Search results for: differential learning

Authors: S. O. Uwaifo

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The paper examines the pivotal role of the library in university education through the provision of a wide range of information materials (print and non- print) required for the teaching, learning and research activities of the university. However certain impediments to the effectiveness of Nigerian university libraries, such as financial constraints, high foreign exchange, global disparities in accessing the internet, lack of local area networks, erratic electric power supply, absence of ICT literacy, poor maintenance culture, etc., were identified. Also, the necessity of repositioning Nigerian university libraries for effective information provision and delivery was stressed by pointing out their dividends, such as users’ access to Directory of Open Access Journals (DOAJ), Online Public Access Catalogue (OPAC), Institutional Repositories, Electronic Document Delivery, Social Media Networks, etc. It therefore becomes necessary for the libraries to be repositioned by way of being adequately automated or digitized for effective service delivery, in this age of globalization. Based on the identified barriers by this paper, some recommendations were proffered.

Keywords: repositioning, Nigerian university libraries, effective information provision and delivery, globalization

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Authors: Samir Sarfraj Terdalkar

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India needs to step into affordable higher education with more focus on skill development and employability. The general scenario of higher education in India revolves around two major branches of higher education ie., Engineering and Medical Sciences. These two branches still cannot be considered as affordable. Hence, skill development of each and every student beginning from the school education should emphasize on learning skills with special focus on physics and mathematics. In India, the Central Government initiated a survey based process of all higher Educational Institutes/ Universities and colleges in India. This survey/ process was – All India Survey On Higher Education (AISHE). The focus of this process was understand and Though the increase is significant, it is necessary to propagate skill and vocational education which would add to the employability factor. Similarly, there has been a significant increase in number of higher education institutes, there is need to rethink on the type of education/ curriculum offered by these institutions. In this regard, vocational education has helped to build skill sets to certain extent. There is need to bring in this vocational educational in main stream education which could be complementary for undergraduate / post graduate education. The paper focuses on different policies to bring in vocational/ skill education.

Keywords: higher education, skill, vocational, India

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Authors: Saba Latif

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The use of social media among university students is a topic of ongoing debate, with conflicting views on its impact on academic achievement. This study aimed to explore the relationship between social media use and academic achievement among university students and to identify factors that may contribute to positive or negative effects. The study used a mixed-methods design, including a survey of 500 university students and qualitative interviews with a subset of participants. The survey results showed that social media use was prevalent among students, with Facebook and Instagram are the most commonly used platforms. The findings also indicated a positive relationship between social media use and academic achievement, with students who reported higher levels of social media use also reporting higher GPAs. However, the qualitative interviews revealed that excessive use of social media could be a distraction that hinders academic performance, especially when students use it to procrastinate or to stay up late at night. Overall, the findings suggest that social media use can have both positive and negative effects on academic achievement among university students. Responsible and balanced use of social media, such as setting limits on usage and avoiding procrastination, may help students maximize the benefits while minimizing the risks.

Keywords: social media, university, achievement, effective, learning

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Authors: Nur Aisyah Jalalludin, Arwindra Rizqiawan, Goro Fujita

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In this study, we aim to demonstrate a microgrid system experimental simulation for an easy understanding of a large-scale microgrid system. This model is required for industrial training and learning environments. However, in order to create an exact representation of a microgrid system, the laboratory-scale system must fulfill the requirements of a grid-connected inverter, in which power values are assigned to the system to cope with the intermittent output from renewable energy sources. Aside from that, during changes in load capacity, the grid-connected system must be able to supply power from the utility grid side and microgrid side in a balanced manner. Therefore, droop control is installed in the inverter’s control board to maintain equal power sharing in both sides. This power control in a stand-alone condition and droop control in a grid-connected condition must be implemented in order to maintain a stabilized system. Based on the experimental results, power control and droop control can both be applied in the system by comparing the experimental and reference values.

Keywords: droop control, droop characteristic, grid-connected inverter, microgrid, power control

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Authors: Jacquelyn Irene Eidson

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Women who leave the workforce due to motherhood and wish to return are a valuable, untapped resource for organizations. Levinson’s theory of adult development defines life as a sequence of transitions requiring difficult decisions that prompt humans to question their identity and their self-efficacy. The experience of being a working mother and the experience of workplace mentorship have received extensive research attention. Merging the two experiences and focusing on feelings of identity and self-efficacy provides a unique and focused opportunity for learning. Through one-on-one interviews and focus group discussion with working mothers that had previously left the workforce for an extended leave due to child-rearing, a meaningful description of their experiences will be obtained. Data is currently being collected via a collaboration with state banking associations in the United States. Results from the study will enable organizations worldwide to more effectively provide mentorship opportunities built around a culture of understanding while more effectively recruiting, supporting, developing, and retaining this valuable talent pool.

Keywords: identity, mentorship, self-efficacy, working mother

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Authors: Getaneh Berie Tarekegn

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Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

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Authors: Nikolina Ribarić

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This paper compares the readiness of chemistry teachers to use information and communication technologies in chemistry in 2018. and 2021. A survey conducted in 2018 on a sample of teachers showed that most teachers occasionally use visualization and digitization tools in chemistry teaching (65%) but feel that they are not educated enough to use them (56%). Also, most teachers do not have adequate equipment in their schools and are not able to use ICT in teaching or digital tools for visualization and digitization of content (44%). None of the teachers find the use of digitization and visualization tools useless. Furthermore, a survey conducted in 2021 shows that most teachers occasionally use visualization and digitization tools in chemistry teaching (83%). Also, the research shows that some teachers still do not have adequate equipment in their schools and are not able to use ICT in chemistry teaching or digital tools for visualization and digitization of content (14%). Advances in the use of ICT in chemistry teaching are linked to pandemic conditions and the obligation to conduct online teaching. The share of 14% of teachers who still do not have adequate equipment to use digital tools in teaching is worrying.

Keywords: chemistry, digital content, e-learning, ICT, visualization

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Authors: Ritika Chaturvedi

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Introduction: Sexual objectification, characterized by the reduction of an individual to a mere object of sexual desire, remains a pervasive societal issue with profound repercussions on individual well-being. Such experiences, often rooted in systemic and cultural norms, have long-lasting implications for mental and emotional health. This study aims to explore the intricate relationship between experiences of sexual objectification and insidious trauma, further investigating the potential moderating effects of overexcitabilities as proposed by Dabrowski's theory of positive disintegration. Methodology: The research involved a comprehensive cohort of 204 women, spanning ages from 18 to 65 years. Participants were tasked with completing self-administered questionnaires designed to capture their experiences with sexual objectification. Additionally, the questionnaire assessed symptoms indicative of insidious trauma and explored overexcitabilities across five distinct domains: emotional, intellectual, psychomotor, sensory, and imaginational. Employing advanced statistical techniques, including multiple regression and moderation analysis, the study sought to decipher the intricate interplay among these variables. Findings: The study's results revealed a compelling positive correlation between experiences of sexual objectification and the onset of symptoms indicative of insidious trauma. This correlation underscores the profound and detrimental effects of sexual objectification on an individual's psychological well-being. Interestingly, the moderation analyses introduced a nuanced understanding, highlighting the differential roles of various overexcitabilities. Specifically, emotional, intellectual, and sensual overexcitabilities were found to exacerbate trauma symptomatology. In contrast, psychomotor overexcitability emerged as a protective factor, demonstrating a mitigating influence on the relationship between sexual objectification and trauma. Implications: The study's findings hold significant implications for a diverse array of stakeholders, encompassing mental health practitioners, educators, policymakers, and advocacy groups. The identified moderating effects of overexcitabilities emphasize the need for tailored interventions that consider individual differences in coping and resilience mechanisms. By recognizing the pivotal role of overexcitabilities in modulating the traumatic consequences of sexual objectification, this research advocates for the development of more nuanced and targeted support frameworks. Moreover, the study underscores the importance of continued research endeavors to unravel the intricate mechanisms and dynamics underpinning these relationships. Such endeavors are crucial for fostering the evolution of informed, evidence-based interventions and strategies aimed at mitigating the adverse effects of sexual objectification and promoting holistic well-being.

Keywords: sexual objectification, insidious trauma, emotional overexcitability, intellectual overexcitability, sensual overexcitability, psychomotor overexcitability, imaginational overexcitability

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Authors: Chuen-arom Janthimachai-amorn, Chirawadee Harnrittha

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This study aims to identify the factors facilitating the knowledge management to develop the graduate study programs to achieve success and to identify the approaches in developing the graduate study programs in the Rajbhat Suansunantha University. The 10 respondents were the administrators, the faculty, and the personnel of its Graduate School. The research methodology was based on Pla-too Model of the Knowledge Management Institute (KMI) by allocating the knowledge indicators, the knowledge creation and search, knowledge systematization, knowledge processing and filtering, knowledge access, knowledge sharing and exchanges and learning. The results revealed that major success factors were knowledge indicators, evident knowledge management planning, knowledge exchange and strong solidarity of the team and systematic and tenacious access of knowledge. The approaches allowing the researchers to critically develop the graduate study programs were the environmental data analyses, the local needs and general situations, data analyses of the previous programs, cost analyses of the resources, and the identification of the structure and the purposes to develop the new programs.

Keywords: program development, knowledge management, graduate study programs, Rajbhat Suansunantha University

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Authors: Alaa Alrakaf, Sk. Md. Mizanur Rahman

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Since the last two decades’ Arabic natural language processing (ANLP) has become increasingly much more important. One of the key issues related to ANLP is ambiguity. In Arabic language different pronunciation of one word may have a different meaning. Furthermore, ambiguity also has an impact on the effectiveness and efficiency of Machine Translation (MT). The issue of ambiguity has limited the usefulness and accuracy of the translation from Arabic to English. The lack of Arabic resources makes ambiguity problem more complicated. Additionally, the orthographic level of representation cannot specify the exact meaning of the word. This paper looked at the diacritics of Arabic language and used them to disambiguate a word. The proposed approach of word sense disambiguation used Diacritizer application to Diacritize Arabic text then found the most accurate sense of an ambiguous word using Naïve Bayes Classifier. Our Experimental study proves that using Arabic Diacritics with Naïve Bayes Classifier enhances the accuracy of choosing the appropriate sense by 23% and also decreases the ambiguity in machine translation.

Keywords: Arabic natural language processing, machine learning, machine translation, Naive bayes classifier, word sense disambiguation

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Authors: Shyam Mehraaj, Samanthi E. R. Siriwardana, Shehara A. K. G. H., Wanigasinghe N. T., Wandana R. A. K., Wedage C. V.

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A presentation is a critical tool for conveying not just spoken information but also a wide spectrum of human emotions. The single most effective thing to make the presentation successful is to practice it beforehand. Preparing for a presentation has been shown to be essential for improving emotional control, intonation and prosody, pronunciation, and vocabulary, as well as the quality of the presentation slides. As a result, practicing has become one of the most critical parts of giving a good presentation. In this research, the main focus is to analyze the audio, video, and slides of the presentation uploaded by the presenters. This proposed solution is based on the Natural Language Processing and Computer Vision techniques to cater to the requirement for the presenter to do a presentation beforehand using a mobile responsive web application. The proposed system will assist in practicing the presentation beforehand by identifying the presenters’ emotions, body language, tonality, prosody, pronunciations and vocabulary, and presentation slides quality. Overall, the system will give a rating and feedback to the presenter about the performance so that the presenters’ can improve their presentation skills.

Keywords: presentation, self-evaluation, natural learning processing, computer vision

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Xian Xie, Qinghua Fang

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This study used quantitative and qualitative methods to explore the characteristics of flipped classroom’s influence on classroom environment of college English reading, writing, and translating, and to summarize and reflect on the teaching characteristics of college English Reading, writing, and translating. The results of the study indicated that after the flipped classroom applied to reading, writing, and translating, students’ performance was improved to a certain extent, the classroom environment was improved to some extent, students of the flipped classroom are generally satisfied with the classroom environment; students showed a certain degree of individual differences to the degree of cooperation, participation, self-responsibility, task-orientation, and the teacher leadership and innovation. The study indicated that the implementation of flipped classroom teaching mode can optimize College English reading, writing, and translating classroom environment and realize target－learner as the center in foreign language teaching and learning, but bring a greater challenge to teachers.

Keywords: classroom environment, college English reading, writing and translating, individual differences, flipped classroom

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Dinesh Khanduja, Pardeep Kumar Sharma

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Education as the most important resource in any country has multiplying effects on all facets of development in a society. The new social realities, particularly, the interplay between democratization of education; unprecedented developments in the IT sector; emergence of knowledge society, liberalization of economy, and globalization have greatly influenced the educational process of all nations. This turbulence entails upon education to undergo dramatic changes to keep up with the new expectations. Growth of entrepreneurship among Indian women is highly important for empowering them and this is highly essential for the socio-economic development of a society. Unfortunately, in India, there is poor acceptance of entrepreneurship among women as unfounded myths and fears restrain them to be enterprising. To remove these inhibitions, the education system needs to be re-engineered to make entrepreneurship more acceptable. This paper empirically analyses the results of a survey done on around 500 female graduates in North India to measure and evaluate various entrepreneurial traits present in them. A formative model has been devised in this context, which should improve the teaching-learning process in our education system, which can lead to a sustainable growth of women entrepreneurship in India.

Keywords: women empowerment, entrepreneurship, education system, women entrepreneurship, sustainable development

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Authors: Mikkel Snorre Wilms Boysen

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From a theoretical perspective, expertise is generally considered a precondition for creativity. The assumption is that an individual needs to master the common and accepted rules and techniques within a certain knowledge-domain in order to create something new and valuable. However, real life cases, and a limited amount of empirical studies, demonstrate that this assumption may be overly simple. In this article, this question is explored through a number of semi-experimental case studies conducted within the fields of music, technology, and youth culture. The studies indicate that, in various ways, expertise plays an important part in creative processes. However, the case studies also indicate that expertise sometimes leads to an entrenched perspective, in the sense that knowledge and experience may work as a path into the well-known rather than into the unknown. In this article, these issues are explored with reference to different theoretical approaches to creativity and learning, including actor-network theory, the theory of blind variation and selective retention, and Csikszentmihalyi’s system model. Finally, some educational aspects and implications of this are discussed.

Keywords: creativity, expertise , education, technology

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Authors: Barbara Behre, Verolien Cauberghe, Dieneke Van de Sompel

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Sustainable consumption is on the rise, yet it has still not entered the mainstream in several industries, such as the fashion industry. In online fashion contexts, sustainability cues have been used to signal the sustainable benefits of certain garments to promote sustainable consumption. These sustainable cues may focus on the ecological or social dimension of sustainability. Since sustainability, in general, relates to distant, abstract benefits, the current study aims to examine if and how psychological distance may mediate the effects of exposure to different sustainability cues on consumption outcomes. Following the framework of Construal Level Theory of Psychological Distance, reduced psychological distance renders the construal level more concrete, which may influence attitudes and subsequent behavior in situations like fashion shopping. Most studies investigated sustainability as a composite, failing to differentiate between ecological and societal aspects of sustainability. The few studies examining sustainability more in detail uncovered that environmental sustainability is rather perceived in abstract cognitive construal, whereas social sustainability is linked to concrete construal. However, the construal level affiliation of the sustainability dimensions likely is not universally applicable to different domains and stages of consumption, which further suggest a need to clarify the relationships between environmental and social sustainability dimensions and the construal level of psychological distance within fashion brand consumption. While psychological distance and construal level have been examined in the context of sustainability, these studies yielded mixed results. The inconsistent findings of past studies might be due to the context-dependence of psychological distance as inducing construal differently in diverse situations. Especially in a hedonic consumption context like online fashion shopping, the role of visual processing of information could determine behavioural outcomes as linked to situational construal. Given the influence of the mode of processing on psychological distance and construal level, the current study examines the moderating role of verbal versus non-verbal presentation of the sustainability cues. In a 3 (environmental sustainability vs. social sustainability vs. control) x 2 (non-verbal message vs. verbal message) between subjects experiment, the present study thus examines how consumers evaluate sustainable brands in online shopping contexts in terms of psychological distance and construal level, as well as the impact on brand attitudes and buying intentions. The results among 246 participants verify the differential impact of the sustainability dimensions on fashion brand purchase intent as mediated by construal level and perceived psychological distance. The ecological sustainability cue is perceived as more concrete, which might be explained by consumer bias induced by the predominance of pro-environmental sustainability messages. The verbal versus non-verbal presentation of the sustainability cue neither had a significant influence on distance perceptions and construal level nor on buying intentions. This study offers valuable contributions to the sustainable consumption literature, as well as a theoretical basis for construal-level framing as applied in sustainable fashion branding.

Keywords: construal level theory, environmental vs social sustainability, online fashion shopping, sustainable fashion

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

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Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

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Authors: Kaling Taki, Rohit Gahlot, Manish Kumar

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Disposal of Sewage Sludge (SS) is a big issue especially in developing nation like India, where there is no control in the dynamicity of SS produced. The present research work demonstrates the potential application of SS amended with varying percentage (0-100%) of Fly Ash (FA) for brick manufacturing as an alternative of SS management. SS samples were collected from Jaspur sewage treatment plant (Ahmedabad, India) and subjected to different preconditioning treatments: (i) atmospheric drying (ii) pulverization (iii) heat treatment in oven (110°C, moisture removal) and muffle furnace (440°C, organic content removal). Geotechnical parameters of the SS were obtained as liquid limit (52%), plastic limit (24%), shrinkage limit (10%), plasticity index (28%), differential free swell index (DFSI, 47%), silt (68%), clay (27%), organic content (5%), optimum moisture content (OMC, 20%), maximum dry density (MDD, 1.55gm/cc), specific gravity (2.66), swell pressure (57kPa) and unconfined compressive strength (UCS, 207kPa). For FA liquid limit, plastic limit and specific gravity was 44%, 0% and 2.2 respectively. Initially, for brick casting pulverized SS sample was heat treated in a muffle furnace around 440℃ (5 hours) for removal of organic matter. Later, mixing of SS, FA and water by weight ratio was done at OMC. 7*7*7 cm3 sample mold was used for casting bricks at MDD. Brick samples were then first dried in room temperature for 24 hours, then in oven at 100℃ (24 hours) and finally firing in muffle furnace for 1000℃ (10 hours). The fired brick samples were then cured for 3 days according to Indian Standards (IS) common burnt clay building bricks- specification (5th revision). The Compressive strength of brick samples (0, 10, 20, 30, 40, 50 ,60, 70, 80, 90, 100%) of FA were 0.45, 0.76, 1.89, 1.83, 4.02, 3.74, 3.42, 3.19, 2.87, 0.78 and 4.95MPa when evaluated through compressive testing machine (CTM) for a stress rate of 14MPa/min. The highest strength was obtained at 40% FA mixture i.e. 4.02MPa which is much higher than the pure SS brick sample. According to IS 1077: 1992 this combination gives strength more than 3.5 MPa and can be utilized as common building bricks. The loss in weight after firing was much higher than the oven treatment, this might be due to degradation temperature higher than 100℃. The thermal conductivity of the fired brick was obtained as 0.44Wm-1K-1, indicating better insulation properties than other reported studies. TCLP (Toxicity characteristic leaching procedure) test of Cr, Cu, Co, Fe and Ni in raw SS was found as 69, 70, 21, 39502 and 47 mg/kg. The study positively concludes that SS and FA at optimum ratio can be utilized as common building bricks such as partitioning wall and other small strength requirement works. The uniqueness of the work is it emphasizes on utilization of FA for stabilizing SS as construction material as a replacement of natural clay as reported in existing studies.

Keywords: Compressive strength, Curing, Fly Ash, Sewage Sludge.

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: C. Amer, M. Sahin

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The design of an optimised horizontal axis 5-meter-long wind turbine rotor blade in according with IEC 61400-2 standard is a research and development project in order to fulfil the requirements of high efficiency of torque from wind production and to optimise the structural components to the lightest and strongest way possible. For this purpose, a research study is presented here by focusing on the structural characteristics of a composite wind turbine blade via finite element modelling and analysis tools. In this work, first, the required data regarding the general geometrical parts are gathered. Then, the airfoil geometries are created at various sections along the span of the blade by using CATIA software to obtain the two surfaces, namely; the suction and the pressure side of the blade in which there is a hat shaped fibre reinforced plastic spar beam, so-called chassis starting at 0.5m from the root of the blade and extends up to 4 m and filled with a foam core. The root part connecting the blade to the main rotor differential metallic hub having twelve hollow threaded studs is then modelled. The materials are assigned as two different types of glass fabrics, polymeric foam core material and the steel-balsa wood combination for the root connection parts. The glass fabrics are applied using hand wet lay-up lamination with epoxy resin as METYX L600E10C-0, is the unidirectional continuous fibres and METYX XL800E10F having a tri-axial architecture with fibres in the 0,+45,-45 degree orientations in a ratio of 2:1:1. Divinycell H45 is used as the polymeric foam. The finite element modelling of the blade is performed via MSC PATRAN software with various meshes created on each structural part considering shell type for all surface geometries, and lumped mass were added to simulate extra adhesive locations. For the static analysis, the boundary conditions are assigned as fixed at the root through aforementioned bolts, where for dynamic analysis both fixed-free and free-free boundary conditions are made. By also taking the mesh independency into account, MSC NASTRAN is used as a solver for both analyses. The static analysis aims the tip deflection of the blade under its own weight and the dynamic analysis comprises normal mode dynamic analysis performed in order to obtain the natural frequencies and corresponding mode shapes focusing the first five in and out-of-plane bending and the torsional modes of the blade. The analyses results of this study are then used as a benchmark prior to modal testing, where the experiments over the produced wind turbine rotor blade has approved the analytical calculations.

Keywords: dynamic analysis, fiber reinforced composites, horizontal axis wind turbine blade, hand-wet layup, modal testing

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

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Authors: M. Rodionov, N. Sharapova, Z. Dedovets

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The article describes methods of preparation of future teachers that includes the entire diversity of traditional and computer-oriented methodological approaches. The authors reveal how, in the specific educational environment, a teacher can choose the most effective combination of educational technologies based on the nature of the learning task. The key conditions that determine such a choice are that the methodological approach corresponds to the specificity of the problem being solved and that it is also responsive to the individual characteristics of the students. The article refers to the training of students in the proper use of mathematical electronic tools for educational purposes. The preparation of future mathematics teachers should be a step-by-step process, building on specific examples. At the first stage, students optimally solve problems aided by electronic means of teaching. At the second stage, the main emphasis is on modeling lessons. At the third stage, students develop and implement strategies in the study of one of the topics within a school mathematics curriculum. The article also recommended the implementation of this strategy in preparation of future teachers and stated the possible benefits.

Keywords: education, methodological approaches, teacher, secondary school

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Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

Abstract:

This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: T. Borja-Alvarez, V. Jiménez-Borja, M. Jiménez Borja, C. J. Jiménez-Mosquera

Abstract:

Research on child sexual abuse has predominantly focused on female survivors. This has resulted in less research looking at the particular context in which this abuse takes place for boys and the impact this abuse may have on male survivors. The aim of this study is to examine the sex and age of the perpetrators of child sexual abuse and explore gender differences in the impact along with the subjective interpretation that survivors attribute to these experiences. The data for this study was obtained from Ecuadorian university students (M = 230, F = 293) who reported sexual abuse using the ISPCAN Child Abuse Screening Tool Retrospective version (ICAST-R). Participants completed Horowitz's Impact of Event Scale (IES) and were also requested to choose among neutral, positive, and negative adjectives to describe these experiences. The results indicate that in the case of males, perpetrators were both males (adults =27%, peers =20%, relatives =10.3%, cousins =7.4%) and young females (girlfriends or ex-girlfriends =25.6%, neighborhood =20.7%, school =16.7%, cousins =15.3%, strangers =12.8%). In contrast, almost all females reported that adult males were the perpetrators (relatives =29.6%, neighborhood =11.9%, strangers =19.9%, family friends =9.7%). Regarding the impact of these events, significant gender differences emerged. More females (50%) than males (20%) presented symptoms of post-traumatic stress disorder (PTSD). Gender differences also surfaced in the way survivors interpret their experiences. Almost half of the male participants selected the word “consensual” followed by the words “normal”, “helped me to mature”, “shameful”, “confusing”, and “traumatic”. In contrast, almost all females chose the word “non-consensual” followed by the words “shameful”, “traumatic”, “scary”, and “confusing”. In conclusion, the findings of this study suggest that young females and adult males were the most common perpetrators of sexually abused boys whereas adult males were the most common perpetrators of sexually abused girls. The impact and the subjective interpretation of these experiences were more negative for girls than for boys. The factors that account for the gender differences in the impact and the interpretation of these experiences need further exploration. It is likely that the cultural expectations of sexual behaviors for boys and girls in Latin American societies may partially explain the differential impact in the way these childhood sexual abuse experiences are interpreted in adulthood. In Ecuador, as is the case in other Latin American countries, the machismo culture not only accepts but encourages early sexual behaviors in boys and negatively judges premature sexual behavior in females. The result of these different sexual expectations may be that sexually abused boys may re-define these experiences as “consensual” and “normal” in adulthood, even though these were not consensual at the time of occurrence. Future studies are needed to more deeply understand the different contexts of sexual abuse for boys and girls in order to analyze the long-term impact of these experiences.

Keywords: abuse, child, gender differences, sexual

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Authors: Wongwarit Nipitwittaya

Abstract:

The objectives of this research is bringing folk song with the teaching of Western music were to examine to investigate, to compare, develop the skill, technique, knowledge of Thai folk song and to preserve folk song of Thailand to be known more widely also learn Thai culture from Thai folk song. Study by bringing Thailand folk song is widely known for learning with Western music in course brass performance. Bringing the melody of Thai folk music and changing patterns to western music notes for appropriate on brass performance. A sample was selected from brass students, using research by assessment of knowledge from test after used Thai folk song lesson. The lesson focus for scales and key signature in western music by divided into two groups, the one study by used research tools and another one used simple lesson and a collection of research until testing. The results of the study were as follows: 1. There are good development skill form research method 2. Sound recognition can be even better. The study was a qualitative research and data collection by observation.

Keywords: Thai folk song, brass instrument, key signature, western music

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Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

Abstract:

Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

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Authors: Sanjay Adhikesaven

Abstract:

Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

Procedia PDF Downloads 105