Search results for: Uncertainty sets

Authors: Nidal Hadadin

Abstract:

This study investigated the effect of cross sectional geometry on sediment transport rate. The processes of sediment transport are generally associated to environmental management, such as pollution caused by the forming of suspended sediment in the channel network of a watershed and preserving physical habitats and native vegetations, and engineering applications, such as the influence of sediment transport on hydraulic structures and flood control design. Many equations have been proposed for computing the sediment transport, the influence of many variables on sediment transport has been understood; however, the effect of other variables still requires further research. For open channel flow, sediment transport capacity is recognized to be a function of friction slope, flow velocity, grain size, grain roughness and form roughness, the hydraulic radius of the bed section and the type and quantity of vegetation cover. The effect of cross sectional geometry of the channel on sediment transport is one of the variables that need additional investigation. The width-depth ratio (W/d) is a comparative indicator of the channel shape. The width is the total distance across the channel and the depth is the mean depth of the channel. The mean depth is best calculated as total cross-sectional area divided by the top width. Channels with high W/d ratios tend to be shallow and wide, while channels with low (W/d) ratios tend to be narrow and deep. In this study, the effects of the width-depth ratio on sediment transport was demonstrated theoretically by inserting the shape factor in sediment continuity equation and analytically by utilizing the field data sets for Yalobusha River. It was found by utilizing the two approaches as a width-depth ratio increases the sediment transport decreases.

Keywords: Sediment transport, shape factor, hydraulicgeometry, flow discharge, width depth ratio.

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Authors: Nadeem Ehsan, Ebtisam Mirza, Muhammad Ahmad

Abstract:

In a complex project environment, project teams face multi-dimensional communication problems that can ultimately lead to project breakdown. Team Performance varies in Face-to-Face (FTF) environment versus groups working remotely in a computermediated communication (CMC) environment. A brief review of the Input_Process_Output model suggested by James E. Driskell, Paul H. Radtke and Eduardo Salas in “Virtual Teams: Effects of Technological Mediation on Team Performance (2003)", has been done to develop the basis of this research. This model theoretically analyzes the effects of technological mediation on team processes, such as, cohesiveness, status and authority relations, counternormative behavior and communication. An empirical study described in this paper has been undertaken to test the “cohesiveness" of diverse project teams in a multi-national organization. This study uses both quantitative and qualitative techniques for data gathering and analysis. These techniques include interviews, questionnaires for data collection and graphical data representation for analyzing the collected data. Computer-mediated technology may impact team performance because of difference in cohesiveness among teams and this difference may be moderated by factors, such as, the type of communication environment, the type of task and the temporal context of the team. Based on the reviewed model, sets of hypotheses are devised and tested. This research, reports on a study that compared team cohesiveness among virtual teams using CMC and non-CMC communication mediums. The findings suggest that CMC can help virtual teams increase team cohesiveness among their members, making CMC an effective medium for increasing productivity and team performance.

Keywords: Computer-mediated Communication, Virtual Teams, Team Performance, Team Cohesiveness.

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Authors: Ching-Tsan Chiang, Sheng-Jie Yang, Jing-Kai Huang

Abstract:

The purpose of this research is to develop and apply the RSCMAC to enhance the dynamic accuracy of Global Positioning System (GPS). GPS devices provide services of accurate positioning, speed detection and highly precise time standard for over 98% area on the earth. The overall operation of Global Positioning System includes 24 GPS satellites in space; signal transmission that includes 2 frequency carrier waves (Link 1 and Link 2) and 2 sets random telegraphic codes (C/A code and P code), on-earth monitoring stations or client GPS receivers. Only 4 satellites utilization, the client position and its elevation can be detected rapidly. The more receivable satellites, the more accurate position can be decoded. Currently, the standard positioning accuracy of the simplified GPS receiver is greatly increased, but due to affected by the error of satellite clock, the troposphere delay and the ionosphere delay, current measurement accuracy is in the level of 5~15m. In increasing the dynamic GPS positioning accuracy, most researchers mainly use inertial navigation system (INS) and installation of other sensors or maps for the assistance. This research utilizes the RSCMAC advantages of fast learning, learning convergence assurance, solving capability of time-related dynamic system problems with the static positioning calibration structure to improve and increase the GPS dynamic accuracy. The increasing of GPS dynamic positioning accuracy can be achieved by using RSCMAC system with GPS receivers collecting dynamic error data for the error prediction and follows by using the predicted error to correct the GPS dynamic positioning data. The ultimate purpose of this research is to improve the dynamic positioning error of cheap GPS receivers and the economic benefits will be enhanced while the accuracy is increased.

Keywords: Dynamic Error, GPS, Prediction, RSCMAC.

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Authors: P. Luangpaiboon

Abstract:

Various intelligences and inspirations have been adopted into the iterative searching process called as meta-heuristics. They intelligently perform the exploration and exploitation in the solution domain space aiming to efficiently seek near optimal solutions. In this work, the bee algorithm, inspired by the natural foraging behaviour of honey bees, was adapted to find the near optimal solutions of the transportation management system, dynamic multi-zone dispatching. This problem prepares for an uncertainty and changing customers- demand. In striving to remain competitive, transportation system should therefore be flexible in order to cope with the changes of customers- demand in terms of in-bound and outbound goods and technological innovations. To remain higher service level but lower cost management via the minimal imbalance scenario, the rearrangement penalty of the area, in each zone, including time periods are also included. However, the performance of the algorithm depends on the appropriate parameters- setting and need to be determined and analysed before its implementation. BEE parameters are determined through the linear constrained response surface optimisation or LCRSOM and weighted centroid modified simplex methods or WCMSM. Experimental results were analysed in terms of best solutions found so far, mean and standard deviation on the imbalance values including the convergence of the solutions obtained. It was found that the results obtained from the LCRSOM were better than those using the WCMSM. However, the average execution time of experimental run using the LCRSOM was longer than those using the WCMSM. Finally a recommendation of proper level settings of BEE parameters for some selected problem sizes is given as a guideline for future applications.

Keywords: Meta-heuristic, Bee Algorithm, Dynamic Multi-Zone Dispatching, Linear Constrained Response SurfaceOptimisation Method, Weighted Centroid Modified Simplex Method

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Authors: Zulhisyam Salleh, Erita M. Mazlan, Saiful A. Mazlan, Norzainariah A. Hassan, Fizatul A. Patakor

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Technical laboratories are typically considered as highly hazardous places in the polytechnic institution when addressing the problems of high incidences and fatality rates. In conjunction with several topics covered in the technical curricular, safety and health precaution should be highlighted in order to connect to few key ideas of being safe. Therefore the assessment of safety awareness in terms of safety and health about hazardous and risks at laboratories is needed and has to be incorporated with technical education and other training programmes. The purpose of this study was to determine the efficacy of technical laboratory safety in one of the polytechnics in northern region. The study examined three related issues that were; the availability of safety material and equipment, safety practice adopted by technical teachers and administrator-s safety attitudes in enforcing safety to the students. A model of efficacy technical laboratory was developed to test the linear relationship between existing safety material and equipment, teachers- safety practice and administrators- attitude in enforcing safety and to identify which of technical laboratory safety issues was the most pertinent factor to realize safety in technical laboratory. This was done by analyzing survey-based data sets particularly those obtained from samples of 210 students in the polytechnic. The Pearson Correlation was used to measure the association between the variables and to test the research hypotheses. The result of the study has found that there was a significant correlation between existing safety material and equipment, safety practice adopted by teacher and administrator-s attitude. There was also a significant relationship between technical laboratory safety and safety practice adopted by teacher and between technical laboratory safety and administrator attitude. Hence, safety practice adopted by teacher and administrator attitude is vital in realizing technical laboratory safety.

Keywords: Polytechnic, Safety attitudes, Safety practices, Technical laboratory

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

Abstract:

Copper is an essential raw material used in the construction industry. During 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two hybrid price prediction models using artificial neural network and long short-term memory (ANN-LSTM), by Python, that can forecast the average monthly copper prices, traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022 and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices, and economic indicators of the three major exporting countries of copper depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation, and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-month prediction model is better than the 1-month prediction model; but still, both models can act as predicting tools for diverse economic situations.

Keywords: Copper prices, prediction model, neural network, time series forecasting.

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Authors: Mohammad R. Kavian Nezhad, Carlos F. Lange, Brian A. Fleck

Abstract:

This research presents the validation study of a computational fluid dynamics (CFD) model developed to simulate the scalar dispersion emitted from rooftop sources around the buildings at the University of Alberta North Campus. The ANSYS CFX code was used to perform the numerical simulation of the wind regime and pollutant dispersion by solving the 3D steady Reynolds-averaged Navier-Stokes (RANS) equations on a building-scale high-resolution grid. The validation study was performed in two steps. First, the CFD model performance in 24 cases (eight wind directions and three wind speeds) was evaluated by comparing the predicted flow fields with the available data from the previous measurement campaign designed at the North Campus, using the standard deviation method (SDM), while the estimated results of the numerical model showed maximum average percent errors of approximately 53% and 37% for wind incidents from the North and Northwest, respectively. Good agreement with the measurements was observed for the other six directions, with an average error of less than 30%. In the second step, the reliability of the implemented turbulence model, numerical algorithm, modeling techniques, and the grid generation scheme was further evaluated using the Mock Urban Setting Test (MUST) dispersion dataset. Different statistical measures, including the fractional bias (FB), the mean geometric bias (MG), and the normalized mean square error (NMSE), were used to assess the accuracy of the predicted dispersion field. Our CFD results are in very good agreement with the field measurements.

Keywords: CFD, plume dispersion, complex urban geometry, validation study, wind flow.

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Authors: Yasin Tadayonrad, Alassane Ballé Ndiaye

Abstract:

Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading/unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is the loading/unloading capacity in each source/destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods (FMCG) industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on Python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.

Keywords: Supply chain management, transportation, multiple-multiple network, timeslots management, mathematical modeling, mixed integer programming.

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Authors: H. Zarei, M. H. Fazel Zarandi, M. Karbasian

Abstract:

A Decision Support System/Expert System for stock portfolio selection presented where at first step, both technical and fundamental data used to estimate technical and fundamental return and risk (1st phase); Then, the estimated values are aggregated with the investor preferences (2nd phase) to produce convenient stock portfolio. In the 1st phase, there are two expert systems, each of which is responsible for technical or fundamental estimation. In the technical expert system, for each stock, twenty seven candidates are identified and with using rough sets-based clustering method (RC) the effective variables have been selected. Next, for each stock two fuzzy rulebases are developed with fuzzy C-Mean method and Takai-Sugeno- Kang (TSK) approach; one for return estimation and the other for risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation method. In parallel, for fundamental expert systems, fuzzy rule-bases have been identified in the form of “IF-THEN" rules through brainstorming with the stock market experts and the input data have been derived from financial statements; as a result two fuzzy rule-bases have been generated for all the stocks, one for return and the other for risk. In the 2nd phase, user preferences represented by four criteria and are obtained by questionnaire. Using an expert system, four estimated values of return and risk have been aggregated with the respective values of user preference. At last, a fuzzy rule base having four rules, treats these values and produce a ranking score for each stock which will lead to a satisfactory portfolio for the user. The stocks of six manufacturing companies and the period of 2003-2006 selected for data gathering.

Keywords: Stock Portfolio Selection, Fuzzy Rule-Base ExpertSystems, Financial Decision Support Systems, Technical Analysis, Fundamental Analysis.

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Authors: A. M. Pashayev, C. Ardil, D. D. Askerov, R. A. Sadiqov, P. S. Abdullayev

Abstract:

In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-bystage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.

Keywords: Gas turbine engines, neural networks, fuzzy logic, fuzzy statistics.

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Authors: Joe Walsh, Mike Climstein, Ian Timothy Heazlewood, Stephen Burke, Jyrki Kettunen, Kent Adams, Mark DeBeliso

Abstract:

Thousands of masters athletes participate quadrennially in the World Masters Games (WMG), yet this cohort of athletes remains proportionately under-investigated. Due to a growing global obesity pandemic in context of benefits of physical activity across the lifespan, the BMI trends for this unique population was of particular interest. The nexus between health, physical activity and aging is complex and has raised much interest in recent times due to the realization that a multifaceted approach is necessary in order to counteract the obesity pandemic. By investigating age based trends within a population adhering to competitive sport at older ages, further insight might be gleaned to assist in understanding one of many factors influencing this relationship.BMI was derived using data gathered on a total of 6,071 masters athletes (51.9% male, 48.1% female) aged 25 to 91 years ( =51.5, s =±9.7), competing at the Sydney World Masters Games (2009). Using linear and loess regression it was demonstrated that the usual tendency for prevalence of higher BMI increasing with age was reversed in the sample. This trend in reversal was repeated for both male and female only sub-sets of the sample participants, indicating the possibility of improved prevalence of BMI with increasing age for both the sample as a whole and these individual sub-groups.This evidence of improved classification in one index of health (reduced BMI) for masters athletes (when compared to the general population) implies there are either improved levels of this index of health with aging due to adherence to sport or possibly the reduced BMI is advantageous and contributes to this cohort adhering (or being attracted) to masters sport at older ages.

Keywords: Aging, masters athlete, Quetelet Index, sport

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Authors: A.Qaderi, A. Heydarinasab, M. Ardjmand

Abstract:

Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.

Keywords: Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB), Hydrogenophaga Pseudoflava, Artificial Neural Network, Leudeking-Piret

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Authors: Suman

Abstract:

In the continuous interaction between the past and the present that historiography is, each time when history gets re/written, a new representation emerges. This new representation is a reflection of the earlier archives and their interpretations, fragmented remembrances of the past, as well as the reactions to the present. Memory, or lack thereof, and stereotyping generally play a major role in this representation. William Dalrymple’s Nine Lives: In Search of the Sacred in Modern India (2009) is one such written account that sets out to narrate the representations of religion and culture of India and contemporary reactions to it. Dalrymple’s nine saints belong to different castes, sects, religions, and regions. By dealing with their religions and expressions of those religions, and through the lived mysticism of these nine individuals, the book engages with some important issues like class, caste and gender in the contexts provided by historical as well as present India. The paper studies the development of religion and accompanied feeling of religiosity in modern as well as historical contexts through a study of these elements in the book. Since, the language used in creation of texts and the literary texts thus produced create a new reality that questions the stereotypes of the past, and in turn often end up creating new stereotypes or stereotypical representations at times, the paper seeks to actively engage with the text in order to identify and study such stereotypes, along with their changing representations. Through a detailed examination of the book, the paper seeks to unravel whether some socio-cultural stereotypes existed earlier, and whether there is development of new stereotypes from Dalrymple’s point of view as an outsider writing on issues that are deeply rooted in the cultural milieu of the country. For this analysis, the paper takes help from the psycho-literary theories of stereotyping and representation.

Keywords: Religion, Representation, Stereotyping, William Dalrymple.

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Authors: Reihaneh Montazeri Shatouri, Rosmini Omar, Kunio Igusa

Abstract:

Although automotive industry has brought different beneficiaries to human life, it is being pointed out as one of the major cause of global air pollution which resulted in climate change, smog, green house gases (GHGs), and human diseases by many reasons. Since auto industry is one of the largest consumers of fossil fuels, the realization of green innovations is becoming a crucial choice to meet the challenges towards sustainable development. Recently, many auto manufacturers have embarked on green technology initiatives to gain a competitive advantage in the global market; however, innovative manufacturing systems and technologies can enhance operational performance only if the human resource management is in place to elicit the motivation of the employees and develop their organizational expertise. No organization can perform at peak levels unless each employee is committed to the company goals and works as an effective team member. Strategic human resource practices are the primary means by which firms can shape the skills, attitudes, and behavior of individuals to align with the business strategic objectives. This study investigates on the comprehensive approach of multiple advanced technology innovations and human resource management at Toyota Motor Corporation as the market leader of full hybrid technology in the automotive industry. Then, HRM framework of the company is described and three sets of human resource practices that support the innovation-oriented HR system, presented. Finally, a conceptual framework for innovativeness in green technology in automotive industry by applying a deliberate strategic HR management system and knowledge management with the intervening factors of organizational culture, knowledge application and knowledge sharing is proposed.

Keywords: Automotive Industry, Green Technology, Innovation, Strategic Human Resource Management

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Authors: Mia Sorensen

Abstract:

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: Nida Rizvi, Deeksha Katyal, Varun Joshi

Abstract:

River Hindon is an important river catering the demand of highly populated rural and industrial cluster of western Uttar Pradesh, India. Water quality of river Hindon is deteriorating at an alarming rate due to various industrial, municipal and agricultural activities. The present study aimed at identifying the pollution sources and quantifying the degree to which these sources are responsible for the deteriorating water quality of the river. Various water quality parameters, like pH, temperature, electrical conductivity, total dissolved solids, total hardness, calcium, chloride, nitrate, sulphate, biological oxygen demand, chemical oxygen demand, and total alkalinity were assessed. Water quality data obtained from eight study sites for one year has been subjected to the two multivariate techniques, namely, principal component analysis and cluster analysis. Principal component analysis was applied with the aim to find out spatial variability and to identify the sources responsible for the water quality of the river. Three Varifactors were obtained after varimax rotation of initial principal components using principal component analysis. Cluster analysis was carried out to classify sampling stations of certain similarity, which grouped eight different sites into two clusters. The study reveals that the anthropogenic influence (municipal, industrial, waste water and agricultural runoff) was the major source of river water pollution. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and elucidation of multifaceted data sets, recognition of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

Keywords: Cluster analysis, multivariate statistical technique, river Hindon, water Quality.

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Authors: Wilailuk Meepracha

Abstract:

The results of the study on the project evaluation to  develop the competencies, capabilities, and skills in repairing  computers of people in Jompluak Local Municipality, Bang Khonthi  District, Samut Songkram Province showed that the overall result  was good (4.33). When considering on each aspect, it was found that  the highest one was on process evaluation (4.60) followed by product  evaluation (4.50) and the least one was on feeding factor (3.97).  When considering in details, it was found that: 1) the context aspect  was high (4.23) with the highest item on the arrangement of the  training situation (4.67) followed by the appropriateness of the target  (4.30) and the least aspect was on the project cooperation (3.73). 2)  The evaluation of average overall primary factor or feeding factor  showed high value (4.23) while the highest aspect was on the  capability of the trainers (4.47) followed by the suitable venue (4.33)  while the least aspect was on the insufficient budget (3.47). 3) The  average result of process evaluation was very high (4.60). The  highest aspect was on the follow-op supervision (4.70) followed by  responsibility of each project staffs (4.50) while the least aspect was  on the present situation and the problems of the community (4.40). 4)  The overall result of the product evaluation was very high (4.50). The  highest aspect was on the diversity of the activities and the  community integration (4.67) followed by project target achievement  (4.63) while the least aspect was on continuation and regularity of the  activities (4.33). The trainees reported high satisfaction on the project  management at very high level (43.33%) while 40% reported high  level and 16.67% reported moderate level. Suggestions for the project  were on the additional number of the computer sets (37.78%)  followed by longer training period especially on computer skills  (43.48%).

 

Keywords: Project evaluation, competency development, the capability on computer repairing and computer skills.

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

Abstract:

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: Walsh Joe, Climstein Mike, Heazlewood Ian Timothy, Burke Stephen, Kettunen Jyrki, Adams Kent, DeBeliso Mark

Abstract:

Whilst there is growing evidence that activity across the lifespan is beneficial for improved health, there are also many changes involved with the aging process and subsequently the potential for reduced indices of health. The nexus between health, physical activity and aging is complex and has raised much interest in recent times due to the realization that a multifaceted approached is necessary in order to counteract a growing obesity epidemic. By investigating age based trends within a population adhering to competitive sport at older ages, further insight might be gleaned to assist in understanding one of many factors influencing this relationship. BMI was derived using data gathered on a total of 6,071 masters athletes (51.9% male, 48.1% female) aged 25 to 91 years ( =51.5, s =±9.7), competing at the Sydney World Masters Games (2009). Using linear and loess regression it was demonstrated that the usual tendency for prevalence of higher BMI increasing with age was reversed in the sample. This trend in reversal was repeated for both male and female only sub-sets of the sample participants, indicating the possibility of improved prevalence of BMI with increasing age for both the sample as a whole and these individual subgroups. This evidence of improved classification in one index of health (reduced BMI) for masters athletes (when compared to the general population) implies there are either improved levels of this index of health with aging due to adherence to sport or possibly the reduced BMI is advantageous and contributes to this cohort adhering (or being attracted) to masters sport at older ages. Demonstration of this proportionately under-investigated World Masters Games population having an improved relationship between BMI and increasing age over the general population is of particular interest in the context of the measures being taken globally to curb an obesity epidemic.

Keywords: Aging, masters athlete, Quetelet Index, sport.

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Authors: Mohsen Nikaein

Abstract:

Because of importance of energy, optimization of power generation systems is necessary. Gas turbine cycles are suitable manner for fast power generation, but their efficiency is partly low. In order to achieving higher efficiencies, some propositions are preferred such as recovery of heat from exhaust gases in a regenerator, utilization of intercooler in a multistage compressor, steam injection to combustion chamber and etc. However thermodynamic optimization of gas turbine cycle, even with above components, is necessary. In this article multi-objective genetic algorithms are employed for Pareto approach optimization of Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective optimization a number of conflicting objective functions are to be optimized simultaneously. The important objective functions that have been considered for optimization are entropy generation of RIGT cycle (Ns) derives using Exergy Analysis and Gouy-Stodola theorem, thermal efficiency and the net output power of RIGT Cycle. These objectives are usually conflicting with each other. The design variables consist of thermodynamic parameters such as compressor pressure ratio (Rp), excess air in combustion (EA), turbine inlet temperature (TIT) and inlet air temperature (T0). At the first stage single objective optimization has been investigated and the method of Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used for multi-objective optimization. Optimization procedures are performed for two and three objective functions and the results are compared for RIGT Cycle. In order to investigate the optimal thermodynamic behavior of two objectives, different set, each including two objectives of output parameters, are considered individually. For each set Pareto front are depicted. The sets of selected decision variables based on this Pareto front, will cause the best possible combination of corresponding objective functions. There is no superiority for the points on the Pareto front figure, but they are superior to any other point. In the case of three objective optimization the results are given in tables.

Keywords: Exergy, Entropy Generation, Brayton Cycle, DesignParameters, Optimization, Genetic Algorithm, Multi-Objective.

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Authors: Asif Ekbal, Sivaji Bandyopadhyay

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Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes 1, defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) 2. In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages.

Keywords: Named Entity (NE), Named Entity Recognition (NER), Support Vector Machine (SVM), Bengali, Hindi.

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Authors: M.karavanmasjedi, N.Hedayat , A.Rohani, H.Shirin

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Sedimentation process resulting from soil erosion in the water basin especially in arid and semi-arid where poor vegetation cover in the slope of the mountains upstream could contribute to sediment formation. The consequence of sedimentation not only makes considerable change in the morphology of the river and the hydraulic characteristics but would also have a major challenge for the operation and maintenance of the canal network which depend on water flow to meet the stakeholder-s requirements. For this reason mathematical modeling can be used to simulate the effective factors on scouring, sediment transport and their settling along the waterways. This is particularly important behind the reservoirs which enable the operators to estimate the useful life of these hydraulic structures. The aim of this paper is to simulate the sedimentation and erosion in the eastern and western water intake structures of the Dez Diversion weir using GSTARS-3 software. This is done to estimate the sedimentation and investigate the ways in which to optimize the process and minimize the operational problems. Results indicated that the at the furthest point upstream of the diversion weir, the coarser sediment grains tended to settle. The reason for this is the construction of the phantom bridge and the outstanding rocks just upstream of the structure. The construction of these along the river course has reduced the momentum energy require to push the sediment loads and make it possible for them to settle wherever the river regime allows it. Results further indicated a trend for the sediment size in such a way that as the focus of study shifts downstream the size of grains get smaller and vice versa. It was also found that the finding of the GSTARS-3 had a close proximity with the sets of the observed data. This suggests that the software is a powerful analytical tool which can be applied in the river engineering project with a minimum of costs and relatively accurate results.

Keywords: Erosion, sedimentation, Dez Diversion weir, GSTARS-3

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Authors: S. Mahalakshmi, B. Senthil Arasu

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As per the recent changes happening in the global policies, climate related changes and the impact it causes across every sector are viewed as green swan events – in essence, climate related changes can happen often and lead to risk and lot of uncertainty, but need to be mitigated instead of considering them as black swan events. This brings about a question on how this risk can be computed, so that the financial institutions can plan to mitigate it. Climate related changes impact all risk types – credit risk, market risk, operational risk, liquidity risk, reputational risk and others. And the models required to compute this have to consider the different industrial needs of the counterparty, as well as the factors that are contributing to this – be it in the form of different risk drivers, or the different transmission channels or the different approaches and the granular form of data availability. This brings out to the suggestion that the climate related changes, though it affects Pillar I risks, will be a Pillar II risk. This has to be modeled specifically based on the financial institution’s actual exposure to different industries, instead of generalizing the risk charge. And this will have to be considered as the additional capital to be met by the financial institution in addition to their Pillar I risks, as well as the existing Pillar II risks. In this paper, we present a risk assessment framework to model and assess climate change risks - for both credit and market risks. This framework helps in assessing the different scenarios, and how the different transition risks affect the risk associated with the different parties. This research paper delves on the topic of increase in concentration of greenhouse gases, that in turn causing global warming. It then considers the various scenarios of having the different risk drivers impacting credit and market risk of an institution, by understanding the transmission channels, and also considering the transition risk. The paper then focuses on the industry that’s fast seeing a disruption: automobile industry. The paper uses the framework to show how the climate changes and the change to the relevant policies have impacted the entire financial institution. Appropriate statistical models for forecasting, anomaly detection and scenario modeling are built to demonstrate how the framework can be used by the relevant agencies to understand their financial risks. The paper also focuses on the climate risk calculation for the Pillar II capital calculations, and how it will make sense for the bank to maintain this in addition to their regular Pillar I and Pillar II capital.

Keywords: Capital calculation, climate risk, credit risk, pillar II risk, scenario modeling.

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Authors: Constantin Z. Leshan

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Hole Vacuum theory is based on discontinuous spacetime that contains vacuum holes. Vacuum holes can explain gravitation, some laws of quantum mechanics and allow teleportation of matter. All massive bodies emit a flux of holes which curve the spacetime; if we increase the concentration of holes, it leads to length contraction and time dilation because the holes do not have the properties of extension and duration. In the limited case when space consists of holes only, the distance between every two points is equal to zero and time stops - outside of the Universe, the extension and duration properties do not exist. For this reason, the vacuum hole is the only particle in physics capable of describing gravitation using its own properties only. All microscopic particles must 'jump' continually and 'vibrate' due to the appearance of holes (impassable microscopic 'walls' in space), and it is the cause of the quantum behavior. Vacuum holes can explain the entanglement, non-locality, wave properties of matter, tunneling, uncertainty principle and so on. Particles do not have trajectories because spacetime is discontinuous and has impassable microscopic 'walls' due to the simple mechanical motion is impossible at small scale distances; it is impossible to 'trace' a straight line in the discontinuous spacetime because it contains the impassable holes. Spacetime 'boils' continually due to the appearance of the vacuum holes. For teleportation to be possible, we must send a body outside of the Universe by enveloping it with a closed surface consisting of vacuum holes. Since a material body cannot exist outside of the Universe, it reappears instantaneously in a random point of the Universe. Since a body disappears in one volume and reappears in another random volume without traversing the physical space between them, such a transportation method can be called teleportation (or Hole Teleportation). It is shown that Hole Teleportation does not violate causality and special relativity due to its random nature and other properties. Although Hole Teleportation has a random nature, it can be used for colonization of extrasolar planets by the help of the method called 'random jumps': after a large number of random teleportation jumps, there is a probability that the spaceship may appear near a habitable planet. We can create vacuum holes experimentally using the method proposed by Descartes: we must remove a body from the vessel without permitting another body to occupy this volume.

Keywords: Border of the universe, causality violation, perfect isolation, quantum jumps.

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Authors: Yung-Chien Sun, O. Grant Clark

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In this paper, the implementation of a rule-based intuitive reasoner is presented. The implementation included two parts: the rule induction module and the intuitive reasoner. A large weather database was acquired as the data source. Twelve weather variables from those data were chosen as the “target variables" whose values were predicted by the intuitive reasoner. A “complex" situation was simulated by making only subsets of the data available to the rule induction module. As a result, the rules induced were based on incomplete information with variable levels of certainty. The certainty level was modeled by a metric called "Strength of Belief", which was assigned to each rule or datum as ancillary information about the confidence in its accuracy. Two techniques were employed to induce rules from the data subsets: decision tree and multi-polynomial regression, respectively for the discrete and the continuous type of target variables. The intuitive reasoner was tested for its ability to use the induced rules to predict the classes of the discrete target variables and the values of the continuous target variables. The intuitive reasoner implemented two types of reasoning: fast and broad where, by analogy to human thought, the former corresponds to fast decision making and the latter to deeper contemplation. . For reference, a weather data analysis approach which had been applied on similar tasks was adopted to analyze the complete database and create predictive models for the same 12 target variables. The values predicted by the intuitive reasoner and the reference approach were compared with actual data. The intuitive reasoner reached near-100% accuracy for two continuous target variables. For the discrete target variables, the intuitive reasoner predicted at least 70% as accurately as the reference reasoner. Since the intuitive reasoner operated on rules derived from only about 10% of the total data, it demonstrated the potential advantages in dealing with sparse data sets as compared with conventional methods.

Keywords: Artificial intelligence, intuition, knowledge acquisition, limited certainty.

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Authors: Fatema-Tuz-Zohra Khanam, Ali Al-Naji, Asanka G. Perera, Kim Gibson, Javaan Chahl

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Conventional contact-based vital signs monitoring sensors such as pulse oximeters or electrocardiogram (ECG) may cause discomfort, skin damage, and infections, particularly in neonates with fragile, sensitive skin. Therefore, remote monitoring of the vital sign is desired in both clinical and non-clinical settings to overcome these issues. Camera-based vital signs monitoring is a recent technology for these applications with many positive attributes. However, there are still limited camera-based studies on neonates in a clinical setting. In this study, the heart rate (HR) and respiratory rate (RR) of eight infants at the Neonatal Intensive Care Unit (NICU) in Flinders Medical Centre were remotely monitored using a digital camera applying color and motion-based computational methods. The region-of-interest (ROI) was efficiently selected by incorporating an image decomposition method. Furthermore, spatial averaging, spectral analysis, band-pass filtering, and peak detection were also used to extract both HR and RR. The experimental results were validated with the ground truth data obtained from an ECG monitor and showed a strong correlation using the Pearson correlation coefficient (PCC) 0.9794 and 0.9412 for HR and RR, respectively. The root mean square errors (RMSE) between camera-based data and ECG data for HR and RR were 2.84 beats/min and 2.91 breaths/min, respectively. A Bland Altman analysis of the data also showed a close correlation between both data sets with a mean bias of 0.60 beats/min and 1 breath/min, and the lower and upper limit of agreement -4.9 to + 6.1 beats/min and -4.4 to +6.4 breaths/min for both HR and RR, respectively. Therefore, video camera imaging may replace conventional contact-based monitoring in NICU and has potential applications in other contexts such as home health monitoring.

Keywords: Neonates, NICU, digital camera, heart rate, respiratory rate, image decomposition.

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Authors: A. Chowdhury, P. Egodawatta, J. M. McGree, A. Goonetilleke

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Hydrologic models are increasingly used as tools to predict stormwater quantity and quality from urban catchments. However, due to a range of practical issues, most models produce gross errors in simulating complex hydraulic and hydrologic systems. Difficulty in finding a robust approach for model calibration is one of the main issues. Though automatic calibration techniques are available, they are rarely used in common commercial hydraulic and hydrologic modelling software e.g. MIKE URBAN. This is partly due to the need for a large number of parameters and large datasets in the calibration process. To overcome this practical issue, a framework for automatic calibration of a hydrologic model was developed in R platform and presented in this paper. The model was developed based on the time-area conceptualization. Four calibration parameters, including initial loss, reduction factor, time of concentration and time-lag were considered as the primary set of parameters. Using these parameters, automatic calibration was performed using Approximate Bayesian Computation (ABC). ABC is a simulation-based technique for performing Bayesian inference when the likelihood is intractable or computationally expensive to compute. To test the performance and usefulness, the technique was used to simulate three small catchments in Gold Coast. For comparison, simulation outcomes from the same three catchments using commercial modelling software, MIKE URBAN were used. The graphical comparison shows strong agreement of MIKE URBAN result within the upper and lower 95% credible intervals of posterior predictions as obtained via ABC. Statistical validation for posterior predictions of runoff result using coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME) was found reasonable for three study catchments. The main benefit of using ABC over MIKE URBAN is that ABC provides a posterior distribution for runoff flow prediction, and therefore associated uncertainty in predictions can be obtained. In contrast, MIKE URBAN just provides a point estimate. Based on the results of the analysis, it appears as though ABC the developed framework performs well for automatic calibration.

Keywords: Automatic calibration framework, approximate Bayesian computation, hydrologic and hydraulic modelling, MIKE URBAN software, R platform.

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Authors: M. A. Putera, N. Yasufuku, A. Alowaisy, R. Ishikura, J. G. Hussary, A. Rifa’i

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Indonesia’s government has planned a project for a high-speed railway connecting the capital cities, Jakarta and Surabaya, about 700 km. Based on that location, it has been planning construction above the lowland soil region. The lowland soil region comprises cohesive soil with high water content and high compressibility index, which in fact, led to a settlement problem. Among the variety of railway track structures, the adoption of the ballastless track was used effectively to reduce the settlement; it provided a lightweight structure and minimized workspace. Contradictorily, deploying this thin layer structure above the lowland area was compensated with several problems, such as lack of bearing capacity and deflection behavior during traffic loading. It is necessary to combine with ground improvement to assure a settlement behavior on the clayey soil. Reflecting on the assurance of strength increment and working period, those were convinced by adopting methods such as cement-treated soil as the substructure of railway track. Particularly, evaluating mechanical properties in the field has been well known by using the plate load test and cone penetration test. However, observing an increment of mechanical properties has uncertainty, especially for evaluating cement-treated soil on the substructure. The current quality control of cement-treated soils was established by laboratory tests. Moreover, using small strain devices measurement in the laboratory can predict more reliable results that are identical to field measurement tests. Aims of this research are to show an intercorrelation of confining pressure with the initial condition of the Young’s modulus (E0), Poisson ratio (υ0) and Shear modulus (G0) within small strain ranges. Furthermore, discrepancies between those parameters were also investigated. Experimental result confirmed the intercorrelation between cement content and confining pressure with a power function. In addition, higher cement ratios have discrepancies, conversely with low mixing ratios.

Keywords: Cement content, confining pressure, high-speed railway, small strain ranges.

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Authors: N. Porzionato, M. Mantiñan, E. Bussi, S. Grinberg, R. Gutierrez, G. Curutchet

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This work sets out to debate the tensions involved in the processes of contamination and self-purification in the urban space, particularly in the streams that run through the Buenos Aires metropolitan area. For much of their course, those streams are piped; their waters do not come into contact with the outdoors until they have reached deeply impoverished urban areas with high levels of environmental contamination. These are peripheral zones that, until thirty years ago, were marshlands and fields. They are now densely populated areas largely lacking in urban infrastructure. The Cárcova neighborhood, where this project is underway, is in the José León Suárez section of General San Martín county, Buenos Aires province. A stretch of José León Suarez canal crosses the neighborhood. Starting upstream, this canal carries pollutants due to the sewage and industrial waste released into it. Further downstream, in the neighborhood, domestic drainage is poured into the stream. In this paper, we formulate a hypothesis diametrical to the one that holds that these neighborhoods are the primary source of contamination, suggesting instead that in the stretch of the canal that runs through the neighborhood the stream’s waters are actually cleaned and the sediments accumulate pollutants. Indeed, the stretches of water that runs through these neighborhoods act as water processing plants for the metropolis. This project has studied the different organic-load polluting contributions to the water in a certain stretch of the canal, the reduction of that load over the course of the canal, and the incorporation of pollutants into the sediments. We have found that the surface water has considerable ability to self-purify, mostly due to processes of sedimentation and adsorption. The polluting load is accumulated in the sediments where that load stabilizes slowly by means of anaerobic processes. In this study, we also investigated the risks of sediment management and the use of the processes studied here in controlled conditions as tools of environmental restoration.

Keywords: Bioremediation, pollutants, sediments, urban streams.

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Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

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This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: Transportation networks, freight delivery, data flow, monitoring, e-services.

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