Search results for: Markov random field

Authors: Levente Varga, Dávid Deritei, Mária Ercsey-Ravasz, Răzvan Florian, Zsolt I. Lázár, István Papp, Ferenc Járai-Szabó

Abstract:

One of the major shortcomings of widely used scientometric indicators is that different disciplines cannot be compared with each other. The issue of cross-disciplinary normalization has been long discussed, but even the classification of publications into scientific domains poses problems. Structural properties of citation networks offer new possibilities, however, the large size and constant growth of these networks asks for precaution. Here we present a new tool that in order to perform cross-field normalization of scientometric indicators of individual publications relays on the structural properties of citation networks. Due to the large size of the networks, a systematic procedure for identifying scientific domains based on a local community detection algorithm is proposed. The algorithm is tested with different benchmark and real-world networks. Then, by the use of this algorithm, the mechanism of the scientometric indicator normalization process is shown for a few indicators like the citation number, P-index and a local version of the PageRank indicator. The fat-tail trend of the article indicator distribution enables us to successfully perform the indicator normalization process.

Keywords: Citation networks, scientometric indicator, cross-field normalization, local cluster detection.

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Authors: M. Saravanan, J. Raja Murugadoss, V. Jayanthi

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Optimal design of structure has a main role in reduction of material usage which leads to deduction in the final cost of construction projects. Evolutionary approaches are found to be more successful techniques for solving size and shape structural optimization problem since it uses a stochastic random search instead of a gradient search. By reviewing the recent literature works the problem found was the optimization of weight. A new meta-heuristic algorithm called as Cuckoo Search (CS) Algorithm has used for the optimization of the total weight of the truss structures. This paper has used set of 10 bars and 25 bars trusses for the testing purpose. The main objective of this work is to reduce the number of iterations, weight and the total time consumption. In order to demonstrate the effectiveness of the present method, minimum weight design of truss structures is performed and the results of the CS are compared with other algorithms.

Keywords: Cuckoo search algorithm, levy’s flight, meta-heuristic, optimal weight.

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Authors: Nipun Manirajan, Heeralal Gargama, Sushil Guhe, Manoj Prabhakaran

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In railway industry, train sets are designed based on contractual requirements (mission profile), where reliability targets are measured in terms of mean distance between failures (MDBF). However, during the beginning of revenue services, trains do not achieve the designed mission profile distance (mileage) within the timeframe due to infrastructure constraints, scarcity of commuters or other operational challenges thereby not respecting the original design inputs. Since trains do not run sufficiently and do not achieve the designed mileage within the specified time, car builder has a risk of not achieving the contractual MDBF target. This paper proposes a constant failure rate based model to deal with the situations where mileage accumulation is not a part of the design mission profile. The model provides appropriate MDBF target to be demonstrated based on actual accumulated mileage. A case study of rolling stock running in the field is undertaken to analyze the failure data and MDBF target demonstration during low mileage accumulation. The results of case study prove that with the proposed method, reliability targets are achieved under low mileage accumulation.

Keywords: Mean distance between failures, mileage based reliability, reliability target normalization, rolling stock reliability.

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Authors: B. O. Olawale, C. R. Chatwin, R. C. D. Young, P. M. Birch, F. O. Faithpraise, A. O. Olukiran

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In this paper, we present a four-step ortho-rectification procedure for real-time geo-referencing of video data from a low-cost UAV equipped with a multi-sensor system. The basic procedures for the real-time ortho-rectification are: (1) decompilation of the video stream into individual frames; (2) establishing the interior camera orientation parameters; (3) determining the relative orientation parameters for each video frame with respect to each other; (4) finding the absolute orientation parameters, using a self-calibration bundle and adjustment with the aid of a mathematical model. Each ortho-rectified video frame is then mosaicked together to produce a mosaic image of the test area, which is then merged with a well referenced existing digital map for the purpose of geo-referencing and aerial surveillance. A test field located in Abuja, Nigeria was used to evaluate our method. Video and telemetry data were collected for about fifteen minutes, and they were processed using the four-step ortho-rectification procedure. The results demonstrated that the geometric measurement of the control field from ortho-images is more accurate when compared with those from original perspective images when used to pin point the exact location of targets on the video imagery acquired by the UAV. The 2-D planimetric accuracy when compared with the 6 control points measured by a GPS receiver is between 3 to 5 metres.

Keywords: Geo-referencing, ortho-rectification, video frame, self-calibration, UAV, target tracking.

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Authors: Jatinderkumar R. Saini

Abstract:

E-mail has become a key mechanism of electronic communication. This is true for professional organizations that like to communicate with their subjects online and are slowly shifting to paper-less office. The current paper focuses specifically on academic institutions offering Engineering course in Gujarat state and attempts for textual analysis of the usernames of the institutional e-mail addresses. We found that the institutions tend to design the username segment of their e-mail addresses by choosing words or combination of words from specific categories. The paper also highlights the use of special characters, digits and random words in designing the usernames. On the sidelines, the paper lists the style of employing department names and designations for the design process. To the best of our knowledge, this is the first formal attempt to analyze the selection of words employed for designing username segment of e-mail addresses of engineering institutions.

Keywords: E-mail address, Institute, Engineering, Username.

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Authors: Muhammad Wajid Tahir, Rubina Kauser, Majid Ali Tahir

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Pakistani doctors (MBBS) are emigrating towards developed countries for professional adjustments. This study aims to highlight causes and consequences of doctors- brain drain from Pakistan. Primary data was collected from Mayo Hospital, Lahore by interviewing doctors (n=100) through systematic random sampling technique. It found that various socio-economic and political conditions are working as push and pull factors for brain drain of doctors in Pakistan. Majority of doctors (83%) declared poor remunerations and professional infrastructure of health department as push factor of doctors- brain drain. 81% claimed that continuous instability in political situation and threats of terrorism are responsible for emigration of doctors. 84% respondents considered fewer opportunities of further studies responsible for their emigration. Brain drain of doctors is affecting health sector-s policies / programs, standard doctor-patient ratios and quality of health services badly.

Keywords: Brain Drain, Emigration, Remuneration, Politicalinstability, MBBS doctors

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled direct normal irradiance field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: Artificial Neural Networks, Resilient Propagation, Solar Radiation, Time Series Forecasting.

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Authors: Zhanna Mingaleva, Irina Mirskikh

Abstract:

Innovational development of regions in Russia is generally faced with the essential influence from federal and local authorities. The organization of effective mechanism of innovation development (and self-development) is impossible without establishment of defined institutional conditions in the analyzed field. Creative utilization of scientific concepts and information should merge, giving rise to continuing innovation and advanced production. The paper presents an analysis of institutional conditions in the field of creation and development of innovation activity infrastructure and transferring of knowledge and skills between different economic agents in Russia. Knowledge is mainly privately owned, developed through R&D investments and incorporated into technology or a product. Innovation infrastructure is a strong concentration mechanism of advanced facilities, which are mainly located inside large agglomerations or city-regions in order to benefit from scale effects in both input markets (human capital, private financial capital) and output markets (higher education services, research services). The empirical results of the paper show that in the presence of more efficient innovation and knowledge transfer and transcoding system and of a more open attitude of economic agents towards innovation, the innovation and knowledge capacity of regional economy is much higher.

Keywords: knowledge economy, innovational development, transfer of knowledge, institutional preconditions, innovation andknowledge capacity.

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Authors: L. N. A. Chandana Jayawardena, Ales Gregar

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Occupational Self Efficacy (OSE) reflects the conviction of a person’s ability to fulfill his job related behavior at a perfectly acceptable level to the employer. Transformational leadership improves followers’ commitment by influencing their needs, values, and self-esteem. Employees also develop a dyadic relationship with their immediate superiors. Study was conducted amongst one hundred and twenty two (122) bank managers in Sri Lanka. They were selected based on multi-stage (seniority in the hierarchy, gender, department-wise etc.) stratified random sampling. Major objectives of this study were to analyze the impact of Transformational leadership style, and OSE along with Sociodemographic factors, and Career, Job and Organizational experience, to the Career satisfaction of managers. SPSS software was used for parametric and non-parametric statistical analyses. Career satisfaction had positive impacts with their Transformational leadership style, and their relationships with the immediate superior. Impact of sociodemographic factors, and career exposure to career satisfaction was assessed.

Keywords: Career success, Relationship with immediate superior, Transformational leadership.

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Features selection, learning with kernels, support vector machine, genetic algorithms and classification.

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Authors: Nino Abesadze, Marine Mindorashvili, Nino Paresashvili

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The main purpose of the article is to make complex statistical analysis of tourist expenses of foreign visitors. We used mixed technique of selection that implies rules of random and proportional selection. Computer software SPSS was used to compute statistical data for corresponding analysis. Corresponding methodology of tourism statistics was implemented according to international standards. Important information was collected and grouped from the major Georgian airports. Techniques of statistical observation were prepared. A representative population of foreign visitors and a rule of selection of respondents were determined. We have a trend of growth of tourist numbers and share of tourists from post-soviet countries constantly increases. Level of satisfaction with tourist facilities and quality of service has grown, but still we have a problem of disparity between quality of service and prices. The design of tourist expenses of foreign visitors is diverse; competitiveness of tourist products of Georgian tourist companies is higher.

Keywords: Tourist, expenses, methods, statistics, analysis.

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Authors: A. Appe, B. Poluparthi, L. Kasivajjula, U. Mv, S. Bagadi, P. Modi, A. Singh, H. Gunupudi, S. Troiano, J. Paul, J. Stovall, J. Yamamoto

Abstract:

The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data are considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP (SHapley Additive exPlanations), to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since it is data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for e.g., quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP, a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: Competition, DAGs, hospital, healthcare, machine learning, market share, random forest, SHAP.

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: Data mining, knowledge discovery in databases, prediction models, student success.

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Authors: M. Bevilacqua, F. E. Ciarapica, G. Marcucci

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Supply Chain Resilience has been broadly studied during the last decade, focusing the research on many aspects of Supply Chain performance. Consequently, different definitions of Supply Chain Resilience have been developed by the research community, drawing inspiration also from other fields of study such as ecology, sociology, psychology, economy et al. This way, the definitions so far developed in the extant literature are therefore very heterogeneous, and many authors have pointed out a lack of consensus in this field of analysis. The aim of this research is to find common points between these definitions, through the development of a framework of study: the Resilience Triangle. The Resilience Triangle is a tool developed in the field of civil engineering, with the objective of modeling the loss of resilience of a given structure during and after the occurrence of a disruption such as an earthquake. The Resilience Triangle is a simple yet powerful tool: in our opinion, it can summarize all the features that authors have captured in the Supply Chain Resilience definitions over the years. This research intends to recapitulate within this framework all these heterogeneities in Supply Chain Resilience research. After collecting a various number of Supply Chain Resilience definitions present in the extant literature, the methodology approach provides a taxonomy step with the scope of collecting and analyzing all the data gathered. The next step provides the comparison of the data obtained with the plotting of a disruption profile, in order to contextualize the Resilience Triangle in the Supply Chain context. The tool and the results developed in this research will allow to lay the foundation for future Supply Chain Resilience modeling and measurement work.

Keywords: Supply chain resilience, resilience definition, supply chain resilience triangle.

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Authors: M. F. Haroun, T. A. Gulliver

Abstract:

In this paper, an encryption algorithm is proposed for real-time image encryption. The scheme employs a dual chaotic generator based on a three dimensional (3D) discrete Lorenz attractor. Encryption is achieved using non-autonomous modulation where the data is injected into the dynamics of the master chaotic generator. The second generator is used to permute the dynamics of the master generator using the same approach. Since the data stream can be regarded as a random source, the resulting permutations of the generator dynamics greatly increase the security of the transmitted signal. In addition, a technique is proposed to mitigate the error propagation due to the finite precision arithmetic of digital hardware. In particular, truncation and rounding errors are eliminated by employing an integer representation of the data which can easily be implemented. The simple hardware architecture of the algorithm makes it suitable for secure real-time applications.

Keywords: Chaotic systems, image encryption, 3D Lorenz attractor, non-autonomous modulation, FPGA.

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Authors: A. T. Asutosh, J. Woo, M. Kouhirostami, M. Sam, A. Khantawang, C. Cuales, W. Ryor, C. Kibert

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Climate change and global warming vortex have grown to alarming proportions. Therefore, the need for a shift in the conceptualization of energy production is paramount. Energy practices have been created in the current situation. Fossil fuels continue their prominence, at the expense of renewable sources. Despite this abundance, a large percentage of the world population still has no access to electricity but there have been encouraging signs in global movement from nonrenewable to renewable energy but means to reverse climate change have been elusive. Worldwide, organizations have put tremendous effort into innovation. Conferences and exhibitions act as a platform that allows a broad exchange of information regarding trends in the renewable energy field. The Solar Power International (SPI) conference and exhibition is a gathering of concerned activists, and probably the largest convention of its kind. This study investigates current development in the renewable energy field, analyzing means by which industry is being applied to the issue. In reviewing the 2019 SPI conference, it was found innovations in recycling and assessing the environmental impacts of the solar products that need critical attention. There is a huge movement in the electrical storage but there exists a large gap in the development of security systems. This research will focus on solar energy, but impacts will be relevant to the entire renewable energy market.

Keywords: Climate change, renewable energy, solar, trends, research, SPI.

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Authors: Reza Hossseynie, Amir Jafari

Abstract:

This paper presents a method for steering velocity bounded mobile robots in environments with partially known stationary obstacles. The exact location of obstacles is unknown and only a probability distribution associated with the location of the obstacles is known. Kinematic model of a 2-wheeled differential drive robot is used as the model of mobile robot. The presented control strategy uses the Artificial Potential Field (APF) method for devising a desired direction of movement for the robot at each instant of time while the Constrained Directions Control (CDC) uses the generated direction to produce the control signals required for steering the robot. The location of each obstacle is considered to be the mean value of the 2D probability distribution and similarly, the magnitude of the electric charge in the APF is set as the trace of covariance matrix of the location probability distribution. The method not only captures the challenges of planning the path (i.e. probabilistic nature of the location of unknown obstacles), but it also addresses the output saturation which is considered to be an important issue from the control perspective. Moreover, velocity of the robot can be controlled during the steering. For example, the velocity of robot can be reduced in close vicinity of obstacles and target to ensure safety. Finally, the control strategy is simulated for different scenarios to show how the method can be put into practice.

Keywords: Steering, obstacle avoidance, mobile robots, constrained directions control, artificial potential field.

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Authors: A. S. Haruna

Abstract:

The incessant discomfort for Voluntary Counselling and Testing (VCT) exhibited by students in some tertiary institutions in Kano State, Nigeria is capable of causing Psychological Resistance as well as jeopardizing the purpose of HIV intervention. This study investigated the Prevalence of Psychological Resistance to VCT of HIV/AIDS among students of tertiary institutions in the state. Two null hypotheses were postulated and tested. Cross- Sectional Survey Design was employed in which 1512 sample was selected from a student population of 104,841 following Stratified Random Sampling technique. A self-developed 20-item scale whose reliability coefficient is 0.83 was used for data collection. Data analyzed via Chi-square and t-test reveals a prevalence of 38% with males (Mean=0.34; SD=0.475) constituting 60% and females (Mean=0.45; SD=0.498) 40%. Also, the calculated chi-square and ttest were not significant at 0.05 as such the null hypotheses were upheld. Recommendation offered suggests the use of reinforcement and social support for students who patronize HIV/AIDS counselling.

Keywords: HIV/AIDS, Prevalence rate, Psychological Resistance, VCT.

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Authors: Norah Alshahrani, Abdulaziz Almaleh

Abstract:

Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD: Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by SVM, achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: Autism Spectrum Disorder, ASD, Machine Learning, ML, Feature Selection, Support Vector Machine, SVM.

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Authors: Fan Li, Chun-Hsien Chen, Li Pheng Khoo

Abstract:

Operators of vessel traffic service (VTS) center provides three different types of services; namely information service, navigational assistance and traffic organization to vessels. To provide these services, operators monitor vessel traffic through computer interface and provide navigational advice based on the information integrated from multiple sources, including automatic identification system (AIS), radar system, and closed circuit television (CCTV) system. Therefore, this information is crucial in VTS operation. However, what information the VTS operator actually need to efficiently and properly offer services is unclear. The aim of this study is to investigate into information requirements for VTS operation. To achieve this aim, field observation was carried out to elicit the information requirements for VTS operation. The study revealed that the most frequent and important tasks were handling arrival vessel report, potential conflict control and abeam vessel report. Current location and vessel name were used in all tasks. Hazard cargo information was particularly required when operators handle arrival vessel report. The speed, the course, and the distance of two or several vessels were only used in potential conflict control. The information requirements identified in this study can be utilized in designing a human-computer interface that takes into consideration what and when information should be displayed, and might be further used to build the foundation of a decision support system for VTS.

Keywords: Vessel traffic service, information requirements, hierarchy task analysis, field observation.

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Authors: M. Ghanbari, S. Hossainpour, G. Rezazadeh

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In this paper, longitudinal vibration of a micro-beam in micro-scale fluid media has been investigated. The proposed mathematical model for this study is made up of a micro-beam and a micro-plate at its free end. An AC voltage is applied to the pair of piezoelectric layers on the upper and lower surfaces of the micro-beam in order to actuate it longitudinally. The whole structure is bounded between two fixed plates on its upper and lower surfaces. The micro-gap between the structure and the fixed plates is filled with fluid. Fluids behave differently in micro-scale than macro, so the fluid field in the gap has been modeled based on micro-polar theory. The coupled governing equations of motion of the micro-beam and the micro-scale fluid field have been derived. Due to having non-homogenous boundary conditions, derived equations have been transformed to an enhanced form with homogenous boundary conditions. Using Galerkin-based reduced order model, the enhanced equations have been discretized over the beam and fluid domains and solve simultaneously in order to obtain force response of the micro-beam. Effects of micro-polar parameters of the fluid as characteristic length scale, coupling parameter and surface parameter on the response of the micro-beam have been studied.

Keywords: Micro-polar theory, Galerkin method, MEMS, micro-fluid.

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Authors: Rangoli Goyal, Rama Bhargava

Abstract:

The triple diffusive boundary layer flow of nanofluid under the action of constant magnetic field over a non-linear stretching sheet has been investigated numerically. The model includes the effect of Brownian motion, thermophoresis, and cross-diffusion; slip mechanisms which are primarily responsible for the enhancement of the convective features of nanofluid. The governing partial differential equations are transformed into a system of ordinary differential equations (by using group theory transformations) and solved numerically by using variational finite element method. The effects of various controlling parameters, such as the magnetic influence number, thermophoresis parameter, Brownian motion parameter, modified Dufour parameter, and Dufour solutal Lewis number, on the fluid flow as well as on heat and mass transfer coefficients (both of solute and nanofluid) are presented graphically and discussed quantitatively. The present study has industrial applications in aerodynamic extrusion of plastic sheets, coating and suspensions, melt spinning, hot rolling, wire drawing, glass-fibre production, and manufacture of polymer and rubber sheets, where the quality of the desired product depends on the stretching rate as well as external field including magnetic effects.

Keywords: FEM, Thermophoresis, Diffusiophoresis, Brownian motion.

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Authors: Ke He, Wumaier Parezhati, Haruka Yamashita

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Recently, online marketplaces in the e-commerce industry, such as Rakuten and Alibaba, have become some of the most popular online marketplaces in Asia. In these shopping websites, consumers can select purchase products from a large number of stores. Additionally, consumers of the e-commerce site have to register their name, age, gender, and other information in advance, to access their registered account. Therefore, establishing a method for analyzing consumer preferences from both the store and the product side is required. This study uses the Doc2Vec method, which has been studied in the field of natural language processing. Doc2Vec has been used in many cases to analyze the extraction of semantic relationships between documents (represented as consumers) and words (represented as products) in the field of document classification. This concept is applicable to represent the relationship between users and items; however, the problem is that one more factor (i.e., shops) needs to be considered in Doc2Vec. More precisely, a method for analyzing the relationship between consumers, stores, and products is required. The purpose of our study is to combine the analysis of the Doc2vec model for users and shops, and for users and items in the same feature space. This method enables the calculation of similar shops and items for each user. In this study, we derive the real data analysis accumulated in the online marketplace and demonstrate the efficiency of the proposal.

Keywords: Doc2Vec, marketing, online marketplace, recommendation system.

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Authors: M. T. Sichani, B. J. Pedersen, S. R. K. Nielsen

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Turbulence of the incoming wind field is of paramount importance to the dynamic response of civil engineering structures. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper an empirical cross spectral density function for the along-wind turbulence component over the wind field area is taken as the starting point. The spectrum is spatially discretized in terms of a Hermitian cross-spectral density matrix for the turbulence state vector which turns out not to be positive definite. Since the succeeding state space and ARMA modelling of the turbulence rely on the positive definiteness of the cross-spectral density matrix, the problem with the non-positive definiteness of such matrices is at first addressed and suitable treatments regarding it are proposed. From the adjusted positive definite cross-spectral density matrix a frequency response matrix is constructed which determines the turbulence vector as a linear filtration of Gaussian white noise. Finally, an accurate state space modelling method is proposed which allows selection of an appropriate model order, and estimation of a state space model for the vector turbulence process incorporating its phase spectrum in one stage, and its results are compared with a conventional ARMA modelling method.

Keywords: Turbulence, wind turbine, complex coherence, state space modelling, ARMA modelling.

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Authors: Ana-Marija Stjepić, Dalia Suša Vugec

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Today, digital transformation is one of the leading topics that occupy the attention of scientific circles and business experts. Organizational success is most often reflected through the successful managing of business processes. Given the growing market for digital innovations and its ever-increasing impact on business, organizations need to be prepared for organizational changes that come with the digital era. In order to maintain their competitive advantage in the global market, organizations must adapt their processes to new digitalization conditions. The main goal of this study is to point out the link between the digital transformation and the business process management concept. Therefore, in order to contribute to the scientific field that explores the potential relation between business process management concept and digital transformation, a literature review has been conducted. Papers have been searched within the Business Process Management Journal by keywords related to the term digital transformation. Selected papers have been analyzed according to the topic, type of publication, year of publication, keywords, etc. The results reveal a growing number of papers published on the topic of digital transformation to the Business Process Management Journal, but the lack of case studies. This paper contributes to the extension of academic literature in this important, yet insufficiently researched, scientific field that creates the bond between two strong concepts of digital transformation and business process management.

Keywords: Business process management, digital transformation, digitalization, process change.

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Authors: Mohammed Benbrahim, Adil Daoudi, Khalid Benjelloun, Aomar Ibenbrahim

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The automatic discrimination of seismic signals is an important practical goal for earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, two classes of seismic signals recorded routinely in geophysical laboratory of the National Center for Scientific and Technical Research in Morocco are considered. They correspond to signals associated to local earthquakes and chemical explosions. The approach adopted for the development of an automatic discrimination system is a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "modified Mexican hat wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.

Keywords: Seismic signals, Wavelets, Dimensionality reduction, Artificial neural networks, Classification.

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Authors: Fahad Suleiman

Abstract:

The aim of this paper is to study students’ view on mathematics learning in Katsina State Senior Secondary Schools of Nigeria, such as their conceptions of mathematics, attitudes toward mathematics learning, etc. A questionnaire was administered to a random sample of 1,225 senior secondary two (SS II) students of Katsina State in Nigeria. The data collected showed a clear picture of the hurdles that affect the teaching and learning of mathematics in our schools. Problems such as logistics and operational which include shortage of mathematics teachers, non–availability of a mathematics laboratory, etc. were identified. It also depicted the substantial trends of changing views and attitudes toward mathematics across secondary schools. Students’ responses to the conception of mathematics were consistent and they demonstrated some specific characteristics of their views in learning mathematics. This survey has provided useful information regarding students’ needs and aspirations in mathematics learning for curriculum planners and frontline teachers for future curriculum reform and implementation.

Keywords: Attitude, education, mathematics, students.

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Authors: Sattam Allahawiah, Haroon Altarawneh, Sameer Alamro

Abstract:

Because of its global reach, reduction of time restraints, and ability to reduce costs and increase sales, use of the Internet, the World Wide Web (WWW), and related technologies can be a competitive tool in the arsenal of small and medium-sized enterprises (SMEs). Countries the world over are interested in the successful adoption of the Internet by SMEs. Because a vast majority of jobs come from that sector, greater financial success of SMEs translates into greater job growth and, subsequently, higher tax revenue to the government. This research investigated the level of Internet usage for business solutions by small and medium enterprises in Jordan. Through the survey of a random sample of 100 firms with less than 500 employees and from data obtained from this survey that formed the basis for our study, we found that a majority of respondents use the Internet in business activities , the adoption of the Internet as a business tool is limited to a brochure where Web site which primarily provides one way. As such, there wasn't interactive information about the company and its products and services.

Keywords: Internet, SMEs, e-commerce, website, e-business, IT.

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Authors: Lubna Shaheen

Abstract:

Palestinian cities face the challenges of land scarcity, high population growth rates, rapid urbanization, uneven development and territorial fragmentation. Due to geopolitical constrains and the absence of an effective Palestinian planning institution, urban development in Palestinian cities has not followed any discernable planning scheme. This has led to a number of internal contradictions in the structure of cities, and adversely affected land use, the provision of urban services, and the quality of the living environment. This paper explores these challenges, and the potential that exists for introducing a more sustainable urban development pattern in Palestinian cities. It assesses alternative development approaches with a particular focus on sustainable development, promoting ecodevelopment imperatives, limiting random urbanization, and meeting present and future challenges, including fulfilling the needs of the people and conserving the scarce land and limited natural resources. This paper concludes by offering conceptual proposals and guidelines for promoting sustainable physical development in Palestinian cities.

Keywords: Palestinian Cities, Rapid urbanization, Sustainableurban development.

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Authors: Manasjyoti Bhuyan, Kandarpa Kumar Sarma, Hirendra Das

Abstract:

Signature represents an individual characteristic of a person which can be used for his / her validation. For such application proper modeling is essential. Here we propose an offline signature recognition and verification scheme which is based on extraction of several features including one hybrid set from the input signature and compare them with the already trained forms. Feature points are classified using statistical parameters like mean and variance. The scanned signature is normalized in slant using a very simple algorithm with an intention to make the system robust which is found to be very helpful. The slant correction is further aided by the use of an Artificial Neural Network (ANN). The suggested scheme discriminates between originals and forged signatures from simple and random forgeries. The primary objective is to reduce the two crucial parameters-False Acceptance Rate (FAR) and False Rejection Rate (FRR) with lesser training time with an intension to make the system dynamic using a cluster of ANNs forming a multiple classifier system.

Keywords: offline, algorithm, FAR, FRR, ANN.

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