketing by allowing students to apply theory in a safe, simulated marketplace. This study addresses the following research questions: Does an online marketing simulation game engage students higher order cognitive skills? Does collaborative activity required develop students’ “soft” skills, such as communication and negotiation? What specific affordances of the online simulation promote learning? This qualitative case study took place in 2014 with 40 postgraduate students on a Business Masters Programme. The two-week intensive module combined lectures with collaborative activity on a marketing simulation game, MMX from Pearsons. The game requires student teams to compete against other teams in a marketplace and design a marketing plan to maximize key performance indicators. The data for this study comprise essays written by students after the module reflecting on their learning on the module. A thematic analysis was conducted of the essays using the following a priori theme sets: 6 levels of the cognitive domain of Blooms taxonomy; 5 principles of Cooperative Learning; affordances of simulation environments including experiential learning; motivation and engagement; goal orientation. Preliminary findings would strongly suggest that the game facilitated students identifying the value of theory in practice, in particular for future employment; enhanced their understanding of group dynamics and their role within that; and impacted very strongly, both positively and negatively on motivation. In particular the game mechanics of MMX, which hinges on the correct identification of a target consumer group, was identified as a key determinant of extrinsic and intrinsic motivation for learners. The findings also suggest that the situation of the simulation game within a broader module which required post-game reflection was valuable in identifying key learning of marketing concepts in both the positive and the negative experiences of the game.

Keywords: simulation, marketing, serious game, cooperative learning, bloom's taxonomy

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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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

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Henriette van Rensburg, Betty Adcock

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Concern about the lack of knowledge of quality teaching and teacher retention in rural and remote areas of Australia, has caused academics to improve pre-service teachers’ understanding of this problem. The participants in this study were forty students enrolled in an undergraduate educational course (EDO3341 Teaching in rural and remote communities) at the University of Southern Queensland in Toowoomba in 2012. This study involved an innovative case-based teaching approach in order to broaden their generally under-informed understanding of teaching in a rural and remote area. Three themes have been identified through analysing students’ critical reflections: learning expertise, case-based learning support and authentic learning. The outcomes identified the changes in pre-service teachers’ understanding after they have deepened their knowledge of the realities of teaching in rural and remote areas.

Keywords: rural and remote education, case based teaching, innovative education approach, higher education

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Authors: Rosa M. Gambier, Jennifer L. Carlson

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In order to revitalize the science curriculum for botany courses for non-science majors, we have introduced the use of the medicinal plants into a first-year botany course. We have connected the use of scientific method, scientific inquiry and active learning in the classroom with the study of Western Traditional Medical Botany. The students have researched models of Botanical medicine and have designed a sustainable medicinal plants garden using native medicinal plants from the northeast. Through the semester, the students have researched their chosen species, planted seeds in the college greenhouse, collected germination ratios, growth ratios and have successfully produced a beginners medicinal plant garden. Phase II of the project will be to tie in SCCCs community outreach goals by involving the public in the expanded development of the garden as a way of sharing learning about medicinal plants and traditional medicine outside the classroom.

Keywords: medicinal plant garden, botany curriculum, active learning, community outreach

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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