Evaluation Indicators Research Articles

An Explainable Data-Driven Optimization Method for Unmanned Autonomous System Performance Assessment

Unmanned autonomous systems (UASs), including drones and robotics, are widely employed across various fields. Despite significant advances in AI-enhanced intelligent systems, there remains a notable deficiency in the interpretability and comprehensive quantitative evaluation of these systems. The existing literature has primarily focused on constructing evaluation frameworks and methods, but has often overlooked the rationality and reliability of these methods. To address these challenges, this paper proposes an innovative optimization evaluation method for data-driven unmanned autonomous systems. By optimizing the weights of existing indicators based on data distribution characteristics, this method enhances the stability and reliability of assessment outcomes. Furthermore, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIMEs) and Partial Dependence Plots (PDPs) were employed to verify the effectiveness of the designed evaluation indicators, thereby ensuring the robustness of the evaluation system. The experimental results validated the effectiveness of the proposed approach.

DGTNet：dynamic graph attention transformer network for traffic flow forecasting

Abstract Graph-based traffic flow prediction plays a crucial role in urban traffic management and planning. In this paper, we propose a novel Dynamic Graph Attention Transformer Network (DGTnet), which is designed to address the issue of inadequately integrating of temporal and spatial dimensions in traditional models. DGTnet maintains temporal continuity while revealing the complex dynamic relationships between key nodes in the urban traffic system, capturing the periodic changes in the rhythm of city life. Specifically, this study adopts adaptive signal decomposition technology to decompose traffic data into multiple intrinsic mode functions (IMFs), effectively capturing the dynamic changes in traffic flow. This decomposition method is key to the implementation of DGTnet dynamic graph construction, enabling the analysis of traffic flow at different time scales, thereby providing a new perspective for traffic flow prediction research. In the traffic prediction module, we comprehensively consider node, edge, and graph structural information, adopting a multi-head self-attention mechanism to achieve direct cross-modeling in both temporal and spatial dimensions. Finally, we introduce a position-wise feed forward network layer to integrate different types of data and capture nonlinear relationships. The experimental results, based on the public transportation network data sets METR_LA, PEMS_BAY, PEMS03 and PEMS07, demonstrate that DGTNet exhibits notable enhancements in three evaluation indicators, namely the Mean Absolute Percentage Error (MAPE), the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The pertinent code has been made available for public access at https://github.com/chenjing0616/DGTNet.

Assessment of Water Disaster Resilience in Mountainous Urban Metro Stations by Combination Weighting Method and Extension Cloud Model

Studying the resilience of metro stations in mountainous cities to heavy rain and flooding is of significant importance for enhancing the stability and safety of metro station operations. Considering the topographical and climatic characteristics of mountainous urban areas, this study analyzes the mechanisms through which heavy rain and flooding affect metro station resilience. Based on this analysis, 27 factors, influencing metro station resilience, are identified across 4 dimensions: absorptive capacity, resistance capacity, recovery capacity, and adaptive capacity. A water disaster resilience evaluation index system and corresponding rating standards are established for metro stations in mountainous cities. By combining the advantages of objective and subjective weighting, the combination weights of evaluation indicators are calculated using game theory. The extension theory is combined with the cloud model to establish a model for assessing the water disaster resilience of metro stations in mountainous urban areas. The applicability and feasibility of the model are validated through its implementation at Shapingba Station within Chongqing Rail Transit. The evaluation results obtained from the established model indicate a resilience level of IV for Shapingba metro station, reflecting a high level of resilience that aligns with real-world conditions. These findings further validate the proposed evaluation standards and the method for assessing the water disaster resilience of metro stations based on the combination weighting method and extension cloud model. This evaluation method considers the uncertainty in the evaluation process, demonstrating good feasibility and reliability. It offers a new perspective and methodology for assessing and analyzing the resilience of similar metro stations in mountainous cities.

A Meta-Learning Approach to Mitigating the Estimation Bias of Q-Learning

It is a longstanding problem that Q-learning suffers from the overestimation bias. This issue originates from the fact that Q-learning uses the expectation of maximum Q-value to approximate the maximum expected Q-value. A number of algorithms, such as Double Q-learning, were proposed to address this problem by reducing the estimation of maximum Q-value, but this may lead to an underestimation bias. Note that this underestimation bias may have a larger performance penalty than the overestimation bias. Different from previous algorithms, this article studies this issue from a fresh perspective, i.e., meta-learning view, which leads to our Meta-Debias Q-learning. The main idea is to extract the maximum expected Q-value with meta-learning over multiple tasks to remove the estimation bias of maximum Q-value and help the agent choose the optimal action more accurately. However, there are two challenges: (1) How to automatically select suitable training tasks? (2) How to positively transfer the meta-knowledge from selected tasks to remove the estimation bias of maximum Q-value? To address the two challenges mentioned above, we quantify the similarity between the training tasks and the test task. This similarity enables us to select appropriate “partial” training tasks and helps the agent extract the maximum expected Q-value to remove the estimation bias. Extensive experiment results show that our Meta-Debias Q-learning outperforms SOTA baselines drastically in three evaluation indicators, i.e., maximum Q-value, policy, and reward. More specifically, our Meta-Debias Q-learning only underestimates \(1.2*10^{-3}\) than the maximum expected Q-value in the multi-armed bandit environment and only differs \(5.04\%-5\%=0.04\%\) than the optimal policy in the two states MDP environment. In addition, we compare the uniform weight and our similarity weight. Experiment results reveal fundamental insights into why our proposed algorithm outperforms in the maximum Q-value, policy, and reward.

Developing a quality indicator system for evaluating internet plus home care nursing services based on the SERVQUAL model: a Delphi-analytic hierarchy process study

Background In the context of global population aging and the rapid development of information technology, the demand for Internet Plus Home Care Nursing (Internet+ HCN) services have been on the rise, especially in China. Internet+ HCN services have the potential to maximize existing human resources to counter the shortage of medical healthcare services. However, at present, Internet+ HCN services are difficult to scale due to the lack of standardized service quality governance. Quality indicators for service evaluation of Internet+ HCN services are under-defined. Objective To develop a quality indicator system for evaluating Internet+ HCN services, and to shed theoretical light on assessing mHealth service quality from a user experience perspective. Methods An initial quality indicator system was established based on scenarios related to Internet+ HCN services. The Delphi Method was applied to modify the indicators according to experts’ opinions, and the analytic hierarchy process (AHP) was applied to calculate the indicator weight. Finally, a quality indicator system for evaluating Internet+ HCN services was developed based on the SERVQUAL model. Results Altogether, 17 experts from relevant fields such as nursing education, clinical nursing, health management, and health informatics were consulted through email surveys. The response rates in both rounds of Delphi and the AHP were 100%. The average expert authority coefficients were 0.912 and 0.925 respectively in the two rounds of Delphi. Kendall’s W, indicating variation coefficients, ranged from 0.262 to 0.265. Finally, a quality indicator system for evaluating Internet+ HCN services, comprising five primary indicators and 15 secondary indicators, was developed. Primary indicators and their AHP generated the following weights: assurance (0.245), reliability (0.240), tangibles (0.192), responsiveness (0.190), and empathy (0.132). Conclusions By measuring the services quality gap between user expectations and perceptions, the proposed SERVQUAL model-based quality indicator system shows potential in improving the quality of Internet+ HCN services through the perspective of user experiences.

Early evaluation of salt-stress tolerance of new released olive cultivars based on physiological and biomass allocation indicators

The evaluation of salt-tolerant olive tree genotypes released from breeding program has become intrinsic in order to develop sustainable agriculture in these regions. The current study aims to i) evaluate the tolerance of two new released olive cultivars (Zeitoun Ennour and Zeitoun Ennwader), issued from a Tunisian breeding program in comparison with the main olive cultivar ‘Chemlali Sfax’, and ii) identify suitable salt-tolerance indicators for this evaluation. The trial was conducted under controlled conditions in a greenhouse, for six months, and plants were irrigated with half-strength Hoagland nutrient solution containing NaCl at various levels (0, 75, 150 and 225 mM). Vegetative growth, biomass allocation and biochemical parameters were considered for the assessment of salt-tolerant capacity. Results revealed that growth and biomass should be considered as useful indicators for early evaluation method of salt-stress tolerance. ‘Chemlali Sfax’ displayed a gradual significant decrease of vegetative growth with increasing salinity. The reduction of shoot elongation and trunk diameter was most evidently at 150 mM with 43.2 % and 80.8 %, respectively. However, the new released cultivars showed unaffected vegetative growth, despite a slight decrease at the salt level of 225 mM. Moreover, results demonstrated the importance of certain physiological and biochemical indicators such as ions contents. The new released cultivars maintained a high K+/Na+ ratio at moderate salinity then a significant decrease occurred at 150 mM. In conclusion, vegetative growth, biomass allocation and K+/Na+ ratio seemed to be the suitable salt-tolerance indicators for early evaluation. Based on these indicators, ‘Zeitoun Ennour’ seems to be the most salt tolerant genotype and ‘Zeitoun Ennwader’ proves to have a similar salt tolerant capacity as ‘Chemlali Sfax’, which is a valuable information for olive growers in arid region.

Study on the skid resistance decay of submerged asphalt pavements based on texture parameters

AbstractIt is well known that prolonged rainwater erosion can adversely affect the surface texture of asphalt pavements, leading to a rapid decline in their skid resistance. This study utilized a small-scale accelerated loading device, a high-precision 3D scanner, and digital image processing technology to investigate the surface texture wear process and skid resistance decay trends of basalt asphalt pavement and steel slag asphalt pavement under water erosion and traffic load. The results indicate that under submerged conditions, the skid resistance (BPN) of asphalt pavement declines rapidly during the first 500,000 load cycles, and the rate of decline gradually stabilizes after 500,000 cycles. After 1.2 million load cycles, the BPN of basalt pavement decreased by 28.10%, while that of steel slag pavement decreased by 21.18%, indicating that the skid resistance of steel slag pavement is significantly better than that of basalt pavement. Texture parameters—namely, root mean square height, peak material volume, core material volume, void volume of the core, and valley void volume—exhibited the same decay trend as BPN. The average correlation coefficients between BPN and texture parameters were 0.846, 0.848, 0.898, and 0.916, respectively, indicating that texture parameters can be used as evaluation indicators for skid resistance decay. Finally, the decay of pavement skid resistance was predicted using an exponential decay equation.

Evaluation of Watershed Development Programme: A Framework for Selection and Aggregation of Key Indicators

The measure of the effectiveness of watershed development programmes depends on selecting the right set of evaluation indicators that reflect the programme’s goals, including resource management, environmental sustainability, and socio-economic benefits. In this paper, the focus is on the process of selecting and aggregating the key indicators to assess the success of watershed development initiatives. By categorizing indicators into relevant themes such as Terrain Management, Water Resources Utilization, Agronomic Vitality, Livestock Vitality, Employability, Human Empowerment, and Sustainability. This paper highlights the importance of using a structured approach to ensure comprehensive evaluation. Proper selection and aggregation of indicators emphasize the importance of context-specific indicators to ensure accurate and effective programme evaluation and lead to providing valuable insights for programme assessment and future improvements.

The reliability and validity of a novel clinical tool for distributing rehabilitation patients: a multicenter cross-sectional study in China

This study aimed to assess the reliability and validity of a novel clinical tool for distributing rehabilitation patients (DRP) for measuring a population of rehabilitation patients based on first-hand data from the field survey in China. A multi-stage sampling scheme was used to select 2512 rehabilitation outpatients from 21 medical institutions in seven cities in China. The evaluation indicators of the DRP tool consisted of five clinical indexes on multiple dysfunctions, self-care ability, vital signs, disease status, and disease course. The evaluation of rehabilitation ways of the DRP tool mainly included outpatient rehabilitation treatment, admission to primary healthcare, admission to secondary hospitals, and admission to tertiary hospitals. The mean age of participants was 54.22 years (SD:17.91), and nearly half (48.68%) were male. The majority (70.15%) were diagnosed with orthopaedic disorders. The Cronbach’s alpha (0.66), the Kendall test (Kendall coefficient = 0.86; P < 0.001), and the Kappa test (Kappa = 0.83; Agreement: 90.48%; P < 0.001) reflected acceptable consistency reliability for the indexes on multiple dysfunctions, self-care ability, vital signs, disease status, and disease course in DRP tool. The modified confirmatory factor analysis (CFA) showed the indexes of multiple dysfunctions (β = 0.31; 95%CI = 0.20–0.43), self-care ability (β = 0.99; 95%CI = 0.65–1.33), disease status (β = 0.11; 95%CI = 0.06–0.16), disease course (β = 0.15; 95%CI = 0.09–0.21), and vital signs (β = 0.15; 95%CI = 0.08–0.21) had significantly factor loadings on the rehabilitation ways. The modified CFA exhibited a satisfactory model fit (RMSEA = 0.03; CFI = 0.99; TLI = 0.95; SRMR = 0.01; AIC = 8268.51; BIC = 8373.43). This is the first study to develop a DRP tool using a relatively large sample from seven cities in China. The DRP tool had acceptable reliability and validity in measuring the rehabilitation ways of patients. Our findings provide a starting point for developing a practical tool in rehabilitation clinical practices and could provide advice on building more effective strategies and tools for other low-and middle-income countries that struggle with integrated healthcare.

Loan Approval Prediction Improved by XGBoost Model Based on Four-Vector Optimization Algorithm

Abstract. This paper discusses an improved XGBoost model based on four-vector optimization algorithm to improve the accuracy of loan approval prediction. Through the analysis of correlation heat maps, we found that there were significant positive and negative correlations among some variables, which laid the foundation for subsequent machine learning analysis. Based on this, we compare the traditional machine learning algorithm with the improved model to evaluate its performance in loan approval forecasting. In the confusion matrix analysis of the training set, the improved XGBoost model demonstrated excellent performance, with all loan approval predictions being correct with 100% accuracy. However, the performance in the test set was slightly different, with 1,182 projects receiving correct loan approval predictions and 99 projects forecasting errors. Of these, 26 projects that should have been predicted to be "unapproved" were incorrectly labeled as "approved," while 73 projects that should have been predicted to be "approved" were incorrectly labeled as "unapproved." These results suggest that we still need to pay attention to the misjudgment of the model in practical application. By synthesizing all model evaluation indicators, we found that the improved XGBoost model based on the four-vector optimization algorithm has a higher accuracy in loan approval prediction than the traditional XGBoost model, with an increase of 1.5%. In addition, the other evaluation indicators also show a trend of significantly better than the traditional model. This study shows that the four-vector optimization algorithm can effectively improve the application effect of XGBoost model in the field of loan approval, and provide more accurate data support and decision-making basis for the financial industry. In the future, we will continue to explore the potential and application prospects of this algorithm in other fields.

Study on the Comprehensive Assessment of High-Quality Development Performance of Low Carbon Tourism in China's Rizhao City Empowered by New Quality Productivity

With the rapid development of China's urban economy, the new quality productivity empowerment to promote the high-quality development of tourism in Rizhao City has become a new trend, and the vigorous development of tourism has gradually become a new growth point under the influence of a series of policies on low-carbon tourism in Shandong Province. In order to effectively assess the performance of new quality productivity empowerment to promote the high-quality development of low-carbon tourism in Rizhao City, this paper utilizes the basic data of Rizhao City, China, for the period of 2013-2022, and comprehensively assesses the status of the high-quality development performance of low-carbon tourism in Rizhao City by constructing the spatial ecological niche integrated assessment model, and it is found that: the performance of the high-quality development of low-carbon tourism in the city of Rizhao City, China, which is empowered by the new quality productivity, has shown a There are some differences in the assessment results of the three models, the absolute ecological niche suitability model has the largest assessment result, the relative ecological niche suitability model has the smallest assessment result, and the assessment result of the spatial ecological niche suitability model is located in between the assessment results of the other two models with the effect of equalization, and the assessment result of the spatial ecological niche suitability model shows that the high-quality development performance of the target city's low-carbon tourism The assessment result in 2013 is 0.7783, and the quality level of low-carbon tourism high-quality development performance is III, and the assessment result in 2022 rises to 0.8972, and the quality level of low-carbon tourism high-quality development performance is II, which has already reached the edge of I. In terms of the degree of influence of the evaluation indicators, the insufficient strength of new quality productivity empowerment in Rizhao City, China, affects the performance of low-carbon tourism high-quality development, and the second most influential of the other guideline level indicators on the assessment results is the weak effect category.

Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning.

There is a lack of objective, accurate, and convenient methods for classification diagnostic hypopigmented dermatoses (HD) and severity evaluation of vitiligo. To achieve an accurate and intelligent classification diagnostic model of HD and severity evaluation model of vitiligo using a deep learning-based method. A total of 11,483 images from 4744 patients with HD were included in this study. An optimal diagnostic model was constructed by merging the squeeze-and-excitation (SE) module with the candidate model, its diagnostic efficiency was compared with that of 98 dermatologists. An objective severity evaluation indicator was proposed through weighting method and combined with a segmentation model to form a severity evaluation model, which was then compared with the assessments conducted by three experienced dermatologists using the naked eye. The improved diagnosis model SE_ResNet-18 outperformed the other 11 classic models with an accuracy of 0.9389, macro-specificity of 0.9878, and macro-f1 score of 0.9395, and outperformed the different categories of 98 dermatologists (P < 0.001). The weighted Kappa test indicated medium consistency between the Indicatorv and the VASIchange (K = 0.567, P < 0.05). The optimal segmented model, HR-Net, had 0.8421 mIOU. The model-based severity evaluation results were not significantly different among the three experienced dermatologists. This study proposes an objective, accurate, and convenient hybrid model for diagnosing HD and evaluating the severity of vitiligo, providing a method for dermatologists especially in grassroots hospitals, and provides a foundation for telemedicine.

Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China

As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation indicators of ecosystem service value for Guangzhou, China, and the evaluation method depends on the land cover type. Based on remote sensing technology and random forest algorithm, this study addresses these gaps by integrating remote sensing technology with a random forest algorithm to enhance the accuracy and rationality of ESV assessments. Focusing on Guangzhou, China, we improved the ecological service value evaluation system and conducted dynamic predictions based on land-use change scenarios. Our results indicate that the total ESV of Guangzhou’s green space was USD 7.323 billion in 2020, with a projected decline to USD 6.496 billion by 2030, representing a 12.37% reduction due to urbanization-driven land-use changes. This research highlights the noticeable role of green spaces in urban sustainability and provides robust, data-driven insights for policymakers to design more effective green space protection and management strategies. The improved assessment framework offers a novel approach for accurately quantifying urban ecosystem services and predicting future trends.

Validation through internal flow physics of response surface methodology optimized mixed flow pump as turbine

In order to determine the optimal working efficiency of mixed flow pumps as turbine (MF-PAT) under a design condition of 10m3kw, this study takes the number of blades, blade wrap angle, impeller outer diameter, and impeller inlet width as design variables. Based on the center combination design method, experimental scheme design is carried out, and the head, shaft power, and efficiency of the turbine are used as evaluation indicators. A response surface model is constructed for optimization analysis, and the optimal geometric parameter combination of the impeller for MF-PAT is determined. For MF-PAT with forward-curved blade impeller in this paper, the optimal parameter combination is recommended as blade number Z = 6, blade wrap angle α = 47°, impeller outer diameter D2 = 140 mm and impeller inlet width b2 = 34 mm. The results show that compared with the original scheme, its efficiency has increased by 7.8 %. The established response surface model can reflect the relationship between evaluation indicators and design variables, and can be used for optimizing the geometric parameters of MF-PAT impellers. It can effectively enhance the blade's constraint ability on liquid flow, reduce hydraulic losses, and improve the performance of MF-PAT. Apply the ns-ds methodology for this and future mixed flow optimized pumps as turbines.

Dynamic fuzzy comprehensive evaluation of wet friction components performance degradation based on G1-CRITIC

High power density wet friction components are crucial in heavy equipment transmission systems, directly influencing equipment reliability. This paper proposes a fuzzy comprehensive evaluation method with combined weighting, which dynamically divides the performance degradation of wet friction components into three stages. It constructs a multi-indicator, multi-level evaluation system, in which the performance degradation data is obtained from the wet clutch accelerated life test. Additionally, we utilize the combined weighting method to dynamically allocate weights for evaluation indicators at different stages and introduce the trapezoidal distribution membership function. The accuracy of the fuzzy comprehensive evaluation model is then verified by using two prediction models as a comparison method. The results indicate that the model achieves an average relative error of 14.79% and a correlation coefficient R2 of 0.9580, indicating higher usability and accuracy than the prediction models. Furthermore, the evaluation indicator weights are more sensitive to pressure variations.

Multi-Objective Optimization for Controlling Conflicts in Roadway Surrounding Rock Induced by Floor Stress-Relief Groove

This paper studies the effectiveness of the stress-relief groove on the floor of deep coal roadway and determines the influence of the stress-relief groove parameters on the surrounding rock through qualitative analysis. Based on the displacement conflict problem, evaluation indicators were established, and the optimal solution set was obtained. The innovations of this research include: 1. For geotechnical numerical simulations, novel stress monitoring and plastic zone monitoring techniques have been introduced to accurately reflect the condition of the surrounding rock; 2. The effects of floor relief grooves in deep roadway on surrounding rock have been analyzed, and the advantages and utilities of central and corner relief grooves have been determined; 3. The usability of small datasets has been enhanced by applying SEGA to optimize machine learning models with data augmentation techniques; 4. Multi-objective optimization algorithms have been applied to geotechnical engineering, providing valuable references for decision making. The results demonstrate that multi-objective optimization can significantly enhance the effectiveness of surrounding rock control, resolve conflicts, and achieve more reasonable construction plans. This research provides new theoretical foundations and practical guidance for deep mine roadway-surrounding rock control.

Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

An innovative approach to post-occupancy evaluation of architectural heritages in the Peking-Mukden railway

AbstractThe architectural heritages of the Peking-Mukden Railway has witnessed the industrialization of China. When it no longer serves its original function, finding a new way for reuse becomes a trend. It requires feedback from reused cases to guide decision-making. This article aims to propose an approach to post-occupancy evaluation of architectural heritages in the Peking-Mukden Railway and apply it to real cases for analysis. The evaluation indicator system used here consists of a hierarchical structure, evaluation indicators, and interpretation indicators. Cosine similarity, entropy weight method, and precedence chart method are utilized to determine final weights. The GRA-TOPSIS method is utilized to identify reuse cases that require additional optimization. The conclusions are as follows: 1. The cosine similarity can be introduced to neutralize individual preferences. The entropy weight method can be used to neutralize subjective factors. 2. Based on the analysis of the sensitivity levels, indicators that need to be improved first can be identified. Priority should be given to spatial accessibility and cultural transmission when making adjustments with limited resources. 3. The GRA-TOPSIS evaluation method can be used to rank and find reuse cases that need further optimization.

Can sustainable finance promote manufacturing green transition trapped in financing dilemma? The influence mechanism of green technology innovation, technology market and government guidance

ABSTRACT Sustainable finance (SF) has become a potential financing channel for the manufacturing sector because green finance has intensified the transition financing dilemma for brown sectors. This study investigates whether SF can promote green transition in the manufacturing sector (MT) under this financing dilemma, and the role of green technology innovation (GTI), the technology market (TM), and government guidance (GVG) in such a relationship. Using the data from 30 Chinese provinces from 2011 to 2020, this study first constructs comprehensive evaluation indicator systems for SF and MT and then applies the chain-mediation model and moderated chain-mediation model. The findings show that SF has a significantly positive influence on MT. The influence mechanism demonstrates that GTI-TM is a vital influence channel for SF to promote MT. GVG can moderate the chain-mediation effect of GTI and TM. These findings have important implications for promoting MT through SF.

High-precision IFM receiver based on random forest algorithm

Abstract The six-port network-based Instantaneous Frequency Measurement (IFM) receiver is widely utilized in industrial applications for signal frequency demodulation. However, current IFM receivers face the issue of low algorithmic demodulation accuracy (ACC). According to the four-channel output characteristics of the IFM receiver, we use the Random Forest algorithm to extract features, train and test the output voltage data set, and recognize frequency labels. The experimental results show that the ACC of the proposed algorithm reaches 0.9583, which is 13.99% higher than that of the traditional polynomial fitting algorithm. To evaluate the noise reduction performance of the algorithm, we add Gaussian white noise to the original data and demodulate the signal with noise. Comparative tests prove the advantages of the algorithm in high stability and flexibility. Finally, multiple model evaluation indicators demonstrate that the algorithm features strong applicability and robustness to the experimental data with IFM receiver.