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.

Evaluation method of cracking resistance of lightweight aggregate concrete

The cracking behavior of lightweight aggregate concrete(LWAC) was investigated by mechanical analysis, SEM and cracking-resistant test where a shrinkage-restrained ring with a clapboard was used. The relationship between the ceramsite type and the cracking resistance of LWAC was built up and compared with that of normal-weight coarse aggregate concrete(NWAC). A new method was proposed to evaluate the cracking resistance of concrete, where the concepts of cracking coefficient ζt(t) and the evaluation index Acr(t) were proposed, and the development of micro-cracks and damage accumulation were recognized. For the concrete with an ascending cracking coefficient curve, the larger Acr(t) is, the lower cracking resistance of concrete is. For the concrete with a descending cracking coefficient curve, the larger Acr(t) is, the stronger the cracking resistance of concrete is. The evaluation results show that in the case of that all the three types of coarse aggregates in concrete are pre-soaked for 24 h, NWAC has the lowest cracking resistance, followed by the LWAC with lower water absorption capacity ceramsite and the LWAC with higher water absorption capacity ceramsite has the strongest cracking resistance. The proposed method has obvious advantages over the cracking age method, because it can evaluate the cracking behavior of concrete even if the concrete has not an observable crack.

XMAM:X-raying models with a matrix to reveal backdoor attacks for federated learning

Federated Learning(FL),a burgeoning technology,has received increasing attention due to its privacy protection capability.However,the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks.Former researchers proposed several robust aggregation methods.Unfortunately,due to the hidden characteristic of backdoor attacks,many of these aggregation methods are unable to defend against backdoor attacks.What's more,the attackers recently have proposed some hiding methods that further improve backdoor attacks'stealthiness,making all the existing robust aggregation methods fail.To tackle the threat of backdoor attacks,we propose a new aggregation method,X-raying Models with A Matrix(XMAM),to reveal the malicious local model updates submitted by the backdoor attackers.Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates,unlike the existing aggregation algorithms,we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior.Specifically,like medical X-ray examinations,we investigate the collected local model updates by using a matrix as an input to get their Softmax layer's outputs.Then,we preclude updates whose outputs are abnormal by clustering.Without any training dataset in the server,the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones.For instance,when other methods fail to defend against the backdoor attacks at no more than 20%malicious clients,our method can tolerate 45%malicious clients in the black-box mode and about 30%in Projected Gradient Descent(PGD)mode.Besides,under adaptive attacks,the results demonstrate that XMAM can still complete the global model training task even when there are 40%malicious clients.Finally,we analyze our method's screening complexity and compare the real screening time with other methods.The results show that XMAM is about 10–10000 times faster than the existing methods.

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.

Design of dense gap-graded friction course mixture

The design procedure of a dense gap-graded friction course（DGGFC） with coarse aggregate void filling method is presented. Testing results show that a DGGFC mixture possesses a dense stone-matrix structure, good stability and almost the same texture depth as stone matrix asphalt （SMA）. It also has a coarse and even surface after paving and has no separation during construction. It is durable and impermeable. It balances and improves the inherent inconsistency of asphalt mixture between the large texture depth for skid resistance and the impermeability for durability. The actual application in the Nanning-Liuzhou Expressway also shows that the performance of the DGGFC is as excellent as that of SMA, while the DGGFC mixture is cheaper than SMA. The DGGFC mixture is good for wearing course of pavement. Further research on DGGFC can be helpful for improving the surface skid resistance, prolonging the life-span period and reducing the construction costs of asphalt pavement.

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.

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.

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.

ORDERED WEIGHTED AVERAGING AGGREGATION METHOD FOR PORTFOLIO SELECTION

Portfolio management is a typical decision making problem under incomplete,sometimes unknown, information. This paper considers the portfolio selection problemsunder a general setting of uncertain states without probability. The investor's preferenceis based on his optimum degree about the nature, and his attitude can be described by anOrdered Weighted Averaging Aggregation function. We construct the OWA portfolio selection model, which is a nonlinear programming problem. The problem can be equivalentlytransformed into a mixed integer linear programming. A numerical example is given andthe solutions imply that the investor's strategies depend not only on his optimum degreebut also on his preference weight vector. The general game-theoretical portfolio selectionmethod, max-min method and competitive ratio method are all the special settings of thismodel.

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.