Current Evaluation Models Research Articles

Performance Evaluation of University-Based Scientific Research Institutions With a Non-Independent Parallel System.

Performance management in university-based scientific research institutions is essential for driving reform, advancing education quality, and fostering innovation. However, current performance evaluation models often focus solely on research indicators, neglecting the critical interdependence between the education and research systems. This oversight leads to inefficiencies in resource allocation and an underestimation of overall institutional performance, particularly in universities with varying development levels. To address these deficiencies, this study introduces two innovative two-stage data envelopment analysis models: the group-frontier and meta-frontier evaluation models. The findings are validated using data from 144 universities across China. They demonstrate that the proposed models effectively mitigate the underestimation of efficiency in traditional models and accurately reflect the intertwined nature of university subsystems and the disparities in university development. These results offer valuable insights for improving the performance of scientific research institutions and informing the strategic decisions of university administrators and government education departments.

Contradictions and Challenges: University Teachers’ Views on Performance Evaluation Models in Portugal and Spain

Teacher performance evaluation is a key component of high performing quality assurance education systems. The main focus of the present paper is to examine how international trends in teacher evaluation policies determine teachers’ working conditions in different higher education contexts. A comparative study was implemented at two public universities of Southern Europe (Portugal and Spain), with the aim of elucidating university teachers’ perceptions regarding the current teacher evaluation model. To achieve this, we conducted 28 semi-structured interviews, employing a comparative approach in their design. The findings suggest that participants encounter discrepancies within the stated objectives of teacher performance evaluation in higher education. Such evaluation is perceived as an external imposition with a very poor democratic process, focusing mostly on the quantitative perspective and with uncertain consequences.

Design of an online education student learning status evaluation model based on dual-improved neural networks

With the continuous development of network technology, online education has become an important form of education. However, in the online education model, it is difficult for educators to effectively evaluate students' learning status, and using a learning status evaluation model can effectively solve this problem. The main goal of this model is to comprehensively evaluate students' learning behavior, progress, and outcomes, in order to understand their learning status, provide effective teaching feedback to teachers, help students improve learning methods, and improve learning efficiency. The current automatic evaluation model for student learning status has certain limitations in terms of applicability and accuracy. A student learning state evaluation model based on Multi task Cascaded Convolutional Networks (MTCNN) is proposed to address the effectiveness of online education student learning state evaluation. Use the facial image acquisition function to extract students' facial features, process each feature through label classification, and then analyze the students' attention and learning emotions. Finally, analyze the effectiveness of the research method application. The results showed that the train_loss value of the learning state evaluation model proposed in the study can be reduced to about 0.1; the train_acc value can reach more than 95 %, and the overall volatility is small; the overall evaluation accuracy of facial expressions can reach 74.71 %, which is significantly better than cpc, VGG19 and other evaluation methods; compared with the comprehensive evaluation results and multi-modal analysis methods, only two evaluations at the critical value are different. The experimental results show that the online education students’ learning status evaluation model designed by the research has a high accuracy rate and has a certain application potential in the field of online education.

Improved evaluation model for macro-bending loss and power variation in single-mode fiber

It is challenging to calculate the macro-bending loss of optical fiber when the bending radius is reduced to sub-millimeter levels, based on Marcuse's theory and its derivative theories. Furthermore, these theories are limited in their ability to assess the distribution and changes of the optical field within the fiber, focusing solely on the analysis of loss. To overcome these limitations, we proposed an evaluation model which can simultaneously provide results for both macro-bending loss and power variation in a bent single-mode fiber. Our model achieves this by conformally transforming the fiber's refractive index profile of the axis section according to the bending radius. The ratio of output power to input power exhibits oscillation with changes in the bending radius, thereby resolving the issue of inability of Marcuse's theory to obtain an oscillation loss curve for sub-millimeter bending radii. The proposed model was validated by finite element method. We also obtained the internal power variation along the axial distance of the bent fiber through this model. This research improves the current fiber macro-bending loss evaluation model and lays a foundation for further studies on bend-insensitive fibers and the design of sub-millimeter bending sensors.

Research on Credit Risk Prediction Method of Blockchain Applied to Supply Chain Finance

INTRODUCTION: From the perspective of blockchain, it establishes a credit risk evaluation index system for supply chain finance applicable to blockchain, constructs an accurate credit risk prediction model, and provides a reliable guarantee for the research of credit risk in supply chain finance.OBJECTIVES: To address the inefficiency of the current credit risk prediction and evaluation model for supply chain finance.METHODS: This paper proposes a combined blockchain supply chain financial credit risk prediction and evaluation method based on kernel principal component analysis and intelligent optimisation algorithm to improve Deep Echo State Network. Firstly, the evaluation system is constructed by describing the supply chain financial credit risk prediction and evaluation problem based on blockchain technology, analysing the evaluation indexes, and constructing the evaluation system; then, the parameters of DeepESN network are optimized by combining the kernel principal component analysis method with the JSO algorithm to construct the credit risk prediction and evaluation model of supply chain finance; finally, the effectiveness, robustness, and real-time performance of the proposed method are verified by simulation experiment analysis.RESULTS: The results show that the proposed method improves the prediction efficiency of the prediction model.CONCLUSION: The problems of insufficient scientific construction of index system and poor efficiency of risk prediction model of B2B E-commerce transaction size prediction method are effectively solved.

A multi-objective optimization evaluation model for seismic performance of slopes reinforced by pile-anchor system

The significance means of the seismic reinforcement effect of a pile-anchor system for slope reinforcement has been widely recognized. However, cases of deformation failure and instability sliding of the pile-anchor system itself and the reinforced slope under seismic action continue to be recorded. Therefore, it is crucial to evaluate the seismic performance of slopes reinforced by a pile-anchor system to prevent the system’s failure. Current evaluation models of a slope reinforced by a pile-anchor system mainly focus on slope stability; however, the safety of the pile-anchor system itself is not sufficiently considered in these models. Consequently, in this study, we propose a multi-objective optimization evaluation (MOE) model for evaluating the seismic performance of slopes reinforced by a pile-anchor system that considers slope stability, safety of the pile-anchor system, and dynamic response of the slope. This model considers slope displacement, acceleration amplification factor of a slope, pile displacement, and anchor displacement as negative indexes, and anti-slide pile bending moment, shear force, and anchor axial force as intermediate indexes. The comprehensive weight of relevant indexes is obtained by combining subjective and objective weights, and the seismic reinforcement effect of the pile-anchor system is evaluated subsequently. In conclusion, the MOE model proposed in this study provides a novel solution for the optimization evaluation of a slope reinforced by a pile-anchor system in forthcoming projects.

Effect of rough screen on speckle suppression by wavelength and angle diversity in laser projection systems

In speckle suppression research, screens play a critical role in formation of speckle. This paper examines screen speckle using diverse light sources and screens of varying roughness. Our findings demonstrate that speckle suppression by screens is the key reason wavelength and angle diversities are not mutually independent in existing literature. Different from the theory, our experiments reveal that increased screen roughness can effectively suppress speckle for narrow-spectrum blending light source. This method also enhances the effectiveness of speckle suppression elements. These results contribute to refining current speckle evaluation models and aid in designing screens that yield lower speckle contrast.

An Analysis of the Values of Project-Based Learning in English Language Teaching and the Cultivating Functions it Displays

Abstract In this paper, an English teaching cultivation evaluation model based on RBF neural network is designed to solve the problems of poor generalization ability, low evaluation accuracy, and correlation coefficient of the current teaching evaluation model. Before establishing the evaluation model, conditional information entropy is used to screen the evaluation indexes of the English teaching cultivation function, and six dimensions of core literacy and five dimensions of thinking ability are selected as evaluation indexes. A new relative merit matrix is constructed under the constraint of relative merit by using the screened conditional attributes. Selection, crossover, and mutation operations are used in the RBF neural network to select the optimal parameters through the introduction of the genetic algorithm. Finally, the assessment data of students after project-based English teaching are analyzed for values and nurturing functions using the evaluation model. According to the findings, project-based English teaching led to the highest average score of 29.15 for students’ values compared to the dimensions of interest, ability, and personality. In terms of the six dimensions of core literacy and the five dimensions of thinking ability, the score difference between the experimental class and the control class is more than 1.2 points, and the Sig value of each dimension is less than 0.05, which indicates that English teaching based on Project-Based Learning promotes students’ values, core literacy and thinking ability.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

Application of an intelligent evaluation model of online teaching based on improved BPNN

Online teaching quality evaluation is essential for university management, but the current evaluation model of online teaching quality has problems such as subjectivity, high randomness and slow convergence speed. Therefore, the study uses adaptive learning rate with momentum term for optimizing and improving the traditional back propagation neural network, and the improved adaptive back propagation neural network rating model has good application in small-scale low-dimensional datasets, and its computational power is weak in large-scale datasets. Therefore, the study uses deep noise reduction autoencoder to improve the model structure, and then obtains an improved backpropagation neural network. The model shows better performance in a self-made university online teaching evaluation dataset. When the quantity of hidden layers of the improved model is 2, the mean square error value of the model is 0.0026; when the quantity of hidden layer neurons is 25, the mean square error of the model is 0.0012; when the model is made comparison with the shallow model, the mean absolute percentage error, the mean square error, and the root mean square error perform the best with the values of 0.0502, 24.53, and 4.95, respectively. the experimental results verify that the experiment verified that the improved model possesses powerful computational ability, and also proved that it also possesses excellent forecasting convergence, which can better help universities to complete the evaluation of online teaching quality.

A new multi-scale pore transport capacity model considering elliptical shape and retention effect of unequal-thickness water films

Most of current permeability evaluation models for shale gas reservoirs ignore the influence of imbibition during fracturing, moreover, several of the considerations are different from real conditions, for example, shale pore shape is circular and water film is with equal thickness, which is convenient for calculation but adverse to evaluation accuracy. In the study, shale pore shapes are closer to ellipse by statistically analyzing the scanning electron microscopy results, the thickness of water film is not equal on elliptical pore surface but related to the curvature. Based on these new insights, a quantitative model for determining the unequal thickness of water film was established, and then a new multi-scale elliptical pore permeability model, considering non-equal-thickness water film, wall roughness, stress sensibility, and retention effect, was established based on the multi-scale flow weighting theory. The prediction results of this permeability model are similar to the test results, and are more accurate than that of the previous models. Finally, factors analysis showed that the effect of roughness on permeability was small, but the negative impact of water film and stress sensitivity on permeability was significant.

Ageing Suitability Evaluation of Residential Districts Based on Active Ageing Theory

Residential districts, the primary locations for older adults, struggle to satisfy the needs of the rising older population due to ageing infrastructure, etc. China has begun to focus on this issue through the popularization of approaches such as active ageing theory (AAT), and has implemented ageing suitability renovations. Meanwhile, recent evaluation models failed to validate their logic and standardization. The current evaluation models prioritize the indoor environment above the external environment. Therefore, this study attempts to provide a comprehensive evaluation model of the ageing suitability of residential districts. First, 23 indicators of ageing suitability were identified by a systematic literature review and expert interviews; next, the indicators were categorized using the active ageing theory. Second, the grey correlation analysis approach was used to calculate the weights of evaluation indicators, and the existing evaluation criteria were incorporated to formulate the evaluation criteria for the project. Eventually, the evaluation model was constructed. Finally, the feasibility of the model was validated by a case study. The findings of this study can help provide directions for the future renovation of old residential districts (for example, the activity area should be over 200 m2) and the construction of suitable houses for older adults, such as the installation of a continuous handrail, to alleviate the increasingly grave problem of ageing and promote the development of the pension industry.

Dynamic stray current evaluation model of metro system considering reversible DC traction power system

ABSTRACT Stray current of DC metro system will accelerate metal corrosion. Therefore, it is necessary to evaluate it. With the application of inverter feedback devices in recent years, stray current is more complex due to the DC traction power system is reversible. To evaluate stray current more accurately, the dynamic evaluation model is proposed in this paper. Firstly, traction power substation (TPS) is equivalent to controlled source, considering the diverse operating characteristics. Based on this, the dynamic evaluation model is built with multiple trains running and TPSs operation. Then, considering the nonlinear and multivalued function characteristics of TPS, the solution method is proposed. The problem of slow convergence or even non-convergence in the stray current solving process of the metro system considering reversible DC traction power system is solved. The model is validated by the field test data. Finally, the model is used to obtain dynamic stray current. Result shows that stray current level tends to be higher as traction trains and braking trains run separately and concentratedly in different areas of the line.

Modeling Methodology for Site Selection Evaluation of Underground Coal Gasification Based on Combination Weighting Method with Game Theory.

The lack of systematic geological work is an essential reason why underground coal gasification (UCG) has not been industrialized for a long time. Building a scientific index system and favorable area evaluation technology for the UCG site selection is the key to breaking through the geological bottleneck. Aiming at the problems of the single index weight determination method, intense subjectivity, and poor reliability of current evaluation models, we put forward an evaluation modeling methodology for the UCG site selection using the combination weighting method with the game theory. The factors of coal resource conditions associated with the potential risk of UCG are systematically analyzed. From the six dimensions of the geological structure, hydrogeology, seam occurrence, coal properties, reserves, and roof lithology, 23 key factors were selected as evaluation indexes to construct a hierarchical model composed of the target layer, category index layer, and index layer. The influence of each index on UCG and its reasonable value range were systematically analyzed. The evaluation index system for UCG site selection was formed. The improved analytic hierarchy process (AHP) was adopted to sequence indices and determine their subjective weight. And the variability, conflict, and information amount of the index data were analyzed by the CRiteria Importance Through Intercriteria Correlation (CRITIC) method to calculate the objective weight. Then, the subjective and objective weights were combined through game theory. On this basis, fuzzy theory was employed to calculate the membership of indices and construct the fuzzy comprehensive judgment matrix. The evaluation model of the UCG site selection was applied to the suitability evaluation of resource conditions of UCG pilot projects at Zhongliangshan (ZLS), Huating (HT), and Shanjiaoshu (SJS) mines in China. The result shows that the resource conditions of HT are the best, followed by ZLS and, finally, SJS, which are consistent with the actual running effects of the three UCG pilot projects. It indicates that the evaluation model can provide a scientific theoretical basis and reliable technical support for the UCG site selection.

Discussion on the current situation and quality evaluation model of online course teaching in universities based on social network analysis

Discussion on the current situation and quality evaluation model of online course teaching in universities based on social network analysis

Discussion on the Current Situation and Quality Evaluation Model of Online Course Teaching in Universities Based on Social Network Analysis

Discussion on the Current Situation and Quality Evaluation Model of Online Course Teaching in Universities Based on Social Network Analysis

Blocking Mechanism and Evaluation Model of Insensitive Effect Under Multi-Frequency Electromagnetic Radiation

Due to the insensitive blocking effect on frequency equipment, the current effect evaluation model based on the third-order power series expansion is disabled to the insensitive blocking effect evaluation under multi-frequency electromagnetic radiation. We adopt the fifth-order power series expansion to analyze the insensitive blocking interference effect. In addition to revealing the mechanism of the insensitive blocking effect under multi-frequency electromagnetic radiation, we also build an improved effect evaluation model in this research. It defines the effect evaluation index. Subsequently, a ranging radar and a communication radio station are employed to verify the effectiveness of the proposed effect evaluation model separately. The results show that the proposed model can be applied to the electromagnetic radiation effect evaluation, regardless of whether the multi-frequency insensitive jamming effect exists on tested equipment or not. All effect evaluation indexes of the new model are smaller than that of the previous model based on the third-order power series expansion method. The absolute value of relative errors could be less than 1 dB. Not only is the theoretical analysis result of the multi-frequency insensitive blocking effect mechanism verified, but also the problem of poor universality of the current electromagnetic radiation effect evaluation model is figured out.

Evaluation of Comprehensive Services of an Online Learning Platform Based on Artificial Intelligence

With the college students having increasing needs for learning diversified knowledge and skills, online learning platforms for essential qualities enhancement have emerged one by one. However, due to lack of feedbacks from students, the comprehensive service quality of these platforms varies greatly. Therefore, it has certain practical significance to study how to evaluate the comprehensive services of online learning platforms. The current evaluation models are not suitable for online learning platforms, nor have they fully considered the dynamic and subjective feedbacks of students about their experience. To this end, this paper takes an online music education platform as an example and studies the evaluation on the comprehensive services of the online learning platform. First, the overall architecture design of an online learning platform for essential qualities enhancement was displayed, and the teacher-student interaction mode for the comprehensive services of the online learning platform was identified and analyzed. Then, the derivation process of the evaluation model was presented, the evaluation indicator system for the comprehensive service quality of the online learning platform constructed, and the comprehensive service evaluation model for the online learning platform consisting of the hierarchical model and the rough set-neural network evaluation model established. The experimental results verified the effectiveness of the constructed indicator system and evaluation model.

Rural Planning Evaluation Based on Artificial Neural Network.

The continuation of human civilization is inseparable from the development and construction of rural areas, and infrastructure is the core of rural development. China has been building large-scale rural infrastructure in recent years. Rural infrastructure building, for example, is huge in both quantity and scope, but it is beset by challenges in its current construction and development, and it urgently requires suitable leadership. Planning assessment, as a technical method, can identify problems in regional development and is a powerful tool for evaluating the impact of planning and construction and promoting the development of complete new areas. This paper is aimed at the planning evaluation of rural construction and the evaluation of rural construction and guides the planning and implementation of the next step of rural construction, to assist China's supervision and inspection of rural construction effect and promote rural construction and development into a good track. In view of the low accuracy and efficiency of the current evaluation model of rural planning and the problem that a single neural network easily produces local extreme value, the neural network method is improved, and the application of LM-BP neural network in the evaluation model of rural planning is proposed. Input sample elements are five factors affecting rural construction, including industrial construction, population distribution, and utilization rate of large-scale facilities, construction of public facilities, and promotion effect of supporting policies. Output sample is the evaluation result. On this foundation, the LM-BP neural network was used to convert the training into a least square problem, and the LM method was used to redefine the number of hidden layer nodes, resulting in the construction of a rural planning evaluation model based on the LM-BP neural network. This approach is used to determine the outcomes of rural planning evaluations. The experimental results show that the designed evaluation model has a small evaluation error, has the advantage of high accuracy compared with similar models, and is a reliable evaluation model for rural planning.

Comprehensive evaluation of power transmission and transformation project based on electric power big data

In order to solve the problems of incomplete factors and inaccurate prediction in the current evaluation model of power transmission and transformation project, this paper uses the BP neural network algorithm optimized by simulated annealing genetic algorithm to solve the shortcomings of low training efficiency and local convergence value of BP neural network in model prediction. The comparison of experimental results shows that the BP neural network optimized by simulated annealing genetic algorithm proposed in this paper is 23.21% higher than that of BP neural network.