Entropy Method Research Articles

Experimental and numerical study on fire development process and fire risk assessment of historic timber lounge bridges

AbstractHistoric timber lounge bridges are precious architectural heritages. But they are sensitive to fire. In order to obtain an adequate understanding of the fire vulnerability of historical timber lounge bridges for their better conservation, this paper proposes a framework based on experiment and computational fluid dynamics (CFD) analysis. Dengyun Bridge, a typical timber lounge bridge with cantilever beams, was chosen to carry out study on the fire development process and fire risk assessment. Firstly, the structural form and fire loads of the bridge were obtained by conducting on-site survey, and then a complex pyrolysis model of ancient wood was established in the Fire Dynamic Simulator (FDS) according to the pyrolysis characteristic of the ancient wood replaced from the bridge during its renovation. Secondly, the fire development process in Dengyun Bridge was simulated and four crucial fire events were defined. Finally, the comprehensive fire risk index for the Dengyun Bridge under those fire events was calculated by applying the Analytic hierarchy process (AHP) and Entropy method, and some mitigation and prevention strategies were discussed accordingly. The obtained results indicated that the flashover point was a boundary of fire development. After the flashover, the comprehensive fire risk index increased and reached its peak value rapidly. In the 1084 s (peak heat release rate), the comprehensive fire risk index was about 1.7 times of that in 847 s (flashover point). Mitigation strategies, such as the flame-retardant treatment and water-mist extinguishing system, were proved to be effective to retard the developed fire. The results of this study can provide a scientific basis for fire risk assessment and conservation of inherited timber lounge bridges.

Analysis of Spatial Effects of New Energy Vehicles Based on Semi-parametric Spatial Durbin Model

The global scientific and technological revolution and industrial change are developing rapidly, the integration of automotive technology with energy, transportation and information and communication fields is accelerating, and electrification, internet connectivity and intelligence have become the main trends. Therefore, studying the impact of digital development on the growth of new energy vehicle industry and consumer purchase intention is of great theoretical and practical significance for the digital transformation of automobile companies. In this paper, based on the provincial panel data of new energy vehicles in China from 2016 to 2023, we constructed the index system of digital economy and economic development, used the entropy method to calculate the level of development, and conducted spatial autocorrelation tests using global and local Moran indices. Considering the non-linear relationship between variables, a semi-parametric spatial Durbin model is established using local polynomial estimation to explore the impact of digital economy on new energy automobile industry. The results of the study show that the development of digital economy promotes the development of new energy automobile industry to a certain extent, but its development level may have a negative impact if it is too low or too high.

CYLINDER AND PISTON: MATERIAL SELECTION IN THE DESIGN PHASE

The piston and cylinder constitute an inseparable pair, playing a crucial role in both the mechanical and hydraulic industries. They are frequently employed to convert linear motion into rotational motion in various types of engines and are especially valuable for heavy-duty applications. Material selection for these components is conducted during the product design phase. This study aimed to identify the optimal material for each type of product. To determine the best material, a ranking of materials was carried out using the CoCoSo (COmbined COmpromise SOlution) method, with scores for criteria calculated using the Entropy method. Nine materials for cylinder construction were evaluated, including S355JR, S275JR, S235JR, BS97007M20, R35, R45, IS1030GRADE, AISI304, and 60-40-18. Additionally, seven materials for piston construction were considered: 332-T5, A336, 242-T5, 333.0-F, A213.0 F, AISI308, and A319.0F. The Entropy-CoCoSo approach was employed to rank the materials for each case (cylinder material and piston material). The results indicated that AISI304 is the optimal material for cylinder manufacturing, while A336 is the best material for piston manufacturing. Furthermore, the study extensively examined the impact of different weighting methods (Entropy, WENSLO, CRITIC, ROC, RS, EW) and normalization techniques (sum, vector, max, max-min, peldschus, decimal) on CoCoSo method results using an innovative sensitivity analysis approach, analyzing the techniques according to their sensitivity levels.

Nanodomains and Their Temperature Dependence in a Phosphonium-Based Ionic Liquid: A Single-Molecule Tracking Study.

Ionic liquids (ILs) exhibit a unique nanoscale structure (i.e., nanodomains) characterized by their organization into distinct domains. We present evidence of nanodomains in trihexyl(tetradecyl)phosphonium chloride, [P66614][Cl], using single-molecule tracking (SMT) and the maximum entropy method (MEM) to analyze single-molecule trajectories. The diffusion properties of ATTO 647N were assessed as the temperature of [P66614][Cl] increased from 20 °C (4020 cP), 35 °C (1239 cP), 45 °C (599 cP) to 50 °C (439 cP). The MEM analysis revealed a distinct two-population distribution of diffusion coefficients representing nanodomains in [P66614][Cl] at 20 °C (4020 cP). The slow population accounts for 16%, with a diffusion coefficient of 0.104 μm2/s, while the fast population constitutes 84% with a diffusion coefficient of 0.634 μm2/s. Two diffusing populations were also measured for the chemically different probes ATTO 647N, DiD, and Nile Blue chloride in [P66614][Cl] at 20 °C. In contrast, only a single fast population was measured in [P66614][Cl] at 50 °C. At a similar viscosity (640 cP) but a lower temperature of 20 °C, trihexyl(tetradecyl)phosphonium bis[(trifluoromethyl)-sulfonyl]imide, [P66614][NTf2], also showed only a single diffusing population. The elimination of the slow population and the presence of a single diffusing population in [P66614][Cl] as the temperature increases and the viscosity decreases is consistent with liquid-liquid phase separation (LLPS) as a mechanism of nanodomain formation. In addition, the measurement of two diffusing populations for three fluorophores with different chemical structures is also consistent with a physical mechanism, and not a chemical mechanism, for nanodomain formation.

Evaluating China’s urbanization trajectory: an overextension or still in progress?

China’s urbanization has undergone a transformative journey since the initiation of the reform and opening-up policy in 1978, catalyzing economic growth while profoundly impacting its ecological and demographic landscapes. This study offers a systematic evaluation of China’s urbanization trajectory over the past 4 decades through the development of an indicator system encompassing three key dimensions: population urbanization, land urbanization, and economic urbanization. Using the entropy method for weight assignment, the analysis reveals a consistent upward trend in China’s comprehensive urbanization, with occasional sharp increases. Although population urbanization influences the overall index, land urbanization has outpaced both population and economic urbanization, indicating a pronounced dependence on land resources. This trend poses a potential risk of unsustainable urban growth if left unaddressed. The findings indicate that to achieve sustainable urban development, China must prioritize balancing the interplay between population and land, moving away from land-dependent strategies, and adopting a more balanced approach to urbanization. The quantitative analysis provides key insights into this imbalance, indicating that policies should integrate land use planning with population dynamics to promote holistic urban growth. Future research should investigate the long-term socio-economic and environmental impacts of land-centric urbanization and propose innovative strategies to guide China towards a sustainable urban future.

African Schistosomiasis: A Framework of Indicators Assessing the Transmission Risk and Intervention Effectiveness

Schistosomiasis, a parasitic disease with a complex transmission mechanism, requiring a snail intermediate host, is influenced by biology, the environment, human behavior and the prevailing socioeconomic situation. This study aimed to systematically investigate the importance and feasibility of indicators related to the factors influencing transmission and intervention measures for Schistosoma mansoni and S. haematobium. Based on a literature review and group discussions according to the Delphi method, a framework questionnaire was designed. A total of 33 experts on schistosomiasis were invited, and 27 were accepted, to rate the importance and feasibility of indicators for transmission with and the control of schistosomiasis, with a focus on intervention measures for S. mansoni and S. haematobium infections in Zimbabwe. After two rounds of Delphi consultations with these experts, calculated to have a high average authority coefficient (0.88), a consensus was reached on a framework that included 2 primary, 6 secondary and 39 tertiary indicators. The Delphi–entropy method was applied to assess the weight of each indicator. The key influencing factors included hazardous water exposure, accessibility to safe drinking water, sanitary facilities and the contamination of water bodies by outdoor defecation/urinary habits. The intervention measures involved improved diagnostics, health education, preventive chemotherapy, the presence of national control plans and the implementation of the strategy on water, sanitation and hygiene (WASH). While these factors are already well known, their detailed order of importance could help to improve the allocation of specific control efforts.

The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations

China attaches great importance to land use and ecological civilization; hence, clarifying the relationship of land use on ecological resilience is crucial for urban development. The aim of this paper is to study the impact of land-use carbon efficiency on ecological resilience and the moderating role played by different environmental regulatory policies between the two, with the aim of providing a research basis and decision-making reference for the country’s ecological high-quality development by proposing suggestions for different subjects based on the results of this study. Taking 30 provinces and cities in mainland China from 2009 to 2022 as samples, the authors constructed an indicator system to measure their ecological resilience using the entropy method, measured their land-use carbon efficiency using the super SBM, and verified the mechanism of land-use carbon efficiency on ecological resilience by using the bidirectional fixed-effects model. Robustness and endogeneity tests confirmed the validity of the regression results. The following is a summary of this study’s findings: (1) Land-use carbon efficiency can enhance ecological resilience through various mechanisms such as scale promotion, structural upgrading, and technological progress. (2) Regional research shows that different regions have distinct effects of land-use carbon efficiency on ecological resilience. The northeastern region shows a non-significant inhibitory effect, whereas the eastern, middle, and western regions show varying degrees of promotion effects. Land-use carbon efficiency contributes to increased ecological resilience in resource-based and non-resource-based provinces, with resource-based provinces witnessing a greater increase in ecological resilience. The effects of land-use carbon efficiency on different aspects of ecological resilience are diverse, with ecosystem resistance and recovery being empowered. However, the precise mechanism through which ecosystem adaptability influences ecological resilience remains unclear. (3) Moreover, there is variation in the moderating impact of environmental legislation. Command-and-control environmental regulation impedes the positive impact of land-use carbon efficiency, and market-incentive environmental regulation strengthens their relationship, while spontaneous-participation environmental regulation does not significantly enhance their connection. It provides a new theoretical perspective for the study of ecological resilience, deepens the understanding of ecological resilience, and provides theoretical support for enhancing the resilience of ecosystems.

Study on Regional Differences, Dynamic Evolution and Convergence of Nutrition-Sensitive Agricultural Development in China

This article constructs an evaluation index system for the development of nutrition-sensitive agriculture in China and measures the development level of nutrition-sensitive agriculture using the entropy method, based on the panel data of 31 provinces from 2000 to 2022. The Dagum Gini coefficient is employed to analyze the magnitude and sources of regional differences in the development level between the whole country and the four major regions. The Kernel density estimation method is applied to describe the dynamic evolutionary characteristics of the development level in different regions. Furthermore, this study delves into the σ convergence and β convergence characteristics of the development level. The results show the following: (a) The level of nutrition-sensitive agricultural development at the national level and in the four major regions has been rising year by year, with a clear spatial pattern of “high in the east and low in the west”. (b) There are significant regional differences at the national level and in the four major regions, which tend to widen. (c) The dynamic evolution characteristics of the development level of nutrition-sensitive agriculture in various regions differ greatly, with polarization in the national, eastern, western and northeastern regions. (d) There is no σ convergence in the development level of nutrition-sensitive agriculture in the country or in the four major regions, but there is absolute β convergence and conditional β convergence in all of them, with the northeastern and central regions having faster convergence speeds; the “catching-up effect” is obvious. The report concludes by outlining the policy ramifications for implementing a methodical and comprehensive strategy approach to support regionally coordinated development plans for leapfrogging and upgrading.

SmoQyDEAC.jl: A differential evolution package for the analytic continuation of imaginary time correlation functions

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package’s output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

Codebase release r1.1 for SmoQyDEAC.jl

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package’s output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

The Electroencephalogram (EEG) Study for Estimating Endurance Sports Performance Base on Eigenvalues Extraction Method

Objectives. Brain–behavior connections are a new means to evaluate sports performance. This electroencephalogram (EEG) study aims to estimate endurance exercise performance by investigating eigenvalue trends and comparing their sensitivity and linearity. Methods. Twenty-three cross-country skiers completed endurance cycling tasks. Twenty-four-channel full-brain EEG signals were recorded in the motor phase and recovery phase continuously. Eighteen EEG eigenvalues calculation methods were collected, commonly used in previous research. Time-frequency, band power, and nonlinear analyses were used to calculate the EEG eigenvalues. Their regression coefficients and correlation coefficients were calculated and compared, with the linear regression method. Results. The time-frequency eigenvalues shift slightly throughout the test. The power eigenvalues changed significantly before and after motor and recovery, but the linearity was not satisfactory. The sensitivity and linearity of the nonlinear eigenvalues were stronger than the other eigenvalues. Of all eigenvalues, Shannon entropy showed completely non-overlapping distribution intervals in the regression coefficients of the two phases, which were −0.1474 ± 0.0806 s−1 in the motor phase and 0.2560 ± 0.1365 s−1 in the recovery phase. Shannon entropy amplitude decreased more in the F region of the brain than in the other regions. Additionally, the higher the level of sport, the slower the decline in Shannon entropy of the athlete. Conclusions. The Shannon entropy method provided more accurate estimations for endurance exercise performance compared to other eigenvalues.

Investigating the effect of non-resonant background variation on the CARS data analysis of bacteria samples and classification using machine learning

Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only one study explored the effect of NRB. Hence, in this work, we systematically investigated the impact of NRB variation on Raman signal retrieval. The NRB is simulated as a linear function with different strengths relative to the resonant Raman signal, and the variance also changes for each NRB strength. The resonant part of nonlinear susceptibility is extracted from real experimental Raman data; hence, the simulated CARS data better approximate the experimental CARS spectra. Then, the corresponding Raman signal is retrieved by four different methods: maximum entropy method (MEM), Kramers-Kronig (KK), convolutional neural network (CNN), and long short-term memory (LSTM) network. Pearson correlation measurements and principal component analysis combined with linear discriminant analysis modeling revealed that MEM and KK methods have an edge over LSTM and CNN for higher NRB strengths. It is also demonstrated that normalizing the input data favors LSTM and CNN predictions. In contrast, background removal from the predictions significantly influenced Pearson correlation but not the classification accuracies for MEM and KK. Further, the LSTM performance is found to be limited and can only be applied for low NRB strengths. This comprehensive study has the potential to impact the CARS spectroscopy and microscopy applications in different areas.

The influence of public environmental concern on the rural living environment in China

BackgroundDespite China's economic growth, rural living environments have often lagged behind. While public participation is gaining importance in environmental governance, the magnitude and mechanism of its impact remain understudied.PurposeThis research investigates the relationship between public environmental concerns and the rural living environment in China and explores how public concerns impact living conditions.MethodologyUsing panel data from 245 prefecture-level cities (2012–2021), we employed the entropy method to measure rural living environment scores and used fixed-effect models to analyze the relationship between public concern and the living environment.ResultsThe findings demonstrate a positive relationship between strong public environmental concerns and improved rural living environments. Further analysis suggests that local government environmental attention acts as a partial mediator in this relationship.ConclusionThis study reveals that public participation can influence government policies, ultimately leading to positive environmental outcomes. Promoting public participation in environmental governance is crucial for improving the rural living environment.

Spatial benefit assessment and marine climate response of coastal zone in Fujian Province under cross-system influence.

To gain a scientific understanding of the cross-system impact of coastal zones and promote the sustainable development and protection of coastal areas, we constructed a spatial benefit evaluation system that encompassed both terrestrial and marine systems, focusing on the ecological, economic, and social dimensions. We employed the entropy method, moving average method, and Mann-Kendall trend test to quantitatively characterize the spatial benefits of the coastal zone in Fujian Province, China, and the evolution of the marine climate from 2005 to 2020. Building on this, the grey relational analysis method was applied to investigate the correlation between spatial benefits and marine climate and to explore the trends and magnitude of the impact of marine climate on spatial benefits. During the study period, the spatial benefits of the coastal zone in Fujian Province exhibited a fluctuating pattern of an initial increase followed by a decrease, with spatial benefits varying among cities. The role of the economic system in enhancing spatial benefits was not considerable. Changes in the marine climate aligned with the global warming trend, with the most considerable changes observed in sea level and tropical cyclone frequency and intensity, which are sensitive to human activities. There was a high degree of correlation between coastal zone spatial benefits and marine climate, with seawater salinity being most closely related to spatial benefits, while tropical cyclones showed the weakest correlation. The results of this study support sustainable development efforts in coastal zones.

Addressing the Global Logistics Performance Index Rankings with Methodological Insights and an Innovative Decision Support Framework

This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade and logistics sectors. In this study, the efficiency of logistics operations in 118 countries was evaluated using an integrated multi-criteria decision-making (MCDM) model objectively weighted by the Entropy method. Countries were ranked using the MCRAT, SAW, TOPSIS, and FUCA methods. According to the findings, large datasets provide more robust insights for sensitivity analyses, and wider weighting coefficient combinations make the data more meaningful. In addition, it is suggested to use low-compensation methods instead of classical additive methods for LPI. Unlike other studies in literature, this research applied an innovative sensitivity analysis to test the robustness of the model and comprehensively examined the effects of weighting techniques based on over 2500 different MCDM results. The findings suggest that the FUCA method should be recommended to decision-makers for calculating LPI rankings due to its simplicity, practicality, low compensatory power, and low sensitivity. This study offers methodological improvements when evaluating logistics performance and provides significant contributions to decision-making processes. The findings are expected to provide a valuable resource for policymakers and businesses in understanding a country’s position in global competition, as well as serving as a reference for researchers evaluating the logistics performance of countries.

Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model

ABSTRACTAccurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.

Forging a path to sustainability: the impact of Fintech on corporate ESG performance

Emerging development of Fintech has raised widespread attention to its important role in shaping corporate ESG performance despite little in-depth research. We study the impact of Fintech on corporate ESG performance using a comprehensive dataset covering major China’s firms over the past decade. The Fintech development at the city level is measured via an entropy method based on information gathering from firms and households. Drawn on a panel data estimation with two-way fixed effects, our findings show that corporate ESG performance improves with Fintech development. Promotion of green innovation, mitigation of financial mismatch, and environmental uncertainty are found to serve as the three mediating channels that drive the relationship between Fintech and corporate ESG performance. The positive impact of Fintech is shown to be more pronounced in firms with non-high-tech, non-high-polluting, and labor-intensive features. Further analysis indicates that Fintech strengthens in the enterprises’ green value through perspectives of green total factor productivity and green investment.

Assessing Flood Risks in Coastal Plain Cities of Zhejiang Province, Southeastern China

Constructing a precise and effective evaluation index system is crucial to flood disaster prevention and management in coastal areas. This study takes Lucheng District, Wenzhou City, Zhejiang Province, southeastern China, as a case study and constructs an evaluation index system comprising three criterion levels: disaster-causing factors, disaster-gestation environments, and disaster-bearing bodies. The weights of each evaluation index are determined by combining the Analytic Hierarchy Process (AHP) and the entropy method. The fuzzy matter-element model is utilized to assess the flood disaster risk in Lucheng District quantitatively. By calculating the correlation degree of each evaluation index, the comprehensive index of flood disaster risk for each street area is obtained, and the flood disaster risk of each street area is classified according to the risk level classification criteria. Furthermore, the distribution of flood disaster risks in Lucheng District under different daily precipitation conditions is analyzed. The results indicate that: (1) the study area falls into the medium-risk category, with relatively low flood risks; (2) varying precipitation conditions will affect the flood resilience of each street in Lucheng District, Wenzhou City. The flood disaster evaluation index system and calculation framework constructed in this study provide significant guidance for flood risk assessment in coastal plain cities.

Global stability for McKean–Vlasov equations on large networks

Abstract We investigate the mean-field dynamics of stochastic McKean differential equations with heterogeneous particle interactions described by large network structures. To express a wide range of graphs, from dense to sparse structures, we incorporate the recently developed graph limit theory of graphops into the limiting McKean–Vlasov equations. Global stability of the splay steady state is proven via a generalised entropy method, leading to explicit graph structure-dependent decay rates. We highlight the robustness of the entropy approach by extending the results to the closely related Sakaguchi–Kuramoto model with intrinsic frequency distributions. We also present central examples of random graphs, such as power law graphs and the spherical graphop, and analyse the limitations of the applied methodology.

Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition–Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy

Existing gearbox fault diagnosis methods are prone to noise interference and cannot extract comprehensive fault signals, leading to misdiagnosis or missed diagnosis. This paper proposes a method for gearbox fault diagnosis based on adaptive variational mode decomposition–stationary wavelet transform (AVMD-SWT) and ensemble refined composite multiscale fluctuation dispersion entropy (ERCMFDE). Initially, the kurtosis coefficient and autocorrelation coefficient are presented, and the Intrinsic Mode Functions are denoised through the application of AVMD-SWT. Secondly, the coarse-grained processing method of composite multiscale fluctuation dispersion entropy is extended to encompass three additional approaches: first-order central moment, second-order central moment, and third-order central moment. This enables the comprehensive extraction of feature information from the time series, thereby facilitating the formation of an initial hybrid feature set. Subsequently, recursive feature elimination (RFE) is employed for feature selection. Ultimately, the outcomes of the faults diagnoses are derived through the utilization of a Support Vector Machine with a Sparrow Search Algorithm (SSA-SVM), with the actual faults data collection and analysis conducted on an experimental platform for gearbox fault diagnosis. The experiments demonstrate that the method can accurately identify gearbox faults and achieve a high diagnostic accuracy of 98.78%.