Search results for: teaching report writing for innovative learning

Authors: N. N. Joshi, G. K. Singh

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We present the stimuli implementation and verification of a Programmable Peripheral Interface (PPI) 8255. It involves a designing and verification of configurable intellectual property (IP) module of PPI protocol using Verilog HDL for implementation part and System Verilog for verification. The overview of the PPI-8255 presented then the design specification implemented for the work following the functional description and pin configuration of PPI-8255. The coverage report of design shows that our design and verification environment covered 100% functionality in accordance with the design specification generated by the Questa Sim 10.0b.

Keywords: Programmable Peripheral Interface (PPI), verilog HDL, system verilog, questa sim

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Authors: Vesa Salminen, Esa Santakallio, Heikki Ruohomaa

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Introduction: The city of Riihimäki has decided robotics on well-being, service and industry as the main focus area on their ecosystem strategy. Robotics is going to be an important part of the everyday life of citizens and present in the working day of the average citizen and employee in the future. For that reason, also education system and education programs on all levels of education from diaper age to doctorate have been directed to fulfill this ecosystem strategy. Goal: The objective of this activity has been to develop education continuity from diaper age to doctorate. The main target of the development activity is to create a unique robotics study entity that enables ongoing robotics studies from preprimary education to university. The aim is also to attract students internationally and supply a skilled workforce to the private sector, capable of the challenges of the future. Methodology: Education instances (high school, second grade, Universities on all levels) in a large area of Tavastia Province have gradually directed their education programs to support this goal. On the other hand, applied research projects have been created to make proof of concept- phases on areal real environment field labs to test technology opportunities and digitalization to change business processes by applying robotic solutions. Customer-oriented applied research projects offer for students in robotics education learning environments to learn new knowledge and content. That is also a learning environment for education programs to adapt and co-evolution. New content and problem-based learning are used in future education modules. Major findings: Joint robotics education entity is being developed in cooperation with the city of Riihimäki (primary education), Syria Education (secondary education) and HAMK (bachelor and master education). The education modules have been developed to enable smooth transitioning from one institute to another. This article is introduced a case study of the change of education of wellbeing education because of digitalization and robotics. Riihimäki's Elderly citizen's service house, Riihikoti, has been working as a field lab for proof-of-concept phases on testing technology opportunities. According to successful case studies also education programs on various levels of education have been changing. Riihikoti has been developed as a physical learning environment for home care and robotics, investigating and developing a variety of digital devices and service opportunities and experimenting and learn the use of equipment. The environment enables the co-development of digital service capabilities in the authentic environment for all interested groups in transdisciplinary cooperation.

Keywords: ecosystem strategy, digitalization and robotics, education continuity, learning environment, transdisciplinary co-operation

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Authors: Tala Toonsi, Marah Alagha, Lina Alnowaiser, Hala Rajab

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This report is about the Photomath app, which is an AI application that uses image recognition technology, specifically optical character recognition (OCR) algorithms. The (OCR) algorithm translates the images into a mathematical equation, and the app automatically provides a step-by-step solution. The application supports decimals, basic arithmetic, fractions, linear equations, and multiple functions such as logarithms. Testing was conducted to examine the usage of this app, and results were collected by surveying ten participants. Later, the results were analyzed. This paper seeks to answer the question: To what level the artificial intelligence features are accurate and the speed of process in this app. It is hoped this study will inform about the efficiency of AI in Photomath to the users.

Keywords: photomath, image recognition, app, OCR, artificial intelligence, mathematical equations.

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Authors: Alejandra G. Barbón, José Vila, Dania Vazquez

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This study seeks to contribute to the advancement of library and archival sciences in the areas of records management, knowledge organization, and information architecture, particularly focusing on the enhancement of bibliographical description through the incorporation of visual interactive designs aimed to enrich the library users’ experience. In an era of heightened awareness about the legacy of hiddenness across special and rare collections in libraries and archives, along with the need for inclusivity in academia, the University of Miami Libraries has embarked on an innovative project that intersects the realms of print culture, decolonization, and digital technology. This proposal presents an exciting initiative to revitalize the study of Artists’ Books collections by employing digital visual representations to decolonize bibliographic records of some of the most unique materials and foster a more holistic understanding of cultural heritage. Artists' Books, a dynamic and interdisciplinary art form, challenge conventional bibliographic classification systems, making them ripe for the exploration of alternative approaches. This project involves the creation of a digital platform that combines multimedia elements for digital representations, interactive information retrieval systems, innovative information architecture, trending bibliographic cataloging and metadata initiatives, and collaborative curation to transform how we engage with and understand these collections. By embracing the potential of technology, we aim to transcend traditional constraints and address the historical biases that have influenced bibliographic practices. In essence, this study showcases a groundbreaking endeavor at the University of Miami Libraries that seeks to not only enhance bibliographic practices but also confront the legacy of hiddenness across special and rare collections in libraries and archives while strengthening conventional bibliographic description. By embracing digital visualizations, we aim to provide new pathways for understanding Artists' Books collections in a manner that is more inclusive, dynamic, and forward-looking. This project exemplifies the University’s dedication to fostering critical engagement, embracing technological innovation, and promoting diverse and equitable classifications and representations of cultural heritage.

Keywords: decolonizing bibliographic cataloging frameworks, digital visualizations information architecture platforms, collaborative curation and inclusivity for records management, engagement and accessibility increasing interaction design and user experience

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Madeline Phillips, Luis Mendez

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The 21st century has seen an increase in the amount and variety of psychological research on Latinx, the largest minority group in the U.S., with great variability from the individual’s cultural origin (e.g., ethnicity) to region (e.g., nationality). We were interested in exploring how scientists recruit, conduct and report research on Latinx samples. Ethnicity and race are important components of individuals and should be addressed to capture a broader and deeper understanding of psychological research findings. In order to explore Latinx/Hispanic work, the Journal of Latinx Psychology (JLP) and Hispanic Journal of Behavioral Sciences (HJBS) were analyzed for 1) measures of ethnicity and race in empirical studies 2) nationalities represented 3) how researchers reported ethnic-racial demographics. The analysis included publications from 2013-2018 and revealed two common themes of reporting ethnicity and race: overrepresentation/underrepresentation and overgeneralization. There is currently not a systematic way of reporting ethnicity and race among Latinx/Hispanic research, creating a vague sense of what and how ethnicity/race plays a role in the lives of participants. Second, studies used the Hispanic/Latinx terms interchangeably and are not consistent across publications. For the purpose of this project, we were only interested in publications with Latinx samples in the U.S. Therefore, studies outside of the U.S. and non-empirical studies were excluded. JLP went from N = 118 articles to N = 94 and HJBS went from N = 174 to N = 154. For this project, we developed a coding rubric for ethnicity/race that reflected the different ways researchers reported ethnicity and race and was compatible with the U.S. census. We coded which ethnicity/race was identified as the largest ethnic group in each sample. We used the ethnic-racial breakdown numbers or percentages if provided. There were also studies that simply did not report the ethnic composition besides Hispanic or Latinx. We found that in 80% of the samples, Mexicans are overrepresented compared to the population statistics of Latinx in the US. We observed all the ethnic-racial breakdowns, demonstrating the overrepresentation of Mexican samples and underrepresentation and/or lack of representation of certain ethnicities (e.g., Chilean, Guatemalan). Our results showed an overgeneralization of studies that cluster their participants to Latinx/Hispanic, 23 for JLP and 63 for HJBS. The authors discuss the importance of transparency from researchers in reporting the context of the sample, including country, state, neighborhood, and demographic variables that are relevant to the goals of the project, except when there may be an issue of privacy and/or confidentiality involved. In addition, the authors discuss the importance to recognize the variability within the Latinx population and how it is reflected in the scientific discourse.

Keywords: Latinx, Hispanic, race and ethnicity, diversity

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Authors: Som Nath Sachdeva, Vanita Aggarwal, S. M. Gupta

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Use of concrete paver blocks is becoming increasingly popular. They are used for paving of approaches, paths and parking areas including their application in pre-engineered buildings. This paper discusses the results of an experimental study conducted on Fly Ash Concrete with the aim to report its suitability for concrete paver blocks. In this study, the effect of varying proportions of fly ash, 20 % to 40 %, on compressive strength and flexural strength of concrete has been evaluated. The mix designs studied are M-30, M-35, M-40 and M-50. It is observed that all the fly ash based mixes are able to achieve the required compressive and flexural strengths. In comparison to control mixes, the compressive and flexural strengths of the fly ash based mixes are found to be slightly less at 7 days and 28 days and a little more at 90 days.

Keywords: fly ash concrete, paver blocks, compressive, flexural strength

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: H. Pasternak, T. Krausche

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The Porsche Pavilion is an innovative stainless steel construction using the principle, often used in ship and car design, as an advantage for building a light but stiff structure. The Pavilion is a one of a kind and outstanding construction that you can find. It fits right in the existing parts of the Autostadt within the lagoon landscape and was built in only eight months. With its curving lines and exiting bends the structure is an extraordinary work which was designed by Henn architects, Munich. The monocoque has a good balance between material and support structure. The stiffness is achieved by the upper and lower side sheathing plates and the intermediate formers. Also the roof shell has no joints and a smooth surface. The assembling of the structure requires a large time and effort cost due to many welds which are necessary to connect all section to one large shell.

Keywords: construction welding, exhibition building, light steel construction, monocoque

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Authors: D. C. Asebiah, D. Saranin, S. Karazhanov, A. R. Tameev, M. Kah

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In this paper, the mesoscopic NiO was used as a hole transport layer in the inverted planar organometallic hybrid perovskite solar cell to study the effect of hysteresis. The devices we fabricated have the structures Fluorine Tin Oxide (FTO)/mesoscopic NiO/perovskite/[6,6]-phenyl C₆₁-butyric acid methyl ester (PC₆₁BM) photovoltaic device. The perovskite solar cell was done by toluene air (TLA) method and horn sonication for the dispersion of the NiO nanoparticles in deionized water. The power conversion efficiency was 12.07% under 1.5 AM illumination. We report hysteresis in the in current-voltage dependence of the solar cells with mesoscopic NiO as a hole transport layer.

Keywords: perovskite, mesoscopic, hysteresis, toluene air

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Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Mohammed Abdurabu, Akram Al-Warqi, Ebrahim M. A. Ebrahim, Jouhar Kollari, Salman Mirza

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The novel coronavirus (COVID-19) is one of the most recent pandemics that invaded earth that left and still leaving hundreds of thousands of patients and ended with high morbidity and mortality rates with no clear cure till this moment. COVID-19 has been proven to be associated with pathologic changes in coagulation, characterized by either thromboembolic or bleeding events. We present this case of a 44-year-old male patient that presented to our Emergency Department with flank pain that later was found to have renal angiomyolipoma (AML) rupture during his COVID-19 infection, ultimately requiring admission for hemorrhage control via Interventional Radiology (IR) drainage. Here, we discuss the role of the front-line physicians and how they should keep a low threshold for the different presentations that could be associated with COVID-19 infection.

Keywords: angiomyolipoma, COVID-19, renal, rupture

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Authors: Rikke Lybæk

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This paper delves into the intricacies of recycling household plastic waste within Denmark, employing an exploratory case study methodology to shed light on the technical, strategic, and market dynamics of the plastic recycling value chain. Focusing on circular economy principles, the research identifies critical gaps and opportunities in recycling processes, particularly regarding plastic packaging waste derived from households, with a notable absence in food packaging reuse initiatives. The study uncovers the predominant practice of downcycling in the current value chain, underscoring a disconnect between the potential for high-quality plastic recycling and the market's readiness to embrace such materials. Through detailed examination of three leading companies in Denmark's plastic industry, the paper highlights the existing support for recycling initiatives, yet points to the necessity of assured quality in sorted plastics to foster broader adoption. The analysis further explores the importance of reuse strategies to complement recycling efforts, aiming to alleviate the pressure on virgin feedstock. The paper ventures into future perspectives, discussing different approaches such as biological degradation methods, watermark technology for plastic traceability, and the potential for bio-based and PtX plastics. These avenues promise not only to enhance recycling efficiency but also to contribute to a more sustainable circular economy by reducing reliance on virgin materials. Despite the challenges outlined, the research demonstrates a burgeoning market for recycled plastics within Denmark, propelled by both environmental considerations and customer demand. However, the study also calls for a more harmonized and effective waste collection and sorting system to elevate the quality and quantity of recyclable plastics. By casting a spotlight on successful case studies and potential technological advancements, the paper advocates for a multifaceted approach to plastic waste management, encompassing not only recycling but also innovative reuse and reduction strategies to foster a more sustainable future. In conclusion, this study underscores the urgent need for innovative, coordinated efforts in the recycling and management of plastic waste to move towards a more sustainable and circular economy in Denmark. It calls for the adoption of comprehensive strategies that include improving recycling technologies, enhancing waste collection systems, and fostering a market environment that values recycled materials, thereby contributing significantly to environmental sustainability goals.

Keywords: case study, circular economy, Denmark, plastic waste, sustainability, waste management

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Authors: Meng-Ying Chiu, Yu-Fang Huang, Pei-Wei Wang, Yi-Ru Wang, Yi-Shuan Shao, Mei-Lien Chen

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Background: Organophosphate pesticides (OPs) are the most heavily used pesticides in agriculture in Taiwan. Therefore, they are commonly detected in general public including pregnant women and children. These compounds are proven endocrine disrupters that may affect the neural development in humans. The aim of this study is to assess the OPs exposure of children in 2 years of age and to examine the association between the exposure concentrations and neurodevelopmental effects in children. Methods: In a prospective cohort of 280 mother-child pairs, urine samples of prenatal and postnatal were collected from each participant and analyzed for metabolites of OPs by using gas chromatography-mass spectrometry. Six analytes were measured including dimethylphosphate (DMP), dimethylthiophosphate (DMTP), dimethyldithiophosphate (DMDTP), diethylphosphate (DEP), diethylthiophosphate (DETP), and diethyldithiophosphate (DEDTP). This study created a combined concentration measure for dimethyl compounds (DMs) consisting of the three dimethyl metabolites (DMP, DMTP, and DMDTP), for diethyl compounds (DEs) consisting of the three diethyl metabolites (DEP, DETP, and DEDTP) and six dialkyl phosphate (DAPs). The Bayley Scales of Infant and Toddler Development (Bayley-III) was used to assess children's cognitive behavior at 2 years old. The association between OPs exposure and Bayley-III scale score was determined by using the Mann-Whitney U test. Results: The measurements of urine samples are still on-going. This preliminary data are the report of 56 children aged 2 from the cohort. The detection rates for DMP, DMTP, DMDTP, DEP, DETP, and DEDTP are 80.4%, 69.6%, 64.3%, 64.3%, 62.5%, and 75%, respectively. After adjusting the creatinine concentrations of urine, the median (nmol/g creatinine) of urinary DMP, DMTP, DMDTP, DEP, DETP, DEDTP, DMs, DEs, and DAPs are 153.14, 53.32, 52.13, 19.24, 141.65, 192.17, 308.8, 311.6, and 702.11, respectively. The concentrations of urine are considerably higher than that in other countries. Children’s cognitive behavior was used three scales for Bayley-III, including cognitive, language and motor. In Mann-Whitney U test, the higher levels of DEs had significantly lower motor score (p=0.037), but no significant association was found between the OPs exposure levels and the score of either cognitive or language. Conclusion: The limited sample size suggests that Taipei children are commonly exposed to OPs and OPs exposure might affect the cognitive behavior of young children. This report will present more data to verify the results. The predictors of OPs concentrations, such as dietary pattern will also be included.

Keywords: biomonitoring, children, neurodevelopment, organophosphate pesticides exposure

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Authors: Francisco Luis Reyes

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European colonization had a profound impact on Latin America. The influence of the old continent can be perceived in the culture, religion, and language of the region as well as the beliefs and attitudes of the population. Music education is not an exception to this phenomenon. With Europeans controlling cultural life and erecting educational institutions across the continent for several centuries, Western European Art Music (WEAM) has polarized music learning in formal spaces. In contrast, the musics from the indigenous population, the African slaves, and the ones that emerged as a result of the cultural mélanges have largely been excluded from primary and secondary schooling. The purpose of this paper is to suggest the inclusion of indigenous music education in primary and secondary music education. The paper employs a philosophical inquiry in order to achieve this aim. Philosophical inquiry seeks to uncover and examine individuals' unconscious beliefs, principles, values, and assumptions to envision potential possibilities. This involves identifying and describing issues within current music teaching and learning practices. High-quality philosophical research tackles problems that are sufficiently narrow (addressing a specific aspect of a single complex topic), realistic (reflecting the experiences of music education), and significant (addressing a widespread and timely issue). Consequently, this methodological approach fits this topic, as the research addresses the omnipresence of WEAM in Latin American music education, the exclusion of indigenous music, and argues about the transformational impact said artistic expressions can have on practices in the region. The paper initially addresses how WEAM became ubiquitous in the region by recounting historical events, and adressing the issues other types of music face entering higher education. According to Shifres and Rosabal-Coto (2017) Latin America still upholds the musical heritage of their colonial period, and its formal music education institutions promote the European ontology instilled during European expansion. In accordance, the work of Reyes and Lorenzo-Quiles (2024), and Soler, Lorenzo-Quiles, and Hargreaves (2014), demonstrate how music institutions in the region uphold foreign narratives. Their studies show that music programs in Puerto Rico and Colombia instruct students in WEAM as well as require skills in said art form to enter the profession, just like other authors have argued (Cain & Walden, 2019, Walden, 2016). Subsequently, the research explains the issues faced by prospective music educators that do not practice WEAM. Roberts (1991a, 1991b, 1993), Green (2012) have found that music education students that do not adhere to the musical culture of their institution, are less likely to finish their degrees. Hence, practicioners of tradional musics might feel out of place in the environment. The ubiquity of WEAM and the exclusion of traditional musics of the region, provide the primary challenges to the inclusion of indigenous musics in formal spaces in primary and secondary education. The presentation then laids the framework for the inclusion indigenous music, and conclusively offers examples of how the musical expressions from the continent can improove the music education practices of the region. As an ending, the article highlights the benefits of these musics that are lacking in current practices.

Keywords: indigenous music education, postmodern music education, decolonization in music education, music education practice, Latin American music education

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Authors: Yan Zhang

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With the development of commercial movies, more and more instructors are drawn to adapt film pedagogy to teach history and culture. By challenging traditional standards of classroom culture, instruction through film represents an intersection of modernity and adaptability which is no longer optional but essential to maintaining educational accessibility. First, this presentation describes special features of the film that can be used in the classroom and help students acquire intercultural communicative competence (ICC) and achieve the learning goal. Second, the author brings forward the 5 A STAIRCASE model (Acknowledge-Adjust-Acculturate-Act-Assess) to explore how students acquire international communicative competence. Third, this article presents the intersections between new digital environments and classroom practice, such as how films can contribute to combining classical and contemporary Chinese cultures seamlessly and how film pedagogy can be an effective way to get students to engage in deeper critical thinking by exposing them to visuals, music, language, and styling which do not exist in traditional learning formats. Last, the student’s final video project will be exemplified at the end, demonstrating how to engage students in the analysis and experience of history and culture.

Keywords: intercultural education, curriculum, media, history

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Authors: Edith Lotachukwu Ochege

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Nations stay together when citizens share enough values and preferences and can communicate with each other. Homogeneity among people can be built with education, teaching a common language to facilitate communication, infrastructure for easier travel, but also by brute force such as prohibiting local cultures. This paper discusses the role of language education in nation building. It defines education, highlights the functions of language. Furthermore, it expresses socialization agents that aid culture which are all embodied in language, problems of nation building.

Keywords: nation building, language education, function of language, socialization

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: In-Hyuck Song, Jang-Hoon Ha

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Recently, porous ceramic membranes have attracted great interest due to their outstanding thermal and chemical stability. In this paper, we report the results of our efforts to determine whether we could prepare a diatomite-kaolin composite coating to be deposited over a sintered diatomite support layer that could reduce the largest pore size of the sintered diatomite membrane while retaining an acceptable level of permeability. We determined under what conditions such a composite coating over a support layer could be prepared without the generation of micro-cracks during drying and sintering. The pore characteristics of the sintered diatomite membranes were studied by scanning electron microscopy and capillary flow porosimetry.

Keywords: ceramic membrane, diatomite, water treatment, sintering

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Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

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Authors: Harvey Hyman, Thomas Hull

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As one of the largest cancer treatment centers in the United States, we continuously confront the challenge of how to leverage the best possible technological solutions, in order to provide the highest quality of service to our customers – the doctors, nurses and patients at Moffitt who are fighting every day for the prevention and cure of cancer. This paper reports on the transition from a vertical to a horizontal IT infrastructure. We discuss how the new frameworks and methods such as public, private and hybrid cloud, brokering cloud services are replacing the traditional vertical paradigm for computing. We also report on the impact of containers, micro services, and the shift to continuous integration/continuous delivery. These impacts and changes in delivery methodology for computing are driving how we accomplish our strategic IT goals across the enterprise.

Keywords: cloud computing, IT infrastructure, IT architecture, healthcare

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Authors: Ayşegül Çabuk, Mine Işıksal

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Mathematics is a sense-making activity so that it requires meaningful learning. Hence based on this idea, meaningful mathematical connections are necessary to learn mathematics. At that point, the major question has become that which educational methods can provide opportunities to provide mathematical connections and to understand mathematics. The amalgam of reasoning and proof can be the one of the methods that creates opportunities to learn mathematics in a meaningful way. However, even if reasoning and proof should be included from prekindergarten to grade 12, studies in literature generally include secondary school students and pre-service mathematics teachers. With the light of the idea that the amalgam of reasoning and proof has significant effect on middle school students' mathematical learning, this study aims to investigate middle grade students' tendencies while reasoning the correctness of statements related to numbers. The sample included 272 middle grade students, specifically 69 of them were sixth grade students (25.4%), 101 of them were seventh grade students (37.1%) and 102 of them were eighth grade students (37.5%). Data was gathered through an achievement test including 2 essay types of problems about algebra. The answers of two items were analyzed both quantitatively and qualitatively in terms of students' solutions strategies while reasoning the correctness of the statements. Similar on the findings in the literature, most of the students, in all grade levels, used numerical examples to judge the statements. Moreover the results also showed that the majority of these students appear to believe that providing one or more selected examples is sufficient to show the correctness of the statement. Hence based on the findings of the study, even students in earlier ages have proving and reasoning abilities their reasoning's generally based on the empirical evidences. Therefore, it is suggested that examples and example-based reasoning can be a fundamental role on to generate systematical reasoning and proof insight in earlier ages.

Keywords: reasoning, mathematics learning, middle grade students

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Authors: Samantha Rix

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The purpose of this research paper is to investigate the cognitive processing of learners who receive input but produce very little or no output, and who, when they do produce output, exhibit a similar language proficiency as do those learners who produced output more regularly in the language classroom. Previous studies have investigated the benefits of output (with somewhat differing results); therefore, the presentation will begin with an investigation of what may underlie gains in proficiency without output. Consequently, a pilot study was designed and conducted to gain insight into the cognitive processing of low-output language learners looking, for example, at quantity and quality of noticing. This will be carried out within the paradigm of action classroom research, observing and interviewing low-output language learners in an intensive English program at a small Midwest university. The results of the pilot study indicated that autonomy in language learning, specifically utilizing strategies such self-monitoring, self-talk, and thinking 'out-loud', were crucial in the development of language proficiency for academic-level performance. The presentation concludes with an examination of pedagogical implication for classroom use in order to aide students in their language development.

Keywords: cognitive processing, language learners, language proficiency, learning strategies

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Authors: A. Eiguren-Munitis, N. Berasategi, J. M. Correa

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Changes in family structures, immigration, economic crisis, among others, hinder the connection between different generations. This situation gives rise to a greater lack of social protection of the groups in vulnerable situations, such as the elderly and children. There is a growing need to search for shared spaces where different generations manage to break negative stereotypes and interact with each other. The school environment provides a favourable context in which the approach of different generations can be worked on. The intergenerational experiences that take place within the school context help to introduce the educational ideology for a lifetime. This induces bilateral learning, which encourages citizen participation. For this reason, the general objective of this research is to deepen the impact that intergenerational experiences have on participating students. The research is carried out based on mixed methods. The qualitative and quantitative evaluation included pre-test and post-test questionnaires (n=148) and group interviews (n=43). The results indicate that the intergenerational experiences influence different levels, on the one hand, help to promote school motivation and on the other hand, help to reduce negative stereotypes towards older people thus contributing to greater social cohesion.

Keywords: intergenerational learning, school, stereotypes, social cohesion

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Noemia Bessa Vilela, Jose Caramelo Gomes

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All law student are familiarized, in the first years of their bachelor of laws with the concepts of “public goods” and “ private goods”; often, such legal concept does not exactly match such economic concept, and there are consequences are some sort of confusion being created. The list of goods that follow under each category is not exhaustive, nor are students given proper mechanisms to acknowledge that some legal fields can, on its own, be considered as a “public good”; this is the case of Competition. Legal authors consider that “competition law is used to promote public interest” and, as such, it is a “public good”; in economics theory, Competition is the first public good in a market economy, as the enabler of allocation efficiency. Competition law is the legal tool to support the proper functioning of the market economy and democracy itself. It is fact that Competition Law only applies to economic activities, still, competition is object of private litigation as an integral part of Public Law. Still, regardless of the importance of Competition Law in the economic activity and market regulation, most student complete their studies in law, join the Bar Associations and engage in their professional activities never having been given sufficient tools to deal with the increasing demands of a globalized world. The lack of knowledge of economics, market functioning and the mechanisms at their reach in order to ensure proper realization of their duties as lawyers/ attorneys-at-law would be tackled if Competition Law would be included as part of the curricula of Law Schools. Proper teaching of Competition Law would combine the foundations of Competition Law, doctrine, case solving and Case Law study. Students should to understand and apply the analytical model. Special emphasis should be given to EU Competition Law, namely the TFEU Articles 101 to 106. Damages Directive should also be part of the curriculum. Students must in the first place acquire and master the economic rationale as competition and the world of competition law are the cornerstone of sound and efficient market. The teaching of Competition Law in undergraduate programs in Law would contribute to fulfill the potential of the students who will deal with matters related to consumer protection, economic and commercial law issues both in private practice and as in-house lawyers for companies.

Keywords: higher education, competition law, legal education, law, market economy, industrial economics

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Mohamad Abdullah

Abstract:

Recent Advancements in technology have stimulated deep change in the language learning classroom. Flipped classroom as a new pedagogical method is at the center of this change. It turns the classroom into a student-centered environment and promotes interactive and autonomous learning. The present study is an attempt to examine the effectiveness of the Flipped Classroom Model (FCM) on students’ motivation level in English speaking performance. This study was carried out with 27 undergraduate female English majors who enrolled in the course of Advanced Communication Skills (ENGL 154) at Buraimi University College (BUC). Data was collected through Motivation in English Speaking Performance Questionnaire (MESPQ) which has been distributed among the participants of this study pre and post the implementation of FCM. SPSS was used for analyzing data. The Paired T-Test which was carried out on the pre-post of (MESPQ) showed a significant difference between them (p < .009) that revealed participants’ tendency to increase their motivation level in English speaking performance after the application of FCM. In addition, respondents of the current study reported positive views about the implementation of FCM.

Keywords: english speaking performance, motivation, flipped classroom model, learner-contentedness

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