Search results for: low order model

Authors: Amir Abbas Kamanbedast, Abbas Saki

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Stepped spillways are composed of several steps, which start from around the spillway crest and continue to the downstream heel. Recently, such spillways have been receiving increasing attention due to the significant effect of the associated stairs on the flow’s rate of energy dissipation. Energy dissipation in the stepped spillways across the overflow can be explained by the watercourse contact with the stairs (i.e., large, harsh surfaces). In this context, less energy must be dissipated at the end of the spillway, and, hence, a smaller (less expensive) energy-dissipating structure is required. In this study, a stepped spillway was simulated using the model Fluent 3, and a standard model was used to model the flow disturbance. For this purpose, the energy dissipation from the stepped spillway was investigated in terms of the different numbers of stairs involved. Using k-ε, the disturbances of the numerical method for velocity and of flow depth at the downstream overflow were obtained, and, then, the energy that was dissipated throughout the spillway was calculated. Our results showed that an increase in the number of stairs can considerably increase the amount of energy dissipation for the fixed, upstream energy. In addition, the results of the numerical analyses were provided as isobar and velocity curves so points that were sensitive to cavitation could be determined.

Keywords: stepped spillway, fluent software, turbulence model of k-ε, VOF model

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Authors: H. Ben Khlifa, W. Cheikhrouhou-Koubaa, A. Cheikhrouhou

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The orthorhombic Pr₀.₈Na₀.₂−ₓKₓMnO₃ (x = 0.10 and 0.15) manganites are prepared by using the solid-state reaction at high temperatures. The critical exponents (β, γ, δ) are investigated through various techniques such as modified Arrott plot, Kouvel-Fisher method, and critical isotherm analysis based on the data of the magnetic measurements recorded around the Curie temperature. The critical exponents are derived from the magnetization data using the Kouvel-Fisher method, are found to be β = 0.32(4) and γ = 1.29(2) at TC ~ 123 K for x = 0.10 and β = 0.31(1) and γ = 1.25(2) at TC ~ 133 K for x = 0.15. The critical exponent values obtained for both samples are comparable to the values predicted by the 3D-Ising model and have also been verified by the scaling equation of state. Such results demonstrate the existence of ferromagnetic short-range order in our materials. The magnetic entropy changes of polycrystalline samples with a second-order phase transition are investigated. A large magnetic entropy change deduced from isothermal magnetization curves, is observed in our samples with a peak centered on their respective Curie temperatures (TC). The field dependence of the magnetic entropy changes are analyzed, which shows power-law dependence ΔSmax ≈ a(μ0 H)n at the transition temperature. The values of n obey the Curie Weiss law above the transition temperature. It is shown that for the investigated materials, the magnetic entropy change follows a master curve behavior. The rescaled magnetic entropy change curves for different applied fields collapse onto a single curve for both samples.

Keywords: manganites, critical exponents, magnetization, magnetocaloric, master curve

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Authors: Plessen Mogens Graf

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Within the context of multivariate time series segmentation, this paper proposes a method inspired by a posteriori optimal trading. After a normalization step, time series are treated channelwise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Trading signals, as well as trading signals obtained on the reversed time series, are used for unsupervised channelwise labeling before a consensus over all channels is reached that determines the final segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, computational efficiency, and adaptability to a wide range of different shapes of time series. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a recent model-based top-down approach fitting Gaussian models and found to be consistently faster while producing more intuitive results in the sense of segmenting time series at peaks and valleys.

Keywords: time series segmentation, model-free, trading-inspired, multivariate data

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Authors: Emrah Ayhan, Puspa Saananta Irfani, Ömer Doğukan Şahin

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Public services, which are important for the order and continuity of social life, have begun to transform into electronic services (E-service) with the development of information and communication technologies in recent years. In particular, as a result of the widespread use of the internet and the increase in citizen demands, it has become necessary to provide public services electronically. In addition to facilitating traditional public services, new types of e-services strengthen the interaction, cooperation, accessibility, transparency, citizen participation (e-governance) and accountability between citizens and the state. In this context, the factors in the literature that are considered to influence the citizens’ satisfaction towards e-services will be examined through the example of student satisfaction with the e-services (Anasis, Mergen, E-mail, library, cafeteria and other transactions) offered by Anadolu University (Eskişehir, Türkiye) through university website and mobile application. The data for the analysis will be obtained from the survey research that will be used to measure user satisfaction with university e-services of 1,000 students studying at 9 different faculties and graduate schools of Anadolu University. These data will be analyzed with a unique methodology that uses the E-GovQual model and Importance Performance Analysis (IPA) methods together. The e-GovQual model serves as a framework for evaluating the quality of e-services, allowing a detailed understanding of students' perceptions. On the other hand, the IPA method will be used to determine the performance level of Anadolu University in the provision of e-services and to understand the areas that require improvement and student expectations. Strategic goals and suggestions will be made to decision-makers, students, and researchers in line with the findings obtained in the research. Thus, it is planned to contribute to e-governance and user satisfaction in educational institutions and to reveal practical implications for optimizing online platforms to better serve student needs.

Keywords: e-service, Anadolu university, student satisfaction, e-governance, e-govqual, importance performance analysis

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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Authors: Elham Allahmoradi, Bahman Allahmoradi, Ali Bonyadi Naeini

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Complex systems have many aspects. A variety of methods have been developed to analyze these systems. The most efficient of these methods should not only be simple, but also provide useful and comprehensive information about many aspects of the system. Matrix methods are considered the most commonly methods used to analyze and design systems. Each matrix method can examine a particular aspect of the system. If these methods are combined, managers can access to more comprehensive and broader information about the system. This study was conducted in four steps. In the first step, a process model of a real project has been extracted through IDEF3. In the second step, activity levels have been attained by writing a process model in the form of a design structure matrix (DSM) and sorting it through triangulation algorithm (TA). In the third step, sub-processes have been obtained by writing the process model in the form of an interface structure matrix (ISM) and clustering it through cluster identification algorithm (CIA). In the fourth step, a mixed model has been developed to provide a unified picture of the project structure through the simultaneous presentation of activities and sub-processes. Finally, the paper is completed with a conclusion.

Keywords: integrated definition for process description capture (IDEF3) method, design structure matrix (DSM), interface structure matrix (ism), mixed matrix model, activity level, sub-process

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Authors: Yew Mun Yip, Dawei Zhang

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Proteins are the biological machinery that executes specific vital functions in every cell of the human body by folding into their 3D structures. When a protein misfolds from its native structure, the machinery will malfunction and lead to misfolding diseases. Although in vitro experiments are able to conclude that the mutations of the amino acid sequence lead to incorrectly folded protein structures, these experiments are unable to decipher the folding process. Therefore, molecular dynamic (MD) simulations are employed to simulate the folding process so that our improved understanding of the folding process will enable us to contemplate better treatments for misfolding diseases. MD simulations make use of force fields to simulate the folding process of peptides. Secondary structures are formed via the hydrogen bonds formed between the backbone atoms (C, O, N, H). It is important that the hydrogen bond energy computed during the MD simulation is accurate in order to direct the folding process to the native structure. Since the atoms involved in a hydrogen bond possess very dissimilar electronegativities, the more electronegative atom will attract greater electron density from the less electronegative atom towards itself. This is known as the polarization effect. Since the polarization effect changes the electron density of the two atoms in close proximity, the atomic charges of the two atoms should also vary based on the strength of the polarization effect. However, the fixed atomic charge scheme in force fields does not account for the polarization effect. In this study, we introduce the polarized structure-specific backbone charge (PSBC) model. The PSBC model accounts for the polarization effect in MD simulation by updating the atomic charges of the backbone hydrogen bond atoms according to equations derived between the amount of charge transferred to the atom and the length of the hydrogen bond, which are calculated from quantum-mechanical calculations. Compared to other polarizable models, the PSBC model does not require quantum-mechanical calculations of the peptide simulated at every time-step of the simulation and maintains the dynamic update of atomic charges, thereby reducing the computational cost and time while accounting for the polarization effect dynamically at the same time. The PSBC model is applied to two different β-peptides, namely the Beta3s/GS peptide, a de novo designed three-stranded β-sheet whose structure is folded in vitro and studied by NMR, and the trpzip peptides, a double-stranded β-sheet where a correlation is found between the type of amino acids that constitute the β-turn and the β-propensity.

Keywords: hydrogen bond, polarization effect, protein folding, PSBC

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Authors: Atif Zafar, Fan Haijun

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A sequence of different Reservoir Engineering methods and tools in reservoir characterization and field development are presented in this paper. The real data of Jin Gas Field of L-Basin of Pakistan is used. The basic concept behind this work is to enlighten the importance of well test analysis in a broader way (i.e. reservoir characterization and field development) unlike to just determine the permeability and skin parameters. Normally in the case of reservoir characterization we rely on well test analysis to some extent but for field development plan, the well test analysis has become a forgotten tool specifically for locations of new development wells. This paper describes the successful implementation of well test analysis in Jin Gas Field where the main uncertainties are identified during initial stage of field development when location of new development well was marked only on the basis of G&G (Geologic and Geophysical) data. The seismic interpretation could not encounter one of the boundary (fault, sub-seismic fault, heterogeneity) near the main and only producing well of Jin Gas Field whereas the results of the model from the well test analysis played a very crucial rule in order to propose the location of second well of the newly discovered field. The results from different methods of well test analysis of Jin Gas Field are also integrated with and supported by other tools of Reservoir Engineering i.e. Material Balance Method and Volumetric Method. In this way, a comprehensive way out and algorithm is obtained in order to integrate the well test analyses with Geological and Geophysical analyses for reservoir characterization and field development. On the strong basis of this working and algorithm, it was successfully evaluated that the proposed location of new development well was not justified and it must be somewhere else except South direction.

Keywords: field development plan, reservoir characterization, reservoir engineering, well test analysis

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Authors: Abderrazek Ben Maatoug, Ibrahim Fatnassi, Wassim Ben Ayed

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In this paper we investigate the issue of market risk quantification for Middle East and North Africa (MENA) Islamic market equity. We use Value-at-Risk (VaR) as a measure of potential risk in Islamic stock market, for long and short position, based on Riskmetrics model and the conditional parametric ARCH class model volatility with normal, student and skewed student distribution. The sample consist of daily data for the 2006-2014 of 11 Islamic stock markets indices. We conduct Kupiec and Engle and Manganelli tests to evaluate the performance for each model. The main finding of our empirical results show that (i) the superior performance of VaR models based on the Student and skewed Student distribution, for the significance level of α=1% , for all Islamic stock market indices, and for both long and short trading positions (ii) Risk Metrics model, and VaR model based on conditional volatility with normal distribution provides the best accurate VaR estimations for both long and short trading positions for a significance level of α=5%.

Keywords: value-at-risk, risk management, islamic finance, GARCH models

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Authors: A. Hidalgo, A. Desportes, E. Bonin, A. Kadaoui, T. Bouaricha

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Present paper is concerned with portfolio management with Black-Litterman (B-L) model. Considered stocks are exclusively limited to large companies stocks on US market. Results obtained by application of the model are presented. From analysis of collected Dow Jones stock data, remarkable explicit analytical expression of optimal B-L parameter τ, which scales dispersion of normal distribution of assets mean return, is proposed in terms of standard deviation of covariance matrix. Implementation has been developed in Matlab environment to split optimization in Markovitz sense from specific elements related to B-L representation.

Keywords: Black-Litterman, Markowitz, market data, portfolio manager opinion

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Authors: Serpil Türkyılmaz, Mesut Balıbey

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In this study, long memory properties in volatility of Turkey Stock Market are being examined through the FIGARCH, FIEGARCH and FIAPARCH models under different distribution assumptions as normal and skewed student-t distributions. Furthermore, structural changes in volatility of Turkey Stock Market are investigated. The results display long memory property and the presence of asymmetric effects of shocks in volatility of Turkey Stock Market.

Keywords: FIAPARCH model, FIEGARCH model, FIGARCH model, structural break

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Authors: Sobhy Fathy A. Hashesh

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The study was carried out in the High School Classes of Andalus Private Schools, boys section, using control and experimental groups that were randomly assigned. The study investigated the effect of Al-Andalus Improvement Model (AIM) on the development of students’ skills acquiring. The society of the study composed of Al-Andalus Private Schools, high school students, boys Section (N=700), while the sample of the study composed of four randomly assigned groups two groups of teachers (N=16) and two groups of students (N=42) with one experimental group and one control group for teachers and their students respectively. The study followed the quantitative and qualitative approaches in collecting and analyzing data to investigate the study hypotheses. Results of the study revealed that there were significant statistical differences in teachers’ performances and students' skills acquiring for the favor of the experimental groups and there was a strong correlation between the teachers performances and the students skills acquiring. The study recommended the implementation of the AIM model for the sake of teachers performances and students’ learning outcomes.

Keywords: AIM, improvement model, Classera, Al-Andalus Improvement Model.

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Authors: Mehrnoosh Pirhayati

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In this study, the researcher tried to propose a model of Translation Criticism (TC) regarding the phenomenon of Translation Quality Assessment (TQA). With changing the general view on re/writing as an illegal act, the researcher defined a scale for the act of translation and determined the redline of translation with other products. This research attempts to show TC as a related phenomenon to TQA. This study shows that TQA with using the rules and factors of TC as depicted in both product-oriented analysis and process-oriented analysis, determines the orientation or the level of the quality of translation. This study also depicts that TC, regarding TQA’s perspective, reveals the aim of the translation of original text and the root of ideological manipulation and re/writing. On the other hand, this study stresses the existence of a direct relationship between the linguistic materials and semiotic codes of a text or book. This study can be fruitful for translators, scholars, translation criticizers, and translation quality assessors, and also it is applicable in the area of pedagogy.

Keywords: a model of translation criticism, a model of translation quality assessment, critical discourse analysis (CDA), re/writing, translation criticism (TC), translation quality assessment (TQA)

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Authors: Shu-Lu Hsu, Fang-Yi Chu

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With the advances in information and communications technology, mobile context-aware applications have become powerful marketing tools. In Apple online store, there are numerous mobile applications (APPs) developed for destination tour. This study investigated the determinants of adoption of context-aware APPs for destination tour services. A model is proposed based on Technology Acceptance Model and privacy concern theory. The model was empirically tested based on a sample of 259 users of a tourism APP published by Kaohsiung Tourism Bureau, Taiwan. The results showed that the fitness of the model is well and, among all the factors, the perceived usefulness and perceived ease of use have the most significant influences on the intention to adopt context-aware destination APPs. Finally, contrary to the findings of previous literature, the effect of privacy concern on the adoption intention of context-aware APP is insignificant.

Keywords: mobile application, context-aware, privacy concern, TAM

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Authors: Chukiat Chaiboonsri, Satawat Wannapan

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This paper used an asymmetric informative concept to apply in the macroeconomic model estimation of the tourism sector in Thailand. The variables used to statistically analyze are Thailand international and domestic tourism revenues, the expenditures of foreign and domestic tourists, service investments by private sectors, service investments by the government of Thailand, Thailand service imports and exports, and net service income transfers. All of data is a time-series index which was observed between 2002 and 2015. Empirically, the tourism multiplier and accelerator were estimated by two statistical approaches. The first was the result of the Generalized Method of Moments model (GMM) based on the assumption which the tourism market in Thailand had perfect information (Symmetrical data). The second was the result of the Maximum Entropy Bootstrapping approach (MEboot) based on the process that attempted to deal with imperfect information and reduced uncertainty in data observations (Asymmetrical data). In addition, the tourism leakages were investigated by a simple model based on the injections and leakages concept. The empirical findings represented the parameters computed from the MEboot approach which is different from the GMM method. However, both of the MEboot estimation and GMM model suggests that Thailand’s tourism sectors are in a period capable of stimulating the economy.

Keywords: TThailand tourism, Maximum Entropy Bootstrapping approach, macroeconomic model, asymmetric information

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Authors: Yanqin Chen, Chao Jiang, Chongdu Cho

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This research presents the three-dimensional mechanical characteristics of a commercial gas diffusion layer by experiment and simulation results. Although the mechanical performance of gas diffusion layers has attracted much attention, its reliability and accuracy are still a major challenge. With the help of simulation analysis methods, it is beneficial to the gas diffusion layer’s extensive commercial development and the overall stress analysis of proton electrolyte membrane fuel cells during its pre-production design period. Therefore, in this paper, a three-dimensional constitutive model of a commercial gas diffusion layer, including its material stiffness matrix parameters, is developed and coded, in the user-defined material model of a commercial finite element method software for simulation. Then, the model is validated by comparing experimental results as well as simulation outcomes. As a result, both the experimental data and simulation results show a good agreement with each other, with high accuracy.

Keywords: gas diffusion layer, proton electrolyte membrane fuel cell, stiffness matrix, three-dimensional mechanical characteristics, user-defined material model

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Authors: Hamadi Cherif, Christophe Coquelet, Paolo Stringari, Denis Clodic, Laura Pellegrini, Stefania Moioli, Stefano Langè

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Biogas is a promising technology which can be used as a vehicle fuel, for heat and electricity production, or injected in the national gas grid. It is storable, transportable, not intermittent and substitutable for fossil fuels. This gas produced from the wastewater treatment by degradation of organic matter under anaerobic conditions is mainly composed of methane and carbon dioxide. To be used as a renewable fuel, biogas, whose energy comes only from methane, must be purified from carbon dioxide and other impurities such as water vapor, siloxanes and hydrogen sulfide. Purification of biogas for this application particularly requires the removal of hydrogen sulfide, which negatively affects the operation and viability of equipment especially pumps, heat exchangers and pipes, causing their corrosion. Several methods are available to eliminate hydrogen sulfide from biogas. Herein, reactive absorption in structured packed column by means of chemical absorption in aqueous sodium hydroxide solutions is considered. This study is based on simulations using Aspen Plus™ V8.0, and comparisons are done with data from an industrial pilot plant treating 85 Nm3/h of biogas which contains about 30 ppm of hydrogen sulfide. The rate-based model approach has been used for simulations in order to determine the efficiencies of separation for different operating conditions. To describe vapor-liquid equilibrium, a γ/ϕ approach has been considered: the Electrolyte NRTL model has been adopted to represent non-idealities in the liquid phase, while the Redlich-Kwong equation of state has been used for the vapor phase. In order to validate the thermodynamic model, Henry’s law constants of each compound in water have been verified against experimental data. Default values available in Aspen Plus™ V8.0 for the properties of pure components properties as heat capacity, density, viscosity and surface tension have also been verified. The obtained results for physical and chemical properties are in a good agreement with experimental data. Reactions involved in the process have been studied rigorously. Equilibrium constants for equilibrium reactions and the reaction rate constant for the kinetically controlled reaction between carbon dioxide and the hydroxide ion have been checked. Results of simulations of the pilot plant purification section show the influence of low temperatures, concentration of sodium hydroxide and hydrodynamic parameters on the selective absorption of hydrogen sulfide. These results show an acceptable degree of accuracy when compared with the experimental data obtained from the pilot plant. Results show also the great efficiency of sodium hydroxide for the removal of hydrogen sulfide. The content of this compound in the gas leaving the column is under 1 ppm.

Keywords: biogas, hydrogen sulfide, reactive absorption, sodium hydroxide, structured packed column

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Authors: Wu Peng, Anders Liljerehn, Martin Magnevall

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In metal cutting, tool wear gradually changes the micro geometry of the cutting edge. Today there is a significant gap in understanding the impact these geometrical changes have on the cutting forces which governs tool deflection and heat generation in the cutting zone. Accurate models and understanding of the interaction between the work piece and cutting tool leads to improved accuracy in simulation of the cutting process. These simulations are useful in several application areas, e.g., optimization of insert geometry and machine tool monitoring. This study aims to develop an extended Kienzle force model to account for the effect of rake angle variations and tool flank wear have on the cutting forces. In this paper, the starting point sets from cutting force measurements using orthogonal turning tests of pre-machined flanches with well-defined width, using triangular coated inserts to assure orthogonal condition. The cutting forces have been measured by dynamometer with a set of three different rake angles, and wear progression have been monitored during machining by an optical measuring collaborative robot. The method utilizes the measured cutting forces with the inserts flank wear progression to extend the mechanistic cutting forces model with flank wear as an input parameter. The adapted cutting forces model is validated in a turning process with commercial cutting tools. This adapted cutting forces model shows the significant capability of prediction of cutting forces accounting for tools flank wear and different-rake-angle cutting tool inserts. The result of this study suggests that the nonlinear effect of tools flank wear and interaction between the work piece and the cutting tool can be considered by the developed cutting forces model.

Keywords: cutting force, kienzle model, predictive model, tool flank wear

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Authors: Joseph Fitoussi, Mohammadali Shirinbayan, Abbas Tcharkhtchi

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Due to material flow during processing, short fiber reinforced composites structures obtained by injection or compression molding generally present strong spatial microstructure variation. On the other hand, quasi-static, dynamic, and fatigue behavior of these materials are highly dependent on microstructure parameters such as fiber orientation distribution. Indeed, because of complex damage mechanisms, SFRC structures design is a key challenge for safety and reliability. In this paper, we propose a micromechanical model allowing prediction of damage behavior of real structures as a function of microstructure spatial distribution. To this aim, a statistical damage criterion including strain rate and fatigue effect at the local scale is introduced into a Mori and Tanaka model. A critical local damage state is identified, allowing fatigue life prediction. Moreover, the multi-scale model is coupled with an experimental intrinsic link between damage under monotonic loading and fatigue life in order to build an abacus giving Tsai-Wu failure criterion parameters as a function of microstructure and targeted fatigue life. On the other hand, the micromechanical damage model gives access to the evolution of the anisotropic stiffness tensor of SFRC submitted to complex thermomechanical loading, including quasi-static, dynamic, and cyclic loading with temperature and amplitude variations. Then, the latter is used to fill out microstructure dependent material cards in finite element analysis for design optimization in the case of complex loading history. The proposed methodology is illustrated in the case of a real automotive component made of sheet molding compound (PSA 3008 tailgate). The obtained results emphasize how the proposed micromechanical methodology opens a new path for the automotive industry to lighten vehicle bodies and thereby save energy and reduce gas emission.

Keywords: short fiber reinforced composite, structural design, damage, micromechanical modelling, fatigue, strain rate effect

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Authors: Hye-Kyung Kang

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Social justice is a central principle of the social work profession and education. However, scholars have long questioned the profession’s commitment to putting social justice values into practice. Clinical social work has been particularly criticized for its lack of attention to social justice and for failing to address the concerns of the oppressed. One prominent criticism of clinical social work is that it often relies on individual intervention and fails to take on system-level changes or advocacy. This concern evokes the historical macro-micro tension of the social work profession where micro (e.g., mental health counseling) and macro (e.g., policy advocacy) practices are conceptualized as separate domains, creating a false binary for social workers. One contributor to this false binary seems to be that most clinical practice models do not prepare social work students and practitioners to make a clear link between clinical practice and social justice. This paper presents a model of clinical social work practice that clearly recognizes the essential and necessary connection between social justice, advocacy, and clinical practice throughout the clinical process: engagement, assessment, intervention, and evaluation. Contemporary relational theories, critical social work frameworks, and anti-oppressive practice approaches are integrated to build a clinical social work practice model that addresses the urgent need for mental health practice that not only helps and heals the person but also challenges societal oppressions and aims to change them. The application of the model is presented through case vignettes.

Keywords: social justice, clinical social work, clinical social work model, integrative model

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Authors: Xuezhang Hou

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In the present paper, a hybrid hyperbolic dynamic system formulated by partial differential equations with initial and boundary conditions is considered. First, the system is transformed to an abstract evolution system in an appropriate Hilbert space, and spectral analysis and semigroup generation of the system operator is discussed. Subsequently, a sliding model control problem is proposed and investigated, and an equivalent control method is introduced and applied to the system. Finally, a significant result that the state of the system can be approximated by an ideal sliding mode under control in any accuracy is derived and examined.

Keywords: hyperbolic dynamic system, sliding model control, semigroup of linear operators, partial differential equations

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Authors: Jillian Correia, Ali Sakkal

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Lunchtime can play a pivotal role in shaping student eating habits. Studies have previously indicated that eating a healthy meal during the school day can improve students’ well-being and academic performance, and potentially prevent childhood obesity. This study investigated the school lunch program in the United Kingdom in order to gain an understanding of the attitudes and beliefs surrounding school meals and the realities of student food patterns. Using a qualitative research methodology, this study was conducted in three primary and secondary school systems in London, United Kingdom. In depth interviews consisting of 14 headteachers, teachers, staff, and chefs and fieldwork observations of approximately 830 primary and secondary school students in the three schools’ cafeterias provided the data. The results of interview responses and fieldwork observation yielded the following set of themes: (a) school meals are publicly portrayed as healthful and nutritious, yet students’ eating habits do not align with this advertising, (b) the level of importance placed on school lunch varies widely among participants and generates inconsistent views concerning who is responsible (government, families, caterers, or schools) for students’ eating habits, (c) role models (i.e. teachers and chefs) present varying levels of interaction with students and conflicting approaches when monitoring students’ eating habits. The latter finding expanded upon Osowski, Göranzon, and Fjellström’s (2013) concept of teacher roles to formulate three education philosophies – the Removed Authority Role Model, the Accommodating Role Model, and the Social Educational Role Model – concluding that the Social Educational Role Model was the most effective at fostering an environment that encouraged healthy eating habits and positive behavior. For schools looking to cultivate strong relationships between students and teachers and facilitate healthier eating habits, these findings were used to construct three key recommendations: (1) elevate the lunch environment by encouraging proper dining etiquette, (2) get teachers eating at the table with students, and (3) shift the focus from monitoring behavior to a teacher-student dialogue centered on food awareness.

Keywords: food culture, eating habits, school meals, student behavior, education, food patterns, lunchtime

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: Robin Molinier

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Industrial symbiosis (I.S) rely on two modes of cooperation that are infrastructure sharing and resource substitution to obtain economic and environmental benefits. The former consists in the intensification of use of an asset while the latter is based on the use of waste, fatal energy (and utilities) as alternatives to standard inputs. Both modes, in fact, rely on the shift from a business-as-usual functioning towards an alternative production system structure so that in a business point of view the distinction is not clear. In order to investigate the way those cooperation modes can be distinguished, we consider the stakeholders' interplay in the business model structure regarding their resources and requirements. For infrastructure sharing (following economic engineering literature) the cost function of capacity induces economies of scale so that demand pooling reduces global expanses. Grassroot investment sizing decision and the ex-post pricing strongly depends on the design optimization phase for capacity sizing whereas ex-post operational cost sharing minimizing budgets are less dependent upon production rates. Value is then mainly design driven. For resource substitution, synergies value stems from availability and is at risk regarding both supplier and user load profiles and market prices of the standard input. Baseline input purchasing cost reduction is thus more driven by the operational phase of the symbiosis and must be analyzed within the whole sourcing policy (including diversification strategies and expensive back-up replacement). Moreover, while resource substitution involves a chain of intermediate processors to match quality requirements, the infrastructure model relies on a single operator whose competencies allow to produce non-rival goods. Transaction costs appear higher in resource substitution synergies due to the high level of customization which induces asset specificity, and non-homogeneity following transaction costs economics arguments.

Keywords: business model, capacity, sourcing, synergies

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Authors: Hameed Olalekan Bolaji

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The digital natives are driving the trends of literacy in the use of electronic devices for learning purposes. This has reconfigured the context of learning in the exploration of knowledge in a social learning environment. This study explores the impact of Ubiquitous Collaborative Mobile Learning (UCML) instructional design model in a quantitative designed-based research approach. The UCML model was a synergetic blend of four models that are relevant to the design of instructional content for a social learning environment. The UCML model serves as the treatment and instructions were transmitted via mobile device based on the principle of ‘bring your own device’ (BYOD) to promote social learning. Three research questions and two hypotheses were raised to guide the conduct of this study. A researcher-designed questionnaire was used to collate data and the it was subjected to reliability of Cronbach Alpha which yielded 0.91. Descriptive statistics of mean and standard deviation were used to answer research questions while inferential statistics of independent sample t-test was used to analyze the hypotheses. The findings reveal that the UCML model was adequately evolved and it promotes social learning its design principles through the use of mobile devices.

Keywords: collaboration, mobile device, social learning, ubiquitous

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Authors: Eliane Abdo, Olivier Colot

Abstract:

SMEs are always considered as a national priority due to their contribution to job creation, innovation and growth. Once the start-up phase is crossed with encouraging results, the company enters the phase of growth. In order to improve its competitiveness, maintain and increase its market share, the company is in the necessity even the obligation to develop its tangible and intangible investments. SMEs are generally closed companies with special and critical financial situation, limited resources and difficulty to access the capital markets; their shareholders are always living in a conflict between their independence and their need to increase capital that leads to the entry of new shareholder. The capital structure was always considered the core of research in corporate finance; moreover, the financial crisis and its repercussions on the credit’s availability, especially for SMEs make SME financing a hot topic. On the other hand, financial theories do not provide answers to capital structure’s questions; they offer tools and mode of financing that are more accessible to larger companies. Yet, SME’s capital structure can’t be independent of their governance structure. The classic financial theory supposes independence between the investment decision and the financing decision. Thus, investment determines the volume of funding, but not the split between internal or external funds. In this context, we find interesting to study the hypothesis that SMEs respond positively to the financial theories applied to large firms and to check if they are constrained by conventional solutions used by large companies. In this context, this research focuses on the analysis of the resource’s structure of SME in parallel with their investments’ structure, in order to highlight a link between their assets and liabilities structure. We founded our conceptual model based on two main theoretical frameworks: the Pecking order theory, and the Trade Off theory taking into consideration the SME’s characteristics. Our data were generated from DIANE database. Five hypotheses were tested via a panel regression to understand the type of dependence between the financing methods of 3,244 French SMEs and the development of their investment over a period of 10 years (2007-2016). The results show dependence between equity and internal financing in case of intangible investments development. Moreover, this type of business is constraint to financial debts since the guarantees provided are not sufficient to meet the banks' requirements. However, for tangible investments development, SMEs count sequentially on internal financing, bank borrowing, and new shares issuance or hybrid financing. This is compliant to the Pecking Order Theory. We, therefore, conclude that unlisted SMEs incur more financial debts to finance their tangible investments more than their intangible. However, they always prefer internal financing as a first choice. This seems to be confirmed by the assumption that the profitability of the company is negatively related to the increase of the financial debt. Thus, the Pecking Order Theory predictions seem to be the most plausible. Consequently, SMEs primarily rely on self-financing and then go, into debt as a priority to finance their financial deficit.

Keywords: capital structure, investments, life cycle, pecking order theory, trade off theory

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Authors: Ali Nadi, Ali Edrissi

Abstract:

Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: L. Yettou

Abstract:

In this study, the calculations of proton emission spectra produced by ⁶³Cu(n,xp) and ⁶⁵Cu(n,xp) reactions are used in the framework of preequilibrium models using the EMPIRE code and TALYS code. Exciton Model predidtions combined with the Kalbach angular distribution systematics and the Hybrid Monte Carlo Simulation (HMS) were used. The effects of levels densities and those of a-parameter have been investigated for our calculations. The comparison with experimental data shows clear improvement over the Exciton Model and HMS calculations.

Keywords: Preequilibrium models , level density, level density a-parameter., Empire code, Talys code.

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%.

Keywords: ADALINE, neural network, gradient decent, process mining, resource behaviour, polynomial regression model

Procedia PDF Downloads 390

Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

Abstract:

Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

Procedia PDF Downloads 427