Search results for: Structural Learning.

Authors: Kathaleeya Chanda, Panupong Chanplin, Suppara Charoenpoom

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This purposes of the development of creative instruction model through digital media are to: 1) enable learners to learn from instruction media application; 2) help learners implementing instruction media correctly and appropriately; and 3) facilitate learners to apply technology for searching information and practicing skills to implement technology creatively. The sample group consists of 130 cases of secondary students studying in Bo Kluea School, Bo Kluea Nuea Sub-district, Bo Kluea District, Nan Province. The probability sampling was selected through the simple random sampling and the statistics used in this research are percentage, mean, standard deviation and one group pretest – posttest design. The findings are summarized as follows: The congruence index of instruction media for occupation and technology subjects is appropriate. By comparing between learning achievements before implementing the instruction media and learning achievements after implementing the instruction media, it is found that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. For the learning achievements from instruction media implementation, pretest mean is 16.24 while posttest mean is 26.28. Besides, pretest and posttest results are compared and differences of mean are tested, the test results show that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. This can be interpreted that the learners achieve better learning progress.

Keywords: Teaching learning model, digital media, creative instruction model, facilitate learners.

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Authors: Nilubon Kurubanjerdjit, Nattakarn Iam-On, Ka-Lok Ng

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MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation.

Keywords: MicroRNA, miRNAs, lung cancer, machine learning, Naïve Bayes, SVM.

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Authors: Taylan Kabbani, Ekrem Duman

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Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining  the financial assets price ”prediction” step and the ”allocation” step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solved the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and achieved a 2.68 Sharpe ratio on the test dataset. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Keywords: Autonomous agent, deep reinforcement learning, MDP, sentiment analysis, stock market, technical indicators, twin delayed deep deterministic policy gradient.

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Authors: Sang Wook Park, Jun Su Park, Byung Kwan Oh, Yousok Kim, Hyo Seon Park

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This study suggests the estimation method of stress distribution for the beam structures based on TLS (Terrestrial Laser Scanning). The main components of method are the creation of the lattices of raw data from TLS to satisfy the suitable condition and application of CSSI (Cubic Smoothing Spline Interpolation) for estimating stress distribution. Estimation of stress distribution for the structural member or the whole structure is one of the important factors for safety evaluation of the structure. Existing sensors which include ESG (Electric strain gauge) and LVDT (Linear Variable Differential Transformer) can be categorized as contact type sensor which should be installed on the structural members and also there are various limitations such as the need of separate space where the network cables are installed and the difficulty of access for sensor installation in real buildings. To overcome these problems inherent in the contact type sensors, TLS system of LiDAR (light detection and ranging), which can measure the displacement of a target in a long range without the influence of surrounding environment and also get the whole shape of the structure, has been applied to the field of structural health monitoring. The important characteristic of TLS measuring is a formation of point clouds which has many points including the local coordinate. Point clouds are not linear distribution but dispersed shape. Thus, to analyze point clouds, the interpolation is needed vitally. Through formation of averaged lattices and CSSI for the raw data, the method which can estimate the displacement of simple beam was developed. Also, the developed method can be extended to calculate the strain and finally applicable to estimate a stress distribution of a structural member. To verify the validity of the method, the loading test on a simple beam was conducted and TLS measured it. Through a comparison of the estimated stress and reference stress, the validity of the method is confirmed.

Keywords: Structural health monitoring, terrestrial laser scanning, estimation of stress distribution, coordinate transformation, cubic smoothing spline interpolation.

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Authors: I. Alberdi, H. Iribas, A. Martin, N. Aginako

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This paper describes a platform that faces the main research areas for e-learning educational contents. Reusability tackles the possibility to use contents in different courses reducing costs and exploiting available data from repositories. In our approach the production of educational material is based on templates to reuse learning objects. In terms of interoperability the main challenge lays on reaching the audience through different platforms. E-learning solution must track social consumption evolution where nowadays lots of multimedia contents are accessed through the social networks. Our work faces it by implementing a platform for generation of multimedia presentations focused on the new paradigm related to social media. The system produces videos-courses on top of web standard SMIL (Synchronized Multimedia Integration Language) ready to be published and shared. Regarding interfaces it is mandatory to satisfy user needs and ease communication. To overcome it the platform deploys virtual teachers that provide natural interfaces while multimodal features remove barriers to pupils with disabilities.

Keywords: Collaborative, multimedia e-learning, reusability, SMIL, virtual teacher

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Authors: Mine Uslu Uysal

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This paper interested in the mechanical deformation behavior of shear deformable functionally graded ceramic-metal (FGM) plates. Theoretical formulations are based on power law theory when build up functional graded material. The mechanical properties of the plate are graded in the thickness direction according to a power-law Displacement and stress is obtained using finite element method (FEM). The load is supposed to be a uniform distribution over the plate surface (XY plane) and varied in the thickness direction only. An FGM’s gradation in material properties allows the designer to tailor material response to meet design criteria. An FGM made of ceramic and metal can provide the thermal protection and load carrying capability in one material thus eliminating the problem of thermo-mechanical deformation behavior. This thesis will explore analysis of FGM flat plates and shell panels, and their applications to r structural problems. FGMs are first characterized as flat plates under pressure in order to understand the effect variation of material properties has on structural response. In addition, results are compared to published results in order to show the accuracy of modeling FGMs using ABAQUS software.

Keywords: Functionally graded material, finite element method, thermal and structural loading.

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Authors: I. F. Fiţigău, F. Peter, C. G. Boeriu

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Five lignin samples were fractionated with Acetone/Water mixtures and the obtained fractions were subjected to extensive structural characterization, including Fourier Transform Infrared (FT-IR), Gel permeation Chromatography (GPC) and Phosphorus-31 NMR spectroscopy (31P-NMR). The results showed that for all studied lignins the solubility increases with the increment of the acetone concentration. Wheat straw lignin has the highest solubility in 90/10 (v/v) Acetone/Water mixture, 400 mg lignin being dissolved in 1 mL mixture. The weight average molecular weight of the obtained fractions increased with the increment of acetone concentration and thus with solubility. 31P-NMR analysis based on lignin modification by reactive phospholane into phosphitylated compounds was used to differentiate and quantify the different types of OH groups (aromatic, aliphatic, and carboxylic) found in the fractions obtained with 70/30 (v/v) Acetone/Water mixture.

Keywords: Lignin, fractionation, FT-IR, GPC, 31P-NMR.

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Authors: Raquel P. F. Guiné, Daniela V. T. A. Costa, Paula M. R. Correia, Moisés Castro, Luis T. Guerra, Cristina A. Costa

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The aim of this work was to characterize a potential target group of people interested in participating into a training program in organic farming in the context of mobile-learning. The information sought addressed in particular, but not exclusively, possible contents, formats and forms of evaluation that will contribute to define the course objectives and curriculum, as well as to ensure that the course meets the needs of the learners and their preferences. The sample was selected among different European countries. The questionnaires were delivered electronically for answering on-line and in the end 135 consented valid questionnaires were obtained. The results allowed characterizing the target group and identifying their training needs and preferences towards m-learning formats, giving valuable tools to design the training offer.

Keywords: Mobile-learning, organic farming, rural development, survey.

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. Our work applies modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: Deep learning, indoor quality, metabolism, predictive model.

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Authors: Ihuarulam Ambrose Ikenna

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In the past few decades, the field of chemistry education has grown tremendously and researches indicated that after traditional chemistry instruction students often lacked deep conceptual understanding and failed to integrate their ideas into coherent conceptual framework. For several concepts in chemistry, students at all levels have demonstrated difficulty in changing their initial perceptions. Their perceptions are most often wrong and don't agree with correct scientific concepts. This study explored the effectiveness of intervention discussion sections for a college general chemistry course designed to apply research on students preconceptions, knowledge integration and student explanation. Three interventions discussions lasting three hours on bond energy and spontaneity were done tested and intervention (treatment) students’ performances were compared with that of control group which did not use the experimental pedagogy. Results indicated that this instruction which was capable of identifying students' misconceptions, initial conceptions and integrating those ideas into class discussion led to enhanced conceptual understanding and better achievement for the experimental group.

Keywords: Intervention Discussion Learning Model, Learning, Remedying, Students’ misconceptions.

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Authors: D. Aboutaleb, B. Safi

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The borate glasses are known by their structural characterized by existence of unit’s structural composed by triangles and tetrahedrons boron in different configurations depending on the percentage of B2O3 in the glass chemical composition. In this paper, effect of lithium oxide addition on the thermal and physical properties of an alumina borate glass, was investigated. It was found that the boron abnormality has a significant effect in the change of glass properties according to the addition rate of lithium oxide.

Keywords: Borate glasses, triangles and tetrahedrons boron, Lithium oxide, Boron anomaly, thermal properties, physical properties.

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Authors: Lori L. Scarlatos, Ryan Courtney

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This paper explores the impact that playing a casual game can have on a player's learning and subsequent behavior. A casual mobile game, Container Chaos, was created to teach undergraduate students about the carbon footprint of various disposable beverage containers. Learning was tested with a short quiz, and behavior was tested by observing which beverage containers players choose when offered a drink and a snack. The game was tested multiple times, under a variety of different circumstances. Findings of these tests indicate that, with extended play over time, players can learn new information and sometimes even change their behavior as a result. This has implications for how other casual games can be used to teach concepts and possibly modify behavior.

Keywords: Behavior, carbon footprint, casual games, environmental impact, material sciences.

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Authors: Khaled Abduesslam. M, Mohammed Ali, Basher H Alsdai, Muhammad Nizam, Inayati

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This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New- England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification.

Keywords: IEEE 39 bus, Least Squares Support Vector Machine, Learning Vector Quantization, Voltage Collapse.

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Authors: Nabil El Kadhi, Minerva M. Bunagan

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The dynamism in education, particularly in the area of teaching, learning and assessment has caused Higher Education Institutions (HEIs) worldwide to seek for ways to continuously improve their educational processes. HEIs use outcomes of institutional audits, assessments and accreditations, for improvement. In this study, the published institutional audit reports of HEIs in the Sultanate of Oman were analyzed to produce features of good practice; identify challenges along Teaching, Learning Assessment (TLA); and propose a framework that puts major emphasis in having a quality-assured TLA, including a set of principles that can be used as basis in succeeding an institutional visit. The TLA framework, which shows the TLA components, characteristics of the components, related expectation, including implementation tool/ strategy and pitfalls can be used by HEIs to have an adequate understanding of the scope of audit and be able to satisfy institutional audit requirements. The scope of this study can be widened by exploring the other requirements of the Institutional Audits in the Sultanate of Oman, particularly the area on Governance and Management and Student Support Services.

Keywords: Accreditation, audit, quality assurance, teaching, learning and assessment.

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Authors: C.W. Kan

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This paper investigates the use of atmospheric pressure plasma for improving the surface hydrophobicity of polyurethane synthetic leather with tetramethylsilane (TMS). The atmospheric pressure plasma treatment with TMS is a single-step process to enhance the hydrophobicity of polyurethane synthetic leather. The hydrophobicity of the treated surface was examined by contact angle measurement. The physical and chemical surface changes were evaluated by scanning electron microscopy (SEM) and infrared spectroscopy (FTIR). The purpose of this paper is to provide learning materials for understanding how to use atmospheric pressure plasma in the textile finishing process to transform a hydrophilic surface to hydrophobic.

Keywords: Learning materials, atmospheric pressure plasma treatment, hydrophobic, hydrophilic, surface.

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Authors: Patricia M. Boechler, Heather M. Gautreau

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In this research study, postsecondary students completed an information learning task in an avatar-based 3D virtual learning environment. Three factors were of interest in relation to learning; 1) the influence of collaborative vs. independent conditions, 2) the influence of the spatial arrangement of the virtual environment (linear, random and clustered), and 3) the relationship of individual differences such as spatial skill, general computer experience and video game experience to learning. Students completed pretest measures of prior computer experience and prior spatial skill. Following the premeasure administration, students were given instruction to move through the virtual environment and study all the material within 10 information stations. In the collaborative condition, students proceeded in randomly assigned pairs, while in the independent condition they proceeded alone. After this learning phase, all students individually completed a multiple choice test to determine information retention. The overall results indicated that students in pairs did not perform any better or worse than independent students. As far as individual differences, only spatial ability predicted the performance of students. General computer experience and video game experience did not. Taking a closer look at the pairs and spatial ability, comparisons were made on pairs high/matched spatial ability, pairs low/matched spatial ability and pairs that were mismatched on spatial ability. The results showed that both high/matched pairs and mismatched pairs outperformed low/matched pairs. That is, if a pair had even one individual with strong spatial ability they would perform better than pairs with only low spatial ability individuals. This suggests that, in virtual environments, the specific individuals that are paired together are important for performance outcomes. The paper also includes a discussion of trends within the data that have implications for virtual environment education.

Keywords: Avatar-based, virtual environment, paired learning, individual differences.

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: Personal information, deep learning, auto fill, NLP, document analysis.

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Authors: S. Tarighat, F. Shateri

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This study explored the relationship between L2 Willingness to Communicate (WTC) of bilinguals and monolinguals in a foreign language using a snowball sampling method to collect questionnaire data from 200 bilinguals and monolinguals studying a foreign language (FL). The results indicated a higher willingness to communicate in a foreign language (WTC-FL) performed by bilinguals compared to that of the monolinguals with a weak significance. Yet a stronger significance was found in the relationship between the age of onset of bilingualism and WTC-FL. The researcher proposed that L2 WTC is indirectly influenced by knowledge of other languages, which can boost L2 confidence and reduce L2 anxiety and consequently lead to higher L2 WTC when learning a different L2. The study also found the age of onset of bilingualism to be a predictor of L2 WTC when learning a FL. The results emphasize the importance of bilingualism and early bilingualism in particular.

Keywords: Bilingualism, foreign language learning, L2 acquisition, willingness to communicate.

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Authors: Zasiah Tafheem, Mahadee Hasan Shourav, Zahidul Islam, Saima Islam Tumpa

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The paper aims at investigating the effect of number of story on different structural components of reinforced concrete building due to gravity and lateral loading. For the study, three building models having same building plan of three, six and nine stories are analyzed and designed using software package. All the buildings are residential and are located in Dhaka city of Bangladesh. Lateral load including wind and earthquake loading are applied to the building along both longitudinal and transverse direction as per Bangladesh National Building Code (BNBC, 2006). Equivalent static force method is followed for the applied seismic loading. The present study investigates as well as compares mainly total steel requirement in different structural components for those buildings. It has been found that total longitudinal steel requirement for beams at each floor is 48.57% for three storied building, 61.36% for six storied building when the total percentage is taken as 100% in case of nine storied building. For an exterior column, the steel ratio is 2.1%, 3.06%, 4.55% for three, six and nine storied building respectively for the first three floors. In addition, it has been noted that total weight of longitudinal reinforcement of an interior column is 14.02 % for threestoried building and 43.12% for six storied building when the total reinforcement is considered 100% for nine storied building for the first three floors.

Keywords: Equivalent Static Force Method, longitudinal reinforcement, seismic loading, steel ratio.

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Authors: A. Bey, T. Bensebaa, H. Benselem

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This paper attempts to explore a new method to improve the teaching of algorithmic for beginners. It is well known that algorithmic is a difficult field to teach for teacher and complex to assimilate for learner. These difficulties are due to intrinsic characteristics of this field and to the manner that teachers (the majority) apprehend its bases. However, in a Technology Enhanced Learning environment (TEL), assessment, which is important and indispensable, is the most delicate phase to implement, for all problems that generate (noise...). Our objective registers in the confluence of these two axes. For this purpose, EASEL focused essentially to elaborate an assessment approach of algorithmic competences in a TEL environment. This approach consists in modeling an algorithmic solution according to basic and elementary operations which let learner draw his/her own step with all autonomy and independently to any programming language. This approach assures a trilateral assessment: summative, formative and diagnostic assessment.

Keywords: Algorithmic, assessment of competences, Technology Enhanced Learning (TEL).

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Authors: Nina Øystad-Larsen, Miran Cemalovic, Amir M. Kaynia

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The nonlinear static and dynamic analysis procedures presented in EN 1998-1 for the structural response of a RC wall-frame building are assessed. The structure is designed according to the guidelines for high ductility (DCH) in 1998-1. The finite element packages SeismoStruct and OpenSees are utilized and evaluated. The structural response remains nearly in the elastic range even though the building was designed for high ductility. The overstrength is a result of oversized and heavily reinforced members, with emphasis on the lower storey walls. Nonlinear response history analysis in the software packages give virtually identical results for displacements.

Keywords: Behaviour factor, Dual system, OpenSEES, Overstrength, SeismoStruct.

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Authors: Kuo Hung Huang, Chong-Ji Ke

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The purpose of this study is to explore the impacts of computer games on the mathematics instruction. First, the research designed and implemented the web-based games according to the content of existing textbook. And the researcher collected and analyzed the information related to the mathematics instruction integrating the computer games. In this study, the researcher focused on the learning motivation of mathematics, mathematics achievement, and pupil-teacher interactions in classroom. The results showed that students under instruction integrating computer games significantly improved in motivation and achievement. The teacher tended to use less direct teaching and provide more time for student-s active learning.

Keywords: computer games, mathematics instruction, pupil-teacher interaction, technology-enhanced learning

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline which ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

Keywords: Machine learning, healthcare, classification, explainability.

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Authors: Réka Sárközi, Péter Iványi, Attila B. Széll

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The aim of this paper is to present the adaptation of the dome construction tool for formex algebra to the parametric design software Grasshopper. Formex algebra is a mathematical system, primarily used for planning structural systems such like truss-grid domes and vaults, together with the programming language Formian. The goal of the research is to allow architects to plan truss-grid structures easily with parametric design tools based on the versatile formex algebra mathematical system. To produce regular structures, coordinate system transformations are used and the dome structures are defined in spherical coordinate system. Owing to the abilities of the parametric design software, it is possible to apply further modifications on the structures and gain special forms. The paper covers the basic dome types, and also additional dome-based structures using special coordinate-system solutions based on spherical coordinate systems. It also contains additional structural possibilities like making double layer grids in all geometry forms. The adaptation of formex algebra and the parametric workflow of Grasshopper together give the possibility of quick and easy design and optimization of special truss-grid domes.

Keywords: Parametric design, structural morphology, space structures, spherical coordinate system.

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: Drive test, LTE, machine learning, uplink throughput prediction.

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Authors: Jihye Jeon

Abstract:

This paper analyzes the conceptual framework of three statistical methods, multiple regression, path analysis, and structural equation models. When establishing research model of the statistical modeling of complex social phenomenon, it is important to know the strengths and limitations of three statistical models. This study explored the character, strength, and limitation of each modeling and suggested some strategies for accurate explaining or predicting the causal relationships among variables. Especially, on the studying of depression or mental health, the common mistakes of research modeling were discussed.

Keywords: Multiple regression, path analysis, structural equation models, statistical modeling, social and psychological phenomenon.

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Authors: Fiona Wahr, Sitalakshmi Venkatraman

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Industry employers require new graduates to bring with them a range of knowledge, skills and abilities which mean these new employees can immediately make valuable work contributions. These will be a combination of discipline and professional knowledge, skills and abilities which give graduates the technical capabilities to solve practical problems whilst interacting with a range of stakeholders. Underpinning the development of these disciplines and professional knowledge, skills and abilities, are “enabling” knowledge, skills and abilities which assist students to engage in learning. These are academic and learning skills which are essential to common starting points for both the learning process of students entering the course as well as forming the foundation for the fully developed graduate knowledge, skills and abilities. This paper reports on a project created to introduce and strengthen these enabling skills into the first semester of a Bachelor of Information Technology degree in an Australian polytechnic. The project uses an action research approach in the context of ongoing continuous improvement for the course to enhance the overall learning experience, learning sequencing, graduate outcomes, and most importantly, in the first semester, student engagement and retention. The focus of this is implementing the new curriculum in first semester subjects of the course with the aim of developing the “enabling” learning skills, such as literacy, research and numeracy based knowledge, skills and abilities (KSAs). The approach used for the introduction and embedding of these KSAs, (as both enablers of learning and to underpin graduate attribute development), is presented. Building on previous publications which reported different aspects of this longitudinal study, this paper recaps on the rationale for the curriculum redevelopment and then presents the quantitative findings of entering students’ reading literacy and numeracy knowledge and skills degree as well as their perceived research ability. The paper presents the methodology and findings for this stage of the research. Overall, the cohort exhibits mixed KSA levels in these areas, with a relatively low aggregated score. In addition, the paper describes the considerations for adjusting the design and delivery of the new subjects with a targeted learning experience, in response to the feedback gained through continuous monitoring. Such a strategy is aimed at accommodating the changing learning needs of the students and serves to support them towards achieving the enabling learning goals starting from day one of their higher education studies.

Keywords: Enabling skills, student retention, embedded learning support, continuous improvement.

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Authors: Jelena R. Pejović, Nina N. Serdar

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This paper presents an analysis of the “Performance-Based” seismic design method, in order to overcome the perceived disadvantages and limitations of the existing seismic design approach based on force, in engineering practice. Bearing in mind, the specificity of the earthquake as a load and the fact that the seismic resistance of the structures solely depends on its behaviour in the nonlinear field, traditional seismic design approach based on force and linear analysis is not adequate. “Performance-Based” seismic design method is based on nonlinear analysis and can be used in everyday engineering practice. This paper presents the application of this method to eight-story high reinforced concrete building with combined structural system (reinforced concrete frame structural system in one direction and reinforced concrete ductile wall system in other direction). The nonlinear time-history analysis is performed on the spatial model of the structure using program Perform 3D, where the structure is exposed to forty real earthquake records. For considered building, large number of results were obtained. It was concluded that using this method we could, with a high degree of reliability, evaluate structural behavior under earthquake. It is obtained significant differences in the response of structures to various earthquake records. Also analysis showed that frame structural system had not performed well at the effect of earthquake records on soil like sand and gravel, while a ductile wall system had a satisfactory behavior on different types of soils.

Keywords: Ductile wall, frame system, nonlinear time-history analysis, performance-based methodology, RC building.

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Authors: Jindrich J. Melcher, Marcela Karmazínová

Abstract:

The paper presents the brief information on particular results of experimental study focused to the problems of behavior of structural plated components made of fiber-cement-based materials and used in building constructions, exposed to atmospheric physical effects given by the weather changes in the summer period. Weather changes represented namely by temperature and rain cause also the changes of the temperature and moisture of the investigated structural components. This can affect their static behavior that means stresses and deformations, which have been monitored as the main outputs of tests performed. Experimental verification is based on the simulation of the influence of temperature and rain using the defined procedure of warming and water sprinkling with respect to the corresponding weather conditions during summer period in the South Moravian region at the Czech Republic, for which the application of these structural components is mainly planned. Two types of components have been tested: (i) glass-fiber-concrete panels used for building façades and (ii) fiber-cement slabs used mainly for claddings, but also as a part of floor structures or lost shuttering, and so on.

Keywords: Atmospheric physical effect, building component, experiment, fiber-cement, glass-fiber-concrete, simulation, static behavior, test, warming, water sprinkling, weather.

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