Nantong Science and Technology Bureau

ABSTRACT As a novel class of nitrogen‐containing heterocyclic compounds, imidazopyridine uracils serve as the core scaffold in this study, where 16 derivatives were designed and synthesized with oxazosulfuron as the lead compound. The chemical structures of the target compounds were fully characterized and confirmed by proton nuclear magnetic resonance spectroscopy ( 1 H NMR ), carbon‐13 nuclear magnetic resonance spectroscopy ( 13 C NMR ), and high‐resolution mass spectrometry ( HRMS ). Nilaparvata lugens , Plutella xylostella , and Mythimna separata were selected as the test insects, and the insecticidal activities of the target compounds were determined in this research. Especially, compound 8a showed a mortality rate of 77.8% against N. lugens at 10 mg/L, which was superior to the activity of oxazosulfyl under the same conditions (51.9% mortality at 10 mg/L). In addition, compound 9j exhibited a mortality rate of 25.0% against M. separata at a concentration of 1 mg/L, which was superior to that of oxazosulfyl under the same conditions (0% mortality at 1 mg/L). This study is intended to provide a theoretical reference for the subsequent research and industrial development of this class of novel insecticides.

Detecting ships using Synthetic Aperture Radar (SAR) in coastal areas is still difficult due to the impact of coherent speckle noise from the ocean surface, complex land clutter and having multi-scale target representations in the radar imagery. Most of the existing ship detection algorithms lose important target features during downsampling and have difficulty recovering those features through upsampling, resulting in a high number of false detections and missed detections. In this work, we present a new ship detection algorithm called Synergistic Star-Attentive Network with Dynamic Feature Refinement (S2D-Net). First, we create a new backbone called Multi-scale PCCA-StarNet to generate robust feature representations. Within the backbone we implement a Progressive Channel-Coordinate Attention (PCCA) mechanism to create a synergy between global channel filtering and adaptive coordinate locking to decouple ship textures from granular speckle noise. Second, we create a Dynamic Feature Refinement Neck. We develop a content-aware dynamic upsampler called DySample to replace conventional interpolation to improve fidelity of the upsampled feature of small targets. Further, we design a Star-PCCA Feature Aggregation module which fuses features together. Using star-operations and the PCCA mechanism, this module refines semantic features and removes background clutter while aggregating features across multiple scales. Third, we develop a Lightweight Shared Convolutional Detection Head with Quality Estimation (LSCD-LQE). The LSCD-LQE decreases parameter redundancy by using shared convolutional layers and adds a localization quality estimation branch. Therefore, the LSCD-LQE effectively reduces false positive detections through alignment of classification scores with localization quality based on Intersection over Union (IoU) in difficult coastal environments. Our experimental results, using the SSDD and HRSID datasets, show that S2D-Net produces results comparable to representative ship detection algorithms. In particular, on the challenging HRSID inshore subset, our proposed method achieved a mean average precision (mAP) of 82.7%, which is 6.9% greater than the YOLOv11n baseline ship detection algorithm. These results demonstrate that S2D-Net is superior at detecting small coastal vessels and mitigating the detrimental effects of the nearshore complex environment on the performance ship detection using SAR.

For MRI brain tumor image segmentation, it is necessary to have high real-time and accurate edge localization. Therefore, we propose a yolov8n based on dynamic serpentine convolution and multi-feature attention method (DMA-YOLOV8n). The method combines dynamic serpentine convolution and multi-feature attention mechanism, which can effectively adapt to different brain tumor tissue edge morphology changes and more accurately segmented to obtain brain tumor and locate its edge position. First, dynamic serpentine convolution is used to replace standard convolution in C2f. module. Then, drawing on the idea of skip connection in U-Net model, multi- feature fusion is used to connect multilayer sampling information to retain more feature details and improve edge segmentation accuracy. Finally, dual attention mechanism is added to multilayer feature fusion to pay more attention to brain tumor tissue. DMA-YOLOV8n is applied to brain MRI images from Kaggle_3M dataset. Experimental results show the method has mAP50: 0.806 and mAP50:95: 0.490.

Eddy current coupler is a non-contact power transmission device. But during the operation of eddy current coupler, eddy current losses are continuously generated in the conductor, leading to temperature rise of components and affecting its performance. A transient analysis model under electromagnetic-thermal coupling is established to predict temperature rise and transmission performance. First, for the electromagnetic analysis the three dimensions vector magnetic potential method is used to calculate the eddy current loss power of the conductor and the relationship between the eddy current loss power and the time is obtained. Subsequently, the structure of eddy current coupler is equivalent to a series of concentric rings to form a T-equivalent thermal circuit. Taking the eddy current loss of the conductor as the heat source, an equivalent thermal network model is established to compute the nodal temperature rise of each component. Obviously, the nodal temperature rise is transient. Finally, temperature rise is then fed back to adjust the conductor's electrical conductivity and PMs' remanent magnetism, with iterative calculations repeated until convergence. The transient thermal model which is relative to time enables real-time prediction and updates parameters via temperature feedback. The transient matrix formula is used to solve the variation laws of nodal temperature rise and transmission torque with time, followed by experimental verification. The model results are in good agreement with the experimental results. This transient analysis model provides effective theoretical support for the thermal analysis of eddy current coupler. • Eddy current loss power formula of eddy current coupling. • Transient temperature rise model of the node of eddy current coupling. • Transient temperature rise effect on the transmission performance.

Abstract Background Patients with depression often exhibit persistent low mood, anhedonia, and social withdrawal, accompanied by reduced physical activity and impaired social functioning. These factors interact to further exacerbate symptom maintenance and relapse risk. Although pharmacotherapy and psychotherapy play a central role in clinical care, some patients still face challenges such as insufficient motivation, low social engagement, and reliance on single-intervention pathways during rehabilitation, which limit the overall nursing outcomes. In recent years, multidimensional intervention strategies integrating physical activity, structured education, and social interaction have gained increasing attention. Physical education can improve bodily arousal and emotional regulation, while social activities help alleviate feelings of isolation and enhance a sense of support. However, systematic nursing research on the synergistic effects of these two approaches remains limited. To address this, the study employs a multidimensional intervention framework, using randomization and multi-time watch evaluations to investigate the therapeutic effects and underlying mechanisms of nursing interventions combining physical education and social activities for depression patients. Methods The study enrolled 342 patients meeting the diagnostic criteria for depressive disorder who completed baseline assessments and were randomly assigned to one of three 12-week nursing interventions: (1) an integrated physical education and social activities intervention group (n = 116), which included structured exercise guidance, health education, and group social activities; (2) a standalone physical education nursing group (n = 112); and (3) a conventional nursing control group (n = 114). Key outcome measures included severity of depressive symptoms, emotional regulation ability, social functioning level, and subjective sense of support. These indicators were evaluated at baseline, intervention completion, and 3-month follow-up. Statistical analysis employed a mixed-effects model to compare the efficacy of different nursing strategies and used regression analysis to examine the predictive role of changes in emotional regulation and social support in symptom improvement. Results The experimental results demonstrated that, compared to the single physical education group and the conventional nursing group, the multidimensional intervention group exhibited a significant reduction in depressive symptoms at the end of the intervention (p<.001, d = 0.53), with sustained improvement maintained during the follow-up phase (p=.003). Mechanistic indicators revealed that the multidimensional intervention group showed a significant enhancement in emotional regulation capacity (p=.005, d = 0.44), along with concurrent improvements in social functioning and subjective sense of support (p=.008, d = 0.40). Further analysis revealed that enhanced emotional regulation capacity (p=.02) and increased sense of social support (p=.01) could significantly predict lower levels of depressive symptoms during the follow-up phase. This indicates that emotional regulation and social support may serve as important mediating pathways for the long-term efficacy of multidimensional nursing interventions. Discussion Multidimensional nursing interventions based on physical education and social activities can significantly improve the emotional state of patients with depression, demonstrating favorable sustained effects during follow-up. Studies indicate that multidimensional integrated interventions help compensate for the limitations of single-nursing models, serving as an important supplement to long-term care and rehabilitation management for depression. Future research could further optimize the combination ratios of different intervention elements and explore their application value in community and primary care systems.

Positive psychological changes in cancer patients have been shown to positively influence disease progression and enhance quality of life. Based on this, the present study explores the psychological dynamics facilitating post-traumatic growth (PTG) in cervical cancer patients, specifically examining the roles of self-disclosure and marital intimacy in this process. This was a descriptive qualitative study.Twelve patients with cervical cancer were recruited from Nantong, China to participate in this study. Data were collected between October 2022 and August 2023 and analyzed using Colaizzi's seven-step analysis. In this study, six thematic keywords were extracted: "Traumatic reactions", "Psychological breakdown", "Need for self-disclosure", "Enhanced marital intimacy", "Cognitive restructuring and life changes", and "Current needs and future expectations". Both self-disclosure and marital intimacy played pivotal roles in facilitating PTG among cervical cancer patients. Nurses should focus on fostering positive psychological changes in cervical cancer patients-specifically, self-disclosure and marital intimacy play significant roles in facilitating post-traumatic growth. This suggests that fostering self-disclosure and marital intimacy could be important therapeutic goals to enhance psychosocial well-being and quality of life in cervical cancer patients.