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This study employs an improved fractional-order grey multivariable convolution model (FGMC(1,N,2r)) to predict abalone aquaculture output in Fujian, Shandong, and Guangdong. By integrating fractional-order accumulation (r1, r2) with a particle-swarm-optimization (PSO) algorithm, the model addresses limitations of handling multivariable interactions and sequence heterogeneity within small-sample regional datasets. Grey relational analysis (GRA) first identified key factors exhibiting the strongest associations with production: abalone production in Fujian and Shandong is predominantly influenced by funding for aquatic-technology extension (GRA degrees of 0.9156 and 0.8357, respectively), while in Guangdong, production was most strongly associated with import volume (GRA degree of 0.9312). Validation confirms that FGMC(1,N,2r) achieves superior predictive accuracy, with mean absolute percentage errors (MAPE) of 0.51% in Fujian, 3.51% in Shandong, and 2.12% in Guangdong, significantly outperforming benchmark models. Prediction of abalone production for 2024–2028 project sustained growth across Fujian, Shandong, and Guangdong. However, risks associated with typhoon disasters (X6 and import dependency (X5) require attention. The study demonstrates that the FGMC(1,N,2r) model achieves high predictive accuracy for regional aquaculture output. It identifies the primary drivers of abalone production: technology-extension funding in Fujian and Shandong, and import volume in Guangdong. These findings support the formulation of region-specific strategies, such as enhancing technological investment in Fujian and Shandong, and strengthening seed supply chains while reducing import dependency in Guangdong. Furthermore, by identifying vulnerabilities such as typhoon disasters and import reliance, the study underscores the need for resilient infrastructure and diversified seed sources, thereby providing a robust scientific basis for production optimization and policy guidance towards sustainable and environmentally sound aquaculture development.

As global sustainability imperatives increase, understanding how green finance policies and technological innovation influence corporate environmental performance has become a relevant issue. This study examines the impact of green finance on corporate environmental practices, particularly focusing on how innovation enhances sustainable energy transitions. A difference-in-differences (DID) approach was employed. This research compares corporate environmental performance before and after the implementation of green finance policies across treated and control groups. This method allows for isolating the effect of green finance by controlling for temporal and individual factors, providing robust insights into policy efficacy. Our findings indicate a statistically significant improvement in environmental performance, particularly among larger, state-owned enterprises in China’s eastern regions. The findings also underscore the moderating role of innovation in optimizing green finance outcomes. Finally, important implications for policymakers aiming to drive corporate sustainability are offered.

Resource, environmental, and ecological issues have become major constraints to the development of many regions. The Yellow River Basin is an important barrier for maintaining ecological security in northern China, but it has been impacted by problems such as severe soil erosion and declining biodiversity. The rational construction of ecological security patterns is important to enhance ecosystem functions and maintain regional ecological security. In this study, a comprehensive ecological security assessment system was constructed by selecting ecosystem service importance, ecological sensitivity, and landscape connectivity to assess the ecological security of Xiliu Ditch, an ecologically fragile region of the Inner Mongolia section of the Yellow River Basin in China. The assessment results showed significant spatial heterogeneity, with medium- and low-security value areas dominating, while high-security value areas accounted for only 18.7% of the study area. Seventeen ecological sources were identified from the high-security areas, which were mainly composed of grassland, woodland, and water bodies, most of which are distributed in the southern part of the study area. Twenty ecological corridors were selected by the minimum cumulative resistance model and gravity model and classified into 15 construction corridors and 5 potential corridors. Forty-six ecological nodes were defined, including twenty strategic points, nine potential strategic points, and seventeen break points. On this basis, we constructed an ecological security pattern of “two belts, three cores, six zones, multiple corridors and multiple nodes” and proposed corresponding ecological governance measures. This study explores the ecological security pattern at the small watershed scale, which helps to realize the fine management of the Xiliu Ditch basin and, on this basis, can provide scientific support for the ecological protection and sustainable development of the Yellow River basin. In addition, the ecological security assessment system proposed in this study can provide new ideas for the construction of ecological security patterns in similar ecologically fragile areas around the globe.

Mangrove ecosystems play a critical role in sequestering heavy metals pollutants, yet the dynamics of heavy metals accumulation during mixed litter decomposition remain poorly understood. This study investigated the seasonal and species-specific variations in heavy metals accumulation during the decomposition of Kandelia obovata (KO) and Avicennia marina (AM) leaf litter mixtures in a subtropical mangrove forest in the Jiulong River Estuary, Fujian, China. Using the litterbag technique, we monitored eight heavy metals (V, Cr, Ni, Cu, Zn, As, Se, Cd) across three mixing ratios (KO:AM = 1:2, 1:1, 2:1) in summer and winter. Results revealed that V concentrations were influenced by both season and litter ratio, with higher KO proportions enhancing V accumulation in summer but reducing it in winter. In contrast, Cr, Ni, Cu, As, Se, and Cd were primarily regulated by litter ratios: KO-dominated mixtures promoted Cr and Ni accumulation, while AM-dominated mixtures favored Cu, As, Se, and Cd. Zn exhibited the highest variability and was unaffected by season or ratio. Total organic carbon (TOC) and carbon/metal (C/M) ratios significantly correlated with reduced bioavailability of most heavy metals, whereas total nitrogen (TN) and C/N ratios showed no consistent relationship. The heavy metals accumulation index (MAI) indicated higher accumulation in summer than in winter, with the highest MAI observed in the KO:AM = 2:1 treatment group during summer (MAI = 1.36), whereas winter decomposition slowed accumulation rates. These findings highlight the dual regulatory roles of species composition and environmental factors in mangrove heavy metals cycling, offering critical insights for ecological risk assessment and contaminated soil remediation strategies in coastal ecosystems.

Background Due to better functional outcomes, pylorus-preserving gastrectomy (PPG) has been widely applied for early gastric cancer (EGC) patients as an alternative to distal gastrectomy (DG). However, controversies still persist regarding the surgical efficacy and oncological safety of PPG. Methods Original studies comparing PPG and DG for EGC were searched in PubMed, Embase, and the Cochrane Register of Controlled Trials up to December 2019. The weight mean difference, standardized mean difference, or odds risk was used to calculate the short-term and long-term outcomes between the two groups. Results Twenty-one comparative studies comprising 4871 patients (1955 in the PPG group and 2916 in the DG group) were enrolled in this systematic review and meta-analysis. PPG showed longer hospital day, decreased harvested lymph nodes, and more delayed gastric emptying. However, PPG had the benefits of lower incidence of anastomosis leakage, early dumping syndrome, gastritis and bile reflux, and better recovery of total protein, albumin, hemoglobin, and weight. No difference was found in operative time, blood loss, and overall complications. Moreover, the long-term survival and recurrence rate were similar in two groups. Conclusion Owing to the non-inferiority of surgery and oncology outcomes and the superiority of function outcomes in PPG, we revealed that PPG can be clinically applicable instead of DG in EGC. However, more high-quality comparative studies and randomized clinical trials would be required for further confirmation.

Assessing Leaf Nitrogen Content (LNC) is critical for evaluating crop nutritional status and monitoring growth. While Unmanned Aerial Vehicle (UAV) remote sensing has become a pivotal tool for nitrogen monitoring at the field scale, current research predominantly relies on uni-modal feature variables. Consequently, the integration of multidimensional feature information for nitrogen assessment remains largely underutilized in existing literature. In this study, the four types of feature variables (two kinds of spectral indices, color space parameters and texture features from UAV images of RGB and multispectral sensors) were extracted from three dimensions, and crop nitrogen-sensitive feature variables were selected by GCA (Gray Correlation Analysis), followed by one fused deep neural network (DNN-F2) for remote sensing monitoring of rice nitrogen and a comparative analysis with five common machine learning algorithms (RF, GPR, PLSR, SVM and ANN). Experimental results indicate that the DNN-F2 model consistently outperformed conventional machine learning algorithms across all three growth stages. Notably, the model achieved an average R2 improvement of 40%, peaking at the rice jointing stage with R2 of 0.72, RMSE of 0.08, and NRMSE of 0.019. The study shows that the fusion of multidimensional feature information from UAVs combined with deep learning algorithms has great potential for nitrogen nutrient monitoring in rice crops, and can also provide technical support to guide decisions on fertilizer application in rice fields.

Ischemic stroke (IS) is an acute neurological disorder causing brain dysfunction, with high mortality and disability. PANoptosis is a synchronized sort of regulated cell demise that combines the characteristics of pyroptosis, apoptosis, and necroptosis. However, its role in ischemic stroke remains unclear. We downloaded the ischemic stroke-related microarray dataset GSE58294 from the Gene Expression Omnibus (GEO) database and identified Differentially expressed PANoptosis-related genes (DE-PRGs). We utilized three algorithms to identify diagnostic DE-PRGs, constructed a nomogram for diagnostic modeling, and evaluated their diagnostic performance with receiver operating characteristic (ROC) analysis. Additionally, we develop interaction networks linking genes with miRNAs, transcription factors, and drugs. We also analyzed gene expression in different cell subgroups using the GSE174574 single-cell dataset. We applied consensus clustering to classify stroke samples into subtypes and compared immune microenvironment differences. Ultimately, we verified the gene expression patterns of candidate markers through the MCAO model. We pinpointed seven diagnostic DE-PRGs, with CASP1, PIK3R5, AVEN, and PSMC3 showing significant protein expression differences. Single-cell transcriptome analysis revealed the link between stroke and immune cells. Furthermore, consensus clustering revealed two clusters with unique immune infiltration patterns and functional characteristics. Our study may provide new theoretical insights for the early diagnosis and targeted therapy of IS and offer support for the clinical application of PANoptosis in IS.

In the transmission line inspection operation, the flying robotic arm with good maneuverability can replace the manpower to complete a variety of scenes that are in danger or difficult to reach by manpower for grasping operations. In this paper, based on the D-H matrix, the tandem robotic arm is initially modeled, combined with the Lagrange equation and the characteristics of the robotic arm, to construct the robotic arm dynamics model. Construct the visual servo frame of the flying robotic arm and define the coordinate system of the visual servo system. Get the center point of the grasping frame by grasping the target, establish the camera coordinate system, and realize the position-based visual servo control. Drawing on the predatory action of the bald eagle in biology, set the grasping strategy of the flying robotic arm. Verify the application effect of visual servo in UAV robotic arm grasping through simulation experiments. The random motion speed of the UAV in three-dimensional space is analyzed, and the maximum speeds of roll, pitch and yaw of the target in the motion process are about 1.311 rad/s, 0.825 rad/s, and 1.239 rad/s. In the estimation accuracy of the flying robotic arm's position, the overlap between the true value of the UAV's position and the trajectory of the estimation value is high, and the x-axis only appears smaller errors in 15s, 26s, and 32s, respectively, at 15s, 26s and 32s. There is a small error of 0.013, 0.003, 0.002m, respectively, and the model has a high accuracy in estimating the position of the UAV.

China is a major goose-raising country, and the geese industry plays a significant role in animal husbandry. Therefore, goose growth performance (body weight) is a critical topic. Goose gut microbiota influences weight gain by regulating its energy metabolism and digestion. Additionally, the impact of cecal microbial community structure on goose growth and development, energy metabolism, and immunity has been examined. However, most studies have used different additives or feeds as variables. Improving the understanding of the dynamic changes in gut microbial communities in geese of different body weights during their growth and development and their correlation with the host’s body weight is necessary. In this study, the cecal microbiota of healthy Yangzhou geese with large (L) and small (S) body weights, all at the same age (70 days old) and under the same feeding conditions, were sequenced using 16S rRNA. The sequencing results were annotated using QIIME2 (classify-sklearn algorithm) software, and the linkET package was used to explore the correlation between intestinal microorganisms and the body weight of the Yangzhou goose (Spearman). At the phylum level, the Firmicutes/Bacteroidetes ratio in the large body weight group was approximately 20% higher than that in the small body weight group, with Bacteroidetes and Firmicutes exhibiting a highly significant negative correlation. At the genus level, Bacteroides constituted the most abundant microbial group in both groups, although the Prevotellaceae_Ga6A1_group exhibited a higher abundance in the large than the small weight group. Spearman correlation analysis and the linkET package were used to analyze the correlation between cecal microflora and production performance indicators that showed significant differences between the two groups and showed that birth weight was significantly positively correlated with Deferribacterota at the phylum level. At the genus level, leg and chest muscle weights exhibited significant positive correlations with Prevotellace-ae_Ga6A1_group, suggesting its critical role in promoting the growth and development of goose leg and chest muscles. A significant negative correlation was observed between [Ruminococ-cus]_torque and Prevotellaceae_Ga6A1_group. These findings offer a crucial theoretical foundation for the study of gastrointestinal microorganisms and provide insights into the development and formulation of poultry probiotics.

Continuous theta burst stimulation (cTBS) is a specific paradigm of repetitive transcranial magnetic stimulation (rTMS) with an inhibitory effect on cortical excitability for up to 60 min after less than 1 min of stimulation. The right posterior superior temporal gyrus (pSTG), homotopic to Wernicke's area in the left hemisphere, may be a potential stimulation target based on its critical role in semantic processing. The objective of this study was to explore whether cTBS over the right pSTG can promote language improvements in aphasic patients and the underlying mechanism. A total of 34 subjects with aphasia were randomly assigned to undergo 15 sessions of either 40-s inhibitory cTBS over the right pSTG (the cTBS group) or sham stimulation (the sham group), followed by 30 min of speech and language therapy. Subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI), and the aphasia quotient (AQ) of the Chinese version of the Western Aphasia Battery (WAB) was calculated before and after the intervention. This randomized controlled trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR210052962). After treatment, the language performance of the cTBS group was higher than that of the sham group in terms of the WAB-AQ score ( = 0.010) and the WAB scores for auditory comprehension ( = 0.022) and repetition ( = 0.035). The fractional amplitude of low-frequency fluctuations (fALFF) was significantly decreased in the pars triangularis of the inferior frontal gyrus (IFG), right middle frontal gyrus, right thalamus, and left cerebellar crus I. Clusters in the left orbitofrontal cortex exhibited increased fALFF. The change in WAB comprehension scores were significantly correlated with the change in the fALFF of the right IFG pars triangularis in both groups. Greatly increased functional connectivity was observed between the right pars triangularis and left paracingulate gyrus and between the right pSTG and right angular gyrus and the posterior cingulate gyrus with pre-and post-treatment between the two groups. Our findings indicate that cTBS of the right pSTG may improve language production by suppressing intrinsic activity of the right fronto-thalamic-cerebellar circuit and enhancing the involvement of the right temporoparietal region.