Search results for: dimensional accuracy (DA)

Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

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Authors: Sun Il Yu, Jung Hyun Joo, Hwa Sung Shin

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Astrocytes are known as dominant cells in CNS and play a role as a supporter of CNS activity and regeneration. Recently, three-dimensional culture of astrocytes were actively applied to understand in vivo astrocyte works. Electrospun nanofibers are attractive for 3D cell culture system because they have a high surface to volume ratio and porous structure, and have already been used for 3D astrocyte cultures. In this research, the stiffness of cellulose acetate (CA) nanofiber was controlled by heat treatment. As stiffness increased, astrocyte cell viability and adhesion increased. Reactivity of astrocyte was also upregulated in stiffer CA nanofiber in terms of GFAP, an intermediate filament protein. Finally, we demonstrated that stiffness-controllable CA is attractive for astrocyte tissue engineering.

Keywords: astrocyte, cellulose acetate, nanofiber, tissue scaffold

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Authors: Sofiqul Islam, V. Murugan, Prema Kumari, Hari

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Gentamicin is a broad spectrum water-soluble aminoglycoside antibiotics produced by the fermentation process of microorganism known as Micromonospora purpurea. It is widely used for the treatment of infection caused by both gram positive and gram negative bacteria. Gentamicin consists of a mixture of aminoglycoside components like C1, C1a, C2a, and C2. The molecular structure of Gentamicin and its related substances showed that it has lack of presence of chromophore group in the molecule due to which the detection of such components were quite critical and challenging. In this study, a simple Reversed Phase-High Performance Liquid Chromatographic (RP-HPLC) method using ultraviolet (UV) detector was developed and validated for quantification of the related substances present in Gentamicin drug substances. The method was achieved by using Thermo Scientific Hypersil Gold analytical column (150 x 4.6 mm, 5 µm particle size) with isocratic elution composed of methanol: water: glacial acetic acid: sodium hexane sulfonate in the ratio 70:25:5:3 % v/v/v/w as a mobile phase at a flow rate of 0.5 mL/min, column temperature was maintained at 30 °C and detection wavelength of 330 nm. The four components of Gentamicin namely Gentamicin C1, C1a, C2a, and C2 were well separated along with the related substance present in Gentamicin. The Limit of Quantification (LOQ) values were found to be at 0.0075 mg/mL. The accuracy of the method was quite satisfactory in which the % recovery was resulted between 95-105% for the related substances. The correlation coefficient (≥ 0.995) shows the linearity response against concentration over the range of Limit of Quantification (LOQ). Precision studies showed the % Relative Standard Deviation (RSD) values less than 5% for its related substance. The method was validated in accordance with the International Conference of Harmonization (ICH) guideline with various parameters like system suitability, specificity, precision, linearity, accuracy, limit of quantification, and robustness. This proposed method was easy and suitable for use for the quantification of related substances in routine analysis of Gentamicin formulations.

Keywords: reversed phase-high performance liquid chromatographic (RP-HPLC), high performance liquid chromatography, gentamicin, isocratic, ultraviolet

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Authors: Anthony R. Hassan, Jacob A. Gbadeyan

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In this paper, the analysis of a reactive hydromagnetic Poiseuille fluid flow under each of sensitized, Arrhenius and bimolecular chemical kinetics through a channel in the presence of heat source is carried out. An exothermic reaction is assumed while the concentration of the material is neglected. Adomian Decomposition Method (ADM) together with Pade Approximation is used to obtain the solutions of the governing nonlinear non – dimensional differential equations. Effects of various physical parameters on the velocity and temperature fields of the fluid flow are investigated. The entropy generation analysis and the conditions for thermal criticality are also presented.

Keywords: chemical kinetics, entropy generation, thermal criticality, adomian decomposition method (ADM) and pade approximation

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Authors: Mohammad Hadi Jalali, Behrooz Shahriari, Mostafa Ghayour, Saeed Ziaei-Rad, Shahram Yousefi

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Most flexible rotors can be considered as beam-like structures. In many cases, rotors are modeled as one-dimensional bodies, made basically of beam-like shafts with rigid bodies attached to them. This approach is typical of rotor dynamics, both analytical and numerical, and several rotor dynamic codes, based on the finite element method, follow this trend. In this paper, a finite element model based on Timoshenko beam elements is utilized to analyze the lateral dynamic behavior of a certain rotor-bearing system in operating conditions.

Keywords: finite element method, Timoshenko beam elements, operational deflection shape, unbalance response

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Authors: Xiaobin Liang, Wei Liang, Laibin Zhang, Xiaoyan Guo

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Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality.

Keywords: bow-tie model, natural gas pipeline, coal mine gob, cloud inference

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Authors: A. Giniatoulline

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A model of the mathematical fluid dynamics which describes the motion of a three-dimensional viscous rotating fluid in a homogeneous gravitational field with the consideration of the salinity and heat transfer is considered in a vertical finite layer. The model is a generalization of the linearized Navier-Stokes system with the addition of the Coriolis parameter and the equations for changeable density, salinity, and heat transfer. An explicit solution is constructed and the proof of the existence and uniqueness theorems is given. The localization and the structure of the spectrum of inner waves is also investigated. The results may be used, in particular, for constructing stable numerical algorithms for solutions of the considered models of fluid dynamics of the Atmosphere and the Ocean.

Keywords: Fourier transform, generalized solutions, Navier-Stokes equations, stratified fluid

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

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Authors: M. Kalbac, T. Verhagen, K. Drogowska, J. Vejpravova

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One- and two-dimensional carbon nano structures with sp2 hybridization of carbon atoms (single walled carbon nano tubes and graphene) are promising materials in future electronic and spintronics devices due to specific character of their electronic structure. In this paper, we present a comparative study of graphene and single-wall carbon nano tubes by Raman spectro-microscopy in strong magnetic field. This unique method allows to study changes in electronic band structure of the two types of carbon nano structures induced by a strong magnetic field.

Keywords: carbon nano structures, magnetic field, raman spectroscopy, spectro-microscopy

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: Vixen Joshua Tan, Siyuan He

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Scanners have been implemented within single point laser rangefinders, to determine the ranges within an environment by sweeping the laser spot across the surface of interest. The research motivation is to exploit a smaller and cheaper alternative scanning component for the emitting portion within current designs of laser rangefinders. This research implements an FPCB (Flexible Programmable Circuit Board) Electromagnetic 1-Dimensional scanning micro-mirror as a scanning component for laser rangefinding by means of triangulation. The prototype uses a laser module, micro-mirror, and receiver. The laser module is infrared (850 nm) with a power output of 4.5 mW. The receiver consists of a 50 mm convex lens and a 45mm 1-dimensional PSD (Position Sensitive Detector) placed at the focal length of the lens at 50 mm. The scanning component is an elliptical Micro-Mirror attached onto an FPCB Structure. The FPCB structure has two miniature magnets placed symmetrically underneath it on either side, which are then electromagnetically actuated by small solenoids, causing the FPCB to mechanically rotate about its torsion beams. The laser module projects a laser spot onto the micro-mirror surface, hence producing a scanning motion of the laser spot during the rotational actuation of the FPCB. The receiver is placed at a fixed distance from the micro-mirror scanner and is oriented to capture the scanning motion of the laser spot during operation. The elliptical aperture dimensions of the micro-mirror are 8mm by 5.5 mm. The micro-mirror is supported by an FPCB with two torsion beams with dimensions of 4mm by 0.5mm. The overall length of the FPCB is 23 mm. The voltage supplied to the solenoids is sinusoidal with an amplitude of 3.5 volts and 4.5 volts to achieve optical scanning angles of +/- 10 and +/- 17 degrees respectively. The operating scanning frequency during experiments was 5 Hz. For an optical angle of +/- 10 degrees, the prototype is capable of detecting objects within the ranges from 0.3-1.2 meters with an error of less than 15%. As for an optical angle of +/- 17 degrees the measuring range was from 0.3-0.7 meters with an error of 16% or less. Discrepancy between the experimental and actual data is possibly caused by misalignment of the components during experiments. Furthermore, the power of the laser spot collected by the receiver gradually decreased as the object was placed further from the sensor. A higher powered laser will be tested to potentially measure further distances more accurately. Moreover, a wide-angled lens will be used in future experiments when higher scanning angles are used. Modulation within the current and future higher powered lasers will be implemented to enable the operation of the laser rangefinder prototype without the use of safety goggles.

Keywords: FPCB electromagnetic 1-D scanning micro-mirror, laser rangefinder, position sensitive detector, PSD, triangulation

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Authors: M. V. A. Raju Bahubalendruni, Bibhuti Bhusan Biswal, B. B. V. L. Deepak

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Achieving an appropriate assembly sequence needs deep verification for its physical feasibility. For this purpose, industrial engineers use several assembly predicates; namely, liaison, geometric feasibility, stability and mechanical feasibility. However, testing an assembly sequence for these predicates requires huge assembly information. Extracting such assembly information from an assembled product is a time consuming and highly skillful task with complex reasoning methods. In this paper, computer aided methods are proposed to extract all the necessary assembly information from computer aided design (CAD) environment in order to perform the assembly sequence planning efficiently. These methods use preliminary capabilities of three-dimensional solid modelling and assembly modelling methods used in CAD software considering equilibrium laws of physical bodies.

Keywords: assembly automation, assembly attributes, assembly, CAD

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Authors: S. M. Hamidi, M. Zamani

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Magneto-plasmonic (MP) structures have turned into essential tools for the amplification of magneto-optical (MO) responses via the combination of MO activity and surface Plasmon resonance (SPR). Both the plasmonic and the MO properties of the resulting MP structure become interrelated because the SPR of the metallic medium. This interconnection can be modified the wave vector of surface plasmon polariton (SPP) in MP multilayer [1] or enhanced the MO activity [2- 3] and also modified the sensor responses [4]. There are several types of MP structures which are studied to enhance MO response in miniaturized configuration. In this paper, we propose a new MP structure based on the nano-metal grating and we investigate the MO and optical properties of this new structure. Our new MP structure fabricate by DC magnetron sputtering method and our home made MO experimental setup use for characterization of the structure.

Keywords: Magneto-plasmonic structures, magneto-optical effect, nano-garting

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Ali Reza Tahavvor, Saeed Hosseini, Nazli Jowkar, Behnam Amiri

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Heat transfer of leaves is a crucial factor in optimal operation of metabolic functions in plants. In order to quantify this phenomenon in different leaves and investigate the influence of leaf shape on heat transfer, natural convection for pine, orange and olive leaves was simulated as representatives of different groups of leaf shapes. CFD techniques were used in this simulation with the purpose to calculate heat transfer of leaves in similar environmental conditions. The problem was simulated for steady state and three-dimensional conditions. From obtained results, it was concluded that heat fluxes of all three different leaves are almost identical, however, total rate of heat transfer have highest and lowest values for orange leaves and pine leaves, respectively.

Keywords: computational fluid dynamic, heat flux, heat transfer, natural convection

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Authors: Jatindranath Gain, Madhumita DasSarkar, Sudakshina Kundu

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Quantum information is stored in states with multiple quasiparticles, which have a topological degeneracy. Topological quantum computation is concerned with two-dimensional many body systems that support excitations. Anyons are elementary building block of quantum computations. When anyons tunneling in a double-layer system can transition to an exotic non-Abelian state and produce Fibonacci anyons, which are powerful enough for universal topological quantum computation (TQC).Here the exotic behavior of Fibonacci Superlattice is studied by using analytical transfer matrix methods and hence Fibonacci anyons. This Fibonacci anyons can build a quantum computer which is very emerging and exciting field today’s in Nanophotonics and quantum computation.

Keywords: quantum computing, quasicrystals, Multiple Quantum wells (MQWs), transfer matrix method, fibonacci anyons, quantum hall effect, nanophotonics

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Authors: Norhashidah Hj. Mohd Ali, Teng Wai Ping

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In this paper, we present a high order group explicit method in solving the two dimensional Helmholtz equation. The presented method is derived from a nine-point fourth order finite difference approximation formula obtained from a 45-degree rotation of the standard grid which makes it possible for the construction of iterative procedure with reduced complexity. The developed method will be compared with the existing group iterative schemes available in literature in terms of computational time, iteration counts, and computational complexity. The comparative performances of the methods will be discussed and reported.

Keywords: explicit group method, finite difference, helmholtz equation, rotated grid, standard grid

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Authors: Marielcio Lacerda, Angelo Paulino, Elcio Shiguemori, Alvaro Damiao, Lamartine Guimaraes, Camila Anjos

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Accurate information about the terrain is extremely important in disaster management activities or conflict. This paper proposes the use of the Unmanned Aircraft Systems (UAS) at the identification of Airdrop Zones (AZs) and Helicopter Landing Zones (HLZs). In this paper we consider the AZs the zones where troops or supplies are dropped by parachute, and HLZs areas where victims can be rescued. The use of digital image processing enables the automatic generation of an orthorectified mosaic and an actual Digital Surface Model (DSM). This methodology allows obtaining this fundamental information to the terrain’s comprehension post-disaster in a short amount of time and with good accuracy. In order to get the identification and classification of AZs and HLZs images from DJI drone, model Phantom 4 have been used. The images were obtained with the knowledge and authorization of the responsible sectors and were duly registered in the control agencies. The flight was performed on May 24, 2017, and approximately 1,300 images were obtained during approximately 1 hour of flight. Afterward, new attributes were generated by Feature Extraction (FE) from the original images. The use of multispectral images and complementary attributes generated independently from them increases the accuracy of classification. The attributes of this work include the Declivity Map and Principal Component Analysis (PCA). For the classification four distinct classes were considered: HLZ 1 – small size (18m x 18m); HLZ 2 – medium size (23m x 23m); HLZ 3 – large size (28m x 28m); AZ (100m x 100m). The Decision Tree method Random Forest (RF) was used in this work. RF is a classification method that uses a large collection of de-correlated decision trees. Different random sets of samples are used as sampled objects. The results of classification from each tree and for each object is called a class vote. The resulting classification is decided by a majority of class votes. In this case, we used 200 trees for the execution of RF in the software WEKA 3.8. The classification result was visualized on QGIS Desktop 2.12.3. Through the methodology used, it was possible to classify in the study area: 6 areas as HLZ 1, 6 areas as HLZ 2, 4 areas as HLZ 3; and 2 areas as AZ. It should be noted that an area classified as AZ covers the classifications of the other classes, and may be used as AZ, HLZ of large size (HLZ3), medium size (HLZ2) and small size helicopters (HLZ1). Likewise, an area classified as HLZ for large rotary wing aircraft (HLZ3) covers the smaller area classifications, and so on. It was concluded that images obtained through small UAV are of great use in calamity situations since they can provide data with high accuracy, with low cost, low risk and ease and agility in obtaining aerial photographs. This allows the generation, in a short time, of information about the features of the terrain in order to serve as an important decision support tool.

Keywords: disaster management, unmanned aircraft systems, helicopter landing zones, airdrop zones, random forest

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Authors: Zina Benouaret, Djamil Aissani

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In this work, we introduce the qualitative and quantitative concept of the strong stability method in the risk process modeling two lines of business of the same insurance company or an insurance and re-insurance companies that divide between them both claims and premiums with a certain proportion. The approach proposed is based on the identification of the ruin probability associate to the model considered, with a stationary distribution of a Markov random process called a reversed process. Our objective, after clarifying the condition and the perturbation domain of parameters, is to obtain the stability inequality of the ruin probability which is applied to estimate the approximation error of a model with disturbance parameters by the considered model. In the stability bound obtained, all constants are explicitly written.

Keywords: Markov chain, risk models, ruin probabilities, strong stability analysis

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Authors: Hee Sun (Sunny) Choi, Gerhard Bruyns, Wang Zhang, Sky Cheng, Saijal Sharma

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The main focus of this paper is to propose a comprehensive framework for the cognitive measurement and modelling of the built environment. This will involve exploring and measuring neural mechanisms. The aim is to create a foundation for further studies in this field that are consistent and rigorous. Additionally, this framework will facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling. By doing so, the broader intention is to design urban spaces that prioritize human needs and well-being, making them more liveable.

Keywords: vertical urbanism, human centric design, spatial cognition and psychology, vertical urban design guidelines

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Authors: Panatda Intanin, Sangutid Thongsawad, Chirapha Tannanonta, Todsaporn Fuangrod

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Stereotactic radiosurgery (SRS) is a highly precision delivery technique that requires comprehensive quality assurance (QA) tests prior to treatment delivery. An isocenter of delivery beam plays a critical role that affect the treatment accuracy. The uncertainty of isocenter is traditionally accessed using circular cone equipment, Winston-Lutz (WL) phantom and film. This technique is considered time consuming and highly dependent on the observer. In this work, the development of multileaf collimator (MLC)-based isocenter verification tool using electronic portal imaging device (EPID) was proposed and evaluated. A mechanical isocenter alignment with ball bearing diameter 5 mm and circular cone diameter 10 mm fixed to gantry head defines the radiation field was set as the conventional WL test method. The conventional setup was to compare to the proposed setup; using MLC (10 x 10 mm) to define the radiation filed instead of cone. This represents more realistic delivery field than using circular cone equipment. The acquisition from electronic portal imaging device (EPID) and radiographic film were performed in both experiments. The gantry angles were set as following: 0°, 90°, 180° and 270°. A software tool was in-house developed using MATLAB/SIMULINK programming to determine the centroid of radiation field and shadow of WL phantom automatically. This presents higher accuracy than manual measurement. The deviation between centroid of both cone-based and MLC-based WL tests were quantified. To compare between film and EPID image, the deviation for all gantry angle was 0.26±0.19mm and 0.43±0.30 for cone-based and MLC-based WL tests. For the absolute deviation calculation on EPID images between cone and MLC-based WL test was 0.59±0.28 mm and the absolute deviation on film images was 0.14±0.13 mm. Therefore, the MLC-based isocenter verification using EPID present high sensitivity tool for SRS QA.

Keywords: isocenter verification, quality assurance, EPID, SRS

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Authors: S. Belhour, A. K. Ferouani, C. Azizi

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In recent years, a considerable effort (experience, numerical simulation, and theoretical prediction models) has characterised by high efficiency and low cost. Then an improved physics analytical model for simulating is proposed. The performance of GaAs MESFETs has been developed for use in device design for high frequency. This model is based on mathematical analysis, and a new approach for the standard model is proposed, this approach allowed to conceive applicable model for MESFET’s operating in the turn-one or pinch-off region and valid for the short-channel and the long channel MESFET’s in which the two dimensional potential distribution contributed by the depletion layer under the gate is obtained by conventional approximation. More ever, comparisons between the analytical models with different values of mobility are proposed, and a good agreement is obtained.

Keywords: analytical, gallium arsenide, MESFET, mobility, models

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Authors: Suleiman Bashir Adamu, Lawan Sani Taura

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We study the role of boundary conditions via -symmetric quantum mechanics, where denotes parity operator and denotes time reversal operator. Using the one-dimensional Schrödinger Hamiltonian for a free particle in an infinite square well, we introduce symmetric boundary conditions. We find solutions of the 1D Klein-Gordon equation for a free particle in an infinite square well with Hermitian boundary and symmetry boundary conditions, where in both cases the energy eigenvalues and eigenfunction, respectively, are obtained.

Keywords: Eigenvalues, Eigenfunction, Hamiltonian, Klein- Gordon equation, PT-symmetric quantum mechanics

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Authors: Ibrahim Ajani Tijani, C. W. Lim

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Limited studies have examined the effect of size on the mechanical behavior of confined concrete subjected to 3-dimensional (3D) test. With the novel 3D testing system to produce passive confinement, concrete cubes were tested to examine the effect of size on stress-strain behavior of the specimens. The effect of size on 3D stress-strain relationship was scrutinized and compared to the stress-strain relationship available in the literature. It was observed that the ultimate stress and the corresponding strain was related to the confining rigidity and size. The size shows a significant effect on the intersection stress and a new model was proposed for the intersection stress based on the conceptual design of the confining plates.

Keywords: concrete, aggregate size, size effect, 3D compression, passive confinement

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Authors: A. R. Manu, V. K. Agrawal, K. N. Balasubramanya Murthy

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Authentication is required in stored database systems so that only authorized users can access the data and related cloud infrastructures. This paper proposes an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. The proposed technique is likely to be more robust as the probability of breaking the password is extremely low. This framework uses a multi-modal biometric approach and SMS to enforce additional security measures with the conventional Login/password system. The robustness of the technique is demonstrated mathematically using a statistical analysis. This work presents the authentication system along with the user authentication architecture diagram, activity diagrams, data flow diagrams, sequence diagrams, and algorithms.

Keywords: design, implementation algorithms, performance, biometric approach

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Authors: Yakup Ulusu, Numan Eczacıoğlu, İsa Gökçe, Helen Waller, Jeremy H. Lakey

Abstract:

Besides having the appropriate amino acid sequence to perform the function of proteins, it is important to have correct conformation after this sequence to process. To consist of this conformation depends on the amino acid sequence at the primary structure, hydrophobic interaction, chaperones and enzymes in charge of folding etc. Misfolded proteins are not functional and tend to be aggregated. Cysteine originating disulfide cross-links make stable this conformation of functional proteins. When two of the cysteine amino acids come side by side, disulfide bond is established that forms a cystine bridge. Due to this feature cysteine plays an important role on the formation of three-dimensional structure of many proteins. There are two cysteine amino acids (C44, C69) in the Tol-A-III protein. Unlike protein disulfide bonds from within his own, any non-specific cystine bridge causes a change in the three dimensional structure of the protein. Proteins can be expressed in various host cells as directly or fusion (chimeric). As a result of overproduction of the recombinant proteins, aggregation of insoluble proteins in the host cell can occur by forming a crystal structure called inclusion body. In general fusion proteins are produced for provide affinity tags to make proteins more soluble and production of some toxic proteins via fusion protein expression system like pTolT. Proteins can be modified by using a site-directed mutagenesis. By this way, creation of non-specific disulfide crosslinks can be prevented at fusion protein expression system via the present cysteine replaced by another amino acid such as serine, glycine or etc. To do this, we need; a DNA molecule that contains the gene that encodes for the target protein, required primers for mutation to be designed according to site directed mutagenesis reaction. This study was aimed to be replaced cysteine encoding codon TGT with serine encoding codon AGT. For this sense and reverse primers designed (given below) and used site-directed mutagenesis reaction. Several new copy of the template plasmid DNA has been formed with above mentioned mutagenic primers via polymerase chain reaction (PCR). PCR product consists of both the master template DNA (wild type) and the new DNA sequences containing mutations. Dpn-l endonuclease restriction enzyme which is specific for methylated DNA and cuts them to the elimination of the master template DNA. E. coli cells obtained after transformation were incubated LB medium with antibiotic. After purification of plasmid DNA from E. coli, the presence of the mutation was determined by DNA sequence analysis. Developed this new plasmid is called PtolT-δ.

Keywords: site directed mutagenesis, Escherichia coli, pTolT, protein expression

Procedia PDF Downloads 368

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 122

Authors: Sina Saadati, Mohammadreza Razzazi

Abstract:

Since Java is an object-oriented language, It can be used to solve a wide range of problems. One of the considerable usages of this language can be found in Agent-based modeling and simulation. Despite the significant power of Java, There is not an easy method to render a 3-dimensional human model. In this article, we are about to develop a library which helps modelers present a 3D human model and control it with Java. The library runs two server programs. The first one is a web page server that can connect to any browser and present an HTML code. The second server connects to the browser and controls the movement of the model. So, the modeler will be able to develop a simulation and display a good-looking human model without any knowledge of any graphical tools.

Keywords: agent-based modeling and simulation, human model, graphics, Java, distributed systems

Procedia PDF Downloads 107

Authors: A. Amiri Delouei

Abstract:

In the current study, two-dimensional unsteady heat conduction in a functionally graded cylinder is studied analytically. The temperature distribution is in radial and longitudinal directions. Heat conduction coefficients are considered a power function of radius both in radial and longitudinal directions. The proposed solution can exactly satisfy the boundary conditions. Analytical unsteady temperature distribution for different parameters of functionally graded cylinder is investigated. The achieved exact solution is useful for thermal stress analysis of functionally graded cylinders. Regarding the analytical approach, this solution can be used to understand the concepts of heat conduction in functionally graded materials.

Keywords: functionally graded materials, unsteady heat conduction, cylinder, temperature distribution

Procedia PDF Downloads 298