Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 813

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813 Attention Based Fully Convolutional Neural Network for Simultaneous Detection and Segmentation of Optic Disc in Retinal Fundus Images

Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

Abstract:

Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords:

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812 A Literature Review on the Effect of Industrial Clusters and the Absorptive Capacity on Innovation

Authors: Enrique Claver Cortes, Bartolome Marco Lajara, Eduardo Sanchez Garcia, Pedro Seva Larrosa, Encarnacion Manresa Marhuenda, Lorena Ruiz Fernandez, Esther Poveda Pareja

Abstract:

In recent decades, the analysis of the effects of clustering as an essential factor for the development of innovations and the competitiveness of enterprises has raised great interest in different areas. Nowadays, companies have access to almost all tangible and intangible resources located and/or developed in any country in the world. However, despite the obvious advantages that this situation entails for companies, their geographical location has shown itself, increasingly clearly, to be a fundamental factor that positively influences their innovative performance and competitiveness. Industrial clusters could represent a unique level of analysis, positioned between the individual company and the industry, which makes them an ideal unit of analysis to determine the effects derived from company membership of a cluster. Also, the absorptive capacity (hereinafter 'AC') can mediate the process of innovation development by companies located in a cluster. The transformation and exploitation of knowledge could have a mediating effect between knowledge acquisition and innovative performance. The main objective of this work is to determine the key factors that affect the degree of generation and use of knowledge from the environment by companies and, consequently, their innovative performance and competitiveness. The elements analyzed are the companies' membership of a cluster and the AC. To this end, 30 most relevant papers published on this subject in the "Web of Science" database have been reviewed. Our findings show that, within a cluster, the knowledge coming from the companies' environment can significantly influence their innovative performance and competitiveness, although in this relationship, the degree of access and exploitation of the companies to this knowledge plays a fundamental role, which depends on a series of elements both internal and external to the company.

Keywords: Innovation, Knowledge, Clusters, absorptive capacity

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811 Machine Learning Techniques in Bank Credit Analysis

Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

Abstract:

The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: Artificial Neural Networks, Machine Learning, Support Vector Machines, Logistic Regression, credit risk assessment, ANNs, Classifier algorithms

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810 Microbial Contaminants in Drinking Water Collected from Different Regions of Kuwait

Authors: Abu Salim Mustafa

Abstract:

Water plays a major role in maintaining life on earth, but it can also serve as a matrix for pathogenic organisms, posing substantial health threats to humans. Although, outbreaks of diseases attributable to drinking water may not be common in industrialized countries, they still occur and can lead to serious acute, chronic, or sometimes fatal health consequences. The analysis of drinking water samples from different regions of Kuwait was performed in this study for bacterial and viral contaminations. Drinking tap water samples were collected from 15 different locations of the six Kuwait governorates. All samples were analyzed by confocal microscopy for the presence of bacteria. The samples were cultured in vitro to detect cultivable organisms. DNA was isolated from the cultured organisms and the identity of the bacteria was determined by sequencing the bacterial 16S rRNA genes, followed by BLAST analysis in the database of NCBI, USA. RNA was extracted from water samples and analyzed by real-time PCR for the detection of viruses with potential health risks, i.e. Astrovirus, Enterovirus, Norovirus, Rotavirus, and Hepatitis A. Confocal microscopy showed the presence of bacteria in some water samples. The 16S rRNA gene sequencing of culture grown organisms, followed by BLAST analysis, identified the presence of several non-pathogenic bacterial species. However, one sample had Acinetobacter baumannii, which often causes opportunistic infections in immunocompromised people, but none of the studied viruses could be detected in the drinking water samples analyzed. The results indicate that drinking water samples analyzed from various locations in Kuwait are relatively safe for drinking and do not contain many harmful pathogens.

Keywords: Microbial diversity, Viruses, drinking water, Kuwait

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809 Fatal Road Accident Causer's Driving Aptitude in Hungary

Authors: A. Juhász, M. Fogarasi

Abstract:

Those causing fatal traffic accidents are traumatized, which negatively influences their cognitive functions and their personality. In order to clarify how much the trauma of causing a fatal accident effects their driving skills and personality traits, the results of a psychological aptitude and a personality test of drivers carelessly causing fatal accidents and of drivers not causing any accidents were compared separately. The sample (N = 354) consists of randomly selected drivers from the Transportation Aptitude and Examination Centre database who caused fatal accidents (Fatal group, n = 177) or did not cause accidents (Control group, n = 177). The aptitude tests were taken between 2014 and 2019. The comparison of the 2 groups was done according to 3 aspects: 1. Categories of aptitude (suitable, restricted, unsuited); 2. Categories of causes (ability, personality, ability and personality) within the restricted or unsuited (altogether: non-suitable subgroups); 3. Categories of ability and personality within the non-suitable subgroups regardless of the cause-category. Within ability deficiency, the two groups include those, whose ability factor is impaired or limited. This is also true in case of personality failure. Compared to the control group, the number of restricted drivers causing fatal accidents is significantly higher (p < .000) and the number of unsuited drivers is higher on a tendency-level (p = .06). Compared to the control group in the fatal non-suitable subgroup, the ratio of restricted suitability and the unsuitability due to ability factors is exclusively significantly lower (p < .000). The restricted suitability and the unsuitability due to personality factors are more significant in the fatal non-suitable subgroup (p < .000). Incapacity due to combination of ability and personality is also significantly higher in the fatal group (p = .002). Compared to the control group both ability and personality factors are also significantly higher in the fatal non-suitable subgroup (p < .000). Overall, the control group is more eligible for driving than drivers who have caused fatalities. The ability and personality factors are significantly higher in the case of fatal accident causers who are non-suitable for driving. Moreover the concomitance of ability and personality factors occur almost exclusively to drivers who caused fatal accidents. Further investigation is needed to understand the causes and how the aptitude test results for the fatal group could improve over time.

Keywords: Personality, ability, aptitude, unsuited, fatal accident

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808 Remote Monitoring and Control System of Potentiostat Based on the Internet of Things

Authors: Liang Zhao, Guangwen Wang, Guichang Liu

Abstract:

Constant potometer is an important component of pipeline anti-corrosion systems in the chemical industry. Based on Internet of Things (IoT) technology, Programmable Logic Controller (PLC) technology and database technology, this paper developed a set of a constant potometer remote monitoring management system. The remote monitoring and remote adjustment of the working status of the constant potometer are realized. The system has real-time data display, historical data query, alarm push management, user permission management, and supporting Web access and mobile client application (APP) access. The actual engineering project test results show the stability of the system, which can be widely used in cathodic protection systems.

Keywords: Internet of Things, Remote Monitoring, potentiostat, pipe corrosion protection

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807 A Comparative Study of Global Power Grids and Global Fossil Energy Pipelines Using GIS Technology

Authors: Wenhao Wang, Xinzhi Xu, Limin Feng, Wei Cong

Abstract:

This paper comprehensively investigates current development status of global power grids and fossil energy pipelines (oil and natural gas), proposes a standard visual platform of global power and fossil energy based on Geographic Information System (GIS) technology. In this visual platform, a series of systematic visual models is proposed with global spatial data, systematic energy and power parameters. Under this visual platform, the current Global Power Grids Map and Global Fossil Energy Pipelines Map are plotted within more than 140 countries and regions across the world. Using the multi-scale fusion data processing and modeling methods, the world’s global fossil energy pipelines and power grids information system basic database is established, which provides important data supporting global fossil energy and electricity research. Finally, through the systematic and comparative study of global fossil energy pipelines and global power grids, the general status of global fossil energy and electricity development are reviewed, and energy transition in key areas are evaluated and analyzed. Through the comparison analysis of fossil energy and clean energy, the direction of relevant research is pointed out for clean development and energy transition.

Keywords: Power Systems, Geographic Information System, Fossil energy, Energy Transition

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806 Automatic Number Plate Recognition System Based on Deep Learning

Authors: T. Damak, N. Masmoudi, M. A. Ben Ayed, O. Kriaa, A. Baccar

Abstract:

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: Deep learning, convolutional neural network, CNN, Character Segmentation, Automatic number plate recognition, number plate localization

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805 A Hybrid Feature Selection and Deep Learning Algorithm for Cancer Disease Classification

Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Genetic Algorithm, Deep learning, Feature selection, cancer classification

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804 Speaker Recognition Using LIRA Neural Networks

Authors: Tetyana Baydyk, Ernst Kussul, Nestor A. Garcia Fragoso

Abstract:

This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: speaker identification, voice recognition, extreme learning, LIRA neural classifier

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803 A Multiple Linear Regression Model to Predict the Price of Cement in Nigeria

Authors: Kenneth M. Oba

Abstract:

This study investigated factors affecting the price of cement in Nigeria, and developed a mathematical model that can predict future cement prices. Cement is key in the Nigerian construction industry. The changes in price caused by certain factors could affect economic and infrastructural development; hence there is need for proper proactive planning. Secondary data were collected from published information on cement between 2014 and 2019. In addition, questionnaires were sent to some domestic cement retailers in Port Harcourt in Nigeria, to obtain the actual prices of cement between the same periods. The study revealed that the most critical factors affecting the price of cement in Nigeria are inflation rate, population growth rate, and Gross Domestic Product (GDP) growth rate. With the use of data from United Nations, International Monetary Fund, and Central Bank of Nigeria databases, amongst others, a Multiple Linear Regression model was formulated. The model was used to predict the price of cement for 2020-2025. The model was then tested with 95% confidence level, using a two-tailed t-test and an F-test, resulting in an R2 of 0.8428 and R2 (adj.) of 0.6069. The results of the tests and the correlation factors confirm the model to be fit and adequate. This study will equip researchers and stakeholders in the construction industry with information for planning, monitoring, and management of present and future construction projects that involve the use of cement.

Keywords: multiple linear regression model, Nigerian construction industry, price prediction, Cement price

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802 The Quality Assessment of Seismic Reflection Survey Data Using Statistical Analysis: A Case Study of Fort Abbas Area, Cholistan Desert, Pakistan

Authors: M. A. Rashid, U. Waqas, M. F. Ahmed, A. Mehmood

Abstract:

In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data Quality, Null hypothesis, Seismic lines, Seismic reflection survey

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801 Content-Based Image Retrieval Using HSV Color Space Features

Authors: Hamid Hassanpour, Hamed Qazanfari, Kazem Qazanfari

Abstract:

In this paper, a method is provided for content-based image retrieval. Content-based image retrieval system searches query an image based on its visual content in an image database to retrieve similar images. In this paper, with the aim of simulating the human visual system sensitivity to image's edges and color features, the concept of color difference histogram (CDH) is used. CDH includes the perceptually color difference between two neighboring pixels with regard to colors and edge orientations. Since the HSV color space is close to the human visual system, the CDH is calculated in this color space. In addition, to improve the color features, the color histogram in HSV color space is also used as a feature. Among the extracted features, efficient features are selected using entropy and correlation criteria. The final features extract the content of images most efficiently. The proposed method has been evaluated on three standard databases Corel 5k, Corel 10k and UKBench. Experimental results show that the accuracy of the proposed image retrieval method is significantly improved compared to the recently developed methods.

Keywords: correlation, Entropy, content-based image retrieval, color difference histogram, efficient features selection

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800 Development of Requirements Analysis Tool for Medical Autonomy in Long-Duration Space Exploration Missions

Authors: Lara Dutil-Fafard, Caroline Rhéaume, Patrick Archambault, Daniel Lafond, Neal W. Pollock

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Improving resources for medical autonomy of astronauts in prolonged space missions, such as a Mars mission, requires not only technology development, but also decision-making support systems. The Advanced Crew Medical System - Medical Condition Requirements study, funded by the Canadian Space Agency, aimed to create knowledge content and a scenario-based query capability to support medical autonomy of astronauts. The key objective of this study was to create a prototype tool for identifying medical infrastructure requirements in terms of medical knowledge, skills and materials. A multicriteria decision-making method was used to prioritize the highest risk medical events anticipated in a long-term space mission. Starting with those medical conditions, event sequence diagrams (ESDs) were created in the form of decision trees where the entry point is the diagnosis and the end points are the predicted outcomes (full recovery, partial recovery, or death/severe incapacitation). The ESD formalism was adapted to characterize and compare possible outcomes of medical conditions as a function of available medical knowledge, skills, and supplies in a given mission scenario. An extensive literature review was performed and summarized in a medical condition database. A PostgreSQL relational database was created to allow query-based evaluation of health outcome metrics with different medical infrastructure scenarios. Critical decision points, skill and medical supply requirements, and probable health outcomes were compared across chosen scenarios. The three medical conditions with the highest risk rank were acute coronary syndrome, sepsis, and stroke. Our efforts demonstrate the utility of this approach and provide insight into the effort required to develop appropriate content for the range of medical conditions that may arise.

Keywords: Decision Support System, space medicine, exploration mission, medical autonomy, scenario-based queries, event sequence diagram

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799 From Electroencephalogram to Epileptic Seizures Detection by Using Artificial Neural Networks

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

Abstract:

Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: Signal Processing, Data Mining, Epilepsy, Feature Extraction, Artificial Neural Network, electroencephalogram, seizure detection

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798 Association of Smoking with Chest Radiographic and Lung Function Findings in Retired Bauxite Mining Workers

Authors: L. R. Ferreira, C. M. Galhardi, L. H. Oliveira, R. C. G. Bianchi, L. C.R. Ferreira, E. P. Baciuk

Abstract:

Inhalation hazards are associated with potentially injurious exposure and increased risk for lung diseases, within the bauxite mining industry, especially for the smelter workers. Smoking is related to decreased lung function and leads to chronic lung diseases. This study had the objective to evaluate whether smoking is related to functional and radiographic respiratory changes in retired bauxite mining workers. Methods: This was a retrospective and cross-sectional study involving the analysis of database information of 140 retired bauxite mining workers from Poços de Caldas-MG evaluated at Worker’s Health Reference Center and at the Social Security Brazilian National Institute, from July 1st, 2015 until June 30th, 2016. The workers were divided into three groups: non-smokers (n = 47), ex-smokers (n = 46), and smokers (n = 47). The data included: age, gender, spirometry results, and the presence or not of pulmonary pleural and/or parenchymal changes in chest radiographs. Chi-Squared test was used (p < 0,05). Results: In the smokers’ group, 83% of spirometry tests and 64% of chest x-rays were altered. In the non-smokers’ group, 19% of spirometry tests and 13% of chest x-rays were altered. In the ex-smokers’ group, 35% of spirometry tests and 30% of chest x-rays were altered. Most of the results were statistically significant. Results demonstrated a significant difference between smokers’ and non-smokers’ groups in regard to spirometric and radiographic pulmonary alterations. Ex-smokers’ and non-smokers’ group demonstrated better results when compared to the smokers’ group in relation to altered spirometry and radiograph findings. These data may contribute to planning strategies to enhance smoking cessation programs within the bauxite mining industry.

Keywords: Smoking, spirometry, chest radiography, Bauxite mining

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797 Foot Recognition Using Deep Learning for Knee Rehabilitation

Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

Abstract:

The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: Deep learning, Convolutional Neural Networks, foot recognition, knee rehabilitation

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796 Optimizing the Probabilistic Neural Network Training Algorithm for Multi-Class Identification

Authors: Abdelkader Benyettou, Abdelhadi Lotfi

Abstract:

In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: Pattern Recognition, classification, Network Optimization, probabilistic neural networks

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795 Information Literacy among Faculty and Students of Medical Colleges of Haryana, Punjab and Chandigarh

Authors: SANJEEV SHARMA, Suman Lata

Abstract:

With the availability of diverse printed, electronic literature and web sites on medical and health related information, it is impossible for the medical professional to get the information he seeks in the shortest possible time. For all these problems information literacy is the only solution. Thus, information literacy is recognized as an important aspect of medical education. In the present study, an attempt has been made to know the information literacy skills of the faculty and students at medical colleges of Haryana, Punjab and Chandigarh. The scope of the study was confined to the 12 selected medical colleges of three States (Haryana, Punjab, and Chandigarh). The findings of the study were based on the data collected through 1018 questionnaires filled by the respondents of the medical colleges. It was found that Online Medical Websites (such as WebMD, eMedicine and Mayo Clinic etc.) were frequently used by 63.43% of the respondents of Chandigarh which is slightly more than Haryana (61%) and Punjab (55.65%). As well, 30.86% of the respondents of Chandigarh, 27.41% of Haryana and 27.05% of Punjab were familiar with the controlled vocabulary tool; 25.14% respondents of Chandigarh, 23.80% of Punjab, 23.17% of Haryana were familiar with the Boolean operators; 33.05% of the respondents of Punjab, 28.19% of Haryana and 25.14% of Chandigarh were familiar with the use and importance of the keywords while searching an electronic database; and 51.43% of the respondents of Chandigarh, 44.52% of Punjab and 36.29% of Haryana were able to make effective use of the retrieved information. For accessing information in electronic format, 47.74% of the respondents rated their skills high, while the majority of respondents (76.13%) were unfamiliar with the basic search technique i.e. Boolean operator used for searching information in an online database. On the basis of the findings, it was suggested that a comprehensive training program based on medical professionals information needs should be organized frequently. Furthermore, it was also suggested that information literacy may be included as a subject in the health science curriculum so as to make the medical professionals information literate and independent lifelong learners.

Keywords: Information, Information literacy, medical professionals, medical colleges

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794 Bibliometric Analysis of the Research Progress on Graphene Inks from 2008 to 2018

Authors: Jean C. A. Sousa, Julio Cesar Maciel Santos, Andressa J. Rubio, Edneia A. S. Paccola, Natália U. Yamaguchi

Abstract:

A bibliometric analysis in the Web of Science database was used to identify overall scientific results of graphene inks to date (2008 to 2018). The objective of this study was to evaluate the evolutionary tendency of graphene inks research and to identify its aspects, aiming to provide data that can guide future work. The contributions of different researches, languages, thematic categories, periodicals, place of publication, institutes, funding agencies, articles cited and applications were analyzed. The results revealed a growing number of annual publications, of 258 papers found, 107 were included because they met the inclusion criteria. Three main applications were identified: synthesis and characterization, electronics and surfaces. The most relevant research on graphene inks has been summarized in this article, and graphene inks for electronic devices presented the most incident theme according to the research trends during the studied period. It is estimated that this theme will remain in evidence and will contribute to the direction of future research in this area.

Keywords: Nanomaterials, Coating, Scientometrics, Bibliometric

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793 Research Trends on Magnetic Graphene for Water Treatment: A Bibliometric Analysis

Authors: J. C. M. Santos, J. C. A. Sousa, A. J. Rubio, L. S. Soletti, F. Gasparotto, N. U. Yamaguchi

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Magnetic graphene has received widespread attention for their capability of water and wastewater treatment, which has been attracted many researchers in this field. A bibliometric analysis based on the Web of Science database was employed to analyze the global scientific outputs of magnetic graphene for water treatment until the present time (2012 to 2017), to improve the understanding of the research trends. The publication year, place of publication, institutes, funding agencies, journals, most cited articles, distribution outputs in thematic categories and applications were analyzed. Three major aspects analyzed including type of pollutant, treatment process and composite composition have further contributed to revealing the research trends. The most relevant research aspects of the main technologies using magnetic graphene for water treatment were summarized in this paper. The results showed that research on magnetic graphene for water treatment goes through a period of decline that might be related to a saturated field and a lack of bibliometric studies. Thus, the result of the present work will lead researchers to establish future directions in further studies using magnetic graphene for water treatment.

Keywords: Nanomaterials, Composite, Scientometrics, Graphene Oxide

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792 Reducing Later Life Loneliness: A Systematic Literature Review of Loneliness Interventions

Authors: Dhruv Sharma, Lynne Blair, Stephen Clune

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Later life loneliness is a social issue that is increasing alongside an upward global population trend. As a society, one way that we have responded to this social challenge is through developing non-pharmacological interventions such as befriending services, activity clubs, meet-ups, etc. Through a systematic literature review, this paper suggests that currently there is an underrepresentation of radical innovation, and underutilization of digital technologies in developing loneliness interventions for older adults. This paper examines intervention studies that were published in English language, within peer reviewed journals between January 2005 and December 2014 across 4 electronic databases. In addition to academic databases, interventions found in grey literature in the form of websites, blogs, and Twitter were also included in the overall review. This approach yielded 129 interventions that were included in the study. A systematic approach allowed the minimization of any bias dictating the selection of interventions to study. A coding strategy based on a pattern analysis approach was devised to be able to compare and contrast the loneliness interventions. Firstly, interventions were categorized on the basis of their objective to identify whether they were preventative, supportive, or remedial in nature. Secondly, depending on their scope, they were categorized as one-to-one, community-based, or group based. It was also ascertained whether interventions represented an improvement, an incremental innovation, a major advance or a radical departure, in comparison to the most basic form of a loneliness intervention. Finally, interventions were also assessed on the basis of the extent to which they utilized digital technologies. Individual visualizations representing the four levels of coding were created for each intervention, followed by an aggregated visual to facilitate analysis. To keep the inquiry within scope and to present a coherent view of the findings, the analysis was primarily concerned the level of innovation, and the use of digital technologies. This analysis highlights a weak but positive correlation between the level of innovation and the use of digital technologies in designing and deploying loneliness interventions, and also emphasizes how certain existing interventions could be tweaked to enable their migration from representing incremental innovation to radical innovation for example. This analysis also points out the value of including grey literature, especially from Twitter, in systematic literature reviews to get a contemporary view of latest work in the area under investigation.

Keywords: Innovation, Ageing, Digital, loneliness

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791 Seismic Performance of Slopes Subjected to Earthquake Mainshock Aftershock Sequences

Authors: Alisha Khanal, Gokhan Saygili

Abstract:

It is commonly observed that aftershocks follow the mainshock. Aftershocks continue over a period of time with a decreasing frequency and typically there is not sufficient time for repair and retrofit between a mainshock–aftershock sequence. Usually, aftershocks are smaller in magnitude; however, aftershock ground motion characteristics such as the intensity and duration can be greater than the mainshock due to the changes in the earthquake mechanism and location with respect to the site. The seismic performance of slopes is typically evaluated based on the sliding displacement predicted to occur along a critical sliding surface. Various empirical models are available that predict sliding displacement as a function of seismic loading parameters, ground motion parameters, and site parameters but these models do not include the aftershocks. The seismic risks associated with the post-mainshock slopes ('damaged slopes') subjected to aftershocks is significant. This paper extends the empirical sliding displacement models for flexible slopes subjected to earthquake mainshock-aftershock sequences (a multi hazard approach). A dataset was developed using 144 pairs of as-recorded mainshock-aftershock sequences using the Pacific Earthquake Engineering Research Center (PEER) database. The results reveal that the combination of mainshock and aftershock increases the seismic demand on slopes relative to the mainshock alone; thus, seismic risks are underestimated if aftershocks are neglected.

Keywords: Earthquake, Landslide, Seismic Slope Stability, aftershock, mainshock, sliding displacement

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790 The Impact of Globalization on the Development of Israel Advanced Changes

Authors: Erez Cohen

Abstract:

The study examines the socioeconomic impact of development of an advanced industry in Israel. The research method is based on data collected from the Israel Central Bureau of Statistics and from the National Insurance Institute (NII) databases, which provided information that allows to examine the Economic and Social Changes during the 1990s. The study examined the socioeconomic effects of the development of advanced industry in Israel. The research findings indicate that as a result of globalization processes, the weight of traditional industry began to diminish as a result of factory closures and the laying off of workers. These circumstances led to growing unemployment among the weaker groups in Israeli society, detracting from their income and thus increasing inequality among different socioeconomic groups in Israel and enhancement of social disparities.

Keywords: Public Policy, Globalization, socio-economic indicators, Israeli advanced industry

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789 Comparison of Irradiance Decomposition and Energy Production Methods in a Solar Photovoltaic System

Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

Abstract:

Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: Energy Production, meteorological data, irradiance decomposition, solar photovoltaic system

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788 Landslide Susceptibility Mapping: A Comparison between Logistic Regression and Multivariate Adaptive Regression Spline Models in the Municipality of Oudka, Northern of Morocco

Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

Abstract:

The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: Morocco, landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

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787 Availability Strategy of Medical Information for Telemedicine Services

Authors: Rozo D. Juan Felipe, Ramírez L. Leonardo Juan, Puerta A. Gabriel Alberto

Abstract:

The telemedicine services require correct computing resource management to guarantee productivity and efficiency for medical and non-medical staff. The aim of this study was to examine web management strategies to ensure the availability of resources and services in telemedicine so as to provide medical information management with an accessible strategy. In addition, to evaluate the quality-of-service parameters, the followings were measured: delays, throughput, jitter, latency, available bandwidth, percent of access and denial of services based of web management performance map with profiles permissions and database management. Through 24 different test scenarios, the results show 100% in availability of medical information, in relation to access of medical staff to web services, and quality of service (QoS) of 99% because of network delay and performance of computer network. The findings of this study suggest that the proposed strategy of web management is an ideal solution to guarantee the availability, reliability, and accessibility of medical information. Finally, this strategy offers seven user profile used at telemedicine center of Bogota-Colombia keeping QoS parameters suitable to telemedicine services.

Keywords: Telemedicine, Strategy, Availability, Medical Information, QoS

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786 Automated, Objective Assessment of Pilot Performance in Simulated Environment

Authors: Maciej Zasuwa, Grzegorz Ptasinski, Antoni Kopyt

Abstract:

Nowadays flight simulators offer tremendous possibilities for safe and cost-effective pilot training, by utilization of powerful, computational tools. Due to technology outpacing methodology, vast majority of training related work is done by human instructors. It makes assessment not efficient, and vulnerable to instructors’ subjectivity. The research presents an Objective Assessment Tool (gOAT) developed at the Warsaw University of Technology, and tested on SW-4 helicopter flight simulator. The tool uses database of the predefined manoeuvres, defined and integrated to the virtual environment. These were implemented, basing on Aeronautical Design Standard Performance Specification Handling Qualities Requirements for Military Rotorcraft (ADS-33), with predefined Mission-Task-Elements (MTEs). The core element of the gOAT enhanced algorithm that provides instructor a new set of information. In details, a set of objective flight parameters fused with report about psychophysical state of the pilot. While the pilot performs the task, the gOAT system automatically calculates performance using the embedded algorithms, data registered by the simulator software (position, orientation, velocity, etc.), as well as measurements of physiological changes of pilot’s psychophysiological state (temperature, sweating, heart rate). Complete set of measurements is presented on-line to instructor’s station and shown in dedicated graphical interface. The presented tool is based on open source solutions, and flexible for editing. Additional manoeuvres can be easily added using guide developed by authors, and MTEs can be changed by instructor even during an exercise. Algorithm and measurements used allow not only to implement basic stress level measurements, but also to reduce instructor’s workload significantly. Tool developed can be used for training purpose, as well as periodical checks of the aircrew. Flexibility and ease of modifications allow the further development to be wide ranged, and the tool to be customized. Depending on simulation purpose, gOAT can be adjusted to support simulator of aircraft, helicopter, or unmanned aerial vehicle (UAV).

Keywords: Human Factors, pilot training, flight simulator, automated assessment

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785 Localization of Geospatial Events and Hoax Prediction in the UFO Database

Authors: Harish Krishnamurthy, Anna Lafontant, Ren Yi

Abstract:

Unidentified Flying Objects (UFOs) have been an interesting topic for most enthusiasts and hence people all over the United States report such findings online at the National UFO Report Center (NUFORC). Some of these reports are a hoax and among those that seem legitimate, our task is not to establish that these events confirm that they indeed are events related to flying objects from aliens in outer space. Rather, we intend to identify if the report was a hoax as was identified by the UFO database team with their existing curation criterion. However, the database provides a wealth of information that can be exploited to provide various analyses and insights such as social reporting, identifying real-time spatial events and much more. We perform analysis to localize these time-series geospatial events and correlate with known real-time events. This paper does not confirm any legitimacy of alien activity, but rather attempts to gather information from likely legitimate reports of UFOs by studying the online reports. These events happen in geospatial clusters and also are time-based. We look at cluster density and data visualization to search the space of various cluster realizations to decide best probable clusters that provide us information about the proximity of such activity. A random forest classifier is also presented that is used to identify true events and hoax events, using the best possible features available such as region, week, time-period and duration. Lastly, we show the performance of the scheme on various days and correlate with real-time events where one of the UFO reports strongly correlates to a missile test conducted in the United States.

Keywords: Feature Extraction, time-series clustering, hoax prediction, geospatial events

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784 Implementation of a Multimodal Biometrics Recognition System with Combined Palm Print and Iris Features

Authors: Rabab M. Ramadan, Elaraby A. Elgallad

Abstract:

With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: Feature Extraction, Feature selection, Particle Swarm Optimization, iris recognition, palm print, scale invariant feature transform

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