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Digital transformation is reshaping agricultural governance of local governments, yet micro-pathways through which policy instruments influence consumer behavior via market mechanisms remain unclear. This study examines the fresh corn industry in Beijing and surrounding areas to explore how local governments employ policy-driven digital marketing strategies to promote agricultural modernization and shape consumer behavior. The research constructs an analytical framework integrating policy instrument theory, multi-level governance, and consumer behavior models. Through surveying 380 consumers, Partial Least Squares Structural Equation Modeling (PLS-SEM) examined policy implementation mechanisms. Findings reveal that policy-supported digital infrastructure (β=0.328, p<0.001) and government-led platform engagement (β=0.296, p<0.001) significantly enhance public service satisfaction, which demonstrates the strongest effect on market participation behavior (β=0.512, p<0.001). Mediation tests indicate indirect effects of policy instruments through satisfaction (0.168 and 0.152) exceed direct effects (0.126 and 0.108), with public service satisfaction playing a crucial mediating role, explaining 52.3% of satisfaction variance and 58.2% of market behavior variance. Regional analysis reveals a 35% gap in policy implementation intensity between urban and rural areas, though 56.3% of consumers frequently use digital channels, indicating positive transformation progress. The study unveils governance innovation pathways of local governments transitioning from direct market intervention to platform empowerment and from technology promotion to service optimization, providing theoretical perspectives for understanding local agricultural governance in the digital era and offering transferable strategies for agricultural digital transformation in other regions. 

Digital transformation is reshaping agricultural governance of local governments, yet micro-pathways through which policy instruments influence consumer behavior via market mechanisms remain unclear. This study examines the fresh corn industry in Beijing and surrounding areas to explore how local governments employ policy-driven digital marketing strategies to promote agricultural modernization and shape consumer behavior. The research constructs an analytical framework integrating policy instrument theory, multi-level governance, and consumer behavior models. Through surveying 380 consumers, Partial Least Squares Structural Equation Modeling (PLS-SEM) examined policy implementation mechanisms. Findings reveal that policy-supported digital infrastructure (β=0.328, p<0.001) and government-led platform engagement (β=0.296, p<0.001) significantly enhance public service satisfaction, which demonstrates the strongest effect on market participation behavior (β=0.512, p<0.001). Mediation tests indicate indirect effects of policy instruments through satisfaction (0.168 and 0.152) exceed direct effects (0.126 and 0.108), with public service satisfaction playing a crucial mediating role, explaining 52.3% of satisfaction variance and 58.2% of market behavior variance. Regional analysis reveals a 35% gap in policy implementation intensity between urban and rural areas, though 56.3% of consumers frequently use digital channels, indicating positive transformation progress. The study unveils governance innovation pathways of local governments transitioning from direct market intervention to platform empowerment and from technology promotion to service optimization, providing theoretical perspectives for understanding local agricultural governance in the digital era and offering transferable strategies for agricultural digital transformation in other regions. 

In the context of agricultural digital transformation, how local government digitalization policies influence enterprise technological innovation has become a critical theoretical and practical issue. This study constructs a multi-level moderated mediation model based on the Technology-Organization-Environment (TOE) framework and Innovation Diffusion Theory to explore how local government digitalization policies affect enterprise AI marketing strategy adoption and market performance in China's fresh corn industry. Using survey data from 128 fresh corn enterprises across 15 provinces and 3,842 consumer questionnaires, we employ Partial Least Squares Structural Equation Modeling (PLS-SEM) for empirical analysis. The findings reveal that: (1) local government digitalization policies significantly promote enterprise AI marketing adoption (β=0.21, p<0.01), which positively impacts market performance (β=0.34, p<0.001), with the model explaining 58.7% of variance (R²=0.587); (2) policy environment characteristics significantly moderate the policy-enterprise relationship, with effects in high-support environments (0.43) being 2.5 times those in low-support environments (0.17); (3) industry competition intensity strengthens the AI adoption-performance relationship (β=0.21, p<0.01); (4) organizational learning capability (indirect effect=0.126) and customer satisfaction (indirect effect=0.187) play partial mediating roles; (5) regional heterogeneity analysis shows policy effects in eastern regions (β=0.38) significantly exceed western regions (β=0.19).This study extends digital governance theory applications in agriculture and provides empirical evidence for local governments to formulate differentiated digital agriculture support policies and for enterprise technological innovation decision-making.

In the context of agricultural digital transformation, how local government digitalization policies influence enterprise technological innovation has become a critical theoretical and practical issue. This study constructs a multi-level moderated mediation model based on the Technology-Organization-Environment (TOE) framework and Innovation Diffusion Theory to explore how local government digitalization policies affect enterprise AI marketing strategy adoption and market performance in China's fresh corn industry. Using survey data from 128 fresh corn enterprises across 15 provinces and 3,842 consumer questionnaires, we employ Partial Least Squares Structural Equation Modeling (PLS-SEM) for empirical analysis.The findings reveal that: (1) local government digitalization policies significantly promote enterprise AI marketing adoption (β=0.21, p<0.01), which positively impacts market performance (β=0.34, p<0.001), with the model explaining 58.7% of variance (R²=0.587); (2) policy environment characteristics significantly moderate the policy-enterprise relationship, with effects in high-support environments (0.43) being 2.5 times those in low-support environments (0.17); (3) industry competition intensity strengthens the AI adoption-performance relationship (β=0.21, p<0.01); (4) organizational learning capability (indirect effect=0.126) and customer satisfaction (indirect effect=0.187) play partial mediating roles; (5) regional heterogeneity analysis shows policy effects in eastern regions (β=0.38) significantly exceed western regions (β=0.19).This study extends digital governance theory applications in agriculture and provides empirical evidence for local governments to formulate differentiated digital agriculture support policies and for enterprise technological innovation decision-making.

Digital transformation is reshaping agricultural governance of local governments, yet micro-pathways through which policy instruments influence consumer behavior via market mechanisms remain unclear. This study examines the fresh corn industry in Beijing and surrounding areas to explore how local governments employ policy-driven digital marketing strategies to promote agricultural modernization and shape consumer behavior. The research constructs an analytical framework integrating policy instrument theory, multi-level governance, and consumer behavior models. Through surveying 380 consumers, Partial Least Squares Structural Equation Modeling (PLS-SEM) examined policy implementation mechanisms. Findings reveal that policy-supported digital infrastructure (β=0.328, p<0.001) and government-led platform engagement (β=0.296, p<0.001) significantly enhance public service satisfaction, which demonstrates the strongest effect on market participation behavior (β=0.512, p<0.001). Mediation tests indicate indirect effects of policy instruments through satisfaction (0.168 and 0.152) exceed direct effects (0.126 and 0.108), with public service satisfaction playing a crucial mediating role, explaining 52.3% of satisfaction variance and 58.2% of market behavior variance. Regional analysis reveals a 35% gap in policy implementation intensity between urban and rural areas, though 56.3% of consumers frequently use digital channels, indicating positive transformation progress. The study unveils governance innovation pathways of local governments transitioning from direct market intervention to platform empowerment and from technology promotion to service optimization, providing theoretical perspectives for understanding local agricultural governance in the digital era and offering transferable strategies for agricultural digital transformation in other regions.

In the context of agricultural digital transformation, how local government digitalization policies influence enterprise technological innovation has become a critical theoretical and practical issue. This study constructs a multi-level moderated mediation model based on the Technology-Organization-Environment (TOE) framework and Innovation Diffusion Theory to explore how local government digitalization policies affect enterprise AI marketing strategy adoption and market performance in China's fresh corn industry. Using survey data from 128 fresh corn enterprises across 15 provinces and 3,842 consumer questionnaires, we employ Partial Least Squares Structural Equation Modeling (PLS-SEM) for empirical analysis.The findings reveal that: (1) local government digitalization policies significantly promote enterprise AI marketing adoption (β=0.21, p<0.01), which positively impacts market performance (β=0.34, p<0.001), with the model explaining 58.7% of variance (R²=0.587); (2) policy environment characteristics significantly moderate the policy-enterprise relationship, with effects in high-support environments (0.43) being 2.5 times those in low-support environments (0.17); (3) industry competition intensity strengthens the AI adoption-performance relationship (β=0.21, p<0.01); (4) organizational learning capability (indirect effect=0.126) and customer satisfaction (indirect effect=0.187) play partial mediating roles; (5) regional heterogeneity analysis shows policy effects in eastern regions (β=0.38) significantly exceed western regions (β=0.19).This study extends digital governance theory applications in agriculture and provides empirical evidence for local governments to formulate differentiated digital agriculture support policies and for enterprise technological innovation decision-making.

With the rapid development of artificial intelligence and computer vision, deep learning has become widely used for aircraft detection. However, aircraft detection is still a challenging task due to the small target size and dense arrangement of aircraft and the complex backgrounds in remote sensing images. Existing remote sensing aircraft detection methods were mainly designed based on algorithms employed in general object detection methods. However, these methods either tend to ignore the key structure and size information of aircraft targets or have poor detection effects on densely distributed aircraft targets. In this paper, we propose a novel multi-task aircraft detection algorithm. Firstly, a multi-task joint training method is proposed, which provides richer semantic structure features for bounding box localization through landmark detection. Secondly, a multi-task inference algorithm is introduced that utilizes landmarks to provide additional supervision for bounding box NMS (non-maximum suppression) filtering, effectively reducing false positives. Finally, a novel loss function is proposed as a constrained optimization between bounding boxes and landmarks, which further improves aircraft detection accuracy. Experiments on the UCAS-AOD dataset demonstrated the state-of-the-art precision and efficiency of our proposed method compared to existing approaches. Furthermore, our ablation study revealed that the incorporation of our designed modules could significantly enhance network performance.

This report investigates prediction on adverse drug reactions (ADR) with kernel and imbalance data mechanisms. The hypothesis is that different types of kernel lead to different prediction results, which suggests deciding the best-fit kernel might be a critical way of improving prediction accuracy. Besides, it was also hypothesized that edge cases in real-life setting would cause imbalance in the dataset, thus further causing inaccuracy in prediction. Similarly, attempting to add class weight to various machine learning models could also be a way to improve prediction accuracy. Hence, these hypotheses are being explored in this study.

Abnormal brain structural connectivity of end-stage renal disease(ESRD) is associated with cognitive impairment. However, the characteristics of cortical structural connectivity have not been investigated in ESRD patients. Here, we study structural connectivity of the entire cerebral cortex using a fiber connectivity density(FiCD) mapping method derived from diffusion tensor imaging(DTI) data of 25 ESRD patients and 20 healthy controls, and between-group differences were compared in a vertexwise manner. We also investigated the associations between these abnormal cortical connectivities and the clinical variables using Pearson correlation analysis and multifactor linear regression analysis. Our results demonstrated that the mean global FiCD value was significantly decreased in ESRD patients. Notably, FiCD values were significantly changed(decreased or increased) in certain cortical regions, which mainly involved the bilateral dorsolateral prefrontal cortex(DLPFC), inferior parietal cortex, lateral temporal cortex and middle occipital cortex. In ESRD patients, we found a trend of negative correlation between the increased FiCD values of bilateral middle frontal gyrus and serum creatinine, urea, parathyroid hormone(PTH) levels and dialysis duration. Only the white matter hyperintensity(WMH) scores were significantly negatively correlated with the global FiCD value in multifactor regression analysis. Our results suggested that ESRD patients exhibited extensive impaired cortical structural connectivity, which was related to the severity of WMHs. A compensation mechanism of cortical structural recombination may play a role in how the brain adapts to maintain optimal network function. Additionally, the serum creatinine, urea and PTH levels may be risk factors for brain structural network decompensation in ESRD patients.

Visual tracking is an important branch in computer vision. In complex scenarios, there exist various interference factors, e.g . background clutter, similar objects etc ., making robust tracking based on correlation filter algorithm still a challenging task. In this paper, a correlation filter algorithm based on a novel adaptive multi-cue fusion strategy was proposed. First, a unified response map evaluation strategy was presented to assess the tracking reliability by combing the average peak correlation energy and the response peak historical information. Second, according to the cue reliability, an adaptive multi-cue fusion strategy was proposed to adaptively fuse two tracking cues, correlation filter and color histogram. The experimental results on OTB-2013 and UAV123 demonstrated that the proposed algorithm achieved competitive performance to the state-of the-art trackers.