Search results for: graph spectra

Authors: Natasha Sharma, Kulbhushan Agnihotri

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Cellular Automata are discrete dynamical systems which are based on local character and spatial disparateness of the spreading process. These factors are generally neglected by traditional models based on differential equations for epidemic spread. The aim of this work is to introduce an SEIR model based on cellular automata on graphs to imitate epidemic spreading. Distinctively, it is an SEIR-type model where the population is divided into susceptible, exposed, infected and recovered individuals. The results obtained from simulations are in accordance with the spreading behavior of a real time epidemics.

Keywords: cellular automata, epidemic spread, graph, susceptible

Procedia PDF Downloads 459

Authors: Gaye Ibrahima, Mendy Gervais, Seck Diaraf, Ouya Samuel

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In this paper, we consider the influence maximization in social networks. Here we give importance to initial diffuser called the seeds. The goal is to find efficiently a subset of k elements in the social network that will begin and maximize the information diffusion process. A new approach which treats the social network before to determine the seeds, is proposed. This treatment eliminates the information feedback toward a considered element as seed by extracting an acyclic spanning social network. At first, we propose two algorithm versions called SCG − algoritm (v1 and v2) (Spanning Connected Graphalgorithm). This algorithm takes as input data a connected social network directed or no. And finally, a generalization of the SCG − algoritm is proposed. It is called SG − algoritm (Spanning Graph-algorithm) and takes as input data any graph. These two algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, two seeds set are determined and those given by our approach give a better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.

Keywords: acyclic spanning graph, centrality measures, information feedback, influence maximization, social network

Procedia PDF Downloads 248

Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

Procedia PDF Downloads 234

Authors: Naoki Ohshima, Ken Kaminishi

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Four types of dependencies, which are 'Finish-to-start', 'Finish-to-finish', 'Start-to-start' and 'Start-to-finish (S-F)' as logical relationship are modeled based on the definition by 'the predecessor activity is defined as an activity to come before a dependent activity in a schedule' in PMBOK. However, it is found a self-contradiction in the precedence diagram for S-F relationship by PMBOK. In this paper, author would like to validate logical relationship of S-F by Graph Theory and propose a new interpretation of the precedence diagram for S-F relationship.

Keywords: project time management, sequence activity, start-to-finish relationship, precedence diagram, PMBOK

Procedia PDF Downloads 270

Authors: Khaled Bahgat

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In the present work, the characterization of 4-Amino-3-phenyl-1H-1,2,4-triazole-5(4H)-thione (APTT) molecule was carried out by quantum chemical method and vibrational spectral techniques. The FT-IR (4000–400 cm_1) and FT-Raman (4000–100 cm_1) spectra of APTT were recorded in solid phase. The UV–Vis absorption spectrum of the APTT was recorded in the range of 200–400 nm. The molecular geometry, harmonic vibrational frequencies and bonding features of APTT in the ground state have been calculated by HF and DFT methods using 6-311++G(d,p) basis set. The complete vibrational frequency assignments were made by normal coordinate analysis (NCA) following the scaled quantum mechanical force field methodology (SQMF). The molecular stability and bond strength were investigated by applying the natural bond orbital analysis (NBO) and natural localized molecular orbital (NLMO) analysis. The electronic properties, such as excitation energies, absorption wavelength, HOMO and LUMO energies were performed by time depended DFT (TD-DFT) approach. The 1H and 13C nuclear magnetic resonance chemical shift of the molecule were calculated using the gauge-including atomic orbital (GIAO) method and compared with experimental results. Finally, the calculation results were analyzed to simulate infrared, FT-Raman and UV spectra of the title compound which shows better agreement with observed spectra.

Keywords: 4-amino-3-phenyl-1H-1, 2, 4-triazole-5(4H)-thione, vibrational assignments, normal coordinate analysis, quantum mechanical calculations

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Authors: S. H. Allehyani, H. S. Ibrahim, F. M. Ali, E. Sayd, T. Abou Aiad

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The present study aimed to analyse the radiation risk associated with the exposure of haemoglobin (Hb) of rat red blood cells (rbcs) exposed to a 50-Hz 6-kV/m electric field, a fast neutron dose of 1 mSv, and mixed radiation from fast neutrons and an electric field distributed over a period of three weeks at a rate of 5 days/week and 8 hours/day. The dielectric measurements and the absorption spectra for the haemoglobin molecule in the frequency range of 1 kHz to 5 MHz were measured for all of the samples. The dielectric relaxation results demonstrated an increase in the dielectric increment (∆ε) for the rbcs from all of the irradiated animals, which indicates an increase in the electric dipole. Moreover, the results revealed a decrease in the relaxation time (τ) and the molecular radius (r) of the irradiated molecules, which indicates that the increase in ∆ε is mainly due to a pronounced increase in the centre of mass of the charge on the electric dipole of the Hb molecule. The results from the absorption spectra indicate that the ratio of met-haemoglobin to oxy-haemoglobin is altered by irradiation. Moreover, the results from the delayed effect studies show that the structure and function of the newly generated Hb molecules are altered and dissimilar to that of healthy Hb.

Keywords: rat red blood cell haemoglobin, dielectric properties, absorption spectra, biochemical analysis

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

Procedia PDF Downloads 153

Authors: Thierry Ganomanana, Dominique Hervé, Solo Randriamahaleo

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Management of Malagasy renewable natural re-sources allows, in the case of forest, the mobilization of several actors with norms and/or territory. The interaction in this socio-ecosystem is represented by a graph of two different relationships in which most of action chains, from individual activities under the continuous of forest dynamic and discrete interventions by institutional, are also studied. The fuzzy set theory is adapted to graduate the elements of the set Illegal Activities in the space of sanction’s institution by his severity and in the space of degradation of forest by his extent.

Keywords: fuzzy set, graph, institution, renewable resource, system

Procedia PDF Downloads 88

Authors: Can Zhang, Qun Li, Yonglin Lei, Zhi Zhu, Dong Guo

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Aiming at the problem of factor screen in distributed collaborative navigation of dense UAV swarm, an efficient distributed collaborative navigation factor screen method is proposed. The method considered the balance between computing load and positioning accuracy. The proposed algorithm utilized the factor graph model to implement a distributed collaborative navigation algorithm. The GNSS information of the UAV itself and the ranging information between the UAVs are used as the positioning factors. In this distributed scheme, a local factor graph is established for each UAV. The positioning factors of nodes with good geometric position distribution and small variance are selected to participate in the navigation calculation. To demonstrate and verify the proposed methods, the simulation and experiments in different scenarios are performed in this research. Simulation results show that the proposed scheme achieves a good balance between the computing load and positioning accuracy in the distributed cooperative navigation calculation of UAV swarm. This proposed algorithm has important theoretical and practical value for both industry and academic areas.

Keywords: screen method, cooperative positioning system, UAV swarm, factor graph, cooperative navigation

Procedia PDF Downloads 79

Authors: Madhuresh Dwivedi, Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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FT-NIR spectroscopy and electronic nose was used to study the kinetics of dough proofing. Spectroscopy was conducted with an optic probe in the diffuse reflectance mode. The dough leavening was carried out at different temperatures (25 and 35°C) and constant RH (80%). Spectra were collected in the range of wave numbers from 12,000 to 4,000 cm-1 directly on the samples, every 5 min during proofing, up to 2 hours. NIR spectra were corrected for scatter effect and second order derivatization was done to transform the spectra. Principal component analysis (PCA) was applied for the leavening process and process kinetics was calculated. PCA was performed on data set and loadings were calculated. For leavening, four absorption zones (8,950-8,850, 7,200-6,800, 5,250-5,150 and 4,700-4,250 cm-1) were involved in describing the process. Simultaneously electronic nose was also used for understanding the development of odour compounds during fermentation. The electronic nose was able to differential the sample on the basis of aroma generation at different time during fermentation. In order to rapidly differentiate samples based on odor, a Principal component analysis is performed and successfully demonstrated in this study. The result suggests that electronic nose and FT-NIR spectroscopy can be utilized for the online quality control of the fermentation process during leavening of bread dough.

Keywords: FT-NIR, dough, e-nose, proofing, principal component analysis

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Authors: Dimitra Alexiou

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In an urban area the allocation placement of an emergency service mobile units, such as ambulances, police patrol must be designed so as to achieve a prompt response to demand locations. In this paper, a partition of a given urban network into distinct sub-networks is performed such that; the vertices in each component are close and simultaneously the difference of the sums of the corresponding population in the sub-networks is almost uniform. The objective here is to position appropriately in each sub-network a mobile emergency unit in order to reduce the response time to the demands. A mathematical model in the framework of graph theory is developed. In order to clarify the corresponding method a relevant numerical example is presented on a small network.

Keywords: graph partition, emergency service, distances, location

Procedia PDF Downloads 499

Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

Procedia PDF Downloads 94

Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

Procedia PDF Downloads 189

Authors: Andrey Lebedev, Ilya Dmitrenok, JooYoung Lee, Leonard Johard

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Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm.

Keywords: graph matching, link prediction, shortest path, similarity

Procedia PDF Downloads 358

Authors: Gonzalez Carlos, Martinez Fransisco

Abstract:

There are various types of analysis that allow us to involve seismic phenomena that cause strong requirements for structures that are designed by society; one of them is a probabilistic analysis which works from prediction equations that have been created based on metadata seismic compiled in different regions. These equations form models that are used to describe the 5% damped pseudo spectra response for the various zones considering some easily known input parameters. The biggest problem for the creation of these models requires data with great robust statistics that support the results, and there are several places where this type of information is not available, for which the use of alternative methodologies helps to achieve adjustments to different models of seismic prediction.

Keywords: GMPM, 5% damped pseudo-response spectra, models of seismic prediction, PSHA

Procedia PDF Downloads 76

Authors: Z. Ž. Lazarević, Č. Jovalekić, V. N. Ivanovski, N. Ž. Romčević

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Nickel-zinc ferrite, Ni0.5Zn0.5Fe2O4 was prepared by mechanochemical route in a planetary ball mill starting from mixture of the appropriate quantities of the Ni(OH)2, Zn(OH)2 and Fe(OH)3 hydroxide powders. In order to monitor the progress of chemical reaction and confirm phase formation, powder samples obtained after 5 h and 10 h of milling were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), IR, Raman and Mössbauer spectroscopy. It is shown that the soft mechanochemical method, i.e. mechanochemical activation of hydroxides, produces high quality single phase Ni0.5Zn0.5Fe2O4 samples in much more efficient way. From the IR spectroscopy of single phase samples it is obvious that energy of modes depends on the ratio of cations. It is obvious that all samples have more than 5 Raman active modes predicted by group theory in the normal spinel structure. Deconvolution of measured spectra allows one to conclude that all complex bands in the spectra are made of individual peaks with the intensities that vary from spectrum to spectrum. The deconvolution of Raman spectra alows to separate contributions of different cations to a particular type of vibration and to estimate the degree of inversion.

Keywords: ferrite, X-ray diffraction, infrared spectroscopy, Raman spectroscopy, Mössbauer spectroscopy

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Authors: Amritpal Singh Nafria, Rohit Sharma, Md. Shami Ansari

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Up to now only five different equations of motion can be derived from velocity time graph without needing to know the normal and frictional forces acting at the point of contact. In this paper we obtained all possible requisite conditions to be considering an equation as an equation of motion. After that we classified equations of motion by considering two equations as fundamental kinematical equations of motion and other three as additional kinematical equations of motion. After deriving these five equations of motion, we examine the easiest way of solving a wide variety of useful numerical problems. At the end of the paper, we discussed the importance and educational benefits of classification of equations of motion.

Keywords: velocity-time graph, fundamental equations, additional equations, requisite conditions, importance and educational benefits

Procedia PDF Downloads 787

Authors: Asma Rebaya, Kaouther Gasmi, Imen Amari, Salem Hasnaoui

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Over these last years, applications such as telecommunications, signal processing, digital communication with advanced features (Multi-antenna, equalization..) witness a rapid evaluation accompanied with an increase of user exigencies in terms of latency, the power of computation… To satisfy these requirements, the use of hardware/software systems is a common solution; where hardware is composed of multi-cores and software is represented by models of computation, synchronous data flow (SDF) graph for instance. Otherwise, the most of the embedded system designers utilize Simulink for modeling. The issue is how to simplify the c code generation, for a multi-cores platform, of an application modeled by Simulink. To overcome this problem, we propose a workflow allowing an automatic transformation from the Simulink model to the SDF graph and providing an efficient schedule permitting to optimize the number of cores and to minimize latency. This workflow goes from a Simulink application and a hardware architecture described by IP.XACT language. Based on the synchronous and hierarchical behavior of both models, the Simulink block diagram is automatically transformed into an SDF graph. Once this process is successfully achieved, the scheduler calculates the optimal cores’ number needful by minimizing the maximum density of the whole application. Then, a core is chosen to execute a specific graph task in a specific order and, subsequently, a compatible C code is generated. In order to perform this proposal, we extend Preesm, a rapid prototyping tool, to take the Simulink model as entry input and to support the optimal schedule. Afterward, we compared our results to this tool results, using a simple illustrative application. The comparison shows that our results strictly dominate the Preesm results in terms of number of cores and latency. In fact, if Preesm needs m processors and latency L, our workflow need processors and latency L'< L.

Keywords: hardware/software system, latency, modeling, multi-cores platform, scheduler, SDF graph, Simulink model, workflow

Procedia PDF Downloads 269

Authors: Behnaz Pahlousay, Farshad Kazemnejad, Elisa Palezzato, Michele Torielli

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In this paper, we study the notions of connected domination number and of outer-connected domination number for middle graphs. Indeed, we obtain tight bounds for these numbers in terms of the order of the middle graph M(G). We also compute the outer-connected domination number of some families of graphs such as star graphs, cycle graphs, wheel graphs, complete graphs, complete bipartite graphs and some operation on graphs, explicitly. Moreover, some Nordhaus-Gaddum-like relations are presented for the outer-connected domination number of middle graphs.

Keywords: connected domination number, outer-connected dom- ination number, domination number, middle graph, nordhaus- gaddum-like relation.

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Authors: Mingmei Zhang, Xinyong Li

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AgInS₂/SnS₂ (AIS) nanocomposites were synthesized by a simple hydrothermal method. The morphology and composition of the fabricated AIS nanocomposites were investigated by field-emission scanning electron microscopy (SEM), X-ray diffraction (XRD), high resolution transmission electron microscopy (HRTEM) and X-ray photoelectron spectroscopy (XPS). Moreover, the as-prepared AIS photocatalysts exhibited excellent photocatalytic activities for the degradation of Norfloxacin (NOR), mainly due to its high optical absorption and separation efficiency of photogenerated electron-hole pairs, as evidenced by UV–vis diffusion reflection spectra (DRS) and Surface photovoltage (SPV) spectra. Furthermore, the interfacial charges transfer mechanism was also discussed by DFT calculations.

Keywords: AIS nanocomposites, electron-hole pairs, charges transfer, DFTcaculations

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Authors: May Fadheel Estephan, Richard Perks

Abstract:

Context: Cancer is a serious health concern that affects millions of people worldwide. Early detection and treatment are essential for improving patient outcomes. However, current methods for cancer detection have limitations, such as low sensitivity and specificity. Research Aim: The aim of this study was to develop an optical sensor for cancer detection using elastic light scattering spectroscopy (ELSS). ELSS is a noninvasive optical technique that can be used to characterize the size and concentration of particles in a solution. Methodology: An optical probe was fabricated with a 100-μm-diameter core and a 132-μm centre-to-centre separation. The probe was used to measure the ELSS spectra of polystyrene spheres with diameters of 2, 0.8, and 0.413 μm. The spectra were then analysed to determine the size and concentration of the spheres. Findings: The results showed that the optical probe was able to differentiate between the three different sizes of polystyrene spheres. The probe was also able to detect the presence of polystyrene spheres in suspension concentrations as low as 0.01%. Theoretical Importance: The results of this study demonstrate the potential of ELSS for cancer detection. ELSS is a noninvasive technique that can be used to characterize the size and concentration of cells in a tissue sample. This information can be used to identify cancer cells and assess the stage of the disease. Data Collection: The data for this study were collected by measuring the ELSS spectra of polystyrene spheres with different diameters. The spectra were collected using a spectrometer and a computer. Analysis Procedures: The ELSS spectra were analysed using a software program to determine the size and concentration of the spheres. The software program used a mathematical algorithm to fit the spectra to a theoretical model. Question Addressed: The question addressed by this study was whether ELSS could be used to detect cancer cells. The results of the study showed that ELSS could be used to differentiate between different sizes of cells, suggesting that it could be used to detect cancer cells. Conclusion: The findings of this research show the utility of ELSS in the early identification of cancer. ELSS is a noninvasive method for characterizing the number and size of cells in a tissue sample. To determine cancer cells and determine the disease's stage, this information can be employed. Further research is needed to evaluate the clinical performance of ELSS for cancer detection.

Keywords: elastic light scattering spectroscopy, polystyrene spheres in suspension, optical probe, fibre optics

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Authors: S. S. Sathiesh

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The aim of this project is to study the radiation mechanism synchrotron and Inverse Compton scattering. Theoretically, we discussed spectral energy distribution for both. Programming is done for plotting the graph of Power-law spectrum for synchrotron Radiation using fortran90. The importance of power law spectrum was discussed and studied to infer its physical parameters from the model fitting. We also discussed how to infer the physical parameters from the theoretically drawn graph, we have seen how one can infer B (magnetic field of the source), γ min, γ max, spectral indices (p1, p2) while fitting the curve to the observed data.

Keywords: blazars/quasars, beaming, synchrotron radiation, Synchrotron Self Compton, inverse Compton scattering, mrk421

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Authors: Daniel Hyun

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Dental implants are one of the top 3 reasons to sue a dentist for malpractice. It involves dental implant complications, usually because of the angle of the implant from the surgery. At present, surgeons usually use a 3D-printed navigator that is customized for the patient’s teeth. However, those can’t be reused for other patients as they require time. Therefore, I made a guiding device to assist the surgeon in implant operations. The surgeon can input the objective of the operation, and the device constantly checks if the surgery is heading towards the objective within the set range, telling the surgeon by manipulating the LED. We tested the prototypes’ consistency and accuracy by checking the graph, average standard deviation, and the average change of the calculated angles. The accuracy of performance was also acquired by running the device and checking the outputs. My first prototype used accelerometer and gyroscope sensors from the Arduino MPU6050 sensor, getting a changeable graph, achieving 0.0295 of standard deviations, 0.25 of average change, and 66.6% accuracy of performance. The second prototype used only the gyroscope, and it got a constant graph, achieved 0.0062 of standard deviation, 0.075 of average change, and 100% accuracy of performance, indicating that the accelerometer sensor aggravated the functionality of the device. Using the gyroscope sensor allowed it to measure the orientations of separate axes without affecting each other and also increased the stability and accuracy of the measurements.

Keywords: implant, guide, accelerometer, gyroscope, handpiece

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Orsolya Kegyes-Brassai, Zsolt Szilvágyi, Ákos Wolf, Richard P. Ray

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Local ground conditions have a substantial influence on the seismic response of structures. Their inclusion in seismic hazard assessment and structural design can be realized at different levels of sophistication. However, response results based on more advanced calculation methods e.g. nonlinear or equivalent linear site analysis tend to show significant discrepancies when compared to simpler approaches. This project's main objective was to compare results from several 1-D response programs to Eurocode 8 design spectra. Data from in-situ site investigations were used for assessing local ground conditions at several locations in Hungary. After discussion of the in-situ measurements and calculation methods used, a comprehensive evaluation of all major contributing factors for site response is given. While the Eurocode spectra should account for local ground conditions based on soil classification, there is a wide variation in peak ground acceleration determined from 1-D analyses versus Eurocode. Results show that current Eurocode 8 design spectra may not be conservative enough to account for local ground conditions typical for Hungary.

Keywords: 1-D site response analysis, multichannel analysis of surface waves (MASW), seismic CPT, seismic hazard assessment

Procedia PDF Downloads 246

Authors: Amin Asgarian, Ghyslaine McClure

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Proper seismic evaluation of Non-Structural Components (NSCs) mandates an accurate estimation of floor seismic demands (i.e. acceleration and displacement demands). Most of the current international codes incorporate empirical equations to calculate equivalent static seismic force for which NSCs and their anchorage system must be designed. These equations, in general, are functions of component mass and peak seismic acceleration to which NSCs are subjected to during the earthquake. However, recent studies have shown that these recommendations are suffered from several shortcomings such as neglecting the higher mode effect, tuning effect, NSCs damping effect, etc. which cause underestimation of the component seismic acceleration demand. This work is aimed to circumvent the aforementioned shortcomings of code provisions as well as improving them by proposing a simplified, practical, and yet accurate approach to generate acceleration Floor Design Spectra (FDS) directly from corresponding Uniform Hazard Spectra (UHS) (i.e. design spectra for structural components). A database of 27 Reinforced Concrete (RC) buildings in which Ambient Vibration Measurements (AVM) have been conducted. The database comprises 12 low-rise, 10 medium-rise, and 5 high-rise buildings all located in Montréal, Canada and designated as post-disaster buildings or emergency shelters. The buildings are subjected to a set of 20 compatible seismic records and Floor Response Spectra (FRS) in terms of pseudo acceleration are derived using the proposed approach for every floor of the building in both horizontal directions considering 4 different damping ratios of NSCs (i.e. 2, 5, 10, and 20% viscous damping). Several effective parameters on NSCs response are evaluated statistically. These parameters comprise NSCs damping ratios, tuning of NSCs natural period with one of the natural periods of supporting structure, higher modes of supporting structures, and location of NSCs. The entire spectral region is divided into three distinct segments namely short-period, fundamental period, and long period region. The derived roof floor response spectra for NSCs with 5% damping are compared with the 5% damping UHS and procedure are proposed to generate roof FDS for NSCs with 5% damping directly from 5% damped UHS in each spectral region. The generated FDS is a powerful, practical, and accurate tool for seismic design and assessment of acceleration-sensitive NSCs particularly in existing post-critical buildings which have to remain functional even after the earthquake and cannot tolerate any damage to NSCs.

Keywords: earthquake engineering, operational and functional components (OFCs), operational modal analysis (OMA), seismic assessment and design

Procedia PDF Downloads 236

Authors: Seedwell Sithole

Abstract:

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 151

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

Abstract:

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

Procedia PDF Downloads 78

Authors: R. Shrestha, D. P. Subedi, R. B. Tyata, C. S. Wong,

Abstract:

In this paper, a technique for the determination of electron temperatures and electron densities in atmospheric pressure Argon/air discharge by the analysis of optical emission spectra (OES) is reported. The discharge was produced using a high voltage (0-20) kV power supply operating at a frequency of 27 kHz in parallel electrode system, with glass as dielectric. The dielectric layers covering the electrodes act as current limiters and prevent the transition to an arc discharge. Optical emission spectra in the range of (300nm-850nm) were recorded for the discharge with different inter electrode gap keeping electric field constant. Electron temperature (Te) and electron density (ne) are estimated from electrical and optical methods. Electron density was calculated using power balance method. The optical methods are related with line intensity ratio from the relative intensities of Ar-I and Ar-II lines in Argon plasma. The electron density calculated by using line intensity ratio method was compared with the electron density calculated by stark broadening method. The effect of dielectric thickness on plasma parameters (Te and ne) have also been studied and found that Te and ne increases as thickness of dielectric decrease for same inter electrode distance and applied voltage.

Keywords: electron density, electron temperature, optical emission spectra,

Procedia PDF Downloads 496

Authors: L. N. Shubha, P. Madhusudana Rao

Abstract:

The Polyaniline / Copper Oxide(PANI / CuO) nanocomposite was prepared by solution mixing of prepared Polyaniline and copper Oxide in Dimethyl sulfoxide (DMSO). The synthesis involved the formation of dark green colored Polyaniline-Copper Oxide nanocomposite. The synthesized polymer nano composites were characterized by XRD, FTIR, SEM and UV-Visible Spectroscopy. The characteristic peaks in XRD, FTIR and UV-Visible spectra confirmed the presence of CuO in the polymer structure. SEM analysis revealed formation of PANI/CuO nano composite The D.C. conductivity measurements were performed using two probe method for various temperatures.

Keywords: polyaniline/copper oxide (PANI/CuO) nanocomposite, XRD, SEM, FTIRand DC- conductivity, UV-visible spectra

Procedia PDF Downloads 307