Search results for: block linear multistep methods

Authors: Seyed Habib Shojaei, Reza Eivazi, Mir Sajad Shojaei, Alireza Akbari, Pooria Mazloom, Seyede Mitra Sadati, Mir Zeinalabedin Shojaei, Farnaz Farbakhsh

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To study the genetic diversity in rapeseeds and agronomic traits, an experiment was conducted using multivariate statistical methods at Agricultural Research Station of Kashmar in 2012-2013.In this experiment, ten genotypes of rapeseed in a Randomized Complete Block designs with three replications were evaluated. The following traits were studied: seed yield, number of days to the fifty percent of flowering, plant height, number of pods on main stem, length of the pod, seed yield per plant, number of seed in pod, harvest index, weight of 100 seeds, number of pods on lateral branch, number of lateral branches. In analyzing the variance, differences between cultivars were significant. The average comparative revealed that the most valuable variety was Licord regarding to the traits while the least valuable variety was Opera. In stepwise regression, harvest index, grain yield per plant and number of pods per lateral branches were entering to model. Correlation analysis showed that the grain yield with the number of pods per lateral branches and seed yield per plant have positive and significant correlation. In the factor analysis, the first five components explained more than 83% of the variance in the data. In the first factor, seed yield and the number of pods per lateral branches were of the highest importance. The traits, seed yield per plant, and pod per main stem were of a great significance in the second factor. Moreover, in the third factor, plant height and the number of lateral branches were more important. In the fourth factor, plant height and one hundred seeds weight were of the highest variance. Finally, days to fifty percent of flowering and one hundred seeds weight were more important in fifth factor.

Keywords: rapeseed, variance analysis, regression, factor analysis

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Authors: Nima Dastanboo, Xiao-Qing Li, Hamed Gharibdoost

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The purpose of this study is to investigate about the geological properties ahead of a tunnel face with using Electrical Resistivity Tomography ERT and Tunnel Seismic Prediction TSP303 methods. In deep tunnels with hydro-geological conditions, it is important to study the geological structures of the region before excavating tunnels. Otherwise, it would lead to unexpected accidents that impose serious damage to the project. For constructing Nosoud tunnel in west of Iran, the ERT and TSP303 methods are employed to predict the geological conditions dynamically during the excavation. In this paper, based on the engineering background of Nosoud tunnel, the important results of applying these methods are discussed. This work demonstrates seismic method and electrical tomography as two geophysical techniques that are able to detect a tunnel. The results of these two methods were being in agreement with each other but the results of TSP303 are more accurate and quality. In this case, the TSP 303 method was a useful tool for predicting unstable geological structures ahead of the tunnel face during excavation. Thus, using another geophysical method together with TSP303 could be helpful as a decision support in excavating, especially in complicated geological conditions.

Keywords: tunnel seismic prediction (TSP303), electrical resistivity tomography (ERT), seismic wave, velocity analysis, low-velocity zones

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Authors: Nureni O. Adeboye, Dawud A. Agunbiade

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This research attempts to investigate the effects of heteroscedasticity and periodicity in a Panel Data Regression Model (PDRM) by extending previous works on balanced panel data estimation within the context of fitting PDRM for Banks audit fee. The estimation of such model was achieved through the derivation of Joint Lagrange Multiplier (LM) test for homoscedasticity and zero-serial correlation, a conditional LM test for zero serial correlation given heteroscedasticity of varying degrees as well as conditional LM test for homoscedasticity given first order positive serial correlation via a two-way error component model. Monte Carlo simulations were carried out for 81 different variations, of which its design assumed a uniform distribution under a linear heteroscedasticity function. Each of the variation was iterated 1000 times and the assessment of the three estimators considered are based on Variance, Absolute bias (ABIAS), Mean square error (MSE) and the Root Mean Square (RMSE) of parameters estimates. Eighteen different models at different specified conditions were fitted, and the best-fitted model is that of within estimator when heteroscedasticity is severe at either zero or positive serial correlation value. LM test results showed that the tests have good size and power as all the three tests are significant at 5% for the specified linear form of heteroscedasticity function which established the facts that Banks operations are severely heteroscedastic in nature with little or no periodicity effects.

Keywords: audit fee lagrange multiplier test, heteroscedasticity, lagrange multiplier test, Monte-Carlo scheme, periodicity

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Authors: Somruay Apichatibutarapong, Chookiat Pudprommart

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The purpose of this study was to develop of statistical analysis by using R programming via internet applied for agriculture experimental design. Data were collected from 65 items in completely randomized design, randomized block design, Latin square design, split plot design, factorial design and nested design. The quantitative approach was used to investigate the quality of learning media on statistical analysis by using R programming via Internet by six experts and the opinions of 100 students who interested in experimental design and applied statistics. It was revealed that the experts’ opinions were good in all contents except a usage of web board and the students’ opinions were good in overall and all items.

Keywords: experimental design, r programming, applied statistics, statistical analysis

Procedia PDF Downloads 350

Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Siva Kumar Reddy, Anwesha Mukherjee, Abha Misra

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Excellent energy absorption capability in carbon nanotubes (CNT) is shown in their bulk structure that behaves like super compressible foam. Furthermore, a tunable mechanical behavior of CNT foam is achieved using several methods like changing the concentration of precursors, polymer impregnation, non covalent functionalization of CNT microstructure etc. Influence of magnetic field on compressive behavior of magnetic CNT demonstrated an enhanced peak stress and energy absorption capability, which does not require any surface and structural modification of the foam. This presentation discusses the mechanical behavior of micro porous CNT foam that is impregnated in magnetic field responsive fluid. Magnetic particles are dispersed in a nonmagnetic fluid so that alignment of both particles and CNT could play a crucial role in controlling the stiffness of the overall structure. It is revealed that the compressive behavior of CNT foam critically depends on the fluid viscosity as well as magnetic field intensity. Both peak Stress and energy absorption in CNT foam followed a power law behavior with the increase in the magnetic field intensity. However, in the absence of magnetic field, both peak stress and energy absorption capability of CNT foam presented a linear dependence on the fluid viscosity. Hence, this work demonstrates the role magnetic filed in controlling the mechanical behavior of the foams prepared at nanoscale.

Keywords: carbon nanotubes, magnetic field, energy absorption capability and viscosity

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Authors: Hamid Gholami

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During the years, the language teaching methods have been the offshoots of schools of thought in psychology. The methods were mainly influenced by their contemporary psychological approaches, as Audiolingualism was based on behaviorism and Communicative Language Teaching on constructivism. In 1950s, the text books were full of repetition exercises which were encouraged by Behaviorism. In 1980s they got filled with communicative exercises as suggested by constructivism. The trend went on to nowadays that sees no specific method as prevalent since none of the schools of thought seem to be illustrative of the complexity in human being learning. But some changes can be notable; some textbooks are giving more and more space to repetition exercises at least to enhance some aspects of language proficiency, namely collocations, rhythm and intonation, and conversation models. These changes may mark the reemergence of one of the once widely accepted schools of thought in psychology; behaviorism.

Keywords: language teaching methods, psychology, schools of thought, Behaviorism

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Authors: Nioushasadat Haji Seyed Javadi, Ailar Hajimohammadi, Nasser Khalili

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Currently, the recycling of plastic wastes has been the subject of much research attention, especially in pavement constructions, where virgin polymers can be replaced by recycled plastics for asphalt binder modification. Among the plastic types, recycled linear low-density polyethylene (RLLDPE) has been one of the common and largely available plastics for bitumen modification. However, it is important to note that during the recycling process, LLDPE can easily be contaminated with other plastic types, especially with low-density polyethylene (LDPE), high-density polyethylene (HDPE), and polypropylene (PP). The cross-contamination of LLDPE with other plastics lowers its quality and, consequently, can affect the asphalt modification process. This study aims to assess the effect of LLDPE cross-contamination on bitumen modification. To do so, samples of bitumen modified with LLDPE and blends of LLDPE with LDPE, HDPE, and PP were prepared and compared through physical and rheological evaluations. The experimental tests, including softening point, penetration, viscosity at 135 °C, and dynamic shear rheometer, were conducted. The results indicated that the effect of cross-contamination on softening point and rutting resistance was negligible. On the other side, penetration and viscosity were highly impacted. The results also showed that among contamination of LLDPE with the other plastic types, PP had the highest influence in comparison with HDPE and LDPE on changing the properties of the LLDPE- modified bitumen.

Keywords: recycled polyethylene, polymer cross-contamination, waste plastic, bitumen, rutting resistance

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Authors: Nicholas B. Beck, Thomas E. Albrecht-Schönzart

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High-pressure results in decreases in the bond lengths of metal-ligand bonds, which has proven to be incredibly informative in uncovering differences in bonding between lanthanide and actinide complexes. The degree of f-electron contribution to the metal ligand bonds has been observed to increase under pressure by a far greater degree in the actinides than the lanthanides, as revealed by spectroscopic studies. However, the actual changes in bond lengths have yet to be quantified, although computationally predicted. By using high-pressure crystallographic techniques, crystal structures of lanthanide complexes have been obtained at pressures up to 5 GPa for both hard and soft-donor ligands. These studies have revealed some unpredicted changes in the coordination environment as well as provided experimental support to computational results

Keywords: crystallography, high-pressure, lanthanide, materials

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Authors: T. Yu, L. Audibert, J. F. Chaix, D. Komatitsch, V. Garnier, J. M. Henault

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Linear Ultrasonic Techniques play a major role in Non-Destructive Evaluation (NDE) for civil engineering structures in concrete since they can meet operational requirements. Interpretation of ultrasonic measurements could be improved by a better understanding of ultrasonic wave propagation in a multiple scattering medium. This work aims to develop a 2D numerical model of ultrasonic wave propagation in a heterogeneous medium, like concrete, integrating the multiple scattering phenomena in SPECFEM software. The coherent field of multiple scattering is obtained by averaging numerical wave fields, and it is used to determine the effective phase velocity and attenuation corresponding to an equivalent homogeneous medium. First, this model is applied to one scattering element (a cylinder) in a homogenous medium in a linear-elastic system, and its validation is completed thanks to the comparison with analytical solution. Then, some cases of multiple scattering by a set of randomly located cylinders or polygons are simulated to perform parametric studies on the influence of frequency and scatterer size, concentration, and shape. Also, the effective properties are compared with the predictions of Waterman-Truell model to verify its validity. Finally, the mortar viscoelastic behavior is introduced in the simulation in order to considerer the dispersion and the attenuation due to porosity included in the cement paste. In the future, different steps will be developed: The comparisons with experimental results, the interpretation of NDE measurements, and the optimization of NDE parameters before an auscultation.

Keywords: attenuation, multiple-scattering medium, numerical modeling, phase velocity, ultrasonic measurements

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.

Keywords: dialogue management, response generation, deep learning, evaluation

Procedia PDF Downloads 146

Authors: Somayeh Nezami

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The management of financial and human resources of police in metropolitans requires many information and exact plans to reduce a rate of crime and increase the safety of the society. Geographical Information Systems have an important role in providing crime maps and their analysis. By using them and identification of crime hot spots along with spatial presentation of the results, it is possible to allocate optimum resources while presenting effective methods for decision making and preventive solutions. In this paper, we try to explain and compare between some of the methods of hot spots analysis such as Mode, Fuzzy Mode and Nearest Neighbour Hierarchical spatial clustering (NNH). Then the spots with the highest crime rates of drug smuggling for one province in Iran with borderline with Afghanistan are obtained. We will show that among these three methods NNH leads to the best result.

Keywords: GIS, Hot spots, nearest neighbor hierarchical spatial clustering, NNH, spatial analysis of crime

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Authors: Mohammed Belloufi, Rachid Benzine, Badreddine Sellami

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Conjugate gradient methods is useful for solving large scale optimization problems in scientific and engineering computation, characterized by the simplicity of their iteration and their low memory requirements. It is well known that the search direction plays a main role in the line search method. In this paper, we propose a search direction with the Wolfe line search technique for solving unconstrained optimization problems. Under the above line searches and some assumptions, the global convergence properties of the given methods are discussed. Numerical results and comparisons with other CG methods are given.

Keywords: unconstrained optimization, conjugate gradient method, strong Wolfe line search, global convergence

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Authors: J. Berčík, A. Mravcová, A. Rusková, P. Jurčišin, R. Virágh

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The online world is changing every day. With this comes the emergence and development of new business models. One of them is the sale of several types of products in one place. This type of sales in the form of online marketplaces has undergone a positive development in recent years and represents a kind of alternative to brick-and-mortar shopping centres. The main philosophy is to buy several products under one roof. Examples of popular e-commerce marketplaces are Amazon, eBay, and Allegro. Their share of total e-commerce turnover is expected to even double in the coming years. The paper highlights possibilities for testing web applications and online marketplace using emerging methods like stationary eye cameras (eye tracking) and facial analysis (FaceReading).

Keywords: emerging methods, consumer neuroscience, e-commerce, marketplace, user experience, user interface

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Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

Procedia PDF Downloads 183

Authors: Shaila Bahl, S. P. Lochab, Pratik Kumar

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Nanotechnology and nanomaterials have attracted researchers from different fields, especially from the field of luminescence. Recent studies on various luminescent nanomaterials have shown their relevance in dosimetry of ionizing radiations for the measurements of high doses using the Thermoluminescence (TL) technique, where the conventional microcrystalline phosphors saturate. Ion beams have been used for diagnostic and therapeutic purposes due to their favorable profile of dose deposition at the end of the range known as the Bragg peak. While dealing with human beings, doses from these beams need to be measured with great precision and accuracy. Henceforth detailed investigations of suitable thermoluminescent dosimeters (TLD) for dose verification in ion beam irradiation are required. This paper investigates the TL response of nanocrystalline BaSO4 doped with Eu to 85 MeV carbon beam. The synthesis was done using Co-precipitation technique by mixing Barium chloride and ammonium sulphate solutions. To investigate the crystallinity and particle size, analytical techniques such as X-ray diffraction (XRD) and Transmission electron microscopy (TEM) were used which revealed the average particle sizes to 45 nm with orthorhombic structure. Samples in pellet form were irradiated by 85 MeV carbon beam in the fluence range of 1X1010-5X1013. TL glow curves of the irradiated samples show two prominent glow peaks at around 460 K and 495 K. The TL response is linear up to 1X1013 fluence after which saturation was observed. The wider linear TL response of nanocrystalline BaSO4: Eu and low fading make it a superior candidate as a dosimeter to be used for detecting the doses of carbon beam.

Keywords: radiation, dosimetry, carbon ions, thermoluminescence

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Authors: Fatima El Zahra Amin, Emine Efe

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This study was carried out to evaluate the effect of the education program for parents of children with Sickle Cell Anemia, on the knowledge level of parents and the reduction of pain relief by non-pharmacological methods used by parents at home. In Turkey, 54 parents and 109 from Chad agreed to participate in the survey. The data were collected by the researcher using a face-to-face interview method. Non-pharmacological treatment information form for parents, face expressions rating scale, and parent education program for non-pharmacological methods used in children with sickle cell anemia were used. It was determined that there was a statistically significant difference between the educational status, occupation, disease status, place of residence, family structure and age of parents of Chad and Turkey. According to the ratings of facial expressions scale, it was concluded that there was no significant difference between the children’s average degree of pain before and after administration of non-pharmacological methods by the groups of Chad and Turkey. It was determined that the educational programs prepared for parents of children with sickle cell anemia in both Turkey and Chad were effective in increasing the knowledge level of parents and also in reducing pain crisis with non-pharmacological methods parents used at home.

Keywords: Chad, child, non-pharmacological treatment methods, nurse, sickle cell anemia, Turkey

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Authors: Merve Kucukali Ozturk, Yesim Beceren, Banu Nergis

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The study discussed in this paper was conducted in an attempt to investigate effects of different drying methods (line dry and tumble dry) on viscose single jersey fabrics knitted with ring yarn.

Keywords: color change, dimensional properties, drying method, fabric tightness, physical properties

Procedia PDF Downloads 271

Authors: Omar Dib, Rita Yaacoub, Luc Eveleigh, Nathalie Locquet, Hussein Dib, Ali Bassal, Christophe B. Y. Cordella

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The potential of front-face fluorescence coupled with chemometric techniques, namely parallel factor analysis (PARAFAC) and multiple linear regression (MLR) as a rapid analysis tool to characterize Lebanese virgin olive oils was investigated. Fluorescence fingerprints were acquired directly on 102 Lebanese virgin olive oil samples in the range of 280-540 nm in excitation and 280-700 nm in emission. A PARAFAC model with seven components was considered optimal with a residual of 99.64% and core consistency value of 78.65. The model revealed seven main fluorescence profiles in olive oil and was mainly associated with tocopherols, polyphenols, chlorophyllic compounds and oxidation/hydrolysis products. 23 MLR regression models based on PARAFAC scores were generated, the majority of which showed a good correlation coefficient (R > 0.7 for 12 predicted variables), thus satisfactory prediction performances. Acid values, peroxide values, and Delta K had the models with the highest predictions, with R values of 0.89, 0.84 and 0.81 respectively. Among fatty acids, linoleic and oleic acids were also highly predicted with R values of 0.8 and 0.76, respectively. Factors contributing to the model's construction were related to common fluorophores found in olive oil, mainly chlorophyll, polyphenols, and oxidation products. This study demonstrates the interest of front-face fluorescence as a promising tool for quality control of Lebanese virgin olive oils.

Keywords: front-face fluorescence, Lebanese virgin olive oils, multiple Linear regressions, PARAFAC analysis

Procedia PDF Downloads 437

Authors: Marin Mandić, Elis Sutlović, Tonći Modrić, Luka Stanić

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With the restructuring and deregulation of the power system, storage owners, generation companies or private producers can offer their multiple services on various power markets and earn income in different types of markets, such as the day-ahead, real-time, ancillary services market, etc. During the COVID-19 pandemic, electricity prices, as well as ancillary services prices, increased significantly. The optimization of the energy storage operation was performed using a suitable model for simulating the operation of a pumped storage hydropower plant under market conditions. The objective function maximizes the income earned through energy arbitration, regulation-up, regulation-down and spinning reserve services. The optimization technique used for solving the objective function is mixed integer linear programming (MILP). In numerical examples, the pumped storage hydropower plant operation has been optimized considering the already achieved hourly electricity market prices from Nord Pool for the pre-pandemic (2019) and the pandemic (2020 and 2021) years. The impact of the electricity market prices during the COVID-19 pandemic on energy storage operation is shown through the analysis of income, operating hours, reserved capacity and consumed energy for each service. The results indicate the role of energy storage during a significant fluctuation in electricity and services prices.

Keywords: electrical market prices, electricity market, energy storage optimization, mixed integer linear programming (MILP) optimization

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Authors: Kiyoumars Roushangar, Saeid Sadaghian

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Although accurate sediment load prediction is very important in planning, designing, operating and maintenance of water resources structures, the transport mechanism is complex, and the deterministic transport models are based on simplifying assumptions often lead to large prediction errors. In this research, firstly, two intelligent ANN methods, Radial Basis and General Regression Neural Networks, are adopted to model of total sediment load transport into Madani Dam reservoir (north of Iran) using the measured data and then applicability of the sediment transport methods developed by Engelund and Hansen, Ackers and White, Yang, and Toffaleti for predicting of sediment load discharge are evaluated. Based on comparison of the results, it is found that the GRNN model gives better estimates than the sediment rating curve and mentioned classic methods.

Keywords: sediment transport, dam reservoir, RBF, GRNN, prediction

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Authors: Fernanda Varella Borges, Silvio Burrattino Melhado

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Motivated by the importance of public infrastructure projects in the civil construction chain, this research shows the study of project management methods and the infrastructure projects’ characteristics. The research aims at the objective of improving management efficiency by proposing guidelines for the application of project management methods in infrastructure projects. Through literature review and case studies, the research analyses two major infrastructure projects underway in Brazil, identifying the critical points for achieving its success. As a result, the proposed guidelines indicate that special attention should be given to the management of stakeholders, focusing on their knowledge and experience, their different interests, the efficient management of their communication, and their behavior in the day-by-day project management process.

Keywords: construction, infrastructure, project management, public projects

Procedia PDF Downloads 473

Authors: Hafiz Md. Hasan Babu, Khandaker Mohammad Mohi Uddin, Nitish Biswas, Sarreha Tasmin Rikta, Nuzmul Hossain Nahid

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Nanomedicine and bioengineering use biological systems that can perform computing operations. In a biocomputational circuit, different types of biomolecules and DNA (Deoxyribose Nucleic Acid) are used as active components. DNA computing has the capability of performing parallel processing and a large storage capacity that makes it diverse from other computing systems. In most processors, the multiplier is treated as a core hardware block, and multiplication is one of the time-consuming and lengthy tasks. In this paper, cost-effective DNA multipliers are designed using algorithms of molecular DNA operations with respect to conventional ones. The speed and storage capacity of a DNA multiplier are also much higher than a traditional silicon-based multiplier.

Keywords: biological systems, DNA multiplier, large storage, parallel processing

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: N. Abbas, S. Lagomarsino, S. Cattari

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Examining existing experimental results for shallow rigid foundations subjected to vertical centric load (N), accompanied or not with a bending moment (M), two main non-linear mechanisms governing the cyclic ‎response of the soil-foundation system can be distinguished: foundation uplift and soil yielding. A soil-foundation failure limit, is defined as a domain of resistance in the two dimensional (2D) load space (N, M) inside of which lie all the admissible combinations of loads; these latter correspond to a pure elastic, non-linear elastic or plastic behavior of the soil-foundation system, while the points lying on the failure limit correspond to a combination of loads leading to a failure of the soil-foundation system. In this study, the proposed resistance domain is constructed analytically based on mechanics. Original elastic limit, uplift initiation ‎limit and iso-uplift limits are constructed inside this domain. These limits give a prediction ‎of the mechanisms activated for each combination of loads applied to the ‎foundation. A comparison of the proposed failure limit with experimental tests existing in the literature shows interesting results. Also, the developed uplift initiation limit and iso-uplift curves are confronted with others already proposed in the literature and widely used due to the absence of other alternatives, and remarkable differences are noted, showing evident errors in the past proposals and relevant accuracy for those given in the present work.

Keywords: foundation uplift, iso-uplift curves, resistance domain, soil yield

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Authors: Diala Bitar, Emmanuel Gourdon, Claude H. Lamarque, Manuel Collet

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Acoustic absorber devices play an important role reducing the noise at the propagation and reception paths. An electroacoustic absorber consists of a loudspeaker coupled to an electric shunt circuit, where the membrane is playing the role of an absorber/reflector of sound. Although the use of linear shunt resistors at the transducer terminals, has shown to improve the performances of the dynamical absorbers, it is nearly efficient in a narrow frequency band. Therefore, and since nonlinear phenomena are promising for their ability to absorb the vibrations and sound on a larger frequency range, we propose to couple a nonlinear electric shunt circuit at the loudspeaker terminals. Then, the equivalent model can be described by a 2 degrees of freedom system, consisting of a primary linear oscillator describing the dynamics of the loudspeaker membrane, linearly coupled to a cubic nonlinear energy sink (NES). The system is analytically treated for the case of 1:1 resonance, using an invariant manifold approach at different time scales. The proposed methodology enables us to detect the equilibrium points and fold singularities at the first slow time scales, providing a predictive tool to design the nonlinear circuit shunt during the energy exchange process. The preliminary results are promising; a significant improvement of acoustic absorption performances are obtained.

Keywords: electroacoustic absorber, multiple-time-scale with small finite parameter, nonlinear energy sink, nonlinear passive shunt

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Authors: Pınar Karbuz, A. Seyhun Kıpcak, Mehmet B. Piskin, Emek Derun, Nurcan Tugrul

Abstract:

Pectin is a complex colloidal polysaccharide, found on the cell walls of all young plants such as fruit and vegetables. It acts as a thickening, stabilizing and gelling agent in foods. Pectin was extracted from mandarin and lemon peels using ultrasound and microwave assisted extraction methods to compare with these two different sources and methods of pectin production. In this work, the effect of microwave power (360, 600 W) and irradiation time (1, 2, 3 min) on the yield of extracted pectin from mandarin and lemon peels for microwave assisted extraction (MAE) were investigated. For ultrasound assisted extraction (UAE), parameters were determined as temperature (60, 75 °C) and sonication time (15, 30, 45 min) and hydrochloric acid (HCl) was used as an extracting agent for both extraction methods. The highest yields of extracted pectin from lemon peels were found to be 8.16 % (w/w) for 75 °C, 45 min by UAE and 8.58 % (w/w) for 360 W, 1 min by MAE. Additionally, the highest yields of extracted pectin from mandarin peels were found to be 11.29 % (w/w) for 75 °C, 45 min by UAE and 16.44 % (w/w) for 600 W, 1 min by MAE. The results showed that the use of microwave assisted extraction promoted a better yield when compared to the two extraction methods. On the other hand, according to the results of experiments, mandarin peels contain more pectin than lemon peels when the compared to the pectin product values of two sources. Therefore, these results suggested that MAE could be used as an efficient and rapid method for extraction of pectin and mandarin peels should be preferred as sources of pectin production compared to lemon peels.

Keywords: mandarin peel, lemon peel, pectin, ultrasound, microwave, extraction

Procedia PDF Downloads 223

Authors: H. Taylan Selamlar, I. Yavuz, G. Yapar

Abstract:

It is difficult to make predictions especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme. In this paper, the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher order ES methods for additive, multiplicative, additive damped and multiplicative damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics.

Keywords: accuracy, exponential smoothing, forecasting, initial value

Procedia PDF Downloads 164

Authors: T. A. Sakr, Hanaa E. Abd-El-Mottaleb

Abstract:

Outrigger-braced wall systems are commonly used to provide high rise buildings with the required lateral stiffness for wind and earthquake resistance. The existence of outriggers adds to the stiffness and strength of walls as reported by several studies. The effects of different parameters on the elasto-plastic dynamic behavior of outrigger-braced wall systems to earthquakes are investigated in this study. Parameters investigated include outrigger stiffness, concrete strength, and reinforcement arrangement as the main design parameters in wall design. In addition to being significant to the wall behavior, such parameters may lead to the change of failure mode and the delay of crack propagation and consequently failure as the wall is excited by earthquakes. Bi-linear stress-strain relation for concrete with limited tensile strength and truss members with bi-linear stress-strain relation for reinforcement were used in the finite element analysis of the problem. The famous earthquake record, El-Centro, 1940 is used in the study. Emphasis was given to the lateral drift, normal stresses and crack pattern as behavior controlling determinants. Results indicated significant effect of the studied parameters such that stiffer outrigger, higher grade concrete and concentrating the reinforcement at wall edges enhance the behavior of the system. Concrete stresses and cracking behavior are sigbificantly enhanced while lesser drift improvements are observed.

Keywords: outrigger, shear wall, earthquake, nonlinear

Procedia PDF Downloads 268

Authors: Ehab El-Dabaa, Omnia Ali, Mohamed Abd El-Hady, Ahmed Osman

Abstract:

Recombinant human interferon alpha 2 (rhIFN-α2) is FDA approved for treatment of some viral and malignant diseases. Approved pegylated rhIFN-α2 drugs have highly improved pharmacokinetics, pharmacodynamics and therapeutic efficiency compared to native protein. In this work, we studied the pegylation of purified properly refolded rhIFN-α2b using linear 20kDa PEG-NHS (polyethylene glycol- N-hydroxysuccinimidyl ester) to prepare pegylated rhIFN-α2b with high stability and activity. The effect of different parameters like rhIFN-α2b final concentration, pH, rhIFN-α2b/PEG molar ratios and reaction time on the efficiency of pegylation (high percentage of monopegylated rhIFN-α2b) have been studied in small scale (100µl) pegylation reaction trials. Study of the percentages of different components of these reactions (mono, di, polypegylated rhIFN-α2b and unpegylated rhIFN-α2b) indicated that 2h is optimum time to complete the reaction. The pegylation efficiency increased at pH 8 (57.9%) by reducing the protein concentration to 1mg/ml and reducing the rhIFN-α2b/PEG ratio to 1:2. Using larger scale pegylation reaction (65% pegylation efficiency), ion exchange chromatography method has been optimized to prepare and purify the monopegylated rhIFN-α2b with high purity (96%). The prepared monopegylated rhIFN-α2b had apparent Mwt of approximately 65 kDa and high in vitro antiviral activity (2.1x10⁷ ± 0.8 x10⁷ IU/mg). Although it retained approximately 8.4 % of the antiviral activity of the unpegylated rhIFN-α2b, its activity is high compared to other pegylated rhIFN-α2 developed by using similar approach or higher molecular weight branched PEG.

Keywords: antiviral activity, rhIFN-α2b, pegylation, pegylation efficiency

Procedia PDF Downloads 160