Search results for: sequential dependence model

Authors: A. El Amri, M. El Yakhloufi Haddou, A. Khamlichi

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The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as fracture. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. The influences of thermal and mechanical loads cause damage. The temperature and strain rate dependent materials’ properties and their modelling are discussed. A Johnson-Cook Model of damage has been selected for the numerical simulations. Virtual software called the ABAQUS 6.11 is used for finite element analysis. This model was introduced in order to give information concerning crack initiation during thermal and mechanical loads.

Keywords: thermo-mechanical fatigue, failure, numerical simulation, fracture, damage

Procedia PDF Downloads 393

Authors: Cau Tran, Tu Nguyen, Tien Pham

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Model-based design (MBD) is a mathematical and visual technique for addressing design issues in the fields of communications, signal processing, and complicated control systems. It is utilized in several automotive, aerospace, industrial, and motion control applications. Virtual models are at the center of the software development process with model based design. A method used in the creation of embedded software is model-based design. In this study, the LAT motor is modeled in a simulation environment, and the LAT motor control is designed with a cascade structure, a speed and current control loop, and a controller that is used in the next part. A PID structure serves as this controller. Based on techniques and motor parameters that match the design goals, the PID controller is created for the model using traditional design principles. The MBD approach will be used to build embedded software for motor control. The paper will be divided into three distinct sections. The first section will introduce the design process and the benefits and drawbacks of the MBD technique. The design of control software for LAT motors will be the main topic of the next section. The experiment's results are the subject of the last section.

Keywords: model based design, limited angle torque, intellectual property core, hardware description language, controller area network, user datagram protocol

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Authors: Deokyeong Choe, Sung Hun Youn, Younggon Kim, Chul Soo Shin

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L-Cysteine, a sulfur-containing amino acid, has been often used in the pharmaceutical, cosmetic, food, and feed additive industries. This amino acid has been usually produced by acid-hydrolysis of human hair and poultry feathers. There are many problems, such as avoidance for use of animal hair, low yields, and formation of harmful waste material. As an alternative, the enzymatic conversion of D, L-2-amino-Δ2-thiazoline-4-carboxylic acid (ATC) to L-cysteine has been developed as an environmental-friendly method. However, the substrate solubility was too low to be used in industry. In this study, high concentrations of eutectic substrate solutions were prepared to solve the problem. Eutectic melting occurred at 39°C after mixing ATC and malonic acid at a molar ratio of 1:1. The characteristics of eutectic mixtures were analyzed by FE-SEM, EDS mapping, and XPS. However, since sorbitol, MnSO4, and NaOH should be added as supplements to the substrate mixture for the activation and stabilization of the enzyme, strategies for sequential addition of total five compounds, ATC, malonic acid, sorbitol, MnSO4, and NaOH were established. As a result, eutectic substrate mixtures of 670 mM ATC were successfully formulated. After 6 h of enzymatic reaction, 550 mM L-cysteine was made.

Keywords: D, L-2-amino-Δ2-thiazoline-4-carboxylicacid, enzymatic conversion, eutectic solution, l-cysteine

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Authors: Kholil, Laksanto Utomo

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The food needs of the Indonesian people will continue increase year to year due to the increase of population growth. For ensuring food securityand and resilience, the government has developed a program food self-resilience village since 2006. Food resilience is a complex system, consisting of subsystem availability, distribution and consumption of the sufficiency of food consumed both in quantity and quality. Low access, and limited assets to food sources is the dominant factor vulnerable of food. Women have a major role in supporting the productive activities of the family to meet food sufficiency and resilience. The purpose of this paper is to discuss the model of food self-resilience village wich gender responsive by using a dynamic system model. Model will be developed into 3 level: family, vilage, and regency in accordance with the concept of village food resilience model wich has been developed by ministry of agriculture. Model development based on the results of experts discussion and field study. By some scenarios and simulation models we will able to develop appropriate policy strategies for family food resilience. The result of study show that food resilience was influenced by many factors: goverment policies, technology, human resource, and in the same time it will be a feed back for goverment policies and number of poor family.

Keywords: food availability, food sufficiency, gender, model dynamic, law enfrocement

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Authors: Nam Chol Yu, Kyong Il Chu

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Using DLTS measurement techniques, we determined the dominant recombination center levels (defects of both A and B) in gold and platinum doped n-type silicon. Also, the injection and temperature dependence of the Shockley-Read-Hall (SRH) carrier lifetime was studied under low-level injection and high-level injection. Here measurements show that the dominant level under low-level injection located at EC-0.25eV(A) correlated to the Pt+G1 and the dominant level under high-level injection located at EC-0.54eV(B) correlated to the Au+G4. Finally, A and B are the same dominant levels for controlling the lifetime in gold-platinum doped n-silicon.

Keywords: recombination center level, lifetime, carrier lifetime control, gold, platinum, silicon

Procedia PDF Downloads 157

Authors: Reza Davoodi, Hamid Abbasi, Heidar Norouzi, Gholamabbas Alipourian

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A dynamic model shaping a process of knowledge management (KM) culture change is suggested. It is aimed at providing effective KM of employees for obtaining desired results in an organization. The essential requirements for obtaining KM culture change are determined. The proposed model realizes these requirements. Dynamics of the model are expressed by a change of its parameters. It is adjusted to the dynamic process of KM culture change. Building the model includes elaboration and integration of interconnected components. The “Result” is a central component of the model. This component determines a desired organizational goal and possible directions of its attainment. The “Confront” component engenders constructive confrontation in an organization. For this reason, the employees are prompted toward KM culture change with the purpose of attaining the desired result. The “Assess” component realizes complex assessments of employee proposals by management and peers. The proposals are directed towards attaining the desired result in an organization. The “Reward” component sets the order of assigning rewards to employees based on the assessments of their proposals.

Keywords: knowledge management, organizational culture change, employee, result

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Felix Böck

Abstract:

Nowadays, data about learners and their digital activities are collected, which could help educational institutions to better understand learning processes, improve them and be able to provide better learning assistance. In this research project, custom knowledge- and data-driven recommendation algorithms will be used to offer students in higher education integrated learning assistance. The pre-requisite for this is a learner model that is as comprehensive as possible, which should first be created and then kept up-to-date largely automatically for being able to individualize and personalize the learning experience. In order to create such a learner model, a roadmap is presented that describes the individual phases up to the creation and evaluation of the finished model. The methodological process for the research project is disclosed, and the research question of how learners can be supported in their learning with personalized, customized learning recommendations is explored.

Keywords: research agenda, user model, learner model, higher education, adaptive educational digital learning environments, personalized learning paths, recommendation system, adaptation, personalization

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Authors: German Osma, Gabriel Ordonez

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The production of metal-rubber spares for vehicles is a sequential process that consists in the transformation of raw material through cutting activities and chemical and thermal treatments, which demand electricity and fossil fuels. The energy efficiency analysis for these cases is mostly focused on studying of each machine or production step, but is not common to study of the quality of the production process achieves from aggregated value viewpoint, which can be used as a quality measurement for determining of impact on the environment. In this paper, the theory of exergetic cost is used for determining of aggregated exergy to three metal-rubber spares, from an exergy analysis and thermoeconomic analysis. The manufacturing processing of these spares is based into batch production technique, and therefore is proposed the use of this theory for discontinuous flows from of single models of workstations; subsequently, the complete exergy model of each product is built using flowcharts. These models are a representation of exergy flows between components into the machines according to electrical, mechanical and/or thermal expressions; they determine the demanded exergy to produce the effective transformation in raw materials (aggregated exergy value), the exergy losses caused by equipment and irreversibilities. The energy resources of manufacturing process are electricity and natural gas. The workstations considered are lathes, punching presses, cutters, zinc machine, chemical treatment tanks, hydraulic vulcanizing presses and rubber mixer. The thermoeconomic analysis was done by workstation and by spare; first of them describes the operation of the components of each machine and where the exergy losses are; while the second of them estimates the exergy-aggregated value for finished product and wasted feedstock. Results indicate that exergy efficiency of a mechanical workstation is between 10% and 60% while this value in the thermal workstations is less than 5%; also that each effective exergy-aggregated value is one-thirtieth of total exergy required for operation of manufacturing process, which amounts approximately to 2 MJ. These troubles are caused mainly by technical limitations of machines, oversizing of metal feedstock that demands more mechanical transformation work, and low thermal insulation of chemical treatment tanks and hydraulic vulcanizing presses. From established information, in this case, it is possible to appreciate the usefulness of theory of exergetic cost for analyzing of aggregated value in manufacturing processes.

Keywords: exergy-aggregated value, exergy efficiency, thermoeconomics, exergy modeling

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Authors: Elena V. Syurina, Sophie Renckens, Martina Valente

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Objective: The objective of this study was to investigate the nature of the conversation around orthorexia nervosa (ON) on Instagram. Methods: The present study was conducted using mixed methods, combining a concurrent triangulation and sequential explanatory design. First, 3027 pictures posted on Instagram using #Orthorexia were analyzed. Then, a questionnaire about Instagram use related to ON was completed entirely by 185 respondents. These two quantitative data sources were statistically analyzed and triangulated afterwards. Finally, 9 interviews were conducted, to more deeply investigate what is being said about ON on Instagram and what the motivations to post about it are. Results: Four main categories of pictures were found to be represented in Instagram posts about ON: ‘food’, ‘people’, ‘text’, and ‘other.’ Savory and unprocessed food was most highly represented within the food category, and pictures of people were mostly pictures of the account holder. People who self-identify as having ON were more likely to post about ON, and they were significantly more likely to post about ‘food’, ‘people’ and ‘text.’ The goal of the posts was to raise awareness around ON, as well as to provide support for people who believe to be suffering from it. Conclusion: Since the conversation around ON on Instagram is supportive, it could be beneficial to consider Instagram use in the treatment of ON. However, more research is needed on a larger scale.

Keywords: orthorexia nervosa, Instagram, social media, disordered eating

Procedia PDF Downloads 138

Authors: Anusha P. Wijesundara, Dulap I. Rathnayake, Nihal D. Perera

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Bathymetric information is fundamental importance to coastal and marine planning and management, nautical navigation, and scientific studies of marine environments. Satellite-derived bathymetry data provide detailed information in areas where conventional sounding data is lacking and conventional surveys are inaccessible. The two empirical approaches of log-linear bathymetric inversion model and non-linear bathymetric inversion model are applied for deriving bathymetry from high-resolution multispectral satellite imagery. This study compares these two approaches by means of geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. Based on the Levenberg-Marquardt method calibrated the parameters of non-linear inversion model and the multiple-linear regression model was applied to calibrate the log-linear inversion model. In order to calibrate both models, Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Residuals were calculated as the difference between the derived depth values and the validation echo sounder bathymetry data and the geographical distribution of model residuals was mapped. The spatial autocorrelation was calculated by comparing the performance of the bathymetric models and the results showing the geographic errors for both models. A spatial error model was constructed from the initial bathymetry estimates and the estimates of autocorrelation. This spatial error model is used to generate more reliable estimates of bathymetry by quantifying autocorrelation of model error and incorporating this into an improved regression model. Log-linear model (R²=0.846) performs better than the non- linear model (R²=0.692). Finally, the spatial error models improved bathymetric estimates derived from linear and non-linear models up to R²=0.854 and R²=0.704 respectively. The Root Mean Square Error (RMSE) was calculated for all reference points in various depth ranges. The magnitude of the prediction error increases with depth for both the log-linear and the non-linear inversion models. Overall RMSE for log-linear and the non-linear inversion models were ±1.532 m and ±2.089 m, respectively.

Keywords: log-linear model, multi spectral, residuals, spatial error model

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Authors: Ishan Arora, Anurag Rathore

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The primary objective of this work was to study the effect of resin chemistry, pH and molarity of binding and elution buffer on aggregate removal using Cation Exchange Chromatography and find the optimum conditions which can give efficient aggregate removal with minimum loss of yield. Four different resins were used for carrying out the experiments: Fractogel EMD SO3-(S), Fractogel EMD COO-(M), Capto SP ImpRes and S Ceramic HyperD. Runs were carried out on the AKTA Avant system. Design of Experiments (DOE) was used for analysis using the JMP software. The dependence of the yield obtained using different resins on the operating conditions was studied. Success has been achieved in obtaining yield greater than 90% using Capto SP ImpRes and Fractogel EMD COO-(M) resins. It has also been found that a change in the operating conditions generally has different effects on the yields obtained using different resins.

Keywords: aggregates, cation exchange chromatography, design of experiments, monoclonal antibodies

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Authors: Nadish Anand, Richard D. Gould

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A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

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Authors: Asowata Osamede

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Solar radiation provides the largest renewable energy potential on earth and photovoltaics (PV) are considered a promising technological solution to support the global transformation to a low-carbon economy and reduce dependence on fossil fuels. The aim of this paper is to evaluate the efficiency of power conditioning devices with a focus on the Buck and Boost DC-DC converters (12 V, 24 V and 48 V) in a basic off grid PV system with a varying load profile. This would assist in harnessing more of the available solar energy. The practical setup consists of a PV panel that is set to an orientation angle of 0º N, with corresponding tilt angles. Preliminary results, which include data analysis showing the power loss in the system and efficiency, indicate that the 12V DC-DC converter coupled with the load profile had the highest efficiency for a latitude of 26º S throughout the year.

Keywords: poly-crystalline PV panels, DC-DC converters, tilt and orientation angles, direct solar radiation, load profile

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Authors: Ballari Bagchi

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The dynamism ingrained in the process of urban-rural integration is manifested in the emergence of rurban settlements, referring to areas that combine the characteristics of agricultural activities found in rural zones with those of suburban living areas and industrialised zones. The concept of rurbanisation refers to the idea of introducing urban conveniences and opportunities, to rural areas in an attempt to stem rural urban migration. In the backdrop of the worldwide problem of disharmonised urban-rural dependence and the associated problems in urban and rural areas, the present study seeks to explore the potentialities of few settlements having a blend of rural and urban characteristics in the urban field of Koch Bihar. The prime concern of the present paper is three-fold: (i) to identify the rurban centres, (ii) to analyse the spatial integration of these identified centres with the rural areas situated in the urban periphery, and (iii) to suggest the necessities to be introduced in these settlements. The methodology applied here includes rurban index, gravity model, and functional classification of rurban centres, correlation and regression analysis and cartographic representation of data collected through primary and secondary sources. The investigation has identified a number of settlements potentially viable to be termed as rurban centres which may render services to the other less equipped rural areas in all aspects of life and thereby would lessen the burden on Koch Bihar urban centre. The levels of infrastructure of these settlements should be such that it might even attract the urban population in a reverse direction. The villages belonging to the lower rung of these service settlements would require metalled road connection with these intermediate settlements in addition to their connection with the core town. That is to say, a proper policy needs to be adopted in this regard to furnish these settlements with required infrastructures for serving their own population as well as the population of other villages. As a consequence of that, the idea of a well-coordinated settlement hierarchy may emerge in future.

Keywords: Hinterland, rurban, settlement hierarchy, urban-rural integration

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Authors: Diogo Pereira, Maria Moura, Joana Campos, João Nunes

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As the Ukraine war highlights the European Economic Area’s vulnerability and external dependence on feed and food, agriculture gains significant importance. Transformative change is necessary to reach a sustainable and resilient agricultural sector. Agriculture is an important drive for bioeconomy and the equilibrium and survival of society and rural fires resilience. The pressure of (1) water stress, (2) nutrient cycle, and (3) social demographic evolution towards 70% of the population in Urban systems and the aging of the rural population, combined with climate change, exacerbates the problem and paradigm of rural and wildfires, especially in Portugal. The Portuguese territory is characterized by (1) 28% of marginal land, (2) the soil quality of 70% of the territory not being appropriate for agricultural activity, (3) a micro smallholding, with less than 1 ha per proprietor, with mainly familiar and traditional agriculture in the North and Centre regions, and (4) having the most vulnerable areas for rural fires in these same regions. The most important difference between the South, North and Centre of Portugal, referring to rural and wildfires, is the agricultural activity, which has a higher level in the South. In Portugal, rural and wildfires represent an average annual economic loss of around 800 to 1000 million euros. The WinBio model is an agrienvironmental metabolism design, with the capacity to create a new agri-food metabolism through Agricultural Organized Areas, a privatepublic partnership. This partnership seeks to grow agricultural activity in regions with (1) abandoned territory, (2) micro smallholding, (3) water and nutrient management necessities, and (4) low agri-food literacy. It also aims to support planning and monitoring of resource use efficiency and sustainability of territories, using agriculture as a barrier for rural and wildfires in order to protect rural population.

Keywords: agricultural organized areas, residues, climate change, drought, nutrients, rural and wild fires

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Authors: Zainab Fadhil Al Toki, Nader Ghareeb

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This study investigates how lightweight a fire resistance door will perform with under types of insulation materials. Data is initially collected from various websites, scientific books and research papers. Results show that different layers of insulation in a single door can perform better than one insulator. Furthermore, insulation materials that are lightweight, high strength and low thermal conductivity are the most preferred for fire-rated doors. Whereas heavy weight, low strength, and high thermal conductivity are least preferred for fire resistance doors. Fire-rated door specifications, theoretical test methodology, structural analysis, and comparison between five different models with diverse layers insulations are presented. Five different door models are being investigated with different insulation materials and arrangements. Model 1 contains an air gap between door layers. Model 2 includes phenolic foam, mild steel and polyurethane. Model 3 includes phenolic foam and glass wool. Model 4 includes polyurethane and glass wool. Model 5 includes only rock wool between the door layers. It is noticed that model 5 is the most efficient model, and its design is simple compared to other models. For this model, numerical calculations are performed to check its efficiency and the results are compared to data from experiments for validation. Good agreement was noticed.

Keywords: fire resistance, insulation, strength, thermal conductivity, lightweight, layers

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Authors: Kolesnik Nadezda A.

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The research aims to elaborate inter-organizational network relationships management model to maximize value co-creation. We propose a network management framework that requires evaluation of network partners with respect to their position and role in network; and elaboration of appropriate relationship development strategy with partners in network. Empirical research and approval is based on the case study method, including structured in-depth interviews with the companies from b2b market.

Keywords: inter-organizational networks, value co-creation, model, B2B market

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Authors: Phanida Phukoetphim, Asaad Y. Shamseldin

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In this study, the novel Stochastic Gradient Boosting (SGB) combination method is addressed for producing daily river flows from four different rain-runoff models of Ohinemuri catchment, New Zealand. The selected rainfall-runoff models are two empirical black-box models: linear perturbation model and linear varying gain factor model, two conceptual models: soil moisture accounting and routing model and Nedbør-Afrstrømnings model. In this study, the simple average combination method and the weighted average combination method were used as a benchmark for comparing the results of the novel SGB combination method. The models and combination results are evaluated using statistical and graphical criteria. Overall results of this study show that the use of combination technique can certainly improve the simulated river flows of four selected models for Ohinemuri catchment, New Zealand. The results also indicate that the novel SGB combination method is capable of accurate prediction when used in a combination method of the simulated river flows in New Zealand.

Keywords: multi-model combination, rainfall-runoff modeling, stochastic gradient boosting, bioinformatics

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Authors: Michele Catalano, Jacopo Di Domenico, Luca Riccetti, Andrea Teglio

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We develop a data-driven agent based model (ABM) for the Italian economy. We calibrate the model for the initial condition and parameters. As a preliminary step, we replicate the Monte-Carlo simulation for the Austrian economy. Then, we evaluate the dynamic properties of the model: the long-run equilibrium and the allocative efficiency in terms of disequilibrium patterns arising in the search and matching process for final goods, capital, intermediate goods, and credit markets. In this perspective, we use a randomized initial condition approach. We perform a robustness analysis perturbing the system for different parameter setups. We explore the empirical properties of the model using a rolling window forecast exercise from 2010 to 2022 to observe the model’s forecasting ability in the wake of the COVID-19 pandemic. We perform an analysis of the properties of the model with a different number of agents, that is, with different scales of the model compared to the real economy. The model generally displays transient dynamics that properly fit macroeconomic data regarding forecasting ability. We stress the model with a large set of shocks, namely interest policy, fiscal policy, and exogenous factors, such as external foreign demand for export. In this way, we can explore the most exposed sectors of the economy. Finally, we modify the technology mix of the various sectors and, consequently, the underlying input-output sectoral interdependence to stress the economy and observe the long-run projections. In this way, we can include in the model the generation of endogenous crisis due to the implied structural change, technological unemployment, and potential lack of aggregate demand creating the condition for cyclical endogenous crises reproduced in this artificial economy.

Keywords: agent-based models, behavioral macro, macroeconomic forecasting, micro data

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Authors: Khaled Mammar

Abstract:

This paper presents an application of adaptive neuro-fuzzy controller for PEM fuel cell system. The model proposed for control include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore, a Fuzzy Logic (FLC) and adaptive neuro-fuzzy controllers are used to control the active power of PEM fuel cell system. The controllers modify the hydrogen flow feedback from the terminal load. The validity of the controller is verified when the fuel cell system model is used in conjunction with the ANFIS controller to predict the response of the active power. Simulation results confirmed the high-performance capability of the neuo-fuzzy to control power generation.

Keywords: fuel cell, PEMFC, modeling, simulation, Fuzzy Logic Controller, FLC, adaptive neuro-fuzzy controller, ANFIS

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Authors: Seyed-Mohammad-Kazem Emami, Behrang Saki, Majid Mohammadian

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The wastewater flow field inside a sewage network including pipe and ‎manhole was investigated using a Computational Fluid Dynamics ‎‎(CFD) model. The numerical model is developed by incorporating a ‎rheological model to calculate the viscosity of wastewater fluid by ‎means of open source toolbox OpenFOAM. The rheological ‎properties of prepared wastewater fluid suspensions are first measured ‎using a BrookField LVDVII Pro+ viscometer with an enhanced UL ‎adapter and then correlated the suitable rheological viscosity model ‎values from the measured rheological properties. The results show the ‎significant effects of rheological characteristics of wastewater fluid on ‎the flow domain of sewer system. Results were compared and ‎discussed with the commonly used Newtonian model to evaluate the ‎differences for velocity profile, pressure and shear stress. ‎

Keywords: Non-Newtonian flows, Wastewater, Numerical simulation, Rheology, Sewage Network

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Authors: Taher M. Abou-deif, Esmail M. El-Bialy, Essam E. Khalil

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The paper is devoted to numerically investigating the influence of the air supply outlets geometry on human comfort inside living looms. A computational fluid dynamics model is developed to examine the air flow characteristics of a room with different supply air diffusers. The work focuses on air flow patterns, thermal behavior in the room with few number of occupants. As an input to the full-scale 3-D room model, a 2-D air supply diffuser model that supplies direction and magnitude of air flow into the room is developed. Air distribution effect on thermal comfort parameters was investigated depending on changing the air supply diffusers type, angles and velocity. Air supply diffusers locations and numbers were also investigated. The pre-processor Gambit is used to create the geometric model with parametric features. Commercially available simulation software “Fluent 6.3” is incorporated to solve the differential equations governing the conservation of mass, three momentum and energy in the processing of air flow distribution. Turbulence effects of the flow are represented by the well-developed two equation turbulence model. In this work, the so-called standard k-ε turbulence model, one of the most widespread turbulence models for industrial applications, was utilized. Basic parameters included in this work are air dry bulb temperature, air velocity, relative humidity and turbulence parameters are used for numerical predictions of indoor air distribution and thermal comfort. The thermal comfort predictions through this work were based on ADPI (Air Diffusion Performance Index),the PMV (Predicted Mean Vote) model and the PPD (Percentage People Dissatisfied) model, the PMV and PPD were estimated using Fanger’s model.

Keywords: thermal comfort, Fanger's model, ADPI, energy effeciency

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Authors: Arian Ebneyamini, Hoda Azimi, Jules Thibaults, F. Handan Tezel

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In this study, a modification of Hennepe model for pervaporation separation of butanol from aqueous solutions using Polydimethylsiloxane (PDMS) mixed matrix membranes has been introduced and validated by experimental data. The model was compared to the original Hennepe model and few other models which are applicable for membrane gas separation processes such as Maxwell, Lewis Nielson and Pal. Theoretical modifications for non-ideal interface morphology have been offered to predict the permeability in case of interface void, interface rigidification and pore-blockage. The model was in a good agreement with experimental data.

Keywords: butanol, PDMS, modeling, pervaporation, mixed matrix membranes

Procedia PDF Downloads 221

Authors: Ishan Arora, Anurag Rathore

Abstract:

The primary objective of this work was to study the effect of resin chemistry, pH and molarity of binding and elution buffer on aggregate removal using Cation Exchange Chromatography and find the optimum conditions which can give efficient aggregate removal with minimum loss of yield. Four different resins were used for carrying out the experiments: Fractogel EMD SO3-(S), Fractogel EMD COO-(M), Capto SP ImpRes and S Ceramic HyperD. Runs were carried out on the AKTA Avant system. Design of Experiments (DOE) was used for analysis using the JMP software. The dependence of the yield obtained using different resins on the operating conditions was studied. Success has been achieved by obtaining yield greater than 90% using Capto SP ImpRes and Fractogel EMD COO-(M) resins. It has also been found that a change in the operating conditions generally has different effects on the yields obtained using different resins.

Keywords: aggregates, cation exchange chromatography, design of experiments, monoclonal antibodies

Procedia PDF Downloads 261

Authors: Fethi Bouksila, Magnus Persson, Ronny Berndtsson, Akissa Bahri

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Under salinity conditions, we evaluate the performance of Hilhost (2000) model to predict pore electrical conductivity ECp from dielectric permittivity and bulk electrical conductivity (ECa) using Time and Frequency Domain Reflectometry sensors (TDR, FDR). Using FDR_WET sensor, RMSE of ECp was 4.15 dS m-1. By replacing the standard soil parameter (K0) in Hilhost model by K0-ECa relationship, the RMSE of ECp decreased to 0.68 dS m-1. WET sensor could give similar accuracy to estimate ECp than TDR if calibrated values of K0 were used instead of standard values in Hilhost model.

Keywords: hilhost model, soil salinity, time domain reflectometry, frequency domain reflectometry, dielectric methods

Procedia PDF Downloads 135

Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

Abstract:

Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

Procedia PDF Downloads 97

Authors: Nataliya Venelinova

Abstract:

The paper proposes a new interdisciplinary model of reconsidering the role of mass communication effects by coverage of terrorism. The idea of 4P model is based on the synergy, created by the information strategy of threat, predominantly used by terrorist groups, the effects of mediating the symbolic action of the terrorist attacks or the taking of responsibility of any attacks, and the reshaped public perception for security after the attacks being mass communicated. The paper defines the mass communication cycle of terrorism, which leads not only to re-agenda setting of the societies, but also spirally amplifying the effect of propagating fears by over-informing on terrorism attacks. This finally results in the outlining of the so called 4P-model of information terrorism: mass propaganda, panic, paranoia and pandemic.

Keywords: information terrorism, mass communication cycle, public perception, security

Procedia PDF Downloads 174

Authors: Hung-Chi Chang

Abstract:

For an extended EPQ model with random yield, the existent study revealed that both the disaggregating and consolidating shipment policies for the imperfect quality items are independent of holding cost, and recommended a model with economic benefit by comparing the least total cost for each of the three models investigated. To better capture the real situation, we generalize the existent study to include different holding costs for perfect and imperfect quality items. Through analysis, we show that the above shipment policies are dependent on holding costs. Furthermore, we derive a simple decision rule solely based on the thresholds of problem parameters to select a superior model. The results are illustrated analytically and numerically.

Keywords: consolidating shipments, disaggregating shipments, EPQ, imperfect quality, inventory

Procedia PDF Downloads 376

Authors: Watchareeporn Chaimongkol

Abstract:

In this paper, we compare the statistical measures accuracy of composite forecasting model to estimate automobile customer demand in Thailand. A modified simple exponential smoothing and autoregressive integrate moving average (ARIMA) forecasting model is built to estimate customer demand of passenger cars, instead of using information of historical sales data. Our model takes into account special characteristic of the Thai automobile market such as sales promotion, advertising and publicity, petrol price, and interest rate for loan. We evaluate our forecasting model by comparing forecasts with actual data using six accuracy measurements, mean absolute percentage error (MAPE), geometric mean absolute error (GMAE), symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), median relative absolute error (MdRAE), and geometric mean relative absolute error (GMRAE).

Keywords: composite forecasting, simple exponential smoothing model, autoregressive integrate moving average model selection, accuracy measurements

Procedia PDF Downloads 362