This study aims to investigate the coupling and harmonization between land ecological security (LES) and high-quality agricultural development (HAD) in the Han River Basin (HRB), China, with the objective of promoting harmonious coexistence between agriculture and ecosystems. Using 17 cities in the HRB as the research objects, an evaluation index system of two systems, LES and HAD, was constructed, analyzed, and evaluated via projective tracer modeling for multiple intelligent genetic algorithms (MIGA-PTM). The degree of coupling coordination (DCC) was used to quantitatively evaluate the coupling coordination development status of the two systems, the obstacle model (OM) was used to identify the main influencing factors, and the gray predictive model first-order univariate model (GM (1, 1)) was used to predict the DCC of the LES and HAD from 2025 to 2040. The results show the following: (1) the LES and HAD levels of the 17 cities in the HRB tended to increase during the study period, and there was a large gap between cities; (2) the spatial distributions of the DCCs of the LES and HAD in the HRB were uneven, with high values in the southern and low values in the central and northern parts, and the overall degree of coupling tended to fluctuate. The overall DCC showed a fluctuating upward trend; (3) the degree of obstacles, per capita water resources, greening coverage, and rate of return on financial expenditure are the main influencing factors; and (4) the prediction results of GM (1, 1) indicate that the LES and HAD of the HRB will be close to reaching the intermediate stage of coupling in 2035. This research offers critical insights into sustainable development practices that facilitate the alignment of agricultural growth with ecological preservation.

Digital technology drives global industrial transformation. The synchronized development of organizational digital transformation and innovation systems is pivotal in corporate strategy and governmental governance. The dynamic interaction mechanisms among digital economy, government innovation policy, and regional innovation capacity remain insufficiently explored. This study employs panel data from 15 prefecture-level cities within the Yangtze River Delta urban agglomeration, spanning the years 2012 to 2020, and uses the panel vector autoregression (PVAR) model to investigate the interrelationships among the digital economy, government innovation preferences (the government’s supportive attitude and policy inclination towards innovative activities in the fields of science and technology as well as economic development), and regional innovation capacity. This research emphasizes the impact of the digital economy on regional innovation capacity and the influence of government innovation preferences on regional innovation capacity. The findings indicate that both the digital economy and government innovation preferences significantly enhance technological and product innovation, with this effect being particularly pronounced in the initial stages but diminishing over time. The three dimensions of the digital economy exert varying effects on technological and product innovation. Specifically, digital application has the most substantial impact on technological innovation, whereas infrastructure has a more pronounced effect on product innovation. Overall, the influence of government innovation preferences on technological and product innovation is less significant than that of the digital economy. The intensity of government innovation preferences has a greater impact than does the structure of government innovation preferences; however, in the long term, the structure of government innovation preferences can exert a more stable and sustainable influence. This study offers policy implications for constructing an innovation ecosystem driven by the synergy between government and market forces, particularly in optimizing data governance systems and planning sustainable transformation pathways, which hold practical value.

The Han River Ecological Economic Belt (HREEB) has a substantial amount of water resources; however, its distribution is uneven, and issues such as seasonal and engineering water shortages are prevalent. This necessitates a thorough assessment of the current water resource situation and trends in water resource carrying capacity (WRCC) to provide scientific support for the rational allocation of water resources. This study employed the RAGA-PP model to establish a WRCC evaluation index system composed of four subsystems: water resources, economy, society, and the ecological environment. The WRCC of the 17 major cities in the HREEB was evaluated from 2008 to 2022. The differentiation method was introduced to compare the reliability of the RAGA-PP model with three evaluation methods: the entropy weight TOPSIS method, the rank sum ratio method, and the principal component analysis method. In addition, an obstacle degree model was introduced to analyze the factors influencing WRCC enhancement. The results indicated the following. (1) In the differentiation test of the four models, the RAGA-PP model was found to have the highest differentiation value, and the results showed that it was more reliable in the WRCC evaluation of HREEB. (2) WRCC in the HREEB underwent significant changes between 2008 and 2022. (3) The WRCC in Shiyan and Wuhan, which are located in the eastern part of the HREEB, were high in Hubei, low in four cities in Henan, and satisfactory in three cities in Shaanxi. (4) The carrying capacity of the subsystems of the cities in the HREEB exhibited fluctuating changes with obvious internal variations. (5) The problems in the WRCC guideline layer were consistent across all cities in the HREEB, with limited per capita water resources being the primary issue in the indicator layer. Assessing WRCC is essential for achieving sustainable water resource use and high-quality regional development.

The Jianghan Plain (JHP) is a key agricultural area in China where efficient agricultural water use (AWUE) is vital for sustainable water management, food security, environmental sustainability, and economic growth. This study introduces a novel AWUE prediction model for the JHP, combining a BP neural network with the Sparrow Search Algorithm (SSA) and an improved Tent Mixing Algorithm (Tent-SSA-BPNN). This hybrid model addresses the limitations of traditional methods by enhancing AWUE forecast accuracy and stability. By integrating historical AWUE data and environmental factors, the model provides a detailed understanding of AWUE’s spatial and temporal variations. Compared to traditional BP neural networks and other methods, the Tent-SSA-BPNN model significantly improves prediction accuracy and stability, achieving an accuracy (ACC) of 96.218%, a root mean square error (RMSE) of 0.952, and a coefficient of determination (R²) of 0.9939, surpassing previous models. The results show that (1) from 2010 to 2022, the average AWUE in the JHP fluctuated within a specific range, exhibiting a decrease of 0.69%, with significant differences in the spatial and temporal distributions across various cities; (2) the accuracy (ACC) of the Tent-SSA-BPNN prediction model was 96.218%, the root mean square error (RMSE) was 0.952, and the coefficient of determination (R²) value was 0.9939. (3) Compared with those of the preoptimization model, the ACC, RMSE, and R² values of the Tent-SSA-BPNN model significantly improved in terms of accuracy and stability, clearly indicating the efficacy of the optimization. (4) The prediction results reveal that the proportion of agricultural water consumption has a significant impact on AWUE. These results provide actionable insights for optimizing water resource allocation, particularly in water-scarce regions, and guide policymakers in enhancing agricultural water management strategies, supporting sustainable agricultural development.

Water resources are fundamental to sustaining life, fostering social development, and maintaining ecological balance. This study focuses on Anhui Province (AP) as the research area, employing 22 indicators from the Water-Socio-Ecological (WSE) framework for the water resource-water environment carrying capacity (WR-WECC) of AP. The WR-WECC of AP is assessed via differential evolution projection pursuit modeling (DE-PPM). Additionally, the degree of coupled coordination model (DCCM) is utilized to analyze the coordinated development among the municipalities of AP, whereas the obstacle degree model is employed to identify the primary obstacles affecting the enhancement of the WR-WECC and to forecast them via autoregressive composite moving averages. The findings of the study are as follows: (1) WR-WECC in AP showed a steady upward trend, and the water, socio-economic, and ecological subsystems showed a fluctuating upward trend, with ES increasing the fastest. The overall WR-WECC of each city shows a fluctuating upward trend, and the spatial gap narrows, with Southern Anhui (SA) > Central Anhui (CA) > Northern Anhui (NA). (2) The DCC of the WSE carrying capacity of AP also shows a fluctuating upward trend, gradually transitioning from barely coordinated to well coordinated. The DCC level of the WR-WECC in SA is better than that in CA and NA, and the growth rate is greater than that in CA and NA, whereas the DCC level in CA is better than that in NA, and the DCCs of the three major regions show an upward trend. (3) The degree of obstruction at the criterion level of AP’s WR-WECC basically maintains the following order: WS > SS > ES. In the indicator layer, the water supply modulus, water production modulus, and proportion of tertiary industry are the main obstacle factors restricting the enhancement of the WR-WECC of AP. (4) The prediction results for 2025–2040 indicated that the WR-WECC level and the three subsystem levels of AP showed a continuous increasing trend. Measuring WR-WECC plays a crucial role in regional sustainable development.

This study investigated the integration of detection and communication techniques. First, the fractional-order Fourier transform (FRFT) is introduced, and the golden section method, parabolic interpolation, and Brent method are applied to search for the optimal fractional-order domain to accurately estimate the parameters of the linear frequency modulation (LFM) signal. Second, the three search algorithms and the performance of the integrated sensing and communication waveform are simulated. The Brent method improves the parameter searching efficiency by approximately 30% compared with the golden section method; the bit error ratio (BER) of the integrated LFM signal can reach 10⁻⁴ with a signal-to-noise ratio (SNR) of 3 dB. The results show that the integrated waveform can realize the detection function with guaranteed communication performance. An anti-frequency sweeping interference method based on the fractional domain matching order was also carried out to optimize the detection performance of the integrated waveform. Through the analysis of the difference-frequency signal under frequency sweeping interference, two methods, direct filtering, and pairwise cancellation filtering, are used to suppress the interference signal and detect the target distance. The simulation evaluated the detection performance of the two methods under different signal-to-interference ratios (SIR) and filter widths. The simulation results show that the pairwise cancellation filtering suppresses the frequency sweeping interference by 4–6 dB more than the direct filtering with an SIR ≤ −15 dB. Both filtering methods can correctly extract the target position information under frequency sweeping interference with a low signal-to-interference ratio (SIR). In conclusion, this study provides an effective solution for parameter estimation optimization and frequency-sweeping interference suppression for FRFT-based sensing communication systems.