Analysis Of Key Factors Research Articles

Recent Advances in Photocatalytic Degradation of Imidacloprid in Aqueous Solutions Using Solid Catalysts

Imidacloprid (IMI), a widely used neonicotinoid pesticide, has led to significant water contamination due to excessive use. As a result, there is an urgent need for effective and straightforward methods to remove IMI residues from water. Photocatalytic technology, an integral part of advanced oxidation processes, is particularly promising due to its renewability, high catalytic efficiency, fast degradation ratio, and cost-effectiveness. This review systematically examines recent progress in the photocatalytic degradation of imidacloprid in aqueous solutions using various solid catalysts. It provides a comparative analysis of key factors affecting catalytic performance, such as catalyst synthesis methods, reaction times, catalyst loading, and IMI concentrations. Among the solid catalysts studied, nano-ZnO achieved a higher degradation rate of IMI in a shorter period and with a reduced catalyst dosage, reaching approximately 95% degradation efficiency within one hour. Additionally, this review explores the types of heterojunctions formed by the catalysts and elucidates the mechanisms involved in the photocatalytic degradation of IMI. In conclusion, this review offers a comprehensive evaluation of solid catalysts for the photocatalytic removal of IMI from water, serving as an important reference for developing innovative catalysts aimed at eliminating organic pollutants from aquatic environments.

Simulation and optimization of heavy fuel oil (HFO) refining platform to low sulfur marine fuel (LS-FO) by oxidative desulfurization

The International Maritime Organization (IMO) has implemented new sulfur content regulations for marine fuels in response to growing environmental concerns, including global warming. These regulations severely and costly restrict refinery operations. Oxidative desulfurization (ODS) is an attractive desulfurization method that has advantages such as mild operating conditions and a hydrogen-free process over traditional processes such as hydrodesulfurization (HDS). One can employ oxidative desulfurization in addition to or instead of hydrodesulfurization. Organic sulfur molecules undergo oxidative desulfurization, which results in the formation of polar sulfones. The refined fuel oil is then separated from the oxidized sulfones using methods such liquid-liquid extraction, distillation, and absorption. This work examines and simulates the process of separating sulfur-containing oxidized chemicals from fuel oil utilizing oxidative desulfurization technology. Acetic acid served as the catalyst and hydrogen peroxide as the chosen oxidant. This effort involved simulating the oxidative desulfurization of fuel oil and then using absorption methods to separate the oxidized sulfones. Subsequent procedures including absorption and distillation were used to separate and recover the resultant effluent, solvent, and oxidant from the remaining components. According to the simulation results, the procedure was run at 80 °C and a modest pressure of less than 5 bar. Sulfur content in the original hydrocarbon fuel was 3.5% by weight; at the output, it was less than 0.5% by weight. The findings of the sensitivity analysis of a few key factors demonstrated that the conversion percentage increased from 84 to 98% when the reactor’s diameter was changed from 0.5 to 1.2 m and its length was extended to the desired amount. The reaction conversion percentage has increased significantly when the mass flow rate of hydrogen peroxide is increased from 0.5 to 1.5 kg/h, the pressure is increased from 1.5 to 5 bar, and the temperature is raised to 30 °C. According to the process optimization data, the ideal state had an exergy efficiency of 55.57%, a solvent waste rate of 1.75%, and a sulfur content of 0.22% in the fuel.

Analysis of key factors for better results during continuous improvement events within engineering environment

There are few in-depth studies in the Management literature related to the development of activities implemented during the Kaizen week. This work contains a theoretical basis related to the engineering and product development environments as well as to the Kaizen tool and the Lean tool, which seek to reduce waste and implement continuous improvement. Through a case study in an aviation transportation industry, some Kaizens are analyzed in the engineering environment. With the analysis of data collected in Kaizen Weeks, a dissertative study uses three sources of data collection, between them, structured interview, and demonstrates the theory presented, proving the existence of impacting factors that influence the Kaizen week. Using percentages and multivariate analysis, it highlights the factors that have the biggest impact and the necessary attention to be given to them.

Analysis of Key Factors and Correlations Influencing the Adoption of Autonomous Ships by Shipping Companies—A Study Integrating Revised DEMATEL-AHP with BOCR

In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and significant changes and challenges. In the future era, where autonomous vessels dominate shipping, with automated operation systems taking the lead, how successfully shipping companies harness these new maritime transport modes will critically impact the safety, efficiency, and reliability of future vessel operations. With the emergence and development of autonomous vessels, it is crucial to effectively assess the importance and correlation of key factors influencing shipping companies’ adoption of autonomous ships. This study utilizes the Analytic Hierarchy Process (AHP) and Revised Decision Making and Trial Evaluation Laboratory (RDEMATEL) to survey senior managers in container and bulk shipping from Taiwan, China, Japan, and the European Union. Through a literature review on the benefits, opportunities, costs, and risks brought by autonomous shipping, this study aims to understand the critical factors important to shipping companies in adopting autonomous shipping, as well as the correlation between these influencing factors across different shipping sectors. The research findings indicate that “emergency response capability” is a critical factor influencing overall and bulk shipping in the adoption of autonomous vessels, while “incomplete regulations” are the primary factor influencing container shipping in the adoption of autonomous ships. Regarding the correlation of critical influencing factors, “vessel technology development” is the main influencing factor for overall, container, and bulk shipping; “operational performance enhancement” is the primary affected factor for overall and container shipping; and “enhancing personnel and vessel safety” is the main affected factor for bulk shipping. It is hoped that the results of this study can serve as a guide for shipping companies in understanding the benefits and opportunities to be emphasized when adopting autonomous shipping and assist in developing effective strategies to reduce costs and risks.

Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models

BackgroundSuicidal behaviors, which may lead to death (suicide) or survival (suicide attempt), are influenced by various factors. Identifying the specific risk factors for suicidal behavior mortality is critical for improving prevention strategies and clinical interventions. Predicting the outcomes of suicidal behaviors can help identify individuals at higher risk of death, enabling timely and targeted interventions. This study aimed to determine the critical risk factors associated with suicidal behavior mortality and identify an effective classification model for predicting suicidal behavior outcomes.Materials and methodsThis study utilized data recorded in the suicidal behavior registry system of hospitals in Ilam Province. In the first phase, duplicate records were removed, and the data was numerically encoded via Python version 3.11; then, the data was analyzed using chi-square and Fisher’s exact tests in SPSS version 22 software to identify the factors influencing suicidal behavior mortality. In the second phase, missing data were removed, and the dataset was standardized. Five binary classification algorithms were utilized, including Random Forest, Logistic Regression, and Decision Trees, with hyperparameters optimized using the area under the receiver operating characteristic curve (AUC) and F1 score metrics. These models were compared based on accuracy, recall, precision, F1 score, and AUC.ResultsAmong 3833 cases of suicidal behavior in various hospitals in Ilam Province, the results indicated that the method of suicidal behavior (P < 0.001), reason for suicidal behavior (P < 0.001), age group (P < 0.001), education level (P < 0.001), marital status (P = 0.004), and employment status (P = 0.042) were significantly associated with suicide. Variables such as the season of suicidal behavior, gender, father’s education, and mother’s education were not significantly related to suicidal behavior mortality. Furthermore, the random forest model demonstrated the highest area under the ROC curve (0.79) and the highest classification accuracy and F1 score on both the training data (0.85 and 0.2, respectively) and test data (0.86 and 0.31, respectively) for predicting suicidal behaviors outcomes among the models tested.ConclusionThis study identified key factors such as older age, lower education, divorce or widowhood, employment, physical methods, and socioeconomic issues as significant predictors of suicidal behavior outcomes. A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. This approach highlights the potential of integrating statistical methods with machine learning to improve suicide risk prediction and intervention strategies.

Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021)

Methane (CH4) is a potent greenhouse gas that significantly impacts climate change due to its rising atmospheric concentrations. Hence, it is crucial to comprehend the spatial and temporal fluctuations in atmospheric CH4 concentration (XCH4) at both national and international levels. This study investigates the correlation between atmospheric XCH4 concentrations (XCH4) and key influencing factors to identify the primary sources and sinks of CH4 across the Middle East (ME). Initially, XCH4 data from the GOSAT satellite, covering the period from 2010 to 2021, were employed to generate spatiotemporal distribution maps of XCH4 across the ME region. Subsequently, the study investigated the single and simultaneous relationship between XCH4 and relevant environmental factors, such as vegetation, temperature, precipitation, and others, across different months using correlation analysis and the Permutation Feature Importance (PFI) method to identify the key factors influencing XCH4 variations. The results reveal significant spatial and temporal variations in XCH4 concentrations, with higher levels detected in the central and southern regions of the ME during the summer months. The results also highlight the presence of both peak positive and negative correlations with temperature and moisture during winter months. Additionally, both precipitation and vegetation demonstrated negative correlations with XCH4, especially during the winter and plant-growing seasons. According to the PFI results, temperature emerged as the most significant factor, accounting for over 40% of the variance in XCH4 concentrations during summer. At the same time, anthropogenic activities exerted minimal influence on these patterns. This comprehensive spatiotemporal analysis provides crucial insights into the variation of CH4 and its primary drivers in this climatically vulnerable region. Identifying emission patterns can support the development of targeted mitigation policies to curb the future rise of CH4.

Emergency Management in Japan: Human Decision‐Making Strategy Analysis During Large‐Scale Earthquake

ABSTRACTIn the context of unexpected disasters, comprehending individual decision‐making strategy is essential for governmental policy formulation and evacuation planning. The main challenges stem from the absence of large‐scale mobility data and the complex influences of multiple factors on personal strategies. To address this, this study focuses on the 2011 Tohoku earthquake in Japan as a case, which struck at 14:46 on March 11, a typical working day. Decision‐making strategies regarding how people return home from their workplaces have emerged as a primary concern. Therefore, we construct a large human mobility database for the Greater Tokyo area, and develop an empirical prediction for decision‐making strategies following earthquakes with the consideration of multiple factors. Except for users' location‐based information, we extract the grid‐based stay areas from historical stay records (normal days before disasters) and discover the functions of these areas combined with points of interest located within them. Experimental results indicate the effectiveness of our proposed framework in strategy prediction. Besides, we conduct an explainable feature importance analysis of key factors, which also provides insights for understanding human decisions during disasters.

Social Reintegration Experiences of Young Adult Cancer Survivors.

This study uses the Q methodology to investigate the subjective perceptions of social reintegration among cancer survivors in their 20s and 30s. We organised a Q population through a pilot study and interviews and finalised 40 Q sample items. For P sample representativeness, we used purposive sampling and selected 12 individuals by age and cancer type. After a Q sorting process, we conducted a key factor analysis using Ken-Q Analysis Desktop Edition. We identified four types of P samples based on their perceptions and noted the main characteristics of each type. We characterised Type 1 as "recovery of presence through social reintegration seeking", Type 2 as "confusion in social reintegration due to social prejudices", Type 3 as "psychosocial support in the process of social reintegration", and Type 4 as "blessing in disguise for post-traumatic growth". The results suggest a need for practical and institutional support reflecting cancer survivors' characteristics by type. This study provides basic data that researchers could use to develop coaching and counselling services to support the social reintegration of cancer survivors in their 20s and 30s.

Analysis of Key Factors Affecting Case-to-Ambient Thermal Resistance in Thermal Modeling of Power Devices

Analysis of Key Factors Affecting Case-to-Ambient Thermal Resistance in Thermal Modeling of Power Devices

Operating Key Factor Analysis of a Rotary Kiln Using a Predictive Model and Shapley Additive Explanations

The global smelting business of nickel using rotary kilns and electric furnaces is expanding due to the growth of the secondary battery market. Efficient operation of electric furnaces requires consistent calcine temperature in rotary kilns. Direct measurement of calcine temperature in rotary kilns presents challenges due to inaccuracies and operational limitations, and while AI predictions are feasible, reliance on them without understanding influencing factors is risky. To address this challenge, various algorithms including XGBoost, LightGBM, CatBoost, and GRU were employed for calcine temperature prediction, with CatBoost achieving the best performance in terms of MAPE and MLSE. The influential factors on calcine temperature were identified using SHAP from XAI in the context of the CatBoost model. SHAP effectively assesses model impacts, accounting for variable interdependencies, and offers visualization in high-dimensional contexts. Given the correlation and dimensionality of variables predicting calcine temperature, SHAP was preferred over Feature Importance or PDP for the analysis. By incorporating seven out of twenty operational factors like burner fuel and reductant feed rate, combustion conditions inside of the rotary kiln and RPM, the calcine temperature increased from 840 °C in 2023 to 910 °C by October 2024, concurrently reducing the electricity unit consumption of the electric furnace by 7.8%. Enhancements to the CatBoost algorithm will enable the provision of guidance values after optimizing key variables. It is expected that managing the rotary kiln’s calcine temperature according to the predictive model’s guidance values will allow for autonomous operation of the rotary kiln through inputting guidance values to the PLC.

LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities

The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of 5.167 between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics.

CFD analysis of key factors impacting the aerodynamic performance of the S830 wind turbine airfoil

The Global Wind Energy Council (GWEC) reported that in 2021, global wind capacity increased by 93.6 GW (+12%), reaching a total of 837 GW, most of which was contributed by wind turbines. Improving wind turbine performance primarily hinges on advancements in blade technology and airfoil design. This study examines the effect of profile thickness on the aerodynamic performance of the S830 airfoil at a Reynolds number of 25,148, as part of the NREL airfoil's aerodynamic performance research. However, the impacts of additional variables—such as angle of attack, Reynolds number, speed range, and particularly ice accretion—have not been thoroughly investigated. This study utilized a 2D CFD model provided by the commercial software ANSYS Fluent and FENSAP-ICE. The lift-to-drag ratio of the S830 wind turbine airfoil was examined, considering the effects of angle of attack, wind speed, Reynolds number, airfoil thickness, and ice accretion. Additionally, design-related solutions will be suggested. The research indicates that the optimal angle of attack increases the lift-to-drag ratio by approximately 250% compared to a zero angle of attack. An increase in wind speed causes this coefficient to rise nonlinearly within the studied velocity range. The Reynolds number directly influences the optimal angle of attack. According to CFD results, the lift-to-drag ratio can be increased by 50% if the airfoil's thickness is reduced by 20% compared to the original profile. For the ice accretion simulation model, a case test of the NACA 0012 airfoil was conducted to verify the model's parameters. Subsequently, a survey of this phenomenon on the S830 airfoil revealed that the lift coefficient decreased by 5.25% after 90 minutes of ice accretion.

Analysis of Key Factors Affecting Employee Performance

The problem in this study is there any effect of the Archive System, Information System and Infrastructure Facilities on the performance of Employees at the Secretariat of the District Parliament of Tapin. This study aims to determine whether there is an influence of the filing system, information systems and infrastructure on the performance of Employees in the Secretariat of the Tapin District Parliament. The population and sample of this study were employees of the Secretariat of the Tapin District Parliament with 21 respondents. There are four variables examined in this study, namely the filing system (X1), the information system (X2), and infrastructure (X3), and the performance of the Tapin District DPRD Staff Secretariat (Y). Based on the results of multiple linear regression analysis calculated using SPSS obtained Y = 19.067 + 0.099X1 + 0.785X2 + 0.812X3 Partial test results obtained tcount 0.706 and tcount = 8.900 with a significance of 0.000 &lt;0.05 then this means that there is an influence from each variable, namely the filing system, information systems and infrastructure for performance. Simultaneous test results obtained by Fcount of 44.810 with a significance of 0,000 &lt;0.05 then this means that there is an influence of the filing system, information system and infrastructure together on performance. The conclusion is that partially or simultaneously the filing system, information system and infrastructure have a positive and significant effect on performance.

Gender Disparity in It- and Stem-Professions: Analysis of Key Factors

Gender Disparity in It- and Stem-Professions: Analysis of Key Factors

Identification and Analysis of Key Factors Affecting Digital Transformation of Small and Medium-Sized Manufacturing Enterprises

The digital transformation of small and medium-sized manufacturing enterprises is influenced by various factors, and the relationship between them is not yet clear. Studying the factors that affect the digital transformation of small and medium-sized enterprises can promote the digital transformation process of small and medium-sized manufacturing enterprises. On the basis of summarizing literature and expert recommendations, this study identified the influencing factors in the digital transformation process of small and medium-sized manufacturing enterprises, and analyzed the key influencing factors using a combined method of DEMATEL-ISM-MICMAC (Decision-Making Test and Evaluation Laboratory-Interpretive Structural Model-Matrices Impacts Croises-Multiplication Appliance Classement). Research has found that digital policies, market demand, business model innovation, third-party data-driven services, and the digital foundation of enterprises are key factors in the process of digital transformation. In addition, the causal factors in the DEMATEL method, the essential factors in the ISM method analysis, and the independent factors in the MICMAC technique analysis can mutually support and complement each other. This study enriches the methodology for studying the factors of digital transformation and provides reference for the digital transformation of small and medium-sized manufacturing enterprises and government policy formulation.

Analysis of Key Factors for Sustainable International Rural Tourism Using the AHP-DEMATEL Model : A Focus on Sichuan Province in China

Analysis of Key Factors for Sustainable International Rural Tourism Using the AHP-DEMATEL Model : A Focus on Sichuan Province in China

THE STATE AND PROSPECTS OF THE DEVELOPMENT OF THE TOURISM INDUSTRY IN UKRAINE IN THE CONDITIONS OF GLOBAL CHALLENGES

The article examines the state and prospects of international tourism development in Ukraine amid global crises, particularly the COVID-19 pandemic and the Russian-Ukrainian war. A comprehensive analysis of key factors affecting the tourism industry is conducted, including international travel restrictions, geopolitical instability, economic difficulties, and changes in consumer behavior. It demonstrates how global crises, such as COVID-19 and the Russian-Ukrainian war, have led to a significant reduction in international tourist flows, decreased foreign exchange earnings from tourism activities, and created additional risks for tourism infrastructure. In such conditions, the recovery of the industry and its integration into the country's economic development require a comprehensive approach that includes not only investments but also the digitalization of tourism services. The research highlights key changes in tourist flows, the level of economic activity in the sector, and the impact of internal and external challenges on tourism infrastructure. It was found that after a significant decline in 2022 due to the war, a gradual recovery began in 2023 thanks to the development of domestic tourism and the opening of new safe routes. Digitalization has become a key factor in the development of the modern tourism industry, affecting all stages of the tourist experience. The implementation of digital technologies, such as online booking, mobile applications, virtual and augmented reality, allows companies to enhance their service and improve competitiveness. The article emphasizes the importance of strengthening tourism infrastructure and supporting entrepreneurship in times of instability. Directions for further research are proposed, including the development of domestic tourism, the assessment of the resilience of tourism entities to crises, and the analysis of the long-term effects of the war on international tourist flows. The study underscores the significance of the tourism industry for Ukraine's economic recovery and its potential for integration into the global tourism market.

How can Dynamic Pricing Models be Successfully Implemented into Digital Marketplaces to Benefit SMEs in Kazakhstan?

​​The increased popularity of digital marketplaces caused many SMEs to start their online journey. However, the lack of systematisation and optimisation means that SMEs are often unproductive and do not fulfil their potential. This is worsened by the cost complexity of import-focused pricing, which is caused by local market conditions and the swift nature of digital marketplaces. The Kazakhstani market is rapidly developing; however, it is relatively unstable for SMEs. The price complexity issue can be solved by developing dynamic pricing models based on regional contexts. This study created an optimisation model with a macroeconomic focus through data analysis of key economic factors in Kazakhstan. The final dynamic pricing model focused on inflation, exchange rates and product demand.

Prognostic model for predicting recurrence in hepatocellular carcinoma patients with high systemic immune-inflammation index based on machine learning in a multicenter study.

This study aims to use machine learning to conduct in-depth analysis of key factors affecting the recurrence of HCC patients with high preoperative systemic immune-inflammation index (SII) levels after receiving ablation treatment, and based on this, construct a nomogram model for predicting recurrence-free survival (RFS) of patients. This study included clinical data of 505 HCC patients who underwent ablation therapy at Beijing You'an Hospital from January 2014 to January 2020, and accepted 65 HCC patients with high SII levels from Beijing Ditan Hospital as an external validation cohort. 505 patients from Beijing You'an Hospital were divided into low SII and high SII groups based on the optimal cutoff value of SII scores. The high SII group was further randomly divided into training and validation cohorts in a 7:3 ratio. eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox regression analysis, were used to explore the factors affecting the post-ablation RFS of HCC patients. Based on the identified key factors, a nomogram model were developed to predict RFS in HCC patients, and their performance were evaluated using the concordance index (C index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). The optimal cutoff value for nomogram scores was used to divide patients into low- and high-risk groups, and the effectiveness of the model in risk stratification was evaluated using Kaplan-Meier (KM) survival curves. This study confirmed that age, BCLC stage, tumor number, and GGT level were independent risk factors affecting RFS in HCC patients. Based on the selected risk factors, an RFS nomogram was successfully constructed. The C-index, ROC curve, calibration curve, and DCA curve each demonstrated the discrimination, accuracy, and decision-making utility of the nomogram, indicating that it has good predictive performance. KM curve revealed the nomogram could significantly differentiate patient populations with different recurrence risk. We developed a reliable nomogram that can accurately predict the 1-, 3-, and 5-year RFS for HCC patients with high SII levels following ablation therapy.

Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran’s I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran’s I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems.