Search results for: the nuclear shell model

Authors: Seyed Sobhan Alvani, Mohammad Gohari

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By developing cities and increasing the population, traffic congestion has become a vital problem. Due to this crisis, urban designers try to present solutions to decrease this difficulty. On the other hand, predicting the model with perfect accuracy is essential for solution-providing. The current study presents a model based on artificial intelligence which can predict traffic index based on city population, growth rate, and area. The accuracy of the model was evaluated, which is acceptable and it is around 90%. Thus, urban designers and planners can employ it for predicting traffic index in the future to provide strategies.

Keywords: traffic index, population growth rate, cities wideness, artificial neural network

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Authors: Andrew Cummings, Rhianne Boag, Sarah Bryson, Gordon Turner

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Packages designed for transport of radioactive material must satisfy rigorous safety regulations specified by the International Atomic Energy Agency (IAEA). Higher Activity Waste (HAW) transport packages have to maintain containment of their contents during normal and accident conditions of transport (NCT and ACT). To ensure containment criteria is satisfied these packages are required to be leak-tight in all transport conditions to meet allowable activity release rates. Package design safety reports are the safety cases that provide the claims, evidence and arguments to demonstrate that packages meet the regulations and once approved by the competent authority (in the UK this is the Office for Nuclear Regulation) a licence to transport radioactive material is issued for the package(s). The standard approach to demonstrating containment in the RWM transport safety case is set out in BS EN ISO 12807. In this document a method for measuring a leak rate from the package is explained by way of a small interspace test volume situated between two O-ring seals on the underside of the package lid. The interspace volume is pressurised and a pressure drop measured. A small interspace test volume makes the method more sensitive enabling the measurement of smaller leak rates. By ascertaining the activity of the contents, identifying a releasable fraction of material and by treating that fraction of material as a gas, allowable leak rates for NCT and ACT are calculated. The adherence to basic safety principles in ISO12807 is very pessimistic and current practice in the demonstration of transport safety, which is accepted by the UK regulator. It is UK government policy that management of HAW will be through geological disposal. It is proposed that the intermediate level waste be transported to the geological disposal facility (GDF) in large cuboid packages. This poses a challenge for containment demonstration because such packages will have long seals and therefore large interspace test volumes. There is also uncertainty on the releasable fraction of material within the package ullage space. This is because the waste may be in many different forms which makes it difficult to define the fraction of material released by the waste package. Additionally because of the large interspace test volume, measuring the calculated leak rates may not be achievable. For this reason a justification for a lower releasable fraction of material is sought. This paper considers the use of aerosol processes to reduce the releasable fraction for both NCT and ACT. It reviews the basic coagulation and removal processes and applies the dynamic aerosol balance equation. The proposed solution includes only the most well understood physical processes namely; Brownian coagulation and gravitational settling. Other processes have been eliminated either on the basis that they would serve to reduce the release to the environment further (pessimistically in keeping with the essence of nuclear transport safety cases) or that they are not credible in the conditions of transport considered.

Keywords: aerosol processes, Brownian coagulation, gravitational settling, transport regulations

Procedia PDF Downloads 109

Authors: Souad Romdhane, Lotfi Belkacem

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When referring to actuarial analysis of lifetime, only models accounting for observable risk factors have been developed. Within this context, Cox proportional hazards model (CPH model) is commonly used to assess the effects of observable covariates as gender, age, smoking habits, on the hazard rates. These covariates may fail to fully account for the true lifetime interval. This may be due to the existence of another random variable (frailty) that is still being ignored. The aim of this paper is to examine the shared frailty issue in the Cox proportional hazard model by including two different parametric forms of frailty into the hazard function. Four estimated methods are used to fit them. The performance of the parameter estimates is assessed and compared between the classical Cox model and these frailty models through a real-life data set from the Quebec Pension Plan and then using a more general simulation study. This performance is investigated in terms of the bias of point estimates and their empirical standard errors in both fixed and random effect parts. Both the simulation and the real dataset studies showed differences between classical Cox model and shared frailty model.

Keywords: life insurance-pension plan, survival analysis, risk factors, cox proportional hazards model, multivariate failure-time data, shared frailty, simulations study

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Authors: Kazuyuki Takase, Yoshihisa Hiraki, Gaku Takase, Isamu Kudo

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One of the most important issue is to ensure the safety of long-term waste storage containers in which fuel debris and radioactive materials are accumulated. In this case, hydrogen generated by water decomposition by radiation is accumulated in the container for a long period of time, so it is necessary to reduce the concentration of hydrogen in the container. In addition, a condition that any power supplies from the outside of the container are unnecessary is requested. Then, radioactive waste storage containers with the passive autocatalytic recombiner (PAR) would be effective. The radioactive waste storage container with PAR was used for moving the fuel debris of the Three Mile Island Unit 2 to the storage location. However, the effect of PAR is not described in detail. Moreover, the reduction of hydrogen concentration during the long-term storage period was performed by the venting system, which was installed on the top of the container. Therefore, development of a long-term storage container with PAR was started with the aim of safely storing fuel debris picked up at the Fukushima Daiichi Nuclear Power Plant for a long period of time. A fundamental experiment for reducing the concentration of hydrogen which generates in a nuclear waste long-term storage container was carried out using a small-scale container with PAR. Moreover, the circulation flow behavior of hydrogen in the small-scale container resulting from the natural convection by the decay heat was clarified. In addition, preliminary numerical analyses were performed to predict the experimental results regarding the circulation flow behavior and the reduction of hydrogen concentration in the small-scale container. From the results of the present study, the validity of the container with PAR was experimentally confirmed on the reduction of hydrogen concentration. In addition, it was predicted numerically that the circulation flow behavior of hydrogen in the small-scale container is blocked by steam which generates by chemical reaction of hydrogen and oxygen.

Keywords: hydrogen behavior, reduction of concentration, long-term storage container, small-scale, PAR, experiment, analysis

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Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer

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This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.

Keywords: non-stationary, BINARMA(1, 1) model, Poisson innovations, conditional maximum likelihood, CML

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Authors: Jana Gutierez Chvalkovska, Michal Mejstrik, Matej Urban

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There are pending discussions over the mapping of country export potential in order to refocus export strategy of firms and its evidence-based promotion by the Export Credit Agencies (ECAs) and other permitted vehicles of governments. In this paper we develop our version of an applied model that offers “stepwise” elimination of unattractive markets. We modify and calibrate the model for the particular features of the Czech Republic and specific pilot cases where we apply an individual approach to each sector.

Keywords: export strategy, modeling export, calibration, export promotion

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Authors: Enrique Barbieri

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The basic disease-spread model described by three states denoting the susceptible (S), infectious (I), and removed (recovered and deceased) (R) sub-groups of the total population N, or SIR model, has been considered. Heuristic mitigating action profiles of the pharmaceutical and non-pharmaceutical types may be developed in a control design setting for the purpose of reducing the transmission rate or improving the recovery rate parameters in the model. Even though the transmission and recovery rates are not control inputs in the traditional sense, a linear observer and feedback controller can be tuned to generate an asymptotic estimate of the transmission rate for a linearized, discrete-time version of the SIR model. Then, a set of mitigating actions is suggested to steer the basic reproduction number toward unity, in which case the disease does not spread, and the infected population state does not suffer from multiple waves. The special case of piecewise constant transmission rate is described and applied to a seventh-order SEIQRDP model, which segments the population into four additional states. The offline simulations in discrete time may be used to produce heuristic policies implemented by public health and government organizations.

Keywords: control of SIR, observer, SEIQRDP, disease spread

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Authors: Anastasis D. Petrou

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Innovation and strategy discussions do highlight open innovation as a new paradigm in business. Yet, a number of stumbling blocks in the form of closed innovation principles weaved into the fabric of a traditional business model stand in the way of the new paradigm’s momentum to increase value in various business contexts. The paper argues that businesses considering an engagement with the open innovation paradigm would need to take steps to improve their multiplicative, absorptive and relational capabilities, respectively. The needed improvements would amount to a business model evolutionary transformation and eventually bring about a paradigm overhaul in business. The transformation is worth staging over time to ensure that open innovation is developed across interconnected and partnered areas of strategic importance. This article develops an open innovation strategy (OIS) capabilities model, and employs examples from different industries to briefly discuss OIS’s potential to augment business value in a number of suggested areas for future research.

Keywords: close innovation, open innovation paradigm, open innovation strategy (OIS) paradigm, OIS capabilities model, multiplicative capability, absorptive capability, relational capability

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Authors: Xian Li, Qing-Guo Wang, Jiangshuai Huang, Jidong Liu, Ming Yu, Tan Kok Poh

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In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly.

Keywords: power industry, electricity demand, modeling, forecasting

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Authors: B. Van Kessel, P. T. Kockelkorn, T. R. Speelman, T. C. Wierikx, C. Mai Van, T. A. Bogaard

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Saltwater intrusion is a common problem in estuaries around the world, as it could hinder the freshwater supply of coastal zones. This problem is likely to grow due to climate change and sea-level rise. The influence of these factors on the saltwater intrusion was investigated for the Cai River in the Khanh Hoa province in Vietnam. In addition, the Cai River has high seasonal fluctuations in discharge, leading to increased saltwater intrusion during the dry season. Sea level rise, river discharge changes, river mouth widening and a proposed saltwater intrusion prevention dam can have influences on the saltwater intrusion but have not been quantified for the Cai River estuary. This research used both an analytical and numerical model to investigate the effect of the aforementioned factors. The analytical model was based on a model proposed by Savenije and was calibrated using limited in situ data. The numerical model was a 3D hydrodynamic model made using the Delft3D4 software. The analytical model and numerical model agreed with in situ data, mostly for tidally average data. Both models indicated a roughly similar dependence on discharge, also agreeing that this parameter had the most severe influence on the modeled saltwater intrusion. Especially for discharges below 10 m/s3, the saltwater was predicted to reach further than 10 km. In the models, both sea-level rise and river widening mainly resulted in salinity increments up to 3 kg/m3 in the middle part of the river. The predicted sea-level rise in 2070 was simulated to lead to an increase of 0.5 km in saltwater intrusion length. Furthermore, the effect of the saltwater intrusion dam seemed significant in the model used, but only for the highest position of the gate.

Keywords: Cai River, hydraulic models, river discharge, saltwater intrusion, tidal barriers

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

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In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: information theory, prediction model, prosthetic alignment, transtibial prosthesis

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Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

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This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

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Authors: Christopher Deekia Nwimae, Nigel Simms, Liyun Lao

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Erosion in pipe bends caused by particles is a major obstacle in the oil and gas fields and might cause the breakdown of production equipment. This work studied the effects imposed by flow velocity and impact of solid particles diameter in an elbow; erosion rate was verified with experimental data using the computational fluid dynamics (CFD) approach. Two-way coupled Euler-Lagrange and discrete phase model was employed to calculate the air/solid particle flow in an elbow. One erosion model and three-particle rebound models were used to predict the erosion rate on the 90° elbows. The generic erosion model was used in the CFD-based erosion model, and after comparing it with experimental data, results showed agreement with the CFD-based predictions as observed.

Keywords: erosion, prediction, elbow, computational fluid dynamics

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Mohammad Y. Badiee, Saeed Golestani, Mir Saman Pishvaee

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In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown.

Keywords: supply chain management, closed-loop supply chain, multi-objective optimization, goal programming, uncertainty, robust optimization

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Authors: Bekir Turedi

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Exploiting the potential of perovskite single-crystal solar cells in optoelectronic applications necessitates overcoming a significant challenge: the low charge collection efficiency at increased thickness, which has restricted their deployment in radiation detectors and nuclear batteries. Our research details a promising approach to this problem, wherein we have successfully fabricated single-crystal MAPbI3 solar cells employing a space-limited inverse temperature crystallization (ITC) methodology. Remarkably, these cells, up to 400-fold thicker than current-generation perovskite polycrystalline films, maintain a high charge collection efficiency even without external bias. The crux of this achievement lies in the long electron diffusion length within these cells, estimated to be around 0.45 mm. This extended diffusion length ensures the conservation of high charge collection and power conversion efficiencies, even as the thickness of the cells increases. Fabricated cells at 110, 214, and 290 µm thickness manifested power conversion efficiencies (PCEs) of 20.0, 18.4, and 14.7% respectively. The single crystals demonstrated nearly optimal charge collection, even when their thickness exceeded 200 µm. Devices of thickness 108, 214, and 290 µm maintained 98.6, 94.3, and 80.4% of charge collection efficiency relative to their maximum theoretical short-circuit current value, respectively. Additionally, we have proposed an innovative, self-consistent technique for ascertaining the electron-diffusion length in perovskite single crystals under operational conditions. The computed electron-diffusion length approximated 446 µm, significantly surpassing previously reported values for this material. In conclusion, our findings underscore the feasibility of fabricating halide perovskite single-crystal solar cells of hundreds of micrometers in thickness while preserving high charge extraction efficiency and PCE. This advancement paves the way for developing perovskite-based optoelectronics necessitating thicker active layers, such as X-ray detectors and nuclear batteries.

Keywords: perovskite, solar cell, single crystal, diffusion length

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Authors: Selin Guney, Andres Riquelme

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The traditional bio-economic method for fisheries modeling uses some estimate of the growth parameters and the system carrying capacity from a biological model for the population dynamics (usually a logistic population growth model) which is then analyzed as a traditional production function. The stock dynamic is transformed into a revenue function and then compared with the extraction costs to estimate the maximum economic yield. In this paper, the logistic population growth model for the population is combined with a forecast of the abundance and location of the stock by using a generalized additive model approach. The paper focuses on the Chilean hake population. This method allows for the incorporation of climatic variables and the interaction with other marine species, which in turn will increase the reliability of the estimates and generate better extraction paths for different conservation objectives, such as the maximum biological yield or the maximum economic yield.

Keywords: bio-economic, fisheries, GAM, production

Procedia PDF Downloads 235

Authors: S. Sonkham, U. Pinsopon, W. Chatlatanagulchai

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This paper presents a method of sliding mode control (SMC) designing and developing for the servo system in a dual-stage actuator (DSA) hard disk drive. Mathematical modelling of hard disk drive actuators is obtained, extracted from measuring frequency response of the voice-coil motor (VCM) and PZT micro-actuator separately. Matlab software tools are used for mathematical model estimation and also for controller design and simulation. A model-reference approach for tracking requirement is selected as a proposed technique. The simulation results show that performance of a model-reference SMC controller design in DSA servo control can be satisfied in the tracking error, as well as keeping the positioning of the head within the boundary of +/-5% of track width under the presence of internal and external disturbance. The overall results of model-reference SMC design in DSA are met per requirement specifications and significant reduction in %off track is found when compared to the single-state actuator (SSA).

Keywords: hard disk drive, dual-stage actuator, track following, hdd servo control, sliding mode control, model-reference, tracking control

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Authors: R. Fernandez-Saavedra, M. B. Gomez-Mancebo, D. Gomez-Briceno

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The gradual corrosion of PWR (Pressurized Water Reactor) feedwater, condensate and drain systems results in the inevitable liberation of corrosion products, principally metallic oxides, to the secondary circuit. In addition, other contaminants and impurities are introduced into the makeup water, auxiliary feedwater and by condenser leaks. All these compounds circulating in the secondary flow can eventually be transported to steam generators and be transformed into deposits on their surfaces. Deposits that accumulate on the tube sheet are known as sludge piles and when they consolidate and harden become into hard sludge. Hard sludge is especially detrimental because it favors tube deformation or denting at the top of tube sheet and further stress corrosion cracking (SCC). These failures affect the efficiency of nuclear power plants. In a recent work, a model for the formation and consolidation of hard sludge has been formulated, highlighting the influence of aluminum and silicon compounds in the initial formation of hard sludge. In this work, an experimental study has been performed in order to get a deeper understanding of the behavior of Al and Si species in hard sludge formation and consolidation. For this purpose, the key components of hard sludge (magnetite, aluminum and/or silicon sources) have been isothermally autoclaved in representative secondary circuit conditions during one week, and the resulting products have been chemically and structurally characterized by XRF and XRD techniques, respectively.

Keywords: consolidation, hard sludge, secondary circuit, steam generator

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Authors: Shahrokh Barati

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In this paper, we introduced AIDS disease at first, then proposed dynamic model illustrate its progress, after expression of a short history of nonlinear modeling by polynomial phasing systems, we considered the stability conditions of the systems, which contained a huge amount of researches in order to modeling and control of AIDS in dynamic nonlinear form, in this approach using a frame work of control any polynomial phasing modeling system which have been generalized by part of phasing model of T-S, in order to control the system in better way, the stability conditions were achieved based on polynomial functions, then we focused to design the appropriate controller, firstly we considered the equilibrium points of system and their conditions and in order to examine changes in the parameters, we presented polynomial phase model that was the generalized approach rather than previous Takagi Sugeno models, then with using case we evaluated the equations in both open loop and close loop and with helping the controlling feedback, the close loop equations of system were calculated, to simulate nonlinear model of AIDS disease, we used polynomial phasing controller output that was capable to make the parameters of a nonlinear system to follow a sustainable reference model properly.

Keywords: polynomial fuzzy, AIDS, nonlinear AIDS model, fuzzy control systems

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Babek Erdebilli

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The aim of this study is to develop and describe a new methodology for the Multi-Criteria Decision-Making (MCDM) problem using IFE (Elimination Et Choix Traduisant La Realite (ELECTRE) model. The proposed models enable Decision-Makers (DMs) on the assessment and use Intuitionistic Fuzzy Numbers (IFN). A numerical example is provided to demonstrate and clarify the proposed analysis procedure. Also, an empirical experiment is conducted to validation the effectiveness.

Keywords: multi-criteria decision-making, IFE, DM’s, fuzzy electre model

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Authors: Sangwoo Han, Saeed Khaleghi Rahimian, Ying Liu

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Vehicle electrification is gaining momentum, and many car manufacturers promise to deliver more electric vehicle (EV) models to consumers in the coming years. In controlling the battery pack, the battery management system (BMS) must maintain optimal battery performance while ensuring the safety of a battery pack. Tasks related to battery performance include determining state-of-charge (SOC), state-of-power (SOP), state-of-health (SOH), cell balancing, and battery charging. Safety related functions include making sure cells operate within specified, static and dynamic voltage window and temperature range, derating power, detecting faulty cells, and warning the user if necessary. The BMS often utilizes an RC circuit model to model a Li-ion cell because of its robustness and low computation cost among other benefits. Because an equivalent circuit model such as the RC model is not a physics-based model, it can never be a prognostic model to predict battery state-of-health and avoid any safety risk even before it occurs. A physics-based Li-ion cell model, on the other hand, is more capable at the expense of computation cost. To avoid the high computation cost associated with a full-order model, many researchers have demonstrated the use of a single particle model (SPM) for BMS applications. One drawback associated with the single particle modeling approach is that it forces to use the average current density in the calculation. The SPM would be appropriate for simulating drive cycles where there is insufficient time to develop a significant current distribution within an electrode. However, under a continuous or high-pulse electrical load, the model may fail to predict cell voltage or Li⁺ plating potential. To overcome this issue, a multi-particle reduced-order model is proposed here. The use of multiple particles combined with either linear or nonlinear charge-transfer reaction kinetics enables to capture current density distribution within an electrode under any type of electrical load. To maintain computational complexity like that of an SPM, governing equations are solved sequentially to minimize iterative solving processes. Furthermore, the model is validated against a full-order model implemented in COMSOL Multiphysics.

Keywords: battery management system, physics-based li-ion cell model, reduced-order model, single-particle and multi-particle model

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Authors: W. H. Wan Zakiyatussariroh, A. A. Nasuhar, W. Y. Wan Fairos, Z. A. Nazatul Shahreen

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Forecasting dengue incidence in a population can provide useful information to facilitate the planning of the public health intervention. Many studies on dengue cases in Malaysia were conducted but are limited in modeling the outbreak and forecasting incidence. This article attempts to propose the most appropriate time series model to explain the behavior of dengue incidence in Malaysia for the purpose of forecasting future dengue outbreaks. Several seasonal auto-regressive integrated moving average (SARIMA) models were developed to model Malaysia’s number of dengue incidence on weekly data collected from January 2001 to December 2011. SARIMA (2,1,1)(1,1,1)52 model was found to be the most suitable model for Malaysia’s dengue incidence with the least value of Akaike information criteria (AIC) and Bayesian information criteria (BIC) for in-sample fitting. The models further evaluate out-sample forecast accuracy using four different accuracy measures. The results indicate that SARIMA (2,1,1)(1,1,1)52 performed well for both in-sample fitting and out-sample evaluation.

Keywords: time series modeling, Box-Jenkins, SARIMA, forecasting

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Authors: Chaitanya R. Mali, V. Vinod, Ashwin W. Patwardhan

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Design of long vertical steam generator (SG) tubes in nuclear power plant involves an understanding of different aspects of flow boiling phenomenon such as flow instabilities, flow regimes, dry out, critical heat flux, pressure drop, etc. The knowledge of the prediction of local thermal hydraulic characteristics is necessary to understand these aspects. For this purpose, the methodology has been developed which covers all the flow boiling regimes to model full range flow boiling phenomenon. In this methodology, the vertical tube is divided into four sections based on vapor fraction value at the end of each section. Different modeling strategies have been applied to the different sections of the vertical tube. Computational fluid dynamics simulations have been performed on a vertical SG tube of 0.0126 m inner diameter and 23 m length. The thermal hydraulic parameters such as vapor fraction, liquid temperature, heat transfer coefficient, pressure drop, heat flux distribution have been analyzed for different designed heat duties (1.1 MW (20%) to 3.3 MW (60%)) and flow conditions (10 % to 80 %). The sensitivity of different boiling parameters such as bubble departure diameter, nucleation site density, bubble departure frequency on the thermal hydraulic parameters was also studied. Flow instability has been observed at 20 % designed heat duty and 20 % flow conditions.

Keywords: thermal hydraulics, boiling, vapor fraction, sensitivity

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Authors: Serkan Sayin, Songul F. Varol

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High attention on the organic light-emitting diodes has been paid since their efficient properties in the flat panel displays, and solid-state lighting was realized. Because of their high efficient electroluminescence, brightness and providing eminent in the emission range, organic light emitting diodes have been preferred a material compared with the other materials consisting of the liquid crystal. Calixarenes obtained from the reaction of p-tert-butyl phenol and formaldehyde in a suitable base have been potentially used in various research area such as catalysis, enzyme immobilization, and applications, ion carrier, sensors, nanoscience, etc. In addition, their tremendous frameworks, as well as their easily functionalization, make them an effective candidate in the applied chemistry. Herein, a calix[4]arene derivative has been synthesized, and its structure has been fully characterized using Fourier Transform Infrared Spectrophotometer (FTIR), proton nuclear magnetic resonance (¹H-NMR), carbon-13 nuclear magnetic resonance (¹³C-NMR), liquid chromatography-mass spectrometry (LC-MS), and elemental analysis techniques. The calixarene derivative has been employed as an emitting layer in the fabrication of the organic light-emitting diodes. The optical and electrochemical features of calixarane-contained organic light-emitting diodes (Clx-OLED) have been also performed. The results showed that Clx-OLED exhibited blue emission and high external quantum efficacy. As a conclusion obtained results attributed that the synthesized calixarane derivative is a promising chromophore with efficient fluorescent quantum yield that provides it an attractive candidate for fabricating effective materials for fluorescent probes and labeling studies. This study was financially supported by the Scientific and Technological Research Council of Turkey (TUBITAK Grant no. 117Z402).

Keywords: calixarene, OLED, supramolecular chemistry, synthesis

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Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal

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Planning models for fresh products is a very useful tool for improving the net profits. To get an efficient supply chain model, several functions should be considered to get a complete simulation of several operational units. We consider a linear programming model to help farmers to decide if it is convenient to choose what area should be planted for three kinds of export fruits considering their future investment. We consider area, investment, water, productivity minimal unit, and harvest restrictions to develop a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability, and initial investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market. Also, this tool help to support decisions for government and individual farmers.

Keywords: mixed integer problem, fresh fruit production, support decision model, agricultural and biosystems engineering

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Authors: Recep Kesli, Onur Turkyilmaz, Cengiz Demir

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Objective: Autoimmune collagen diseases occur with the immune reactions against the body’s own cell or tissues which cause inflammation and damage the tissues and organs. In this study, it was aimed to compare seropositivity rates and patterns of the anti-nuclear antibodies (ANA) in the diagnosis of four different autoimmune collagen tissue diseases (Rheumatoid Arthritis-RA, Systemic Lupus Erythematous-SLE, Scleroderma-SSc and Sjogren Syndrome-SS) with each other. Methods: One hundred eighty-eight patients applied to different clinics in Afyon Kocatepe University ANS Practice and Research Hospital between 11.07.2014 and 14.07.2015 that thought the different collagen disease such as RA, SLE, SSc and SS have participated in the study retrospectively. All the data obtained from the patients participated in the study were evaluated according to the included criteria. The historical archives belonging to the patients have been screened, assessed in terms of ANA positivity. The obtained data was analysed by using the descriptive statistics; chi-squared, Fischer's exact test. The evaluations were performed by SPSS 20.0 version and p < 0.05 level was considered as significant. Results: Distribution rates of the totally one hundred eighty-eight patients according to the diagnosis were found as follows: 82 (43.6%) were RA, 38 (20.2%) were SLE, 22 (11.7%) were SSc, and 46 (24.5%) were SS. Distribution of ANA positivity rates according to the collagen tissue diseases were found as follows; for RA were 54 (65,9 %), for SLE were 36 (94,7 %), for SSc were 18 (81,8 %), and for SS were 43 (93,5 %). Rheumatoid arthritis should be evaluated and classified as a different class among all the other investigated three autoimmune illnesses. ANA positivity rates were found as differently higher (91.5 %) in the SLE, SSc, and SS, from the RA (65.9 %). Differences at ANA positivity rates for RA and the other three diseases were found as statistically significant (p=0.015). Conclusions: Systemic autoimmune illnesses show broad spectrum. ANA positivity was found as an important predictor marker in the diagnosis of the rheumatologic illnesses. ANA positivity should be evaluated as more valuable and sensitive a predictor diagnostic marker in the laboratory findings of the SLE, SSc, and SS according to RA.

Keywords: antinuclear antibody (ANA), rheumatoid arthritis, scleroderma, Sjogren syndrome, systemic lupus Erythemotosus

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Authors: Sujit K. Basak

Abstract:

The aim of this paper is to analyze the impact of literature review software on researchers. The aim of this study was achieved by analyzing models in terms of perceived usefulness, perceived ease of use, and acceptance level. Collected data was analyzed using WarpPLS 4.0 software. This study used two theoretical frameworks namely Technology Acceptance Model and the Training Needs Assessment Model. The study was experimental and was conducted at a public university in South Africa. The results of the study showed that acceptance level has a high impact on research workload and productivity followed by perceived usefulness and perceived ease of use.

Keywords: technology acceptance model, training needs assessment model, literature review software, research productivity

Procedia PDF Downloads 485