Evaluation Index Research Articles

Joint guarantee rate index assessment of the contribution of agricultural water-saving measures to river ecological flow in water shortage areas

Reconciling higher ecological water demands and economic purposes with finite freshwater resources remains one of the great management dilemmas. Low water efficiency and large water consumption in agricultural irrigation hinder the protection of river ecological flow in the water shortage areas of Northwest China. By analyzing the surplus and shortage of river ecological protection targets, the intensity guarantee rate index is innovatively proposed to quantitatively evaluate the important contribution of improved water-saving measures for river flow restoration. It compensates for the shortcomings of the duration guarantee rate index in evaluation that may mask ecological problems. Considering ecological baseflow, fish species, and landscape water demand, the ecological flow interval (6.00–12.42 m3/s) was determined. Three scenarios are highlighted for different typical years and irrigation regimes in combination with planting structure adjustment and irrigation water utilization coefficient. In 75 %, 90 %, and 95 % typical years, the maximum total annual water-savings (Scenario 3) are 57.3, 57.1, and 87.2 million m3 (sufficient irrigation); 32.2, 33.9, and 35.5 million m3 (deficit irrigation) respectively. The guarantee degree of river ecological flow under both irrigation regimes increased from the previous 65.6 % to a maximum of 91.7 % and 81.2 %. The increase in the daily guarantee rate and decrease in the intensity guarantee rate under the aforementioned three scenarios indicate a reduction of damage depth and duration of ecological flow. By using a quantitative study, it is pointed out that improving agricultural water-saving is a reasonable and necessary way to protect the river flow.

A TransISP Based Image Enhancement Method for Visual Disbalance in Low‐light Images

AbstractExisting image enhancement algorithms often fail to effectively address issues of visual disbalance, such as brightness unevenness and color distortion, in low‐light images. To overcome these challenges, we propose a TransISP‐based image enhancement method specifically designed for low‐light images. To mitigate color distortion, we design dual encoders based on decoupled representation learning, which enable complete decoupling of the reflection and illumination components, thereby preventing mutual interference during the image enhancement process. To address brightness unevenness, we introduce CNNformer, a hybrid model combining CNN and Transformer. This model efficiently captures local details and long‐distance dependencies between pixels, contributing to the enhancement of brightness features across various local regions. Additionally, we integrate traditional image signal processing algorithms to achieve efficient color correction and denoising of the reflection component. Furthermore, we employ a generative adversarial network (GAN) as the overarching framework to facilitate unsupervised learning. The experimental results show that, compared with six SOTA image enhancement algorithms, our method obtains significant improvement in evaluation indexes (e.g., on LOL, PSNR: 15.59%, SSIM: 9.77%, VIF: 9.65%), and it can improve visual disbalance defects in low‐light images captured from real‐world coal mine underground scenarios.

Integrating remote sensing modeling for drought assessment and vegetation monitoring in El Tarf, Algeria: a 40-year analysis

The escalating frequency and intensity of drought events, exacerbated by anthropogenic climate change, present critical challenges to hydrological resources, ecosystem resilience, and agricultural productivity. In North Africa, and specifically Algeria, comprehending the spatiotemporal patterns of drought is essential for sustainable water resource management and ecological conservation. This investigation focuses on the El Tarf region, located in northeastern Algeria, recognized for its ecological diversity and agricultural relevance. Employing a synergistic methodology, this study integrates the Standardized Precipitation Evapotranspiration Index (SPEI) for long-term drought severity evaluation with the Normalized Difference Vegetation Index (NDVI) to monitor vegetation phenology and health over time. Utilizing the computational power of Google Earth Engine, we process extensive temporal datasets of SPEI spanning from 1980 to 2023, complemented by high-resolution satellite imagery for NDVI computation. This comprehensive approach facilitates an in-depth analysis of drought dynamics, their severity, and impacts on vegetative cover in El Tarf over the past four decades. Our findings indicate significant long-term drought trends, with notable dry spells occurring between 1994 and 2003. Severe drought events were particularly pronounced in December 2022 (SPI = -2.50) and May 2002 (SPI = -2.2), correlating with substantial precipitation deficits. Although areas experiencing extreme drought have diminished, there has been an increase in moderate drought occurrences, suggesting an uptick in drought frequency, which poses risks to hydrological resources and ecosystem health. These observed patterns align with regional climate change projections, highlighting an urgent necessity for adaptive management strategies in agriculture and water resources.

Acute appendicitis due to extrapulmonary tuberculosis

Introduction and ImportanceTuberculous appendicitis is very infrequent, estimated to occur in only 0.1% of all appendectomies. Appendicular tuberculosis can manifest as either a primary or secondary infection. Primary appendicular tuberculosis is even more uncommon and occurs in the absence of any detectable primary focus elsewhere in the body. Case presentationIn this report, we present a challenging case of primary tuberculous appendicitis that does not appear to have originated from the lungs. This case highlights the importance of considering tuberculosis in the differential diagnosis of appendicitis, especially in regions where tuberculosis is endemic or in patients with a history of tuberculosis exposure. Clinical DiscussionDiagnosing tuberculous appendicitis can be challenging due to its rarity and the non-specific nature of its symptoms, which often mimic those of more common conditions such as acute appendicitis and because of these non-specific symptoms, tuberculous appendicitis is mostly diagnosed only after surgical intervention. ConclusionThis case underscores the need for a high index of suspicion and thorough diagnostic evaluation in patients with atypical presentations of appendicitis specifically in patients with chronic complaints. Treatment typically involves a combination of surgical intervention to remove the affected appendix and a prolonged course of anti-tuberculous medication to eradicate the infection and prevent recurrence.

Use of Vegetation Activity Index for Evaluation of L-Alpha Amino Acid Treatment in Sugarcane

Use of Vegetation Activity Index for Evaluation of L-Alpha Amino Acid Treatment in Sugarcane

Research on the evaluation method for safety cognitive ability of workers in high-risk construction positions

PurposeSafety cognitive ability is a key factor influencing unsafe behavior. However, the existing achievements have not yet involved the division of the hierarchical relationship of factors influencing safety cognition and lack a quantitative evaluation system of safety cognitive ability. The purpose of this paper is to find out the deficiencies in the safety cognition of workers in high-risk construction positions and to provide practical suggestions for improving their safety cognitive ability and reducing unsafe behavior.Design/methodology/approachBased on the iceberg model, the factors influencing the safety cognitive ability of workers in high-risk construction positions and their hierarchical relationship were determined, and an evaluation index system consisting of four primary indicators and 20 secondary indicators was constructed. The game theory algorithm was used to optimize the subjective and objective weights of the indicators calculated by the sequential analysis method (G1) and the entropy weighting method (EWM) to obtain the optimal combination weight value. The Matlab software was used for cloud mapping and similarity calculation to determine the safety cognitive ability level of the object to be evaluated.FindingsThe research results indicate that the comprehensive level of safety cognitive ability of scaffolders in this construction project is at “Level III”, the fundamental factors and compliance factors are at “Level IV”, the auxiliary factors and driving factors are at “Level III”. This conclusion aligns with the situation learned from the previous field investigation, which validates the feasibility and scientificity of the proposed evaluation method.Research limitations/implicationsConsidering that the safety cognitive ability of construction workers is constantly changing, this study has not yet delved into the specific impacts of various influencing factors on the level of safety cognitive ability. Future research can utilize simulation software, such as MATLAB and Vensim, to construct dynamic simulation models that accurately simulate the changing rules of construction workers’ safety cognitive ability under the influence of different factors.Practical implicationsThis research broadens the application scope of the iceberg model, enriches the analysis model of the factors influencing the safety cognitive ability of workers in high-risk construction positions and provides a novel perspective for similar research. The safety cognitive ability evaluation method proposed in this paper can not only accurately evaluate the safety cognitive ability level of workers in high-risk positions such as scaffolders but also provide practical suggestions for improving the safety cognitive ability of workers, which is of great significance to improve the safety management level and reduce unsafe behavior in the construction field.Originality/valueThis research fills the research gap of workers in high-risk construction positions and the quantification of safety cognitive ability. The iceberg model is used to realize the hierarchical division of the factors influencing safety cognitive ability. Additionally, an evaluation method for the safety cognitive ability of workers in high-risk construction positions based on the game theory combination weighting method and cloud model is proposed, which realizes the quantitative evaluation of safety cognitive ability.

Improvement of hydrogen reciprocating compressor efficiency: A novel capacity control system and its multi-objective optimization

To enhance the operational efficiency of hydrogen compressors and reduce the costs associated with the storage and transportation of hydrogen, this paper has developed a novel capacity control system (CCS). This system ensures that the compressor accurately compresses hydrogen according to actual demand, driven by an electromagnetic actuator, which simplifies the system design and enhances operational precision. To optimize the system's performance, a multi-objective optimization was conducted. Initially, a hydrogen compressor backflow working model and a comprehensive evaluation index system were established. Subsequently, a three-tier computational multi-objective optimization framework was proposed based on the working model, ultimately determining the optimal combination of key system parameters. A prototype was manufactured and subjected to validation testing and a full life cycle analysis. The results indicate that the developed system can accurately regulate the amount of compressed hydrogen within a range of 0%–100% load. Compared to traditional systems, for the same compressor group, carbon emissions and costs were reduced by 41.93% and 36.49%, respectively. The optimized design of the internal and external resistance angles and the limiter plate thickness are 11°, 16°, and 1 mm, respectively. Compared to the baseline state, the specific energy consumption of the hydrogen compressor per unit of exhaust volume has been reduced by 28.22%. Additionally, during the ejection and retraction processes of the actuator, the peak vibration impact on the hydrogen compressor has been reduced by 77.6% and 73.3%, respectively, achieving optimal comprehensive performance.

Evaluation Indexes of Skid Resistance of Epoxy Chip Seal Based on Texture Features at Different Scales

Epoxy chip seals are widely used to improve the skid resistance of cement concrete pavements, which is significantly affected by surface texture. However, current methods for characterizing the surface texture structure are not precise, and the standard is not uniform. Therefore, a high-toughness modified epoxy resin chip seal structure was developed to conduct an indoor accelerated abrasion test. The elevation data of the epoxy chip seal under different abrasion times and different abrasion loads were obtained using laser scanning, and the density/sharpness combination parameters were obtained using the Hilbert–Huang transform and spectral analysis. The correlation between the texture parameters and the dynamic friction coefficient was analyzed using a stepwise multivariate quadratic polynomial method. The results showed that the texture structure of the epoxy chip seal surface is positively distributed, and the macroscopic texture occupies 49.45%, while the probability of the microscopic texture is only 4.55%. Meanwhile, the correlation coefficient between the texture parameters and the dynamic friction coefficient is 0.9307, which demonstrates that the selected texture parameters discard the texture components unrelated to skid resistance performance and reflect the distribution of the pavement texture and the skid resistance performance well.

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.

Time-varying cable force identification in cable-stayed bridges by a high-resolution time-frequency method

Accurate monitoring of time-varying cable force plays a vital role in the condition assessment of cables. However, most current methods for identifying the cable force can only provide an average cable force over a period. The vibration method correlates the cable force with cable frequency via a formula, transforming the time-varying cable force identification into the instantaneous frequency (IF) identification of the cable. Synchroextracting transform (SET) is a powerful time-frequency method with the ability to identify the cable’s IF from the measured response. However, the performance of SET in identifying the IF is heavily influenced by the choice of window length, limiting its practical application. In this study, an improved SET (ISET) is proposed to overcome this limitation and is utilized to identify the time-varying force in bridge cables. In ISET, the variational mode decomposition is first employed to decompose the measured response into several mono-component modes. A hyperparameter optimization algorithm, adopting Rényi entropy as the evaluation index, is then introduced to determine the optimal window length for each mono-component mode. Furthermore, the proposed ISET with the optimal window length is used to identify the cable’s IF, thereby calculating the time-varying cable force using the vibration method formula. Cases involving finite element model of the cable, along with laboratory experiments on a scaled cable, show that the proposed method successfully addresses the limitations of SET and accurately identifies time-varying cable force. Finally, to validate the practicality of the proposed method, the field test data from an unsymmetric cable-stayed bridge is adopted. The results demonstrate that the proposed method is straightforward and efficient, capable of accurately monitoring the time-varying cable force in both new and existing cables of cable-stayed bridges.

Education, Science and Technology, and Talent Integrated Development: Evidence from China

Education, science and technology, and talent are significant factors driving economic growth. Coordinating the integrated development of education, science and technology, and talent is not only an important way to achieve the development of education, technology, and talent themselves, but it is also a crucial path to sustainable development. The research objectives of this paper are to explore the coupling coordination relationship among the three subsystems of education, science and technology, and talent and to investigate their spatiotemporal pattern evolution characteristics. This paper, based on panel data from 30 provinces in China from 2001 to 2022, constructs an evaluation index system for “education–science and technology–talent” (EST) development from three dimensions: education, science and technology, and talent. It comprehensively employs methods such as spatiotemporal pattern evolution, LISA time path, and spatial transitions to evaluate the level of China’s EST. The results indicate the following: (1) During the research period, the level of China’s EST shows a significant upward trend, with the educational subsystem being the most prominent. (2) The level of China’s EST development has a significant spatial differentiation characteristic, generally showing a decreasing trend from the eastern to the central and western regions. (3) In terms of the stability of the spatiotemporal pattern, the spatial pattern of China’s EST development is relatively stable, and there is no significant spatial shift during the research period. This study provides a new method for the evaluation of complex systems and also offers a reference for the economic growth of many developing countries.

Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.

The interchange stations siting model of urban rail transit network

The "node anchor weaving method" is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the "anchor nodes" to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

The impact of pilot policies for low carbon city construction on the development of urban circular economy multiple time-point difference-in-differences model

IntroductionAs a means of environmental regulation, the low-carbon city pilot policy aims to address climate change and alleviate environmental resource pressures in economic development. The development of low-carbon cities often requires changes to the existing industrial and energy structures, which will have a significant impact on the regional economy.MethodsThe study is based on panel data from 273 prefecture level cities in China from 2012 to 2022, and uses the Analytic Hierarchy Process and Entropy Comprehensive Evaluation Method to construct an evaluation index system for urban circular benefits. Finally, the multiple time point differences model and mediation effect model were used to evaluate the impact and influencing factors of policies on urban economy.ResultsThe results indicate that at the 1% level, the explanatory variable coefficient significantly improves the level of urban circular economy development after adding control variables. The construction of low-carbon pilot cities has improved the performance of circular economy development by about 6.42%. The mediating effect of industrial structure rationalization is significantly positive at the 1% significance level, indicating that the construction of low-carbon pilot cities has greatly promoted the coordinated development of industrial relations within China’s low-carbon development regions.DiscussionIt can be seen that the low-carbon city pilot policy has a significant promoting effect on China’s economic development and the development of urban circular economy. Therefore, it is recommended to further expand the pilot scope of low-carbon cities and fully leverage their promoting role.

Association between estimated pulse wave velocity and the risk of mortality in patients with subarachnoid hemorrhage: a retrospective cohort study based on the MIMIC database

BackgroundThe estimated pulse wave velocity (ePWV) is a recently developed, simple and useful tool to measure arterial stiffness and to predict long-term cardiovascular mortality. However, the association of ePWV with mortality risk in patients with subarachnoid hemorrhage (SAH) is unclear. Herein, this study aims to assess the potential prediction value of ePWV on short- and long-term mortality of SAH patients.MethodsData of adult patients with no traumatic SAH were extracted from the Medical Information Mart for Intensive Care (MIMIC) III and IV database in this retrospective cohort study. Weighted univariate and multivariable Cox regression analyses were used to explore the associations of ePWV levels with 30-day mortality and 1-year mortality in SAH patients. The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). In addition, subgroup analyses of age, the sequential organ failure assessment (SOFA) score, surgery, atrial fibrillation (AF), renal failure (RF), hepatic diseases, chronic obstructive pulmonary disease (COPD), sepsis, hypertension, and diabetes mellitus (DM) were also performed.ResultsAmong 1,481 eligible patients, 339 died within 30 days and 435 died within 1 year. After adjusting for covariates, ePWV ≥ 12.10 was associated with higher risk of both 30-day mortality (HR = 1.77, 95%CI: 1.17–2.67) and 1-year mortality (HR = 1.97, 95%CI: 1.36–2.85), compared to ePWV < 10.12. The receiver operator characteristic (ROC) curves showed that compared to single SOFA score, ePWV combined with SOFA score had a relative superior predictive performance on both 30-day mortality and 1-year mortality, with the area under the curves (AUCs) of 0.740 vs. 0.664 and 0.754 vs. 0.658. This positive relationship between ePWV and mortality risk was also found in age ≥ 65 years old, SOFA score < 2, non-surgery, non-hepatic diseases, non-COPD, non-hypertension, non-DM, and sepsis subgroups.ConclusionBaseline ePWV level may have potential prediction value on short- and long-term mortality in SAH patients. However, the application of ePWV in SAH prognosis needs further clarification.

Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions

Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management.

Spatio-temporal patterns and driving factors of green development level in alpine meadow regions: A case study in Gannan Tibetan Autonomous Prefecture

Green development is an approach that harmonizes human beings with nature and leads to sustainable social and economic development. However, the factors influencing the level of green development are uncertain due to regional differences, especially in the alpine meadow regions. To explore the factors affecting green development in the alpine meadow region, we took the Gannan Tibetan Autonomous Prefecture as an example. Through a green development level evaluation index system, the DPSIR (Driver, Pressure, State, Impact, and Response) was constructed to comprehensively evaluate and analyze the green development level of the study area from 1990 to 2020. On this basis, the key influencing factors of the green development level was analyzed based on the GeoDetector. The findings indicate that the total level of green development in Gannan has an upward trend within the range of 0.044–0.535, moving towards high quality and sustainable development. Except for 2005, the spatial clustering characteristics of the green development level did not display obvious high values of spatial autocorrelation. The green development space is concentrated and stable, and the centre of gravity is in Zhuoni. The impact and response dimensions make the greatest contribution to green development. Different evaluation indicators have different impacts on green development during the study period. Specifically, people’s productive lives, the impact of social production and positive policy responses are the main factors contributing to green development. The findings will provide theoretical basis and case support for ecological security and green sustainable development in the alpine meadow regions.

Cooling characteristics of nanofluids in a snowflake and squid fin composite bionic microchannel heat sink

To improve the heat dissipation performance of electronic components, this study proposed a novel biomimetic microchannel design inspired by snowflakes and squid fins. A numerical simulation method was employed to investigate the cooling characteristics of nanofluids at different concentrations within this biomimetic microchannel heat sink. The microchannel is shaped like a snowflake, while the base plate features a biomimetic squid fin structure. The study focused on analyzing the effects of waveform height (amplitude A = 0.1–0.4 mm) and waveform frequency (B = 0.5–2π) on heat transfer performance. The results indicated that when nanofluids flow over a base plate with a wavelength frequency of B = 0.5π, there is a significant enhancement in heat exchange while simultaneously reducing flow resistance. In the microchannel heat sink (A = 0.3 mm, B = π), the thermal conductivity of the nanofluids can be increased by up to 32.56 %, with a maximum comprehensive evaluation index of 1.68. This research provides important guidance for the application of nanofluids and biomimetic microchannels in the thermal control of electronic devices.

Innovative Design and Optimization of High-Quality Peanut Digging-Inverter

To rapidly dry peanut pods and effectively address mold issues resulting from rainy weather, this article provides an analysis of the current research status of global peanut digging, inverting, and harvesting technologies. Based on a two-stage peanut harvesting model, the operating principles of a high-quality peanut digging-inverter are elaborated upon, and the design of key machine components is discussed. The evaluation metrics for the operation of a high-quality peanut digging-inverter include the rate of vine inverting, the soil content rate, the rate of fallen pods, and the rate of buried pods. Utilizing theoretical analysis, the Box–Behnken experimental design is employed to investigate the operating parameters of these machines, with the tractor’s running speed, the linear speed of the inverting roller, and the linear speed of the conveyor chain serving as the experimental factors in a three-factor, three-level experimental study. A regression model is established to analyze the impact of each factor on operational quality and to comprehensively optimize the influencing factors. The experimental results indicate that all factors significantly affect the rate of vine inverting. The tractor’s running speed (X1) and the linear speed of the inverting roller (X3) significantly influence the rate of buried pods, while the linear speed of the conveyor chain (X2) does not have a significant effect on this rate. Similarly, the tractor’s running speed (X1) and the linear speed of the inverting roller (X3) significantly affect the rate of fallen pods, whereas the linear speed of the conveyor chain (X2) does not. Furthermore, the linear speed of the conveyor chain (X2) and the linear speed of the inverting roller (X3) significantly impact the soil content rate, while the tractor’s running speed (X1) does not. The optimal combination of operating parameters is a tractor running speed of 1 m/s, a conveyor chain linear speed of 1.3 m/s, and an inverting roller linear speed of 2.1 m/s. Under these conditions, the high-quality peanut digging-inverter achieves a vine inverting rate of 89.29%, a buried pod rate of 0.31%, a fallen pod rate of 0.74%, and a soil content rate of 8.11%. The experimental values for each evaluation index exhibit relative errors of 1.14%, 6.45%, 1.35%, and 0.13% compared to the optimized values provided by the Design-Expert software. The findings of this research will facilitate the development of hardware conditions for the future intelligent and information-based harvesting of peanuts, thereby establishing a solid foundation for the high-quality initial processing of oilseeds.

Node Importance Evaluation of Urban Rail Transit Based on Signaling System Failure: A Case Study of the Nanjing Metro

Assessing the importance of nodes in urban rail transit systems helps enhance their ability to respond to emergencies and improve reliability in view of the fact that most of the existing methods for evaluating the importance of rail transit nodes ignore the disturbance effect of signaling system failures and are unable to objectively identify critical stations in specific disturbance scenarios. Therefore, this paper proposed a method for evaluating the importance of urban rail transit nodes in signaling system failure scenarios. The method was based on the research background of the signaling system failure that occurs most frequently and analyzed the network failure mechanism after the occurrence of a disturbance. The node importance evaluation indices were selected from the network topology and network operation performance in two aspects. The variation coefficient–VIKOR method was employed to comprehensively assess the significance of urban rail transit stations during signaling system failures. The Nanjing Metro network was also used as an example to evaluate the importance of network stations. The results showed that under the attack method of signaling system failure, most ECC and interlocking stations experienced significantly higher network performance losses compared to the original attack method, and a few interchange stations showed smaller performance losses. The critical stations identified based on the proposed method are mainly distributed in the passenger flow backbone of the Nanjing Metro and were constructed in the early stage; of these, 85% are ECC stations or interlocking stations, which are easily neglected in daily management, in contrast to interchange stations with heavy passenger flow. The results of this study can provide an important reference for the stable operation and sustainable construction of urban rail transit.