A Union of Dynamic Hydrological Modeling and Satellite Remotely-Sensed Data for Spatiotemporal Assessment of Sediment Yields

(1) The existing frameworks for water quality modeling overlook the connection between multiple dynamic factors affecting spatiotemporal sediment yields (SY). This study aimed to implement satellite remotely sensed data and hydrological modeling to dynamically assess the multiple factors within basin-scale hydrologic models for a realistic spatiotemporal prediction of SY in watersheds. (2) A connective algorithm was developed to incorporate dynamic models of the crop and cover management factor (C-factor) and the soil erodibility factor (K-factor) into the Soil and Water Assessment Tool (SWAT) with the aid of the Python programming language and Geographic Information Systems (GIS). The algorithm predicted the annual SY in each hydrologic response unit (HRU) of similar land cover, soil, and slope characteristics in watersheds between 2002 and 2013. (3) The modeled SY closely matched the observed SY using the connective algorithm with the inclusion of the two dynamic factors of K and C (predicted R2 (PR2): 0.60–0.70, R2: 0.70–0.80, Nash Sutcliffe efficiency (NS): 0.65–0.75). The findings of the study highlight the necessity of excellent spatial and temporal data in real-time hydrological modeling of catchments.

A New General Equilibrium Welfare Measure, with Application to Labor Income Taxes

In this paper we extend the Hicksian compensating variation welfare measure in two directions. First, we adjust the size of the compensating variation in order to account for the fact that the compensating transfers will result in changes in output, as well as in prices, because labor and, in dynamic models, capital will adjust in response to these transfers. Second, we extend the measure to a dynamic setting with possibly time non-separable preferences. We find that these considerations become more significant for the welfare cost of higher labor income taxes as one moves from static to dynamic models, to models with time non-separable preferences, and finally to models with uncertainty.

The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading

In network models of propagation processes, the individual, microscopic level perspective is the norm, with aggregations studied as possible outcomes. On the contrary, we adopted a mesoscale perspective with groups as the core element and in this sense we present a novel agent-group dynamic model of propagation in networks. In particular, we focus on ephemeral groups that dynamically form, create new links, and dissolve. The experiments simulated 160 model configurations and produced results describing cases of consecutive and non-consecutive dynamic grouping, bounded or unbounded in the number of repetitions. Results revealed the existence of complex dynamics and multiple behaviors. An efficiency metric is introduced to compare the different cases. A Null Model analysis disclosed a pattern in the difference between the group and random models, varying with the size of groups. Our findings indicate that a mesoscopic construct like the ephemeral group, based on assumptions about social behavior and absent any microscopic level change, could produce and describe complex propagation dynamics. A conclusion is that agent-group dynamic models may represent a powerful approach for modelers and a promising new direction for future research in models of coevolution between propagation and behavior in society.

Robust dynamic experiments for the precise estimation of respiration and fermentation parameters of fruit and vegetables

Dynamic models based on non-linear differential equations are increasingly being used in many biological applications. Highly informative dynamic experiments are valuable for the identification of these dynamic models. The storage of fresh fruit and vegetables is one such application where dynamic experimentation is gaining momentum. In this paper, we construct optimal O2 and CO2 gas input profiles to estimate the respiration and fermentation kinetics of pear fruit. The optimal input profiles, however, depend on the true values of the respiration and fermentation parameters. Locally optimal design of input profiles, which uses a single initial guess for the parameters, is the traditional method to deal with this issue. This method, however, is very sensitive to the initial values selected for the model parameters. Therefore, we present a robust experimental design approach that can handle uncertainty on the model parameters.

An Improved Fast Estimation of Satellite Phase Fractional Cycle Biases

Satellite phase fractional cycle biases (FCBs) are crucial to precise point positioning with ambiguity resolution (PPP–AR), and they can improve the accuracy and reliability of a solution. Traditional methods need multiple iterations and need to keep the same reference when estimating satellite phase fractional cycle biases. In this paper, we propose an improved fast estimation of FCB, which does not need any iterations and can select any reference when estimating FCB. We compare the suitability and precision of a traditional and a proposed method by BDS-3 experiments. The results of the FCB experiments show that the calculated time of the proposed method is less than the traditional method and that computation efficiency is increased by 34.71%. These two methods have a similar rate of fixed epochs and ambiguities in the static and dynamic models. However, the time to first fix (TTFF) of the proposed method decreased by 19.69% and 28.83% for the static and dynamic models, respectively. The results show that the proposed method has a better convergence time in PPP–AR.

Simulation and Formation Mechanisms of Urban Landscape Design Based on Discrete Dynamic Models Driven by Big Data

Urban landscape design as a contemporary art embodies postmodernist philosophical thinking, aesthetic thinking, and breaking the traditional concept of art, and it is a new way of creating and presenting art. Big data technology characterized by large scale, speed, variety, value, and uncertainty of data is used to achieve urban landscape design. In this article, during the research process, we strive to raise the revelation of the design layer rather than the brand new level of cross-fertilization and interaction between big data-driven discrete dynamic model and urban landscape design; we also reveal how the benefits of promoting urban development and harmonious life are achieved in the interactive expression of the urban landscape after the application of the big data-driven discrete dynamic model, which provides designers and related professionals with more detailed and novel design ideas at the theoretical level and makes the theory of big data-driven discrete dynamic models in landscape design interactive methods more enriched. Finally, this article puts forward its thinking and outlook on the design of the big data-driven discrete dynamic model in the interactivity of urban landscape design, hoping that artists will strengthen its functional and material design elements when creating performance. Moreover, more design means of emerging technologies of modern science and technology should be integrated so that modern urban landscape can achieve ordinary and uncommon benefits and promote the rapid development of the big data-driven discrete dynamic model in urban landscape design development.

Mini-batch optimization enables training of ODE models on large-scale datasets

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Comparative study of offshore spar-buoy oscillating water column dynamic models for captured power estimation

The prompt estimation of power and geometrical aspects enables faster and more accurate financial assessment of wave energy converters to be deployed. This may lead to better commercialisation of wave energy technologies, as they require location-based customisation, unlike the mature wind energy technologies with developed benchmark. The adopted approach provides simple and efficient modelling tool allowing the study of the system from different perspective. The aim of this study is to select the optimum dynamic model to predict the captured power of a spar-buoy Oscillating Water Column (OWC) wave energy converter. Four dynamic models were developed to predict the system dynamics and results were validated experimentally. In-depth investigations on the effect of the mass and damping ratios of the oscillating bodies on the accuracy of the adopted models were performed. Such investigations included the proposed one-way coupling model and three two-degree of freedom models and three reduced-scale models, in addition to analytical and numerical solutions. Pneumatic power was calculated for the reduced-scale model where orifices’ covers simulated the power take-off mechanism damping experimentally. Analysis and comparisons between the adopted models are finally provided.

Parameter Estimation in the Age of Degeneracy and Unidentifiability

Parameter estimation from observable or experimental data is a crucial stage in any modeling study. Identifiability refers to one’s ability to uniquely estimate the model parameters from the available data. Structural unidentifiability in dynamic models, the opposite of identifiability, is associated with the notion of degeneracy where multiple parameter sets produce the same pattern. Therefore, the inverse function of determining the model parameters from the data is not well defined. Degeneracy is not only a mathematical property of models, but it has also been reported in biological experiments. Classical studies on structural unidentifiability focused on the notion that one can at most identify combinations of unidentifiable model parameters. We have identified a different type of structural degeneracy/unidentifiability present in a family of models, which we refer to as the Lambda-Omega (Λ-Ω) models. These are an extension of the classical lambda-omega (λ-ω) models that have been used to model biological systems, and display a richer dynamic behavior and waveforms that range from sinusoidal to square wave to spike like. We show that the Λ-Ω models feature infinitely many parameter sets that produce identical stable oscillations, except possible for a phase shift (reflecting the initial phase). These degenerate parameters are not identifiable combinations of unidentifiable parameters as is the case in structural degeneracy. In fact, reducing the number of model parameters in the Λ-Ω models is minimal in the sense that each one controls a different aspect of the model dynamics and the dynamic complexity of the system would be reduced by reducing the number of parameters. We argue that the family of Λ-Ω models serves as a framework for the systematic investigation of degeneracy and identifiability in dynamic models and for the investigation of the interplay between structural and other forms of unidentifiability resulting on the lack of information from the experimental/observational data.

A Dynamic Statistical Subseasonal Forecast Model for OLR Over Tropical Pacific Region

How to improve the subseasonal forecast skills of dynamic models has always been an important issue in atmospheric science and service. This study proposes a new dynamical-statistical forecast method and a stable components dynamic statistical forecast (STsDSF) for subseasonal outgoing long-wave radiation (OLR) over the tropical Pacific region in January-February from 2004 to 2008. Compared with 11 advanced multi-model ensemble (MME) daily forecasts, the STsDSF model was able to capture the change characteristics of OLR better when the lead time was beyond 30 days in 2005 and 2006. The average pattern correlation coefficients (PCC) of STsDSF are 0.24 and 0.16 in 2005 and 2006, while MME is 0.10 and 0.05, respectively. In addition, the average value of PCC of the STsDSF model in five years is higher than MME in 7–11 pentads. Although both the STsDSF model and MME show a similar temporal correlation coefficient (TCC) pattern over the tropical Pacific region, the STsDSF model error grows more slowly than the MME error during 8–12 pentads in January 2005. This phenomenon demonstrates that STsDSF can reduce dynamical model error in some situations. According to the comparison of subseasonal forecasts between STsDSF and MME in five years, STsDSF model skill depends strictly on the predictability of the dynamical model. The STsDSF model shows some advantages when the dynamical model could not forecast well above a certain level. In this study, the STsDSF model can be used as an effective reference for subseasonal forecast and could feasibly be used in real-time forecast business in the future.