Search results for: utility model

Authors: K. Sekulová, M. Šimon

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This article focuses on upper-extremity musculoskeletal disorders risk assessment model at workplace. In this model are used risk factors that are responsible for musculoskeletal system damage. Based on statistic calculations the model is able to define what risk of MSD threatens workers who are under risk factors. The model is also able to say how MSD risk would decrease if these risk factors are eliminated.

Keywords: ergonomics, musculoskeletal disorders, occupational diseases, risk factors

Procedia PDF Downloads 551

Authors: Wen-Chung Wang, Xue-Lan Qiu

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Ipsative tests have been widely used in vocational and career counseling (e.g., the Jackson Vocational Interest Survey). Pairwise-comparison items are a typical item format of ipsative tests. When the two statements in a pairwise-comparison item measure two different constructs, the item is referred to as a multidimensional pairwise-comparison (MPC) item. A typical MPC item would be: Which activity do you prefer? (A) playing with young children, or (B) working with tools and machines. These two statements aim at the constructs of social interest and investigative interest, respectively. Recently, new item response theory (IRT) models for ipsative tests with MPC items have been developed. Among them, the Rasch ipsative model (RIM) deserves special attention because it has good measurement properties, in which the log-odds of preferring statement A to statement B are defined as a competition between two parts: the sum of a person’s latent trait to which statement A is measuring and statement A’s utility, and the sum of a person’s latent trait to which statement B is measuring and statement B’s utility. The RIM has been extended to polytomous responses, such as preferring statement A strongly, preferring statement A, preferring statement B, and preferring statement B strongly. To promote the new initiatives, in this study we developed computerized adaptive testing algorithms for MFC items and evaluated their performance using simulations and two real tests. Both the RIM and its polytomous extension are multidimensional, which calls for multidimensional computerized adaptive testing (MCAT). A particular issue in MCAT for MPC items is the within-person statement exposure (WPSE); that is, a respondent may keep seeing the same statement (e.g., my life is empty) for many times, which is certainly annoying. In this study, we implemented two methods to control the WPSE rate. In the first control method, items would be frozen when their statements had been administered more than a prespecified times. In the second control method, a random component was added to control the contribution of the information at different stages of MCAT. The second control method was found to outperform the first control method in our simulation studies. In addition, we investigated four item selection methods: (a) random selection (as a baseline), (b) maximum Fisher information method without WPSE control, (c) maximum Fisher information method with the first control method, and (d) maximum Fisher information method with the second control method. These four methods were applied to two real tests: one was a work survey with dichotomous MPC items and the other is a career interests survey with polytomous MPC items. There were three dependent variables: the bias and root mean square error across person measures, and measurement efficiency which was defined as the number of items needed to achieve the same degree of test reliability. Both applications indicated that the proposed MCAT algorithms were successful and there was no loss in measurement proficiency when the control methods were implemented, and among the four methods, the last method performed the best.

Keywords: computerized adaptive testing, ipsative tests, item response theory, pairwise comparison

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Authors: Anthony Usoro

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In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data.

Keywords: autoregressive model, bilinear autoregressive model, bilinear moving average model, moving average model

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Authors: Yohanes Kristianto

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The objective of this paper is to compose a sustainable supply chain that integrates product, process and networks design. An integrated life cycle assessment and techno-economic optimization is proposed that might deliver more economically feasible operations, minimizes environmental impacts and maximizes social contributions. Closed loop economy of the supply chain is achieved by reusing waste to be raw material of final products. Societal benefit is given by the supply chain by absorbing waste as source of raw material and opening new work opportunities. A case study of ethanol supply chain from rice straws is considered. The modeling results show that optimization within the scope of LCA is capable of minimizing both CO₂ emissions and energy and utility consumptions and thus enhancing raw materials utilization. Furthermore, the supply chain is capable of contributing to local economy through jobs creation. While the model is quite comprehensive, the future research recommendation on energy integration and global sustainability is proposed.

Keywords: life cycle assessment, techno-economic optimization, sustainable supply chains, closed loop economy

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Authors: Atefeh Abedini, Fatemeh Razavi, Mihan Pourabdollah Toutkaboni, Hossein Mehravaran, Arda Kiani

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In countries with the highest prevalence of tuberculosis, such as Iran, the differentiation of malignant tumors from non-malignant is very important. In this study, which was conducted for the first time among the Iranian population, the utility of the ultrasonographic morphological characteristics in patients undergoing EBUS was used to distinguish the non-malignant versus malignant lymph nodes. The morphological characteristics of lymph nodes, which consist of size, shape, vascular pattern, echogenicity, margin, coagulation necrosis sign, calcification, and central hilar structure, were obtained during Endobronchial Ultrasound-Guided Trans-Bronchial Needle Aspiration and were compared with the final pathology results. During this study period, a total of 253 lymph nodes were evaluated in 93 cases. Round shape, non-hilar vascular pattern, heterogeneous echogenicity, hyperechogenicity, distinct margin, and the presence of necrosis sign were significantly higher in malignant nodes. On the other hand, the presence of calcification and also central hilar structure were significantly higher in the benign nodes (p-value ˂ 0.05). Multivariate logistic regression showed that size>1 cm, heterogeneous echogenicity, hyperechogenicity, the presence of necrosis signs and, the absence of central hilar structure are independent predictive factors for malignancy. The accuracy of each of the aforementioned factors is 42.29 %, 71.54 %, 71.90 %, 73.51 %, and 65.61 %, respectively. Of 74 malignant lymph nodes, 100% had at least one of these independent factors. According to our results, the morphological characteristics of lymph nodes based on Endobronchial Ultrasound-Guided Trans-Bronchial Needle Aspiration can play a role in the prediction of malignancy.

Keywords: EBUS-TBNA, malignancy, nodal characteristics, pathology

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Shank Kulkarni, Alireza Tabarraei

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The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear hyper-viscoelastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive loading conditions at various strain rates. Four parameter Ogden model and Mooney Rivlin model are used to modeling the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using both a three-parameter standard linear solid (SLS) model and a K-BKZ model. Comparison of the modeling results with experiments shows that Odgen and SLS model can more accurately predict the behavior of polyurea. The material parameters of the model are found by curve fitting of the proposed model to the uniaxial compression test data. The proposed model can closely reproduce the stress-strain behavior of polyurea for strain rates up to 6500 /s.

Keywords: constitutive modelling, ogden model, polyurea, SLS model, uniaxial compression test

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Authors: Hussein Altartouri

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In this paper the kinematic and kinetic models of an omnidirectional holonomic mobile robot is presented. The kinematic and kinetic models form the OmniDrive model. Therefore, a mathematical model for the robot equipped with three- omnidirectional wheels is derived. This model which takes into consideration the kinematics and kinetics of the robot, is developed to state space representation. Relative analysis of the velocities and displacements is used for the kinematics of the robot. Lagrange’s approach is considered in this study for deriving the equation of motion. The drive train and the mechanical assembly only of the Festo Robotino® is considered in this model. Mainly the model is developed for motion control. Furthermore, the model can be used for simulation purposes in different virtual environments not only Robotino® View. Further use of the model is in the mechatronics research fields with the aim of teaching and learning the advanced control theories.

Keywords: mobile robot, omni-direction wheel, mathematical model, holonomic mobile robot

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Authors: T. N. Mac, B. Shahbodaghkhan, N. Khalili

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A new elastic-viscoplastic (EVP) constitutive model is proposed for the analysis of time-dependent behavior of clay. The proposed model is based on the bounding surface plasticity and the concept of viscoplastic consistency framework to establish continuous transition from plasticity to rate dependent viscoplasticity. Unlike the overstress based models, this model will meet the consistency condition in formulating the constitutive equation for EVP model. The procedure of deriving the constitutive relationship is also presented. Simulation results and comparisons with experimental data are then presented to demonstrate the performance of the model.

Keywords: bounding surface, consistency theory, constitutive model, viscosity

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Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

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Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

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Authors: Anna Avramenko, Heikki Haario

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This paper reports the development and application of a 2D depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. K-E and 2D LES turbulence models were consider in this article. 2D CFD simulations for one hill was done to check the depth-averaged model in practise.

Keywords: depth-averaged equations, numerical modeling, CFD, wind park model

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Authors: Christoph Ostermair

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We deal with the effect of correlation between prospects on human decision making under uncertainty as proposed by the comparatively new and promising model of “salience theory of choice under risk”. In this regard, we show that the theory entails the prediction that the inconsistency of choices, known as the Allais paradox, should not be an issue in the context of “real-life decision making”, which typically corresponds to situations of subjective uncertainty. The Allais paradox, probably the best-known anomaly regarding expected utility theory, would then essentially have no practical relevance. If, however, empiricism contradicts this prediction, salience theory might suffer a serious setback. Explanations of the model for variable human choice behavior are mostly the result of a particular mechanism that does not come to play under perfect correlation. Hence, if it turns out that correlation between prospects – as typically found in real-world applications – does not influence human decision making in the expected way, this might to a large extent cost the theory its explanatory power. The empirical literature regarding the Allais paradox under subjective uncertainty is so far rather moderate. Beyond that, the results are hard to maintain as an argument, as the presentation formats commonly employed, supposably have generated so-called event-splitting effects, thereby distorting subjects’ choice behavior. In our own incentivized experimental study, we control for such effects by means of two different choice settings. We find significant event-splitting effects in both settings, thereby supporting the suspicion that the so far existing empirical results related to Allais paradoxes under subjective uncertainty may not be able to answer the question at hand. Nevertheless, we find that the basic tendency behind the Allais paradox, which is a particular switch of the preference relation due to a modified common consequence, shared by two prospects, is still existent both under an event-splitting and a coalesced presentation format. Yet, the modal choice pattern is in line with the prediction of salience theory. As a consequence, the effect of correlation, as proposed by the model, might - if anything - only weaken the systematic choice pattern behind the Allais paradox.

Keywords: Allais paradox, common consequence effect, models of decision making under risk and uncertainty, salience theory

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Authors: Jui-Ching Chou

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Numerical simulation is a popular method used to evaluate the effects of soil liquefaction on a structure or the effectiveness of a mitigation plan. Many constitutive models (UBCSAND model, PM4 model, SANISAND model, etc.) were presented to model the liquefaction phenomenon. In general, inputs of a constitutive model need to be calibrated against the soil cyclic resistance before being applied to the numerical simulation model. Then, simulation results can be compared with results from simplified liquefaction potential assessing methods. In this article, inputs of the UBCSAND model, a simple elastic-plastic stress-strain model, are calibrated against several popular generic liquefaction triggering curves of simplified liquefaction potential assessing methods via FLAC program. Calibrated inputs can provide engineers to perform a preliminary evaluation of an existing structure or a new design project.

Keywords: calibration, liquefaction, numerical simulation, UBCSAND Model

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Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

Abstract:

Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies.

Keywords: runoff, roughness coefficient, PAR, WRM model

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

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Authors: Alokananda Ghosh, Shraban Sarkar

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The study aims to identify suitable sites for new hospitals with critical obstetric care facilities in Birbhum, one of the vulnerable and underserved districts of Eastern India, considering six main and 14 sub-criteria, using GIS-based Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) approach. The criteria were identified through field surveys and previous literature. After collecting expert decisions, a pairwise comparison matrix was prepared using the Saaty scale to calculate the weights through AHP. On the contrary, objective weighting methods, i.e., Entropy and Criteria Importance through Interaction Correlation (CRITIC), were used to perform the MAUT. Finally, suitability maps were prepared by weighted sum analysis. Sensitivity analyses of AHP were performed to explore the effect of dominant criteria. Results from AHP reveal that ‘maternal death in transit’ followed by ‘accessibility and connectivity’, ‘maternal health care service (MHCS) coverage gap’ were three important criteria with comparatively higher weighted values. Whereas ‘accessibility and connectivity’ and ‘maternal death in transit’ were observed to have more imprint in entropy and CRITIC, respectively. While comparing the predictive suitable classes of these three models with the layer of existing hospitals, except Entropy-MAUT, the other two are pointing towards the left-over underserved areas of existing facilities. Only 43%-67% of existing hospitals were in the moderate to lower suitable class. Therefore, the results of the predictive models might bring valuable input in future planning.

Keywords: hospital site suitability, analytic hierarchy process, multi-attribute utility theory, entropy, criteria importance through interaction correlation, multi-criteria decision analysis

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Authors: Adji Achmad Rinaldo Fernandes

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SEM analysis is a complex multivariate analysis because it involves a number of exogenous and endogenous variables that are interconnected to form a model. The measurement model is divided into two, namely, the reflective model (reflecting) and the formative model (forming). Before carrying out further tests on SEM, there are assumptions that must be met, namely the linearity assumption, to determine the form of the relationship. There are three modeling approaches to path analysis, including parametric, nonparametric and semiparametric approaches. The aim of this research is to develop semiparametric SEM and obtain the best model. The data used in the research is secondary data as the basis for the process of obtaining simulation data. Simulation data was generated with various sample sizes of 100, 300, and 500. In the semiparametric SEM analysis, the form of the relationship studied was determined, namely linear and quadratic and determined one and two knot points with various levels of error variance (EV=0.5; 1; 5). There are three levels of closeness of relationship for the analysis process in the measurement model consisting of low (0.1-0.3), medium (0.4-0.6) and high (0.7-0.9) levels of closeness. The best model lies in the form of the relationship X1Y1 linear, and. In the measurement model, a characteristic of the reflective model is obtained, namely that the higher the closeness of the relationship, the better the model obtained. The originality of this research is the development of semiparametric SEM, which has not been widely studied by researchers.

Keywords: semiparametric SEM, measurement model, structural model, reflective model, formative model

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Alan Wan

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In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

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Authors: Myung Hwan Na, Ho- Chun Song, EunHee Hong

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We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model.

Keywords: reliability, tires, field data, linear mixed-effects model

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Authors: A. Al Hajeri, M. E. Minton, B. Haskins, F. H. Cummins

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The pre-hospital endotracheal intubation is fraught with difficulties; one solution offered has been video laryngoscopy (VL) which permits better visualization of the glottis than the standard method of direct laryngoscopy (DL). This method has resulted in a higher first attempt success rate and fewer failed intubations. However, VL has mainly been evaluated by experienced providers (experienced anesthetists), and as such the utility of this device for those whom infrequently intubate has not been thoroughly assessed. We sought to evaluate this equipment to determine whether in the hands of novice providers this equipment could prove an effective airway management adjunct. DL and two VL methods (C-Mac with distal screen/C-Mac with attached screen) were evaluated by simulating practice on a Laerdal airway management trainer manikin. Twenty Emergency Medical Technicians (basics) were recruited as novice practitioners. This group was used to eliminate bias, as these clinicians had no pre-hospital experience of intubation (although they did have basic airway skills). The following areas were assessed: Time taken to intubate, number of attempts required to successfully intubate, ease of use of equipment VL (attached screen) took on average longer for novice clinicians to successfully intubate and had a lower success rate and reported higher rating of difficulty compared to DL. However, VL (with distal screen) and DL were comparable on intubation times, success rate, gastric inflation rate and rating of difficulty by the user. This study highlights the routine use of VL by inexperienced clinicians would be of no added benefit over DL. Further studies are required to determine whether Emergency Medical Technicians (Paramedics) would benefit from this airway adjunct, and ascertain whether after initial mastery of VL (with a distal screen), lower intubation times and difficulty rating may be achievable.

Keywords: direct laryngoscopy, endotracheal intubation, pre-hospital, video laryngoscopy

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Authors: Abdoullah Fath-Allah, Laila Cheikhi, Rafa E. Al-Qutaish, Ali Idri

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The e-government emerging concept transforms the way in which the citizens are dealing with their governments. Thus, the citizens can execute the intended services online anytime and anywhere. This results in great benefits for both the governments (reduces the number of officers) and the citizens (more flexibility and time saving). Therefore, building a maturity model to assess the e-government portals becomes desired to help in the improvement process of such portals. This paper aims at proposing an e-government maturity model based on the measurement of the best practices’ presence. The main benefit of such maturity model is to provide a way to rank an e-government portal based on the used best practices, and also giving a set of recommendations to go to the higher stage in the maturity model.

Keywords: best practices, e-government portal, maturity model, quality model

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Authors: Chung Danbi, Linton Oliver, Whang Yoon-Jae

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Supermodularity, or complementarity, is a popular concept in economics which can characterize many objective functions such as utility, social welfare, and production functions. Further, supermodular dominance captures a preference for greater interdependence among inputs of those functions, and it can be applied to examine which input set would produce higher expected utility, social welfare, or production. Therefore, we propose and justify a consistent testing for a useful implication of supermodular dominance. We also conduct Monte Carlo simulations to explore the finite sample performance of our test, with critical values obtained from the recentered bootstrap method, with and without the selective recentering, and the subsampling method. Under various parameter settings, we confirmed that our test has reasonably good size and power performance. Finally, we apply our test to compare the geographic and distant knowledge spillover in terms of their effects on social welfare using the National Bureau of Economic Research (NBER) patent data. We expect localized citing to supermodularly dominate distant citing if the geographic knowledge spillover engenders greater social welfare than distant knowledge spillover. Taking subgroups based on firm and patent characteristics, we found that there is industry-wise and patent subclass-wise difference in the pattern of supermodular dominance between localized and distant citing. We also compare the results from analyzing different time periods to see if the development of Internet and communication technology has changed the pattern of the dominance. In addition, to appropriately deal with the sparse nature of the data, we apply high-dimensional methods to efficiently select relevant data.

Keywords: supermodularity, supermodular dominance, stochastic dominance, Monte Carlo simulation, bootstrap, subsampling

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Authors: I. C. Tolias, A. G. Venetsanos, N. Markatos

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In the present work, CFD simulations of a large scale open deflagration experiment are performed. Stoichiometric hydrogen-air mixture occupies a 20 m hemisphere. Two combustion models are compared and are evaluated against the experiment. The Eddy Dissipation Model and a Multi-physics combustion model which is based on Yakhot’s equation for the turbulent flame speed. The values of models’ critical parameters are investigated. The effect of the turbulence model is also examined. k-ε model and LES approach were tested.

Keywords: CFD, deflagration, hydrogen, combustion model

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Authors: J. Rhodes, V. Cheng, P. Lok

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The aim of this study is to develop a parsimonious model that explains the effect of different stimulus on a tourist’s intention to visit a new destination. The model consists of destination trust and interest as the mediating variables. The model was tested using two different types of stimulus; both studies empirically supported the proposed model. Furthermore, the first study revealed that advertising has a stronger effect than positive online reviews. The second study found that the peripheral route of the elaboration likelihood model has a stronger influence power than the central route in this context.

Keywords: advertising, electronic word-of-mouth, elaboration likelihood model, intention to visit, trust

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Authors: Ahmad Sarfaraz, Raiyad Herwies

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In this paper, a multi-criteria decision making analysis is used to help any organization selects the best KM tool that fits and serves its needs. The AHP model is used based on a previous study to highlight and identify the main criteria and sub-criteria that are incorporated in the selection process. Different KM tools alternatives with different criteria are compared and weighted accurately to be incorporated in the GP model. The main goal is to combine the GP model with the AHP model to ensure that selecting the KM tool considers the resource constraints. Two important issues are discussed in this paper: how different factors could be taken into consideration in forming the AHP model, and how to incorporate the AHP results into the GP model for better results.

Keywords: knowledge management, analytical hierarchy process, goal programming, multi-criteria decision making

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

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Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

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Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

Abstract:

Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

Procedia PDF Downloads 257

Authors: Coskun Serefoglu

Abstract:

The study aims to analyse how a traditional sector such as agri-food could be mobilized through regional innovation strategies. A principal component analysis as well as qualitative information, such as in-depth interviews, focus group and surveys, were employed to find the priority sectors. An agri-food model was developed which includes both a linear model and interactive model. The model consists of two main components, one of which is technological integration and the other one is agricultural extension which is based on Land-grant university approach of U.S. which is not a common practice in Turkey.

Keywords: regional innovation strategy, interactive model, agri-food sector, local development, planning, regional development

Procedia PDF Downloads 152