Search results for: transportation model

Authors: Roberto Machado

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We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Retius Chifurira

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Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe.

Keywords: generalized extreme value distribution, general linear model, mean annual rainfall, meteorological drought probabilities

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Authors: Dorota Miroslaw-Swiatek, Mateusz Grygoruk, Sylwia Szporak

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We applied a two-dimensional surface water flow model with irregular wet boundaries. In this model, flow equations are in the form of a 2-D, non-linear diffusion equations which allows to account spatial variations in flow resistance and topography. Calculation domain to simulate the flow pattern in the floodplain is congruent with a Digital Elevation Model (DEM) grid. The rate and direction of sheet flow in wetlands is affected by vegetation type and density, therefore the developed model take into account spatial distribution vegetation resistance to the water flow. The model was tested in a part of the Biebrza Valley, of an outstanding heterogeneity in the elevation and flow resistance distributions due to various ecohydrological conditions and management measures. In our approach we used the highest-possible quality of the DEM in order to obtain hydraulic slopes and vegetation distribution parameters for the modelling. The DEM was created from the cloud of points measured in the LiDAR technology. The LiDAR reflects both the land surface as well as all objects on top of it such as vegetation. Depending on the density of vegetation cover the ability of laser penetration is variable. Therefore to obtain accurate land surface model the “vegetation effect” was corrected using data collected in the field (mostly the vegetation height) and satellite imagery such as Ikonos (to distinguish different vegetation types of the floodplain and represent them spatially). Model simulation was performed for the spring thaw flood in 2009.

Keywords: floodplain flow, Biebrza valley, model simulation, 2D surface flow model

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Authors: Young-Hyun Seo, Hyunwoo Park, Dong-Kyu Kim, Seung-Young Kho

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The purpose of this study is providing an improved mode choice model considering parameters including age grouping of prime-aged and old age. In this study, 2010 Household Travel Survey data were used and improper samples were removed through the analysis. Chosen alternative, date of birth, mode, origin code, destination code, departure time, and arrival time are considered from Household Travel Survey. By preprocessing data, travel time, travel cost, mode, and ratio of people aged 45 to 55 years, 55 to 65 years and over 65 years were calculated. After the manipulation, the mode choice model was constructed using LIMDEP by maximum likelihood estimation. A significance test was conducted for nine parameters, three age groups for three modes. Then the test was conducted again for the mode choice model with significant parameters, travel cost variable and travel time variable. As a result of the model estimation, as the age increases, the preference for the car decreases and the preference for the bus increases. This study is meaningful in that the individual and households characteristics are applied to the aggregate model.

Keywords: age grouping, aging, mode choice model, multinomial logit model

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Authors: Li Xiaoge, Wang Chaodong

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We introduce the first Chinese Coreference Resolution Model based on BERT (CCRM-BERT) and show that it significantly outperforms all previous work. The key idea is to consider the features of the mention, such as part of speech, width of spans, distance between spans, etc. And the influence of each features on the model is analyzed. The model computes mention embeddings that combine BERT with features. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the Chinese OntoNotes benchmark.

Keywords: BERT, coreference resolution, deep learning, nature language processing

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Authors: Awol Seid Ebrie

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HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Y. Y. Babanyara, D. B. Ibrahim, T. Garba

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Proper management of medical waste is a crucial issue for maintaining human health and the environment. The waste generated in the hospitals has the potential for spreading infections and causing diseases. The study is aimed at assessing the medical waste management practices in Nigeria. Three instruments, questionnaire administration, in-depth interview and observation method for data collection were adopted in the study. The results revealed that the hospital does not quantify medical waste. Segregation of medical wastes is not conducted according to definite rules and standards. Wheeled trolleys are used for on-site transportation of waste from the points of production to the temporary storage area. Offsite transportation of the hospital waste is undertaken by a private waste management company. Small pickups are mainly used to transport waste daily to an off-site area for treatment and disposal. The main treatment method used in the final disposal of infectious waste is incineration. Non-infectious waste is disposed off using land disposal method. The study showed that the hospital does not have a policy and plan in place for managing medical waste. The study revealed number of problems the hospital faces in terms of medical waste management, including; lack of necessary rules, regulations and instructions on the different aspects of collections and disposal of waste, failure to quantify the waste generated in reliable records, lack of use of coloured bags by limiting the bags to only one colour for all waste, the absence of a dedicated waste manager, and no committee responsible for monitoring the management of medical waste. Recommendations are given with the aim of improving medical waste management in the hospital.

Keywords: medical waste, treatment, disposal, public health

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Authors: Heung Sun Yoon, Jong Tae Kim

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In this paper, we present an activity diagram model for double-loop control self-adaptive braking system. Since activity diagram helps to improve visibility of self-adaption, we can easily find where improvement is needed on double-loop control. Double-loop control is adopted since the design conditions and actual conditions can be different. The system is reconfigured in runtime by using double-loop control. We simulated to verify and validate our model by using MATLAB. We compared single-loop control model with double-loop control model. Simulation results show that double-loop control provides more consistent brake power control than single-loop control.

Keywords: activity diagram, automotive, braking system, double-loop, self-adaptive, UML, vehicle

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Authors: Ahmad Zaini, F. Muhammad Reza Hadafi, Surya Sumpeno, Muhtadin, Mochamad Hariadi

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The lack of information about museum’s collection reduces the number of visits of museum. Museum’s revitalization is an urgent activity to increase the number of visits. The research's roadmap is building a web-based application that visualizes museum in the virtual form including museum's statue reconstruction in the form of 3D. This paper describes implementation of three-dimensional model reconstruction method based on light-strip pattern on the museum statue using 3D scanner. Noise removal, alignment, meshing and refinement model's processes is implemented to get a better 3D object reconstruction. Model’s texture derives from surface texture mapping between object's images with reconstructed 3D model. Accuracy test of dimension of the model is measured by calculating relative error of virtual model dimension compared against the original object. The result is realistic three-dimensional model textured with relative error around 4.3% to 5.8%.

Keywords: 3D reconstruction, light pattern structure, texture mapping, museum

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Authors: Anjali Singh, Ashwani Raju, Vandana Devi, Mohmad Mohsin Atique, Satyendra Singh, Munendra Singh

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India is the biggest consumer of mercury and, consequently, a major emitter too. The increasing mercury contamination in India’s water resources has gained widespread attention and, therefore, atmospheric deposition is of critical concern. However, little emphasis was placed on the role of precipitation in the aquatic mercury cycle of the Ganga Alluvial Plain which provides drinking water to nearly 7% of the world’s human population. A majority of the precipitation here occurs primarily in 10% duration of the year in the monsoon season. To evaluate the sources and transportation of mercury, water sample analysis has been conducted from two selected sites near Lucknow, which have a strong hydraulic gradient towards the river. 31 groundwater samples from Jehta village (26°55’15’’N; 80°50’21’’E; 119 m above mean sea level) and 31 river water samples from the Behta Nadi (a tributary of the Gomati River draining into the Ganga River) were collected during the monsoon season on every alternate day between 01 July to 30 August 2019. The total mercury analysis was performed by using Flow Injection Atomic Absorption Spectroscopy (AAS)-Mercury Hybride System, and daily rainfall data was collected from the India Meteorological Department, Amausi, Lucknow. The ambient groundwater and river-water concentrations were both 2-4 ng/L as there is no known geogenic source of mercury found in the area. Before the onset of the monsoon season, the groundwater and the river-water recorded mercury concentrations two orders of magnitude higher than the ambient concentrations, indicating the regional transportation of the mercury from the non-point source into the aquatic environment. Maximum mercury concentrations in groundwater and river-water were three orders of magnitude higher than the ambient concentrations after the onset of the monsoon season characterizing the considerable mobilization and redistribution of mercury by monsoonal precipitation. About 50% of both of the water samples were reported mercury below the detection limit, which can be mostly linked to the low intensity of precipitation in August and also with the dilution factor by precipitation. The highest concentration ( > 1200 ng/L) of mercury in groundwater was reported after 6-days lag from the first precipitation peak. Two high concentration peaks (>1000 ng/L) in river-water were separately correlated with the surface flow and groundwater outflow of mercury. We attribute the elevated mercury concentration in both of the water samples before the precipitation event to mercury originating from the extensive use of agrochemicals in mango farming in the plain. However, the elevated mercury concentration during the onset of monsoon appears to increase in area wetted with atmospherically deposited mercury, which migrated down from surface water to groundwater as downslope migration is a fundamental mechanism seen in rivers of the alluvial plain. The present study underscores the significance of monsoonal precipitation in the transportation of mercury to drinking water resources of the Ganga Alluvial Plain. This study also suggests that future research must be pursued for a better understand of the human health impact of mercury contamination and for quantification of the role of Ganga Alluvial Plain in the Global Mercury Cycle.

Keywords: drinking water resources, Ganga alluvial plain, india, mercury

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Authors: Mengke Zhan, Cheng-Gang Xie, Jian-Jun Shu

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A venturi flowmeter is a common device used in multiphase flow rate measurement in the upstream oil and gas industry. Having a robust computational model for multiphase flow in a venturi is desirable for understanding the gas-liquid and fluid-pipe interactions and predicting pressure and phase distributions under various flow conditions. A steady Eulerian-Eulerian framework is used to simulate upward gas-liquid flow in a vertical venturi. The simulation results are compared with experimental measurements of venturi differential pressure and chord-averaged gas holdup in the venturi throat section. The choice of turbulence model is nontrivial in the multiphase flow modelling in a venturi. The performance cross-comparison of the k-ϵ model, Reynolds stress model (RSM) and shear-stress transport (SST) k-ω turbulence model is made in the study. In terms of accuracy and computational cost, the SST k-ω turbulence model is observed to be the most efficient.

Keywords: computational fluid dynamics (CFD), gas-liquid flow, turbulence modelling, venturi

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Authors: Kyeong Hoon Park, Taiji Mazuda

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High damping rubber (HDR) bearings are dissipating devices mainly used in seismic isolation systems and have a great damping performance. Although many studies have been conducted on the dynamic model of HDR bearings, few models can reflect phenomena such as dependency of experienced shear strain on initial history. In order to develop a model that can represent the dependency of experienced shear strain of HDR by Mullins effect, dynamic loading test was conducted using HDR specimen. The reaction of HDR was measured by applying a horizontal vibration using a hybrid actuator under a constant vertical load. Dynamic program analysis was also performed after dynamic loading test. The dynamic model applied in program analysis is a bilinear type double-target model. This model is modified from typical bilinear model. This model can express the nonlinear characteristics related to the initial history of HDR bearings. Based on the dynamic loading test and program analysis results, equivalent stiffness and equivalent damping ratio were calculated to evaluate the mechanical properties of HDR and the feasibility of the bilinear type double-target model was examined.

Keywords: base-isolation, bilinear model, high damping rubber, loading test

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Authors: Anurag Savarnya

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The reliability of the various parts of electric mining shovel has been assessed through the application of Weibull Model. The study was initiated to find reliability of components of electric mining shovel. The paper aims to optimize the reliability of components and increase the life cycle of component. A multilevel decomposition of the electric mining shovel was done and maintenance records were used to evaluate the failure data and appropriate system characterization was done to model the system in terms of reasonable number of components. The approach used develops a mathematical model to assess the reliability of the electric mining shovel components. The model can be used to predict reliability of components of the hydraulic mining shovel and system performance. Reliability is an inherent attribute to a system. When the life-cycle costs of a system are being analyzed, reliability plays an important role as a major driver of these costs and has considerable influence on system performance. It is an iterative process that begins with specification of reliability goals consistent with cost and performance objectives. The data were collected from an Indian open cast coal mine and the reliability of various components of the electric mining shovel has been assessed by following a Weibull Model.

Keywords: reliability, Weibull model, electric mining shovel

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Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

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Authors: Watcharin Sangma, Onsiri Chanmuang, Pitsanu Tongkhow

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A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.

Keywords: forecasting model, steel demand uncertainty, hierarchical Bayesian framework, exponential smoothing method

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Authors: Soroor K. H. Al-Khafaji, Manal Mohammad Abed

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The research aim of this paper is to evaluate the reliability of a complex engineering system and to design a fuzzy model for the reliability estimation. The designed model has been applied on Vegetable Oil Purification System (neutralization system) to help the specialist user based on the concept of FMEA (Failure Mode and Effect Analysis) to estimate the reliability of the repairable system at the vegetable oil industry. The fuzzy model has been used to predict the system reliability for a future time period, depending on a historical database for the two past years. The model can help to specify the system malfunctions and to predict its reliability during a future period in more accurate and reasonable results compared with the results obtained by the traditional method of reliability estimation.

Keywords: fuzzy logic, reliability, repairable systems, FMEA

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Authors: Marco Camilli

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This article examines the impact of the pandemic outbreak of COVID-19 in the tourism sector in Italy, analyzing the current scenario, the government decisions and the private company reaction for the summer season 2020. The framework of the data analyzed shows how massive it’s the impact of the pandemic outbreak in the tourism revenue, and the weaknesses of the measures proposed. Keywords Travel &Tourism, Transportation, Sustainability, COVID-19, Businesses Introduction The current COVID-19 scenario shows a shocking situation for the tourism and transportation sectors: it could be the most affected by the Coronavirus in Italy. According to forecasts, depending on the duration of the epidemic outbreak and the lockdown strategy applied by the Government, businesses in the supply chain could lose between 24 and 66 billion in turnover in the period of 2020-21, with huge diversified impacts at the national and regional level. Many tourist companies are on the verge of survival and if there are no massive measures by the government they risk closure. Data analysis The tourism and transport sector could be among the sectors most damaged by Covid-19 in Italy. Considering the two-year period 2020-21, companies operating in the travel & tourism sector (Tour operator, Travel Agencies, Hotel, Guides, Bus Company, etc..) could in suffer losses in revenues of 24 to 64 billion euros, especially in the sectors such as the travel agencies, hotel and rental. According to Statista Research Department, from April 2020 estimated that the coronavirus (COVID-19) pandemic will have a significant impact on revenues of the tourism industry in Italy. Revenues are expected to decrease by over 40 billion euros in the first semester of 2020, compared to the same period of the previous year. According to the study, hotel and non-hotel accommodations will experience the highest loss. Revenues of this sector are expected to decrease by 13 billion euros compared to the first semester of 2019 when accommodations registered revenues for about 17 billion euros. According to Statista.com, in 2020, Italy is expected to register a decrease of roughly 28.5 million tourist arrivals due to the impact of coronavirus (COVID-19) on the country's tourist sector. According to the estimate, the region of Veneto will record the highest drop with a decrease of roughly 4.61 million arrivals. Similarly, Lombardy is expected to register a decrease of about 3.87 million arrivals in 2020.

Keywords: travel and tourism, sustainability, COVID-19, businesses, transportation

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Authors: Kuan-Chou Chen

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This paper will demonstrate a simulation model of an information security system by using the systems dynamic approach. The relationships in the system model are designed to be simple and functional and do not necessarily represent any particular information security environments. The purpose of the paper aims to develop a generic system dynamic information security system model with implications on information security research. The interrelated and interdependent relationships of five primary sectors in the system dynamic model will be presented in this paper. The integrated information security systems model will include (1) information security characteristics, (2) users, (3) technology, (4) business functions, and (5) policy and management. Environments, attacks, government and social culture will be defined as the external sector. The interactions within each of these sectors will be depicted by system loop map as well. The proposed system dynamic model will not only provide a conceptual framework for information security analysts and designers but also allow information security managers to remove the incongruity between the management of risk incidents and the management of knowledge and further support information security managers and decision makers the foundation for managerial actions and policy decisions.

Keywords: system thinking, information security systems, security management, simulation

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Authors: Semih Sözer

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One of the most common issues in economic systems is understanding characteristics of economic activities in cities and regions. Although there are critics to economic base models in conceptual and empirical aspects, these models are useful tools to examining the economic structure of a nation, regions or cities. This paper uses one of the methodologies of economic base models namely the location quotients model. Data for this model includes employment numbers of provinces and NUTS II regions in Turkey. Time series of data covers the years of 1990, 2000, 2003, and 2009. Aim of this study is finding which sectors are export-base and which sectors are import-base in provinces and regions. Model results show that big provinces or powerful regions (population, size etc.) mostly have basic sectors in their economic system. However, interesting facts came from different sectors in different provinces and regions in the model results.

Keywords: economic base, location quotients model, regional economics, regional development

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Authors: Albert Gyamfi

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Cocoa plays critical role in the socio-economic development of Ghana. Meanwhile, smallholder farmers most of whom are illiterate dominate the industry. According to the cocoa-based agricultural knowledge and information system (AKIS) model knowledge is created and transferred to the industry between three key actors: cocoa researchers, extension experts, and cocoa farmers. Dwelling on the SECI model, the media richness theory (MRT), and the AKIS model, a conceptual model of web 2.0-based AKIS model (AKIS 2.0) is developed and used to assess the possible effects of social media usage for knowledge creation in the Ghanaian cocoa industry. A mixed method approach with a survey questionnaire was employed, and a second-order multi-group structural equation model (SEM) was used to analyze the data. The study concludes that the use of web 2.0 applications for knowledge creation would lead to sustainable interactions among the key knowledge actors for effective knowledge creation in the cocoa industry in Ghana.

Keywords: agriculture, cocoa, knowledge, media, web 2.0

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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Authors: Ross J. Maestas

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Biofuels are derived from multiple renewable bioenergy feedstocks including animal fats, wood, starchy grains, and oil seeds. Transportation agencies have considered growing the latter two on underutilized and nontraditional lands that they manage, such as in the Right of Way (ROW), abandoned weigh stations, and at maintenance yards. These crops provide the opportunity to generate revenue or supplement fuel once converted and offer a solution to increasing fuel costs and instability by creating a ‘home-grown’ alternative. Biofuels are non-toxic, biodegradable, and emit less Green House Gasses (GHG) than fossil fuels, therefore allowing agencies to meet sustainability goals and regulations. Furthermore, they enable land managers to achieve soil erosion and roadside aesthetic strategies. The research sought to understand if the cultivation of a biofuel feedstock within the Arkansas State Highway Transportation Department’s (AHTD) managed and marginalized lands is feasible by identifying potential land areas and crops. To determine potential plots the parcel data was downloaded from Arkansas’s GIS office. ArcGIS was used to query the data for all variations of the names of property owned by AHTD and a KML file was created that identifies the queried parcel data in Google Earth. Furthermore, biofuel refineries in the state were identified to optimize the harvest to transesterification process. Agricultural data was collected from federal and state agencies and universities to assess various oil seed crops suitable for conversion and suited to grow in Arkansas’s climate and ROW conditions. Research data determined that soybean is the best adapted biofuel feedstock for Arkansas with camelina and canola showing possibilities as well. Agriculture is Arkansas’s largest industry and soybean is grown in over half of the state’s counties. Successful cultivation of a feedstock in the aforementioned areas could potentially offer significant employment opportunity for which the skilled farmers already exist. Based on compiled data, AHTD manages 21,489 acres of marginalized land. The result of the feasibility assessment offer suggestions and guidance should AHTD decide to further investigate this type of initiative.

Keywords: Arkansas highways, biofuels, renewable energy initiative, marginalized lands

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Authors: Anderson Ngowa Chembe, John Olukuru

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Greenhouse emissions by human activities are known to irreversibly increase global temperatures through the greenhouse effect. This study seeks to propose a mortality model with sensitivity to heat-change effects as one of the underlying parameters in the model. As such, the study sought to establish the relationship between greenhouse emissions and mortality indices in five OECD countries (USA, UK, Japan, Canada & Germany). Upon the establishment of the relationship using correlation analysis, an additional parameter that accounts for the sensitivity of heat-changes to mortality rates was incorporated in the Lee-Carter model. Based on the proposed model, new parameter estimates were calculated using iterative algorithms for optimization. Finally, the goodness of fit for the original Lee-Carter model and the proposed model were compared using deviance comparison. The proposed model provides a better fit to mortality rates especially in USA, UK and Germany where the mortality indices have a strong positive correlation with the level of greenhouse emissions. The results of this study are of particular importance to actuaries, demographers and climate-risk experts who seek to use better mortality-modeling techniques in the wake of heat effects caused by increased greenhouse emissions.

Keywords: climate risk, greenhouse emissions, Lee-Carter model, OECD

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Authors: Ibrahim A. Aref, Tarek El-Mihoub, Khadiga Ben Musa

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This paper presents Carrier Sense Multiple Access (CSMA) communication model based on SoC design methodology. Such model can be used to support the modelling of the complex wireless communication systems, therefore use of such communication model is an important technique in the construction of high performance communication. SystemC has been chosen because it provides a homogeneous design flow for complex designs (i.e. SoC and IP based design). We use a swarm system to validate CSMA designed model and to show how advantages of incorporating communication early in the design process. The wireless communication created through the modeling of CSMA protocol that can be used to achieve communication between all the agents and to coordinate access to the shared medium (channel).

Keywords: systemC, modelling, simulation, CSMA

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

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Health analytics (HA) is used in healthcare systems for effective decision-making, management, and planning of healthcare and related activities. However, user resistance, the unique position of medical data content, and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. The success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose an HA process model with features from the rational unified process (RUP) model and agile methodology.

Keywords: agile methodology, health analytics, unified process model, UML

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Authors: Robert Setekera, Ramses van der Toorn

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We present an extensive analysis of the temperature dependence of the local avalanche model used in most of the modern compact models for bipolar transistors. This local avalanche model uses the Chynoweth's empirical law for ionization coefficient to define the generation of the avalanche current in terms of the local electric field. We carry out the model analysis using DC-measurements taken on both Si and advanced SiGe bipolar transistors. For the advanced industrial SiGe-HBTs, we consider both high-speed and high-power devices (both NPN and PNP transistors). The limitations of the local avalanche model in modeling the temperature dependence of the avalanche current mostly in the weak avalanche region are demonstrated. In addition, the model avalanche parameters are analyzed to see if they are in agreement with semiconductor device physics.

Keywords: avalanche multiplication, avalanche current, bipolar transistors, compact modeling, electric field, impact ionization, local avalanche

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Authors: Jiri Dufek

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The national model of the Czech Republic has been modified in a detailed way to get detailed travel demand in the municipality level (cities, villages over 300 inhabitants). As a technique for this detailed modelling, three-dimensional procedure for calibrating gravity models, was used. Besides of zone production and attraction, which is usual in gravity models, the next additional parameter for trip distribution was introduced. Usually it is called by “third dimension”. In the model, this parameter is a demand between regions. The distribution procedure involved calculation of appropriate skim matrices and its multiplication by three coefficients obtained by iterative balancing of production, attraction and third dimension. This type of trip distribution was processed in R-project and the results were used in the Czech Republic transport model, created in PTV Vision. This process generated more precise results in local level od the model (towns, villages)

Keywords: trip distribution, three dimension, transport model, municipalities

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Authors: Ksenija Dumičić, Anita Čeh Časni, Berislav Žmuk

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The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which accounts for trend and seasonality. The results of the empirical analysis showed that the optimal model for forecasting unemployment rate in Greece was Holt-Winters` additive method. In the case of Spain, according to MAPE, the optimal model was double exponential smoothing model. Furthermore, for Croatia and Italy the best forecasting model for unemployment rate was Holt-Winters` multiplicative model, whereas in the case of Portugal the best model to forecast unemployment rate was Double exponential smoothing model. Our findings are in line with European Commission unemployment rate estimates.

Keywords: European Union countries, exponential smoothing methods, forecast accuracy unemployment rate

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Authors: Mohsen Solimani Babarsad, Payam Taheri

Abstract:

Numerical results of vertical slot fishways with and without cylinders study are presented. The simulated results and the measured data in the fishways are compared to validate the application of the model. This investigation is made using FLUENT V.6.3, a Computational Fluid Dynamics solver. Advantages of using these types of numerical tools are the possibility of avoiding the St.-Venant equations’ limitations, and turbulence can be modeled by means of different models such as the k-ε model. In general, the present study has demonstrated that the CFD model could be useful for analysis and design of vertical slot fishways with cylinders.

Keywords: slot Fish-way, CFD, k-ε model, St.-Venant equations’

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