Multi-dimensional Evaluation Index System Research Articles

Continue nursing education: an action research study on the implementation of a nursing training program using the Holton Learning Transfer System Inventory

ObjectiveTo address the gap in effective nursing training for quality management, this study aims to implement and assess a nursing training program based on the Holton Learning Transfer System Inventory, utilizing action research to enhance the practicality and effectiveness of training outcomes.MethodsThe study involved the formation of a dedicated training team, with program development informed by an extensive situation analysis and literature review. Key focus areas included motivation to transfer, learning environment, and transfer design. The program was implemented in a structured four-step process: plan, action, observation, reflection.ResultsOver a 11-month period, 22 nurses completed 14 h of theoretical training and 18 h of practical training with a 100% attendance rate and 97.75% satisfaction rate. The nursing team successfully led and completed 22 quality improvement projects, attaining a practical level of application. Quality management implementation difficulties, literature review, current situation analysis, cause analysis, formulation of plans, implementation plans, and report writing showed significant improvement and statistical significance after training.ConclusionThe study confirms the efficacy of action research guided by Holton’s model in significantly enhancing the capabilities of nursing staff in executing quality improvement projects, thereby improving the overall quality of nursing training. Future research should focus on refining the training program through long-term observation, developing a multidimensional evaluation index system, exploring training experiences qualitatively, and investigating the personality characteristics of nurses to enhance training transfer effects.

Coupling and Coordination Relationship of Economic–Social–Natural Composite Ecosystem in Central Yunnan Urban Agglomeration

Exploring the spatio-temporal differentiation characteristics and coupling coordination relationship of the composite ecosystem of urban agglomerations is of great significance for promoting the synergistic sustainable development and integration construction of urban agglomerations. This paper is based on LUCC (Land Use and Land Cover Change) data, a DEM (Digital Elevation Model), and temperature, precipitation, and other multi-source data, using the central Yunnan urban agglomeration as an example and constructing a multi-dimensional evaluation index system highlighting development quality and efficiency. The entropy weight method was first used to determine the comprehensive weights to evaluate the regional economic and social development level. Then, the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to quantitatively calculate and analyze the spatial and temporal evolution characteristics of the four key ecosystem services, evaluating the spatio-temporal evolution characteristics of the natural subsystem. The coupling coordination model was used to quantitatively analyze the evolution of the coupling coordination of the composite ecosystems in the central Yunnan urban agglomeration during the period of 2010–2020, and to reveal its development law. The results show the following: ➀ During the study period, the socio-economic subsystems in the central Yunnan urban agglomeration demonstrated an outward radiative growth from Kunming City, marked by underdeveloped sub-centers and prevalent low-level areas. ② Trends in ecosystem services varied, with water and soil conservation showing fluctuating increases, carbon sequestration remaining stable, and habitat quality declining. The critically important integrated ecosystem services zone of the natural subsystem is mainly located in the northeastern region and southwestern edge of the study area. ③ In 2020, the coupling coordination degree of the composite ecosystem was 0.9492. This showed that economic, social, and ecological subsystems are highly coupled, with consistent overall development trends and strong interactions. ④ The increase in the degree of harmonization amounted to 7.82%, with lagging subsystems varying in the degree of harmonization in subregions. This study can provide a scientific foundation for the development of policies for the Central Yunnan urban agglomeration, promoting regional sustainable development and optimizing the spatial pattern of the national territory.

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.

Research on Evaluation Method of Temporary Road Scheme in Mechanized Construction of Transmission Line

During the construction of overhead transmission lines, it is difficult for construction equipment to arrival the site, and it is difficult to evaluate the optimal approach comprehensively. In this paper, the evaluation of construction temporary road schemes is built, and a multi-dimensional evaluation index system covering the indicators such as technical factors, economy, environment, safety is constructed. The weight of each evaluation index is determined by analytic hierarchy process (AHP), and the comprehensive score of the scheme is obtained, so as to realize the comprehensive ranking of the approach scheme, which can help make decisions of the optimal approach scheme.

Developing a Multi-Dimensional Health Index System for a General Occupational Population in the Light of Health Ecology Theory: A Delphi Study.

The health status of the occupational population is critical to the development of countries and regions as it is the main force of social and economic development. However, there is a dearth of comprehensive and systematic indicators to evaluate the health of occupational groups. This study aimed to construct a multi-dimensional evaluation index system for the general population. This study combined a literature review and initially established a multidimensional health system framework for the occupational population based on health ecology theory and then used two rounds of Delphi expert consultation to construct the final multidimensional health index system for the occupational population. Fifteen experts from related fields were selected for two rounds of Delphi expert consultation. The recovery rates of the two rounds of expert questionnaires were 100.00% and 93.33% respectively, the expert authority coefficient were 0.90, and the Kendall's coordination coefficients of the first and second level indexes were 0.32 and 0.42 (P ≥ 0. 001). The final index system includes four primary indicators (individual characteristics, health knowledge, health behavior, and health skills), 13 second-level indicators, and 41 third-level indicators; the weight coefficients of the four primary health dimensions are relatively close, and the "health knowledge" is slightly higher. The multi-dimensional health index system of the occupational population established in this study is comprehensive and reasonable from the perspective of health ecology, which can provide a solid foundation for the further development of a comprehensive health status prediction model for the occupational population.

Spatial Risk Assessment of the Effects of Obstacle Factors on Areas at High Risk of Geological Disasters in the Hengduan Mountains, China

The Hengduan Mountains in China are known for their complex geological environment, which leads to frequent geological disasters that pose significant threats to the safety and economic and social development of the local population. In this study, we developed develop a multi-dimensional evaluation index system from the aspects of economy, society, ecology, and infrastructure, and the resilience inference measurement (RIM) model was developed to assess resilience to regional disasters. The clustering evaluation of exposure, damage, and recovery variables in four states was conducted by way of K-means clustering. The results of K-means clustering are confirmed by discriminant analysis, and the disaster resilience index was empirically verified once. At the same time, the obstacle factor was further analyzed with the obstacle degree model. The results indicate that there are 8 susceptible areas, 23 recovering areas, 27 resistant areas, and 7 usurper areas. The classification accuracy of the model is 95.4%. The disaster resilience of high-risk areas was found to be low, with “extremely poor” differentiation, where the majority of the areas had low resilience and only a minority had high resilience. A “high in the southeast and low in the northwest” spatial distribution was observed. High-resilience areas were “dotted” and mainly concentrated in core areas with a high population density and strong economic activity, while low-resilience areas had a pattern of “edge extension” and were mainly distributed in the transition zone between the Qinghai–Tibet and Yunnan Plateaus. There were clear differences in the barriers of disaster resilience among the 65 counties (cities). The economic barrier degree was found to be the largest barrier to disaster resilience, followed by ecological, social, and infrastructure barrier degrees. The main factors affecting the distribution of disaster resilience in the high-risk areas were topographic relief, proportion of female population, cultivated land area, industrial structure, number of industrial enterprises above a designated size, and drainage pipeline density in the built-up area. Additionally, primary barrier factors classify the 65 counties (cities) into three types: economic constraint, natural environment constraint, and population structure constraint.

Multi-Dimensional Evaluation of Teachers’ Leading Role in Art-Training Courses

Art-training courses are an important form of practical education, with cultivating students’ innovative thinking and practical skills as their primary objective. In classroom teaching, teachers have a vital influence on students’ learning results. However, conventional methods for evaluating teachers of art-training courses generally have few dimensions and thus are unable to comprehensively and accurately reflect teachers’ leading role in art-training courses. To fill in this blank, this study created a multi-dimensional evaluation index system (EIS) for assessing teachers’ leading role in art-training courses and built a multi-dimensional model based on a satisfaction function and using a cobweb chart. The proposed model can be used to evaluate the overall performance of teachers and analyze the relationship and balance between each evaluation index. The model can output more equitable evaluation results, increasing the trust of evaluation objects for the evaluation process. Lastly, validity of the model was verified by empirical analysis.

Evaluation of Urban Ecological Livability from a Synergistic Perspective: A Case Study of Beijing City, China

The ecological livability of a city reflects the overall civilization degree and sustainable development ability of this region. The establishment of a scientific and reasonable evaluation index system for eco-livable cities helps analyze the level of sustainable development of cities and the quality of life of residents, thus promoting high-quality development of cities. However, few scholars construct a universal evaluation index system for eco-livable cities from synergistic perspective. This paper establishes a multidimensional evaluation index system based on the Theory of Synergism of Five Laws (considering five aspects of economy, society, technology, environment and nature) and analyzes the trends of the evaluation value and coupling coordination degree of five systems in Beijing from 2010 to 2021 by using the systematic methods of a five-law analysis and a five-law synergy integration included in the Theory of Synergism of Five Laws, respectively. The results show that the ecological livability of Beijing generally maintains an upward trend and reaches a well-coordinated level in 2021, but the development level of each system is constrained by different factors. Based on the above results, this paper proposes policy recommendations to improve the ecological livability of cities in combination with the comprehensive model of policy-oriented coupling of multiple subjects to promote the coupling of five systems, with a view to providing references for decision-making in building an eco-livable city.

Multi-Dimensional Evaluation of Land Comprehensive Carrying Capacity Based on a Normal Cloud Model and Its Interactions: A Case Study of Liaoning Province

Studying land comprehensive carrying capacity (LCCC) is the foundational and key requirement for determining land development planning and urban spatial development patterns of a region. However, the traditional evaluation method discounts the fuzziness and randomness of the evaluation index and its results. The cloud model combines randomness and fuzziness to reveal the correlation between randomness and fuzziness using numerical feature entropy and is used to represent the granularity of a qualitative concept. This study used the Liaoning Province as the study area, and developed a multi-dimensional evaluation index system for LCCC using a normal cloud model. Based on this, the relationship between the different elements of geological condition, resources and environment, economic scale and urban construction were studied using the coupling coordination degree model that reflected not only the system interactions but also the strengths of its degree of coordination. Our results were as follows: (1) numerical feature entropy were evaluated to determine the carrying capacity level of the land, and comprehensive land carrying capacity evaluations were conducted in terms of both quantitative results and the reliability of the results, promoting the scientific application of uncertainty theory in the field of comprehensive land evaluation as well as carrying capacity. (2) Liaoning Province’s prefecture-level cities had distinctly different LCCC, demonstrating “low in the west and high in the east” spatial distribution characteristics. Cities with established economies and relatively strong infrastructures had larger comprehensive land carrying capacities. Overall, there was considerable consistency across the region, though the “low in the west and high in the east” spatial distribution characteristics affected the degree of coordination.

Optimization Design and Performance Evaluation of U-Shaped Area Operation Procedures in Complex Apron

In view of the common U-shaped apron structure of large- and medium-sized airports at home and abroad, this study considered the optimization design and performance evaluation of the U-shaped apron operation procedure. First, by analyzing the physical structure characteristics and traffic operation characteristics of the U-shaped area, exclusive, partition-shared, and global-shared operation procedures of the U-shaped area were designed, and differentiated apron-operation rules and traffic models were constructed for different types of operation procedures. Then, from the perspectives of safety, efficiency, and environmental protection, a multi-dimensional evaluation index system of U-shaped area operation performance is established, and a classification measurement and comprehensive evaluation method based on critique is proposed. Finally, a traffic simulation model was established based on airport network topology modeling. We used Monte Carlo methods for the simulation in Python 3.6, and the experimental results show that, in the scenario of high-density traffic operation, compared with exclusive and partition-shared procedures, the implementation effect of the global shared procedure is very significant, and the apron operation capacity increased by 14.8% and 5.0%, respectively. The probability of aircraft conflict decreased by 32.2% and 11.8%, respectively, and the time of single conflict relief decreased by 16.1 s and 10.6 s, respectively. The average resource utilization in each U-shaped area increased by 66% and 25%, respectively, while the average daily carbon emissions of a single aircraft were reduced by 16.7 kg and 11.0 kg and the average daily fuel consumption of a single aircraft were reduced by 3.6 kg and 2.4 kg, respectively. The proposed method is scientific and effective and can provide theoretical and methodological support for optimizing the configuration of the scene operation mode of complex airports and for improving flight operation efficiency.

Vibration Signal Augmentation Method for Fault Diagnosis of Low-Voltage Circuit Breaker Based on W-CGAN

Low-voltage circuit breaker (LVCB) fault diagnosis based on artificial intelligence (AI) algorithm has always been a research hotspot and got some recent advances. However, AI algorithms usually require sufficient data to train the model, so intelligent fault diagnosis is a challenging task when lack of fault signals. To solve this problem, a fault vibration signal augmentation method based on Wasserstein distance (WD) and conditional generative adversarial networks (CGANs) is proposed in this article. The proposed method uses WD to optimize the adversarial training of generator and discriminator, and thus, the generator can generate vibration signals under different fault conditions, which can be used to extend the training dataset. In order to verify the improvement effect of this method on the accuracy of LVCB fault diagnosis, multiple fault classifiers are trained using generated and real fault signals, and a multidimensional evaluation index system is built to evaluate the classification effect. Experimental results reveal that the method can generate fault signals with high similarity and improve the accuracy of fault diagnosis.

Complex Network Construction and Pattern Recognition of China’s Provincial Low-Carbon Economic Development with Long Time Series: Based on the Detailed Spatial Relationship

Low-carbon economic development has become the orientation of high-quality economic construction in the era of climate change. Because it involves multi-dimensional elements and is driven by the concept of regional integration and common development, the development pattern among China’s provinces should show the characteristics of complex networked spatial correlation. However, most of the existing research are for is based on attribute data and combined with traditional spatial econometric models, which cannot describe the complex networked spatial correlation. Concurrently, the network construction is mainly based on undirected-unweighted sparseness, which makes it difficult to truly restore the asymmetric detailed spatial relationship, making the related research about development pattern recognition and its evolution based on the detailed spatial relationship relatively lacking. Therefore, based on the perspective of relational data, this study constructs a multi-dimensional gravity model by using the comprehensive quality of China’s provincial low-carbon economic development, measured by the multi-dimensional evaluation index system and dynamic weight method, and the comprehensive distance based on geography, society, economy, and adjacency. And then the spatial correlation strength among China’s provincial low-carbon economic development is determined, and constructs a directed-weighted complex network. Then, it further explores the inter-provincial detailed spatial relationship of low-carbon economy development from the three dimensions of overall, individual and group, to recognize the development patterns and evolution rules of low-carbon economic development in different provinces, and clarify the development orientation. The results show that, during the study period, the spatial correlation of China’s provincial low-carbon economic development has become increasingly close, showing a complex networked correlation structure with multi-linearity, asymmetry, and geographical proximity as a whole. The division of general structure is consistent with China’s regional economic development strategy. The unbalanced development of China’s provincial low-carbon economy is the result of multi-center drive in the eastern and central regions, and the distance from the network center in the western regions. In general, China’s provincial low-carbon economic development patterns can be roughly divided into spillover, main bridge, auxiliary bridge, and benefit type. In the future, we should make full use of the power source role of spillover provinces, and the bridge support role of bridge provinces to drive the development of benefit provinces. Among them, Chongqing, Shaanxi, Hebei and Sichuan are expected to become new regional growth poles. The research on the inter-provincial spatial correlation effect and development pattern recognition of low-carbon economic development is expected to provide guidance for the sustainable development of low-carbon economy in China.

Multi-Dimensional Analysis of Load Characteristics of Electrical Vehicles Based on Power Supply Side Data and Unsupervised Learning Method

This study set out to extract the charging characteristics of an electrical vehicle (EV) from massive real operating data. Firstly, an unsupervised learning method based on self-organizing map (SOM) is developed to deal with the power supply side data of various charging operators. Secondly, a multi-dimensional evaluation index system is constructed for charging operation and vehicle-to-grid (V2G). Finally, according to more than five million pieces of charging operating data collected over a period of two years, the charging load composition and characteristics under different charging station types, daily types and weather conditions are analyzed. The results show that bus, high-way, and urban public charging loads are different in concentration and regulation flexibility, however, they all have the potential to synergy with power grid and cooperate with renewable energy. Especially in an urban area, more than 37 GWh of photovoltaic (PV) power can be consumed by smart charging at the current penetration rate of EVs.

An Evaluation Model of the Online Learning Effect Based on Fuzzy Mathematics

This study derived an evaluation model that overcomes the various problems in the evaluation of online learning effect. Firstly, the influencing factors in the online learning process were discussed in the paper, pointing out the direction to enhance the online learning effect. Next, a multidimensional evaluation index system was established for online learning effect. Meanwhile, the evaluation of online learning effect was treated as a fuzzy evaluation problem with multiple attributes, and an improved evaluation model for online learning effect was proposed based on fuzzy mathematics theory. The proposed model can evaluate the online learning effect effectively, and enjoy a good prospect of application.

Ancient and Famous Trees Risk Assessment in Changting Based with Multi-Level and Multi-Dimensional Indicator System

According to the results of the second census of ancient and famous trees and field investigation in Changting County, Through VTA method and AHP method, the multi-level and multi-dimensional evaluation index system of risk degree of ancient and famous trees was constructed. The results show that: The higher weight of growth factors are the main trunk diseases and insect pests, abnormal cracks in the main trunk, hollow trunk, branch diseases and insect pests, abnormal cracks in the branches, dangerous dead branches, root diseases and insect pests, leaf diseases and insect pests. The growth space of tree roots is the most important factor in environmental factors. Among the anthropogenic factors, the most important ones are the artificial dumping of pollutants and the lack of professional management. Hazard index of Taxus chinensis var. mairei is maximum, followed by Schima superba, Cinnamomum camphora and Liquidambar formosana. The most dangerous indicators are the professional level of managers, lack of professional management, V-angle trunk and plant parasitism. Through the risk assessment, this paper seeks for the key points of management and protection of ancient and famous trees, aims at solving problems and reducing the risk of ancient and famous trees, so as to promote the management and protection of ancient and famous trees.

Measurement and Evaluation of Collaborative Development Level of Higher Education

With the aid of grey correlation theory and technique for order of preference by similarity to ideal solution (TOPSIS), this paper sets up a multi-dimensional evaluation index system (EIS) for the comprehensive development level (CDL) of China’s higher education, and quantifies the state of collaborative development in 2005-2017 with an improved distance collaboration model. The results show that: Being the premises and bases of higher education, the supply subsystem develops much slower than the other subsystems (i.e. participation, output, and environment), and clearly pulls the development of the other subsystems. From 2012 onwards, the subsystems conformed to basically the same trend for the degree of collaboration: the degree of collaboration tended to be stable. There was even a slight dent in overall degree of collaboration between 2012 and 2016. Hence, the overall degree of collaborative development was far slower than the overall degree of development. After more than a decade of development, the degree of development, degree of collaboration, and degree of collaborative development reached basically the same level in 2017. The empirical results shed new lights on the focal points of higher education development in China.

Power Grid Morphological Positioning and Assessment Method in the Energy Revolution

The energy revolution will affect the development and utilization of energy, the production and distribution of electricity, and then has profound impacts on the function and form of the power grid. In this paper, to analyze the impacts of energy revolution in China, we generator of electric system planning (GESP) model to simulate energy structures and layouts that simulate different scenarios. Then we improve the calculation methods of the index scores and weights to improve the TOPSIS evaluation method, composing a comprehensive assessment system of TOPSIS-AHP-Entropy method. Furthermore, we construct a multi-dimensional evaluation index system that includes economics, technology, and environmental protection. The assessment results of two scenarios demonstrate, in the context of the energy revolution, that the Reinforcement scenario with large-scale renewable energy, has much better benefits, which requires the grid stronger, more efficient, cleaner. In the new energy revolution, there are four main changes of the morphological positioning and functional patterns of future power grid.

The Forecasts Study of City Economy and Cyclic Integrating Development

In this paper, we select the multi-dimensional evaluation index system based on principal component analysis for quantitative evaluation of circular economy in Chongqing . We use ARIMA model to predict Chongqing city cycle economic trends which rely on a comprehensive study in the development of circular economy status quote.