scholarly journals A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps

2021 ◽  
Vol 8 ◽  
Author(s):  
Gianluca Selvaggio ◽  
Serena Cristellon ◽  
Luca Marchetti
Keyword(s):  
Hybrid Approach ◽  
Quantitative Information ◽  
Notch Signaling Pathway ◽  
Equation Model ◽  
Qualitative Description ◽  
Model Parameters ◽  
Modeling Approach ◽  
Qualitative Models ◽  
Logical Formalism

Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity to describe the underlying kinetic processes. We propose a hybrid modeling approach that integrates ordinary differential equations and logical formalism to describe distinct biological layers and their communication. We focused on a multicellular system as a case study by applying the hybrid formalism to the well-known Delta-Notch signaling pathway. We used a differential equation model to describe the intracellular pathways while the cell–cell interactions were defined by logic rules. The hybrid approach herein employed allows us to combine the pros of different modeling techniques by overcoming the lack of quantitative information with a qualitative description that discretizes activation and inhibition processes, thus avoiding complexity.

scholarly journals Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1265 ◽  
Author(s):  
Johanna Geis-Schroer ◽  
Sebastian Hubschneider ◽  
Lukas Held ◽  
Frederik Gielnik ◽  
Michael Armbruster ◽  
...  
Keyword(s):  
Low Voltage ◽  
Measurement Data ◽  
Analytical Calculation ◽  
Laboratory Measurement ◽  
Model Parameters ◽  
Calculation Methods ◽  
Modeling Approach ◽  
Measurement Results ◽  
Simulation Results ◽  
Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

scholarly journals Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

2021 ◽  
Vol 9 (7) ◽  
pp. 761
Author(s):  
Liang Zhang ◽  
Junmin Mou ◽  
Pengfei Chen ◽  
Mengxia Li
Keyword(s):  
Path Planning ◽  
Local Minimum ◽  
Hybrid Approach ◽  
Artificial Potential Field ◽  
Whole Process ◽  
International Regulations ◽  
Local Path ◽  
Velocity Obstacle ◽  
Global And Local

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

scholarly journals Co-creating narratives for WEF nexus governance: a Quantitative Story-Telling case study in the Canary Islands

2021 ◽  
Author(s):  
Violeta Cabello ◽  
David Romero ◽  
Ana Musicki ◽  
Ângela Guimarães Pereira ◽  
Baltasar Peñate
Keyword(s):  
Water Resources ◽  
Canary Islands ◽  
Sustainable Use ◽  
Quantitative Information ◽  
Practical Case ◽  
Story Telling ◽  
Practical Applications ◽  
Narrative Assessment ◽  
Alternative Water

AbstractThe literature on the water–energy–food nexus has repeatedly signaled the need for transdisciplinary approaches capable of weaving the plurality of knowledge bodies involved in the governance of different resources. To fill this gap, Quantitative Story-Telling (QST) has been proposed as a science for adaptive governance approach that aims at fostering pluralistic and reflexive research processes to overcome narrow framings of water, energy, and food policies as independent domains. Yet, there are few practical applications of QST and most run on a pan-European scale. In this paper, we apply the theory of QST through a practical case study regarding non-conventional water sources as an innovation for water and agricultural governance in the Canary Islands. We present the methods mixed to mobilize different types of knowledge and analyze interconnections between water, energy, and food supply. First, we map and interview relevant knowledge holders to elicit narratives about the current and future roles of alternative water resources in the arid Canarian context. Second, we run a quantitative diagnosis of nexus interconnections related to the use of these resources for irrigation. This analysis provides feedback to the narratives in terms of constraints and uncertainties that might hamper the expectations posed on this innovation. Thirdly, the mixed analysis is used as fuel for discussion in participatory narrative assessment workshops. Our experimental QST process succeeded in co-creating new knowledge regarding the water–energy–food nexus while addressing some relational and epistemological uncertainties in the development of alternative water resources. Yet, the extent to which mainstream socio-technical imaginaries surrounding this innovation were transformed was rather limited. We conclude that the potential of QST within sustainability place-based research resides on its capacity to: (a) bridge different sources of knowledge, including local knowledge; (b) combine both qualitative and quantitative information regarding the sustainable use of local resources, and (c) co-create narratives on desirable and viable socio-technical pathways. Open questions remain as to how to effectively mobilize radically diverse knowledge systems in complex analytical exercises where everyone feels safe to participate.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

scholarly journals Empirical Underidentification with the Bifactor Model: A Case Study

2017 ◽  
Vol 78 (5) ◽  
pp. 717-736 ◽  
Author(s):  
Samuel Green ◽  
Yanyun Yang
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Bifactor Model ◽  
Data Sets ◽  
Bifactor Models

Bifactor models are commonly used to assess whether psychological and educational constructs underlie a set of measures. We consider empirical underidentification problems that are encountered when fitting particular types of bifactor models to certain types of data sets. The objective of the article was fourfold: (a) to allow readers to gain a better general understanding of issues surrounding empirical identification, (b) to offer insights into empirical underidentification with bifactor models, (c) to inform methodologists who explore bifactor models about empirical underidentification with these models, and (d) to propose strategies for structural equation model users to deal with underidentification problems that can emerge when applying bifactor models.

scholarly journals Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

2021 ◽  
Vol 11 (8) ◽  
pp. 3487
Author(s):  
Helge Nordal ◽  
Idriss El-Thalji
Keyword(s):  
Simulation Model ◽  
Simulation Modeling ◽  
Study Data ◽  
Opportunistic Maintenance ◽  
Maintenance Strategies ◽  
Modeling Approach ◽  
Intelligent Maintenance ◽  
Diagnosis And Prognosis ◽  
Case Study Data

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

Subjective Design Evaluation With Multiple Attributes

1990 ◽  
Author(s):  
Deborah L. Thurston
Keyword(s):  
Automotive Industry ◽  
The United States ◽  
Quantitative Information ◽  
Design Decision ◽  
Utility Analysis ◽  
Materials Selection ◽  
Design Alternatives ◽  
Multiattribute Utility Analysis ◽  
Alternative Designs

Abstract A formal methodology is presented which may be used to evaluate design alternatives in the iterative design/redesign process. Deterministic multiattribute utility analysis is used to compare the overall utility or value of alternative designs as a function of the levels of several performance characteristics of a manufactured system. The evaluation function reflects the designers subjective preferences. Sensitivity analysis provides quantitative information as to how a design should be modified in order to increase its utility to the design decision maker. Improvements in one or more areas or performance and tradeoffs between attributes which would increase desirability of a design most may be quantified. A case study of materials selection and design in the automotive industry is presented. The methodology was applied to 6 automotive companies in the United States and Europe, and results are used to illustrate the steps followed in application.

A stochastic differential equation model of diurnal cortisol patterns

2001 ◽  
Vol 280 (3) ◽  
pp. E450-E461 ◽  
Author(s):  
Emery N. Brown ◽  
Patricia M. Meehan ◽  
Arthur P. Dempster
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Plasma Cortisol ◽  
Equation Model ◽  
Plasma Cortisol Level ◽  
Model Parameters ◽  
Kinetic Properties ◽  
Simulation Studies ◽  
Physiological Factors ◽  
Diurnal Patterns

Circadian modulation of episodic bursts is recognized as the normal physiological pattern of diurnal variation in plasma cortisol levels. The primary physiological factors underlying these diurnal patterns are the ultradian timing of secretory events, circadian modulation of the amplitude of secretory events, infusion of the hormone from the adrenal gland into the plasma, and clearance of the hormone from the plasma by the liver. Each measured plasma cortisol level has an error arising from the cortisol immunoassay. We demonstrate that all of these three physiological principles can be succinctly summarized in a single stochastic differential equation plus measurement error model and show that physiologically consistent ranges of the model parameters can be determined from published reports. We summarize the model parameters in terms of the multivariate Gaussian probability density and establish the plausibility of the model with a series of simulation studies. Our framework makes possible a sensitivity analysis in which all model parameters are allowed to vary simultaneously. The model offers an approach for simultaneously representing cortisol's ultradian, circadian, and kinetic properties. Our modeling paradigm provides a framework for simulation studies and data analysis that should be readily adaptable to the analysis of other endocrine hormone systems.

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