Search results for: module based teaching and learning

Authors: P. Charvin, R. Bourrou, F. Lemont, C. Lafon, A. Russello

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For nuclear waste treatment and confinement, a specific IN-CAN melting module based on low-frequency induction heating have been designed. The frequency of 50Hz has been chosen to improve penetration length through metal. In this design, the liquid metal, strongly stirred by electromagnetic effects, presents shape of a dome caused by strong Laplace forces developing in the bulk of bath. Because of a lower density, the glass phase is located above the metal phase and is heated and stirred by metal through interface. Electric parameters (Intensity, frequency) give precious information about metal load and composition (resistivity of alloy) through impedance modification. Then, power supply can be adapted to energy transfer efficiency for suitable process supervision. Modeling of this system allows prediction of metal dome shape (in agreement with experimental measurement with a specific device), glass and metal velocity, heat and motion transfer through interface. MHD modeling is achieved with COMSOL and Fluent. First, a simplified model is used to obtain the shape of the metal dome. Then the shape is fixed to calculate the fluid flow and the thermal part.

Keywords: electromagnetic stirring, induction heating, interface modeling, metal load

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Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Maria Sheila S. Garcia

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A good number of researches indicate that living in another country may create different and unexpected adjustment problems, and foreign students are not exempted from this. To provide an understanding of this process, 30 foreign college students studying English in Tarlac City were asked to answer questionnaires. This is to determine their sociocultural adaptation, openness to the host culture and success of sojourn. Through statistical analysis, it was found that the students experience greater difficulty in the academic area. Moderate difficulty was attributed to everyday life and social interactions. Albeit difficult, what they like best is the school’s methods of teaching English while the areas that need improvement are the libraries and internet connection. The only significant relationship was found between sociocultural adaptation and success of sojourn. Negatively correlated, if students experience greater difficulties in their host country, they are likely to regret their stay and will not recommend it to anyone. Openness to the host culture did not have an effect on the adaptation and success of sojourn. The short period of time that the students have are spent in studying rather than making friends. Nonetheless, this indicates the need to look deeper into the academic, extra-curricular activities and facilities provided by learning institutions.

Keywords: foreign students, sociocultural adaptation, success of sojourn, Tarlac Philippines

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Authors: Samuel Olugbenga King

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Using a reflective analysis tool to provide both local and global perspectives, this study focuses on the longitudinal evaluation of the Graduate Student Development (GSD) initiative, the Preparing Future Faculty (PFF) program. The reflection process involves examining the past and present to identify challenges, and culminates in the creation of an action plan to address barriers to further growth and teaching development of graduate students, thus positively impacting student experience. The outcomes of the reflective critique of the PFF program indicate that lack of mentoring as well as inadequate feedback and funding are barriers that need to be addressed to positively impact the graduate student experience. Consequently, interventions, such as peer and student evaluations, and alumni surveys are highlighted as pragmatic modes of addressing the inadequate feedback and mentoring barriers. However, funding remains an ongoing challenge. This article is a contribution to the literature on the use of critical reflection approaches to investigate and evaluate specific programming that focuses on enhancing the graduate student experience and development.

Keywords: graduate student experience, longitudinal reflection, quality enhancement, teaching

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Authors: Nahid Nariman

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The prosperity of any nation depends on its ability to use human potential, in particular, to offer an education that builds learners' competencies to become effective workforce participants and true citizens of the world. Ever since the Second World War, the United States has been a dominant player in the world politically, economically, socially, and culturally. The rapid rise of technological advancement and consumer technologies have made it clear that science, technology, engineering, and math (STEM) play a crucial role in today’s world economy. Exploring the top qualities demanded from new hires in the industry—i.e., problem-solving skills, teamwork, dependability, adaptability, technical and communication skills— sheds light on the kind of path that is needed for a successful educational system to effectively support STEM. The focus of 21st century education has been to build student competencies by preparing them to acquire and apply knowledge, to think critically and creatively, to competently use information, be able to work in teams, to demonstrate intellectual and moral values as well as cultural awareness, and to be able to communicate. Many educational reforms pinpoint various 'ideal' pathways toward STEM that educators, policy makers, and business leaders have identified for educating the workforce of tomorrow. This study will explore how problem-based learning (PBL), an instructional strategy developed in the medical field and adopted with many successful results in K-12 through higher education, is the proper approach to stimulate underrepresented high school students' interest in pursuing STEM careers. In the current study, the effect of a problem-based STEM model on students' attitudes and career interests was investigated using qualitative and quantitative methods. The participants were 71 low-income, native Hawaiian high school students who would be first-generation college students. They were attending a summer STEM camp developed as the result of a collaboration between the University of Hawaii and the Upward Bound Program. The project, funded by the National Science Foundation's Innovative Technology Experiences for Students and Teachers (ITEST) program, used PBL as an approach in challenging students to engage in solving hands-on, real-world problems in their communities. Pre-surveys were used before camp and post-surveys on the last day of the program to learn about the implementation of the PBL STEM model. A Career Interest Questionnaire provided a way to investigate students’ career interests. After the summer camp, a representative selection of students participated in focus group interviews to discuss their opinions about the PBL STEM camp. The findings revealed a significantly positive increase in students' attitudes towards STEM disciplines and STEM careers. The students' interview results also revealed that students identified PBL to be an effective form of instruction in their learning and in the development of their 21st-century skills. PBL was acknowledged for making the class more enjoyable and for raising students' interest in STEM careers, while also helping them develop teamwork and communication skills in addition to scientific knowledge. As a result, the integration of PBL and a STEM learning experience was shown to positively affect students’ interest in STEM careers.

Keywords: problem-based learning, science education, STEM, underrepresented students

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Authors: Benjamin D Reese Jr

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Implicit bias is understood as unconscious attitudes, stereotypes, or associations that can influence the cognitions, actions, decisions, and interactions of an individual without intentional control. These unconscious attitudes or stereotypes are often targeted toward specific groups of people based on their gender, race, age, perceived sexual orientation or other social categories. Since the late 1980s, there has been a proliferation of research that hypothesizes that the operation of implicit bias is the result of the brain needing to process millions of bits of information every second. Hence, one’s prior individual learning history provides ‘shortcuts’. As soon as one see someone of a certain race, one have immediate associations based on their past learning, and one might make assumptions about their competence, skill, or danger. These assumptions are outside of conscious awareness. In recent years, an alternative conceptualization has been proposed. The ‘bias of crowds’ theory hypothesizes that a given context or situation influences the degree of accessibility of particular biases. For example, in certain geographic communities in the United States, there is a long-standing and deeply ingrained history of structures, policies, and practices that contribute to racial inequities and bias toward African Americans. Hence, negative biases among groups of people towards African Americans are more accessible in such contexts or communities. This theory does not focus on individual brain functioning or cognitive ‘shortcuts.’ Therefore, attempts to modify individual perceptions or learning might have negligible impact on those embedded environmental systems or policies that are within certain contexts or communities. From the ‘bias of crowds’ perspective, high levels of racial bias in a community can be reduced by making fundamental changes in structures, policies, and practices to create a more equitable context or community rather than focusing on training or education aimed at reducing an individual’s biases. The current paper acknowledges and supports the foundational role of long-standing structures, policies, and practices that maintain racial inequities, as well as inequities related to other social categories, and highlights the critical need to continue organizational, community, and national efforts to eliminate those inequities. It also makes a case for providing individual leaders with a deep understanding of the dynamics of how implicit biases impact cognitions, actions, decisions, and interactions so that those leaders might more effectively develop structural changes in the processes and systems under their purview. This approach incorporates both the importance of an individual’s learning history as well as the important variables within the ‘bias of crowds’ theory. The paper also offers a model for leadership education, as well as examples of structural changes leaders might consider.

Keywords: implicit bias, unconscious bias, bias, inequities

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Milene Magalhães Pinto, Irlen Antônio Gonçalves

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This paper is within the area of History of Education in Brazil, having a descriptive and exploratory nature. It has been built on the belief that professional education is organized under political guidelines and solidifies through institutionalized discourses, allowing to know its mission concerning the society in which it operates by studying these speeches. Our purpose is to analyze how the Republican political culture yielded changes in public education through reforms to professional education in the First Republic, based on seven procedures of law that occurred in the Legislature of State of Minas Gerais. The Republican effort to reform the teaching was the result of a conception of society that aspired to advance the country by way of the national worker.

Keywords: professional education, republican political culture, education reforms, Brazil

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Damou Mehdi, Nouri Keltouma, Fahem Mohammed

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In this paper, the concept of substrate integrated waveguide (SIW) technology is used to design filter for 30 GHz communication systems. SIW is created in the substrate of RT/Duroid 5880 having relative permittivity ε_r= 2.2 and loss tangent tanφ = 0.0009. Four Via are placed on the century filter the structures of SIW are modeled using and have been optimized in software HFSS (High Frequency Structure Simulator), à transition is designed for a Ka-band transceiver module with a 28.5GHz center frequency, . and then the results are verified using another simulation CST Microwave Studio (Computer Simulation Technology). The return loss are less than -18 dB, and -13 dB respectively. The insertion loss is divided equally -1.2 dB and -1.4 respectively.

Keywords: transition, microstrip, substrat integrated wave guide, filter, via

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Authors: Sora Suh, Catherine Michener

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This paper examines the collaborative forms of advocacy that a group of four public school teachers took for their emergent bilingual students in one public school district. While teacher advocacy takes many forms in and out of the classroom, much advocacy work is done by individuals and less by collective action. As a result, individual teachers risk isolation or marginalization in their school contexts when they advocate for immigrant youth. This paper is intended to contribute to the documentation and understanding of teachers’ advocacy work as a collaborative act in teacher education research. The increase of ELs in US classrooms and a corresponding lack of teacher preparation to meet the needs of ELs has motivated the training of educators in linguistically responsive education (e.g., ESL, sheltered English instruction [SEI], bilingual education). Drawing from educational theories of linguistically responsive teaching for preparing educators, we trace the linguistically responsive advocacy work of the teachers. The paper is a multiple case study that tracks how teachers’ discussions on advocacy during a teacher preparation program leading to collaborative actions in their daily teaching lives in and out of school. Data collected includes online discussion forums on the topic of advocacy, course assignments on the topic of advocacy, video-audio recordings of classroom teaching observations, and video-audio recordings of individual and focus group interviews. The findings demonstrate that the teachers’ understanding of advocacy developed through collaborative partnerships formed in the teacher preparation program and grew into active forms of collaborative advocacy in their teaching practice in and out of school. The teachers formed multi-level and collaborative partnerships with teachers, families, community members, policymakers from the local government, and educational researchers to advocate for their emergent bilingual students by planning advocacy events such as new family orientations for emergent bilinguals, professional development for general education teachers on the topic of linguistically responsive instruction, and family nights hosted by the district. The paper’s findings present types of advocacy work in which teachers engage (pedagogical, curricular, out-of-school work) and provide evidence of collaborative advocacy work by a group of engaged educators. The paper highlights the increased agency and effective advocacy of teachers through teacher education and collaborative partnerships and suggests a need for more research on collaborative forms of teacher advocacy for emergent bilinguals.

Keywords: language education, teacher advocacy, language instruction, teacher education

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: E. N. Jansen van Vuuren

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When visiting Foundation Phase (FP) students during their Teaching Practice at schools in rural Mpumalanga, the lack of music education is evident through the absence of musical sounds, with the exception of a limited repertoire of songs that are sung by all classes everywhere you go. The absence of music teaching resources such as posters and music instruments add to the perception that generalist teachers in the FP are not teaching music. Pre-service students also acknowledge that they have never seen a music class being taught during their teaching practice visits at schools. This lack of music mentoring impacts the quality of teachers who are about to enter the workforce and ultimately results in the perpetuation of no music education in many rural schools. The situation in more affluent schools present a contrasting picture with music education being given a high priority and generalist teachers often being supported by music specialists, paid for by the parents. When student teachers start their music course, they have limited knowledge to use as a foundation for their studies. The aim of the study was to ascertain the music knowledge that students gained throughout their school careers so that the curriculum could be adapted to suit their needs. By knowing exactly what pre-service teachers know about music, the limited tuition time at tertiary level can be used in the most suitable manner and concentrate on filling the knowledge gaps. Many scholars write about the decline of music education in South African schools and mention reasons, but the exact music knowledge void amongst students does not feature in the studies. Knowing the parameters of students’ music knowledge will empower lecturers to restructure their curricula to meet the needs of pre-service students. The research question asks, “what is the extent of the music void amongst rural pre-service teachers in a B.Ed. FP course at an African university?” This action research was done using a pragmatic paradigm and mixed methodology. First year students in the cohort studying for a B.Ed. in FP were requested to complete an online baseline assessment to determine the status quo. This assessment was compiled using the CAPS music content for Grade R to 9. The data was sorted using the elements of music as a framework. Findings indicate that students do not have a suitable foundation in music education despite supposedly having had music tuition from grade R to grade 9. Knowing the content required to fill the lack of knowledge provides academics with valuable information to amend their curricula and to ensure that future teachers will be able to provide rural learners with the same foundations in music as those received by learners in more affluent schools. It is only then that the rich music culture of the African continent will thrive.

Keywords: generalist educators, music education, music curriculum, pre-service teachers

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Authors: Salihu Mohammed Umar

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Perceived physical exercise benefits among staff of tertiary institutions in Adamawa State was investigated as a basis for formulating proper exercise intervention strategies. The study utilized descriptive survey design. The purpose of the study was to determine perceived exercise benefits among staff of tertiary institutions in Adamawa state, Nigeria. The instrument used for data collection was a questionnaire adapted from Exercise Benefit/Barrier Scale (EBBS) developed by Sechrist, Walker and Pender (1985) which was validated by five experts. Three hundred and thirty (330) copies of the questionnaire were distributed among study participants in six institutions of higher learning in Adamawa state. The scale comprised two components; Benefits and Barriers dimensions. To achieve this purpose, three research questions were posed. The instrument had a four response forced-choice Likert-type format with responses ranging from 4 = strongly agree (SA), 3 = Agree (A), 2 = Disagree (D) and 1 = Strongly Disagree (SD). The findings of the study revealed that both male and female staff in institutions of higher learning in Adamawa state perceived exercise as highly beneficial. However, male staff had higher perceived benefits score than their female counterparts. (Male: x̄ = 95.02. SD = 3.08) > female: x̄ = 94.04, SD = 4.35. There was also no significant difference in perceived exercise barriers between staff and students of tertiary institutions in Adamawa state. Based on the finding of the study, it was concluded that staff of tertiary institutions perceived exercise as highly beneficial. It was recommended that since staff of institutions of higher learning in Adamawa State irrespective of gender and religious affiliations have basic knowledge of perceived benefits of exercise, there is the need to explore programmes that will enable staff across the sub-groups to overcome barriers that could discourage physical exercise participation.

Keywords: perception, physical exercise, staff, benefits

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Authors: Pitimanus Bunlue

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The objectives of this study were to identify the unique characteristics of Salaya Old market, Phutthamonthon, Nakhon Pathom and develop the effective video media to promote the homeland awareness among local people and the characteristic features of this community were collectively summarized based on historical data, community observation, and people’s interview. The acquired data were used to develop a media describing prominent features of the community. The quality of the media was later assessed by interviewing local people in the old market in terms of content accuracy, video, and narration qualities, and sense of homeland awareness after watching the video. The result shows a 6-minute video media containing historical data and outstanding features of this community was developed. Based on the interview, the content accuracy was good. The picture quality and the narration were very good. Most people developed a sense of homeland awareness after watching the video also as well.

Keywords: audio-visual, creating homeland awareness, Phutthamonthon Nakhon Pathom, research and development

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Authors: Sarantos Psycharis

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Physical Computing, as an instructional model, is applied in the framework of the Engineering Pedagogy to teach “transversal/cross-cutting ideas” in a STEM content approach. Labview and Arduino were used in order to connect the physical world with real data in the framework of the so called Computational Experiment. Tertiary prospective engineering educators were engaged during their course and Computational Thinking (CT) concepts were registered before and after the intervention across didactic activities using validated questionnaires for the relationship between self-efficacy, computer programming, and CT concepts when STEM content epistemology is implemented in alignment with the Computational Pedagogy model. Results show a significant change in students’ responses for self-efficacy for CT before and after the instruction. Results also indicate a significant relation between the responses in the different CT concepts/practices. According to the findings, STEM content epistemology combined with Physical Computing should be a good candidate as a learning and teaching approach in university settings that enhances students’ engagement in CT concepts/practices.

Keywords: arduino, computational thinking, computer programming, Labview, self-efficacy, STEM

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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: Jiyoung Yoon

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This study examines Spanish textbook explanations of infinitival complements and how they can affect a learner’s second-language acquisition process. Verbs taking infinitival complements are commonly found in the mandate, volition, and emotion verbs, both for Spanish and English. However, while some English verbs take gerunds (María avoids eating/*to eat meat), in Spanish a gerund never functions as the complement of a verb (María evita comer/*comiendo carne). Because of these differences, English learners of Spanish often have difficulty acquiring infinitival complement constructions in Spanish. Specifically, they may employ English-like complement structures, producing such ungrammatical utterances as *Odio comiendo tacos ‘I hate eating tacos.' A compounding factor is that many Spanish textbooks do not emphasize the usages of infinitival complements and, when explanations are provided, they are often vague and insufficient. This study examines Spanish textbook explanations of infinitival complements (intermediate and advanced college-level Spanish textbooks and grammar reference books published in the United States) to determine areas that are problematic and insufficient and how they can affect learners’ second-language acquisition process. In this study, alternative principle-driven explanations are proposed as a replacement.

Keywords: Spanish, teaching, second language, infinitival complement, textbook

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Authors: Joanna Estkowska

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The Sensory Integration Method is aimed primarily at children with learning disabilities. It can also be used as a complementary method in treatment of children with cerebral palsy, autistic, mentally handicapped, blind and deaf. Autism is holistic development disorder that manifests itself in the specific functioning of a child. The most characteristic are: disorders in communication, difficulties in social relations, rigid patterns of behavior and impairment in sensory processing. In addition to these disorders may occur abnormal intellectual development, attention deficit disorders, perceptual disorders and others. This study was focused on the application sensory integration techniques in science education of autistic students. The lack of proper sensory integration causes problems with complicated processes such as motor coordination, movement planning, visual or auditory perception, speech, writing, reading or counting. Good functioning and cooperation of proprioceptive, tactile and vestibular sense affect the child’s mastery of skills that require coordination of both sides of the body and synchronization of the cerebral hemispheres. These include, for example, all sports activities, precise manual skills such writing, as well as, reading and counting skills. All this takes place in stages. Achieving skills from the first stage determines the development of fitness from the next level. Any deficit in the scope of the first three stages can affect the development of new skills. This ultimately reflects on the achievements at school and in further professional and personal life. After careful analysis symptoms from the emotional and social spheres appear to be secondary to deficits of sensory integration. During our research, the students gained knowledge and skills in the classroom of experience by learning biology, chemistry and physics with application sensory integration techniques. Sensory integration therapy aims to teach the child an adequate response to stimuli coming to him from both the outside world and the body. Thanks to properly selected exercises, a child can improve perception and interpretation skills, motor skills, coordination of movements, attention and concentration or self-awareness, as well as social and emotional functioning.

Keywords: autism spectrum disorder, science education, sensory integration, special educational needs

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Authors: Shelley Y. Hawkins

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Introduction/Background: As technology has grown exponentially in healthcare, nurse educators must prepare Advanced Practice Registered Nurse (APRN) graduates with the knowledge and skills in information systems/technology to support and improve patient care and health care systems. APRN’s are expected to lead in caring for populations who lack accessibility and availability through the use of technology, specifically telehealth. The capacity to effectively and efficiently use technology in patient care delivery is clearly delineated in the American Association of Colleges of Nursing (AACN) Doctor of Nursing Practice (DNP) and Master of Science in Nursing (MSN) Essentials. However, APRN’s have minimal, or no, exposure to formalized telehealth education and lack necessary technical skills needed to incorporate telehealth into their patient care. APRN’s must successfully master the technology using telehealth/telemedicine, electronic health records, health information technology, and clinical decision support systems to advance health. Furthermore, APRN’s must be prepared to lead the coordination and collaboration with other healthcare providers in their use and application. Aim/Goal/Purpose: The purpose of this presentation is to establish and operationalize telehealth-focused educational partnerships between one University School of Nursing and two health care systems in order to enhance the preparation of APRN NP students for practice, teaching, and/or scholarly endeavors. Methods: The proposed project was initially presented by the project director to selected multidisciplinary stakeholders including leadership, home telehealth personnel, primary care providers, and decision support systems within two major health care systems to garner their support for acceptance and implementation. Concurrently, backing was obtained from key university-affiliated colleagues including the Director of Simulation and Innovative Learning Lab and Coordinator of the Health Care Informatics Program. Technology experts skilled in design and production in web applications and electronic modules were secured from two local based technology companies. Results: Two telehealth-focused APRN Program academic/practice partnerships have been established. Students have opportunities to engage in clinically based telehealth experiences focused on: (1) providing patient care while incorporating various technology with a specific emphasis on telehealth; (2) conducting research and/or evidence-based practice projects in order to further develop the scientific foundation regarding incorporation of telehealth with patient care; and (3) participating in the production of patient-level educational materials related to specific topical areas. Conclusions: Evidence-based APRN student telehealth clinical experiences will assist in preparing graduates who can effectively incorporate telehealth into their clinical practice. Greater access for diverse populations will be available as a result of the telehealth service model as well as better care and better outcomes at lower costs. Furthermore, APRN’s will provide the necessary leadership and coordination through interprofessional practice by transforming health care through new innovative care models using information systems and technology.

Keywords: academic/practice partnerships, advanced practice nursing, nursing education, telehealth

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Alexis Aldo Mendoza Villarroel, Ademir Clemente Villena Zevallos, Cristian Jose Lopez Del Alamo

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With the advancement of technology it is now possible to scan entire objects and obtain their digital representation by using point clouds or polygon meshes. However, some objects may be broken or have missing parts; thus, several methods focused on this problem have been proposed based on Geometric Deep Learning, such as GCNN, ACNN, PointNet, among others. In this article an approach from a different paradigm is proposed, using metric data structures to index global descriptors in the spectral domain and allow the recovery of a set of similar models in polynomial time; to later use the Iterative Close Point algorithm and recover the parts of the incomplete model using the geometry and topology of the model with less Hausdorff distance.

Keywords: 3D reconstruction method, point cloud completion, shape completion, similarity search

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Authors: N. K. Bhute, B. B. Bhosle, D. G. More, B. V. Bhede

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The field experiments were conducted during kharif season of the year 2007-08 at the experimental farm of the Department of Agricultural Entomology, Vasantrao Naik Marathwada Krishi Vidyapeeth, Studies on evaluation of different IPM modules for Bt cotton in relation to yield economics and ICBR revealed that MAU and CICR IPM modules proved superior. It was, however, on par with chemical control. Considering the ICBR and safety to natural enemies, an inference can be drawn that Bt cotton with IPM module is the most ideal combination. Besides reduction in insecticide use, it is also expected to ensure favourable ecological and economic returns in contrast to the adverse effects due to conventional insecticides. The IPM approach, which takes care of varying pest situation, appears to be essential for gaining higher advantage from Bt cotton.

Keywords: yield, economics, ICBR, IPM Modules, Bt cotton

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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

Procedia PDF Downloads 349

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

Procedia PDF Downloads 83

Authors: Chia-Ling Hsieh, Kankanit Potikit

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Metadiscourse refers to linguistic resources employed by writers to organize text and interact with readers. While metadiscourse has received considerable attention within the field of discourse analysis, few studies have explored the use of metadiscourse in email, one of the most popular forms of computer-mediated communication. Furthermore, the diversity of cross-linguistic research required to uncover the influence of cultural factors on metadiscourse use is lacking. The present study compares metadiscourse markers employed in Chinese and Thai-language request emails with the purpose of discovering cross-cultural similarities and differences that are meaningful and applicable to foreign language teaching. The analysis is based on a corpus of 200 request emails: 100 composed in Chinese and 100 in Thai, with half of the emails from each language data set addressed to professors and the other half addressed to classmates. Adopting Hyland’s model as an analytical framework, two primary categories of metadiscourse are identified. Textual metadiscourse helps to create text coherence, while interpersonal metadiscourse functions to convey authorial stance. Results of the study make clear that both Chinese and Thai-language emails use significantly more interpersonal markers than textual markers, indicating that email, as a unique communicative medium, is characterized by high degrees of concision and interactivity. Users of both languages further deploy similar patterns in writing emails to recipients of different social statuses. Compared with emails addressed to classmates, emails addressed to professors are notably longer and include more transition and engagement markers. Nevertheless, cultural factors do play a role. Emails composed in Thai, for example, include more textual markers than those in Chinese, as Thai favors formal expressions and detailed explanations, while in contrast, emails composed in Chinese employ more interpersonal markers than those in Thai, since Chinese tends to emphasize recipient involvement and attitudinal warmth. These findings thereby demonstrate the combined effects of email as a communicative medium, social status, and cultural values on metadiscourse usage. The study concludes by applying these findings to pedagogical suggestions for teaching email writing to Chinese and Thai language learners based on similarities and differences in metadiscourse strategy between the two languages.

Keywords: discourse analysis, email, metadiscourse, writing instruction

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Authors: Bianca W. O. Silva, Adriana C. M. Andrade, Gustavo C. M. Lucena, Virna M. S. Lima

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Introduction: Basic life support (BLS) involves the immediate recognition of cardiopulmonary arrest. Each year, 359.400 and 275.000 individuals with cardiac arrest are attended in emergency departments in USA and Europe. Brazilian data shows that 200.000 cardiac arrests occur every year, and half of them out of the hospital. Medical schools around the world teach BLS in the first years of the course, but studies show that there is a decline of the knowledge as the years go by, affecting the chain of survival. The objective was to analyze the knowledge of medical students about BLS and the retention of this learning throughout the course. Methods: This study included 150 students who were at the second and third year of a medical school in Salvador, Bahia, Brazil. The instrument of data collection was a structured questionnaire composed of 20 questions based on the 2015 American Heart Association guideline. The Pearson Chi-square test was used in order to study the association between previous training, sex and semester with the degree of knowledge of the students. The Kruskal-Wallis test was used to evaluate the different yields obtained between the various semesters. The number of correct answers was described by average and quartiles. Results: Regarding the degree of knowledge, 19.6% of the female students reached the optimal classification, a better outcome than the achieved by the male participants. Of those with previous training, 33.33% were classified as good and optimal, none of the students reached the optimal classification and only 2.2% of them were classified as bad (those who did not have 52.6% of correct answers). The analysis of the degree of knowledge related to each semester revealed that the 5th semester had the highest outcome: 30.5%. However, the acquaintance presented by the semesters was generally unsatisfactory, since 50% of the students, or more, demonstrated knowledge levels classified as bad or regular. When confronting the different semesters and the achieved scores, the value of p was 0.831. Conclusion: It is important to focus on the training of medical professionals that are capable of facing emergency situations, improving the systematization of care, and thereby increasing the victims' possibility of survival.

Keywords: basic life support, cardiopulmonary ressucitacion, education, medical students

Procedia PDF Downloads 188