Search results for: batch-constrained reinforcement learning

Authors: Lin Xie, Yang Wang

Abstract:

An extensive body of research studies suggest that students, teachers, and supervisors can enhance the likelihood of reaching their goals by improving their communication skills. It is highly important to learn how and when to provide different kinds of feedback, e.g. anticipatory, corrective and positive) will gain better result and higher morale. The purpose of this mixed methods research is twofold: 1) To find out what factors affect effective communication among different stakeholders and how these factors affect student learning2) What are the good practices for improving communication among different stakeholders and improve student achievement. This presentation first begins with an introduction to the recent research on Marshall’s Nonviolent Communication Techniques (NVC), including four important components: observations, feelings, needs, requests. These techniques can be effectively applied at all levels of communication. To develop an in-depth understanding of the relationship among different techniques within, this research collected, compared, and combined qualitative and quantitative data to better improve communication and support student learning.

Keywords: education, communication, psychology, student learning, language teaching

Procedia PDF Downloads 51

Authors: B. A. Mir, A. Juneja

Abstract:

In this paper, shear behavior of reconstituted clay reinforced with varying diameter of sand compaction piles with area replacement-ratio (a_s) of 6.25, 10.24, 16, 20.25 and 64% in 100mm diameter and 200mm long clay specimens is modeled using consolidated drained and undrained triaxial tests under different confining pressures ranging from 50kPa to 575kPa. The test results show that the stress-strain behavior of the clay was highly influenced by the presence of SCP. The insertion of SCPs into soft clay has shown to have a positive effect on the load carrying capacity of the clay, resulting in a composite soil mass that has greater shear strength and improved stiffness compared to the unreinforced clay due to increased reinforcement area ratio. In addition, SCP also acts as vertical drain in the clay thus accelerating the dissipation of excess pore water pressures that are generated during loading by shortening the drainage path and activating radial drainage, thereby reducing post-construction settlement. Thus, sand compaction piles currently stand as one of the most viable and practical techniques for improving the mechanical properties of soft clays.

Keywords: reconstituted clay, SCP, shear strength, stress-strain response, triaxial tests

Procedia PDF Downloads 409

Authors: Yi-Ning Chang

Abstract:

With the rapid advancement of technology, it has become crucial for educators to adeptly integrate technology into their teaching and develop a robust Computer-Assisted Language Learning (CALL) competency. Addressing this need, the present study adopted a technology-based Lesson Study approach to assess its impact on the CALL competency and professional capabilities of EFL teachers. Additionally, the study delved into teachers' perceptions of the benefits derived from participating in the creation of technologically integrated lesson plans. The iterative process of technology-based Lesson Study facilitated ample peer discussion, enabling teachers to flexibly design and implement lesson plans that incorporate various technological tools. This 15-week study included 10 in- service teachers from a university of science and technology in the central of Taiwan. The collected data included pre- and post- lesson planning scores, pre- and post- TPACK survey scores, classroom observation forms, designed lesson plans, and reflective essays. The pre- and post- lesson planning and TPACK survey scores were analyzed employing a pair-sampled t test; students’ reflective essays were respectively analyzed applying content analysis. The findings revealed that the teachers’ lesson planning ability and CALL competencies were improved. Teachers perceived a better understanding of integrating technology with teaching subjects, more effective teaching skills, and a deeper understanding of technology. Pedagogical implications and future studies are also discussed.

Keywords: CALL, language learning, lesson study, lesson plan

Procedia PDF Downloads 40

Authors: Marc Dorval, Marie-Hélène Jobin

Abstract:

This work presents the various perspectives, dimensions, components and definitions given to quality in the operations management (OM) and healthcare services (HCS) literature in time, highlighting gaps and learning opportunities between the two disciplines through a thorough search into their rich and distinct body of knowledge. Greater and new insights about the general nature of quality are obtained with findings such as in OM, quality has been approached in six fairly distinct paradigms (excellence, value, conformity to specifications, attributes, satisfaction and meeting or exceeding customer expectations), whereas in HCS, two approaches are prominent (Donabedian’s structure, process and outcomes model and Lohr and Schroeder’s circumscribed definition). The two disciplines views on quality seem to have progressed much in parallel with little cross-learning from each other. This work then proposes an encompassing definition of quality as a lever and suggests further research and development avenues for a better use of the concept of quality by academics and practitioners alike toward the goals of greater organizational performance and improved management in healthcare and possibly other service domains.

Keywords: healthcare, management, operations, quality, services

Procedia PDF Downloads 229

Authors: Fangfei Li

Abstract:

This research was conducted to explore how Chinese overseas students, who rarely received teacher feedback during their undergraduate studies in China, engaged in a different feedback provision context in the UK universities. In particular, this research provides some insights into Chinese students’ perspectives on how they made sense of the teacher feedback they obtained and how they took it on board in their assignments. Research questions in this study are 1) What are Chinese overseas students’ perceptions of teacher feedback on courses in UK higher education? 2) How do they respond to the teacher feedback they obtained? 3) What factors might influence their engagement with teacher feedback? Multiple case studies of five Chinese overseas students in a UK university have been carried out to address the research questions. The main data collection instruments are various types of semi-structured interviews, consisting of background interviews, scenario-based activities, stimulated recall sessions and retrospective interviews. Research findings indicate that student engagement with teacher feedback is a complex learning process incorporating several stages: from initial teacher input to ultimate transformational learning. Apart from students interpreting teachers’ comments/suggestions by themselves, students’ understandings of and responses to teacher feedback could also be influenced by pre-submission guidance, peer discussion, use of exemplars and post-submission discussion with teachers. These are key factors influencing students to make use of teacher feedback. Findings also reveal that the level of students’ reflections on tutor feedback influences the quality of their assignments and even their future learning. To sum up, this paper will discuss the current concepts of teacher feedback in existing studies and research findings of this study from which reconceptualization of teacher feedback has occurred.

Keywords: Chinese students, student engagement, teacher feedback, the UK higher education

Procedia PDF Downloads 348

Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

Abstract:

Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

Procedia PDF Downloads 71

Authors: Michael R. Taber, Kristin Stanec

Abstract:

This paper presents year-two of a longitudinal study on implementing Possibility Books into undergraduate courses to develop a student's creativity mindset: tolerating ambiguity, willingness to risk failure, curiosity, and openness to embrace possibility thinking through unexpected connections. Courses involved in this research span disciplines in the natural and social sciences and the humanities. Year one of the project developed indices from which baseline data could be analyzed. The two significant indices ( > 0.7) were "creativity mindset" and "intentional interactions." Preliminary qualitative and quantitative data analysis indicated that students found the new pedagogical intervention as a safe space to learn new strategies, recognize patterns, and define structures through innovative notetaking forms. Possibility Books in Natural Science courses were designed to develop students' conceptualization of science and math. Using Possibility Books in all disciplines provided a space for students to practice divergent thinking (i.e.,Possibilities), convergent thinking (i.e., forms that express meaning), and risk-taking (i.e., the vulnerability associated with expression). Qualitative coding of open responses on a post-survey revealed two major themes: 1) Possibility Books provided a mind space for learning about self, and 2) provided a calming opportunity to connect concepts. Quantitative analysis indicated significant correlations between focused headspace and notetaking (r = 0.555, p < 0.001), focused headspace, and connecting with others (r = 0.405, p < 0.001).

Keywords: pedagogy, science education, learning methods, creativity mindsets

Procedia PDF Downloads 23

Authors: Hoong C. Chin, Soon H. Chew, Zhaoxia Wang

Abstract:

Work on sustainable developments and the call for action in education for sustainable development have been ongoing for a number of years. Training engineering students with the relevant competencies, particularly in sustainable development literacy, has been identified as an urgent task in universities. This requires not only a holistic, multi-disciplinary approach to education but also a suitable training environment to develop the needed skills and to inculcate the appropriate attitudes in students towards sustainable development. To demonstrate how this can be done, a module involving an overseas field trip was introduced in 2013 at the National University of Singapore. This paper provides details of the module and describes its training philosophy and methods. Measured against the student learning outcomes, stipulated by the Engineering Accreditation Board, the module scored well on all of them, particularly those related to complex problem solving, environmental and sustainability awareness, multi-disciplinary team work and varied-level communications.

Keywords: civil engineering education, socio-economically sustainable infrastructure, student learning outcome, sustainable development

Procedia PDF Downloads 351

Authors: Pascal Roubides

Abstract:

The presentation proposed herein describes a research case study of a hybrid application of the competency-based education model best exemplified by Western Governor’s University, within the general temporal confines of an accelerated (8-week) term of a College Algebra course at the author’s institution. A competency-based model was applied to an accelerated online College Algebra course, built as an Open Educational Resources (OER) course, seeking quantifiable evidence of any differences in the academic achievement of students enrolled in the competency-based course and the academic achievement of the current delivery of the same course. Competency-based learning has been gaining in support in recent times and the author’s institution has also been involved in its own efforts to design and develop courses based on this approach. However, it is unknown whether there had been any research conducted to quantify evidence of the effect of this approach against traditional approaches prior to the author’s case study. The research question sought to answer in this experimental quantitative study was whether the online College Algebra curriculum at the author’s institution delivered via an OER-based competency-based model can produce statistically significant improvement in retention and success rates against the current delivery of the same course. Results obtained in this study showed that there is no statistical difference in the retention rate of the two groups. However, there was a statistically significant difference found between the rates of successful completion of students in the experimental group versus those in the control group.

Keywords: competency-based learning, online mathematics, online math education, online courses

Procedia PDF Downloads 128

Authors: Sheryl Bernardo-Hinesley

Abstract:

Heritage language learners are students who have been raised in a home where a minority language is spoken, who speaks or merely understand the minority heritage language, but to some degree are bilingual in the majority and the heritage language. In view of the rising university enrollment by Hispanics in the United States who have chosen to study Spanish, university language programs are currently faced with challenges of accommodating the language needs of heritage language learners of Spanish. The present study investigates the heritage language perception and language attitudes by heritage language learners of Spanish, as well as their classroom language learning experiences and needs. In order to carry out the study, a qualitative survey was used to gather data from university students. Analysis of students' responses indicates that heritage learners are motivated to learn the heritage language. In relation to the aspects of focus of a language course for heritage learners, results show that the aspects of interest are accent marks and spelling, grammatical accuracy, vocabulary, writing, reading, and culture.

Keywords: heritage language learners, language acquisition, linguistic attitudes, Spanish in the US

Procedia PDF Downloads 212

Authors: Chen-Jung Chen, Yi-Chang Chen

Abstract:

While traditional virtual reality and augmented reality only allow for small movement learning and cannot provide a truly immersive teaching experience to give it the illusion of movement, the new technology of both content creation and immersive interactive simulation of the metaverse can just reach infinite close to the natural teaching situation. However, the mixed reality virtual classroom of metaverse has not yet explored its theory, and it is rarely implemented in the situational simulation teaching of nursing education. Therefore, in the first year, the study will intend to use grounded theory and case study methods and in-depth interviews with nursing education and information experts. Analyze the interview data to investigate the uniqueness of metaverse development. The proposed analysis will lead to alternative theories and methods for the development of nursing education. In the second year, it will plan to integrate the metaverse virtual situation simulation technology into the alternate teaching strategy in the pediatric nursing technology course and explore the nursing students' use of this teaching method as the construction of personal technology and experience. By leveraging the unique features of distinct teaching platforms and developing processes to deliver alternative teaching strategies in a nursing technology teaching environment. The aim is to increase learning achievements without compromising teaching quality and teacher-student relationships in the post-pandemic era. A descriptive and convergent mixed methods design will be employed. Sixty third-grade nursing students will be recruited to participate in the research and complete the pre-test. The students in the experimental group (N=30) agreed to participate in 4 real-time mixed virtual situation simulation courses in self-practice after class and conducted qualitative interviews after each 2 virtual situation courses; the control group (N=30) adopted traditional practice methods of self-learning after class. Both groups of students took a post-test after the course. Data analysis will adopt descriptive statistics, paired t-tests, one-way analysis of variance, and qualitative content analysis. This study addresses key issues in the virtual reality environment for teaching and learning within the metaverse, providing valuable lessons and insights for enhancing the quality of education. The findings of this study are expected to contribute useful information for the future development of digital teaching and learning in nursing and other practice-based disciplines.

Keywords: metaverse, post-pandemic era, online virtual classroom, immersive teaching

Procedia PDF Downloads 68

Authors: Wang Zhanquan, Yang Zeping, Gu Chunhua, Zhu Fazhi, Guo Weibin

Abstract:

Recently, Massive Open Online Courses (MOOCs) are becoming the new trend of education. There are many problems under the environment of Database Principle curriculum teaching process in MOOCs, such as teaching ideas and theories which are out of touch with the reality, how to carry out the technical teaching and interactive practice in the MOOCs environment, thus the methods of database course under the environment of MOOCs are proposed. There are three processes to deal with problem solving in the research, which are problems proposed, problems solved, and inductive analysis. The present research includes the design of teaching contents, teaching methods in classroom, flipped classroom teaching mode under the environment of MOOCs, learning flow method and large practice homework. The database designing ability is systematically improved based on the researching methods.

Keywords: problem solving-driven, MOOCs, teaching art, learning flow;

Procedia PDF Downloads 363

Authors: Ehsan Atefi, Michael Grigware

Abstract:

Respiratory failure secondary to surfactant deficiency resulting from respiratory distress syndrome is considered one major cause of morbidity in preterm infants. Surfactant replacement treatment (SRT) is considered an effective treatment for this disease. Here, we introduce an AI-mediated approach for estimating the distribution of surfactant in the lung airway of a newborn infant during SRT. Our approach implements machine learning to precisely estimate the splitting ratio of a liquid drop during bifurcation at different injection velocities and patient orientations. This technique can be used to calculate the surfactant residue remaining on the airway wall during the surfactant injection process. Our model works by minimizing the pressure drop difference between the two airway branches at each generation, subject to mass and momentum conservation. Our platform can be used to generate feedback for immediately adjusting the velocity of injection and patient orientation during SRT.

Keywords: respiratory failure, surfactant deficiency, surfactant replacement, machine learning

Procedia PDF Downloads 126

Authors: Kittivate Boonyopakorn

Abstract:

The purposes of this study were to find the English competence of the public health volunteers and to develop the use of their English. The samples for the study were 41 public health village volunteers at Ban Prasukchai, in Thailand. The findings showed that the sum of all scores for the pre-test was 452, while the score for the post-test was 1,080. Therefore, the results of the experiment confirm that the post-test scores (1,080) significantly are higher than the pre-test (452). The mean score (N=41) for the pre-test was 11.02 while the mean score (N=41) for the post-test was 18.10. The standard deviation for the pre-test was 2.734; however, for the post-test it was 1.934. In addition to the experts-evaluated research tools, the results of the evaluation for the structured interviews (IOC) were 1 in value. The evaluation of congruence for the content with learning objectives (IOC) were 0.66 to 1.00 in value. The evaluation of congruence for the pre and post-test with learning objectives (IOC) are 1 in value.

Keywords: finding the English competency, developing public health, village volunteers

Procedia PDF Downloads 450

Authors: Shao-Di Feng, Von-Wun Soo

Abstract:

To generate symbolic music with specific emotions is a challenging task due to symbolic music datasets that have emotion labels are scarce and incomplete. This research aims to generate more refined emotions based on the training datasets that are only labeled with four quadrants in Russel’s 2D emotion model. We focus on the theory of Music Fadernet and map arousal and valence to the low-level attributes, and build a symbolic music generation model by combining transformer and GM-VAE. We adopt an in-attention mechanism for the model and improve it by allowing modulation by conditional information. And we show the music generation model could control the generation of music according to the emotions specified by users in terms of high-level linguistic expression and by manipulating their corresponding low-level musical attributes. Finally, we evaluate the model performance using a pre-trained emotion classifier against a pop piano midi dataset called EMOPIA, and by subjective listening evaluation, we demonstrate that the model could generate music with more refined emotions correctly.

Keywords: music generation, music emotion controlling, deep learning, semi-supervised learning

Procedia PDF Downloads 89

Authors: I Made Ginarsa, Agung Budi Muljono, I Made Ari Nrartha

Abstract:

Transient voltage response appears in power system operation when an additional loading is forced to load bus of power systems. In this research, improvement of transient voltage response is done by using power system stabilizer-static var compensator (PSS-SVC) based on adaptive neuro-fuzzy inference system (ANFIS)-algorithm. The main function of the PSS is to add damping component to damp rotor oscillation through automatic voltage regulator (AVR) and excitation system. Learning process of the ANFIS is done by using off-line method where data learning that is used to train the ANFIS model are obtained by simulating the PSS-SVC conventional. The ANFIS model uses 7 Gaussian membership functions at two inputs and 49 rules at an output. Then, the ANFIS-PSS and ANFIS-SVC models are applied to power systems. Simulation result shows that the response of transient voltage is improved with settling time at the time of 4.25 s.

Keywords: improvement, transient voltage, PSS-SVC, ANFIS, settling time

Procedia PDF Downloads 577

Authors: Julieta Daniela Chelaru, Maria Gorea

Abstract:

The massive demolition of old buildings in recent years has generated tons of waste, especially brick waste. Thus, a concern of recent research is the use of this waste for the production of environmentally friendly concrete. At the same time, corrosion in classical concrete is a current problem. In this context, in the present paper a study was carried out on the corrosion of metal reinforcement in cement mortars with brick waste. The corrosion process was analyzed on four compositions of mortars without and with 15 %, 25 % and 35 % bricks waste replacing the sand. The brick waste has a majority content in SiO2, Al₂O₃, FeO₃ and CaO. The grain size distribution of brick waste was close to that of the sand (dₘₐₓ = 3 mm). The preparation method of the samples was similar to ordinary mortars. The corrosion properties of concrete, at different waste bricks concentrations, on rebar, were investigated by electrochemical measurements (Tafel curves and EIS) at 1 and 6 months. The results obtained at 6 months revealed that the addition of the bricks waste in mortar are improved the anticorrosion properties, in the case of all samples compared with the sample with 0% bricks waste. The best results were obtained in the case of the sample with 15% bricks waste (the efficiency was ≈ 90 %). The corrosion intermediary layer formed on the rebar surface was determined by SEM-EDX.

Keywords: EIS, steel corrosion, steel reinforced concrete, waste materials

Procedia PDF Downloads 338

Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

Abstract:

The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

Procedia PDF Downloads 122

Authors: Rodrigo Arnaldo Scarpel

Abstract:

Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

Procedia PDF Downloads 89

Authors: R. R. Govind

Abstract:

In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

Procedia PDF Downloads 61

Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

Abstract:

Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

Procedia PDF Downloads 102

Authors: Rie Adachi

Abstract:

In Japan, it is required to improve students’ English communicative proficiency and the Education Ministry will start English education for the third grade and upper from year 2020 on. Considering the problems with the educational system, Content and Language Integrated Learning (CLIL) is more appropriate to be employed in elementary schools rather than just introducing English lessons. Effective CLIL takes place in the 4Cs Framework, and different strategies are used in various activities, such as arts and crafts, bodily expression, singing, playing roles, etc. After a CLIL workshop for local teachers focused on the 4Cs, the writer conducted a survey of the 36 participants using a questionnaire and found that they did not know the word CLIL, but seemed to have an interest after attending the workshop. The writer concluded that researchers and practitioners need to spread awareness of the 4Cs framework, to apply CLIL into Japanese educational context, to provide CLIL teacher training program and so on, in order to practice CLIL in Japanese elementary schools and nurture students with a global mindset.

Keywords: CLIL, 4Cs, homeroom teachers, intercultural understanding

Procedia PDF Downloads 168

Authors: Joy Aworookoroh

Abstract:

Writing is one of the active skills in language learning. Students of English as a foreign language are expected to write efficiently and proficiently in the language; however, there are usually challenges to optimal performance and competence in writing. Errors, on the other hand, in a foreign language learning situation are more positive than negative as they provide the basis for solving the limitations of the students. This paper investigates the situation in the Department of English, Taraba State University Jalingo. Students are administered a descriptive writing test across different levels of study. The target students are multilingual with an L1 of either Kuteb, Hausa or Junkun languages. The essays are accessed to identify the different kinds of errors in them alongside the classification of the order. Errors of correctness, clarity, engagement, and delivery were identified. However, the study identified that the degree of errors reduces alongside the experience and exposure of the students to an EFL classroom.

Keywords: errors, writings, descriptive essay, multilingual

Procedia PDF Downloads 63

Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

Abstract:

The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

Procedia PDF Downloads 126

Authors: Laaziz El Hassan

Abstract:

The Service Network Design Problem (SNDP) is a tactical issue in freight transportation firms. The existing formulations of the problem for intermodal rail-road transportation were not always adapted to the intermodality in terms of full asset utilization and modal shift reinforcement. The objective of the article is to propose a model having a more compliant formulation with intermodality, including constraints highlighting the imperatives of asset management, reinforcing modal shift from road to rail and reducing, by the way, road mode CO2 emissions. The model is a fixed charged, path based integer nonlinear program. Its objective is to minimize services total cost while ensuring full assets utilization to satisfy freight demand forecast. The model's main feature is that it gives as output both the train sizes and the services frequencies for a planning period. We solved the program using a commercial solver and discussed the numerical results.

Keywords: intermodal transport network, service network design, model, nonlinear integer program, path-based, service frequencies, modal shift

Procedia PDF Downloads 118

Authors: Waleed Maqableh, Ahmad Al-Hamad, Manjit Sidhu

Abstract:

Augmented reality (AR) technology has the capability to provide many benefits in the field of education as a modern technology which aided learning and improved the learning experience. This paper evaluates AR based application with multiple markers interaction technique (touch-to-print) which is designed for analyzing the kinematics of 4BL mechanism in mechanical engineering. The application is termed as AR-4BL-MAST and it allows the users to touch the symbols on a paper in natural way of interaction. The evaluation of this application was performed with mechanical engineering students and human–computer interaction (HCI) experts to test its effectiveness as a tangible user interface application where the statistical results show its ability as an interaction technique, and it gives the users more freedom in interaction with the virtual mechanical objects.

Keywords: augmented reality, multimedia, user interface, engineering, education technology

Procedia PDF Downloads 575

Authors: Man Singh Meena

Abstract:

Structural engineers are facing different kind of challenges due to innovative & bold ideas of architects who are trying to design every structure with uniqueness. In RCC frame structures different geometry of columns can be used in design and rectangular columns can be placed with different type orientation. The analysis is design of structures can also be carried out by different type of software available i.e., STAAD Pro, ETABS and TEKLA. In recent times high-rise building modeling & analysis is done by ETABS due to its certain features which are superior to other software. The case study in this paper mainly emphasizes on structural behavior of high rise building for different column shape configurations like Circular, Square, Rectangular and Rectangular with 90-degree Rotation and rectangular shape plan. In all these column shapes the areas of columns are kept same to study the effect on design of concrete area is same. Modelling of 20-storeys R.C.C. framed building is done on the ETABS software for analysis. Post analysis of the structure, maximum bending moments, shear forces and maximum longitudinal reinforcement are computed and compared for three different story structures to identify the effectiveness of geometry of column.

Keywords: high-rise building, column geometry, building modelling, ETABS analysis, building design, structural analysis, structural optimization

Procedia PDF Downloads 81

Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

Abstract:

In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

Procedia PDF Downloads 137

Authors: B. Bricklin Zeff

Abstract:

This research explores the development and application of a specialized chatbot tailored for a nursing English course, with a primary objective of augmenting student engagement through situational content and responsiveness to key expressions and vocabulary. Introducing the chatbot, elucidating its purpose, and outlining its functionality are crucial initial steps in the research study, as they provide a comprehensive foundation for understanding the design and objectives of the specialized chatbot developed for the nursing English course. These elements establish the context for subsequent evaluations and analyses, enabling a nuanced exploration of the chatbot's impact on student engagement and language learning within the nursing education domain. The subsequent exploration of the intricate language model development process underscores the fusion of scientific methodologies and artistic considerations in this application of artificial intelligence (AI). Tailored for educators and curriculum developers in nursing, practical principles extending beyond AI and education are considered. Some insights into leveraging technology for enhanced language learning in specialized fields are addressed, with potential applications of similar chatbots in other professional English courses. The overarching vision is to illuminate how AI can transform language learning, rendering it more interactive and contextually relevant. The presented chatbot is a tangible example, equipping educators with a practical tool to enhance their teaching practices. Methodologies employed in this research encompass surveys and discussions to gather feedback on the chatbot's usability, effectiveness, and potential improvements. The chatbot system was integrated into a nursing English course, facilitating the collection of valuable feedback from participants. Significant findings from the study underscore the chatbot's effectiveness in encouraging more verbal practice of target expressions and vocabulary necessary for performance in role-play assessment strategies. This outcome emphasizes the practical implications of integrating AI into language education in specialized fields. This research holds significance for educators and curriculum developers in the nursing field, offering insights into integrating technology for enhanced English language learning. The study's major findings contribute valuable perspectives on the practical impact of the chatbot on student interaction and verbal practice. Ultimately, the research sheds light on the transformative potential of AI in making language learning more interactive and contextually relevant, particularly within specialized domains like nursing.

Keywords: chatbot, nursing, pragmatics, role-play, AI

Procedia PDF Downloads 65

Authors: Jiao Li

Abstract:

Language teachers have been expected to play diversified new roles in times of educational changes. Considering the critical role that materials play in teaching and learning, language teachers have been increasingly involved in developing materials. Using identity as an analytic lens, this study aims to explore language teachers’ experiences as materials developers in China, focusing on the challenges they face and responses to them. It will adopt a multimethod approach. At the first stage, about 12 language teachers who have developed or are developing materials will be interviewed to have a broad view of their experiences. At the second stage, three language teachers who are developing materials will be studied by collecting interview data, policy documents, and data obtained from online observation of their group meetings so as to gain a deeper understanding of their experiences in materials development. It is expected that this study would have implications for teacher development, materials development, and curriculum development as well.

Keywords: educational changes, teacher development, teacher identity, teacher learning, materials development

Procedia PDF Downloads 129