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This paper aims to effectively reduce CO2 emissions by examining the impact of three distinct incentive and constraint policies on the quality of rating and certification information in China’s green bond issuance market. To accomplish this, the government has implemented incentives, while regulators have introduced constraints to curb the spread of inflated rating and certification information. We build on the integrated rating and certification regulation mechanism by presenting a two-stage Stackelberg game model that involves four key participants: the China Securities Regulatory Commission, local governments, green evaluation and certification agencies, and credit rating agencies. We incorporate environmental effects indicators into the expected utility of rating and certification agencies to investigate the equilibrium conditions under three policy scenarios: a single financial incentive policy, a single regulatory constraint policy, and a combined incentive and constraint policy. The paper employs Stackelberg game theory to analyze how different policies mitigate the occurrence of “inflated” ratings and “greenwashing” in certifications. Numerical analysis is conducted to validate the theoretical findings. Moreover, we assess the impact of these policies on the quality of rating and evaluation information, using data from China’s green bond issuance market between 2016 and 2021. Our research offers valuable management insights and regulatory recommendations for both regulators and local governments.

This study was conducted to examine the effect on airflow of the shape of an urban road green belt in an asymmetrical street canyon. In this paper, the airflow field at pedestrian height in an asymmetrical street with different building height ratios (ASF) on both sides of the street is modeled and simulated using computational fluid dynamics (CFD) software, ANSYS FLUENT, and the flow rate characteristic distribution index and the average airflow intensity index are used to evaluate and analyze the airflow at the pedestrian level. The study shows that: (1) in an empty street scheme with different building ratios, the static wind area is located on the roof of the downstream building; the closer to the ground in a street with an ASF = 1/3, the lower the airflow rate. However, the situation is the opposite of that in other streets (2/3, 3/1, and 3/2). (2) The position of the green belt makes the windward side flow rate in the step-up street higher than that of the leeward side, and the flow rate of the leeward side in the step-down street is higher than that of the windward side. (3) Compared with other green belt forms, the use of two plates and three belts in the incremental street can increase the circumferential sinking at the roofs of the windward side of the street, thereby improving the wind environment in the entire street. The use of one plate, two-belt and three-plate, four-belt scenarios in the step-down street allows the two ends of the corner vortex to carry more airflow into the interior of the street and reduces both the “wind shadow effect” area in the middle of the street and the “air outlet effect” at both ends.

Background Erector spinae plane block (ESPB) is a promising technique for effective analgesia. It is still uncertain if ESPB offers the same opioid-sparing effect as thoracic paravertebral block (PVB) in midline incision for upper abdominal surgery. Methods The study is a prospective, bi-center, randomized, controlled, non-inferior trial. One hundred fifty-eight patients scheduled for upper abdominal surgery will be randomly assigned to receive bilateral ESPB or PVB before surgery. The primary outcome will be the equivalent cumulative analgesia dosage of sufentanil during the surgery, which is defined as the total dosage of sufentanil from anesthesia induction to tracheal extubation. The main secondary outcomes include postoperative complications and the quality of recovery-15 score at 24 h, 48 h, and 30 days after surgery. Discussion This study will assess the opioid-sparing efficacy of ESPB and PVB, complications, and the quality of recovery of two blocks. Trial registration ChiCTR2300073030 ( https://www.chictr.org.cn/ ). Registered on 30 June 2023.

The regulation of bile acid pathways has become a particularly promising therapeutic strategy for a variety of metabolic disorders, cancers, and diseases. However, the hydrophobicity of bile acids has been an obstacle to clinical efficacy due to off-target effects from rapid drug absorption. In this report, we explored a novel strategy to design new structure fragments based on lithocholic acid (LCA) with improved hydrophilicity by introducing a polar “oxygen atom” into the side chain of LCA, then (i) either retaining the carboxylic acid group or replacing the carboxylic acid group with (ii) a diol group or (iii) a vinyl group. These novel fragments were evaluated using luciferase-based reporter assays and the MTS assay. Compared to LCA, the result revealed that the two lead compounds 1a–1b were well tolerated in vitro, maintaining similar potency and efficacy to LCA. The MTS assay results indicated that cell viability was not affected by dose dependence (under 25 µM). Additionally, computational model analysis demonstrated that compounds 1a–1b formed more extensive hydrogen bond networks with Takeda G protein-coupled receptor 5 (TGR5) than LCA. This strategy displayed a potential approach to explore the development of novel endogenous bile acids fragments. Further evaluation on the biological activities of the two lead compounds is ongoing.

In improving agricultural yields and ensuring food security, precise detection of maize leaf diseases is of great importance. Traditional disease detection methods show limited performance in complex environments, making it challenging to meet the demands for precise detection in modern agriculture. This paper proposes a maize leaf disease detection model based on a state-space attention mechanism, aiming to effectively utilize the spatiotemporal characteristics of maize leaf diseases to achieve efficient and accurate detection. The model introduces a state-space attention mechanism combined with a multi-scale feature fusion module to capture the spatial distribution and dynamic development of maize diseases. In experimental comparisons, the proposed model demonstrates superior performance in the task of maize disease detection, achieving a precision, recall, accuracy, and F1 score of 0.94. Compared with baseline models such as AlexNet, GoogLeNet, ResNet, EfficientNet, and ViT, the proposed method achieves a precision of 0.95, with the other metrics also reaching 0.94, showing significant improvement. Additionally, ablation experiments verify the impact of different attention mechanisms and loss functions on model performance. The standard self-attention model achieved a precision, recall, accuracy, and F1 score of 0.74, 0.70, 0.72, and 0.72, respectively. The Convolutional Block Attention Module (CBAM) showed a precision of 0.87, recall of 0.83, accuracy of 0.85, and F1 score of 0.85, while the state-space attention module achieved a precision of 0.95, with the other metrics also at 0.94. In terms of loss functions, cross-entropy loss showed a precision, recall, accuracy, and F1 score of 0.69, 0.65, 0.67, and 0.67, respectively. Focal loss showed a precision of 0.83, recall of 0.80, accuracy of 0.81, and F1 score of 0.81. State-space loss demonstrated the best performance in these experiments, achieving a precision of 0.95, with recall, accuracy, and F1 score all at 0.94. These results indicate that the model based on the state-space attention mechanism achieves higher detection accuracy and better generalization ability in the task of maize leaf disease detection, effectively improving the accuracy and efficiency of disease recognition and providing strong technical support for the early diagnosis and management of maize diseases. Future work will focus on further optimizing the model’s spatiotemporal feature modeling capabilities and exploring multi-modal data fusion to enhance the model’s application in real agricultural scenarios.

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model’s computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection.

Recently, growing evidence of the relationship between G-protein coupled receptor 44 (GPR44) and the inflammation-cancer system has garnered tremendous interest, while the exact role of GPR44 has not been fully elucidated. Currently, there is a strong and urgent need for the development of non-invasive in vivo GPR44 positron emission tomography (PET) radiotracers that can be used to aid the exploration of the relationship between inflammation and tumor biologic behavior. Accordingly, the choosing and radiolabeling of existing GPR44 antagonists containing a fluorine group could serve as a viable method to accelerate PET tracers development for in vivo imaging to this purpose. The present study aims to evaluate published (2000-present) indole-based and cyclopentenyl-indole-based analogues of the GPR44 antagonist to guide the development of fluorine-18 labeled PET tracers that can accurately detect inflammatory processes. The selected analogues contained a crucial fluorine nuclide and were characterized for various properties including binding affinity, selectivity, and pharmacokinetic and metabolic profile. Overall, 26 compounds with favorable to strong binding properties were identified. This review highlights the potential of GPR44 analogues for the development of PET tracers to study inflammation and cancer development and ultimately guide the development of targeted clinical therapies.

In this study, seven thermoresponsive methacrylate terpolymers with the same molar mass (MM) and composition but various architectures were successfully synthesized using group transfer polymerization (GTP). These terpolymers were based on tri(ethylene glycol) methyl ether methacrylate (TEGMA, A unit), n-butyl methacrylate (BuMA, B unit), and 2-(dimethylamino)ethyl methacrylate (DMAEMA, C unit). Along with the more common ABC, ACB, BAC, and statistical architectures, three diblock terpolymers were also synthesized and investigated for the first time, namely (AB)C, A(BC), and B(AC); where the units in the brackets are randomly copolymerized. Two BC diblock copolymers were also synthesized for comparison. Their hydrodynamic diameters and their effective pKas were determined by dynamic light scattering (DLS) and hydrogen ion titrations, respectively. The self-assembly behavior of the copolymers was also visualized by transmission electron microscopy (TEM). Both dilute and concentrated aqueous copolymer solutions were extensively studied by visual tests and their cloud points (CP) and gel points were determined. It is proven that the aqueous solution properties of the copolymers, with specific interest in their thermoresponsive properties, are influenced by the architecture, with the ABC and A(BC) ones to show clear sol-gel transition.

The effects of high-temperature treatment and ball-mill treatment on recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) were investigated in this study by comparing the physical properties such as water absorption, void ratio, density, crushing index and residual paste content of recycled aggregates before and after treatments and the flexural and compressive strengths of concrete/mortar made from 100% RCA/RFA. The results show that the combination of high temperature and ball milling significantly enhanced the physical properties of the recycled aggregates (RA), and improved the mechanical properties of the recycled aggregate concrete/mortar. The mechanism of thermal treatment to RA is that the aggregate and attached mortar show different thermal expansion at high temperature, and the connection between aggregate and residual mortar will be weakened, which makes it easier to separate during grinding. Through the ball milling, frictional collisions between different aggregate particles as well as between aggregate and grinding balls can strip and separate the attached surface mortar. In addition, ball milling improves the appearance and shape of the aggregate, resulting in smoother edges and smoother corners, and breaks up and removes light materials such as mortar adhering to the RA. Besides, the cost calculation shows that the treatment cost of RCA and RFA could be lower than the direct usage cost of natural aggregates. From the perspective of considering the actual ecological benefits and resource utilization, more low-cost treatment methods such as ball milling could be a good option to optimize the RA.

This study aims to investigate the effect of probiotic Lactobacillus paracasei N1115 on Type 2 diabetes mellitus (T2DM) rats. SD rats were randomly divided into 5 groups, including control group (ND), diabetes mellitus (DM) group, and high (10 9 CFU/mL), medium (10 8 CFU/mL), or low (10 7 CFU/mL) doses of probiotics intervention groups. The ND rats were fed a normal diet, and the others were fed a high‐fat diet and injected with streptozotocin (STZ) to build a T2DM model. In the three probiotic intervention groups, rats were given high, medium, or low doses of L. paracasei N1115 by gavage for 12 weeks. Results indicated that probiotics could prevent weight loss and decrease the blood glucose levels of T2DM rats, in a dose‐dependent manner. A high dose of L. paracasei N1115 also led to a significant decline in the inflammatory response by regulating the TLR4/NF‐κB inflammatory pathway and reducing proinflammatory cytokines, such as tumor necrosis factor‐ α (TNF‐α), interleukin‐1β (IL‐1β), and lipopolysaccharide (LPS) ( p < 0.05). Besides, the memory factor cAMP and the cognitive regulatory pathway BDNF/TrkB were increased by high dose of L. paracasei N1115 treatment ( p < 0.05), revealing an enhanced cognitive memory competence. In addition, analysis of the gut microbiome showed that a high dose of L.paracasei N1115 decreased Firmicutes/Bacteroidota ratio and increased the relative abundance of beneficial bacteria, such as unclassified_Lachnospiraceae, Ligilactobacillus, and Lachnospiraceae_NK4A136_group. The microbial gene contents associated with metabolic pathways, nicotinate and nicotinamide metabolism, glycine, serine, and threonine metabolism and the citrate cycle were upregulated under L. paracasei N1115 treatment. In summary, this study elucidated the regulatory effects of L. paracasei N1115 on T2DM in rats and revealed a promising dietary supplement for the treatment of T2DM.