Performance Evaluation Index Research Articles

Semi-Active Suspension Control Strategy Based on Negative Stiffness Characteristics

This paper investigates the potential of negative stiffness suspensions for enhanced vehicle vibration isolation. By analyzing and improving traditional control algorithms, we propose and experimentally validate novel skyhook, groundhook, and hybrid control strategies for suspensions with negative stiffness characteristics. We establish pavement models, incorporate negative stiffness into suspension modeling, and develop a performance evaluation index. Our research identifies shortcomings of classical semi-active control algorithms and introduces a new band selector to combine improved control methods. Simulation results demonstrate that the proposed semi-active suspension control strategy based on negative stiffness effectively reduces body vibration and enhances vehicle ride performance.

Investigation on fatigue damage of buton rock asphalt mixtures using semi-circular bending (SCB) and digital image correlation (DIC) techniques

The fatigue performance evaluation indices of Buton rock asphalt (BRA) modified asphalt mixture struggles to accurately characterize damage evolution process under repeated loads of the traffic. This study employs digital image correlation (DIC) technology to assess the fatigue behavior of BRA modified asphalt mixture, quantify the correlation between macroscopic response and local deformation, and verify the accuracy of evaluation indices. By capturing strain and displacement fields, the fatigue damage evolution of BRA modified asphalt is analyzed. Based on fracture mechanics theory, BRA’ s improvement of fatigue performance is quantified. Results show that vertical displacement captured by DIC can divide the three stages of fatigue cracking. The global horizontal strain(GExx)and cumulative horizontal strain(CExx) proposed in this study better characterize the nonlinear fatigue damage evolution compared to horizontal strain(Exx). Adding BRA delays crack initiation, reduces propagation rate, and enhances fatigue performance. Strain field evolution was quantitatively analyzed, and horizontal strain density(DE) and global horizontal strain density(GDE) were proposed as fatigue damage indicators. At the same load level, these indicators in BRA modified asphalt mixture, both pre- and post-aging, exceeded those of neat asphalt mixture. These findings provide a novel and accurate approach for evaluating the fatigue damage behavior of BRA modified asphalt mixture, laying the foundation for future performance-based designs.

A Framework for Monitoring the Effectiveness of Ecosystem-Based Adaptation Strategies Using Internet of Things and Machine Learning Techniques

Climate change poses a threat to the global ecosystem. Many countries adopt various approaches, including ecosystem-based adaptation (EbA), to address this problem. However, the assessment of the effectiveness of the EbA interventions is conducted manually, is resource-intensive, and is focused on short-term outputs. These limitations underscore a critical gap: the need for a comprehensive, automated system that enables long-term monitoring and predictive analysis. This study aimed to address this gap by developing an innovative framework that integrates Internet of Things (IoT) devices and machine learning (ML) algorithms to continuously monitor weather, hydrological, environmental, and other variables. We conducted a thorough analysis to design an appropriate framework. In addition, to obtain the relevant information and data, we conducted interviews with the local community and collected secondary data from various sources. The proposed framework consists of five layers: (i) EbA interventions; (ii) IoT-based key performance indicators (KPI) for monitoring and evaluation (M&E); (iii) primary data collection; (iv) data storage; and (v) application. As a proof of concept, we developed a system that supports early flood and drought alerts while simultaneously providing long-term evaluations of the effectiveness of EbA strategies. The developed system consists of IoT devices and a web application integrated with machine learning (ML). We set up and tested the IoT devices before deploying them in the study area. The devices capture data for two primary purposes: (1) short-term: flood detection and alerting, and (2) long-term: drought prediction and evaluation of EbA effectiveness through continuous data analysis. This research represents a significant advancement in the automation and long-term assessment of climate adaptation measures, offering a scalable and effective solution to disaster risk reduction.

Degradation mechanism and strength prediction of aeolian sand concrete under sulphate dry-wet cycles

In this paper, aeolian sand (AS) with different contents (0 %, 25 %, 50 %, 75 % and 100 %) was used as fine aggregate to prepare aeolian sand concrete (ASC). The effects of AS content and dry-wet cycle (DWC) times on the durability of ASC under sulfate (5 % Na2SO4) erosion conditions were investigated by DWC tests. The mass loss rate, compressive strength loss rate and relative dynamic elastic modulus (RDEM) were used as the macroscopic performance evaluation indexes, while the damage degradation mechanism of ASC was revealed by combining the microscopic technical means such as SEM, XRD, TG-DSC and NMR. The results show that the macroscopic properties of ASC under the action of DWC of sulphate all show the first increase and then decrease, as when the dosage of AS is below 50 %, the "bead effect" and "padding effect" of AS can further improve the internal densification of ASC, thus effectively resisting external sulphate erosion. During sulphate erosion, SO42- firstly reacts with cement hydration product Ca(OH)2 to generate erosion products such as Ettringite (AFt) and fills them within the original defects, with short-term rnhancement of macroscopic properties. As the DWC continue, the excessive erosion products accumulated, resulting in the destruction of pore structure and extension through the phenomenon, the matrix compactness is reduced, and the macroscopic properties degraded. The grey entropy correlation analysis yielded that the grey entropy correlation grade of bound fluid saturation, harmless pore percentage and less harmful pore percentage with compressive strength were all above 0.8, and the GM(1,4) strength prediction model established on the basis of the above pore structure parameters has a high prediction accuracy, and the relative errors between the predicted strength values and the actual strength values are within 10 %, and the research results can provide certain theoretical support for the study of the durability of ASC in sulfate erosion environment.

Mechanism and spatial spillover effect of digital economy on common prosperity in the Yellow River Basin of China

The digital economy has emerged as a new trend in economic development and has profoundly influenced the process of achieving common prosperity. However, current research on the correlation between the digital economy and common prosperity from the perspective of a river basin still needs to be strengthened. Based on this, the present study first theoretically elaborates the conceptual meanings of “digital economy” and “common prosperity”, as well as the mechanism by which the digital economy empowers common prosperity. Subsequently, a scientifically-constructed performance evaluation index system for the digital economy and common prosperity is established. Considering the Yellow River Basin as an empirical case study area, this study investigates the mechanism and spatial spillover effects of the digital economy in empowering common prosperity from 2005 to 2020. The research findings reveal that: (1) The Yellow River Basin exhibits a basin characteristic with downstream > midstream > upstream areas regarding the level of common prosperity and digital economy. It indicates that a distinct spatial correlation exists between the two factors. However, the ongoing decrease in both high-level and very high-level areas reflects the lengthy and challenging journey of enhancing the quality and efficiency of the digital economy in empowering common prosperity. (2) The digital economy not only directly impacts common prosperity, but also fosters its development through spatial spillover effects. Among the control factors, informatization and housing levels have a major stimulating effect. (3) There exists a clear regional heterogeneity in how the digital economy affects common prosperity in the Yellow River Basin. Specifically, common prosperity of downstream cities is significantly impacted by the digital economy. The spatial spillover effects of the digital economy on common prosperity exhibit a pronounced “neighborhood as a moat” characteristic. (4) The digital economy facilitates the achievement of shared prosperity through the implementation of mechanisms centered on sharing, affluence, and sustainability. These research findings illuminate the empowering mechanisms and spatial spillover pathways of the digital economy in promoting shared prosperity, aligning with national strategies for ecological conservation and high-quality development in the Yellow River Basin.

A new performance evaluation indicator for the LEE Jong-wook Fellowship Program of Korea Foundation for International Healthcare to better assess its long-term educational impacts: a Delphi study.

The Dr. LEE Jong-wook Fellowship Program, established by the Korea Foundation for International Healthcare (KOFIH), aims to strengthen healthcare capacity in partner countries. The aim of the study was to develop new performance evaluation indicators for the program to better assess long-term educational impact across various courses and professional roles. A 3-stage process was employed. First, a literature review of established evaluation models (Kirkpatrick's 4 levels, CIPP model, OECD DAC criteria) was conducted to devise evaluation criteria. Second, these criteria were validated via a 2-round Delphi survey with 18 experts in training projects from May 2021 to June 2021. Third, the relative importance of the evaluation criteria was determined using the analytic hierarchy process (AHP), calculating weights and ensuring consistency through the consistency index (CI) and consistency ratio (CR), with CR values below 0.1 indicating acceptable consistency. The literature review led to a combined evaluation model, resulting in 4 evaluation areas, 20 items, and 92 indicators. The Delphi surveys confirmed the validity of these indicators, with content validity ratio values exceeding 0.444. The AHP analysis assigned weights to each indicator, and CR values below 0.1 indicated consistency. The final set of evaluation indicators was confirmed through a workshop with KFIH and adopted as the new evaluation tool. The developed evaluation framework provides a comprehensive tool for assessing the long-term outcomes of the Dr. LEE Jong-wook Fellowship Program. It enhances evaluation capabilities and supports improvements in the training program's effectiveness and international healthcare collaboration.

Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6–14-2. Simultaneously, three swarm intelligence algorithms were integrated—the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)—to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 × 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT–BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 and 1.958, MAPE = 0.071 and 0.041, and RMSE = 4.686 and 1.594, respectively. BAT significantly improves the predictive accuracy of the BPNN model, enabling the prediction of PMM mechanical properties and providing a scientific basis for its mix proportion design.

Innovative server simulator for data centers and critical indices of performance evaluation

Server simulators, also known as dummy loads, are used to simulate real IT equipment for performance examining of data centers. Without these tests, potential failures could result in significant losses. Due to oversimplified designs and lack of evaluation methods, commercial dummy loads often fail to reflect accurately the true conditions of the real servers in data centers. In this study, a novel dummy-chip server simulator (DCSS) is developed, which can well reproduce the working mechanism of real servers. In this paper, three indices are proposed for the first time to evaluate the performance from different perspectives, i.e. Coefficient of Heat Removal (CHR) for heat removal capability, Coefficient of Temperature Deviation (α) for temperature rating, and Power Usage per Computing (PUC) for energy and computation efficiency. Experimental tests are conducted in this study, and results show that the good performance of the DCSS approaching the real server, and the values of CHR, α, and PUC indicate significant improvements compared to the commercial dummy load. The findings of this study present straightforward guidance for IT server simulator design, and demonstrate the advancement of DCSS and proposed index for data center performance evaluation.

A human reliability analysis method based on STPA-IDAC and BN-SLIM for driver take-over in Level 3 automated driving

Human factors play an important role in the take-over process of Level 3 (L3) automated driving. This paper combines Systems Theoretic Process Analysis (STPA) and Information, Decision and Action in Crew context (IDAC) for qualitative analysis and Bayesian Network (BN) and Success Likelihood Index Method (SLIM) for quantitative calculation to obtain the main performance shaping factors (PSFs) and evaluation indicators that cause human errors. Firstly, the STPA-IDAC method is used to analyze unsafe control actions (UCAs) for take-over process and form the mapping relationship of UCAs-IDA-PSFs. Secondly, the BN of human reliability analysis for take-over process is constructed based on the BN-SLIM method. Uncertainty in rates of PSFs and evaluation indicators is addressed in a probabilistic manner using expert opinions and empirical data. After diagnostic reasoning of BN, mean variation is used to identify the main PSFs and evaluation indicators. This method can effectively identify the main PSFs and evaluation indicators that cause human errors, facilitate risk assessment and management, and reduce the human error probability (HEP).

Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.

A performance evaluation index for student satisfaction in online live classes of Chinese language and literature

A performance evaluation index for student satisfaction in online live classes of Chinese language and literature

Performance evaluation of industry-education integration in higher education from the perspective of coupling coordination-an empirical study based on Chongqing.

The coupling coordination between higher education and regional industries is an effective way to enhance the performance of industry-education integration and an important driving force for promoting sustainable economic and social development. This article aims to evaluate the performance level of industry-education integration in higher education from the perspective of coupling coordination between industry and education, and to analyze its sustainable development trends. To achieve this, a framework for the industry-education complex system is constructed based on the theory of coupling coordination, and a performance evaluation model for industry-education integration is established. The "structure-conduct-performance" (S-C-P) analysis paradigm of industrial organization theory is used to design a performance evaluation indicator system, and the entropy weight method is applied to assign weights to the indicator system. Empirical research is conducted on Chongqing based on statistical data from 2000 to 2022. The results show that: (1) During the sample period, the construction effectiveness of industry-education integration in higher education in Chongqing is relatively significant, and the coordination level between regional industries and higher education continues to rise. (2) The orderly development level of the two subsystems of regional industries and higher education alternately rises, jointly promoting the continuous improvement of the performance level of industry-education integration, which is the main driving factor for performance improvement. (3) The stagnation of the coupling strength between regional industries and higher education is a key obstacle to further improving the performance of industry-education integration. Industry-education integration is a systematic project that should be promoted collaboratively from the perspectives of the regional industrial subsystem, higher education subsystem, industry-education complex system, and external environment to continuously enhance the performance level.

Using magnetic marketing tools to enhance the financial performance of the Iraqi general insurance company

The research aims to use magnetic marketing tools across its dimensions of (message, market, and media) to enhance the financial performance of the Iraqi General Insurance Company, as well as to identify the strengths and weaknesses in the company’s performance. Adopting the descriptive approach And Analytical Data and information were collected through the financial statements, annual reports and statistics of the company under study, relying on the annual reports issued by the Iraqi Insurance Bureau, as well as the performance evaluation indicators issued by the Federal Financial Supervision Bureau, represented by (the index of relative importance of insurance activity revenues, and production follow-up indicators) The fulfillment of plans, the development of production, the productivity of producers, the number of documents, and the loss rate) for the years (2018-2022). The research reached a set of results, the most important of which was that adopting magnetic marketing tools would enhance the revenues of insurance activity, reflected in the financial performance of the researched company.

Promotion or inhibition? Exploring the influence of the digital economy on urban-rural integration: A case study of yellow River basin

The digital economy actively disrupts the "urban depression effect" and the "wealth gap" between urban and rural areas, playing a crucial role in advancing urban-rural integration. Based on this, a scientifically constructed performance evaluation indicator system for the digital economy and urban-rural integration was developed. Using the Yellow River Basin as an empirical case study area, the projection pursuit model, simulated annealing algorithm and spatial Durbin model were employed to explore the impact of the digital economy on urban-rural integration in the Yellow River Basin from 2011 to 2021. The findings reveal the following: 1) The degree of digital economy and urban-rural integration display a characteristic pattern resembling that of a downstream > midstream > upstream watershed, showcasing discernible spatial correlation features between them. 2) The digital economy in the Yellow River Basin directly fosters urban-rural integration development. Government behavior stand out as the most influential factor in promoting urban-rural integration among the control variables. 3) The impact of the digital economy on urban-rural integration in the Yellow River Basin displays regional heterogeneity, with the most pronounced effects observed in downstream areas. Moreover, the spatial spillover effect of the digital economy exhibits a distinct "good-neighborliness" characteristic. 4) The digital economy in the Yellow River Basin substantially advances urban-rural integration development through the adjustment effect of industrial structure rationalization and the mediating effect of entrepreneurial activity. These research findings shed light on the impact and spatial spillover pathways of the digital economy on urban-rural integration, aligning with the national strategy of ecological protection and high-quality development in the Yellow River Basin.

SO2 removal performance and breakthrough prediction of activated carbon-based chemical filters for electronics cleanroom air purification

Electronic cleanrooms have strict control requirements for SO2 concentration due its significant influence on product yield, and application of chemical filters is the main measure of SO2 control. In this study, 8 activated carbon (AC)-based chemical filters were collected and tested for their SO2 purification performance and resistance. These filters had very similar initial efficiencies (all >95 %) but large differences in 20 % breakthrough adsorption capacity (49–294 g at 9 ppm and 0.35 m/s). The adsorption capacity is found to be positively correlated with pleat density, which is a main structural parameter of pleated chemical filter. Meanwhile, the filter resistance (9.3–34 Pa at 0.35 m/s) demonstrates a decreasing and then increasing trend with increasing pleat density. This indicated the existence of an optimal range of pleat numbers that can be used to achieve a balance between resistance and adsorption capacity. A comprehensive performance evaluation index (Z′-index) is proposed for assessing the integrated resistant and adsorption capacity performance of chemical filters (19–210 g/W for 8 samples). A lumped parameter model was proposed for predicting the breakthrough curves at low concentration based on the traditional semi-empirical breakthrough models. The prediction deviation of the breakthrough curve at real cleanroom scenario (2 ppb) is less than 15 %. In addition, a rapid filter life prediction method based on filter design parameters is developed from the lumped parameter model. This work helps the understanding of the impact of filter structure parameters on performance and facilitate the development of high-performance chemical filters for SO2 contaminant control in electronic cleanrooms.

Upstream process condition monitoring for froth flotation based on feature performance evaluation and parameter-mapped GRNN

Timely and reliable estimation of the upstream process condition in flotation process is significant for whole flotation operations. An upstream process condition monitoring model based on feature performance evaluation and parameter-mapped generalized regression neural network (GRNN) is proposed to evaluate visual feature performance and improve the generalization ability of ensemble models for zinc flotation. Firstly, different visual features are extracted, and the froth video representation is obtained. Then, feature performance evaluation is constructed, considering the correlation between different visual features and flotation indicators, and stability of feature representation ability vary. Meanwhile, feature performance evaluation indexes are calculated introducing a scale factor to integrate the Pearson correlation with feature stability coefficient. Finally, a parameter-mapped GRNN, whose base model parameters are mapped into a certain interval, is designed to monitor the upstream process condition based on the feature performance evaluation index-weighted feature vector. Experiments using real-world lead–zinc flotation data were implemented to show the potential of the feature performance evaluation and effectiveness of the proposed model.

A grey target performance evaluation model for aeroengine pressure test bench

PurposeThis paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.Design/methodology/approachBased on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.FindingsA case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.Originality/valueThe pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

Fuzzy Evaluation Model for Operational Performance of Air Cleaning Equipment

Global warming has led to the continuous deterioration of the living environment, in which air quality directly affects human health. In addition, the severity of the COVID-19 pandemic has further increased the attention to indoor air quality. Indoor clean air quality is not only related to human health but also related to the quality of the manufacturing environment of clean rooms for numerous high-tech processes, such as semiconductors and packaging. This paper proposes a comprehensive model for evaluating, analyzing, and improving the operational performance of air cleaning equipment. Firstly, three operational performance evaluation indexes, such as the number of dust particles, the number of colonies, and microorganisms, were established. Secondly, the 100(1− α)% upper confidence limits of these three operational performance evaluation indexes were deduced to construct a fuzzy testing model. Meanwhile, the accumulated value of ϕ was used to derive the evaluation decision-making value. The proposed model can help companies identify the key quality characteristics that need to be improved. Furthermore, the competitiveness of cooperative enterprises towards smart manufacturing can be strengthened, so that enterprises can not only fulfill their social responsibilities while developing the economy but also take into account the sustainable development of enterprises and the environment.

Motivating for environmental protection: Evidence from county officials in China

This paper investigates the impact of changes in the officials’ performance evaluation system on ecological environment quality, as well as the effect of ecological improvements on officials’ promotion prospects. Analyses are conducted using the changes in the officials’ evaluation mechanism in China’s ecological function counties in 2013 as a quasi-natural experiment, incorporating a newly developed set of county-level comprehensive ecological environment index data and manually collated data on county leaders. Multiple methods are employed to address issues such as sample selection bias, reverse causality, and heterogeneity of treatment effects. The empirical analysis shows that the adjustment of officials’ performance evaluation indicators contributes to the improvement of ecological environment quality. This effect is more pronounced in regions where GDP evaluation is abolished, regions with lower economic development but better ecological environment foundations, regions closer to provincial capitals, and officials with shorter tenure, younger age, and male gender. The rationale behind this policy is that post-reform improvements in the ecological environment will increase the promotion prospects of officials in ecological function counties, enable these areas to secure additional ecological transfer payments, thereby enhancing their capacity for environmental expenditure, and establish a credible commitment mechanism for central-local contracts. This study not only examines the relationships between political incentives for officials and environmental protection, but also enriches the literature on environmental decentralization, multi-target governance, and environmental political economy.

Numerical model of debris flow susceptibility using slope stability failure machine learning prediction with metaheuristic techniques trained with different algorithms

In this work, intelligent numerical models for the prediction of debris flow susceptibility using slope stability failure factor of safety (FOS) machine learning predictions have been developed. These machine learning techniques were trained using novel metaheuristic methods. The application of these training mechanisms was necessitated by the need to enhance the robustness and performance of the three main machine learning methods. It was necessary to develop intelligent models for the prediction of the FOS of debris flow down a slope with measured geometry due to the sophisticated equipment required for regular field studies on slopes prone to debris flow and the associated high project budgets and contingencies. With the development of smart models, the design and monitoring of the behavior of the slopes can be achieved at a reduced cost and time. Furthermore, multiple performance evaluation indices were utilized to ensure the model’s accuracy was maintained. The adaptive neuro-fuzzy inference system, combined with the particle swarm optimization algorithm, outperformed other techniques. It achieved an FOS of debris flow down a slope performance of over 85%, consistently surpassing other methods.