Search Results (2,170)

Digital twins represent a transformative innovation for battery energy storage systems (BESS), offering real-time virtual replicas of physical batteries that enable accurate monitoring, predictive analytics, and advanced control strategies. These capabilities promise to significantly enhance system efficiency, reliability, and lifespan. Yet, despite the clear technical potential, large-scale deployment of digital twin-enabled battery systems faces critical governance barriers. This study identifies three major challenges: fragmented standards and lack of interoperability, weak or misaligned market incentives, and insufficient cybersecurity safeguards for interconnected systems. The central contribution of this research is the development of a comprehensive governance framework that aligns these three pillars—standards, market and regulatory incentives, and cybersecurity—into an integrated model. Findings indicate that harmonized standards reduce integration costs and build trust across vendors and operators, while supportive regulatory and market mechanisms can explicitly reward the benefits of digital twins, including improved reliability, extended battery life, and enhanced participation in energy markets. For example, simulation-based evidence suggests that digital twin-guided thermal and operational strategies can extend usable battery capacity by up to five percent, providing both technical and economic benefits. At the same time, embedding robust cybersecurity practices ensures that the adoption of digital twins does not introduce vulnerabilities that could threaten grid stability. Beyond identifying governance gaps, this study proposes an actionable implementation roadmap categorized into short-, medium-, and long-term strategies rather than fixed calendar dates, ensuring adaptability across different jurisdictions. Short-term actions include establishing terminology standards and piloting incentive programs. Medium-term measures involve mandating interoperability protocols and embedding digital twin requirements in market rules, and long-term strategies focus on achieving global harmonization and universal plug-and-play interoperability. International examples from Europe, North America, and Asia–Pacific illustrate how coordinated governance can accelerate adoption while safeguarding energy infrastructure. By combining technical analysis with policy and governance insights, this study advances both the scholarly and practical understanding of digital twin deployment in BESSs. The findings provide policymakers, regulators, industry leaders, and system operators with a clear framework to close governance gaps, maximize the value of digital twins, and enable more secure, reliable, and sustainable integration of energy storage into future power systems. Full article

Radio frequency (RF) fingerprinting, as an emerging physical layer security technology, demonstrates significant potential in the field of Internet of Things (IoT) security. However, most existing methods operate under a ‘closed-set’ assumption, failing to effectively address the continuous emergence of unknown devices in real-world scenarios. To tackle this challenge, this paper proposes an open-set radio frequency fingerprint identification (RFFI) method based on Multi-Task Prototype Learning (MTPL). The core of this method is a multi-task learning framework that simultaneously performs discriminative classification, generative reconstruction, and prototype clustering tasks through a deep network that integrates an encoder, a decoder, and a classifier. Specifically, the classification task aims to learn discriminative features with class separability, the generative reconstruction task aims to preserve intrinsic signal characteristics and enhance detection capability for out-of-distribution samples, and the prototype clustering task aims to promote compact intra-class distributions for known classes by minimizing the distance between samples and their class prototypes. This synergistic multi-task optimization mechanism effectively shapes a feature space highly conducive to open-set recognition. After training, instead of relying on direct classifier outputs, we propose to adopt extreme value theory (EVT) to statistically model the tail distribution of the minimum distances between known class samples and their prototypes, thereby adaptively determining a robust open-set discrimination threshold. Comprehensive experiments on a real-world dataset with 16 Wi-Fi devices show that the proposed method outperforms five mainstream open-set recognition methods, including SoftMax thresholding, OpenMax, and MLOSR, achieving a mean AUROC of 0.9918. This result is approximately 1.7 percentage points higher than the second-best method, demonstrating the effectiveness and superiority of the proposed approach for building secure and robust wireless authentication systems. This validates the effectiveness and superiority of our approach in building secure and robust wireless authentication systems. Full article

Designing an accessible, financially viable healthcare system is a key challenge for society. The value-based healthcare (VBHC) strategic model aims to simultaneously improve the quality of healthcare and the efficiency of health systems. The aim of this research was to describe the perceptions of different stakeholders in the Portuguese health industry about the creation of value and the understanding of VBHC as a competitive advantage. A qualitative study was conducted using the inductive method of Braun and Clarke, designed according to the COREQ criteria. Based on the results of the literature review, a semi-structured script for an interview was created, consisting of eight questions. The initial interview script was based on a thorough narrative literature review and tested with two professionals with practical experience in VBHC. The final version of the semi-structured interview guide consisted of eight open-ended questions. The questions were designed to elicit in-depth, reflective responses, and their neutrality was reviewed to avoid leading language that might introduce bias. As the interviews progressed, minor iterative changes were made to include participant-suggested additions, always maintaining alignment with the research objectives. This iterative process was essential to capture the nuanced perspectives of stakeholders and conformed to COREQ standards for qualitative research. A total of 15 stakeholders in VBHC were interviewed. The interviews were transcribed and coded, and 605 codes were created, divided into subthemes and themes. VBHC implementation faces several challenges, requiring a collaborative effort by the stakeholders involved, to achieve a comprehensive vision of value and appropriate multi-stakeholder alignment. The implementation of VBHC can confer a sustainable competitive advantage, and its adoption as a strategic model will be inevitable in the future. Full article

Thin-walled structures have been extensively adopted as energy absorbers in various engineering fields. The energy accumulated in the coal and rock is released instantly, resulting in varying degrees of damage and failure to support equipment. To improve the crushing performance of underground support equipment, a metal thin-walled tube with high-bearing capacities is placed in the column as an energy-absorbing column. Based on the characteristics of non-dimensional parameters governing the crashworthiness of thin-walled tubes by the author’s team, a type of high-performance bi-tubular tube (HPBT) with mixed multicellular configurations is innovatively proposed. First, the finite element models of the HPBTs are established in LS-DYNA, and the accuracy of the FE model is verified by crushing tests. Second, the theoretical model of the mean crushing force (MCF) is derived. Moreover, the effects of the cross-sectional shapes and the wall thickness gradient distribution on the deformation modes and crashworthiness are investigated. The results show that the design strategies of the bi-tubular structures mixed multicellular configurations significantly improve the values of ω. The MCF of HPBT_C2 is 4458.0 kN, which is 28% and 56% higher than those of the conventional circular tube and square tube. The theoretical MCF is consistent with the simulated MCF, with a maximum discrepancy of 6.0%. The gradient distribution (k) of wall thickness significantly affects the crushing behaviors of the HPBT. Considering the energy absorption efficiency, the crushing stability, and the wall thickness gradient distribution, the HPBT_C2 with k = 0.6 has the best overall performance. The results can provide insights and guidelines for designing energy absorption devices with superior crashworthiness for support equipment. Full article

With the maturity of haptic technology and complex systems, tactile interaction has gradually become realized through specific hardware and software configurations in the e-commerce and business industries. As an innovative form depending on haptic systems, tactile interactive advertising could help both advertisers and consumers enhance the haptic experience of products through technology-mediated virtual environments and provide tactile information for purchase decision making that relies on restoring the real sense of touch. On the basis of the value-based adoption model (VAM) and the need for touch (NFT) from a preference for haptic information in a system, we conduct quantitative research and construct a partial least squares structural equation model, which aims to study the influencing factors that characterize the user preference of tactile interactive advertisements empowered by haptic systems among Chinese consumers. A total of 509 valid questionnaires were collected through online and offline channels. The study revealed that the perceived enjoyment (PE) and telepresence (TEL) of tactile interactive advertisements as benefit factors positively influence the perceived value (PV) and that the perceived fee (PF) as a sacrifice factor negatively influences PV, which further impacts the attitude and intention to use (IU). In addition, the study verified that a higher NFT positively affected PE, PU, and PF and IU for the perception of tactile interactive advertising. Through this study, we aim to provide insights from a consumer perspective to enhance the advertising effect and user experience through tactile interaction in further e-commerce, which transforms how we interact with digital systems and virtual environments. Full article

This study evaluates the trade-offs and environmental impacts of transitioning from coal to natural gas (NG) for electricity generation in San Juan County, with a focus on greenhouse gas emissions and water consumption. It addresses key questions, including how water use and emissions change as the county shifts from coal to natural gas. The research analyzes water usage and emissions of CO₂, NO_x, and SO₂ during both the extraction and combustion phases of coal and natural gas. Specifically, it compares water consumption and direct emissions from coal-fired and natural gas-fired power plants. The analysis utilizes ten years of combustion-phase data from the Four Corners (coal-fired) and Afton (natural gas-fired) power plants in New Mexico. Linear regression was applied to the historical data, and four transition scenarios were modeled: (1) 100% coal-generated electricity, (2) a 20% reduction in coal with a corresponding increase in NG, (3) a 50% reduction in coal with a corresponding increase in NG, and (4) a complete transition to NG. Regression analysis and scenario calculations indicate that switching to NG results in significant water savings and reduced emissions. Water savings in the combustion phase decrease by up to 2750 gallons per MWh, valued at USD 0.743 per MWh when electricity is generated 100% from NG. CO₂ emissions are substantially reduced, with the largest decrease being 0.6127 metric tons per MWh, valued at USD 61.26 per MWh. NO_x emissions in the combustion phase decline by 0.0018 metric tons per MWh, with an economic valuation of USD 14.61 per MWh, while SO₂ emissions decrease by 0.0006 metric tons per MWh, valued at USD 11.91 per MWh when electricity generation is 100% NG-based. The results highlight the environmental and economic advantages of transitioning from coal to NG. The findings underscore the environmental and economic advantages of transitioning from coal to natural gas. Water conservation is particularly vital in San Juan County’s semi-arid climate. Additionally, lower emissions support climate change mitigation, enhance air quality, and improve public health. The economic valuation of emissions reductions further highlights the financial benefits of this transition, positioning natural gas as a more sustainable and economically viable energy source for the region. Ultimately, this study emphasizes the need to adopt cleaner energy sources such as renewable energy to achieve long-term environmental sustainability and economic efficiency. Full article

Background: Colon capsule endoscopy (CCE) has seen increased adoption since the COVID-19 pandemic, offering a non-invasive alternative for lower gastrointestinal investigations. However, inadequate bowel preparation remains a key limitation, often leading to higher conversion rates to colonoscopy. Manual assessment of bowel cleanliness is inherently subjective and marked by high interobserver variability. Recent advances in artificial intelligence (AI) have enabled automated cleansing scores that not only standardise assessment and reduce variability but also align with the emerging semi-automated AI reading workflow, which highlights only clinically significant frames. As full video review becomes less routine, reliable, and consistent, cleansing evaluation is essential, positioning bowel preparation AI as a critical enabler of diagnostic accuracy and scalable CCE deployment. Objective: This CESCAIL sub-study aimed to (1) evaluate interobserver agreement in CCE bowel cleansing assessment using two established scoring systems, and (2) determine the impact of AI-assisted scoring, specifically a TransUNet-based segmentation model with a custom Patch Loss function, on both interobserver and intraobserver agreement compared to manual assessment. Methods: As part of the CESCAIL study, twenty-five CCE videos were randomly selected from 673 participants. Nine readers with varying CCE experience scored bowel cleanliness using the Leighton–Rex and CC-CLEAR scales. After a minimum 8-week washout, the same readers reassessed the videos using AI-assisted CC-CLEAR scores. Interobserver variability was evaluated using bootstrapped intraclass correlation coefficients (ICC) and Fleiss’ Kappa; intraobserver variability was assessed with weighted Cohen’s Kappa, paired t-tests, and Two One-Sided Tests (TOSTs). Results: Leighton–Rex showed poor to fair agreement (Fleiss = 0.14; ICC = 0.55), while CC-CLEAR demonstrated fair to excellent agreement (Fleiss = 0.27; ICC = 0.90). AI-assisted CC-CLEAR achieved only moderate agreement overall (Fleiss = 0.27; ICC = 0.69), with weaker performance among less experienced readers (Fleiss = 0.15; ICC = 0.56). Intraobserver agreement was excellent (ICC > 0.75) for experienced readers but variable in others (ICC 0.03–0.80). AI-assisted scores were significantly lower than manual reads by 1.46 points (p < 0.001), potentially increasing conversion to colonoscopy. Conclusions: AI-assisted scoring did not improve interobserver agreement and may even reduce consistency amongst less experienced readers. The maintained agreement observed in experienced readers highlights its current value in experienced hands only. Further refinement, including spatial analysis integration, is needed for robust overall AI implementation in CCE. Full article

Aiming at the stochasticity, uncertainty, and strong perturbation of the linear Fresnel solar thermal power collection subsystem, this study establishes a multivariate prediction model for the linear Fresnel collector subsystem based on complex environmental characteristics and designs a PID controller and MPC controller for the tracking and control of the outlet temperature. By analyzing the heat transfer process of the collector, constructing a model in Multiphysics for three-dimensional modeling of the collector, extracting data through simulation, fuzzy clustering the data and using different clustering centers for parameter identification in order to obtain the multi-model. By using the field data from the site of Dunhuang Dacheng Linear Fresnel Molten Salt Collector Field, considering the inlet temperature, normal direct irradiance and wind speed are used as the perturbation quantities, and the flow rate of molten salt is used as the control quantity. Considering three representative weather conditions, the switching criterion of minimizing the real-time point error is adopted for switching the outlet temperature of the collector. Simulation analysis results show that under the same conditions, the tracking error of the single model is relatively large, with the output temperature error fluctuating between −100 °C and 100 °C and containing many burrs. In contrast, the output temperature error of the multi-model switching control is controlled within 50 °C, which features a smaller tracking error and a faster tracking speed compared with the single-model control. When faced with large disturbances, the multi-model MPC switching control achieves better tracking performance than the multi-model PID switching control. It tracks temperatures closer to the set value, with a faster tracking speed and more excellent anti-interference performance. Full article

The development of unconventional oil and gas resources highly relies on hydraulic fracturing technology, and the fracturing effect directly affects the level of oil and gas recovery. Carrying out fracturing evaluation is the main way to understand the fracturing effect. However, the current fracturing evaluation methods are usually carried out after the completion of fracturing operations, making it difficult to achieve real-time monitoring and dynamic regulation of the fracturing process. In order to solve this problem, an intelligent prediction method for fracture propagation based on the attention mechanism and Long Short-Term Memory (LSTM) neural network was proposed to improve the fracturing effect. Firstly, the GOHFER software was used to simulate the fracturing process to generate 12,000 groups of fracture geometric parameters. Then, through parameter sensitivity analysis, the key factors affecting fracture geometric parameters are identified. Next, the time-series data generated during the fracturing process were collected. Missing values were filled using the K-nearest neighbor algorithm. Outliers were identified by applying the 3-sigma method. Features were combined through the binomial feature transformation method. The wavelet transform method was adopted to extract the time-series features of the data. Subsequently, an LSTM model integrated with an attention mechanism was constructed, and it was trained using the fracture geometric parameters generated by GOHFER software, forming a surrogate model for fracture propagation. Finally, the surrogate model was applied to an actual fracturing well in Block Ma 2 of the Mabei Oilfield to verify the model performance. The results show that by correlating the pumping process with the fracture propagation process, the model achieves the prediction of changes in fracture geometric parameters and Stimulated Reservoir Volume (SRV) throughout the entire fracturing process. The model’s prediction accuracy exceeds 75%, and its response time is less than 0.1 s, which is more than 1000 times faster than that of GOHFER software. The model can accurately capture the dynamic propagation of fractures during fracturing operations, providing reliable guidance and decision-making basis for on-site fracturing operations. Full article

Aiming at the nonlinear, nonstationary, and multiscale fluctuation characteristics of wind speed series, this study proposes a wind speed-forecasting framework that integrates multi-resolution turbulence intensity features and a Transformer-based hybrid neural network. Firstly, based on multi-resolution turbulence intensity and stationary wavelet transform (SWT), the original wind speed series is decomposed into eight pairs of mean wind speeds and turbulence intensities at different time scales, which are then modeled and predicted in parallel using eight independent LSTM sub-models. Unlike traditional methods treating meteorological variables such as air pressure, temperature, and wind direction as static input features, WaveNet, LSTM, and TCN neural networks are innovatively adopted here to independently model and forecast these meteorological series, thoroughly capturing their dynamic influences on wind speed. Finally, a Transformer-based self-attention mechanism dynamically integrates multiple outputs from the four sub-models to generate final wind speed predictions. Experimental results averaged over three datasets demonstrate superior accuracy and robustness, with MAE, RMSE, MAPE, and $R^2$ values around 0.65, 0.87, 23.24%, and 0.92, respectively, for a 6 h forecast horizon. Moreover, the proposed framework consistently outperforms all baselines across four categories of comparative experiments, showing strong potential for practical applications in wind power dispatching. Full article

The precise identification, classification, sorting, and rapid and accurate quality assessment of soybean seeds are extremely important in terms of the continuity of agricultural production, varietal purity, seed processing, protein extraction, and food safety. Currently, commonly used methods for the identification and quality assessment of soybean seeds include morphological analysis, chemical analysis, protein electrophoresis, liquid chromatography, spectral analysis, and image analysis. The use of image analysis and artificial intelligence is the aim of the presented research, in which a method for the automatic classification of soybean varieties, the assessment of the degree of damage, and the identification of geometric features of soybean seeds based on numerical models obtained using a 3D scanner has been proposed. Unlike traditional two-dimensional images, which only represent height and width, 3D imaging adds a third dimension, allowing for a more realistic representation of the shape of the seeds. The research was conducted on soybean seeds with a moisture content of 13%, and the seeds were stored in a room with a temperature of 20–23 °C and air humidity of 60%. Individual soybean seeds were scanned to create 3D models, allowing for the measurement of their geometric parameters, assessment of texture, evaluation of damage, and identification of characteristic varietal features. The developed 3D-CNN network model comprised an architecture consisting of an input layer, three hidden layers, and one output layer with a single neuron. The aim of the conducted research is to design a new, three-dimensional 3D-CNN architecture, the main task of which is the classification of soybean seeds. For the purposes of network analysis and testing, 22 input criteria were defined, with a hierarchy of their importance. The training, testing, and validation database of the SB3D-NET network consisted of 3D models obtained as a result of scanning individual soybean seeds, 100 for each variety. The accuracy of the training process of the proposed SB3D-NET model for the qualitative classification of 3D models of soybean seeds, based on the adopted criteria, was 95.54%, and the accuracy of its validation was 90.74%. The relative loss value during the training process of the SB3D-NET model was 18.53%, and during its validation process, it was 37.76%. The proposed SB3D-NET neural network model for all twenty-two criteria achieves values of global error (GE) of prediction and classification of seeds at the level of 0.0992. Full article

In system identification, augmenting time series data via time shifting and nonlinearisation can lead to both feature and sample redundancy. However, research has mainly focused on feature redundancy while largely ignoring the issue of sample redundancy. This paper proposes a novel data pruning method, called mini-batch FastCan, to reduce sample-wise redundancy based on dictionary learning. Time series data is represented by some representative samples via dictionary learning. The useful samples are selected based on their correlation with the representative samples. The method is tested on two simulated datasets and two benchmark datasets. The R-squared value between the coefficients of models trained on the full datasets and the coefficients of models trained on pruned datasets is adopted to evaluate the performance of data pruning methods. It is found that the proposed method significantly outperforms the random pruning method, with a higher median or mean and a lower variance of R-squared values. Full article

The development of software applications using multiple programming languages has increased in recent years, as it allows the selection of the most suitable language and runtime for each component of the system and the integration of third-party libraries. However, this practice involves complexity and error proneness, due to the absence of an adequate system for the interoperability of multiple programming languages. Developers are compelled to resort to workarounds, such as library reimplementation or language-specific wrappers, which are often dependent on C as the common denominator for interoperability. These challenges render the use of multiple programming languages a burdensome and demanding task that necessitates highly skilled developers for implementation, debugging, and maintenance, and raise doubts about the benefits of interoperability. To overcome these challenges, we propose MetaFFI, introducing a fully in-process, plugin-oriented, runtime-independent architecture based on a minimal C abstraction layer. It provides deep binding without relying on a shared object model, virtual machine bytecode, or manual glue code. This architecture is scalable (O(n) integration for n languages) and supports true polymorphic function and object invocation across languages. MetaFFI is based on leveraging FFI and embedding mechanisms, which minimize restrictions on language selection while still enabling full-duplex binding and deep integration. This is achieved by exploiting the less restrictive shallow binding mechanisms (e.g., Foreign Function Interface) to offer deep binding features (e.g., object creation, methods, fields). MetaFFI provides a runtime-independent framework to load and xcall (Cross-Call) foreign entities (e.g., getters, functions, objects). MetaFFI uses Common Data Types (CDTs) to pass parameters and return values, including objects and complex types, and even cross-language callbacks and dynamic calling conventions for optimization. The indirect interoperability approach of MetaFFI has the significant advantage of requiring only $2n$ mechanisms to support n languages, compared to direct interoperability approaches that need $n^2$ mechanisms. We developed and tested a proof of concept tool interoperating three languages (Go, Python, and Java), on Windows and Ubuntu. To evaluate the approach and the tool, we conducted a user study, with promising results. The MetaFFI framework is available as open source software, including its full source code and installers, to facilitate adoption and collaboration across academic and industrial communities. Full article

Background/Objectives: Cardiac sarcoidosis (CS) is an inflammatory cardiomyopathy with a strong clinical impact on patients affected by the disease and a challenging diagnosis. Methods: This comprehensive narrative review evaluates the role of [18F]fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET)-based radiomics and machine learning (ML) analyses in the assessment of CS. Results: The value of [18F]FDG PET-based radiomics and ML has been investigated for the clinical settings of diagnosis and prognosis of patients affected by CS. Even though different radiomics features and ML models have proved their clinical role in these settings in different cohorts, the clear superiority and added value of one of them across different studies has not been demonstrated. In particular, textural analysis and ML showed high diagnostic value for the diagnosis of CS in some papers, but had controversial results in other works, and may potentially provide prognostic information and predict adverse clinical events. When comparing these analyses with the classic semiquantitative evaluation, a conclusion about which method best suits the final objective cannot be drawn with the available references. Different methodological issues are present when comparing different papers, such as image segmentation and feature extraction differences that are more evident. Furthermore, the intrinsic limitations of radiomics analysis and ML need to be overcome with future research developed in multicentric settings with protocol harmonization. Conclusions: [18F]FDG PET-based radiomics and ML show preliminary promising results for CS evaluation, but remain investigational tools since the current evidence is insufficient for clinical adoption due to methodological heterogeneity, small sample sizes, and lack of standardization. Full article

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region. Full article