关于软科学项目函评的公正性评价指标探析

科学、公正地评估管理研究成果的水平和价值一直是管理领域的难题,本研究针对管理成果函评组织管理工作中普遍存在的一些问题,如评价方法不完善、评委个人素质差异、监督考核机制不健全等,从制度和机制上进行了分析。为提高评审质量,需建立评审工作质量考核办法,组建结构合理的评委队伍。运用相关评价指标,加强评委工作质量的量化评估和考核,并以实例加以分析,最终就如何确保管理成果函评的公正性问题进行了讨论。

首都医学发展科研基金函评立项指标体系的分析与评价

目的根据首都医学发展科研基金的实际函评结果，对函评立项指标体系进行全面的分析与评价，为进一步完善指标体系提供依据的方式和方法。方法应用克朗巴赫系数、分半信度法、结构方程模型的验证性因子分析、专家评定法的内容效度指数、高低分组法鉴别指数，结合实际函评结果，对指标体系的信度、效度、区分度、合理可行性等分析评价。结果①信度：一级指标及指标体系总体的克朗巴赫系数、分半信度均在0．8以上。②校度：指标体系的条目层次分析，标化因子载荷范围在0．57～0．86均大于0．5，路径分析具有统计学意义（t值大于1．96），标准误范围在0．01～0．02，二级指标的路径性能表现良好；模型整体拟合效果的主要拟合指数均达到标准（RMSEA=0．065、NNFI=0．99、IFI=0．99、CFI=0．99、GFI=0．95、AGFI=0．93、x^2=1028．21），指标体系结构效度良好。③内容效度：指标体系内容效度指数为0．97（均值S-CVI），一致性好；每项二级指标条目内容与研究目的关联认可的一致性，除“学术思想”的内容效度指数0．57（I-CVI）较低外，其他二级指标的I—CVI均为1．00。④区分度：各二级指标，一级指标、总分的鉴别指数范围在0．51～0．76，均大于0．40，区分度良好。⑤合理可行性：对2007年立项评审项目的指标体系得分分析，中位数为57．67，均值与中位数相近，分布趋近正态分布，符合函审的要求，合理可行。结论首发基金函评立项指标体系的信度、效度、区分度良好，能较好的反应实际情况，合理可行，所应用的方法对分析、评价及完善科研基金函评立项指标体系，具有实际的应用和借鉴价值。

国家自然科学基金项目的函评意见质量现况与分析——以2019年医学科学部两学科的面上、青年科学基金项目为例

以2019年医学科学部呼吸、血液两个学科受理的面上项目和青年科学基金项目的函评意见为研究对象,就函评意见的专业性和从函评意见中的收获对项目申请人进行了问卷调查,同时学科管理人员也对函评意见专业性进行了评价。其结果在一定意义上反映了国家自然科学基金项目函评意见的基本情况,表明保持和进一步提高同行评议质量仍然是国家自然科学基金项目同行评议工作中的重要任务。

表面工程领域自然科学基金项目的申请和同行评议情况分析

文中统计了国家自然科学基金金属材料学科表面工程领域内自2000年以来的申请和资助情况,并从申请项目的关键词和项目函评意见方面对资助特点进行了统计和分析。结果表明表面工程类的基金项目在研究内涵和研究方案的阐述方面普遍存在着不足,核心问题是没有从工程项目中凝练出基础的科学问题。通过对统计结果的分析,对表面工程类的基金申请项目提出了相应的建议。

Data processing method for aerial testing of rotating accelerometer gravity gradiometer

A novel method for noise removal from the rotating accelerometer gravity gradiometer(MAGG)is presented.It introduces a head-to-tail data expansion technique based on the zero-phase filtering principle.A scheme for determining band-pass filter parameters based on signal-to-noise ratio gain,smoothness index,and cross-correlation coefficient is designed using the Chebyshev optimal consistent approximation theory.Additionally,a wavelet denoising evaluation function is constructed,with the dmey wavelet basis function identified as most effective for processing gravity gradient data.The results of hard-in-the-loop simulation and prototype experiments show that the proposed processing method has shown a 14%improvement in the measurement variance of gravity gradient signals,and the measurement accuracy has reached within 4E,compared to other commonly used methods,which verifies that the proposed method effectively removes noise from the gradient signals,improved gravity gradiometry accuracy,and has certain technical insights for high-precision airborne gravity gradiometry.

Fuzzy Comprehensive Evaluation of Urban Traffic Environment Quality

In this paper, traffic environment quality assessment is achieved by applying fuzzy mathematics methods. Set up an assessment system, determine assessment criterion, formulate membership function, make program designs and conduct example analysis. The evaluation result is consistent with the real case. So that the method of the fuzzy evaluation is a good one for the environment quality assessment.

Product reliability assessment based on proportional hazard degradation model

In order to evaluate the reliability of long-lifetime products with degradation data, a new proportional hazard degradation model is proposed. By the similarity between time-degradation data and stress-accelerated lifetime, and the failure rate function of degradation data which is assumed to be proportional to the time covariate, the reliability assessment based on a proportional hazard degradation model is realized. The least squares method is used to estimate the model＇s parameters. Based on the failure rate of the degradation data and the proportion function of the known time, the failure rate and the reliability function under the given time and the predetermined failure threshold can be extrapolated. A long life GaAs laser is selected as a case study and its reliability is evaluated. The results show that the proposed method can accurately describe the degradation process and it is effective for the reliability assessment of long lifetime products.

Evaluation Model of Carrying Capacity of Water Resources Based on Standardized Indices of Radial Basis Function

[Objective] The aim was to study on RBF model about evaluation on carrying capacity of water resources based on standardized indices. [Method] The indices were transformed and the averages of standard values in different levels were taken as the standardized values of components of central vectors for basic functions of RBF hidden nodes. Hence, the basic functions are suitable for most indices, simplifying expression and calculation of basic functions. [Result] RBF models concluded through Monkey-king Genetic Algorithm with weights optimization are used in evaluation on water carrying capacity in three districts in Changwu County in Shaanxi Province, which were in consistent with that through fuzzy evaluation. [Conclusion] RBF, simple and practical, is universal and popular.

Evaluation on the Two Filling Functions for the Recovery of Forest Information in Mountainous Shadows on Landsat ETM + Image

In general,topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images.In this paper,information in shadow was synthesized by using two filling techniques,namely,roifill and imfill,in order to improve the accuracy of forest mapping over mountainous regions.These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County,Zhejiang Province,China.The performance of these methods was compared with two conventional techniques,including cosine correction and multisource classification.The results showed that by applying filling approaches,average overall accuracy of classification was improved by 14 percent.However,through conventional methods this value increased only by 9 percent.The results also revealed that estimated forest area on the basis of shadow-corrected images by 'roifill' technique was much closer to the survey data compared to traditional algorithms.Apart from this finding,our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions.

Alternatives of Strategic Environmental Assessment for Road Traffic Development Planning——Case of Changchun City,China

On analyzing the achievement of the goal in the modem urban road traffic development planning, the alternative of Strategic Environmental Assessment for urban traffic planning should include the basic scheme, the extended scheme and the environmental protection scheme. This study from different perspectives designed the alternatives for Changchun＇s county-level road and urban road system planning, and used the method of System Dynamics to simulate, optimize and analyze those alternatives. Thereafter, some methods including the correlation function method were used to comprehensively assess and rank those alternatives for recommending two best alternatives with the consideration to the indicators, such as the total emission amount of CO, the total emission amount of nitrogen oxides, the noise value, the road construction cost, the fossil oil consumption and the traffic capacity. The result showed that the study would provide substantial supports for decision-makers to make more scientific decisions and promote the sustainable urban traffic in Changchun City.

On the Lucas Base and Computation of Counting Function Mean Value

In this paper, we introduce a new counting function a(m) related to the Lucas number, then use conjecture and induction methods to give an exact formula Ar(N)=α(n), (r=1,2,3) and prove them.

Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm

Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.

Improving Land Resource Evaluation Using Fuzzy Neural Network Ensembles

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.

Multiagent reinforcement learning through merging individually learned value functions

In cooperative multiagent systems, to learn the optimal policies of multiagents is very difficult. As the numbers of states and actions increase exponentially with the number of agents, their action policies become more intractable. By learning these value functions, an agent can learn its optimal action policies for a task. If a task can be decomposed into several subtasks and the agents have learned the optimal value functions for each subtask, this knowledge can be helpful for the agents in learning the optimal action policies for the whole task when they are acting simultaneously. When merging the agents’ independently learned optimal value functions, a novel multiagent online reinforcement learning algorithm LU-Q is proposed. By applying a transformation to the individually learned value functions, the constraints on the optimal value functions of each subtask are loosened. In each learning iteration process in algorithm LU-Q, the agents’ joint action set in a state is processed. Some actions of that state are pruned from the available action set according to the defined multiagent value function in LU-Q. As the items of the available action set of each state are reduced gradually in the iteration process of LU-Q, the convergence of the value functions is accelerated. LU-Q’s effectiveness, soundness and convergence are analyzed, and the experimental results show that the learning performance of LU-Q is better than the performance of standard Q learning.

Determination of Optical Constant and Thickness for Thin Film by Using Improved FTM

A new method to determine the optical constant and thickness of thin films is proposed. Based on the Fresnel’s optical expression, the improved flexible tolerance method(FTM) is employed in the case of a digital model of thin film to fit the curve of measured reflectance spectrum. The simulation results show a satisfactory correlation of the optical constant with the thickness of the target film. By taking the influence of nonlinear condition into account as well as more direct and indirect limitation, the precision and value-searching efficiency have been improved. Furthermore, the problem of dimension degradation, which exists in “Downhill Simplex”, has been successfully avoided. No initial input is needed for the procedure of optimization to achieve optical solution, which makes the whole processing of value calculation much more convenient and efficient.

A New Statistical Tool： Scalar Score Function

The basic inference function of mathematical statistics, the score function, is a vector function. The author has introduced the scalar score, a scalar inference function, which reflects main features of a continuous probability distribution and which is simple. Its simplicity makes it possible to introduce new relevant numerical characteristics of continuous distributions. The t-mean and score variance are descriptions of distributions without the drawbacks of the mean and variance, which may not exist even in cases of regular distributions. Their sample counterparts appear to be alternative descriptions of the observed data. The scalar score itself appears to be a new mathematical tool, which could be used in solving traditional statistical problems for models far from the normal one, skewed and heavy-tailed.

Multi-Level Fuzzy Comprehensive Assessment of Tourism Resources in Laiyuan County of Hebei Province

The membership of every target and the mathematic model of multi-level fuzzy comprehensive assessment are set up by using fuzzy theories and means in this study.Tourism resources of main scenic spots areas in Laiyuan County of Hebei Province are evaluated and classified by applying the model.The results of evaluation indicate that 10 of these scenic spots such as Baoziwo and Qingyunfeng are grade A,and 6 of them such as Yunpan Valley and Xianrenqiao are grade B.The peak forest scenic area in the Baishishan Geological Park and Shipuxia Scenic Area are grade A,and Jumayuan Scenic Area is grade B.Furthermore,suggestions are put forward based on the scientific and feasible development of tourism resources.

A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network

A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating compressors.Aiming at the current situation that the accuracy rate of reciprocating compressor fault diagnosis which depends on manual work in engineering is very low,we apply information entropy evaluation to select the sensitive features and make clear the corresponding relationship of characteristic parameters and failures.This method could reduce the feature dimension.Then,a complete fault diagnosis architecture has been built combining with radial basis function network which has the fast and efficient characteristics.According to the test results using experimental and engineering data,it is observed that the proposed fault diagnosis method improves the accuracy of fault automatic diagnosis effectively and it could improve the practicability of the monitoring system.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

A new algorithm for estimating gillnet selectivity

The estimation of gear selectivity is a critical issue in fishery stock assessment and management.Several methods have been developed for estimating gillnet selectivity,but they all have their limitations,such as inappropriate objective function in data fitting,lack of unique estimates due to the difficulty in finding global minima in minimization,biased estimates due to outliers,and estimations of selectivity being influenced by the predetermined selectivity functions.In this study,we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet selectivity.The proposed algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers.According to the estimated gillnet selectivity curve,the selectivity function can also be determined.This study suggests that the proposed algorithm is not sensitive to outliers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool.We suggest the proposed approach be used in estimating gillnet selectivity.