Integrating renewable energy into green transportation is critical for achieving sustainable development goals. This study investigates the key factors driving the integration of renewable energy into green transportation systems. Through a comprehensive literature review and expert consultation, 11 drivers were identified and ranked using the Dynamic Grey Relational Analysis (DGRA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Government Subsidies and Incentives, Electric Vehicles and Charging Infrastructure, and Energy Security and Reduced Fuel Dependency were the most important drivers. In addition, Kruskal-Wallis test was used to assess the consistency of rankings between different stakeholder groups. Although there were slight differences between the rankings produced by the DGRA and TOPSIS methods, the top five drivers for both methods remained consistent, underscoring their critical importance. The findings highlight that government interventions, such as subsidies and incentives, play a key role in promoting renewable energy adoption. The development of electric vehicles and charging infrastructure, as well as the need to enhance energy security and reduce dependence on fossil fuels, further highlights the systemic nature of these factors. This study provides a robust framework for policymakers and industry stakeholders to prioritize initiatives and efficiently allocate resources to facilitate the integration of renewable energy sources.

Addressing the nonlinear, fluctuating nature of system data and the insufficient sensitivity of existing grey accumulation to new information, an improved grey Bernoulli model with a damping trend factor (DANGBM(1,N)) is proposed. A damping accumulation operator is introduced, assigning greater weight to new data during preprocessing to enhance sensitivity to recent trends. Combined with the grey Bernoulli model, it better identifies nonlinear characteristics and flexibly adjusts prediction trends, overcoming limitations in existing techniques. Hyperparameters are optimized via particle swarm optimization for adaptability. Applied to China’s high-tech industry output value, the model demonstrates superior predictive performance over five benchmarks, offering an effective method for complex nonlinear small-sample forecasting.

In this study, the complex-order damping accumulated GM(1,1) model (CDAGMz(1,1)) and the complex-order damping accumulated discrete GM(1,1) model (CDADGMz(1,1)) are based on the damping accumulated generating operator and the complex-order accumulated generating operator. By expanding the parameter value range from the real domain to the complex domain, these two new models can better balance the weighting between new and old information, extract more effective features from limited data, and enhance the model accuracy. The relationship between the two models and their respective application scopes was also clarified. An empirical analysis is conducted using China's oil import and export volumes from 2012 to 2024 as the research subject, verifying the applicability and effectiveness of the new models in predicting oil import and export volumes and predicting the oil import and export volumes from 2025 to 2027.

The energy consumption system represents a complex, nonlinear, and chaotic system. Starting from multiple nonlinearities within the energy consumption chaotic system. In this paper leverages the characteristics of the Lorenz chaotic system to establish multiple nonlinear differential equations that embody the features of the energy consumption system. Simultaneously, by utilizing the grey differential information inherent in differential and difference equations, a multivariate grey prediction model based on the energy consumption chaotic system is developed. This model not only organically integrates the nonlinear chaoticity of the system with the prediction accuracy of grey prediction models, enabling the simple grey prediction model to reflect the chaotic nature of the energy consumption system, but also meets the adaptive prediction requirements of the energy consumption system and addresses the challenge of predicting multiple energy sources simultaneously. Subsequently, employing the modeling mechanism of the grey prediction model, parameter estimates for the model are obtained. The differential equation set is solved using the discretization method to derive the model's time-response formula, ultimately yielding key modeling steps. To validate the model's effectiveness, the new model is applied to predict the consumption of natural gas, coal, and crude oil in China. Its validity is verified through three types of experiments: the first involves simulation and prediction for different modeling objects, the second effectively tests the model's robustness, and the third compares the new model with other classical grey models. These three types of experiments demonstrate that the new model can effectively capture the complex nonlinear relationships among various variables in the energy system, exhibiting unique stability and significant advantages, along with precise predictive capabilities. It provides robust data support for the planning, scheduling, and management of energy systems.

Accurate determination of nursing levels for disabled elderly constitutes a critical issue in long-term care systems. Grounded in grey systems theory, this paper proposes a nursing level discriminative method that integrates nursing task decomposition with discrete grey number operations. Drawing upon Work Breakdown Structure from project management, disabled elderly care is decomposed into eight fundamental nursing tasks. Nursing time data are collected through three consecutive days of observation, discrete grey numbers are constructed, and grey possibility functions are designed for level determination. Case analysis involving five disabled elderly from three nursing institutions successfully assigns each case to the corresponding nursing level. The study reveals that possibility function values for adjacent nursing levels are relatively close for certain elderly, indicating their position in boundary regions and suggesting the need for enhanced monitoring and timely level adjustment. The proposed method effectively handles uncertainty in nursing time, provides definitive level determination while identifying boundary cases, and offers decision support for dynamic monitoring and care resource allocation.

Aiming at the problems of scarce data, poor dynamic adaptability of models, and difficulty in uncertainty quantification faced in the prediction of high-temperature creep performance of SiC/SiC ceramic matrix composites for aero-engines, this paper proposes a degradation trajectory model integrating the metabolic GM(1,1) model, stochastic process, and grey cloud theory. By dynamically updating the data sequence, the model captures the creep trend in real time, and uses the grey cloud model to quantify the randomness and fuzziness in the creep process, realizing the cloud droplet distribution characterization of the performance degradation range. Case analysis shows that the proposed model outperforms traditional methods in both prediction accuracy and uncertainty quantification capability, providing an effective tool for the life assessment and reliability design of blade materials under extreme environments.

Attention has emerged as a critical determinant of learning outcomes in an increasingly digital educational landscape. Traditional assessment methods, however, inadequately capture the uncertainty and dynamic fluctuations characteristic of attention data. We developed a Dynamic Nonlinear Grey Relational Analysis (DN-GRA) model integrated with electroencephalography (EEG)neurofeedback technology to enable real-time detection and prediction of attention patterns, thereby facilitating personalized educational interventions. A 12-week randomized controlled trial with 200 primary school students (grades 3-4) revealed that the experimental group significantly outperformed controls across multiple dimensions: attention stability increased 175%, sustained attention duration improved 120%, and average attention values rose 129% (all p < 0.001). Students also demonstrated substantial gains in memory capacity (28.5%), logical reasoning (30.1%), and overall academic performance (effect size d = 0.82). Grey Relational Analysis validation confirmed the effectiveness of personalized intervention strategies, establishing a practical framework for integrating grey system theory (GST) with neurotechnology to advance educational equity and promote sustainable learning outcomes.

The inherent speckle noise in Synthetic Aperture Radar (SAR) images brings difficulty of SAR image segmentation. Especially in the case of high resolution, the real data information of many details is submerged by the speckle noise. A SAR image thresholdsegmentation method based on grey number and improved flower pollination algorithm (SDEFPA) is proposed in this paper.Segmentation threshold can be regarded as an interval grey number, whitening process can be finished via improved flower pollination algorithm (SDEFPA). In SDEFPA, Sobol sequence strategy is used to initialize the population to improve the population uniformity, and differential evolution mutation strategy is introduced to enhance the ability of flower pollination algorithm (FPA) to escape from local optima. The experiment results on the CEC 2017 benchmark functions show that SDEFPA has better optimization accuracy and convergence speed than 5 comparison algorithms. Application of SDEFPA to SAR image segmentation, grey entropy is selected as the fitness function of SDEFPA. Between global pollination and local pollination randomly switching of multiple cyclesby switch probability 0.8, the best flower is gradually close to the optimal threshold. The optimal segmentation is searched quickly by SDEFPA and meanwhile the grey numbers are whitened. Then achieve the final segmented image by whitened threshold. The proposed segmentation method obtains the best effect using the least time compared with contrast segmentation methods. The results demonstrate effectiveness of the proposed method.

This paper is oriented towards investigating the changes in Romanian job market, more specifically focusing on examining the employees’ skills in computers and information technology sector. Initially, 12 representative competences (Cognitive skills, Basic technical skills, Advanced technical skills, Software development, Cloud computing, Databases, Data analysis, Machine Learning & Deep Learning, Cybersecurity, Programming engineering, Soft skills, and Personal skills) were selected, and then a Google Forms�based online questionnaire was created and distributed for assessing the respondents’ skills with respect to the mentioned categories, involving a Likert scale with 5 points. The case study covers the period between November and December 2024 and consists of 154 valid responses from individuals that are either working or looking for a job in the field of computers and information technology. A grey clustering approach was then used for splitting the respondents into multiple categories, considering their core capabilities. Moreover, the actors in the job market were further described based on the information collected from various sources, and a series of hypotheses were made. Finally, a context-specific ABM framework is proposed, including the agent’s types, characteristics, and even possible interconnections, while the use of NetLogo software offers an interface which exposes the identified attributes of the agents under consideration. The insights uncovered in this paper represent essential data for the job market sector, highlighting the most common employees’ skills, underdeveloped competences, along with the current employers’ distribution and key categories of learning suppliers. This information can assist in further expanding the domain and better comprehending how the technology’s evolution influences this area.

Accurately forecasting thermal power generation is essential for China’s sustainable energy planning and green development. This paper proposes a novel multivariable time-delay grey model to forecast China’s thermal power generation from 2025 to 2030. The proposed model combines time-delay effects and dummy variables to capture the complex, nonlinear relationships between thermal power generation and economic drivers under conditions of limited data. By optimizing parameters with the Aquila Optimizer, the proposed model achieves improved adaptability and forecasting accuracy. The proposed model’s fitting MAPE is 0.58%, and the test MAPE is 0.29%, outperforming other comparison models. The forecasting results indicate that China’s thermal power generation may grow unstably through 2030. The growth rate may slow due to the increasing integration of renewable energy sources and the implementation of carbon neutrality policies. It offers referenceable and foresight insights for the Chinese government to support energy security management and future sustainable development.