Search results for: data dependency graph

Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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Authors: Sazzad Hossain Talukder

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Survival in the competitive international market with unforeseen environmental contingencies has always been a concern of the firms that led to adopting different strategies to deal with different situations. Language strategy is considered to enhance the international performance of a firm by organizing language diversity and fostering communications within and outside the firm. Yet there is a lack of theoretical attention or model development on the role of language strategy on firm international survival. From resource dependence perspective, the adoption of language strategy and its relationship with firm survival are determined by the firm´s capability to prevent dependency concentration and/or increase relative power on the external environment. However, the impact of language strategy on firm survival is complex and multifaceted as the strategy influence firm performance indirectly through communication, coordination, learning and value creation. The evidence of various types of language strategies and different forms of firm survival also bring in complexities to understand the effects of a language strategy on the international survival of a firm. Based on language literatures and resource dependence logic, certain propositions are developed to conceptualize the relationship between language strategy and firm international survival in this conceptual paper. For the purpose of this paper, a conceptual model is proposed to examine how different kinds of language strategy foster reduction of resource dependency that lead to firm international survival in respond to local responsiveness and global integration. In this proposed model, it is theorized that language strategy has a positive relationship with the international survival of the firm, as the strategy is likely to reduce external resource dependency and increase the ability to continue independent operations both in short and long term.

Keywords: language strategy, language diversity, firm international survival, resource dependence logic

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Authors: Seedwell Sithole

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One of the persistent problems in accounting education is how to effectively support students’ learning. A promising technique to this issue is to investigate the extent that learning is determined by the design of instructional material. This study examines the academic performance of students using three instructional designs in financial accounting. Student’s performance scores and reported mental effort ratings were used to determine the instructional effectiveness. The findings of this study show that accounting students prefer graph and text designs that are integrated. The results suggest that spatially separated graph and text presentations in accounting should be reorganized to align with the requirements of human cognitive architecture.

Keywords: accounting, cognitive load, education, instructional preferences, students

Procedia PDF Downloads 119

Authors: Xinyuan Chen, Mohd Nizam Husen, Zhongmei Zhou, Gongde Guo, Wei Gao

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Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention for its enormous value; however, existing models lack capabilities to capture both local interactions and global dependencies simultaneously with evolutionary dynamics, while the latest achievements in convolutions and Transformers haven't been employed in this area. What’s more, periodic patterns in TKGs haven’t been fully explored either. To this end, a multi-stage hybrid architecture with convolution-backed Transformers is introduced in TKGC tasks for the first time combining the Hawkes process to model evolving event sequences in a continuous-time domain. In addition, the seasonal-trend decomposition is adopted to identify periodic patterns. Experiments on six public datasets are conducted to verify model effectiveness against state-of-the-art (SOTA) methods. An extensive ablation study is carried out accordingly to evaluate architecture variants as well as the contributions of independent components in addition, paving the way for further potential exploitation. Besides complexity analysis, input sensitivity and safety challenges are also thoroughly discussed for comprehensiveness with novel methods.

Keywords: temporal knowledge graph completion, convolution, transformer, Hawkes process, periodicity

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Authors: Vathna Suy, Ki-Il Song

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In order to store highly pressurized gas such as an isothermal compressed air energy storage, Lined Rock Caverns (LRC) are constructed underground and supported by layers of concrete, steel and rock masses. This study aims to numerically investigate the performance of rock masses which serve as a support of Lined Rock Cavern subjected to high cyclic pressure loadings. FLAC3D finite different software is used for the simulation since the software can effectively model the behavior of concrete lining and steel plate with its built-in structural elements. Cyclic pressure loadings are applied onto the inner surface of the cavern which then transmitted to concrete, steel and eventually to the surrounding rock masses. Changes of stress and strain are constantly monitored throughout all the process of loading operations. The results at various monitoring locations are then extracted and analyzed to assess the response of the rock masses, specifically on its ability to absorb energy during loadings induced by the changes of cyclic pressure loadings inside the cavern. By analyzing the obtained data of stress-strain relation and taking into account the behavior of materials under the effect of strain-dependency, conclusions on the performance of rock masses subjected to high cyclic loading conditions are drawn.

Keywords: cyclic loading, FLAC3D, lined rock cavern (LRC), strain-dependency

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Authors: Ujjal Chattaraj, Pbharat Saikumar, Thinley Dorji

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Earth is going to be a planet where no further life can sustain if people continue to pollute the environment. We need energy and fuels everyday for heating and lighting purposes in our life. It’s high time we know this problem and take measures at-least to reduce pollution and take alternative measures for everyday livelihood. Biogas is one of them. It is very essential to define and control the parameters for optimization of biogas production. Biogas plants can be made of different size, but it is very vital to make a biogas which will be cost effective, with greater efficiency (more production) and biogas plants that will sustain for a longer period of time for usage. In this research, experiments were carried out only on cow dung and Chicken manure depending on the substrates people out there (Bhutan) used. The experiment was done within 25 days and was tested for different temperatures and found out which produce more amount. Moreover, it was also statistically tested for their dependency and non-dependency which gave clear idea more on their production.

Keywords: digester, mesophilic temperature, organic manure, statistical analysis, thermophilic temperature, t-test

Procedia PDF Downloads 180

Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Sumit Kumar, Sunil Kumar, Chandan Deep Singh

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This paper presents modeling and simulation of flexible robot in an underwater environment. The underwater environment completely contrasts with ground or space environment. The robot in an underwater situation is subjected to various dynamic forces like buoyancy forces, hydrostatic and hydrodynamic forces. The underwater robot is modeled as Rayleigh beam. The developed model further allows estimating the deflection of tip in two directions. The complete dynamics of the underwater robot is analyzed, which is the main focus of this investigation. The control of robot trajectory is not discussed in this paper. Simulation is performed using Symbol Shakti software.

Keywords: bond graph modeling, dynamics. modeling, rayleigh beam, underwater robot

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Authors: Ralph Adaimy, Wassim El-Hajj, Ghassen Ben Brahim, Hazem Hajj, Haidar Safa

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Huge amounts of data and personal information are being sent to and retrieved from web applications on daily basis. Every application has its own confidentiality and integrity policies. Violating these policies can have broad negative impact on the involved company’s financial status, while enforcing them is very hard even for the developers with good security background. In this paper, we propose a framework that enforces security-by-construction in web applications. Minimal developer effort is required, in a sense that the developer only needs to annotate database attributes by a security class. The web application code is then converted into an intermediary representation, called Extended Program Dependence Graph (EPDG). Using the EPDG, the provided annotations are propagated to the application code and run against generic security enforcement rules that were carefully designed to detect insecure information flows as early as they occur. As a result, any violation in the data’s confidentiality or integrity policies is reported. As a proof of concept, two PHP web applications, Hotel Reservation and Auction, were used for testing and validation. The proposed system was able to catch all the existing insecure information flows at their source. Moreover and to highlight the simplicity of the suggested approaches vs. existing approaches, two professional web developers assessed the annotation tasks needed in the presented case studies and provided a very positive feedback on the simplicity of the annotation task.

Keywords: web applications security, secure information flow, program dependence graph, database annotation

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Authors: Hamsini Pulugurtha, P. V. S. L. Jagadamba

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Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations

Keywords: Siamese neural network, English speech, English text, natural language processing, unsupervised extractive text summarization

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Authors: K. Kiniwa, S. Kitagawa, M. Kawamoto, H. Uchiyama

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This study aims to identify phenomenologically the embodiment of soccer in order to analyze soccer games. In this paper the authors focused on the embodiment of sports and the embodiment of the goal type ball games of team sports. The authors revealed that the embodiment of sports is represented by inverse proportional body. It is possible for this structure (body scheme) of intercorporeality of sports to be compared to the symbolic figure of Uroboros which is a monster connected the tails of two snakes. The embodiment of the goal type ball games of team sports has dependency on situation and complexity. In doing this, it revealed that soccer is sensitive and emotional sports.

Keywords: intercorporeality, structure, body scheme, Uroboros, inverse proportional body, dependency on situation, complexity

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Authors: Rajnish Shrivastava, Y. R. Sood, Priti Pundir, Rahul Srivastava

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Power transformer is one of the most important devices that are used in power station. Due to several fault impending upon it or due to ageing, etc its life gets lowered. So, it becomes necessary to have diagnosis of oil for fault analysis. Due to the chemical, electrical, thermal and mechanical stress the insulating material in the power transformer degraded. It is important to regularly assess the condition of oil and the remaining life of the power transformer. In this paper UV-VIS absorption graph area is correlated with moisture content, Flash point, IFT and Density of Transformer oil. Since UV-VIS absorption graph area varies accordingly with the variation in different transformer parameters. So by obtaining the correlation among different oil parameters for oil with respect to UV-VIS absorption area, decay contents of transformer oil can be predicted

Keywords: breakdown voltage (BDV), interfacial Tension (IFT), moisture content, ultra violet-visible rays spectroscopy (UV-VIS)

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Authors: Fulufhelo Oscar Makananise

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Digital technologies have played a significant role in bridging the information gap between the haves and the have nots in society. In developing countries such as South Africa, historically marginalised groups such as women in rural communities have an opportunity to use digital technologies to network among themselves as well as interact with their government, thereby enhancing prospects for poverty eradication, political participation, community development and democracy. However, the extent to which these goals can be achieved in a developing context through harnessing digital technologies is not quite clear, particularly given the fact that access to these technologies is not evenly distributed and the fact that women’s access to digital technologies is hampered by factors that go beyond the question of infrastructure. Informed by the technological dependency theory, this paper is about how female youth in rural South Africa are deploying digital media tools for socio-economic empowerment. In particular, the study investigated the extent to which female youth in Limpopo province, South Africa access and use digital media platforms and gadgets and the extent to which those technologies are breaking down barriers that stand in the way of female youth empowerment. Data were gathered using a self-administered questionnaire disseminated to selected 100 female youth in Limpopo Province, South Africa. The data were analysed using SPSS version 9, and the results were analysed using descriptive statistics. The paper argues that wider and constant access to digital media by female youth in rural areas is indicative of the great potential for empowering female youth in rural areas through harnessing digital media. The study established that the majority of female youth had access to digital media technologies and used them to share valuable information among themselves. The study further established that female youth are active users of digital media in South Africa, which is the significant driver for socio-economic empowerment.

Keywords: digital technologies, empowerment, female youth, South Africa, survey, technological dependency

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Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Ho-Won Jung, Donghun Lee, Hyung-Jin Kim

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Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size.

Keywords: explanatory modeling, metabolic syndrome, predictive analytics, theory building

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Authors: Masoud Charkhabi

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We examine trade theory with this motivation. The full set of World Trade Organization data are organized into country-year pairs, each treated as a different entity. Topological Data Analysis reveals that among the 16 region and 240 region-year pairs there exists in fact a distinguishable group of region-period pairs. The generally accepted periods of shifts from dissimilar-dissimilar to similar-similar trade in goods among regions are examined from this new perspective. The period breaks are treated as cumulative and are flexible. This type of all-subsets analysis is motivated from computer science and is made possible with Lossy Compression and Graph Theory. The results question many patterns in similar-similar to dissimilar-dissimilar trade. They also show indications of economic shifts that only later become evident in other economic metrics.

Keywords: econometrics, globalization, network science, topological data, analysis, trade theory, visualization, world trade

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Authors: Imene Skhakhfa, Lahbaci Ouerdachi

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To ensure the overall coherence of simulated results, it is necessary to develop a robust validation process. In many applications, it is no longer content to calibrate and validate the model only in relation to the hydro graph measured at the outlet, but we try to better simulate the functioning of the watershed in space. Therefore the timing also performs compared to other variables such as water level measurements in intermediate stations or groundwater levels. As part of this work, we limit ourselves to modeling flood of short duration for which the process of evapotranspiration is negligible. The main parameters to identify the models are related to the method of unit hydro graph (HU). Three different models were tested: SNYDER, CLARK and SCS. These models differ in their mathematical structure and parameters to be calibrated while hydrological data are the same, the initial water content and precipitation. The models are compared on the basis of their performance in terms six objective criteria, three global criteria and three criteria representing volume, peak flow, and the mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent and also highlight the problems associated with the simulation of low flow events and intermittent precipitation.

Keywords: model calibration, intensity, runoff, hydrograph

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Authors: Sai Sudarshan, Harsh Vyas, Riddhiman Sherlekar

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The oil and gas industry has been unwilling to adopt stochastic definition of reserves. Nevertheless, Monte Carlo simulation methods have gained acceptance by engineers, geoscientists and other professionals who want to evaluate prospects or otherwise analyze problems that involve uncertainty. One of the common applications of Monte Carlo simulation is the estimation of recoverable hydrocarbon from a reservoir.Monte Carlo Simulation makes use of random samples of parameters or inputs to explore the behavior of a complex system or process. It finds application whenever one needs to make an estimate, forecast or decision where there is significant uncertainty. First, the project focuses on performing Monte-Carlo Simulation on a given data set using U. S Department of Energy’s MonteCarlo Software, which is a freeware e&p tool. Further, an algorithm for simulation has been developed for MATLAB and program performs simulation by prompting user for input distributions and parameters associated with each distribution (i.e. mean, st.dev, min., max., most likely, etc.). It also prompts user for desired probability for which reserves are to be calculated. The algorithm so developed and tested in MATLAB further finds implementation in Python where existing libraries on statistics and graph plotting have been imported to generate better outcome. With PyQt designer, codes for a simple graphical user interface have also been written. The graph so plotted is then validated with already available results from U.S DOE MonteCarlo Software.

Keywords: simulation, probability, confidence interval, sensitivity analysis

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Authors: Siddharth Krishnaswamy

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Problem Statement: Currently, when analyzing household food security, the most commonly studied food access indicators are household income and expenditure. Larger studies do take into account other indices such as credit and employment. But these are baselines studies and by definition are conducted infrequently. Food security analysis for access is usually dedicated to analyzing income and expenditure indicators. And both these indicators are notoriously inconsistent. Yet this data can very often end up being the basis on which household food access is calculated; and by extension, be used for decision making. Objectives: This paper argues that along with income and expenditure, credit and debit information should be collected so that an accurate analysis of household food security (and in particular) food access can be determined. The lack of collection and analysis of this information routinely means that there is often a “masking” of the actual situation; a household’s food access and food availability patterns may be adequate mainly as a result of borrowing and may even be due to a long- term dependency (a debt cycle). In other words, such a household is, in reality, worse off than it appears a factor masked by its performance on basic access indicators. Procedures/methodologies/approaches: Existing food security data sets collected in 2005 in Azerbaijan, 2010 across Myanmar and 2014-15 across Uganda were used to support the theory that analyzing income and expenditure of a HHs and analyzing the same in addition to data on credit & borrowing patterns will result in an entirely different scenario of food access of the household. Furthermore, the data analyzed depicts food consumption patterns across groups of households and then relates this to the extent of dependency on credit, i.e. households borrowing money in order to meet food needs. Finally, response options that were based on analyzing only income and expenditure; and response options based on income, expenditure, credit, and borrowing – from the same geographical area of operation are studied and discussed. Results: The purpose of this work was to see if existing methods of household food security analysis could be improved. It is hoped that food security analysts will collect household level information on credit and debit and analyze them against income, expenditure and consumption patterns. This will help determine if a household’s food access and availability are dependent on unsustainable strategies such as borrowing money for food or undertaking sustained debts. Conclusions: The results clearly show the amount of relevant information that is missing in Food Access analysis if debit and borrowing of the household is not analyzed along with the typical Food Access indicators that are usually analyzed. And the serious repercussions this has on Programmatic response and interventions.

Keywords: analysis, food security indicators, response, resilience analysis

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Authors: Taysir Soliman

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One of the current popular clustering techniques is Spectral Clustering (SC) because of its advantages over conventional approaches such as hierarchical clustering, k-means, etc. and other techniques as well. However, one of the disadvantages of SC is the time consuming process because it requires computing the eigenvectors. In the past to overcome this disadvantage, a number of attempts have been proposed such as the Power Iteration Clustering (PIC) technique, which is one of versions from SC; some of PIC advantages are: 1) its scalability and efficiency, 2) finding one pseudo-eigenvectors instead of computing eigenvectors, and 3) linear combination of the eigenvectors in linear time. However, its worst disadvantage is an inter-class collision problem because it used only one pseudo-eigenvectors which is not enough. Previous researchers developed Deflation-based Power Iteration Clustering (DPIC) to overcome problems of PIC technique on inter-class collision with the same efficiency of PIC. In this paper, we developed Parallel DPIC (PDPIC) to improve the time and memory complexity which is run on apache spark framework using sparse matrix. To test the performance of PDPIC, we compared it to SC, ESCG, ESCALG algorithms on four small graph benchmark datasets and nine large graph benchmark datasets, where PDPIC proved higher accuracy and better time consuming than other compared algorithms.

Keywords: spectral clustering, power iteration clustering, deflation-based power iteration clustering, Apache spark, large graph

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Authors: Hugo Daneluzzo, Christelle Rabbat, Alan Jean-Marie

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Water electrolysis is one of the most advanced technologies for producing hydrogen and can be easily combined with electricity from different sources. Under the influence of electric current, water molecules can be split into oxygen and hydrogen. The production of hydrogen by water electrolysis favors the integration of renewable energy sources into the energy mix by compensating for their intermittence through the storage of the energy produced when production exceeds demand and its release during off-peak production periods. Among the various electrolysis technologies, anion exchange membrane (AEM) electrolyser cells are emerging as a reliable technology for water electrolysis. Modeling and simulation are effective tools to save time, money, and effort during the optimization of operating conditions and the investigation of the design. The modeling and simulation become even more important when dealing with multiphysics dynamic systems. One of those systems is the AEM electrolysis cell involving complex physico-chemical reactions. Once developed, models may be utilized to comprehend the mechanisms to control and detect flaws in the systems. Several modeling methods have been initiated by scientists. These methods can be separated into two main approaches, namely equation-based modeling and graph-based modeling. The former approach is less user-friendly and difficult to update as it is based on ordinary or partial differential equations to represent the systems. However, the latter approach is more user-friendly and allows a clear representation of physical phenomena. In this case, the system is depicted by connecting subsystems, so-called blocks, through ports based on their physical interactions, hence being suitable for multiphysics systems. Among the graphical modelling methods, the bond graph is receiving increasing attention as being domain-independent and relying on the energy exchange between the components of the system. At present, few studies have investigated the modelling of AEM systems. A mathematical model and a bond graph model were used in previous studies to model the electrolysis cell performance. In this study, experimental data from literature were simulated using OpenModelica using bond graphs and mathematical approaches. The polarization curves at different operating conditions obtained by both approaches were compared with experimental ones. It was stated that both models predicted satisfactorily the polarization curves with error margins lower than 2% for equation-based models and lower than 5% for the bond graph model. The activation polarization of hydrogen evolution reactions (HER) and oxygen evolution reactions (OER) were behind the voltage loss in the AEM electrolyzer, whereas ion conduction through the membrane resulted in the ohmic loss. Therefore, highly active electro-catalysts are required for both HER and OER while high-conductivity AEMs are needed for effectively lowering the ohmic losses. The bond graph simulation of the polarisation curve for operating conditions at various temperatures has illustrated that voltage increases with temperature owing to the technology of the membrane. Simulation of the polarisation curve can be tested virtually, hence resulting in reduced cost and time involved due to experimental testing and improved design optimization. Further improvements can be made by implementing the bond graph model in a real power-to-gas-to-power scenario.

Keywords: hydrogen production, anion-exchange membrane, electrolyzer, mathematical modeling, multiphysics modeling

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Suglo Tohari Luri

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Data analysis is an important aspect of business performance. With the application of artificial intelligence within databases, selecting a suitable database engine for an application design is also very crucial for business data analysis. The application of business intelligence (BI) software into some relational databases such as Neo4j has proved highly effective in terms of customer data analysis. Yet what remains of great concern is the fact that not all business organizations have the neo4j business intelligence software applications to implement for customer data analysis. Further, those with the BI software lack personnel with the requisite expertise to use it effectively with the neo4j database. The purpose of this research is to demonstrate how the Neo4j program code alone can be applied for the analysis of e-commerce website customer visits. As the neo4j database engine is optimized for handling and managing data relationships with the capability of building high performance and scalable systems to handle connected data nodes, it will ensure that business owners who advertise their products at websites using neo4j as a database are able to determine the number of visitors so as to know which products are visited at routine intervals for the necessary decision making. It will also help in knowing the best customer segments in relation to specific goods so as to place more emphasis on their advertisement on the said websites.

Keywords: data, engine, intelligence, customer, neo4j, database

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Authors: Omar Dib, Alexandre Caminada, Marie-Ange Manier

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The purpose of this study is to introduce a novel approach to solve the one-to-one shortest path problem. A directed connected graph is assumed in which all edges’ weights are positive. Our method is based on a memetic algorithm in which we combine a genetic algorithm (GA) and a variable neighborhood search method (VNS). We compare our approximate method with two exact algorithms Dijkstra and Integer Programming (IP). We made experimentations using random generated, complete and real graph instances. In most case studies, numerical results show that our method outperforms exact methods with 5% average gap to the optimality. Our algorithm’s average speed is 20-times faster than Dijkstra and more than 1000-times compared to IP. The details of the experimental results are also discussed and presented in the paper.

Keywords: shortest path problem, Dijkstra’s algorithm, integer programming, memetic algorithm

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Authors: Shantam Gupta

Abstract:

The objective of this study is to address the critical need for automation in the domestic food processing sector and study its impact. Food is the one of the most basic physiological needs essential for the survival of a living being. Some of them have the capacity to prepare their own food (like most plants) and henceforth are designated as primary food producers; those who depend on these primary food producers for food form the primary consumers’ class (herbivores). Some of the organisms relying on the primary food are the secondary food consumers (carnivores). There is a third class of consumers called tertiary food consumers/apex food consumers that feed on both the primary and secondary food consumers. Humans form an essential part of the apex predators and are generally at the top of the food chain. But still further disintegration of the food habits of the modern human i.e. Homo sapiens, reveals that humans depend on other individuals for preparing their own food. The old notion of eating raw/brute food is long gone and food processing has become very trenchant in lives of modern human. This has led to an increase in dependence on other individuals for ‘processing’ the food before it can be actually consumed by the modern human. This has led to a further shift of humans in the classification of food chain of consumers. The effects of the shifts shall be systematically investigated in this paper. The processing of food has a direct impact on the economy of the individual (consumer). Also most individuals depend on other processing individuals for the preparation of food. This dependency leads to establishment of a vital link of dependency in the food web which when altered can adversely affect the food web and can have dire consequences on the health of the individual. This study investigates the challenges arising out due to this dependency and the impact of food processing on the economy of the individual. A comparison of Industrial food processing and processing at domestic platforms (households and restaurants) has been made to provide an idea about the present scenario of automation in the food processing sector. A lot of time and energy is also consumed while processing food at home for consumption. The high frequency of consumption of meals (greater than 2 times a day) makes it even more laborious. Through the medium of this study a pressing need for development of an automatic cooking machine is proposed with a mission to reduce the inter-dependency & human effort of individuals required for the preparation of food (by automation of the food preparation process) and make them more self-reliant The impact of development of this product has also further been profoundly discussed. Assumption used: The individuals those who process food also consume the food that they produce. (They are also termed as ‘independent’ or ‘self-reliant’ modern human beings.)

Keywords: automation, food processing, impact on economy, processing individual

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Authors: M. Münch, R. Brandt

Abstract:

Martensitic steels with an ultimate tensile strength beyond 2000 MPa are applied in the powertrain of vehicles due to their excellent fatigue strength and high creep resistance. However, the creep controlling mechanism in martensitic steels at ambient temperatures up to 423 K is not evident. The purpose of this study is to review the low temperature creep (LTC) behavior of martensitic steels at temperatures from 363 K to 523 K. Thus, the validity of a logarithmic creep law is reviewed and the stress and temperature dependence of the creep parameters α and β are revealed. Furthermore, creep tests are carried out, which include stepped changes in temperature or stress, respectively. On one hand, the change of the creep rate due to a temperature step provides information on the magnitude of the activation energy of the LTC controlling mechanism and on the other hand, the stress step approach provides information on the magnitude of the activation volume. The magnitude, the temperature dependency, and the stress dependency of both material specific activation parameters may deliver a significant contribution to the disclosure of the nature of the LTC rate controlling mechanism.

Keywords: activation parameters, creep mechanisms, high strength steels, low temperature creep

Procedia PDF Downloads 149

Authors: E. M. Rifqi Wilda Pradana, Bagus Bayu Prabowo, Meida Riski Pujiyati, Efraim Maykhel Hagana Ginting, Virgiawan Arya Hangga Reksa

Abstract:

The VLF (Very Low Frequency) method is an electromagnetic method that uses low frequencies between 10-30 KHz which results in a fairly deep penetration. In this study, the VLF method was used for zonation of disaster-prone areas by identifying geological structures in the form of faults. Data acquisition was carried out in Trimulyo Region, Jetis District, Bantul Regency, Special Region of Yogyakarta, Indonesia with 8 measurement paths. This study uses wave transmitters from Japan and Australia to obtain Tilt and Elipt values that can be used to create RAE (Rapat Arus Ekuivalen or Current Density) sections that can be used to identify areas that are easily crossed by electric current. This section will indicate the existence of a geological structure in the form of faults in the study area which is characterized by a high RAE value. In data processing of VLF method, it is obtained Tilt vs Elliptical graph and Moving Average (MA) Tilt vs Moving Average (MA) Elipt graph of each path that shows a fluctuating pattern and does not show any intersection at all. Data processing uses Matlab software and obtained areas with low RAE values that are 0%-6% which shows medium with low conductivity and high resistivity and can be interpreted as sandstone, claystone, and tuff lithology which is part of the Semilir Formation. Whereas a high RAE value of 10% -16% which shows a medium with high conductivity and low resistivity can be interpreted as a fault zone filled with fluid. The existence of the fault zone is strengthened by the discovery of a normal fault on the surface with strike N550W and dip 630E at coordinates X= 433256 and Y= 9127722 so that the activities of residents in the zone such as housing, mining activities and other activities can be avoided to reduce the risk of natural disasters.

Keywords: current density, faults, very low frequency, zonation

Procedia PDF Downloads 152

Authors: Ching-Shoei Chiang

Abstract:

The Malfatti’s Problem is to ask for fitting 3 circles into a right triangle such that they are tangent to each other, and each circle is also tangent to a pair of the triangle’s side. This problem has been extended to any triangle (called general Malfatti’s Problem). Furthermore, the problem has been extended to have 1+2+…+n circles, we call it extended general Malfatti’s problem, these circles whose tangency graph, using the center of circles as vertices and the edge connect two circles center if these two circles tangent to each other, has the structure as Pascal’s triangle, and the exterior circles of these circles tangent to three sides of the triangle. In the extended general Malfatti’s problem, there are closed-form solutions for n=1, 2, and the problem becomes complex when n is greater than 2. In solving extended general Malfatti’s problem (n>2), we initially give values to the radii of all circles. From the tangency graph and current radii, we can compute angle value between two vectors. These vectors are from the center of the circle to the tangency points with surrounding elements, and these surrounding elements can be the boundary of the triangle or other circles. For each circle C, there are vectors from its center c to its tangency point with its neighbors (count clockwise) pi, i=0, 1,2,..,n. We add all angles between cpi to cp(i+1) mod (n+1), i=0,1,..,n, call it sumangle(C) for circle C. Using sumangle(C), we can reduce/enlarge the radii for all circles in next iteration, until sumangle(C) is equal to 2πfor all circles. With a similar idea, this paper proposed an algorithm to find the radii of circles whose tangency has the structure of Pascal’s triangle, and the exterior circles of these circles are tangent to the unit Realeaux Triangle.

Keywords: Malfatti’s problem, geometric constraint solver, computer-aided geometric design, circle packing, data visualization

Procedia PDF Downloads 104

Authors: Simeon Mayala, Ida Herdlevær, Jonas Bull Haugsøen, Shamundeeswari Anandan, Sonia Gavasso, Morten Brun

Abstract:

In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the standard gold segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.

Keywords: brain tumor, brain tumor segmentation, minimum spanning tree, segmentation, image processing

Procedia PDF Downloads 102

Authors: Fahimeh Palizban, Farshad Noravesh, Amir Hossein Saeidian, Mahya Mehrmohamadi

Abstract:

In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the blood and contribute to a pool of circulating fragments called cell-free DNA. Accordingly, identifying the tissue origin of these DNA fragments from the plasma can result in more accurate and fast disease diagnosis and precise treatment protocols. Open chromatin regions are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. There have been several studies in the area of cancer liquid biopsy that integrate distinct genomic and epigenomic features for early cancer detection along with tissue of origin detection. However, multimodal analysis requires several types of experiments to cover the genomic and epigenomic aspects of a single sample, which will lead to a huge amount of cost and time. To overcome these limitations, the idea of predicting OCRs from WGS is of particular importance. In this regard, we proposed a computational approach to target the prediction of open chromatin regions as an important epigenetic feature from cell-free DNA whole genome sequence data. To fulfill this objective, local sequencing depth will be fed to our proposed algorithm and the prediction of the most probable open chromatin regions from whole genome sequencing data can be carried out. Our method integrates the signal processing method with sequencing depth data and includes count normalization, Discrete Fourie Transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of the clustering (open chromatin region+, open chromatin region-) with previously validated open chromatin regions related to human blood samples of the ATAC-DB database. The percentage of overlap between predicted open chromatin regions and the experimentally validated regions obtained by ATAC-seq in ATAC-DB is greater than 67%, which indicates meaningful prediction. As it is evident, OCRs are mostly located in the transcription start sites (TSS) of the genes. In this regard, we compared the concordance between the predicted OCRs and the human genes TSS regions obtained from refTSS and it showed proper accordance around 52.04% and ~78% with all and the housekeeping genes, respectively. Accurately detecting open chromatin regions from plasma cell-free DNA-seq data is a very challenging computational problem due to the existence of several confounding factors, such as technical and biological variations. Although this approach is in its infancy, there has already been an attempt to apply it, which leads to a tool named OCRDetector with some restrictions like the need for highly depth cfDNA WGS data, prior information about OCRs distribution, and considering multiple features. However, we implemented a graph signal clustering based on a single depth feature in an unsupervised learning manner that resulted in faster performance and decent accuracy. Overall, we tried to investigate the epigenomic pattern of a cell-free DNA sample from a new computational perspective that can be used along with other tools to investigate genetic and epigenetic aspects of a single whole genome sequencing data for efficient liquid biopsy-related analysis.

Keywords: open chromatin regions, cancer, cell-free DNA, epigenomics, graph signal processing, correlation clustering

Procedia PDF Downloads 124