Method and Application for Dynamic Comprehensive Evaluation with Subjective and Objective Information

In an effort to deal with more complicated evaluation situations, scientists have focused their efforts on dynamic comprehensive evaluation research. How to make full use of the subjective and objective information has become one of the noteworthy content. In this paper, a dynamic comprehensive evaluation method with subjective and objective information is proposed. We use the combination weighting method to determine the index weight. Analysis hierarchy process method is applied to dispose the subjective information, and criteria importance through intercriteria correlation method is used to handle the objective information. And for the time weight determination, we consider both time distance and information size to embody the principle of esteeming the present over the past. And then the linear weighted average model is constructed to make the evaluation process more practicable. Finally, an example is presented to illustrate the effectiveness of this method. Overall, the results suggest that the proposed method is reasonable and effective.     

“满意度”与可持续发展评价研究进展

可持续发展是全人类共同追求并且不断探索完善的一种社会经济发展模式。各国研究机构和学者针对如何评价城市与区域可持续发展水平,提出了大量的分析方法、评价指标和研究框架。分析了"满意度"在城市与区域可持续发展评价中应用的发展历程、存在问题和研究展望。研究认为,在生态文明建设背景下,城市与区域可持续发展评价需从可持续发展的核心内涵出发,结合主观与客观的方法对指标体系进行判定,在充分利用客观信息提高城市可持续发展能力的同时,考虑居民对于城市可持续发展的要求与"满意度",提出建立与之相适应的指标动态监测体系,构建适应生态文明体制的可持续发展模式。     

半定规划鲁棒不可行性研究

本文给出了一种判别半定规划鲁棒不可行的准则,通过该准则我们可判断模型对输入数据是否敏感,进而决定是否应该利用鲁棒优化建模的方法对模型进行重新建模,使其所求解具有鲁棒性.避免了所给模型在实际应用中存在较大的风险性,为更好的应用鲁棒优化建模方法提供了理论依据.     

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks

Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias.     

矿区生态产业评价指标体系

论述了煤炭矿区生态产业评价指标体系设置的理论依据,针对煤炭矿区生态产业链的特点和发展模式,从自然资源、经济效益、环境效益和社会效益四个角度出发选择筛选指标,构建了煤炭矿区生态产业评价指标体系.建立了模糊综合评价模型,探讨了运用层次分析法并通过熵值法修正的确定评价指标权重的新方法,构建了各因素指标的模糊隶属度函数.应用龙口矿区2010年的指标数据对所建模型和方法进行检验,对龙口矿区生态产业发展及生态产业链延伸提出了具体建议.     

ConvAn: a convergence analyzing tool for optimization of biochemical networks

Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic properties and the convergence of the objective function to the global optimum is hardly predictable. Determination of suitable optimization method and necessary duration of optimization becomes critical in case of evaluation of high number of combinations of adjustable parameters or in case of large dynamic models. This task is complex due to variety of optimization methods, software tools and nonlinearity features of models in different parameter spaces. A software tool ConvAn is developed to analyze statistical properties of convergence dynamics for optimization runs with particular optimization method, model, software tool, set of optimization method parameters and number of adjustable parameters of the model. The convergence curves can be normalized automatically to enable comparison of different methods and models in the same scale. By the help of the biochemistry adapted graphical user interface of ConvAn it is possible to compare different optimization methods in terms of ability to find the global optima or values close to that as well as the necessary computational time to reach them. It is possible to estimate the optimization performance for different number of adjustable parameters. The functionality of ConvAn enables statistical assessment of necessary optimization time depending on the necessary optimization accuracy. Optimization methods, which are not suitable for a particular optimization task, can be rejected if they have poor repeatability or convergence properties. The software ConvAn is freely available on www.biosystems.lv/convan.     

Numerical aspects of optimal control of penicillin production

Since their discovery, fermentation processes have gone along not only with the industrial beverages production and breweries, but since the times of Alexander Fleming, they have become a crucial part of the health care due to antibiotics production. However, complicated dynamics and strong nonlinearities cause that the production with the use of linear control methods achieves only suboptimal yields. From the variety of nonlinear approaches, gradient method has proved the ability to handle these issues—nevertheless, its potential in the field of fermentation processes has not been revealed completely. This paper describes constant vaporization control strategy based on a double-input optimization approach with a successful reduction to a single-input optimization task. To accomplish this, model structure used in the previous work is modified so that it corresponds with the new optimization strategy. Furthermore, choice of search step is explored and various alternatives are evaluated and compared.     

Intervertebral disc lesions; surgical treatment,end results,disability ratings and cost in industrial accident injuries

In lumbar intervertebral disc operations there is sometimes pronounced disparity between the clinical results and the results as reflected in the disability rating given by the Industrial Accident Commission. It is believed that the difference must be due to excessive complaining and exaggeration on the part of the claimant and his representative before the Commission, and that undue credence and importance are given to these subjective symptoms as compared with the objective findings as reported by the surgeon or clinician. Data in the present study indicate that the belief that surgical decompression of a single nerve root and removal of an injured or degenerated disc necessarily entails a certain degree of disability is not justified. Satisfactory clinical results were obtained in 93 per cent of compensation cases, but when settled or rated by the Industrial Accident Commission, only 59 per cent were closed as having good or excellent results.     

On sparse Fisher discriminant method for microarray data analysis

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calculate a weight for each gene and use the weight values as an indicator to identify the subsets of relevant genes that categorize patient and normal samples. A l(2) - l(1) norm minimization method is implemented to the discriminant process to automatically compute the weights of all genes in the samples. The experiments on two microarray data sets have shown that the new algorithm can generate classification results as good as other classification methods, and effectively determine relevant genes for classification purpose. In this study, we demonstrate the gene selection's ability and the computational effectiveness of the proposed algorithm. Experimental results are given to illustrate the usefulness of the proposed model.     

大学附属医院临床科主任工作业绩考核评价体系研究

为了改善医院绩效,以目标管理法为理论基础,采用关键绩效指标法建立大学附属医院临床科主任工作业绩考核指标评价体系,并应用层次分析法进行权重分析,得出各层级指标的权重值,各指标的重要程度符合科主任的实际工作要求。以此对临床科主任的工作进行科学、有效的考核,提升临床科室绩效管理水平。     

Modelling the Valuesphere and the Ecosphere: Integrating the Decision Makers' Perspectives into LCA

Methods for Life Cycle Impact Assessment have to cope with two critical aspects, the uncertainty in values and the (unknown) system behaviour. LCA methodology should cope explicitly with these subjective elements. A structured aggregation procedure is proposed that differentiates between the technosphere and the ecosphere and embeds them in the valuesphere. LCA thus becomes a decision support system that models and combines these three spheres. We introduce three structurally identical types of LCA, each based on one coherent but different set of values. These sets of values can be derived from the Cultural Theory and are labeled as ‘egalitarian’, ‘individualistic’, and ‘hierarchic’. Within Life Cycle Impact Assessment, a damage oriented assessment model is complemented with both a newly developed precautionary indicator designed to address unknown damage and an indicator for the manageability of environmental damages. The indicators for unknown damage and for manageability complete the set of indicators judged to be relevant by decision makers. The weights given to these indicators are also value-dependent. The framework proposed here answers the criticisms that present LCA methodology does not strictly enough separate subjective from objective elements and that it fails to accurately model environmental impacts.     

Modelling the Valuesphere and the Ecosphere:

Methods for Life Cycle Impact Assessment have to cope with two critical aspects, the uncertainty in values and the (unknown) system behaviour. LCA methodology should cope explicitly with these subjective elements. A structured aggregation procedure is proposed that differentiates between the technosphere and the ecosphere and embeds them in the valuesphere. LCA thus becomes a decision support system that models and combines these three spheres. We introduce three structurally identical types of LCA, each based on one coherent but different set of values. These sets of values can be derived from the Cultural Theory and are labeled as ‘egalitarian’, ‘individualistic’, and ‘hierarchic’. Within Life Cycle Impact Assessment, a damage oriented assessment model is complemented with both a newly developed precautionary indicator designed to address unknown damage and an indicator for the manageability of environmental damages. The indicators for unknown damage and for manageability complete the set of indicators judged to be relevant by decision makers. The weights given to these indicators are also value-dependent. The framework proposed here answers the criticisms that present LCA methodology does not strictly enough separate subjective from objective elements and that it fails to accurately model environmental impacts.     

Multicriteria optimization of biochemical systems by linear programming: application to production of ethanol by Saccharomyces cerevisiae

In this study we present a method for simultaneous optimization of several metabolic responses of biochemical pathways. The method, based on the use of the power law formalism to obtain a linear system in logarithmic coordinates, is applied to ethanol production by Saccharomyces cerevisiae. Starting from an experimentally based kinetic model, we translated it to its power law equivalent. With this new model representation, we then applied the multiobjective optimization method. Our intent was to maximize ethanol production and minimize each of the internal metabolite concentrations. To ensure cell viability, all optimizations were carried out under imposed constraints. The different solutions obtained, which correspond to alternative patterns of enzyme overexpression, were implemented in the original model. We discovered few discrepancies between the S-system-optimized steady state and the corresponding optimized state in the original kinetic model, thus demonstrating the suitability of the S-system representation as the basis for the optimization procedure. In all optimized solutions, the ATP level reached its maximum and any increase in its activity positively affected the optimization process. This work illustrates that in any optimization study no single criteria is of general application being the multiobjective and constrained task the proper way to address it. It is concluded that the proposed multiobjective method can serve to carry out, in a single study, the general pattern of behavior of a given metabolic system with regard to its control and optimization.     

Subjective and objective components of resource value additively increase aggression in parasitoid contests

Two major categories of factors are predicted to influence behaviour in dyadic contests; differences in the abilities of the contestants to acquire and retain resources (resource holding potential), and the value of the contested resource (resource value, RV; which comprises objective and subjective components). Recent studies indicate that subjective components affect contest behaviour in several animal taxa but few have simultaneously investigated objective RV components. We find that both an objective (host size) and a subjective (contestant age) component of RV affect contest intensity in the parasitoid wasp Goniozus legneri. These additively influence aggressiveness, with a larger effect from the subjective component than the objective component. The greater influence of subjective RV adds weight to the recent surge of recognition of this RV component''s importance in contest behaviour.     

Modelling the valuesphere and the ecosphere: Integrating the decision makers’ perspectives into LCA

Methods for Life Cycle Impact Assessment have to cope with two critical aspects, the uncertainty in values and the (unknown) system behaviour. LCA methodology should cope explicitly with these subjective elements. A structured aggregation procedure is proposed that differentiates between the technosphere and the ecosphere and embeds them in the valuesphere. LCA thus becomes a decision support system that models and combines these three spheres. We introduce three structurally identical types of LCA, each based on one coherent but different set of values. These sets of values can be derived from the Cultural Theory and are labeled as ‘egalitarian’, ‘individualistic’, and ‘hierarchic’. Within Life Cycle Impact Assessment, a damage oriented assessment model is complemented with both a newly developed precautionary indicator designed to address unknown damage and an indicator for the manageability of environmental damages. The indicators for unknown damage and for manageability complete the set of indicators judged to be relevant by decision makers. The weights given to these indicators are also value-dependent. The framework proposed here answers the criticisms that present LCA methodology does not strictly enough separate subjective from objective elements and that it fails to accurately model environmental impacts.     

An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism

Biochemical network reconstructions represent valuable tools for the computational metabolic modeling of organisms that present a great biotechnological interest. An in silico multi-compartmental model of the central metabolism of the plant Brassica napus (Rapeseed) was constructed, aiming to investigate the metabolic properties of the Brassicaceae family. This family comprises many plants with major importance for the energy and nutrition sector, including the model plant Arabidopsis thaliana. The model utilized as objective function to be subsequently optimized, the biomass production of rapeseed developing embryos, which are characterized by a very high, oil content, up to 60% of biomass weight. In order to study global network properties of seed metabolism, various methods were employed, like Flux Balance Analysis, Principal Component Analysis of the flux space and reaction deletion studies, which simulate the effect of gene knock-out experiments. The model successfully simulated seed growth during the stage of oil accumulation and provided insight, regarding certain aspects of network plasticity, with the emphasis given in lipid biosynthesis regulation.     

The Contraction-Expansion Algorithm and Its Use in Fitting Nonlinear Equation

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逻辑斯谛曲线的一种拟合方法

一、前言 逻辑斯谛方程(Logistic equation)指出了有限空间种群增长的基本规律,是研究种群动态的重要工具。长期以来,人们运用逻辑斯谛方程处理实验数据,得出了种种曲线拟合的方法。然而,这些方法都存在着一定的缺点,使逻辑斯谛方程的应用受到某些限制。随着种群生态学的不断发展,对实验数据的处理要求更精确、更迅速,因此进一步探讨曲线拟合     

Comparison of subjective and objective methods to assess flax straw cultivars and fibre quality after dew-retting

Subjective and objective methods were compared for the assessment of the quality of unretted and retted straw, and the fibres obtained after dew-retting of four flax cultivars, Ariane, Evelin, Laura and Viola. Maturity of straw, degree of retting, ease of decortication after retting, divisibility, handle, cleanliness and colour were assessed as subjective factors and of these, degree of retting and handle appear to be useful for assessing quality. The samples were also analysed for physical and chemical differences using a range of objective methods. Straw quality could not be graded using height and maturity, although stem diameter and technical length could indicate quality. Changes in N, lipid and ash content, as a result of retting, were observed. The increase in the proportions of fibre fractions present in retted compared with unretted straw, due to microbial degradation of pectin, could be identified by thermal analysis. Analyses of the fibre samples to determine fineness, strength, cellulose, hemicellulose, lignin and minerals were carried out and significant differences in some of the parameters were identified. Cultivar Laura produced the best fibre. Fibre fineness, ash, lipid, caustic weight loss and fibre fractions as determined by thermal analysis were found to be important objective parameters for quality assessment. The assessment of fibre samples performed subjectively by an expert grader and by selected objective methods gave similar quality rankings.     

Identifying Node Role in Social Network Based on Multiple Indicators

It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn''t comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators'' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.