Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.

SYSTEM AGGREGATION METHOD FOR FAILURE PRONE PRODUCTION LINES WITH UNRELIABLE LIMITED BUFFERS

Different from traditional aggregation method, the unreliable buffers are originally considered and a more general aggregation method is offered, in which not only the unreliable buffers are considered, but also the probabilities of system states are obtained by a discrete model rather than the continuous flow model of unreliable manufacturing systems. The solution technique is offered to get the system sate probabilities. The method advances the traditional system aggregation techniques. Numerical results specify the extended aggregation method and also show that the unreliable limited buffers have a strong impact on the efficiency of the production lines.

Research on the Optimal Aggregation Method of Judgment Matrices Based on Spatial Steiner-Weber Point

This paper is to provide a novel approach for the spatial aggregation of judgment matrices.The optimal aggregation method of judgment matrices based on spatial Steiner-Weber point can effectively aggregate the preference information of group members and achieve the optimization of group preference.The method comprises three key elements:The spatial mapping of the judgment matrices,the spatial optimal aggregation model of the judgment matrices,and the plant growth simulation algorithm(PGSA)is used to find the optimal aggregation points.Firstly,the judgment matrices are mapped into a set of spatial multidimensional coordinates by using spatial mapping rules.Secondly,the spatial Steiner-Weber point is used as the prototype to construct the spatial aggregation model.Thirdly,the PGSA algorithm is used to find the spatial aggregation points,whose spatial weighted Euclidean distance to all the decision makers’preference points is minimal.The optimal aggregation matrix is composed of these optimal aggregation points,which can accurately reflect the decision maker's comprehensive opinions.Finally,the effectiveness and rationality of this method are verified by comparing with the classical group preference aggregation methods.

Wind and Photovoltaic Power Time Series Data Aggregation Method Based on an Ensemble Clustering and Markov Chain

Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.

Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency

More demand-side flexible resources(DFRs)are participating in the frequency regulation of renewable power systems,whose heterogeneous characteristics have a significant impact on the system frequency response.Consequently,selecting suitable DFRs poses a formidable challenge for independent system operators(ISO).In this paper,a reserve allocation methodology for heterogeneous DFRs is proposed to manage the risk of power system frequency.Firstly,a performance curve is developed to describe the cost,capacity,and response speed of DFRs.Moreover,a clustering method for multiple distributed DFRs is conducted to calculate the aggregated performance curves and uncertainty coefficients.Then,the frequency security criterion considering DFRs’performance is constructed,whose linearity makes it can be easily coupled into the system scheduling model and solved.Furthermore,a risk management model for DFRs considering frequency-chance-constraint is proposed to make a trade-off between cost and frequency security.Finally,the model is transformed into mixed integer second-order cone programming(MISOCP)and solved by the commercial solver.The proposed model is validated by the IEEE 30 and IEEE 118 bus systems.

Predictive value of antiplatelet resistance on early stent thrombosis in patients with acute coronary syndrome

Background Despite outstanding antiplatelet properties of aspirin and clopidogrel, some patients taking these drugs continue to suffer complications. Antiplatelet resistance appears to be a new prognostic factor in acute coronary syndrome patients for clinical events associated with stent thrombosis （ST）. However, there is no optimal method to identify it and assess its correlation to clinical outcomes. This study sought to evaluate the predictive value of antiplatelet resistance assessed by whole blood impedance aggregometry for the risk of early ST in patients with acute coronary syndrome who underwent coronary stenting. Methods Platelet responses to aspirin and clopidogrel in 86 patients with acute coronary syndrome were measured by whole blood impedance aggregometry. Spontaneous platelet aggregation was defined as antiplatelet resistance identified by the increased electrical impedance. The clinical endpoint was early stent thrombosis during 30-day follow-up after coronary stenting. Results The prevalence of aspirin resistance, clopidogrel resistance and dual resistance of combined clopidogrel and aspirin resistance were 19.8%, 12.8% and 5.8% respectively. Diabetes, female and higher platelet counts were more frequently detected in clopidogrel-resistant and dual-resistant patients. During 30-day follow-up, the patients with clopidogrel resistance and dual resistance had higher incidence of early stent thrombosis （18.2% vs. 1.3%, 40.0% vs. 1.2%, P 〈0.05）. Binary Logistic Regression analysis indicated that dual resistance remained an independent predicator for early stent thrombosis （odds ratio 34.064, 95% CI 1.919-604.656, P=-0.016）. Conclusions Antiplatelet resistance assessed by whole blood impedance aggregometry is paralleled to clinical events, and dual antiplatelet resistance is an independent predicator for early stent thrombosis in patients with acute coronary syndrome. As a physiological assessment of platelet reactivity, whole blood impedance aggregometry is a convenient and accurate option for measurin.q antiplatelet resistance and hence predicting early stent thrombosis.

Two-component Brownian coagulation: Monte Carlo simulation and process characterization

The compositional distribution within aggregates of a given size is essential to the functionality of com- posite aggregates that are usually enlarged by rapid Brownian coagulation, There is no analytical solution for the process of such two-component systems, Monte Carlo method is an effective numerical approach for two-component coagulation, In this paper, the differentially weighted Monte Carlo method is used to investigate two-component Brownian coagulation, respectively, in the continuum regime, the free-molecular regime and the transition regime. It is found that （1） for Brownian coagulation in the continuum regime and in the free-molecular regime, the mono-variate compositional distribution, i.e., the number density distribution function of one component amount （in the form of volume of the component in aggregates） satisfies self-preserving form the same as particle size distribution in mono-component Brownian coagulation; （2） however, for Brownian coagulation in the transition regime the mono-variate compositional distribution cannot reach self-similarity; and （3） the bivariate compositional distribution, i.e., the combined number density distribution function of two component amounts in the three regimes satisfies a semi self-preserving form. Moreover, other new features inherent to aggregative mixing are also demonstrated; e.g., the degree of mixing between components, which is largely controlled by the initial compositional mass fraction, improves as aggregate size increases.