Academic literature on the topic 'Sequential Equation Modeling'
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Journal articles on the topic "Sequential Equation Modeling"
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Vatsala, Aghalaya S., Govinda Pageni, and V. Anthony Vijesh. "Analysis of Sequential Caputo Fractional Differential Equations versus Non-Sequential Caputo Fractional Differential Equations with Applications." Foundations 2, no. 4 (2022): 1129–42. http://dx.doi.org/10.3390/foundations2040074.
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It is known that, from a modeling point of view, fractional dynamic equations are more suitable compared to integer derivative models. In fact, a fractional dynamic equation is referred to as an equation with memory. To demonstrate that the fractional dynamic model is better than the corresponding integer model, we need to compute the solutions of the fractional differential equations and compare them with an integer model relative to the data available. In this work, we will illustrate that the linear nq-order sequential Caputo fractional differential equations, which are sequential of order q where q<1 with fractional initial conditions and/or boundary conditions, can be solved. The reason for choosing sequential fractional dynamic equations is that linear non-sequential Caputo fractional dynamic equations with constant coefficients cannot be solved in general. We used the Laplace transform method to solve sequential Caputo fractional initial value problems. We used fractional boundary conditions to compute Green’s function for sequential boundary value problems. In addition, the solution of the sequential dynamic equations yields the solution of the corresponding integer-order differential equations as a special case as q→1.
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Samimi, Rabeah. "Sequential Bayesian SEM for Task Technology Fit." international journal of advanced research in computer science 16, no. 1 (2025): 1–5. https://doi.org/10.26483/ijarcs.v16i1.7176.
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The Task Technology Fit (TTF) model is a key framework in information systems research that examines the relationship between user task needs and technological capabilities. Structural Equation Modeling (SEM) and Bayesian Structural Equation Modeling (BSEM) are effective tools for analyzing the TTF model. SEM reveals complex relationships between observed and latent variables, while BSEM is particularly useful for dynamic analyses, incorporating prior information and updating the model in sequential steps. This study compared the performance of SEM, BSEM, and sequential Bayesian SEM in analyzing the TTF model, using Normal and Beta prior distributions. The Bayesian Information Criterion (BIC) assessed model fit, and the Root Mean Square Error (RMSE) evaluated coefficient accuracy. The results indicate that sequential BSEM effectively analyzes models like TTF in sequential conditions. The Beta distribution, known for its stability, is more suitable for sequential Bayesian models. This study introduces a new analytical framework to aid future research in information systems and sequential Bayesian analysis.
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Hajibeygi, H., and H. A. A. Tchelepi. "Compositional Multiscale Finite-Volume Formulation." SPE Journal 19, no. 02 (2013): 316–26. http://dx.doi.org/10.2118/163664-pa.
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Summary The multiscale finite-volume (MSFV) method is extended to include compositional processes in heterogeneous porous media, which require accurate modeling of the mass transfer and associated phase behaviors. A sequential-implicit strategy is used to deal with the coupling of the flow (pressure) and transport (component overall concentration) problems. In this compositional formulation, the overall continuity equation is used to formulate the pressure equation. The resulting pressure equation conserves total mass by construction and depends weakly on the distributions of the phase compositions. The transport equations are expressed in terms of the overall composition; hence, phase-appearance and -disappearance effects do not appear explicitly in these expressions. The details of the MSFV strategy for the pressure equation are described. The only source of error in this MSFV framework is the localization assumption. No additional assumptions related to the complex physics are used. For 1D problems, the sequential strategy is validated against solutions obtained by a fully implicit simulator. The accuracy of the MSFV method for compositional simulations is then illustrated for different test cases.
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Yaseen, Muhammad, Sadia Mumtaz, Reny George, Azhar Hussain, and Hossam A. Nabwey. "Darbo’s Fixed-Point Theorem: Establishing Existence and Uniqueness Results for Hybrid Caputo–Hadamard Fractional Sequential Differential Equations." Fractal and Fractional 8, no. 6 (2024): 326. http://dx.doi.org/10.3390/fractalfract8060326.
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This work explores the existence and uniqueness criteria for the solution of hybrid Caputo–Hadamard fractional sequential differential equations (HCHFSDEs) by employing Darbo’s fixed-point theorem. Fractional differential equations play a pivotal role in modeling complex phenomena in various areas of science and engineering. The hybrid approach considered in this work combines the advantages of both the Caputo and Hadamard fractional derivatives, leading to a more comprehensive and versatile model for describing sequential processes. To address the problem of the existence and uniqueness of solutions for such hybrid fractional sequential differential equations, we turn to Darbo’s fixed-point theorem, a powerful mathematical tool that establishes the existence of fixed points for certain types of mappings. By appropriately transforming the differential equation into an equivalent fixed-point formulation, we can exploit the properties of Darbo’s theorem to analyze the solutions’ existence and uniqueness. The outcomes of this research expand the understanding of HCHFSDEs and contribute to the growing body of knowledge in fractional calculus and fixed-point theory. These findings are expected to have significant implications in various scientific and engineering applications, where sequential processes are prevalent, such as in physics, biology, finance, and control theory.
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A., Bagbe, Obilade T.O., and Olatayo T.O. "A Sequential Structural Equation Modeling of Patient Satisfaction and Compliance to Treatment." African Journal of Mathematics and Statistics Studies 5, no. 3 (2022): 87–98. http://dx.doi.org/10.52589/ajmss-ttzdf8ad.
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With the renewed concern about health care quality, there is a need for improved surveillance tools and focus on special age groups. While Structural Equation Model (SEM) is an important tool for surveillance, prediction and measuring intervention impact, the need to focus on reduction in bias necessitates the modification of SEM with Sequential Structural Equation Model (SSEM) to study some specific groups of health care delivery. This study formulated a model on patient satisfaction and compliance to treatment using SSEM of two stopping times with some exogenous, endogenous and mediating variables that generally influence health care delivery. SSEM modelling of Patient Satisfaction (PS) and Compliance to Treatment (CT) involves four latent variables (factors) and some manifest (dependent) variables. Statistical Packages for Social Sciences (SPSS) and Linear Structural Relationship (LISREL) 8.80 were adopted for the analysis. The study established that the fitted indices for the second stopping time meet the threshold rules in all cases when various fitting indices were used, and the fitted model result revealed an insignificant influence of PE on HS [R2 =0.012, F = 3.199, P >.05]. This indicates that PE contributed insignificantly to HS. Therefore, this study concluded that the procedure of sequential stopping time for hypothesised relationship showed that SSEM is useful in the drive towards quality patient health care and satisfaction. Hence, this confirmed that demographic variables are significant to patient experience.
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Gao, Zhenhao, Yukang Jin, Zhiqin Cai, and Haijun Peng. "Optimal control design for enhanced stability in the transition phase of tilt-rotor UAVs." Journal of Physics: Conference Series 2977, no. 1 (2025): 012012. https://doi.org/10.1088/1742-6596/2977/1/012012.
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Abstract This article completes the dynamic modeling and optimal flight control of tilt-rotor unmanned aerial vehicles during the transition phase through multi-body dynamics and optimization theory. We derive the first type of Lagrange dynamic model of a tilt-rotor unmanned aerial vehicle using the constraint equations of the Newton-Euler equation and virtual power principle combination system and solve the optimal control problem during the transition phase using a sequential quadratic programming algorithm. The numerical results indicate that this method can smoothly switch the flight mode of tilt or unmanned aerial vehicles.
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Choi, Hyunhee, and Yunji Lee. "Sequential relationship analysis of potential learning concepts: using structural equation modeling and HMM." Korean Data Analysis Society 25, no. 1 (2023): 417–29. http://dx.doi.org/10.37727/jkdas.2022.25.1.417.
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As people use non-face-to-face learning frequently, interest in adaptive learning for efficient learning is increasing. In order for students to easily learn, it is necessary to set up a learning path that considers students' learning outcomes. However, as everyone recognizes, learning pathways are not created based on learning logs, but rather rely on the curriculum that experienced teachers have used. However, the answer to the question “Can today's students learn efficiently with the existing curriculum system?” is perhaps not certain. In addition to studies whose subjects were creating a learning path, this study uses a structural equation model, Hidden Markov Model (HMM) and inductive reasoning to create a hierarchical learning path between latent factors at a higher level. Using the mathematics learning data of company D in Korea, a learning path of concepts rather than a learning path of subdivided concepts was created. It was confirmed that the subdivided concepts showed reliable observation values for the upper concepts and that the subdivided concepts conveyed the contents whose concepts were conveyed with validity.
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Sung, Jiwoo, and Eunjung Kim. "The Relationship between Covert Narcissism and Reactive Aggression: The Sequential Mediation Effect of Hostile Attribution Bias and Anger Rumination." Society for Cognitive Enhancement and Intervention 15, no. 4 (2024): 281–97. https://doi.org/10.21197/jcei.15.4.14.
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Objective: This study examined the sequential mediating effects of hostile attribution bias and anger rumination in the relationship between covert narcissism and reactive aggression. Methods: Self-report questionnaires assessing covert narcissism, hostile attribution bias, anger rumination, and reactive aggression were administered to 300 adults aged 19 years and above. Structural equation modeling was used to examine the sequential mediating effects of hostile attribution bias and anger rumination in the relationship between covert narcissis and reactive aggression. Results: The relationship between covert narcissism and reactive aggression was found to be partially mediated by hostile attribution bias and anger rumination, sequentially. In addition, the relationship was partially mediated by anger rumination. Finally, the studied relationship was not found to be mediated by hostile attribution bias. Conclusions: Our findings suggest the need to focus on hostile attribution bias and anger rumination as well as covert narcissism in therapeutic interventions for people with reactive aggression.
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Lima, Pedro M., Wolfram Erlhagen, Gennady Yu Kulikov, and Maria V. Kulikova. "Mathematical Modeling of Working Memory in the Presence of Random Disturbance using Neural Field Equations." EPJ Web of Conferences 248 (2021): 01021. http://dx.doi.org/10.1051/epjconf/202124801021.
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In this paper, we describe a neural field model which explains how a population of cortical neurons may encode in its firing pattern simultaneously the nature and time of sequential stimulus events. Moreover, we investigate how noise-induced perturbations may affect the coding process. This is obtained by means of a two-dimensional neural field equation, where one dimension represents the nature of the event (for example, the color of a light signal) and the other represents the moment when the signal has occurred. The additive noise is represented by a Q-Wiener process. Some numerical experiments reported are carried out using a computational algorithm for two-dimensional stochastic neural field equations.
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Velegol, Darrell, and Kyle J. M. Bishop. "Analyzing Sequential Betting with a Kelly-Inspired Convective-Diffusion Equation." Entropy 26, no. 7 (2024): 600. http://dx.doi.org/10.3390/e26070600.
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The purpose of this article is to analyze a sequence of independent bets by modeling it with a convective-diffusion equation (CDE). The approach follows the derivation of the Kelly Criterion (i.e., with a binomial distribution for the numbers of wins and losses in a sequence of bets) and reframes it as a CDE in the limit of many bets. The use of the CDE clarifies the role of steady growth (characterized by a velocity U) and random fluctuations (characterized by a diffusion coefficient D) to predict a probability distribution for the remaining bankroll as a function of time. Whereas the Kelly Criterion selects the investment fraction that maximizes the median bankroll (0.50 quantile), we show that the CDE formulation can readily find an optimum betting fraction f for any quantile. We also consider the effects of “ruin” using an absorbing boundary condition, which describes the termination of the betting sequence when the bankroll becomes too small. We show that the probability of ruin can be expressed by a dimensionless Péclet number characterizing the relative rates of convection and diffusion. Finally, the fractional Kelly heuristic is analyzed to show how it impacts returns and ruin. The reframing of the Kelly approach with the CDE opens new possibilities to use known results from the chemico-physical literature to address sequential betting problems.
Dissertations / Theses on the topic "Sequential Equation Modeling"
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Zahery, Mahsa. "OPTIMIZATION FOR STRUCTURAL EQUATION MODELING: APPLICATIONS TO SUBSTANCE USE DISORDERS." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5261.
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Substance abuse is a serious issue in both modern and traditional societies. Besides health complications such as depression, cancer and HIV, social complications such as loss of concentration, loss of job, and legal problems are among the numerous hazards substance use disorder imposes on societies. Understanding the causes of substance abuse and preventing its negative effects continues to be the focus of much research.
Substance use behaviors, symptoms and signs are usually measured in form of ordinal data, which are often modeled under threshold models in Structural Equation Modeling (SEM). In this dissertation, we have developed a general nonlinear optimizer for the software package OpenMx, which is a SEM package in widespread use in the fields of psychology and genetics. The optimizer solves nonlinearly constrained optimization problems using a Sequential Quadratic Programming (SQP) algorithm. We have tested the performance of our optimizer on ordinal data and compared the results with two other optimizers (implementing SQP algorithm) available in the OpenMx package. While all three optimizers reach the same minimum, our new optimizer is faster than the other two.
We then applied OpenMx with our optimization engine to a very large population-based drug abuse dataset, collected in Sweden from over one million pairs, to investigate the effects of genetic and environmental factors on liability to drug use.
Finally, we investigated the reasons behind better performance of our optimizer by profiling all three optimizers as well as analyzing their memory consumption. We found that objective function evaluation is the most expensive task for all three optimizers, and that our optimizer needs fewer number of calls to this function to find the minimum. In terms of memory consumption, the optimizers use the same amount of memory.
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Arakaki, Thais Keiko. "Simulação de rede de distribuição de gás." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-03082017-090203/.
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O gás natural é uma fonte alternativa ao petróleo. É uma fonte de energia considerada mais limpa e segura dentre as fontes de energias não renováveis e, por isso, vem aumentando sua participação na matriz energética. Deste modo, impulsionou a criação e ampliação de diversas malhas de gasodutos. O objetivo do presente trabalho é simular redes de gasodutos utilizando ferramentas computacionais disponíveis no mercado. O HYSYS® foi adequado para simular longos gasodutos lineares. Portanto, a estratégia sequencial modular foi adequada para estas configurações. Contudo, não apresentaram bons resultados para redes. O software gratuito iiSE apresenta bons resultados para redes complexas, comprovando que os softwares orientados a equações são mais apropriados para sistemas com paralelismo.<br>Natural gas is an alternative source for oil. It is an energy source considered more clean and safe among the non-renewable energy sources and, therefore, its participation is increasing in the energy matrix. In this way, spurred the creation and expansion of several meshes of pipelines. The objective of this study is to simulate gas pipeline networks using computational tools available on the market. The HYSYS ® was suitable to simulate long linear gas pipelines. Therefore, the sequential modular strategy was appropriate for these settings. However, did not provide good results for networks. The free software iiSE presents good results for complex networks, proving that the software orixented to equations are more suitable for systems with parallelism.
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Al-Saadony, Muhannad. "Bayesian stochastic differential equation modelling with application to finance." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1530.
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In this thesis, we consider some popular stochastic differential equation models used in finance, such as the Vasicek Interest Rate model, the Heston model and a new fractional Heston model. We discuss how to perform inference about unknown quantities associated with these models in the Bayesian framework. We describe sequential importance sampling, the particle filter and the auxiliary particle filter. We apply these inference methods to the Vasicek Interest Rate model and the standard stochastic volatility model, both to sample from the posterior distribution of the underlying processes and to update the posterior distribution of the parameters sequentially, as data arrive over time. We discuss the sensitivity of our results to prior assumptions. We then consider the use of Markov chain Monte Carlo (MCMC) methodology to sample from the posterior distribution of the underlying volatility process and of the unknown model parameters in the Heston model. The particle filter and the auxiliary particle filter are also employed to perform sequential inference. Next we extend the Heston model to the fractional Heston model, by replacing the Brownian motions that drive the underlying stochastic differential equations by fractional Brownian motions, so allowing a richer dependence structure across time. Again, we use a variety of methods to perform inference. We apply our methodology to simulated and real financial data with success. We then discuss how to make forecasts using both the Heston and the fractional Heston model. We make comparisons between the models and show that using our new fractional Heston model can lead to improve forecasts for real financial data.
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Skydánek, Libor. "Nástroj pro modelování a simulaci technologických procesů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236697.
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This thesis focuses on the problem of technological process modelling and simulation. It is based on the realistic requirements of the Faculty of Mechanical Engineering, where they lacked an extensible and open simulation tool with the ability to model biomass processing. In this work we explain the main concepts in the field of (physical) chemistry, the systematic approach to process modelling and simulation and we discuss various problems of simulation methods. We also describe the differences between simulation and design specifications and give reasons for choosing the sequential modular approach. An important part of the work is aimed at the design of the simulation tool's architecture, where the main emphasis is laid on the extensibility of future application. The tool has been implemented and it is now sucessfully being used at the Faculty of Mechanical Engineering.
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Panda, Nishith. "Uncertainty modeling of ore body and grades using single normal equation simulation and sequential gaussian simulation: an application to an iron ore mine." Thesis, 2013. http://ethesis.nitrkl.ac.in/5028/1/109MN0584.pdf.
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A conventional, deterministic orebody model would lead to over estimation or under-estimation of the grade, volume and other parameters related to a deposit. This will lead to improper mine planning and thus incur huge financial risk. A proper orebody and grade modeling provide better confidence to mine owners regarding financial decision. However, only using few number of borehole data it is always difficult to come up with such type of accurate decision. Always there are certain amount of risk are associated with the estimation as well as decision. This thesis aims at providing a better risk assessment at minimizing the grade and volumetric uncertainty of the ore body. The multipoint simulation algorithms eliminate the demerits of variogram based geostatistics modeling and preserve multi-point information borrowed from training image. In this thesis, a case study of iron ore deposit from India is performed to analyses the volumetric and grade uncertainty the volumetric and grade uncertainty. Single normal equation simulation (SNESIM), a multi-point categorical simulation algorithm, was performed to measure the volumetric uncertainty of orebody. Ore volume uncertainty was performed by generating. 10 equiprobable orebody simulated models are developed. The grade uncertainty modeling was performed by applying sequential Gaussian simulation (SGM) with orebody model generated by SNESIM algorithm. The result shows that if the training image –based multi-point simulation is applied for ore body modeling, there would have been 7 % increase in volume as compared to traditional method. The grade-tonnage uncertainty reveals that uncertainty-based generates more high grade ores when compared with ordinary kriging method.
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Ssemugabi, Samuel. "Development and validation of an integrated model for evaluating e-service quality, usability and user experience (e-SQUUX) of Web-based applications in the context of a University web portal." Thesis, 2019. http://hdl.handle.net/10500/26005.
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Text in English<br>Developments in Internet technology and pervasive computing over the past two and half decades have resulted in a variety of Web-based applications (WBAs) that provide products and services to online users or customers. The Internet is used not only to transfer information via the web but is increasingly used to provide electronic services including business transactions, information-delivery and social networking, as well as e-government, e-health and e-learning. For such organisations, e-service quality, usability and user experience are considered to be critical determinants of their products’ or services’ success. Many studies to model these three concepts separately have been undertaken as part of broader studies of software quality or service quality modelling. However, to the current researcher’s knowledge, none of the studies have focussed on proposing an evaluation model that integrates and combines the three of them. This research is an effort to fill that gap.
The primary purpose of this mixed-methods research was to develop a conceptual integrated model for evaluating e-service quality, usability and user experience (e-SQUUX) of WBAs and then contextualise it to evaluation of a University web portal (UWP). This was undertaken using an exploratory sequential research design. During a qualitative phase, an extensive extensive systematic literature review of 264 relevant sources relating to dimensions of e-service quality, usability and user experience, was undertaken to derive an integrated conceptual e-service quality, usability and user experience (e-SQUUX) Model for evaluating WBAs. The model was then empirically refined through a sequential series of validations, thus developing various versions of the e-SQUUX Model. First, it was content validated by a set of four expert reviewers. Second, during the quantitative phase, in the context of a University web portal, a questionnaire survey was conducted that included a comprehensive pilot study with 29 partipants, prior to the main survey. The main survey data from 174 particiapants was used to determine a validated model, using Exploratory factor analysis (EFA), followed by producing a structural model, using partial least square – structural equation modelling (PLS-SEM). This version consisted of the components of the final e-SQUUX Model. Consequently, the research enriches the body of knowledge on IS and HCI by providing the e-SQUUX Model as an evaluation tool. For designers, developers and managers of UWPs, the model serves as a customisable set of evaluation criteria and also provides specific recommendations for design.
In line with the Exploratory sequential design of mixed methods research, the findings of the qualitative work in this research influenced the subsequent quantitative study, since the potential Likert-scale questionnaire items were derived from the definitions and meanings of the components that emanated from the qualitative phase of the study. Consequently, this research is an exemplar for developing an integrated evaluation model for specific facets or domains, and of its application in a particular context, in this case, a University web portal.
Keywords: e-service quality, usability, user experience, evaluation model, integrated model, exploratory factor analysis, partial least square – structural equation modelling (PLS-SEM), mixed methods research, Exploratory sequential design, quantitative study, qualitative study, validation, Web-based applications, University web portal<br>Information System<br>Ph D. (Information Systems)
Books on the topic "Sequential Equation Modeling"
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Morimoto, Hiraoki. Stochastic control and mathematical modeling: Applications in economics. Cambridge University Press, 2010.
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Stochastic control and mathematical modeling: Applications in economics. Cambridge University Press, 2010.
Find full textBook chapters on the topic "Sequential Equation Modeling"
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Vatsala, Aghalaya S., and Govinda Pageni. "Caputo Sequential Fractional Differential Equations with Applications." In Synergies in Analysis, Discrete Mathematics, Soft Computing and Modelling. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7014-6_6.
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Krishna Pasupuleti, Murali. "Integrating Quantitative and Qualitative Approaches Using Advanced Statistical Tools SPSS Amos and NVivo in Modern Research." In Advanced Statistical Tools and Techniques in Modern Research: Integrating Quantitative and Qualitative Approaches With SPSS Amos and NVivo. National Education Services, 2024. http://dx.doi.org/10.62311/nesx/48547.
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Abstract: This chapter explores the integration of quantitative and qualitative research methodologies using advanced statistical tools, specifically SPSS Amos for structural equation modeling (SEM) and NVivo for qualitative data analysis. It highlights the importance of mixed-methods research in addressing complex research questions by providing both statistical rigor and contextual depth. The chapter offers frameworks for integrating these methodologies, discusses sequential and concurrent designs, and provides practical strategies for synthesizing data to generate holistic insights. Emerging technologies such as AI and machine learning are also examined for their potential to enhance mixed-methods research. Through real-world case examples, researchers are guided on how to effectively use SPSS Amos and NVivo together, ensuring validity, reliability, and impactful research outcomes. This chapter serves as a valuable resource for academics and professionals looking to adopt a comprehensive approach to modern research challenges. Keywords: Quantitative research, qualitative research, SPSS Amos, NVivo, structural equation modeling, mixed-methods research, data integration, thematic analysis, sequential design, concurrent triangulation, AI in research, machine learning, research validity, research reliability, data synthesis
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"Marketing Modeling and Validation." In Systems Thinking and Process Dynamics for Marketing Systems. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0969-3.ch008.
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Most existing mathematical models of consumer choice behavior assume that individuals’ preferences remain invariant over time. Although the assumption of invariant preference functions may be reasonable in some choice contexts, consumer preferences are largely influenced by variety-seeking effects. This chapter presents four models on consumer choice behavior, shopping performance, marketplace advantage, and buying decisions involving several cognitive, economic, and relational determinants affecting consumer behavior. These models are based on sequential structural equations using interrelated variables and can be applied in research on consumer shopping channel choices based on cognitive characteristics and consumer risk profiles. The framework for measuring the consumer behavior discussed in this chapter provides analytical dimensions for delineating the prediction of intangible variables affecting consumer preferences in order to optimize the purchase decisions in a given marketplace.
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Steffen, Vilmar, Maiquiel Schmidt de Oliveira, and Edson Antonio da Silva. "A Systematic Review of the Literature on Steady-State Reactive Distillation Modeling and Simulation: Challenges and Opportunities." In Solvents - Dilute, Dissolve, and Disperse - Insights on Green Solvents and Distillation [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1005434.
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Reactive distillation has already been extensively studied, many applications have been evaluated by simulation and laboratory experiments, and many modifications in the configuration of the column are being proposed, such as the reactive dividing wall column. The steady state plays a key role because it is required to operate a process steadily at the optimal point, and it is necessary to use control strategies to keep the operation at this point. Thus, this chapter presents a systematic literature review, about the modeling and simulation of reactive distillation in steady state presenting some bibliometric results. It also presented some content related to the variations in the configurations of a reactive distillation column, mathematical modeling of the process, and computational simulation. Lately, simulation studies reported in the literature about this subject have mainly used sequential modular simulators like Aspen Plus. But, the solution process of the model’s equations still can be improved. Some opportunities are pointed out, like the use of heuristic algorithms for the solutions of the mathematical equations, better initial guess algorithm for the unknowns, and the use of a more generic scheme for representation of the mathematical model of a reactive distillation stage.
Conference papers on the topic "Sequential Equation Modeling"
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Aminaddinov, Khumoyun, and Lionel Randall Kharkrang. "Estimating Software Project Performance Using Factor Analysis and Sequential Equation Modelling." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00147.
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Xu, Jiechi, and Joseph R. Baumgarten. "A Sequential Implicit-Explicit Integration Method in Solving Nonlinear Differential Equations From Flexible System Modeling." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0425.
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Abstract The dynamic equations of motion obtained in the modeling of flexible structural systems with unknown rigid body gross motion are often highly nonlinear and possess time-varying coefficient matrices. The inherent characters of nonlinear large overall rigid body motion and linear small vibration are also involved in the system equations. Neither the implicit nor the explicit algorithm seems optimally suited and efficient by itself in dealing with these kinds of equations. This paper, therefore, presents a sequential implicit-explicit method in which it is attempted to achieve the attributes of both classes of algorithms. The equation system expressed in matrix form is first mapped to a subsystem in which the specified generalized coordinates are eliminated. The subsystem is then partitioned into two sets of coupled equations. One set of equations, describing the elastic motion, is linear with respect to the elastic generalized coordinates and is integrated implicitly. The other set of equations, governing the rigid body motion, contains the highly nonlinear coupling terms and is integrated explicitly with the back substitutions of the elastic kinematic properties already calculated in solving the first set of equations. A Newmark algorithm is employed to integrate the second order system of differential equations directly. A predictor-corrector scheme also coming from the Newmark algorithm is applied to the explicit integration. The procedures developed in the current paper are applied to simulating dynamic response of a complicated flexible system with mutually dependent rigid body unconstrained spherical motion and small elastic deformation.
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Jo, Minju, Seungji Kook, and Noseong Park. "Hawkes Process Based on Controlled Differential Equations." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/239.
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Hawkes processes are a popular framework to model the occurrence of sequential events, i.e., occurrence dynamics, in several fields such as social diffusion. In real-world scenarios, the inter-arrival time among events is irregular. However, existing neural network-based Hawkes process models not only i) fail to capture such complicated irregular dynamics, but also ii) resort to heuristics to calculate the log-likelihood of events since they are mostly based on neural networks designed for regular discrete inputs. To this end, we present the concept of Hawkes process based on controlled differential equations (HP-CDE), by adopting the neural controlled differential equation (neural CDE) technology which is an analogue to continuous RNNs. Since HP-CDE continuously reads data, i) irregular time-series datasets can be properly treated preserving their uneven temporal spaces, and ii) the log-likelihood can be exactly computed. Moreover, as both Hawkes processes and neural CDEs are first developed to model complicated human behavioral dynamics, neural CDE-based Hawkes processes are successful in modeling such occurrence dynamics. In our experiments with 4 real-world datasets, our method outperforms existing methods by non-trivial margins.
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Paknikar, Rahul, Nikhil Sharma, Priyam Nayak, Kannan Moudgalya, and Bhaskaran Raman. "Simulation scheduling of variable-structure models in OpenModelica." In Asian Modelica Conference 2022, Tokyo, Japan, November 24-25, 2022. Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp193136.
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We propose and implement a generic scheduling framework for OpenModelica to eliminate the simulation code corresponding to inactive components in a system-level model. This framework allows the model developer to auto-generate models corresponding to the discrete behavior of the underlying system, and then schedule their simulations. It also provides a Scheduler library in the Modelica language to help the model developer easily generate the schedule. The benefit of this approach is demonstrated with and without real-time simulations of a batch distillation system. The proposed approach also helps implement a sequential modular simulation to arrive at initial guesses for flowsheets, whose equations can then be solved simultaneously using the standard, equation-oriented, approach of Modelica.
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Shao, Hongwei, Ying He, and Lizhong Mu. "Numerical Analysis of Temperature Distribution in a Three-Dimensional Image-Based Hand Model." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22559.
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In the present study, a simulation has been developed to investigate the blood and temperature distribution in the human hand. The simulation consists of image-based mesh generation, blood flow modeling in large vessels, and finite element analysis of heat transfer in tissues based on the porous media theory. In order to reconstruct a real geometric mesh model of the human hand, sequential MR images of a volunteer’s hand was taken firstly. Furthermore, a MATLAB program was developed to detect the edge information of the target by applying several image preprocessing operators. Finally, a FORTRAN program based on the transfinite interpolation method was developed to generate mesh from the preprocessed images automatically, and the positions of simplified bones and vessels were set according to the anatomic structure. The blood flow in large vessels adopted in this study was provided from the one-dimensional simulation of blood circulation in the upper limb, which was completed by He [1]. On the other hand, blood flow perfused in solid tissues through the micro vessels was expressed by Darcy model. The heat transfer in tissues was described by the energy equation for porous media with assuming that a local equilibrium was achieved between the blood and tissue phase. The primary results for the distribution of the blood flow perfused in tissues and temperature were obtained in this study, and they were similar to the real state of the human hand. The improvement of this simulation will be the next work.
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Chen, Zhen, Tareq Shaalan, and Ali Dogru. "A Multi-Level Non-Linear Solver for Complex Well Modelling." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/204009-ms.
Full textAbstract:
Abstract Complex well model has proved to be important for capturing the full physics in wellbore, including pressure losses, multiphase effects, and advanced device modelling. Numerical instability may be observed especially when the well is produced at a low rate from a highly productive multi-phase zone. In this paper, a new multi-level nonlinear solver is presented in a state-of-the-art parallel complex wellbore model for addressing some difficult numerical convergence problems. A sequential two-level nonlinear solver is implemented, where the inner solver is used to address the convergence in the constraint rate equation, and then the entire complex network is solved using an outer solver. Finally, the wellbore model is coupled with the grid solution explicitly, sequentially, or implicitly. This novel formulation is robust enough to greatly improve the numerical stability due to the lagging in the computation of mixture density in wellbore constraint rate equation and the variation in the fluid composition over Newton iterations in network nonlinear solver. The numerical challenge in the complex well model and the improvement of performance with the new nonlinear solver are demonstrated using reservoir simulation. Models with complex wells running into convergence problems are constructed and simulated. With this novel nonlinear solver, simulation gives much more reliable results on well productions without numerical oscillations and computational cost is much less.
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Zhang, Bi, and Xiaolin Zheng. "Dynamic Modeling of Linear Piezomotor." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-1013.
Full textAbstract:
Abstract A two degree-of-freedom dynamic model is presented to simulate the motion characteristics of an inchvvorm type linear piezomotor. With the consideration of the sequential motions of the piezoelectric actuators in the piezomotor, the model handles the piezomotor as a two degree-of-freedom dynamic system with varying interface stiffness and damping coefficient. It incorporates the electromechanical coupling effect into its governing equations. The model is demonstrated using co-quad plots, root locus method as well as frequency and step response techniques, and verified by an experimental study. It turns out that the model can precisely predict the motion behavior of the linear piezomotor. The model can be used to improve the performance of the existing linear piezomotors and to help design new linear piezomotors.
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Duan, Shanzhong, and Kurt S. Anderson. "A Hybrid Highly Parallelizable Algorithm for the Dynamics of Rigid Body Chain Systems." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8207.
Full textAbstract:
Abstract The paper presents a new hybrid parallelizable low order algorithm for modeling the dynamic behavior of multi-rigid-body chain systems. The method is based on cutting certain system interbody joints so that largely independent multibody subchain systems are formed. These subchains interact with one another through associated unknown constraint forces f¯c at the cut joints. The increased parallelism is obtainable through cutting the joints and the explicit determination of associated constraint loads combined with a sequential O(n) procedure. In other words, sequential O(n) procedures are performed to form and solve equations of motion within subchains and parallel strategies are used to form and solve constraint equations between subchains in parallel. The algorithm can easily accommodate the available number of processors while maintaining high efficiency. An O[(n+m)Np+m(1+γ)Np+mγlog2Np](0&lt;γ&lt;1) performance will be achieved with Np processors for a chain system with n degrees of freedom and m constraints due to cutting of interbody joints.
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van Dijk, Peter J., and David C. Swailes. "Hermite-DG Method for PDF-Equations Modelling Dispersions in Turbulent Boundary Layers Including Streamwise Velocity." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31156.
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A novel methodology is presented for the numerical treatment of multi-dimensional pdf (probability density function) models used to study particle transport in turbulent boundary layers. A system of coupled Fokker-Planck type equations is constructed to describe the transport of phase-space conditioned moments of particle and fluid velocities, both streamwise and wall-normal. Unlike conventional moment-based transport equations this system allows for an exact treatment of particle deposition at the flow boundary. Moreover, the equations in the system are linear and can be solved in a sequential fashion; there is no closure problem to address. A Hermite-Discontinuous Galerkin scheme is employed to treat the system. The choice of Hermite basis functions in combination with an iterative rescaling approach, allows for efficient discretization of the, effectively, 5-dimensional phase-space domain. Results demonstrate the effectiveness of the methodology in resolving distributions near an absorbing boundary.
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Pierse, John H., and Arturo Ferna´ndez. "A Semi-Implicit Scheme for Modeling the Interaction Between Biological Cells and Electric Fields." In ASME/JSME 2007 5th Joint Fluids Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/fedsm2007-37487.
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A numerical method for computing the simultaneous solution to the fluid flow equations and the electrostatic field equations is described. The methodology focuses on the modeling of biological cells suspended in fluid plasma. The fluid flow is described using the Navier-Stokes equations for incompressible flows. The electric field is computed trough the Maxwell equations neglecting magnetic effects. The effect of the electric field on the fluid flow is accounted for through the Maxwell stresses. The systems are described by a set of partial differential equations where the solution requires the simultaneous computation of the velocity, pressure and electric potential fields. A semi-implicit numerical scheme is proposed. In order to decrease the computational time required, it is proposed to use a semi-implicit splitting scheme where the Navier-Stokes and Maxwell equations are solved sequentially. The method is used to reproduce the response of human leukocytes immersed in a rotating electric field. An agreement between the numerical results and the data from experiments is observed.