Search results for: discrete choice models

Authors: J. K. Visser, H. T. Malan

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A need exists to identify the sources of risks associated with the process automation systems within petrochemical companies or similar energy related industries. These companies use many different process automation technologies in its value chain. A crucial part of the process automation system is the information technology component featuring in the supervisory control layer. The ever-changing technology within the process automation layers and the rate at which it advances pose a risk to safe and predictable automation system performance. The age of the automation equipment also provides challenges to the operations and maintenance managers of the plant due to obsolescence and unavailability of spare parts. The main objective of this research was to determine the risk sources associated with the equipment that is part of the process automation systems. A secondary objective was to establish whether technology managers and technicians were aware of the risks and share the same viewpoint on the importance of the risks associated with automation systems. A conceptual model for risk sources of automation systems was formulated from models and frameworks in literature. This model comprised six categories of risk which forms the basis for identifying specific risks. This model was used to develop a questionnaire that was sent to 172 instrument technicians and technology managers in the company to obtain primary data. 75 completed and useful responses were received. These responses were analyzed statistically to determine the highest risk sources and to determine whether there was difference in opinion between technology managers and technicians. The most important risks that were revealed in this study are: 1) the lack of skilled technicians, 2) integration capability of third-party system software, 3) reliability of the process automation hardware, 4) excessive costs pertaining to performing maintenance and migrations on process automation systems, and 5) requirements of having third-party communication interfacing compatibility as well as real-time communication networks.

Keywords: Distributed control system, identification of risks, information technology, process automation system.

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Authors: A. Megalingam, M.M.Mayuram

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Evaluation of contact pressure, surface and subsurface contact stresses are essential to know the functional response of surface coatings and the contact behavior mainly depends on surface roughness, material property, thickness of layer and the manner of loading. Contact parameter evaluation of real rough surface contacts mostly relies on statistical single asperity contact approaches. In this work, a three dimensional layered solid rough surface in contact with a rigid flat is modeled and analyzed using finite element method. The rough surface of layered solid is generated by FFT approach. The generated rough surface is exported to a finite element method based ANSYS package through which the bottom up solid modeling is employed to create a deformable solid model with a layered solid rough surface on top. The discretization and contact analysis are carried by using the same ANSYS package. The elastic, elastoplastic and plastic deformations are continuous in the present finite element method unlike many other contact models. The Young-s modulus to yield strength ratio of layer is varied in the present work to observe the contact parameters effect while keeping the surface roughness and substrate material properties as constant. The contacting asperities attain elastic, elastoplastic and plastic states with their continuity and asperity interaction phenomena is inherently included. The resultant contact parameters show that neighboring asperity interaction and the Young-s modulus to yield strength ratio of layer influence the bulk deformation consequently affect the interface strength.

Keywords: Asperity interaction, finite element method, rough surface contact, single layered solid

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Authors: Atiq U. Zaman, Juliet Arnott

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This paper analyses the various benefits and barriers of residential deconstruction in the context of environmental performance and circular economy based on a case study project in Christchurch, New Zealand. The case study project “Whole House Deconstruction” which aimed, firstly, to harvest materials from a residential house, secondly, to produce new products using the recovered materials, and thirdly, to organize an exhibition for the local public to promote awareness on resource conservation and sustainable deconstruction practices. Through a systematic deconstruction process, the project recovered around 12 tonnes of various construction materials, most of which would otherwise be disposed of to landfill in the traditional demolition approach. It is estimated that the deconstruction of a similar residential house could potentially prevent around 27,029 kg of carbon emission to the atmosphere by recovering and reusing the building materials. In addition, the project involved local designers to produce 400 artefacts using the recovered materials and to exhibit them to accelerate public awareness. The findings from this study suggest that the deconstruction project has significant environmental benefits, as well as social benefits by involving the local community and unemployed youth as a part of their professional skills development opportunities. However, the project faced a number of economic and institutional challenges. The study concludes that with proper economic models and appropriate institutional support a significant amount of construction and demolition waste can be reduced through a systematic deconstruction process. Traditionally, the greatest benefits from such projects are often ignored and remain unreported to wider audiences as most of the external and environmental costs have not been considered in the traditional linear economy.

Keywords: Circular economy, construction and demolition waste, resource recovery, systematic deconstruction, sustainable waste management.

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Authors: Eronini Umez-Eronini

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Compressed air energy storage (CAES) coupled with wind farms have gained attention as a means to address the intermittency and variability of wind power. However, most existing studies and implementations focus on bulk or centralized CAES plants. This study presents a dynamic model of a hybrid wind farm with distributed CAES, using air storage tanks and compressor and expander trains at each wind turbine station. It introduces the concept of a distributed CAES with linked air cooling and heating, and presents an approach to scheduling and regulating the production of compressed air and power in such a system. Mathematical models of the dynamic components of this hybrid wind farm system, including a simple transient wake field model, were developed and simulated using MATLAB, with real wind data and Transmission System Operator (TSO) absolute power reference signals as inputs. The simulation results demonstrate that the proposed ad hoc supervisory controller is able to track the minute-scale power demand signal within an error band size comparable to the electrical power rating of a single expander. This suggests that combining the global distributed CAES control with power regulation for individual wind turbines could further improve the system’s performance. The round trip electrical storage efficiency computed for the distributed CAES was also in the range of reported round trip storage electrical efficiencies for improved bulk CAES. These findings contribute to the enhancement of efficiency of wind farms without access to large-scale storage or underground caverns.

Keywords: Distributed CAES, compressed air, energy storage, hybrid wind farm, wind turbines, dynamic simulation.

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Authors: Emanuel Teixeira, Anderson Ramos, Marisa Lourenço, Fernando J. Velez, Jon M. Peha

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This article discusses the benefit cost analysis aspects of millimetre wavebands (mmWaves) and Super High Frequency (SHF). The devaluation along the distance of the carrier-to-noise-plus-interference ratio with the coverage distance is assessed by considering two different path loss models, the two-slope urban micro Line-of-Sight (UMiLoS) for the SHF band and the modified Friis propagation model, for frequencies above 24 GHz. The equivalent supported throughput is estimated at the 5.62, 28, 38, 60 and 73 GHz frequency bands and the influence of carrier-to-noise-plus-interference ratio in the radio and network optimization process is explored. Mostly owing to the lessening caused by the behaviour of the two-slope propagation model for SHF band, the supported throughput at this band is higher than at the millimetre wavebands only for the longest cell lengths. The benefit cost analysis of these pico-cellular networks was analysed for regular cellular topologies, by considering the unlicensed spectrum. For shortest distances, we can distinguish an optimal of the revenue in percentage terms for values of the cell length, R ≈ 10 m for the millimeter wavebands and for longest distances an optimal of the revenue can be observed at R ≈ 550 m for the 5.62 GHz. It is possible to observe that, for the 5.62 GHz band, the profit is slightly inferior than for millimetre wavebands, for the shortest Rs, and starts to increase for cell lengths approximately equal to the ratio between the break-point distance and the co-channel reuse factor, achieving a maximum for values of R approximately equal to 550 m.

Keywords: 5G, millimetre wavebands, super high-frequency band, SINR, signal-to-interference-plus-noise ratio, cost benefit analysis.

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

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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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Authors: Olivier Adam, Maciej Lopatka, Christophe Laplanche, Jean-François Motsch

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This article presents the results using a parametric approach and a Wavelet Transform in analysing signals emitting from the sperm whale. The extraction of intrinsic characteristics of these unique signals emitted by marine mammals is still at present a difficult exercise for various reasons: firstly, it concerns non-stationary signals, and secondly, these signals are obstructed by interfering background noise. In this article, we compare the advantages and disadvantages of both methods: Auto Regressive models and Wavelet Transform. These approaches serve as an alternative to the commonly used estimators which are based on the Fourier Transform for which the hypotheses necessary for its application are in certain cases, not sufficiently proven. These modern approaches provide effective results particularly for the periodic tracking of the signal's characteristics and notably when the signal-to-noise ratio negatively effects signal tracking. Our objectives are twofold. Our first goal is to identify the animal through its acoustic signature. This includes recognition of the marine mammal species and ultimately of the individual animal (within the species). The second is much more ambitious and directly involves the intervention of cetologists to study the sounds emitted by marine mammals in an effort to characterize their behaviour. We are working on an approach based on the recordings of marine mammal signals and the findings from this data result from the Wavelet Transform. This article will explore the reasons for using this approach. In addition, thanks to the use of new processors, these algorithms once heavy in calculation time can be integrated in a real-time system.

Keywords: Autoregressive model, Daubechies Wavelet, Fourier Transform, marine mammals, signal processing, spectrogram, sperm whale, Wavelet Transform.

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Authors: Kelly Grani Yuen

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Accounting scandals and auditing frauds are perceived to be driven by aggressive companies and misrepresentation of audit reports. However, local legal systems and law enforcements may affect the services auditors provide to their ‘important’ clients. Under the civil law and common law jurisdictions, the standard setters, the government, and the regulatory bodies treat cases differently. As such, whether or not different forms of legal systems and extent of law enforcement plays an important role in auditor’s Audit Quality is a question this paper attempts to explore. The paper focuses on the investigation in Asia, where Hong Kong represents the common-law jurisdiction, while Taiwan and China represent the civil law jurisdiction. Only the ten reputable accounting firms are used in this study due to the differences in rankings and establishments of some of the small local audit firms. This will also contribute to the data collected between the years 2007-2013. By focusing on the use of multiple regression based on the dependent (Audit Quality) and independent variables (Client Importance, Law Enforcement, and Press Freedom), six different models are established. Results demonstrate that since different jurisdictions have different legal systems and market regulations, auditor’s treatment on ‘important’ clients will vary. However, with the moderators in place (law enforcement and press freedom), the relationship between client importance and audit quality may be smoothed out. With that in mind, this study contributes to local governments and standard setters’ consideration on legal reform and proper law enforcement in the market. Perhaps, with such modifications on the economic systems, collusion between companies and auditors can finally be put to a halt.

Keywords: Audit quality, client importance, jurisdiction, modified audit opinions.

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Authors: Yongsheng Guo, Mirza Muhammad Naseer, Xiaoxian Zhu

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This study explores the perceptions and conducts of Chinese foreign direct investment (FDI) in Africa from ethical and cultural perspectives. It offers a better understanding of how ethical and cultural factors affect Chinese investment in Africa and how the investment projects performed in Africa from both Chinese investors and African stakeholders’ perceptions. It adopted a grounded theory approach and conducted 30 in-depth interviews with corporate managers. Grounded theory models are developed to link the ethical and cultural factors, actions, and consequences. Results reveal that some ethical concepts like the unity of humans and nature, benevolence, virtue and responsibility, and cultural traits including propriety, righteousness, sincerity, equilibrium, long-term orientation, and principles affect Chinese investors when making investments in Africa. Most Chinese investors harmonize with local managers, cooperate with each other, and are gentle and courteous to partners. They take stable and steady actions and invest in infrastructure and agriculture projects and adopt a virtue governance system in the organization. This study finds that consequently, Chinese investors and local partners take complementary advantages, make achievements sequentially, and therefore both sides can win. They recognize great potentials and make sustainable development in Africa to achieve the Great Together in the future. This study proposes a Chinese ethics and governance system including economic, social, and political perspectives and compares it with alternative systems. It makes implications to the world island theory and propose suggestions to solve the Clash of Civilizations problem.

Keywords: Foreign Direct Investment, Ethics, National Culture, China, Africa.

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Authors: Marco Antonio Lara De la Calleja, María Catalina Ovando Chico, Eduardo Lopez Ruiz

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Community empowerment has been proved to be a key element in the solution of the food security problem. As a result of a conceptual analysis, it was found that agricultural production, economic development and governance, are the traditional basis of food security models. Although the literature points to social inclusion as an important factor for food security, no model has considered it as the basis of it. The aim of this research is to identify different dimensions that make an integral model for food security, with emphasis on community empowerment. A diagnosis was made in the study community (Tatoxcac, Zacapoaxtla, Puebla), to know the aspects that impact the level of food insecurity. With a statistical sample integrated by 200 families, the Latin American and Caribbean Food Security Scale (ELCSA) was applied, finding that: in households composed by adults and children, have moderated food insecurity, (ELCSA scale has three levels, low, moderated and high); that result is produced mainly by the economic income capacity and the diversity of the diet on its food. With that being said, a model was developed to promote food security through five dimensions: 1. Regional context of the community; 2. Structure and system of local food; 3. Health and nutrition; 4. Information and technology access; and 5. Self-awareness and empowerment. The specific actions on each axis of the model, allowed a systemic approach needed to attend food security in the community, through the empowerment of society. It is concluded that the self-awareness of local communities is an area of extreme importance, which must be taken into account for participatory schemes to improve food security. In the long term, the model requires the integrated participation of different actors, such as government, companies and universities, to solve something such vital as food security.

Keywords: Community empowerment, food security, model, systemic approach.

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Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

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The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: Educative model, good life, professional social responsibility (SR), values.

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Authors: Miguel A. Gómez, Enrique Hilario, Francisco J. Alvarez, Elena Gastiasoro, Antonia Alvarez, Jose A. Casla, Jorge Arguinchona, Juan L. Larrabe

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Total liquid ventilation can support gas exchange in animal models of lung injury. Clinical application awaits further technical improvements and performance verification. Our aim was to develop a liquid ventilator, able to deliver accurate tidal volumes, and a computerized system for measuring lung mechanics. The computer-assisted, piston-driven respirator controlled ventilatory parameters that were displayed and modified on a real-time basis. Pressure and temperature transducers along with a lineal displacement controller provided the necessary signals to calculate lung mechanics. Ten newborn lambs (<6 days old) with respiratory failure induced by lung lavage, were monitored using the system. Electromechanical, hydraulic and data acquisition/analysis components of the ventilator were developed and tested in animals with respiratory failure. All pulmonary signals were collected synchronized in time, displayed in real-time, and archived on digital media. The total mean error (due to transducers, A/D conversion, amplifiers, etc.) was less than 5% compared to calibrated signals. Improvements in gas exchange and lung mechanics were observed during liquid ventilation, without impairment of cardiovascular profiles. The total liquid ventilator maintained accurate control of tidal volumes and the sequencing of inspiration/expiration. The computerized system demonstrated its ability to monitor in vivo lung mechanics, providing valuable data for early decision-making.

Keywords: Immature lamb, perfluorocarbon, pressure-limited, total liquid ventilation, ventilator, volume-controlled.

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Authors: Acumsiri Tantiakrnpanit

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This paper aims to examine the relationship between urbanization and income inequality in Thailand during the period 2002–2020, using a panel of data for 76 provinces collected from Thailand’s National Statistical Office (Labor Force Survey: LFS), as well as geospatial data from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite Day/Night band (VIIRS-DNB) satellite for 19 selected years. This paper employs two different definitions to identify urban areas: 1) Urban areas defined by Thailand's National Statistical Office (LFS), and 2) Urban areas estimated using nighttime light data from the DMSP and VIIRS-DNB satellite. The second method includes two sub-categories: 2.1) Determining urban areas by calculating nighttime light density with a population density of 300 people per square kilometer, and 2.2) Calculating urban areas based on nighttime light density corresponding to a population density of 1,500 people per square kilometer. The empirical analysis based on Ordinary Least Squares (OLS), fixed effects, and random effects models reveals a consistent U-shaped relationship between income inequality and urbanization. The findings from the econometric analysis demonstrate that urbanization or population density has a significant and negative impact on income inequality. Moreover, the square of urbanization shows a statistically significant positive impact on income inequality. Additionally, there is a negative association between logarithmically transformed income and income inequality. This paper also proposes the inclusion of satellite imagery, geospatial data, and spatial econometric techniques in future studies to conduct quantitative analysis of spatial relationships.

Keywords: Income inequality, nighttime light, population density, Thailand, urbanization.

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Authors: Yuan-Jye Tseng, Yi-Shiuan Chen

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In this paper, a new concept of closed-loop design for a product is presented. The closed-loop design model is developed by integrating forward design and reverse design. Based on this new concept, a closed-loop design model for sustainable manufacturing by integrated evaluation of forward design, reverse design, and green manufacturing using a fuzzy analytic network process is developed. In the design stage of a product, with a given product requirement and objective, there can be different ways to design the detailed components and specifications. Therefore, there can be different design cases to achieve the same product requirement and objective. Subsequently, in the design evaluation stage, it is required to analyze and evaluate the different design cases. The purpose of this research is to develop a model for evaluating the design cases by integrated evaluating the criteria in forward design, reverse design, and green manufacturing. A fuzzy analytic network process method is presented for integrated evaluation of the criteria in the three models. The comparison matrices for evaluating the criteria in the three groups are established. The total relational values among the three groups represent the total relational effects. In applications, a super matrix model is created and the total relational values can be used to evaluate the design cases for decision-making to select the final design case. An example product is demonstrated in this presentation. It shows that the model is useful for integrated evaluation of forward design, reverse design, and green manufacturing to achieve a closed-loop design for sustainable manufacturing objective.

Keywords: Design evaluation, forward design, reverse design, closed-loop design, supply chain management, closed-loop supply chain, fuzzy analytic network process.

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Authors: Mohsen Soori, Fooad Karimi Ghaleh Jough

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The integration of Artificial Intelligence (AI) techniques in the optimization of steel moment frame structures represents a transformative approach to enhance the design, analysis, and performance of these critical engineering systems. The review encompasses a wide spectrum of AI methods, including machine learning algorithms, evolutionary algorithms, neural networks, and optimization techniques, applied to address various challenges in the field. The synthesis of research findings highlights the interdisciplinary nature of AI applications in structural engineering, emphasizing the synergy between domain expertise and advanced computational methodologies. This synthesis aims to serve as a valuable resource for researchers, practitioners, and policymakers seeking a comprehensive understanding of the state-of-the-art in AI-driven optimization for steel moment frame structures. The paper commences with an overview of the fundamental principles governing steel moment frame structures and identifies the key optimization objectives, such as efficiency of structures. Subsequently, it delves into the application of AI in the conceptual design phase, where algorithms aid in generating innovative structural configurations and optimizing material utilization. The review also explores the use of AI for real-time structural health monitoring and predictive maintenance, contributing to the long-term sustainability and reliability of steel moment frame structures. Furthermore, the paper investigates how AI-driven algorithms facilitate the calibration of structural models, enabling accurate prediction of dynamic responses and seismic performance. Thus, by reviewing and analyzing the recent achievements in applications artificial intelligent in optimization of steel moment frame structures, the process of designing, analysis, and performance of the structures can be analyzed and modified.

Keywords: Artificial Intelligent, optimization process, steel moment frame, structural engineering.

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Authors: Mia Sorensen

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The introduction of haptic elements in a graphic user interfaces are becoming more widespread. Since haptics are being introduced rapidly into computational tools, investigating how these models affect Human-Computer Interaction would help define how to integrate and model new modes of interaction. The interest of this paper is to discuss and investigate the issues surrounding Haptic and Graphic User Interface designs (GUI) as separate systems, as well as understand how these work in tandem. The development of these systems is explored from a psychological perspective, based on how usability is addressed through learning and affordances, defined by J.J. Gibson. Haptic design can be a powerful tool, aiding in intuitive learning. The problems discussed within the text is how can haptic interfaces be integrated within a GUI without the sense of frivolity. Juxtaposing haptics and Graphic user interfaces has issues of motivation; GUI tends to have a performatory process, while Haptic Interfaces use affordances to learn tool use. In a deeper view, it is noted that two modes of perception, foveal and ambient, dictate perception. These two modes were once thought to work in tandem, however it has been discovered that these processes work independently from each other. Foveal modes interpret orientation is space which provide for posture, locomotion, and motor skills with variations of the sensory information, which instructs perceptions of object-task performance. It is contended, here, that object-task performance is a key element in the use of Haptic Interfaces because exploratory learning uses affordances in order to use an object, without meditating an experience cognitively. It is a direct experience that, through iteration, can lead to skill-sets. It is also indicated that object-task performance will not work as efficiently without the use of exploratory or kinesthetic learning practices. Therefore, object-task performance is not as congruently explored in GUI than it is practiced in Haptic interfaces.

Keywords: Affordances, Graphic User Interface, HapticInterfaces, Tool-Use, Object-Naming, Object-Task Performance

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant.

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: Big data, bus headway prediction, machine learning, public transportation.

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Authors: F. J. Moral García, P. Valiente González, F. López Rodríguez

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Ground-level tropospheric ozone is one of the air pollutants of most concern. It is mainly produced by photochemical processes involving nitrogen oxides and volatile organic compounds in the lower parts of the atmosphere. Ozone levels become particularly high in regions close to high ozone precursor emissions and during summer, when stagnant meteorological conditions with high insolation and high temperatures are common. In this work, some results of a study about urban ozone distribution patterns in the city of Badajoz, which is the largest and most industrialized city in Extremadura region (southwest Spain) are shown. Fourteen sampling campaigns, at least one per month, were carried out to measure ambient air ozone concentrations, during periods that were selected according to favourable conditions to ozone production, using an automatic portable analyzer. Later, to evaluate the ozone distribution at the city, the measured ozone data were analyzed using geostatistical techniques. Thus, first, during the exploratory analysis of data, it was revealed that they were distributed normally, which is a desirable property for the subsequent stages of the geostatistical study. Secondly, during the structural analysis of data, theoretical spherical models provided the best fit for all monthly experimental variograms. The parameters of these variograms (sill, range and nugget) revealed that the maximum distance of spatial dependence is between 302-790 m and the variable, air ozone concentration, is not evenly distributed in reduced distances. Finally, predictive ozone maps were derived for all points of the experimental study area, by use of geostatistical algorithms (kriging). High prediction accuracy was obtained in all cases as cross-validation showed. Useful information for hazard assessment was also provided when probability maps, based on kriging interpolation and kriging standard deviation, were produced.

Keywords: Kriging, map, tropospheric ozone, variogram.

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Authors: Kaznah Alshammari, Thomas Beach, Yacine Rezgui

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Technological advances in the construction sector are helping to make smart cities a reality by means of Cyber-Physical Systems (CPS). CPS integrate information and the physical world through the use of Information Communication Technologies (ICT). An increasingly common goal in the built environment is to integrate Building Information Models (BIM) with Internet of Things (IoT) and sensor technologies using CPS. Future advances could see the adoption of digital twins, creating new opportunities for CPS using monitoring, simulation and optimisation technologies. However, researchers often fail to fully consider the security implications. To date, it is not widely possible to assimilate BIM data and cybersecurity concepts and, therefore, security has thus far been overlooked. This paper reviews the empirical literature concerning IoT applications in the built environment and discusses real-world applications of the IoT intended to enhance construction practices, people’s lives and bolster cybersecurity. Specifically, this research addresses two research questions: (a) How suitable are the current IoT and CPS security stacks to address the cybersecurity threats facing digital twins in the context of smart buildings and districts? and (b) What are the current obstacles to tackling cybersecurity threats to the built environment CPS? To answer these questions, this paper reviews the current state-of-the-art research concerning digital twins in the built environment, the IoT, BIM, urban cities and cybersecurity. The results of the findings of this study confirmed the importance of using digital twins in both IoT and BIM. Also, eight reference zones across Europe have gained special recognition for their contributions to the advancement of IoT science. Therefore, this paper evaluates the use of digital twins in CPS to arrive at recommendations for expanding BIM specifications to facilitate IoT compliance, bolster cybersecurity and integrate digital twin and city standards in the smart cities of the future.

Keywords: BIM, cybersecurity, digital twins, IoT, urban cities.

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Authors: I. Ludviga, A. Kalvina

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This paper aims to link together the concepts of job satisfaction, work engagement, trust, job meaningfulness and loyalty to the organisation focusing on specific type of employment – academic jobs. The research investigates the relationships between job satisfaction, work engagement and loyalty as well as the impact of trust and job meaningfulness on the work engagement and loyalty. The survey was conducted in one of the largest Latvian higher education institutions and the sample was drawn from academic staff (n=326). Structured questionnaire with 44 reflective type questions was developed to measure the constructs. Data was analysed using SPSS and Smart-PLS software. Variance based structural equation modelling (PLS-SEM) technique was used to test the model and to predict the most important factors relevant to employee engagement and loyalty. The first order model included two endogenous constructs (loyalty and intention to stay and recommend to work in this organisation, and employee engagement), as well as six exogenous constructs (feeling of fair treatment and trust in management; career growth opportunities; compensation, pay and benefits; management; colleagues and teamwork; and finally job meaningfulness). Job satisfaction was developed as second order construct and both: first and second order models were designed for data analysis. It was found that academics are more engaged than satisfied with their work and main reason for that was found to be job meaningfulness, which is significant predictor for work engagement, but not for job satisfaction. Compensation is not significantly related to work engagement, but only to job satisfaction. Trust was not significantly related neither to engagement, nor to satisfaction, however, it appeared to be significant predictor of loyalty and intentions to stay with the University. Paper revealed academic jobs as specific kind of employment where employees can be more engaged than satisfied and highlighted the specific role of job meaningfulness in the University settings.

Keywords: Job satisfaction, job meaningfulness, higher education, work engagement.

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Authors: Kazuki Kamada, Haruka Yamashita

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In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method.

Keywords: Taxi industry, decision making, recommendation system, embedding model.

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Authors: Rony Reátegui, Cesar Chácara, Benjamin Castañeda, Rafael Aguilar

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Nowadays, Heritage Building Information Modeling (HBIM) is considered an efficient tool to represent and manage information of Cultural Heritage (CH). The basis of this tool relies on a 3D model generally obtained from a Cloud-to-BIM procedure. There are different methods to create an HBIM model that goes from manual modeling based on the point cloud to the automatic detection of shapes and the creation of objects. The selection of these methods depends on the desired Level of Development (LOD), Level of Information (LOI), Grade of Generation (GOG) as well as on the availability of commercial software. This paper presents the 3D modeling of a stone masonry chapel using Recap Pro, Revit and Dynamo interface following a three-step methodology. The first step consists of the manual modeling of simple structural (e.g., regular walls, columns, floors, wall openings, etc.) and architectural (e.g., cornices, moldings and other minor details) elements using the point cloud as reference. Then, Dynamo is used for generative modeling of complex structural elements such as vaults, infills and domes. Finally, semantic information (e.g., materials, typology, state of conservation, etc.) and pathologies are added within the HBIM model as text parameters and generic models’ families respectively. The application of this methodology allows the documentation of CH following a relatively simple to apply process that ensures adequate LOD, LOI and GOG levels. In addition, the easy implementation of the method as well as the fact of using only one BIM software with its respective plugin for the scan-to-BIM modeling process means that this methodology can be adopted by a larger number of users with intermediate knowledge and limited resources, since the BIM software used has a free student license.

Keywords: Cloud-to-BIM, cultural heritage, generative modeling, HBIM, parametric modeling, Revit.

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Authors: Yehia A. Eldrainy, Mohammad Nazri Mohd. Jaafar, Tholudin Mat Lazim

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This paper presents a cold flow simulation study of a small gas turbine combustor performed using laboratory scale test rig. The main objective of this investigation is to obtain physical insight of the main vortex, responsible for the efficient mixing of fuel and air. Such models are necessary for predictions and optimization of real gas turbine combustors. Air swirler can control the combustor performance by assisting in the fuel-air mixing process and by producing recirculation region which can act as flame holders and influences residence time. Thus, proper selection of a swirler is needed to enhance combustor performance and to reduce NOx emissions. Three different axial air swirlers were used based on their vane angles i.e., 30°, 45°, and 60°. Three-dimensional, viscous, turbulent, isothermal flow characteristics of the combustor model operating at room temperature were simulated via Reynolds- Averaged Navier-Stokes (RANS) code. The model geometry has been created using solid model, and the meshing has been done using GAMBIT preprocessing package. Finally, the solution and analysis were carried out in a FLUENT solver. This serves to demonstrate the capability of the code for design and analysis of real combustor. The effects of swirlers and mass flow rate were examined. Details of the complex flow structure such as vortices and recirculation zones were obtained by the simulation model. The computational model predicts a major recirculation zone in the central region immediately downstream of the fuel nozzle and a second recirculation zone in the upstream corner of the combustion chamber. It is also shown that swirler angles changes have significant effects on the combustor flowfield as well as pressure losses.

Keywords: cold flow, numerical simulation, combustor;turbulence, axial swirler.

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Authors: Timothy Akinwande, Eddie C. M. Hui

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Housing the urban poor has remained a challenge for many years across the world, especially in developing economies, despite the apparent research attention and policy interventions. It is apt to investigate the prevalent affordable housing (AH) provision challenges using unconventional approaches. It is pragmatic to thoroughly examine housing experts to provide supply-side solutions to AH challenges and investigate informal settlers to deduce solutions from AH demand viewpoints. This study, being the supply-side investigation of an ongoing research, interrogated housing experts to determine significant expert solutions. Focus group discussions and in-depth interviews were conducted with housing experts in Nigeria. Through descriptive, content, and systematic thematic analyses of data, major findings are that deliberate finance models designed for the urban poor are the most significant housing finance solution in developing economies. Other findings are that adequately implemented rent control policies, deliberate Public-Private Partnership (PPP) approaches like inclusionary housing and land-value capture, and urban renewal programs to enlighten and tutor the urban poor on how to earn more, spend wisely, and invest in their own better housing will effectively solve AH finance challenges. Study findings are informative for the best approaches to achieve effective, AH finance for the urban poor in Nigeria, which is indispensable for the achievement of sustainable development goals. This research’s originality lies in the exploration of experts’ opinions in relation to AH finance to produce an equation model of critical solutions to AH finance challenges. Study data are useful resources for future pro-poor housing studies. This study makes housing policy-oriented recommendations toward effective, AH for the urban poor in developing countries.

Keywords: Affordable housing, effective affordable housing, housing policy, housing research, sustainable development, urban poor.

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: Time-series, features engineering methods for forecasting, energy demand forecasting, Azure machine learning.

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Authors: Levent Basayigit, Sinan Demir, Yusuf Ucar, Burhan Kara

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Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.

Keywords: Gaussian mixture discriminant analysis, spectral mixture model, World View-2, land parcels.

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Authors: Mustafa Qaseem Hielat, Ahmad Mohammad Al-Shabatat

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Jordan exerts many efforts to nurture their academically gifted students in special schools since 2001. During the past nine years of launching these schools, their learning and excellence environments were believed to be distinguished compared to public schools. This study investigated the environments of gifted students compared with other non-gifted, using a survey instrument that measures the dimensions of family, peers, teachers, school- support, society, and resources –dimensions rooted deeply in supporting gifted education, learning, and achievement. A total number of 109 were selected from excellence schools for academically gifted students, and 119 non-gifted students were selected from public schools. Around 8.3% of the non-gifted students reported that they “Never" received any support from their surrounding environments, 14.9% reported “Seldom" support, 23.7% reported “ Often" support, 26.0% reported “Frequent" support, and 32.8% reported “Very frequent" support. Where the gifted students reported more “Never" support than the non-gifted did with 11.3%, “Seldom" support with 15.4%, “Often" support with 26.6%, “Frequent" support with 29.0%, and reported “Very frequent" support less than the non-gifted students with 23.6%. Unexpectedly, statistical differences were found between the two groups favoring non-gifted students in perception of their surrounding environments in specific dimensions, namely, school- support, teachers, and society. No statistical differences were found in the other dimensions of the survey, namely, family, peers, and resources. As the differences were found in teachers, school- support, and society, the nurturing environments for the excellence schools need to be revised to adopt more creative teaching styles, rich school atmosphere and infrastructures, interactive guiding for the students and their parents, promoting for the excellence environments, and re-build successful identification models. Thus, families, schools, and society should increase their cooperation, communication, and awareness of the gifted supportive environments. However, more studies to investigate other aspects of promoting academic giftedness and excellence are recommended.

Keywords: Academic giftedness, Supportive environment, Excellence schools, Gifted grouping, Gifted nurturing.

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Authors: Abdulraaof H. Alqaili, Hamad A. Alsoliman

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Through progress in pavement design developments, a pavement design method was developed, which is titled the Mechanistic Empirical Pavement Design Guide (MEPDG). Nowadays, the evolution in roads network and highways is observed in Saudi Arabia as a result of increasing in traffic volume. Therefore, the MEPDG currently is implemented for flexible pavement design by the Saudi Ministry of Transportation. Implementation of MEPDG for local pavement design requires the calibration of distress models under the local conditions (traffic, climate, and materials). This paper aims to prepare data for calibration of MEPDG in Central Saudi Arabia. Thus, the first goal is data collection for the design of flexible pavement from the local conditions of the Riyadh region. Since, the modifying of collected data to input data is needed; the main goal of this paper is the analysis of collected data. The data analysis in this paper includes processing each: Trucks Classification, Traffic Growth Factor, Annual Average Daily Truck Traffic (AADTT), Monthly Adjustment Factors (MAFi), Vehicle Class Distribution (VCD), Truck Hourly Distribution Factors, Axle Load Distribution Factors (ALDF), Number of axle types (single, tandem, and tridem) per truck class, cloud cover percent, and road sections selected for the local calibration. Detailed descriptions of input parameters are explained in this paper, which leads to providing of an approach for successful implementation of MEPDG. Local calibration of MEPDG to the conditions of Riyadh region can be performed based on the findings in this paper.

Keywords: Mechanistic-empirical pavement design guide, traffic characteristics, materials properties, climate, Riyadh.

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Authors: C. Ardil

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This paper aims to select the best military attack helicopter to purchase by the Armed Forces and provide greater reconnaissance and offensive combat capability in military operations. For this purpose, a multiple criteria decision analysis method integrated with the variance weight procedure was applied to the military attack helicopter selection problem. A real military aviation case problem is conducted to support the Armed Forces decision-making process and contributes to the better performance of the Armed Forces. Application of the methodology resulted in ranking lists for ordering and prioritizing attack helicopters, providing transparency and simplicity to the decision-making process. Nine military attack helicopter models were analyzed in the light of strategic, tactical, and operational criteria, considering attack helicopters. The selected military attack helicopter would be used for fire support and reconnaissance activities required by the Armed Forces operation. This study makes a valuable contribution to the problem of military attack helicopter selection, as it represents a state-of-the-art application of the MCDMA method to contribute to the solution of a real problem of the Armed Forces. The methodology presented in this paper can be used to solve real problems of a wide variety, especially strategic, tactical and operational, and is, therefore, a very useful method for decision making.

Keywords: aircraft selection, military attack helicopter selection, attack helicopter fleet planning, MCDMA, multiple criteria analysis, multiple criteria decision making analysis, distance function measure

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