Search results for: activity learning

Authors: Argyro-Maria Skourmalla

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Today’s reality is characterized by diversity in different levels and aspects of everyday life. Focusing on the aspect of language and communication in Higher Education (HE), the present paper draws on the example of the University of Luxembourg as a multilingual and international setting. The University of Luxembourg, which is located between France, Germany, and Belgium, adopted its new multilingualism policy in 2020, establishing English, French, German, and Luxembourgish as the official languages of the Institution. In addition, with around 10.000 students and staff coming from various countries around the world, linguistic diversity in this university is seen as both a resource and a challenge that calls for an inclusive and multilingual approach. The present paper includes data derived from semi-structured interviews with lecturing staff from different disciplines and an online survey with undergraduate students at the University of Luxembourg. Participants shared their experiences and point of view regarding linguistic diversity in this context. Findings show that linguistic diversity in this university is seen as an asset but comes with challenges, and even though there is progress in the use of multilingual practices, a lot needs to be done towards the recognition of staff and students’ linguistic repertoires for inclusion and education equity.

Keywords: linguistic diversity, higher education, Luxembourg, multilingual practices, teaching, learning

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Authors: S. F. H. Tasfy, N. A. M. Zabidi, M.-S. Shaharun

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Utilization of CO2 as a carbon source to produce valuable chemicals is one of the important ways to reduce the global warming caused by increasing CO2 in the atmosphere. Supported metal catalysts are crucial for the production of clean and renewable fuels and chemicals from the stable CO2 molecules. The catalytic conversion of CO2 into methanol is recently under increased scrutiny as an opportunity to be used as a low-cost carbon source. Therefore, series of the bimetallic Cu/ZnO-based catalyst supported by SBA-15 were synthesized via impregnation technique with different total metal loading and tested in the catalytic hydrogenation of CO2 to methanol. The morphological and textural properties of the synthesized catalysts were determined by transmission electron microscopy (TEM), temperature programmed desorption, reduction, oxidation and pulse chemisorption (TPDRO), and N2-adsorption. The CO2 hydrogenation reaction was performed in microactivity fixed-bed system at 250 °C, 2.25 MPa, and H2/CO2 ratio of 3. Experimental results showed that the catalytic structure and performance was strongly affected by the loading of the active site. Where, the catalytic activity, methanol selectivity as well as the space-time yield increased with increasing the metal loading until it reaches the maximum values at a metal loading of 15 wt% while further addition of metal inhibits the catalytic performance. The higher catalytic activity of 14 % and methanol selectivity of 92 % were obtained over Cu/ZnO-SBA-15 catalyst with total bimetallic loading of 15 wt%. The excellent performance of 15 wt% Cu/ZnO-SBA-15 catalyst is attributed to the presence of well disperses active sites with small particle size, higher Cu surface area, and lower catalytic reducibility.

Keywords: hydrogenation of carbon dioxide, methanol synthesis, metal loading, Cu/ZnO-SBA-15 catalyst

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Authors: Sneha Thakur, Sanjeev Karmakar

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This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively.

Keywords: long short-term memory, particle swarm optimization, prediction, deep learning, groundwater level

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Authors: Reza Zakerinejad

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Gully erosion is a serious problem that threading the sustainability of agricultural area and rangeland and water in a large part of Iran. This type of water erosion is the main source of sedimentation in many catchment areas in the north of Iran. Since in many national assessment approaches just qualitative models were applied the aim of this study is to predict the spatial distribution of gully erosion processes by means of detail terrain analysis and GIS -based logistic regression in the loess deposition in a case study in the Golestan Province. This study the DEM with 25 meter result ion from ASTER data has been used. The Landsat ETM data have been used to mapping of land use. The TreeNet model as a stochastic modeling was applied to prediction the susceptible area for gully erosion. In this model ROC we have set 20 % of data as learning and 20 % as learning data. Therefore, applying the GIS and satellite image analysis techniques has been used to derive the input information for these stochastic models. The result of this study showed a high accurate map of potential for gully erosion.

Keywords: TreeNet model, terrain analysis, Golestan Province, Iran

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Authors: Essam Amerian

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The aim of this study was to investigate the effects of improving waist-to-hip ratio (WHR) and visceral fat components on the health of shift workers in an offshore petrochemical company. A total of 100 male shift workers participated in the study, with an average age of 40.5 years and an average BMI of 28.2 kg/m². The study employed a randomized controlled trial design, with participants assigned to either an intervention group or a control group. The intervention group received a 12-week program that included dietary counseling, physical activity recommendations, and stress management techniques. The control group received no intervention. The outcomes measured were changes in WHR, visceral fat components, blood pressure, and lipid profile. The results showed that the intervention group had a statistically significant improvement in WHR (p<0.001) and visceral fat components (p<0.001) compared to the control group. Furthermore, there were statistically significant improvements in systolic blood pressure (p=0.015) and total cholesterol (p=0.034) in the intervention group compared to the control group. These findings suggest that implementing a 12-week program that includes dietary counseling, physical activity recommendations, and stress management techniques can effectively improve WHR, visceral fat components, and cardiovascular health among shift workers in an offshore petrochemical company.

Keywords: body composition, waist-hip-ratio, visceral fat, shift worker, work efficiency

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Authors: Yenni Anggrayni

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The lack of awareness of the significant prevalence of school absenteeism in Indonesia, which ultimately results in high rates of school dropouts, is an unresolved issue. Therefore, this study aims to investigate the root causes of chronic absenteeism qualitatively and quantitatively using the bioecological systems paradigm in secondary schools for any reason. This study used an open-ended questionnaire to collect data from 1,148 students in six West Java Province districts/cities. Univariate and stepwise multiple logistic regression analyses produced a prediction model for the components. Analysis results show that poor teacher-student relationships, bullying by peers or teachers, negative perception of education, and lack of parental involvement in learning activities are the leading causes of chronic absenteeism. Another finding is to promote home-school partnerships to improve school climate and parental involvement in learning to address chronic absenteeism.

Keywords: bullying, chronic absenteeism, dropout of school, home-school partnerships, parental involvement

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Authors: David Taillis, Oussama Becissa, Julien Gabaston, Jean-Michel Merillon, Tristan Richard, Stephanie Cluzet

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Nowadays, there is a strong pressure to reduce the phytosanitary inputs of synthetic chemistry in vineyards. It is, therefore, necessary to find viable alternatives in order to protect the vine against its major diseases. For this purpose, we suggest the use of a plant extract enriched in antimicrobial compounds. Being produced from vine trunks and roots, which are co-products of wine production, the extract produced is part of a circular economy. The antimicrobial molecules present in this plant material are polyphenols and, more particularly, stilbenes, which are derived from a common base, the resveratrol unit, and that are well known vine phytoalexins. The stilbenoids were extracted from trunks and roots (30/70, w/w) by a double extraction with ethyl acetate followed by enrichment by liquid-liquid extraction. The produced extract was characterized by UHPLC-MS, then its antimicrobial activities were tested on Plasmopara viticola and Botrytis cinerea in the laboratory and/or in greenhouse and in vineyard. The major compounds were purified, and their antimicrobial activity was evaluated on B. cinerea. Moreover, after its spraying, the effect of the stilbene extract on the plant defence status was evaluated by analysis of defence gene expression. UHPLC-MS analysis revealed that the extract contains 50% stilbenes with resveratrol, ε-viniferin and r-viniferin as major compounds. The extract showed antimicrobial activities on P. viticola with IC₅₀ and IC₁₀₀ respectively of 90 and 300 mg/L in the laboratory. In addition, it inhibited 40% of downy mildew development in greenhouse. However, probably because of the sensitivity of stilbenes to the environment, such as UV degradation, no activity has been observed in vineyard towards P. viticola development. For B. cinerea, the extract IC50 was 123 mg/L, with resveratrol and ε-viniferin being the most active stilbenes (IC₅₀ of 88 and 142 mg/L, respectively). The analysis of the expression of defence genes revealed that the extract can induce the expression of some defence genes 24, 48, and 72 hours after treatment, meaning that the extract has a defence-stimulating effect at least for the first three days after treatment. In conclusion, we produced a plant extract enriched in stilbenes with antimicrobial properties against two major grapevine pathogenic agents P. viticola and B. cinerea. In addition, we showed that this extract displayed eliciting activity of plant defences. This extract can therefore represent, after formulation development, a viable eco-friendly alternative for vineyard protection. Subsequently, the effect of the stilbenoid extract on primary metabolism will be evaluated by quantitative NMR.

Keywords: antimicrobial, bioprotection, grapevine, Plasmopara viticola, stilbene

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Kazeem Oluwatoyin Ajape

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The study is concerned with the problem of how to improve the teaching of Arabic as a foreign language in Nigerian Higher Education System. The paper traces the historical background of Arabic education in Nigeria and also outlines the problems facing the language in Nigerian Institutions. It lays down some of the essential foundation work necessary for bringing about systematic and constructive improvements in the Teaching of Arabic as a Foreign Language (TAFL) by giving answers to the following research questions: what is the appropriate medium of instruction in teaching a foreign or second language? What is the position of English language in the teaching and learning of Arabic/Islamic education? What is the relevance of the present curriculum of Arabic /Islamic education in Nigerian institutions to the contemporary society? A survey of the literature indicates that a revolution is currently taking place in FL teaching and that a new approach known as the Communicative Approach (CA), has begun to emerge and influence the teaching of FLs in general, over the last decade or so. Since the CA is currently being adapted to the teaching of most major FLs and since this revolution has not yet had much impact on TAPL, the study explores the possibility of the application of the CA to the teaching of Arabic as a living language and also makes recommendations towards the development of the language in Nigerian Institutions of Higher Learning.

Keywords: Arabic Language, foreign language, Nigerian institutions, curriculum, communicative approach

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Authors: Juan-Pedro Rica-Peromingo

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The important change taking place with respect to the media and the audiovisual world in Europe needs to benefit all populations, in particular those with special needs, such as the deaf and hard-of-hearing population (SDH) and blind and partially-sighted population (AD). This recent interest in the field of audiovisual translation (AVT) can be observed in the teaching and learning of the different modes of AVT in the degree and post-degree courses at Spanish universities, which expand the interest and practice of AVT linguistic accessibility. We present a research project led at the UCM which consists of the compilation of AVT activities for teaching purposes and tries to analyze the creation and reception of SDH and AD: the AVLA Project (Audiovisual Learning Archive), which includes audiovisual materials carried out by the university students on different AVT modes and evaluations from the blind and deaf informants. In this study, we present the materials created by the students. A group of the deaf and blind population has been in charge of testing the student's SDH and AD corpus of audiovisual materials through some questionnaires used to evaluate the students’ production. These questionnaires include information about the reception of the subtitles and the audio descriptions from linguistic and technical points of view. With all the materials compiled in the research project, a corpus with both the students’ production and the recipients’ evaluations is being compiled: the CALING (Corpus de Accesibilidad Lingüística) corpus. Preliminary results will be presented with respect to those aspects, difficulties, and deficiencies in the SDH and AD included in the corpus, specifically with respect to the length of subtitles, the position of the contextual information on the screen, and the text included in the audio descriptions and tone of voice used. These results may suggest some changes and improvements in the quality of the SDH and AD analyzed. In the end, demand for the teaching and learning of AVT and linguistic accessibility at a university level and some important changes in the norms which regulate SDH and AD nationally and internationally will be suggested.

Keywords: audiovisual translation, corpus linguistics, linguistic accessibility, teaching

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Authors: Shaun G. Abbott, Rebecca C. Reynolds, John B. F. de Wit

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Low physical activity (PA) levels are a major public health concern in Australia. There is some evidence that PA interventions can increase PA levels via various methods, including online delivery. Low Socioeconomic Status (SES) people participate in less PA than the rest of the population, partly due to poor self-regulation behaviors associated with socioeconomic characteristics. Interventions that involve a particular method of self-regulation, Mental Contrasting with Implementation Intentions (MCII), has regularly achieved healthy behavior change, but few studies focus on PA behavior outcomes and no studies examining the effect of MCII on the PA behaviors of low SES people has been done. In this study, a pilot randomized controlled trial (RCT) will deliver MCII for PA behavior change to individuals of relative disadvantage for the first time. The current pilot study will predict sample size for a future full RCT and test the hypothesis that sedentary participants from areas of relative socioeconomic disadvantage of Sydney, who learn the MCII technique will be more physically active, have improved anthropometry and psychological indicators at the completion of a 12-week intervention compared to baseline and control. Eligible participants of relative socioeconomic disadvantage will be randomly assigned to either the ‘PA Information Plus MCII Intervention Group’ or a ‘PA Information-Only Control Group’. Both groups will attend a baseline and 12-week face-to-face consultation; where PA, anthropometric and psychological data will be gathered. The intervention group will be guided through an MCII session at the baseline appointment to establish a PA goal to aim to achieve over 12 weeks. Other than these baseline and 12-week consultations, all participant interaction will occur online. All participants will receive a ‘Fitbit’ accelerometer to record objectively. PA as a daily step count, along with a PA diary for the duration of the study. PA data will be recorded on a personalized online spreadsheet. Both groups will receive a standard PA information email at weeks 2, 4, and 8. The intervention group will also receive scripted follow-up online appointments to discuss goal progress. The current pilot study is in recruitment stage with findings to be presented at the conference in December if selected.

Keywords: implementation intentions, mental contrasting, motivation, pedometer, physical activity, socioeconomic

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Authors: Nihar Kanta Patra, Akhtar Hussain

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This comprehensive study, through the perspective of librarians, explores the library as a metaphor and its profound significance in representing knowledge and learning. It delves into how librarians perceive the library as a metaphor and the ways in which it symbolizes the acquisition, preservation, and dissemination of knowledge. The research investigates the most common metaphors used to describe libraries, as witnessed by librarians, and analyzes how these metaphors reflect the evolving role of libraries in society. Furthermore, the study examines how the library metaphor influences the perception of librarians regarding academic libraries as physical places and academic library websites as virtual spaces, exploring their potential for learning and exploration. It investigates the evolving nature of the library as a metaphor over time, as seen by librarians, considering the changing landscape of information and technology. The research explores the ways in which the library metaphor has expanded beyond its traditional representation, encompassing digital resources, online connectivity, and virtual realms, and provides insights into its potential evolution in the future. Drawing on the experiences of librarians in their interactions with library users, the study uncovers any specific cultural or generational differences in how people interpret or relate to the library as a metaphor. It sheds light on the diverse perspectives and interpretations of the metaphor based on cultural backgrounds, educational experiences, and technological familiarity. Lastly, the study investigates the evolving roles of libraries as observed by librarians and explores how these changing roles can influence the metaphors we use to represent them. It examines the dynamic nature of libraries as they adapt to societal needs, technological advancements, and new modes of information dissemination. By analyzing these various dimensions, this research provides a comprehensive understanding of the library as a metaphor through the lens of librarians, illuminating its significance, evolution, and its transformative impact on knowledge, learning, and the changing role of libraries in society.

Keywords: library, librarians, metaphor, perception

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: J. K. D. M. P. Madara, R. B. L. Dharmawickreme, Linu John, Ivee Boiss

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Flemingia vestita, commonly known as ‘Sohphlang’ is an important medicinal plant found in the North-Eastern region of India, which is traditionally recognized for its anthelmintic properties. This study was aimed to evaluate the phytochemical constituents and antibacterial activity of the tuber skin extracts of the plant species. Methanol, acetone, and water were used to obtain the solvent extractions of the skin peel extracts. Concentrated extracts of skin peel were tested using previously established qualitative phytochemical assays. The antibacterial efficacy of methanol tuber skin extract was tested against Gram-negative and positive microorganisms, namely, Klebsiella pneumonia, Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, and Mycobacterium tuberculosis strains. Agar well diffusion method was employed to determine the zone of inhibition of the plant extracts. Obtained data were statistically analyzed. Methanol extracts of Flemingia vestita were found to be effective against Bacillus subtilis and Mycobacterium tuberculosis at concentrations of 0.5 mg/ml. The reported zone of inhibition for the two strains was 13.3mm ± 0.57 and 16.3mm ± 4.9, respectively. However Klebsiella pneumoniae, Pseudomonas aeruginosa and Escherichia coli were resistant to the plant extracts with no zone of inhibition. Alkaloids, glycosides, and phenols were found to be present in aqueous, methanol, and acetone extracts of the plant in qualitative phytochemical analysis.

Keywords: flemingia vestita, antibacterial activity, phytochemical screening, well diffusion method

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Authors: S. C. Lin, W. W. Cheng, M. S. Wu

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This study attempted to investigate the connection between teacher professional development program and students’ Learning. This study took Readers’ Theater Teaching Program (RTTP) for professional development as an example to inquiry how participants apply their new knowledge and skills learned from RTTP to their teaching practice and how the impact influence students learning. The goals of the RTTP included: 1) to enhance teachers RT content knowledge; 2) to implement RT instruction in teachers’ classrooms in response to their professional development. 2) to improve students’ ability of reading fluency in professional development teachers’ classrooms. This study was a two-year project. The researchers applied mixed methods to conduct this study including qualitative inquiry and one-group pretest-posttest experimental design. In the first year, this study focused on designing and implementing RTTP and evaluating participants’ satisfaction of RTTP, what they learned and how they applied it to design their English reading curriculum. In the second year, the study adopted quasi-experimental design approach and evaluated how participants RT instruction influenced their students’ learning, including English knowledge, skill, and attitudes. The participants in this study composed two junior high school English teachers and their students. Data were collected from a number of different sources including teaching observation, semi-structured interviews, teaching diary, teachers’ professional development portfolio, Pre/post RT content knowledge tests, teacher survey, and students’ reading fluency tests. To analyze the data, both qualitative and quantitative data analysis were used. Qualitative data analysis included three stages: organizing data, coding data, and analyzing and interpreting data. Quantitative data analysis included descriptive analysis. The results indicated that average percentage of correct on pre-tests in RT content knowledge assessment was 40.75% with two teachers ranging in prior knowledge from 35% to 46% in specific RT content. Post-test RT content scores ranged from 70% to 82% correct with an average score of 76.50%. That gives teachers an average gain of 35.75% in overall content knowledge as measured by these pre/post exams. Teachers’ pre-test scores were lowest in script writing and highest in performing. Script writing was also the content area that showed the highest gains in content knowledge. Moreover, participants hold a positive attitude toward RTTP. They recommended that the approach of professional learning community, which was applied in RTTP was benefit to their professional development. Participants also applied the new skills and knowledge which they learned from RTTP to their practices. The evidences from this study indicated that RT English instruction significantly influenced students’ reading fluency and classroom climate. The result indicated that all of the experimental group students had a big progress in reading fluency after RT instruction. The study also found out several obstacles. Suggestions were also made.

Keywords: teacher’s professional development, program evaluation, readers’ theater, english reading instruction, english reading fluency

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Authors: A.L. Tamm, N. Šott, J. Jürimäe, E. Lätt, A. Orav, Ü. Parm

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Coffee is one of the most frequently consumed beverages in the world but still its effects on human organism are not completely understood. Coffee has also been used as a method for weight loss, but its effectiveness has not been proved. There is also not similar comprehension in classifying overweight in choosing between body mass index (BMI) and fat percentage (fat%). The aim of the study was to determine associations between coffee consumption and body composition. Secondly, to detect which measure (BMI or fat%) is more accurate to use describing overweight. Altogether 103 persons enrolled the study and divided into three groups: coffee non-consumers (n=39), average coffee drinkers, who consumed 1 to 4 cups (1 cup = ca 200ml) of coffee per day (n=40) and excessive coffee consumers, who drank at least five cups of coffee per day (n=24). Body mass (medical electronic scale, A&D Instruments, Abingdon, UK) and height (Martin metal anthropometer to the nearest 0.1 cm) were measured and BMI calculated (kg/m2). Participants´ body composition was detected with dual energy X-ray absorptiometry (DXA, Hologic) and general data (history of chronic diseases included) and information about coffee consumption, and physical activity level was collected with questionnaires. Results of the study showed that excessive coffee consumption was associated with increased fat-free mass. It could be foremost due to greater physical activity level in school time or greater (not significant) male proportion in excessive coffee consumers group. For estimating the overweight the fat% in comparison to BMI recommended, as it gives more accurate results evaluating chronical disease risks. In conclusion coffee consumption probably does not affect body composition and for estimating the body composition fat% seems to be more accurate compared with BMI.

Keywords: body composition, body fat percentage, body mass index, coffee consumption

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Authors: Nugraheni Widianingtyas, Niko Albert Setiawan

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For ELESP Teachers’ College, teaching learning strategies such as presentation and group discussion are classical ones to be implemented in the class. In order to create a breakthrough which can bring about more positive advancements in the learning process, a Focus Group Discussion (FGD) is being offered and implemented in certain classes. Interestingly, FGD is frequently used in the social-business inquiries such as for recruiting employees. It is then interesting to investigate FGD when it is implemented in the educational scope, especially in the Sociolinguistics class which regarded as one of the most arduous subjects in this study program. Thus, this study focused on how FGD enhances students Sociolinguistics mastery. In response to that, a quantitative survey research was conducted in which observation, questionnaire, and interview (triangulation method) became the instruments. The respondents of this study were 29 sixth-semester students who take Sociolinguistics of ELESP, Sanata Dharma University in 2017. The findings indicated that FGD could help students in enhancing Sociolinguistics mastery. In addition, it also revealed that FGD was exploring students’ logical thinking, English communication skill, and decision-making.

Keywords: focus group discussion, material mastery, sociolinguistics, teaching strategy

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Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: Hassan Nour, Nouh Mounadi, Oussama Abchir, Belaidi Salah, Samir Chtita

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Cholinesterase enzymes are biological catalysts essential for the transformation of acetylcholine, which is a neurotransmitter implicated in memory and learning, into acetic acid and choline, altering the neurotransmission process in Alzheimer’s disease patients. Therefore, inhibition of cholinesterase enzymes is a relevant strategy for the symptomatic treatment of Alzheimer’s disease. The current investigation aims to explore potential Cholinesterase (ChE) inhibitors through a comprehensive computational approach. Forty-nine phytoconstituents extracted from Cannabis sativa L were in-silico screened using molecular docking, pharmacokinetic and toxicological analysis to evaluate their possible inhibitory effect towards the cholinesterase enzymes. Two phytoconstituents belonging to cannabinoid derivatives were revealed to be promising candidates for Alzheimer therapy by acting as cholinesterase inhibitors. They have exhibited high binding affinities towards the cholinesterase enzymes and showed their ability to interact with key residues involved in cholinesterase enzymatic activity. In addition, they presented good ADMET profiles allowing them to be promising oral drug candidates. Furthermore, molecular dynamics (MD) simulations were executed to explore their interactions stability under mimetic biological conditions and thus support our findings. To corroborate the docking results, the binding free energy corresponding to the more stable ligand-ChE complexes was re-estimated by applying the MM-PBSA method. MD and MM-PBSA studies affirmed that the ligand-ChE recognition is spontaneous reaction leading to stable complexes. The conducted investigations have led to great findings that would strongly guide the pharmaceutical industries towards the rational development of potent anti-Alzheimer agents.

Keywords: alzheimer’s disease, molecular docking, cannabis sativa l, cholinesterase inhibitors

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

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Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

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Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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Authors: Sofia Serra-Dawa

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Adult relationships accrue on a variety of past social experiences, intentions, and emotions that might predispose and influence the approach to and construction of subsequent relationships. The Adult Attachment Theory (AAT) proposes four types of adult attachment, where attachment is built over two dimensions of anxiety and avoidance: secure, anxious-preoccupied, dismissive-avoidant, and fearful-avoidant. The AAT has been studied in multiple settings such as personal and therapeutic relationships, educational settings, sexual orientation, health, and religion. In music scholarship, the AAT has been used to frame class learning of student singers and study the relational behavior between voice teachers and students. Building on this study, the present inquiry studies how attachment types might characterize learning relationships of music students (in the Western Conservatory tradition), and whether particular instrumental experiences might correlate to given attachment styles. Given certain behavioral cohesive features of established traditions of instrumental playing and performance modes, it is hypothesized that student musicians will display specific characteristics correlated to instrumental traditions, demonstrating clear tendency of attachment style, which in turn has implications on subsequent professional interactions. This study is informed by the methodological framework of Adult Attachment Scale-R (Collins and Read, 1990), which was particularly chosen given its non-invasive questions and classificatory validation. It is further hypothesized that the analytical comparison of musicians’ profiles has the potential to serve as the baseline for other comparative behavioral observation studies [this component is expected to be verified and completed well before the conference meeting]. This research may have implications for practitioners concerned with matching and improving musical teaching and learning relationships and in (professional and amateur) long-term musical settings.

Keywords: adult attachment, music education, musicians attachment profile, musicians relationships

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Authors: Sobhy El-Sohaimy, Abduvali Toshev, Hanem Mansour

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(1) Objective: The main objective of the current study was to evaluate in an In Vitro and In Vivo, the potential activity of olive leaves extract (OLE) in the treatment and/or preventing the diabetes mellitus type II and related implications; (2) Methodology: Five groups of male rats were used in the current study: group (1)- negative control (normal); group (2)- positive control, streptozotocin (STZ) induced rats; group (3)-diabetic rats treated with metformin (200 mg/kg) plus OLE (200 mg/kg); group 4- diabetic rats treated with metformin (200 mg/kg); group 5- diabetic rats treated with OLE (200 mg/kg). A four-week regime of oral treatment was administered once daily; (3) Results: Diabetic rats treated with metformin + OLE clearly showed normal blood glucose level (121.67 ± 5.49 mg/dl), and glycated hemoglobin (HbA1c) (3.70 ± 0.10%). The combination of metformin + OLE obviously showed a superior improvement in the lipid profile (TG, TC, HDL and LDL) compared to both metformin and OLE individually. The histological examination revealed that the combination of metformin + Olive leaves extract successfully repaired of the liver, kidneys, and pancreatic tissues in diabetic rats to be near to the normal status; (4) Conclusion: Finally, it can be concluded that, the combination of metformin and OLE exhibited a superior improvement than metformin and OLE individually which emphasized the promising adjuvant role of the OLE in the treatment protocol of diabetes mellitus type II.

Keywords: olive (olea europaea) leaves extracts, hypoglycemic agents, cytotoxicity, nitic oxide scavenging activity, α-glucose oxidase inhibitor

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Authors: Rahul Shrivastava, Manish Tripathi, Mohmmad Yasir, Shailesh Singh

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Life style changes and depletion in atmospheric ozone layer in recent decades lead to increase in skin cancer including both melanoma and nonmelanomas. Natural products which were obtained from different plant species have the potential of anti skin cancer activity. In regard of this, present study focuses the potential effect of Glycosmis pentaphylla against anti skin cancer activity. Different Phytochemical constituents which were present in the roots of Glycosmis pentaphylla were identified and were used as ligands after sketching of their structures with the help of ACD/Chemsketch. These ligands are screened for their anticancer potential with proteins which are involved in skin cancer effects with the help of pyrx software. After performing docking studies, results reveal that Noracronycine secondary metabolite of Glycosmis pentaphylla shows strong affinity of their binding energy with Ribosomal S6 Kinase 2 (2QR8) protein. Ribosomal S6 Kinase 2 (2QR8) has an important role in the cell proliferation and transformation mediated through by N-terminal kinase domain and was induced by the tumour promoters such as epidermal growth factor. It also plays a key role in the neoplastic transformation of human skin cells and in skin cancer growth. Noracronycine interact with THR-493 and MET-496 residue of Ribosomal S6 Kinase 2 protein with binding energy ΔG = -8.68 kcal/mole. Thus on the basis of this study we can say that Noracronycine which present in roots of Glycosmis pentaphylla can be used as lead compound against skin cancer.

Keywords: glycosmis pentaphylla, pyrx, ribosomal s6 kinase, skin cancer

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Authors: M. Al-Jepoori, D. Bennett

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Teaching Object-Oriented Programming (OOP) as part of a Computing-related university degree is a very difficult task; the road to ensuring that students are actually learning object oriented concepts is unclear, as students often find it difficult to understand the concept of objects and their behavior. This problem is especially obvious in advanced programming modules where Design Pattern and advanced programming features such as Multi-threading and animated GUI are introduced. Looking at the students’ performance at their final year on a university course, it was obvious that the level of students’ understanding of OOP varies to a high degree from one student to another. Students who aim at the production of Games do very well in the advanced programming module. However, the students’ assessment results of the last few years were relatively low; for example, in 2016-2017, the first quartile of marks were as low as 24.5 and the third quartile was 63.5. It is obvious that many students were not confident or competent enough in their programming skills. In this paper, the reasons behind poor performance in Advanced OOP modules are investigated, and a suggested practice for teaching OOP based on a complex case study is described and evaluated.

Keywords: complex programming case study, design pattern, learning advanced programming, object oriented programming

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Kang-Hyun Leem, Myung-Gyou Kim, Hye Kyung Kim

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The prickly pear cactus (Opuntia ficus-indica) has a global distribution and have been used for medicinal benefits such as artherosclerosis, diabetes, gastritis, and hyperglycemia. However, very little information is currently available for their mechanism. The prikly pear variety Opuntia ficus-indica var. Saboten (OFS) is widely cultivated in Cheju Island, southwestern region of Korea, and used as a functional food. Present study investigated the effects of OFS on pancreatic β-cell function using pancreatic islet β cells (HIT cell). Alpha-glucosidase inhibition, glucose uptake, insulin secretion, insulin sensitivity, and pancreatic β cell proliferation were determined. The inhibitory effect of ethanol extract of OFS stem on α-glucosidase enzyme was measured in a cell free system. Glucose uptake was determined using fluorescent glucose analogue, 2-NBDG. Insulin secretion was measured by ELISA assay. Cell proliferation was measured by MTT assay. Ethanol extracts of OFS dose-dependently inhibited α-glucosidase activity as well as glucose uptake. Insulinotrophic effect of OFS extract was observed at high glucose media in pancreatic β-islet cells. Furthermore, pancreatic β cell regeneration was also observed.These results suggest that OFS mediates the antidiabetic activity mainly via α-glucosidase inhibition, glucose uptake, and improved insulin sensitivity.

Keywords: prickly pear cactus, Opuntia ficus-indica var. Saboten, pancreatic islet HIT cells, α-glucosidase, glucose uptake, insulinotrophic

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Authors: Afy Syahidan Achmad

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Tangkuban Perahu is located in West Java, Indonesia. It is active stratovolcano type and still showing hidrothermal activity. The main purpose of this study is to find correlation between subsurface structure and hidrothermal activity on the surface. Using topographic map, SRTM images, and field observation, geological condition and alteration area was mapped. Alteration sample analyzed trough petrographic analysis and X-Ray Diffraction (XRD) analysis. Altered rock in study area showing white-yellowish white colour, and texture changing variation from softening to hardening because of alteration by sillica and sulphur. Alteration mineral which can be observed in petrographic analysis and XRD analysis consist of crystobalite, anatase, alunite, and pyrite. This mineral assemblage showing advanced argillic alteration type with West-East alteration area orientation. Alteration area have correlation with manifestation occurance such as steam vents, solfatara, and warm to hot pools. Most of manifestation occured in main crater like Ratu Crater and Upas crater, and parasitic crater like Domas Crater and Jarian Crater. This manifestation indicates permeability in subsurface which can be created trough structural process with same orientation. For further study geophysics method such as Magneto Telluric (MT) and resistivity can be required to find permeability zone pattern in Tangkuban Perahu subsurface.

Keywords: alteration, advanced argillic, Tangkuban Perahu, XRD, crystobalite, anatase, alunite, pyrite

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Authors: Nagore Guerra Bilbao, Clemente Lobato Fraile

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Mentoring is provided by professionals with a higher level of experience and competence as part of the professional development of a university faculty. This paper explores the characteristics of the mentoring provided by those teachers participating in the development of an active methodology program run at the University of the Basque Country: to examine and to analyze mentors’ performance with the aim of providing empirical evidence regarding its value as a lifelong learning strategy for teaching staff. A total of 183 teachers were trained during the first three programs. The analysis method uses a coding technique and is based on flexible, systematic guidelines for gathering and analyzing qualitative data. The results have confirmed the conception of mentoring as a methodological innovation in higher education. In short, university teachers in general assessed the mentoring they received positively, considering it to be a valid, useful strategy in their professional development. They highlighted the methodological expertise of their mentor and underscored how they monitored the learning process of the active method and provided guidance and advice when necessary. Finally, they also drew attention to traits such as availability, personal commitment and flexibility in. However, a minority critique is pointed to some aspects of the performance of some mentors.

Keywords: higher education, mentoring, professional development, university teachers

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