Search results for: dynamic time-lagged correlation based method

Authors: Rohan Putatunda, Aryya Gangopadhyay

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Acoustic sensors, if embedded in smart street lights, can help in capturing the activities (car honking, sirens, events, traffic, etc.) in cities. Needless to say, the acoustic data from such scenarios are complex due to multiple audio streams originating from different events, and when decomposed to independent signals, the amount of retrieved data volume is small in quantity which is inadequate to train deep neural networks. So, in this paper, we address the two challenges: a) separating the mixed signals, and b) developing an efficient acoustic classifier under data paucity. So, to address these challenges, we propose an architecture with supervised deep learning, where the initial captured mixed acoustics data are analyzed with Fast Fourier Transformation (FFT), followed by filtering the noise from the signal, and then decomposed to independent signals by fast independent component analysis (Fast ICA). To address the challenge of data paucity, we propose a multi feature-based deep neural network with high performance that is reflected in our experiments when compared to the conventional convolutional neural network (CNN) and multi-layer perceptron (MLP).

Keywords: FFT, ICA, vehicle classification, multi-feature DNN, CNN, MLP.

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Authors: Mohsen Mohammadi, Toshio Fujimi

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High population and irregular urban development in Kabul city, Afghanistan's capital, are among factors that increase its vulnerability to earthquake disasters (on top of its location in a high seismic region); this can lead to widespread economic loss and casualties. This study aims to evaluate earthquake risks in Kabul's 13th district based on scientific data. The research data, which include hazard curves of Kabul, vulnerability curves, and a questionnaire survey through sampling in district 13, have been incorporated to develop risk curves. To estimate potential casualties, we used a set of M parameters in a model developed by Coburn and Spence. The results indicate that in the worst case scenario, more than 90% of district 13, which comprises mostly residential buildings, is exposed to high risk; this may lead to nearly 1000 million USD economic loss and 120 thousand casualties (equal to 25.88% of the 13th district's population) for a nighttime earthquake. To reduce risks, we present the reconstruction of the most vulnerable buildings, which are primarily adobe and masonry buildings. A comparison of risk reduction between reconstructing adobe and masonry buildings indicates that rebuilding adobe buildings would be more effective.

Keywords: Earthquake risk evaluation, Kabul, mitigation, vulnerability.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: S. B. Jaju, R. R. Lakhe

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Quality costs are the costs associated with preventing, finding, and correcting defective work. Since the main language of corporate management is money, quality-related costs act as means of communication between the staff of quality engineering departments and the company managers. The objective of quality engineering is to minimize the total quality cost across the life of product. Quality costs provide a benchmark against which improvement can be measured over time. It provides a rupee-based report on quality improvement efforts. It is an effective tool to identify, prioritize and select quality improvement projects. After reviewing through the literature it was noticed that a simplified methodology for data collection of quality cost in a manufacturing industry was required. The quantified standard methodology is proposed for collecting data of various elements of quality cost categories for manufacturing industry. Also in the light of research carried out so far, it is felt necessary to standardise cost elements in each of the prevention, appraisal, internal failure and external failure costs. . Here an attempt is made to standardise the various cost elements applicable to manufacturing industry and data is collected by using the proposed quantified methodology. This paper discusses the case study carried in luggage manufacturing industry.

Keywords: Quality Costs, PAF model, quantified methodology, Case study.

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Authors: Shayan Rezvanigilkolaei, Shayesteh Vefaghnematollahi

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Electrical impedance tomography is a non-invasive medical imaging technique suitable for medical applications. This paper describes an electrical impedance tomography device with the ability to navigate a robotic arm to manipulate a target object. The design of the device includes various hardware and software sections to perform medical imaging and control the robotic arm. In its hardware section an image is formed by 16 electrodes which are located around a container. This image is used to navigate a 3DOF robotic arm to reach the exact location of the target object. The data set to form the impedance imaging is obtained by having repeated current injections and voltage measurements between all electrode pairs. After performing the necessary calculations to obtain the impedance, information is transmitted to the computer. This data is fed and then executed in MATLAB which is interfaced with EIDORS (Electrical Impedance Tomography Reconstruction Software) to reconstruct the image based on the acquired data. In the next step, the coordinates of the center of the target object are calculated by image processing toolbox of MATLAB (IPT). Finally, these coordinates are used to calculate the angles of each joint of the robotic arm. The robotic arm moves to the desired tissue with the user command.

Keywords: Electrical impedance tomography, EIT, Surgeon robot, image processing of Electrical impedance tomography.

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Authors: A. I. M. Ismail, N. Ryden

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This paper is concerned with the study of mineralogy and engineering characteristics of soil materials derived from the eastern part of Nile Delta. The clay minerals of the studied soil by using X- ray diffraction are mainly illite (average 72.6 %) and kaolinite (average 2.6 %), expandable portion in illite-smectite mixed layer (average 7 %). Smectite is more abundant in fluviatile clays, whereas kaolinite is more abundant in lagoonal clays. On the other hand, illite and illite-smectite are more abundant in marine clays. The geotechnical results show that the soil under study consists mainly of about 0.3 % gravel, 5 % sand, 51.5 % silt and 42.2 % clay in average. The average shrinkage limit attains 11 % whereas the average value of the plasticity index is 23.4 %. The free swelling ranges from 40 % to 75 % and has a value of 55 % giving an indication about the inadequacy of such soil under foundations. From a construction point of view, the soil under investigation poses many problems even under light foundations due to the swelling and shrinkage. Such swelling and shrinkage is due to the high content of soil materials in the expandable clay minerals of illite and smectite. Based on the results of the present and earlier studies, trial application of soil stabilisation is recommended.

Keywords: Engineering Geological Investigations, Nile Delta, Swelling, Shrinkage

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Authors: Adriano Z. Zambom, Preethi Ravikumar

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One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified.

Keywords: Additive models, local polynomial regression, residuals, mean square error, variable selection.

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Authors: Ghada A. El Khayat

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This paper argues that a product development exercise involves in addition to the conventional stages, several decisions regarding other aspects. These aspects should be addressed simultaneously in order to develop a product that responds to the customer needs and that helps realize objectives of the stakeholders in terms of profitability, market share and the like. We present a framework that encompasses these different development dimensions. The framework shows that a product development methodology such as the Quality Function Deployment (QFD) is the basic tool which allows definition of the target specifications of a new product. Creativity is the first dimension that enables the development exercise to live and end successfully. A number of group processes need to be followed by the development team in order to ensure enough creativity and innovation. Secondly, packaging is considered to be an important extension of the product. Branding strategies, quality and standardization requirements, identification technologies, design technologies, production technologies and costing and pricing are also integral parts to the development exercise. These dimensions constitute the proposed framework. The paper also presents a mathematical model used to calculate the design targets based on the target costing principle. The framework is used to study a case of a new product development in the telecommunications services sector.

Keywords: Product Development Framework, Quality FunctionDeployment, Mathematical Models, Telecommunications.

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Authors: V.Manikandan, N.Devarajan

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The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.

Keywords: Artificial neural network, Fault Diagnosis, Identification, Markov parameters.

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Authors: Arkaprabha Bhattacharyya, Makarand Hastak

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The COVID-19 pandemic is unprecedented in terms of its global reach and economic impacts. Historically, investment in infrastructure development projects has been touted to boost the economic growth of a nation. The State and Local governments responsible for delivering infrastructure assets work under tight budgets. Therefore, it is important to understand which infrastructure projects have the highest potential of boosting economic growth in the post-pandemic era. This paper presents relationships between infrastructure projects and economic growth. Statistical relationships between investment in different types of infrastructure projects (transit, water and wastewater, highways, power, manufacturing etc.) and indicators of economic growth are presented using historic data between 2002 and 2020 from the U.S. Census Bureau and U.S. Bureau of Economic Analysis (BEA). The outcome of the paper is the comparison of statistical correlations between investment in different types of infrastructure projects and indicators of economic growth. The comparison of the statistical correlations is useful in ranking the types of infrastructure projects based on their ability to influence economic prosperity. Therefore, investment in the infrastructures with the higher rank will have a better chance of boosting the economic growth. Once, the ranks are derived, they can be used by the decision-makers in infrastructure investment related decision-making process.

Keywords: Economic growth, infrastructure development, infrastructure projects, strategic investment.

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Authors: Ming Ling Lai, Zalilawati Yaacob, Normah Omar, Norashikin Abdul Aziz, Bee Wah Yap

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This paper aims to (1) analyze the profiles of transgressors (detected evaders); (2) examine reason(s) that triggered a tax audit, causes of tax evasion, audit timeframe and tax penalty charged; and (3) to assess if tax auditors followed the guidelines as stated in the 'Tax Audit Framework' when conducting tax audits. In 2011, the Inland Revenue Board Malaysia (IRBM) had audited and finalized 557 company cases. With official permission, data of all the 557 cases were obtained from the IRBM. Of these, a total of 421 cases with complete information were analyzed. About 58.1% was small and medium corporations and from the construction industry (32.8%). The selection for tax audit was based on risk analysis (66.8%), information from third party (11.1%), and firm with low profitability or fluctuating profit pattern (7.8%). The three persistent causes of tax evasion by firms were over claimed expenses (46.8%), fraudulent reporting of income (38.5%) and overstating purchases (10.5%). These findings are consistent with past literature. Results showed that tax auditors took six to 18 months to close audit cases. More than half of tax evaders were fined 45% on additional tax raised during audit for the first offence. The study found tax auditors did follow the guidelines in the 'Tax Audit Framework' in audit selection, settlement and penalty imposition.

Keywords: Corporate tax fraud, tax non-compliance, tax evasion, tax audit, fraudulent reporting.

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Authors: X. Zhao, F. Tian

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Mycotoxins are secondary metabolic products of fungi. These are poisonous, carcinogens and mutagens in nature and pose a serious health threat to both humans and animals, causing severe illnesses and even deaths. The rapid, simple and cheap detection methods of mycotoxins are of immense importance and in great demand in the food and beverage industry as well as in agriculture and environmental monitoring. Lateral flow immunochromatographic strips (ICSTs) have been widely used in food safety, environment monitoring. 46 papers were identified and reviewed on Google Scholar and Scopus for their limit of detection and nanomaterial on Lateral flow ICSTs on different types of mycotoxins. The papers were dated 2001-2021. 25 papers were compared to identify the lowest limit of detection of among different mycotoxins (Aflatoxin B1: 10, Zearalenone: 5, Fumonisin B1: 5, Trichothecene-A: 5). Most of these highly sensitive strips are competitive. Sandwich structures are usually used in large scale detection. In conclusion, the limit of detection of Aflatoxin B1 is the lowest among these mycotoxins. Gold-nanoparticle based immunochromatographic test strips have the lowest limit of detection. Five papers involve smartphone detection and they all detect aflatoxin B1 with gold nanoparticles.

Keywords: Aflatoxin B1, limit of detection, gold nanoparticle, lateral flow immunochromatographic strips, mycotoxins, smartphone.

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Authors: Akiko Kondo, Akio Kondo

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We are facing regional problems to low birth rate and longevity in Japan. Under this situation, there are some local municipalities which lose their vitality. The aims of this study are to clarify the present state of local public transportation services in local municipalities and relation between local public transportation services and population quantitatively. We conducted a questionnaire survey concerning regional agenda in all local municipalities in Japan. We obtained responses concerning the present state of convenience in use of public transportation and local public transportation services. Based on the data gathered from the survey, it is apparent that we should some sort of measures concerning public transportation services. Convenience in use of public transportation becomes an object of public concern in many rural regions. It is also clarified that some local municipalities introduce a demand bus for the purpose of promotion of administrative and financial efficiency. They also introduce a demand taxi in order to secure transportation to weak people in transportation and eliminate of blank area related to public transportation services. In addition, we construct a population model which includes explanatory variables of present states of local public transportation services. From this result, we can clarify the relation between public transportation services and population quantitatively.

Keywords: Public transportation, local municipality, regional analysis, regional issue.

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Authors: Ahmed Amin Mousa, M. Abd El-Salam

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This study discusses a simple solution for the problem of shortage in learning resources for kindergarten teachers. Occasionally, kindergarten teachers cannot access proper resources by usual search methods as libraries or search engines. Furthermore, these methods require a long time and efforts for preparing. The study is expected to facilitate accessing learning resources. Moreover, it suggests a potential direction for using QR code inside the classroom. The present work proposes that QR code can be used for digitizing kindergarten curriculums and accessing various learning resources. It investigates using QR code for saving information related to the concepts which kindergarten teachers use in the current educational situation. The researchers have established a guide for kindergarten teachers based on the Egyptian official curriculum. The guide provides different learning resources for each scientific and mathematical concept in the curriculum, and each learning resource is represented as a QR code image that contains its URL. Therefore, kindergarten teachers can use smartphone applications for reading QR codes and displaying the related learning resources for students immediately. The guide has been provided to a group of 108 teachers for using inside their classrooms. The results showed that the teachers approved the guide, and gave a good response.

Keywords: Kindergarten, child, learning resources, QR code, smart phone, mobile.

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Authors: Sukir Maryanto, Didik R. Santosa, Iyan Mulyana, Muhammad Hendrasto

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Successive event of explosion earthquake and harmonic tremor recorded at Semeru volcano were analyzed to investigate the dynamical system regarding to their eruptive mechanism. The eruptive activity at Semeru volcano East Java, Indonesia is intermittent emission of ash and bombs with Strombolian style which occurred at interval of 15 to 45 minutes. The explosive eruptions accompanied by explosion earthquakes and followed by volcanic tremor which generated by continuous emission of volcanic ash. The spectral and Lyapunov exponent of successive event of explosion and harmonic tremor were analyzed. Peak frequencies of explosion earthquakes range 1.2 to 1.9 Hz and those of the harmonic tremor have peak frequency range 1.5 — 2.2 Hz. The phase space is reconstructed and evaluated based on the Lyapunov exponents. Harmonic tremors have smaller Lyapunov exponent than explosion earthquakes. It can be considerably as correlated complexity of the mechanism from the variance of spectral and fractal dimension and can be concluded that the successive event of harmonic tremor and explosions are chaotic.

Keywords: Semeru volcano, explosion earthquakes, harmonic tremor, lyapunov exponent, chaotic.

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Authors: Emari Hossien

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This study examined the underlying dimensions of brand equity in the chocolate industry. For this purpose, researchers developed a model to identify which factors are influential in building brand equity. The second purpose was to assess brand loyalty and brand images mediating effect between brand attitude, brand personality, brand association with brand equity. The study employed structural equation modeling to investigate the causal relationships between the dimensions of brand equity and brand equity itself. It specifically measured the way in which consumers’ perceptions of the dimensions of brand equity affected the overall brand equity evaluations. Data were collected from a sample of consumers of chocolate industry in Iran. The results of this empirical study indicate that brand loyalty and brand image are important components of brand equity in this industry. Moreover, the role of brand loyalty and brand image as mediating factors in the intention of brand equity are supported. The principal contribution of the present research is that it provides empirical evidence of the multidimensionality of consumer based brand equity, supporting Aaker´s and Keller´s conceptualization of brand equity. The present research also enriched brand equity building by incorporating the brand personality and brand image, as recommended by previous researchers. Moreover, creating the brand equity index in chocolate industry of Iran particularly is novel.

Keywords: brand equity, brand personality, structural equationmodeling, Iran.

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Authors: Andy Tirta, Yus Prasetyo, Eung-Ryul. Baek, Chul-Jin. Choi , Hye-Moon. Lee

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Recently, the advanced technologies that offer high precision product, relative easy, economical process and also rapid production are needed to realize the high demand of ultra precision micro part. In our research, micromanufacturing based on soft lithography and nanopowder injection molding was investigated. The silicone metal pattern with ultra thick and high aspect ratio succeeds to fabricate Polydimethylsiloxane (PDMS) micro mold. The process followed by nanopowder injection molding (PIM) by a simple vacuum hot press. The 17-4ph nanopowder with diameter of 100 nm, succeed to be injected and it forms green sample microbearing with thickness, microchannel and aspect ratio is 700μm, 60μm and 12, respectively. Sintering process was done in 1200 C for 2 hours and heating rate 0.83oC/min. Since low powder load (45% PL) was applied to achieve green sample fabrication, ~15% shrinkage happen in the 86% relative density. Several improvements should be done to produce high accuracy and full density sintered part.

Keywords: Micromanufacturing, Nano PIM, PDMS micro mould.

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Authors: Özge Alptoğa, Nuray Uçar, Nilgün Karatepe Yavuz, Ayşen Önen

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Sulfur dioxide (SO₂) is a very toxic air pollutant gas and it causes the greenhouse effect, photochemical smog, and acid rain, which threaten human health severely. Thus, the capture of SO₂ gas is very important for the environment. Graphene which is two-dimensional material has excellent mechanical, chemical, thermal properties, and many application areas such as energy storage devices, gas adsorption, sensing devices, and optical electronics. Further, graphene oxide (GO) is examined as a good adsorbent because of its important features such as functional groups (epoxy, carboxyl and hydroxyl) on the surface and layered structure. The SO₂ adsorption properties of the fibers are usually investigated on carbon fibers. In this study, potential adsorption capacity of GO fibers was researched. GO dispersion was first obtained with Hummers’ method from graphite, and then GO fibers were obtained via wet spinning process. These fibers were converted into a disc shape, dried, and then subjected to SO₂ gas adsorption test. The SO₂ gas adsorption capacity of GO fiber discs was investigated in the fields of utilization of different coagulation baths and reduction by hydrazine hydrate. As coagulation baths, single and triple baths were used. In single bath, only ethanol and CaCl₂ (calcium chloride) salt were added. In triple bath, each bath has a different concentration of water/ethanol and CaCl₂ salt, and the disc obtained from triple bath has been called as reference disk. The fibers which were produced with single bath were flexible and rough, and the analyses show that they had higher SO₂ adsorption capacity than triple bath fibers (reference disk). However, the reduction process did not increase the adsorption capacity, because the SEM images showed that the layers and uniform structure in the fiber form were damaged, and reduction decreased the functional groups which SO₂ will be attached. Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD) analyzes were performed on the fibers and discs, and the effects on the results were interpreted. In the future applications of the study, it is aimed that subjects such as pH and additives will be examined.

Keywords: Coagulation bath, graphene oxide fiber, reduction, SO2 gas adsorption.

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Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

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This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: Designing, education management, information systems, matrix technology, process affinity.

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Authors: Fan Ye

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Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: Low visibility, RWIS, traffic safety, visibility.

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Authors: F. Guspi, E. García

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This exploratory study gives an overview of the evolution of the main financial and performance indicators of the Academic Spin-Off’s and High Growth Academic Spin-Off’s in year 3 and year 6 after its creation in the region of Catalonia in Spain. The study compares and evaluates results of these different measures of performance and the degree of success of these companies for each University. We found that the average Catalonian Academic Spin-Off is small and have not achieved the sustainability stage at year 6. On the contrary, a small group of High Growth Academic Spin-Off’s exhibits robust performance with high profits in year 6. Our results support the need to increase selectivity and support for these companies especially near year 3, because are the ones that will bring wealth and employment. University role as an investor has rigid norms and habits that impede an efficient economic return from their ASO investment. Universities with high performance on sales and employment in year 3 not always could sustain this growth in year 6 because their ASO’s are not profitable. On the contrary, profitable ASO exhibit superior performance in all measurement indicators in year 6. We advocate the need of a balanced growth (with profits) as a way to obtain subsequent continuous growth.

Keywords: Academic Spin-Off (ASO), University Entrepreneurship, Entrepreneurial University, high growth, New Technology Based Companies (NTBC), University Spin-Off.

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Authors: Hidehiko Okada, Takayuki Akiba

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It is well-known as Fitts’ law that the time for a user to point a target on a GUI screen can be modeled as a linear function of “index of difficulty (ID).” In this paper, the authors investigate whether the traditional ID formulation is appropriate independently of device screen sizes. Result of our experiment reveals that the ID formulation may not consistently capture actual difficulty: users’ pointing performances are not consistent among pointing target variations of which index of difficulty are consistent. The term A/W may not be appropriate because the term causes the observed inconsistency. Based on this finding, the authors then evaluate the applicability of possible models other than Fitts’ one. Multiple regression models are found to be able to appropriately represent the effects of target design variations. The authors next make an attempt to improve the definition of ID in Fitts’ model. Our idea is to raise the size or the distance values depending on the screen size. The modified model is found to fit well to the users’ pointing data, which supports the idea. 

Keywords: Fitts’ law, pointing device, small screen, touch user interface, usability.

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Authors: Azlina Binti Abu Bakar, Fahmi Zaidi Bin Abdul Razak, Wan Salihin Wong Abdullah

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Psychographic is a psychological study of values, attitudes, interests and it is used mostly in prediction, opinion research and social research. This study predicts the influence of performance expectancy, effort expectancy, social influence and facilitating condition on e-government acceptance among Malaysian citizens. The survey responses of 543 e-government users have been validated and analyzed by means of covariance-based Structural Equation Modeling. The findings indicate that e-government acceptance among Malaysian citizens are mainly influenced by performance expectancy (β = 0.66, t = 11.53, p < 0.01) and social influence (β = 0.20, t = 4.23, p < 0.01). Surprisingly, there is no significant effect of facilitating condition and effort expectancy on e-government continuance intention (β = 0.01, t = 0.27, p > 0.05; β = -0.01, t = -0.40, p > 0.05). This study offers government and vendors a frame of reference to analyze citizen’s situation before initiating new innovations. In case of Malaysian e-government technology, adoption strategies should be built around fostering level of citizens’ technological expectation and social influence on e-government usage.

Keywords: Continuance intention, Malaysian citizens, media psychology, structural equation modeling.

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Authors: Hong Son Hoang, Remy Baraille

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This paper addresses the problem of how one can improve the performance of a non-optimal filter. First the theoretical question on dynamical representation for a given time correlated random process is studied. It will be demonstrated that for a wide class of random processes, having a canonical form, there exists a dynamical system equivalent in the sense that its output has the same covariance function. It is shown that the dynamical approach is more effective for simulating and estimating a Markov and non- Markovian random processes, computationally is less demanding, especially with increasing of the dimension of simulated processes. Numerical examples and estimation problems in low dimensional systems are given to illustrate the advantages of the approach. A very useful application of the proposed approach is shown for the problem of state estimation in very high dimensional systems. Here a modified filter for data assimilation in an oceanic numerical model is presented which is proved to be very efficient due to introducing a simple Markovian structure for the output prediction error process and adaptive tuning some parameters of the Markov equation.

Keywords: Statistical simulation, canonical form, dynamical system, Markov and non-Markovian processes, data assimilation.

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Authors: Vasco A. P. Cadavez, Fernando C. Monteiro

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The objective of this study was to develop and compare alternative prediction equations of lean meat proportion (LMP) of lamb carcasses. Forty (40) male lambs, 22 of Churra Galega Bragançana Portuguese local breed and 18 of Suffolk breed were used. Lambs were slaughtered, and carcasses weighed approximately 30 min later in order to obtain hot carcass weight (HCW). After cooling at 4º C for 24-h a set of seventeen carcass measurements was recorded. The left side of carcasses was dissected into muscle, subcutaneous fat, inter-muscular fat, bone, and remainder (major blood vessels, ligaments, tendons, and thick connective tissue sheets associated with muscles), and the LMP was evaluated as the dissected muscle percentage. Prediction equations of LMP were developed, and fitting quality was evaluated through the coefficient of determination of estimation (R2 e) and standard error of estimate (SEE). Models validation was performed by k-fold crossvalidation and the coefficient of determination of prediction (R2 p) and standard error of prediction (SEP) were computed. The BT2 measurement was the best single predictor and accounted for 37.8% of the LMP variation with a SEP of 2.30%. The prediction of LMP of lamb carcasses can be based simple models, using as predictors the HCW and one fat thickness measurement.

Keywords: Bootstrap, Carcass, Lambs, Lean meat

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

Abstract:

Industrial end users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both Machine Learning and First Principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed from breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems and humans – including asset maintenance operations – in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: Degradation models, ageing, anomaly detection, soft sensor, incremental learning.

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Authors: Myint Myint Yi, Khin Sandar Linn, Marlar Kyaw

Abstract:

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. The model was evaluated with the electricity load data of Electronic Engineering Department in Mandalay Technological University in Myanmar. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in STLF.

Keywords: Neural network, Load forecast, Time series, wavelettransform.

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Authors: G. Erkomaishvili

Abstract:

The paper discusses mineral water consumer market and development policy in Georgia, the tools and measures, which will contribute to production of mineral waters and increase its export. The paper studies and analyses current situation in mineral water production sector as well as the factors affecting increase and reduction of its export. It’s noted that in order to gain and maintain competitive advantage, it’s necessary to provide continuous supply of high quality goods with modern design, open new distribution channels to enter new markets, carry out broad promotional activities, organize e-commerce. Economic policy plays an important role in protecting markets from counterfeit goods. The state also plays an important role in attracting foreign direct investments. Stable business environment and export oriented strategy is the basis for the country’s economic growth. Based on the research, the paper suggests the strategy for improving competitiveness of Georgian mineral waters; relevant conclusions and recommendations are provided.

Keywords: Mineral waters, consumer market for mineral waters, export of mineral waters, mineral water development policy in Georgia.

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Authors: M. Saidi, I. Djefour, F. Ait Medjber, A. Melouane, A. Gacem

Abstract:

The objective of this research is to study the physical and mechanical properties and durability of self-compacting mortars prepared by substituting a part of cement up to a percentage of 30% pozzolan according to different Blaine specific surface area (SSB1=7000 cm²/g and SSB=9000 cm²/g)). Order to evaluate durability, mortars were subjected to chemical attacks in various aggressive environments, a solution of a mixture of nitric acid and ammonium nitrate (HNO₃ + NH₄NO₃) and a magnesium sulfate salt solution (MgSO₄)) with a concentration of 10%, for a period of one month. This study is complemented by a comparative study of the durability of mortars elaborated with sulphate resistant cement (SRC). The results show that these mortars develop long-term, mechanical and chemical resistance better than mortars based Portland cement with 5% gypsum (CEM 1) and SRC. We found that the mass losses are lowest in mortars elaborated with pozzolanic cement (30% substitution with SSB₂) in both of chemical attack solutions (3.28% in the solution acid and 1.16% in the salt solution) and the compressive strength gains of 14.68% and 8.5% respectively in the two media. This is due to the action of pozzolan which fixes portlandite to form hydrated calcium silicate (CSH) from the hydration of tricalcic silicate (C₃S).

Keywords: Aggressive environments, durability, mechanical strengths, pozzolanic cement, self-compacting mortar.

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

Abstract:

Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: Clusters of Microcalcifications, Ductal Carcinoma in Situ, Least-Square Support Vector Machine, Particle Swarm Optimization.

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