Search results for: machine learning tools and techniques

Authors: Heichia Wang, Yalan Chao

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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

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Authors: Daniela Velichová

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Plenty of ideas on how to adopt active learning methods in education are available nowadays. Mathematics is a subject where the active involvement of students is required in particular in order to achieve desirable results regarding sustainable knowledge and deep understanding. The present article is based on the outcomes of an Erasmus+ project DrIVE-MATH, that was aimed at developing a novel and integrated framework to teach maths classes in engineering courses at the university level. It is fundamental for students from the early years of their academic life to have agile minds. They must be prepared to adapt to their future working environments, where enterprises’ views are always evolving, where all collaborate in teams, and relations between peers are thought for the well-being of the whole - workers and company profit. This reality imposes new requirements on higher education in terms of adaptation of different pedagogical methods, such as project-based and active-learning methods used within the course curricula. Active learning methodologies are regarded as an effective way to prepare students to meet the challenges posed by enterprises and to help them in building critical thinking, analytic reasoning, and insight to the solved complex problems from different perspectives. Fostering learning-by-doing activities in the pedagogical process can help students to achieve learning independence, as they could acquire deeper conceptual understanding by experimenting with the abstract concept in a more interesting, useful, and meaningful way. Clear information about learning outcomes and goals might help students to take more responsibility for their learning results. Active learning methods implemented by the project team members in their teaching practice, eduScrum and Jigsaw in particular, proved to provide better scientific and soft skills support to students than classical teaching methods. EduScrum method enables teachers to generate a working environment that stimulates students' working habits and self-initiative as they become aware of their responsibilities within the team, their own acquired knowledge, and their abilities to solve problems independently, though in collaboration with other team members. This method enhances collaborative learning, as students are working in teams towards a common goal - knowledge acquisition, while they are interacting with each other and evaluated individually. Teams consisting of 4-5 students work together on a list of problems - sprint; each member is responsible for solving one of them, while the group leader – a master, is responsible for the whole team. A similar principle is behind the Jigsaw technique, where the classroom activity makes students dependent on each other to succeed. Students are divided into groups, and assignments are split into pieces, which need to be assembled by the whole group to complete the (Jigsaw) puzzle. In this paper, analysis of students’ perceptions concerning the achievement of deeper conceptual understanding in mathematics and the development of soft skills, such as self-motivation, critical thinking, flexibility, leadership, responsibility, teamwork, negotiation, and conflict management, is presented. Some new challenges are discussed as brought by introducing active learning methods in the basic mathematics courses. A few examples of sprints developed and used in teaching basic maths courses at technical universities are presented in addition.

Keywords: active learning methods, collaborative learning, conceptual understanding, eduScrum, Jigsaw, soft skills

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Authors: Aleksandra Nocoń (Szunke)

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The study is a review of the literature concerning the consequences of non-standard monetary policy, which are used by central banks during unconventional periods, threatening instability of the banking sector. In particular, the attention was paid to the effects of non-standard monetary policy tools for financial markets. However, the empirical evidence about their effects and real consequences for the financial markets are still not final. The main aim of the study is to survey the consequences of standard and non-standard monetary policy instruments, implemented during the global financial crisis in the United States, United Kingdom and Euroland, with particular attention to the results for the stabilization of global financial markets. The study analyses the consequences for short and long-term market interest rates, interbank interest rates and LIBOR-OIS spread. The study consists mainly of the empirical review, indicating the impact of the implementation of these tools for the financial markets. The following research methods were used in the study: literature studies, including domestic and foreign literature, cause and effect analysis and statistical analysis.

Keywords: asset purchase facility, consequences of monetary policy instruments, non-standard monetary policy, quantitative easing

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Authors: Kevin Wang, Jian Ge, Yinan Zhao, Kevin Willis

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Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars).

Keywords: algorithms, astronomy data analysis, deep learning, exoplanet detection methods, small planets, habitable planets, transit photometry

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Authors: Slim Namouchi, Bruno Vallet, Imed Riadh Farah

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3D urban models provide powerful tools for decision making, urban planning, and smart city services. The accuracy of this 3D based systems is directly related to the quality of these models. Since manual large-scale modeling, such as cities or countries is highly time intensive and very expensive process, a fully automatic 3D building generation is needed. However, 3D modeling process result depends on the input data, the proprieties of the captured objects, and the required characteristics of the reconstructed 3D model. Nowadays, producing 3D real-world model is no longer a problem. Remotely sensed data had experienced a remarkable increase in the recent years, especially data acquired using unmanned aerial vehicles (UAV). While the scanning techniques are developing, the captured data amount and the resolution are getting bigger and more precise. This paper presents a literature review, which aims to identify different methods of automatic 3D buildings extractions either from LiDAR or the combination of LiDAR and satellite or aerial images. Then, we present open source technologies, and data models (e.g., CityGML, PostGIS, Cesiumjs) used to integrate these models in geospatial base layers for smart city services.

Keywords: CityGML, LiDAR, remote sensing, SIG, Smart City, 3D urban modeling

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Authors: Anwar Elsadat Elmahal, Ahmed Abdalla

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The population in Sudan has recently grown to a significant level, Khartoum city the capital has the major portion of this growth. Khartoum is separated by three Niles and linked by eight bridges to Khartoum North and Omdurman. The construction of these bridges disrupted the natural flow of water and sediments which will consequently be reflected on the geomorphological settings of fluvial islands including erosion and sedimentation patterns. The objective of this study is to monitor and assess the development of fluvial islands in Khartoum State up to Sabaloka Gorge using Remote Sensing (RS) and Geographical Information System (GIS) techniques. Landsat Images captured during the period from 1975-2015 with standard false color and standardized 30 m resolution were found useful in understanding the impacts of bridges on disrupting the fluvial cycle. Consequently, the rates, trends of erosions and deposition, and the development of fluvial islands are explained. GIS provides the-state-of-the-art tools in mapping, delineating the fluvial islands during different periods and in quantifying the changes that occurred to fluvial islands as well as creating the geographic databases for the Islands in Khartoum State. It was found that, the developments, shapes and sizes of the islands are directly affected by the construction of bridges, specifically in the Nile River from Tutti Island to Sabaloka gorge.

Keywords: fluvial islands, fluvial cycle, GIS and remote Sensing, Khartoum State, landsat, Sabaloka Gorge

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Authors: Shri Krishna Mishra, Badri Yadav

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Effect of collaborative learning on development of process skills and attitude towards science is It rightly said that the destiny of the nation is shaped inside its classroom. Classroom is a place where the pupil and teacher interact purposefully to gain knowledge. Teaching is the principal mode of education. It can be called a transaction between teacher and pupil, in which one transmits knowledge to other. The teaching learning process consists of three important components, the pupils, the teacher and the curriculum; the classroom is the collection of students of their own individual abilities and needs. In the present classroom teaching learners are either persuasive recipient or passive observant. The school environment leading to low-achievement we have to try better to develop in the young mind. Children are the sticks of dynamite, bundles of energy and potential power waiting to be ignited. Guide them carefully to a place where their potentialities and strength will be used to build a better world. Man’s future depends to large extent on scientific advances and development of productive activity. Science is considered as an important subject in school curricular. The education commission (1964-66) has suggested that science education is necessary for all children at school stage. It is essential to develop children’s logical and critical thinking. But these days thinking process and academic achievement of students have been suppressed by competitive environment of our schools. How the students perceive each other and interact with one another is a neglected aspect of instruction. In the constructivist perspective learning in a process of construction of knowledge. Learners actively construct their own knowledge by connecting new ideas to existing ideas on the basis of materials/ activities presented to them (experience).

Keywords: effect of collaborative learning, development of process skills, science education, attitude towards science

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Authors: Ouafa Benaida, Abdelhamid Loukil, Adda Ali Pacha

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In recent years, the growing interest of scientists in the field of image processing and analysis of underwater images and videos has been strengthened following the emergence of new underwater exploration techniques, such as the emergence of autonomous underwater vehicles and the use of underwater image sensors facilitating the exploration of underwater mineral resources as well as the search for new species of aquatic life by biologists. Indeed, underwater images and videos have several defects and must be preprocessed before their analysis. Underwater landscapes are usually darkened due to the interaction of light with the marine environment: light is absorbed as it travels through deep waters depending on its wavelength. Additionally, light does not follow a linear direction but is scattered due to its interaction with microparticles in water, resulting in low contrast, low brightness, color distortion, and restricted visibility. The improvement of the underwater image is, therefore, more than necessary in order to facilitate its analysis. The research presented in this paper aims to implement and evaluate a set of classical techniques used in the field of improving the quality of underwater images in several color representation spaces. These methods have the particularity of being simple to implement and do not require prior knowledge of the physical model at the origin of the degradation.

Keywords: underwater image enhancement, histogram normalization, histogram equalization, contrast limited adaptive histogram equalization, single-scale retinex

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Authors: Huma Naqeeb

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Epidemiological studies have shown the robust link between breast cancer and dietary pattern. There has been no previous study conducted in Pakistan, which specifically focuses on dietary patterns among breast cancer women. This study aims to examine the association of breast cancer with dietary patterns among Pakistani women. This case-control research was carried in multiple tertiary care facilities. Newly diagnosed primary breast cancer patients were recruited as cases (n = 408); age matched controls (n = 408) were randomly selected from the general population. Data on required parameters were systematically collected using subjective and objective tools. Factor and Principal Component Analysis (PCA) techniques were used to extract women’s dietary patterns. Four dietary patterns were identified based on eigenvalue >1; (i) veg-ovo-fish, (ii) meat-fat-sweet, (iii) mix (milk and its products, and gourds vegetables) and (iv) lentils - spices. Results of the multiple regressions were displayed as adjusted odds ratio (Adj. OR) and their respective confidence intervals (95% CI). After adjusted for potential confounders, veg-ovo-fish dietary pattern was found to be robustly associated with a lower risk of breast cancer among women (Adj. OR: 0.68, 95%CI: (0.46-0.99, p<0.01). The study findings concluded that attachment to the diets majorly composed of fresh vegetables, and high quality protein sources may contribute in lowering the risk of breast cancer among women.

Keywords: breast cancer, dietary pattern, women, principal component analysis

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Authors: F. Uriel, M. M. Fernandez Liporace

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In the last decade, academic achievement in higher education has become a topic of agenda in Argentina, regarding the high figures of adjustment problems, academic failure and dropout, and the low graduation rates in the context of massive classes and traditional teaching methods. Psychological variables, such as perceived social support, academic motivation and learning styles and strategies have much to offer since their measurement by tests allows a proper diagnose of their influence on academic achievement. Framed in a major research, several studies analysed multiple samples, totalizing 5135 students attending Argentinean public universities. The first goal was aimed at the identification of statistically significant differences in psychological variables -perceived social support, learning styles, learning strategies, and academic motivation- by age, gender, and degree of academic advance (freshmen versus sophomores). Thus, an inferential group differences study for each psychological dependent variable was developed by means of student’s T tests, given the features of data distribution. The second goal, aimed at examining associations between the four psychological variables on the one hand, and academic achievement on the other, was responded by correlational studies, calculating Pearson’s coefficients, employing grades as the quantitative indicator of academic achievement. The positive and significant results that were obtained led to the formulation of different predictive models of academic achievement which had to be tested in terms of adjustment and predictive power. These models took the four psychological variables above mentioned as predictors, using regression equations, examining predictors individually, in groups of two, and together, analysing indirect effects as well, and adding the degree of academic advance and gender, which had shown their importance within the first goal’s findings. The most relevant results were: first, gender showed no influence on any dependent variable. Second, only good achievers perceived high social support from teachers, and male students were prone to perceive less social support. Third, freshmen exhibited a pragmatic learning style, preferring unstructured environments, the use of examples and simultaneous-visual processing in learning, whereas sophomores manifest an assimilative learning style, choosing sequential and analytic processing modes. Despite these features, freshmen have to deal with abstract contents and sophomores, with practical learning situations due to study programs in force. Fifth, no differences in academic motivation were found between freshmen and sophomores. However, the latter employ a higher number of more efficient learning strategies. Sixth, freshmen low achievers lack intrinsic motivation. Seventh, models testing showed that social support, learning styles and academic motivation influence learning strategies, which affect academic achievement in freshmen, particularly males; only learning styles influence achievement in sophomores of both genders with direct effects. These findings led to conclude that educational psychologists, education specialists, teachers, and universities must plan urgent and major changes. These must be applied in renewed and better study programs, syllabi and classes, as well as tutoring and training systems. Such developments should be targeted to the support and empowerment of students in their academic pathways, and therefore to the upgrade of learning quality, especially in the case of freshmen, male freshmen, and low achievers.

Keywords: academic achievement, academic motivation, coping, learning strategies, learning styles, perceived social support

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Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: John White

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This study investigated the effects of practice schedules on learning and performance in golf putting, specifically focusing on the impact of increasing contextual interference (CI). University students (n=7) were randomly assigned to blocked, random, or increasing practice schedules. During acquisition, participants performed 135 putting trials using different weighted golf balls. The blocked group followed a specific sequence of ball weights, while the random group practiced with the balls in a random order. The increasing group started with a blocked schedule, transitioned to a serial schedule, and concluded with a random schedule. Retention and transfer tests were conducted 24 hours later. The results indicated that high levels of CI (random practice) were more beneficial for learning than low levels of CI (blocked practice). The increasing practice schedule, incorporating blocked, serial, and random practice, demonstrated advantages over traditional blocked and random schedules. Additionally, EEG was used to explore the neurophysiological effects of the increasing practice schedule.

Keywords: skill acquisition, motor control, learning, contextual interference

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Authors: Farrukh Shahzad

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The rich Islamic resources related to religious text, Islamic sciences, and history are widely available in print and in electronic format online. However, most of these works are only available in Arabic language. In this research, an attempt is made to utilize these resources to create interactive web applications in Arabic, English and other languages. The system utilizes the Pattern Recognition, Knowledge Management, Data Mining, Information Retrieval and Management, Indexing, storage and data-analysis techniques to parse, store, convert and manage the information from authentic Arabic resources. These interactive web Apps provide smart multi-lingual search, tree based search, on-demand information matching and linking. In this paper, we provide details of application architecture, design, implementation and technologies employed. We also presented the summary of web applications already developed. We have also included some screen shots from the corresponding web sites. These web applications provide an Innovative On-line Learning Systems (eLearning and computer based education).

Keywords: Islamic resources, Muslim scholars, hadith, narrators, history, fiqh

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Authors: Angsumalin Puntho

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Disruptive technology has had a great influence on our everyday lives and the existence of an organization. Geologists in the public sector need to keep up with digital technology and be able to work and collaborate in a more effective manner. The result from SWOT and 7S McKinsey analyses suggest that there are inadequate IT personnel, no individual digital literacy development plan, and a misunderstanding of management policies. The Office of Civil Service Commission develops digital literacy skills that civil servants and government officers should possess in order to work effectively; it consists of nine dimensions, including computer skills, internet skills, cyber security awareness, word processing, spreadsheets, presentation programs, online collaboration, graphics editors and cyber security practices; and six steps of digital literacy development including self-assessment, individual development plan, self-learning, certified test, learning reflection, and practices. Geologists can use digital literacy as a learning tool to develop themselves for better career opportunities.

Keywords: disruptive technology, digital technology, digital literacy, computer skills

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Authors: Tung-Kuang Wu, Hsing-Pei Hsieh, Ying-Ru Meng

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Special Education in Taiwan has been facing difficulties including shortage of teachers and lack in resources. Some students need to receive special education are thus not identified or admitted. Fortunately, information technologies can be applied to relieve some of the difficulties. For example, on-line multimedia courseware can be used to assist the learning of special education students and take pretty much workload from special education teachers. However, there may exist cognitive variations between students in special or regular educations, which suggests the design of online courseware requires different considerations. This study aims to investigate the difference in focus of attention (FOA) between special and regular education students of primary school in viewing the computer screen. The study is essential as it helps courseware developers in determining where to put learning elements that matter the most on the right position of screen. It may also assist special education specialists to better understand the subtle differences among various subtypes of learning disabilities. This study involves 76 special education students (among them, 39 are students with mental retardation, MR, and 37 are students with learning disabilities, LDs) and 42 regular education students. The participants were asked to view a computer screen showing a picture partitioned into 3 × 3 areas with each area filled with text or icon. The subjects were then instructed to mark on the prior given paper sheets, which are also partitioned into 3 × 3 grids, the areas corresponding to the pictures on the computer screen that they first set their eyes on. The data are then collected and analyzed. Major findings are listed: 1. In both text and icon scenario, significant differences exist in the first preferred FOA between special and regular education students. The first FOA for the former is mainly on area 1 (upper left area, 53.8% / 51.3% for MR / LDs students in text scenario; and 53.8% / 56.8% for MR / LDs students in icons scenario), while the latter on area 5 (middle area, 50.0% and 57.1% in text and icons scenarios). 2. The second most preferred area in text scenario for students with MR and LDs are area 2 (upper-middle, 20.5%) and 5 (middle area, 24.3%). In icons scenario, the results are similar, but lesser in percentage. 3. Students with LDs that show similar preference (either in text or icons scenarios) in FOA to regular education students tend to be of some specific sub-type of learning disabilities. For instance, students with LDs that chose area 5 (middle area, either in text or icon scenario) as their FOA are mostly ones that have reading or writing disability. Also, three (out of 13) subjects in this category, after going through the rediagnosis process, were excluded from being learning disabilities. In summary, the findings suggest when designing multimedia courseware for students with MR and LDs, the essential learning elements should be placed on area 1, 2 and 5. In addition, FOV preference may also potentially be used as an indicator for diagnosing students with LDs.

Keywords: focus of attention, learning disabilities, mental retardation, on-line multimedia courseware, special education

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Authors: Lucas Paganin, Viliam Makis

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With the advent of globalization, the market competition has become a major issue for most companies. One of the main strategies to overcome this situation is the quality improvement of the product at a lower cost to meet customers’ expectations. In order to achieve the desired quality of products, it is important to control the process to meet the specifications, and to implement the optimal maintenance policy for the machines and the production lines. Thus, the overall objective is to reduce process variation and the production and maintenance costs. In this paper, an integrated model involving Statistical Process Control (SPC) and maintenance is developed to achieve this goal. Therefore, the main focus of this paper is to develop the jointly optimal maintenance and statistical process control policy minimizing the total long run expected average cost per unit time. In our model, the production process can go out of control due to either the deterioration of equipment or other assignable causes. The equipment is also subject to failures in any of the operating states due to deterioration and aging. Hence, the process mean is controlled by an Xbar control chart using equidistant sampling epochs. We assume that the machine inspection epochs are the times when the control chart signals an out-of-control condition, considering both true and false alarms. At these times, the production process will be stopped, and an investigation will be conducted not only to determine whether it is a true or false alarm, but also to identify the causes of the true alarm, whether it was caused by the change in the machine setting, by other assignable causes, or by both. If the system is out of control, the proper actions will be taken to bring it back to the in-control state. At these epochs, a maintenance action can be taken, which can be no action, or preventive replacement of the unit. When the equipment is in the failure state, a corrective maintenance action is performed, which can be minimal repair or replacement of the machine and the process is brought to the in-control state. SMDP framework is used to formulate and solve the joint control problem. Numerical example is developed to demonstrate the effectiveness of the control policy.

Keywords: maintenance, semi-Markov decision process, statistical process control, Xbar control chart

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Authors: Monisha Isaac

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Biotechnology is a discipline which explains the use of living organisms and systems to construct a product, or we can define it as an application or technology developed to use biological systems and organisms processes for a specific use. Particularly, it includes cells and its components use for new technologies and inventions. The tools developed can be further used in diverse fields such as agriculture, industry, research and hospitals etc. The 21^st century has seen a drastic development and advancement in biotechnology in India. Significant increase in Government of India’s outlays for biotechnology over the past decade has been observed. A sectoral break up of biotechnology-based companies in India shows that most of the companies are agriculture-based companies having interests ranging from tissue culture to biopesticides. Major attention has been given by the companies in health related activities and in environmental biotechnology. The biopharmaceutical, which comprises of vaccines, diagnostic, and recombinant products is the most reliable and largest segment of the Indian Biotech industry. India has developed its vaccine markets and supplies them to various countries. Then there are the bio-services, which mainly comprise of contract researches and manufacturing services. India has made noticeable developments in the field of bio industries including manufacturing of enzymes, biofuels and biopolymers. Biotechnology is also playing a crucial and significant role in the field of agriculture. Traditional methods have been replaced by new technologies that mainly focus on GM crops, marker assisted technologies and the use of biotechnological tools to improve the quality of fertilizers and soil. It may only be a small contributor but has shown to have huge potential for growth. Bioinformatics is a computational method which helps to store, manage, arrange and design tools to interpret the extensive data gathered through experimental trials, making it important in the design of drugs.

Keywords: biotechnology, advancement, agriculture, bio-services, bio-industries, bio-pharmaceuticals

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Yi-Hsuan Gloria Lo

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The educational objective of vocational high schools (VHSs) is to equip VHS students with practical skills and knowledge that can be applied in the job-related market. However, with the increasing number of technological universities over the past two decades, the majority of VHS students have chosen to receive higher education rather than enter the job market. VHS English education has been confronting a dilemma: Should an English for specific purposes (ESP) approach, which aligns with the educational goal of VHS education, be taken or should an English for general purposes (EGP) approach, which prepares VHS students for advanced studies in universities, be followed? While ESP theorists proposed that that ESP can be taught to secondary learners, little was known about VHS students’ perspective on this ESP-versus-EGP dilemma. Scant research has investigated different facets of students’ needs (necessities, wants, and lacks) for both ESP and EGP in terms of the four language skills and the factors that contribute to any differences. To address the gap in the literature, 100 VHS students responded to statements related to their necessities, wants, and lacks in learning ESP and EGP on a 6-point Likert scale. Six VHS students were interviewed to tap into the reasons for different facets of the needs for learning EGP and ESP. The statistical analysis indicates that at this stage of learning English, VHS subjects believed that EGP was more necessary than ESP; EGP was more desirable than ESP. However, they reported that they were more lacking in ESP than in EGP learning. Regarding EGP, the results show that the VHS subjects rated speaking as their most necessary skill, speaking as the most desirable skill, and writing as the most lacking skill. A significant difference was found between perceived learning necessities and lacks and between perceived wants and lacks. No statistical difference was found between necessities and wants. In the aspect of ESP, the results indicate that the VHS subjects marked reading as their most necessary skill, speaking as the most desirable skill, and writing as the most lacking skill. A significant difference exists between their perceived necessities and lacks and between their wants and lacks. However, there is no statistically significant difference between their perceived lacks and wants. Despite the lack of a significant difference between learning necessities and wants, the qualitative interview data reveal that the reasons for their perceived necessities and wants were different. The findings of the study confirm previous research that demonstrates that ‘needs’ is a multiple and conflicting construct. What VHS students felt most lacking was not necessarily what they believed they should learn or would like to learn. Although no statistical difference was found, different reasons were attributed to their perceived necessities and wants. Both theoretical and practical implications have been drawn and discussed for ESP research in general and teaching ESP in VHSs in particular.

Keywords: vocational high schools (VHSs), English for General Purposes (EGP), English for Specific Purposes (ESP), needs analysis

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Authors: Hirofumi Miyajima, Kazuya Kishida, Noritaka Shigei, Hiromi Miyajima

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Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed.

Keywords: Box-Jenkins's problem, double-input rule module, fuzzy inference model, obstacle avoidance, single-input rule module

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Authors: Nathan Reeves

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Aim: The aim of this study was to evaluate if there was an impact on student learning when peer role play was substituted for a professional actor in the role of simulated patient in a simulation-based scenario. Method: Third-year exercise science students enrolled in a field project course in 2015 (n=24), and 2016 (n=20) participated in a simulation-based case scenario designed to develop their client-centred exercise prescription skills. During the simulation, students were provided with feedback from the simulated patients. In 2015, three professional actors played the part of the simulated patient, and in 2016 one of the simulated patients was a student from another exercise science cohort (peer role play). The student learning experience, consistency in case fidelity and feedback provided by the simulated patients was evaluated using a 5-point Likert scale survey and collecting phenomenological data. Results: Improvements to student pre and post confidence remained constant between the 2015 and 2016 cohorts (1.04 and 0.85). The perceived usefulness and enjoyability also remained high across the two cohorts (4.96 and 4.71). The feedback provided by all three simulated patients in 2016 was seen to strongly support student learning experience (4.82), and was of a consistent level (4.47). Significance of the findings to allied health: Simulation-based education is rapidly expanding in the curricula across the allied health professions. The simulated patient methodology continues to receive support as a pedagogy to develop a range of clinical skills including communication, engagement and client-centeredness. Upskilling students to peer role play can be a reasonable alternative to engaging paid actors.

Keywords: exercise science, simulation-based learning, simulated patient, peer role play

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Authors: Rajpal Kaur, Pooja Choudhary

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Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods.

Keywords: offline signature verification, offline signature recognition, signatures, SURF features, HMM

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Authors: Hadeel Al Obaidy, Amani Al Heela

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The main purpose of this paper is to examine the concept of semantic web and the role that ontology and semantic annotation plays in the development of semantic web services. The paper focuses on semantic web infrastructure illustrating how ontology and annotation work to provide the learning capabilities for building content semantically. To improve productivity and quality of software, the paper applies approaches, notations and techniques offered by software engineering. It proposes a conceptual model to develop semantic web services for the infrastructure of web information retrieval system of digital libraries. The developed system uses ontology and annotation to build a knowledge based system to define and link the meaning of a web content to retrieve information for users’ queries. The results are more relevant through keywords and ontology rule expansion that will be more accurate to satisfy the requested information. The level of results accuracy would be enhanced since the query semantically analyzed work with the conceptual architecture of the proposed system.

Keywords: semantic web services, software engineering, semantic library, knowledge representation, ontology

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Authors: Taher Eid Shaaban Ahmed Mousa

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Non-Alcoholic fatty liver disease (NAFLD) is a major cause of multiple liver disorders, which strongly linked to a poor lifestyle. This study aiming to elucidate the exercise program’s effectiveness on hepatic fat mobilization among nonalcoholic fatty liver patients. Subjects: A purposive sample of 150 adult male & female patients. Setting: National institute of liver out patient's clinics of Menoufia University. Tools: three tools I: An interviewing structured questionnaire, II: International Physical Activity Questionnaire, III: compliance assessment sheet. Results: There was statistically significant difference pre and post exercise program regarding total body weight, physical activity level and compliance that prevent new fat development with resolution of existing one. Conclusion: regular exercise is the best implemented approach as an initial step for the prevention, treatment and management of NAFLD. Recommendation: It is highly important to unravel the mechanism and dose by which each exercise specifically resolve various stages of liver diseases.

Keywords: exercise program, hebatic fat mobilization, nonalcoholic fatty liver patients, sport science

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Authors: Till Gramberg

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In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management.

Keywords: servitization, product portfolio management, generative AI, disruptive innovation, machine and plant engineering

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Authors: Anass Hsissi, Hakim Allali, Abdelmajid Hajami

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This article presents the results of a doctoral research which aims to integrate metacognitive dimension in the design of human learning computing environments (ILE). We conducted a detailed study on the relationship between metacognitive processes and learning, specifically their positive impact on the performance of learners in the area of reading comprehension. Our contribution is to implement methods, using an intelligent agent based on BDI paradigm to ensure intelligent and reliable support for low readers, in order to encourage regulation and a conscious and rational use of their metacognitive abilities.

Keywords: metacognition, text comprehension EIAH, autoregulation, BDI agent

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Authors: Champa Gunawardana, Anishka Hettiarachchi

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Conducive Architecture for learning environments is an area of interest for many scholars around the world. Loss of sense of hearing leads to the assumption that deaf students are visual learners. Comprehending favorable non-hearing attributes of architecture can lead to effective, rich and friendly learning environments for hearing impaired. The objective of the current qualitative investigation is to explore the nature and parameters of a sense of place of deaf children to support optimal learning. The investigation was conducted with hearing-impaired children (age: between 8-19, Gender: 15 male and 15 female) of Yashodhara deaf and blind school at Balangoda, Sri Lanka. A sensory ethnography study was adopted to identify the nature of perception and the parameters of most preferred and least preferred spaces of the learning environment. The common perceptions behind most preferred places in the learning environment were found as being calm and quiet, sense of freedom, volumes characterized by openness and spaciousness, sense of safety, wide spaces, privacy and belongingness, less crowded, undisturbed, availability of natural light and ventilation, sense of comfort and the view of green colour in the surroundings. On the other hand, the least preferred spaces were found to be perceived as dark, gloomy, warm, crowded, lack of freedom, smells (bad), unsafe and having glare. Perception of space by deaf considering the hierarchy of sensory modalities involved was identified as; light - color perception (34 %), sight - visual perception (32%), touch - haptic perception (26%), smell - olfactory perception (7%) and sound – auditory perception (1%) respectively. Sense of freedom (32%) and sense of comfort (23%) were the predominant psychological parameters leading to an optimal sense of place perceived by hearing impaired. Privacy (16%), rhythm (14%), belonging (9%) and safety (6%) were found as secondary factors. Open and wide flowing spaces without visual barriers, transparent doors and windows or open port holes to ease their communication, comfortable volumes, naturally ventilated spaces, natural lighting or diffused artificial lighting conditions without glare, sloping walkways, wider stairways, walkways and corridors with ample distance for signing were identified as positive characteristics of the learning environment investigated.

Keywords: deaf, visual learning environment, perception, sensory ethnography

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Authors: Leila Goosen, Toppie N. Mukasa-Lwanga

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The purpose of this paper relates to exploring academics’ use of distance education technologies for empowerment and equity in an Information Technology environment. Literature was studied on academics’ technology use towards effective teaching and meaningful learning in a distance education Information Technology environment. Main arguments presented center on formulating and situating significant concepts within an appropriate theoretical and conceptual framework, including those related to distance education, throughput and other measures of academic efficiency. The research design, sampling, data collection instrument and the validity and reliability thereof, as well as the data analysis method used is described. The paper discusses results related to academics’ use of technology towards effective teaching and meaningful learning in a distance education Information Technology environment. Conclusions are finally presented on the way in which this paper makes a significant and original contribution regarding academics’ use of technology towards effective teaching and meaningful learning in a distance education Information Technology environment.

Keywords: distance, education, technologies, Information Technology Environment

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Authors: Vladimira Hornackova, Jana Kottasova, Zuzana Vanova, Anna Jungrova

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Present study deals with drama education in preschool education. The research made in this field brings a qualitative comparative survey with the aim to find out the use of methods and techniques of drama education in preschool education at university or secondary school graduate preschool teachers. The research uses a content analysis and an unstandardized questionnaire for preschool teachers and obtained data are processed with the help of descriptive methods and correlations. The results allow a comparison of aspects applied through drama in preschool education. The research brings impulses for education improvement in kindergartens and inspiration for university study programs of drama education in the professional training of preschool teachers.

Keywords: drama education, preschool education, preschool teacher, research

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

Procedia PDF Downloads 152