Search results for: Big Data Movement

Authors: Jianfeng Hu

Abstract:

Personal authentication based on electroencephalography (EEG) signals is one of the important field for the biometric technology. More and more researchers have used EEG signals as data source for biometric. However, there are some disadvantages for biometrics based on EEG signals. The proposed method employs entropy measures for feature extraction from EEG signals. Four type of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE) and spectral entropy (PE), were deployed as feature set. In a silhouettes calculation, the distance from each data point in a cluster to all another point within the same cluster and to all other data points in the closest cluster are determined. Thus silhouettes provide a measure of how well a data point was classified when it was assigned to a cluster and the separation between them. This feature renders silhouettes potentially well suited for assessing cluster quality in personal authentication methods. In this study, “silhouettes scores” was used for assessing the cluster quality of k-means clustering algorithm is well suited for comparing the performance of each EEG dataset. The main goals of this study are: (1) to represent each target as a tuple of multiple feature sets, (2) to assign a suitable measure to each feature set, (3) to combine different feature sets, (4) to determine the optimal feature weighting. Using precision/recall evaluations, the effectiveness of feature weighting in clustering was analyzed. EEG data from 22 subjects were collected. Results showed that: (1) It is possible to use fewer electrodes (3-4) for personal authentication. (2) There was the difference between each electrode for personal authentication (p<0.01). (3) There is no significant difference for authentication performance among feature sets (except feature PE). Conclusion: The combination of k-means clustering algorithm and silhouette approach proved to be an accurate method for personal authentication based on EEG signals.

Keywords: personal authentication, K-mean clustering, electroencephalogram, EEG, silhouettes

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Authors: Antar Bouhank, Youcef Beellal, Samir Adjel, Abdelmadjid Ababsa

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This paper investigates the impact of 3D printing parameters using the cold metal transfer (CMT) technique on the morphological, mechanical, and tribological properties of walls and massive parts made from aluminum alloy. The parameters studied include current intensity, torch movement speed, printing increment, and the flow rate of shielding gas. The manufactured parts, using the technique mentioned above, are walls and massive parts with different filling strategies, using grid and zigzag patterns and at different current intensities. The main goal of the article is to find out the welding parameters suitable for having parts with low defects and improved properties from the previously mentioned properties point of view. It has been observed from the results thus obtained that the high current intensity causes rapid solidification, resulting in high porosity and low hardness values. However, the high current intensity can cause very rapid solidification, which increases the melting point, and the part remains in the most stable shape. Furthermore, the results show that there is an evident relationship between hardness, coefficient of friction and wear test where the high intensity is, the low hardness is. The same note is for the coefficient of friction. The micrography of the walls shows a random granular structure with fine grain boundaries with a different grain size. Some interesting results are presented in this paper.

Keywords: aluminum alloy, porosity, microstructures, hardness

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Authors: Yana Xiao

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The invention of Triboelectric Nanogenerators (TENGs) provides an effective approach to the sustainable power of energy. Liquid-solid interfaces-based TENGs have been researched in virtue of less friction for harvesting energy from raindrops, rivers, and oceans in the form of water flows. However, TENGs based on droplets have rarely been investigated. In this study, we have proposed a new kind of droplet tube-based TENG (DT-TENG) with free-standing and reformative grating electrodes. Both straight and curved DT-TENGs were designed, fabricated, and evaluated, including straight tubes TENG with 27 electrodes and curved tubes TENG of 25cm radius curvature- at the inclination of 30°, 45° and 60° respectively. Different materials and hydrophobicity treatments for the tubes have also been studied, together with a discussion on the mechanism and applications of DT-TENGs. As different types of liquid discrepant energy performance, this kind of DT-TENG can be potentially used in laboratories to identify liquid or solvent. In addition, a smart fishing float is contrived, which can recognize different levels of movement speeds brought about by different weights and generate corresponding electric signals to remind the angler. The electric generation performance when using a PVC helix tube around a cylinder is similar in straight situations under the inclination of 45° in this experiment. This new structure changes the direction of a water drop or flows without losing kinetic energy, which makes utilizing Helix-Tube-TENG to harvest energy from different building morphologies possible.

Keywords: triboelectric nanogenerator, energy harvest, liquid tribomaterial, structure innovation

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Authors: Jagriti Mishra, Vasundhara Chaudhary

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Introduction: The case highlights various issues and challenges faced by Khanna Enterprises while conceptualizing and execution of launching Active Leisure wear in the domestic market, where different steps involved in the range planning and production have been elaborated. Although Khanna Enterprises was an established company which dealt in the production of knitted and woven garments, they took the risk of launching a new concept- Active Leisure wear for Millennials. Methodology: It is based on primary and secondary research where data collection has been done through survey, in-depth interviews and various reports, forecasts, and journals. Findings: The research through primary and secondary data and execution of active leisure wear substantiated the acceptance, not only by the millennials but also by the generation X. There was a demand of bigger sizes as well as more muted colours. Conclusion: The sales data paved the way for future product development in tune with the strengths of Khanna Enterprises.

Keywords: millennials, range planning, production, active leisure wear

Procedia PDF Downloads 194

Authors: David Levy

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The newly released Distributed System Interface 3 (DSI3) Bus Standard specification defines 3 modulation levels from which 16 valid symbols are coded. This structure creates power consumption variations depending on the transmitted data of a factor of more than 2 between minimum and maximum. The power generation unit has to consider therefore the worst case maximum consumption all the time and be built accordingly. This paper proposes a method to reduce both the average current consumption and worst case current consumption. The transmitter randomizes the data using several pseudo-random sequences. It then estimates the energy consumption of the generated frames and selects to transmit the one which consumes the least. The transmitter also prepends the index of the pseudo-random sequence, which is not randomized, to allow the receiver to recover the original data using the correct sequence. We show that in the case that the frame occupies most of the DSI3 synchronization period, we achieve average power consumption reduction by up to 13% and the worst case power consumption is reduced by 17.7%.

Keywords: DSI3, energy, power consumption, randomization

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Andhiani Sharfina Arnellya, Mochammad Indra Permana, Dika Pramita Destiani, Ellin Febrina

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Health Research Data, Ministry of Health of Indonesia in 2007, showed coronary heart disease (CHD) or coronary artery disease (CAD) was the third leading cause of death in Indonesia after hypertension and stroke with 7.2% incidence rate. Antiplatelet is one of the important therapy in management of patients with CHD. In addition to therapeutic effect on patients, quality of life is one aspect of another assessment to see the success of antiplatelet therapy. The purpose of this study was to determine the quality of life of patients on oral antiplatelet therapy in outpatient cardiac department Dr. Hasan Sadikin central general hospital, Bandung, Indonesia. This research is a cross sectional by collecting data through quality of life questionnaire of patients which performed prospectively as primary data and secondary data from medical record of patients. The results of this study showed that 54.3% of patients had a good quality of life, 45% had a moderate quality of life, and 0.7% had a poor quality of life. There are no significant differences in quality of life-based on age, gender, diagnosis, and duration of drug use.

Keywords: antiplatelet, quality of life, coronary artery disease, coronary heart disease

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Authors: Safiqul Islam, Anamul Haque, Mohammad Amran Hossain

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Aim: To compare the dosimetric parameters of the flattened and flattening filter free (FFF) beam and to validate the beam data using anisotropic analytical algorithm (AAA). Materials and Methods: All the dosimetric data’s (i.e. depth dose profiles, profile curves, output factors, penumbra etc.) required for the beam modeling of AAA were acquired using the Blue Phantom RFA for 6 MV, 6 FFF, 10MV & 10FFF. Progressive resolution Optimizer and Dose Volume Optimizer algorithm for VMAT and IMRT were are also configured in the beam model. Beam modeling of the AAA were compared with the measured data sets. Results: Due to the higher and lover energy component in 6FFF and 10 FFF the surface doses are 10 to 15% higher compared to flattened 6 MV and 10 MV beams. FFF beam has a lower mean energy compared to the flattened beam and the beam quality index were 6 MV 0.667, 6FFF 0.629, 10 MV 0.74 and 10 FFF 0.695 respectively. Gamma evaluation with 2% dose and 2 mm distance criteria for the Open Beam, IMRT and VMAT plans were also performed and found a good agreement between the modeled and measured data. Conclusion: We have successfully modeled the AAA algorithm for the flattened and FFF beams and achieved a good agreement with the calculated and measured value.

Keywords: commissioning of a Flattening Filter Free (FFF) , using an Anisotropic Analytical Algorithm (AAA), flattened beam, parameters

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Authors: Rajendra K. Meena, Maneesh S. Bhandari, Santan Barthwal, Harish S. Ginwal

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Microsatellite markers are the most valuable tools for the characterization of plant genetic resources or population genetic analysis. Since it is codominant and allelic markers, utilizing them in polyploid species remained doubtful. In such cases, the microsatellite marker is usually analyzed by treating them as a dominant marker. In the current study, it has been showed that despite losing the advantage of co-dominance, microsatellite markers are still a powerful tool for genotyping of polyploid species because of availability of large number of reproducible alleles per locus. It has been studied by genotyping of 19 subpopulations of Dendrocalamus hamiltonii (hexaploid bamboo species) with 17 polymorphic simple sequence repeat (SSR) primer pairs. Among these, ten primers gave typical banding pattern of microsatellite marker as expected in diploid species, but rest 7 gave an unusual pattern, i.e., more than two bands per locus per genotype. In such case, genotyping data are generally analyzed by considering as dominant markers. In the current study, data were analyzed in both ways as dominant and co-dominant. All the 17 primers were first scored as nonallelic data and analyzed; later, the ten primers giving standard banding patterns were analyzed as allelic data and the results were compared. The UPGMA clustering and genetic structure showed that results obtained with both the data sets are very similar with slight variation, and therefore the SSR marker could be utilized to characterize polyploid species by considering them as a dominant marker. The study is highly useful to widen the scope for SSR markers applications and beneficial to the researchers dealing with polyploid species.

Keywords: microsatellite markers, Dendrocalamus hamiltonii, dominant and codominant, polyploids

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Authors: Yongqi Dong, Zuo Zhang, Rui Fu, Li Li

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The records generated by taxicabs which are equipped with GPS devices is of vital importance for studying human mobility behavior, however, here we are focusing on taxi drivers' operation strategies between workdays and weekends temporally and spatially. We identify a group of valuable characteristics through large scale drivers' behavior in a complex metropolis environment. Based on the daily operations of 31,000 taxi drivers in New York City, we classify drivers into top, ordinary and low-income groups according to their monthly working load, daily income, daily ranking and the variance of the daily rank. Then, we apply big data analysis and visualization methods to compare the different characteristics among top, ordinary and low income drivers in selecting of working time, working area as well as strategies between workdays and weekends. The results verify that top drivers do have special operation tactics to help themselves serve more passengers, travel faster thus make more money per unit time. This research provides new possibilities for fully utilizing the information obtained from urban taxicab data for estimating human behavior, which is not only very useful for individual taxicab driver but also to those policy-makers in city authorities.

Keywords: big data, operation strategies, comparison, revenue, temporal, spatial

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Authors: Saad Bakkali

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Morocco is a major producer of phosphate, with an annual output of 19 million tons and reserves in excess of 35 billion cubic meters. This represents more than 75% of world reserves. Resistivity surveys have been successfully used in the Oulad Abdoun phosphate basin. A Schlumberger resistivity survey over an area of 50 hectares was carried out. A new field procedure based on analytic signal response of resistivity data was tested to deal with the presence of phosphate deposit disturbances. A resistivity map was expected to allow the electrical resistivity signal to be imaged in 2D. 2D wavelet is standard tool in the interpretation of geophysical potential field data. Wavelet transform is particularly suitable in denoising, filtering and analyzing geophysical data singularities. Wavelet transform tools are applied to analysis of a moroccan phosphate deposit ‘disturbances’. Wavelet approach applied to modeling surface phosphate “disturbances” was found to be consistently useful.

Keywords: resistivity, Schlumberger, phosphate, wavelet, Morocco

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Authors: Mohamed Dakki, Genevieve Robin, Marie Suet, Abdeljebbar Qninba, Mohamed A. El Agbani, Asmâa Ouassou, Rhimou El Hamoumi, Hichem Azafzaf, Sami Rebah, Claudia Feltrup-Azafzaf, Nafouel Hamouda, Wed a.L. Ibrahim, Hosni H. Asran, Amr A. Elhady, Haitham Ibrahim, Khaled Etayeb, Essam Bouras, Almokhtar Saied, Ashrof Glidan, Bakar M. Habib, Mohamed S. Sayoud, Nadjiba Bendjedda, Laura Dami, Clemence Deschamps, Elie Gaget, Jean-Yves Mondain-Monval, Pierre Defos Du Rau

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In biodiversity monitoring, large datasets are becoming more and more widely available and are increasingly used globally to estimate species trends and con- servation status. These large-scale datasets challenge existing statistical analysis methods, many of which are not adapted to their size, incompleteness and heterogeneity. The development of scalable methods to impute missing data in incomplete large-scale monitoring datasets is crucial to balance sampling in time or space and thus better inform conservation policies. We developed a new method based on penalized Poisson models to impute and analyse incomplete monitoring data in a large-scale framework. The method al- lows parameterization of (a) space and time factors, (b) the main effects of predic- tor covariates, as well as (c) space–time interactions. It also benefits from robust statistical and computational capability in large-scale settings. The method was tested extensively on both simulated and real-life waterbird data, with the findings revealing that it outperforms six existing methods in terms of missing data imputation errors. Applying the method to 16 waterbird species, we estimated their long-term trends for the first time at the entire North African scale, a region where monitoring data suffer from many gaps in space and time series. This new approach opens promising perspectives to increase the accuracy of species-abundance trend estimations. We made it freely available in the r package ‘lori’ (https://CRAN.R-project.org/package=lori) and recommend its use for large- scale count data, particularly in citizen science monitoring programmes.

Keywords: biodiversity monitoring, high-dimensional statistics, incomplete count data, missing data imputation, waterbird trends in North-Africa

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Authors: Marisa Rio, Sharanya Bola, Richard H. W. Funk, Gerald Gerlach

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Cell migration, wound healing and regeneration are some of the physiological phenomena in which electric fields (EFs) have proven to have an important function. Physiologically, cells experience electrical signals in the form of transmembrane potentials, ion fluxes through protein channels as well as electric fields at their surface. As soon as a wound is created, the disruption of the epithelial layers generates an electric field of ca. 40-200 mV/mm, directing cell migration towards the wound site, starting the healing process. In vitro electrotaxis, experiments have shown cells respond to DC EFs polarizing and migrating towards one of the poles (cathode or anode). A standard electrotaxis experiment consists of an electrotaxis chamber where cells are cultured, a DC power source and agar salt bridges that help delaying toxic products from the electrodes to attain the cell surface. The electric field strengths used in such an experiment are uniform and homogeneous. In contrast, the endogenous electric field strength around a wound tend to be multi-field and non-homogeneous. In this study, we present a custom device that enables electrotaxis experiments in non-homogeneous DC electric fields. Its main feature involves the replacement of conventional metallic electrodes, separated from the electrotaxis channel by agarose gel bridges, through electrolyte-filled microchannels. The connection to the DC source is made by Ag/AgCl electrodes, incased in agarose gel and placed at the end of each microfluidic channel. An SU-8 membrane closes the fluidic channels and simultaneously serves as the single connection from each of them to the central electrotaxis chamber. The electric field distribution and current density were numerically simulated with the steady-state electric conduction module from ANSYS 16.0. Simulation data confirms the application of nonhomogeneous EF of physiological strength. To validate the biocompatibility of the device cellular viability of the photoreceptor-derived 661W cell line was accessed. The cells have not shown any signs of apoptosis, damage or detachment during stimulation. Furthermore, immunofluorescence staining, namely by vinculin and actin labelling, allowed the assessment of adhesion efficiency and orientation of the cytoskeleton, respectively. Cellular motility in the presence and absence of applied DC EFs was verified. The movement of individual cells was tracked for the duration of the experiments, confirming the EF-induced, cathodal-directed motility of the studied cell line. The in vitro monolayer wound assay, or “scratch assay” is a standard protocol to quantitatively access cell migration in vitro. It encompasses the growth of a confluent cell monolayer followed by the mechanic creation of a scratch, representing a wound. Hence, wound dynamics was monitored over time and compared for control and applied the electric field to quantify cellular population motility.

Keywords: DC non-homogeneous electric fields, electrotaxis, microfluidic biochip, wound healing

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Authors: Muhammet Şahal, Oğuz Köklü

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As statistical thinking and problem-solving processes have become increasingly important, teachers need to be more rigorously prepared with statistical knowledge to teach their students effectively. This study examined preservice mathematics teachers' development of statistical investigation projects using data and exploratory data analysis tools, following a design-based research perspective and statistical investigation cycle. A total of 26 pre-service senior mathematics teachers from a public university in Turkiye participated in the study. They formed groups of 3-4 members voluntarily and worked on their statistical investigation projects for six weeks. The data sources were audio recordings of pre-service teachers' group discussions while working on their projects in class, whole-class video recordings, and each group’s weekly and final reports. As part of the study, we reviewed weekly reports, provided timely feedback specific to each group, and revised the following week's class work based on the groups’ needs and development in their project. We used content analysis to analyze groups’ audio and classroom video recordings. The participants encountered several difficulties, which included formulating a meaningful statistical question in the early phase of the investigation, securing the most suitable data collection strategy, and deciding on the data analysis method appropriate for their statistical questions. The data collection and organization processes were challenging for some groups and revealed the importance of comprehensive planning. Overall, preservice senior mathematics teachers were able to work on a statistical project that contained the formulation of a statistical question, planning, data collection, analysis, and reaching a conclusion holistically, even though they faced challenges because of their lack of experience. The study suggests that preservice senior mathematics teachers have the potential to apply statistical knowledge and techniques in a real-world context, and they could proceed with the project with the support of the researchers. We provided implications for the statistical education of teachers and future research.

Keywords: design-based study, pre-service mathematics teachers, statistical investigation projects, statistical model

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Authors: Pornnapat Berndt

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The purpose of this research is to study the tourism routes in Bangnoi Floating Market, Bangkhontee District, Samut Songkhram province, Thailand in order to create type and form of interpretation to promote cultural tourism based on local community and visitor requirement. To accomplish the goals and objectives, qualitative research will be applied. The research instruments used are observation, questionnaires, basic interviews, in-depth interviews, focus group, interviewed of key local informants including site visitors. The study also uses both primary data and secondary data. A Statistical Package for Social Sciences (SPSS) was used to analyze the data. Descriptive and inferential statistics such as tables, percentage, mean and standard deviation were used for data analysis and summary. From research result, it is revealed that the local community requirement on types of interpretation conforms to visitors require which need guide post, guide book, etc. with up to date and informally content to present Bangnoi Floating Market which got the most demand score (3.78) considered as most wanted demand.

Keywords: interpretation, cultural tourism, tourism route, local community, stakeholders participated

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Authors: Tim Wollert, Fabian Behrendt

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Value Stream Management (VSM) is a widely used methodology in the context of Lean Management for improving end-to-end material and information flows from a supplier to a customer from a company’s perspective. Whereas the design principles, e.g. Pull, value-adding, customer-orientation and further ones are still valid against the background of an increasing digitalized and dynamic environment, the methodology itself for mapping a value stream is characterized as time- and resource-intensive due to the high degree of manual activities. The digitalization of processes in the context of Industry 4.0 enables new opportunities to reduce these manual efforts and make the VSM approach more agile. The paper at hand aims at providing a modular data and calculation framework, utilizing the available business data, provided by information and communication technologies for automizing the value stream mapping process with focus on the manufacturing process.

Keywords: lean management 4.0, value stream management (VSM) 4.0, dynamic value stream mapping, enterprise resource planning (ERP)

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Authors: Peng, Chun-Yi, Chen, Wei-Hsuan, Ueng, Shyh-Kuang

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Yanjie Zhu, André Jesus, Irwanda Laory

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Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment.

Keywords: damage detection, ensemble empirical mode decomposition (EEMD), environmental variations, independent component analysis (ICA), principal component analysis (PCA), structural health monitoring (SHM)

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Authors: Ruanzhenghao Shi

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Student movements have persisted in mainland China, especially in elite universities since the Tiananmen Prodemocracy Movement, contrary to the lack of studies on them. However, the participants' repertoire, mobilization and mode of interaction with authorities are vastly different from their predecessors in the 1980s as well as their western counterparts. In most of the cases, agents, cognizant of the high cost of action and their vulnerability to the authorities, consciously curtailed certain repertoire and themes of resistance. Thus these movements, without appreciable organized force, were self-interested, fragmentally mobilized, lowly integrated and limited within the campus. This study documents the 2014 protest against Yanching Academy program at Peking University, a top-tier Chinese university that played the leading role in the 1989 protest. The 2014 case is different from abovementioned trend of submissive resistance in the last twenty years, insofar as it is a value-oriented and emotion-driven collective action with the resurgence of some repertoire. The participants perceived the university's contemporary ineffectiveness and clumsiness in control and administration, higher Party authorities' indifference to less-political themes, and an increasing number of potential advocates, including students, intellectuals and social media. It shows that resisters' perception of their relative strength to their opponents - in this case, the university and its system for controlling students - under specific circumstances, not merely political opportunities or institutional changes, stimulates the participants and thus contributes to the mobilization and organization of a collective action, even under severe social control.

Keywords: collective action, China, university students, resistance

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Authors: Hana Maresova, Daniel Ecler

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The paper focuses on the use of 3D multi-user virtual environments (MUVE) in language teaching and presents the results of four years of research at the Faculty of Education, Palacký University in Olomouc (Czech Republic). In the form of an experiment, mother tongue language teaching in the 3D virtual worlds Second Life and Kitely (experimental group) and parallel traditional teaching on identical topics representing teacher's interpretation using a textbook (control group) were implemented. The didactic test, which was presented to the experimental and control groups in an identical form before and after the instruction, verified the effect of the instruction in the experimental group by comparing the results obtained by both groups. Within the three components of mother-tongue teaching (vocabulary, literature, style and communication education), the students in the literature group achieved partially better results (statistically significant in the case of items devoted to the area of visualization of the learning topic), while in the case of grammar and style education the respondents of the control group achieved better results. On the basis of the results obtained, we can conclude that the most appropriate use of MUVE can be seen in the teaching of those topics that provide the possibility of dramatization, experiential learning and group involvement and cooperation, on the contrary, with regard to the need to divide students attention between the topic taught and the control of avatar and movement in virtual reality as less suitable for teaching in the area of memorization of the topic or concepts.

Keywords: distance learning, 3D virtual environments, online teaching, language teaching

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Authors: Heeyeul Kwon, Yongtae Park

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Despite the highly touted benefits, emerging technologies have unleashed pervasive concerns regarding unintended and unforeseen social impacts. Thus, those wishing to create safe and socially acceptable products need to identify such side effects and mitigate them prior to the market proliferation. Various methodologies in the field of technology assessment (TA), namely Delphi, impact assessment, and scenario planning, have been widely incorporated in such a circumstance. However, literatures face a major limitation in terms of sole reliance on participatory workshop activities. They unfortunately missed out the availability of a massive untapped data source of futuristic information flooding through the Internet. This research thus seeks to gain insights into utilization of futuristic data, future-oriented documents from the Internet, as a supplementary method to generate social impact scenarios whilst capturing perspectives of experts from a wide variety of disciplines. To this end, network analysis is conducted based on the social keywords extracted from the futuristic documents by text mining, which is then used as a guide to produce a comprehensive set of detailed scenarios. Our proposed approach facilitates harmonized depictions of possible hazardous consequences of emerging technologies and thereby makes decision makers more aware of, and responsive to, broad qualitative uncertainties.

Keywords: emerging technologies, futuristic data, scenario, text mining

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Authors: Alina Trifan, António J. R. Neves

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Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods.

Keywords: bayer image, CFA, lossless compression, image coding standards

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Authors: Azwan Hamdan, Mohd Qayyum Ab Latip, Hasliza Abu Hassim, Tengku Rinalfi Putra Tengku Azizan, Hafandi Ahmad

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Animal cognition provides some evidence for self-recognition, which is described as the ability to recognize oneself as an individual separate from the environment and other individuals. The mirror self-recognition (MSR) or mark test is a behavioral technique to determine whether an animal have the ability of self-recognition or self-awareness in front of the mirror. It also describes the capability for an animal to be aware of and make judgments about its new environment. Thus, the objectives of this study are to measure and to compare the ability of wild and captive wildlife in mirror self-recognition. Wild animals from the Royal Belum Rainforest Malaysia were identified based on the animal trails and salt lick grounds. Acrylic mirrors with wood frame (200 x 250cm) were located near to animal trails. Camera traps (Bushnell, UK) with motion-detection infrared sensor are placed near the animal trails or hiding spot. For captive wildlife, animals such as Malayan sun bear (Helarctos malayanus) and chimpanzee (Pan troglodytes) were selected from Zoo Negara Malaysia. The captive animals were also marked using odorless and non-toxic white paint on its forehead. An acrylic mirror with wood frame (200 x 250cm) and a video camera were placed near the cage. The behavioral data were analyzed using ethogram and classified through four stages of MSR; social responses, physical inspection, repetitive mirror-testing behavior and realization of seeing themselves. Results showed that wild animals such as barking deer (Muntiacus muntjak) and long-tailed macaque (Macaca fascicularis) increased their physical inspection (e.g inspecting the reflected image) and repetitive mirror-testing behavior (e.g rhythmic head and leg movement). This would suggest that the ability to use a mirror is most likely related to learning process and cognitive evolution in wild animals. However, the sun bear’s behaviors were inconsistent and did not clearly undergo four stages of MSR. This result suggests that when keeping Malayan sun bear in captivity, it may promote communication and familiarity between conspecific. Interestingly, chimp has positive social response (e.g manipulating lips) and physical inspection (e.g using hand to inspect part of the face) when they facing a mirror. However, both animals did not show any sign towards the mark due to lost of interest in the mark and realization that the mark is inconsequential. Overall, the results suggest that the capacity for MSR is the beginning of a developmental process of self-awareness and mental state attribution. In addition, our findings show that self-recognition may be based on different complex neurological and level of encephalization in animals. Thus, research on self-recognition in animals will have profound implications in understanding the cognitive ability of an animal as an effort to help animals, such as enhanced management, design of captive individuals’ enclosures and exhibits, and in programs to re-establish populations of endangered or threatened species.

Keywords: mirror self-recognition (MSR), self-recognition, self-awareness, wildlife

Procedia PDF Downloads 254

Authors: Afroditi Zamanidou, Dionysios Giannakopoulos, Konstantinos Manolitsis

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A notable amount of electrical energy demand in many countries worldwide is used to cover public energy demand for road, square and other public spaces’ lighting. Renewable energy can contribute in a significant way to the electrical energy demand coverage for public lighting. This paper focuses on the sizing and design of a hybrid energy system (HES) exploiting the solar-wind energy potential to meet the electrical energy needs of lighting roads, squares and other public spaces. Moreover, the proposed HES provides coverage of the electrical energy demand for a Wi-Fi hotspot and a charging hotspot for the end-users. Alongside the sizing of the energy production system of the proposed HES, in order to ensure a reliable supply without interruptions, a storage system is added and sized. Multiple scenarios of energy consumption are assumed and applied in order to optimize the sizing of the energy production system and the energy storage system. A database with meteorological prediction data for 51 areas in Greece is developed in order to assess the possible deployment of the proposed HES. Since there are detailed meteorological prediction data for all 51 areas under investigation, the use of these data is evaluated, comparing them to real meteorological data. The meteorological prediction data are exploited to form three hourly production profiles for each area for every month of the year; minimum, average and maximum energy production. The energy production profiles are combined with the energy consumption scenarios and the sizing results of the energy production system and the energy storage system are extracted and presented for every area. Finally, the economic performance of the proposed HES in terms of Levelized cost of energy is estimated by calculating and assessing construction, operation and maintenance costs.

Keywords: energy production system sizing, Greece’s deployment potential, meteorological prediction data, wind-solar hybrid energy system, levelized cost of energy

Procedia PDF Downloads 134

Authors: Ikhwanuddin Harahap

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This paper investigates the contribution of culture to divorce prevention in Indonesia in the "new normal" era, especially in Batak, Malay and Minangkabau tribes. This research is qualitative with an anthropological approach. Data were collected by interview and observation techniques. Checking the validity of the data is done by triangulation technique, and the data is analyzed by content analysis. The results of the research showed that culture has a strategic role in preventing divorce. In Batak, Malay and Minangkabau-as, major ethnic groups in Indonesian cultures, have a set of norms and dogmas conveyed at the wedding party, namely “marriage must be eternal and if divorced by death.” In addition, cultural figures actively become arbiters in resolving family conflicts, such as Harajaon in Batak, Datuk in Malay and Mamak in Minangkabau. Cultural dogmas and cultural figures play a very important role in preventing divorce.

Keywords: culture, divorce, prevention, contribution, new normal, era

Procedia PDF Downloads 154

Authors: Chao-Linag Hsieh, Zheng-Wei Ye, Chen-Kang Huang, Yeun-Chung Lee, Chih-Hong Sun, Tzai-Hung Wen, Jehn-Yih Juang, Joe-Air Jiang

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Air pollution and climate warming become more and more intensified in many areas, especially in urban areas. Environmental parameters are critical information to air pollution and weather monitoring. Thus, it is necessary to develop a suitable air pollution and weather monitoring system for urban areas. In this study, a vehicle monitoring system (VMS) based on the IoT technique is developed. Cars are selected as the research tool because it can reach a greater number of streets to collect data. The VMS can monitor different environmental parameters, including ambient temperature and humidity, and air quality parameters, including PM2.5, NO₂, CO, and O₃. The VMS can provide other information, including GPS signals and the vibration information through driving a car on the street. Different sensor modules are used to measure the parameters and collect the measured data and transmit them to a cloud server through the LoRa protocol. A user interface is used to show the sensing data storing at the cloud server. To examine the performance of the system, a researcher drove a Nissan x-trail 1998 to the area close to the Da’an District office in Taipei to collect monitoring data. The collected data are instantly shown on the user interface. The four kinds of information are provided by the interface: GPS positions, weather parameters, vehicle information, and air quality information. With the VMS, users can obtain the information regarding air quality and weather conditions when they drive their car to an urban area. Also, government agencies can make decisions on traffic planning based on the information provided by the proposed VMS.

Keywords: LoRa, monitoring system, smart city, vehicle

Procedia PDF Downloads 378

Authors: Bruno D. Souza, Gerson G. Cunha, Michael O. Ferreira, Roberto Rosenhaim, Robson C. Santos, Sergio O. Santos

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Devices and applications that use satellite-based positioning are becoming more popular day-by-day. Thus, evolution and improvement in this technology are mandatory. Accordingly, satellite georeferenced systems need to accomplish the same evolution rhythm. Either GPS (Global Positioning System) or its similar Russian GLONASS (Global Navigation Satellite System) are system samples that offer us powerful tools to plot coordinates on the earth surface. The development of this research aims the study of several aspects related to use of GPS and GLONASS technologies, given its application and collected data improvement during geodetic data acquisition. So, both relevant theoretic and practical aspects are considered. In this context, at the theoretical part, the main systems' characteristics are shown, observing its similarities and differences. At the practical part, a series of experiences are performed and obtained data packages are compared in order to demonstrate equivalence or differences among them. The evaluation methodology targets both quantitative and qualitative analysis provided by GPS and GPS/GLONASS receptors. Meanwhile, a specific collected data storage system was developed to better compare and analyze them (SisGeo - Georeferenced Research Comparison Support System).

Keywords: satellites, systems, applications, experiments, receivers

Procedia PDF Downloads 237

Authors: Haowei Lu, Anaya Aaron

Abstract:

Understanding historical solar generation and forecasting future solar generation from interconnected Distributed Energy Resources (DER) is crucial for utility planning and interconnection studies. The existing methodology, which relies on solar radiation, weather data, and common inverter models, is becoming less accurate. Rapid advancements in DER technologies have resulted in more diverse project sites, deviating from common patterns due to various factors such as DC/AC ratio, solar panel performance, tilt angle, and the presence of DC-coupled battery energy storage systems. In this paper, the authors review 10,000 DER projects within the system and analyze the Advanced Metering Infrastructure (AMI) data for various types to demonstrate the impact of different parameters. An updated methodology is proposed for redefining historical and future solar generation in distribution feeders.

Keywords: photovoltaic system, solar energy, fluctuations, energy storage, uncertainty

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Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

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The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

Procedia PDF Downloads 36

Authors: Chetneti Srisa-an

Abstract:

In Knowledge and Data Engineering field, relational database is the best repository to store data in a real world. It has been using around the world more than eight decades. Normalization is the most important process for the analysis and design of relational databases. It aims at creating a set of relational tables with minimum data redundancy that preserve consistency and facilitate correct insertion, deletion, and modification. Normalization is a major task in the design of relational databases. Despite its importance, very few algorithms have been developed to be used in the design of commercial automatic normalization tools. It is also rare technique to do it automatically rather manually. Moreover, for a large and complex database as of now, it make even harder to do it manually. This paper presents a new complete automated relational database normalization method. It produces the directed graph and spanning tree, first. It then proceeds with generating the 2NF, 3NF and also BCNF normal forms. The benefit of this new algorithm is that it can cope with a large set of complex function dependencies.

Keywords: relational database, functional dependency, automatic normalization, primary key, spanning tree

Procedia PDF Downloads 338