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Both the deformation and rupture characteristics of rocks are related to geomechanics. In this paper, we identify the evaluation indexes related to coalbed methane (CBM) accumulation in strongly deformed strike-slip fault zones considering tectonics and fractures. We found that fault scale, the fault combination, the tectonic stress, the preservation conditions and fractures all have important effects on the CBM distribution. Areas near the large-scale opening faults are unfavourable to the preservation of coalbed methane. The distribution of gas wells with different capacities is influenced by tectonic extension and convergence. A 3D geomechanical method was used to analyse the influence of the ‘ribbon effect’ of strike-slip faults on the CBM distribution. Due to the influence of the ‘ribbon effect’, the tectonic stress presents a plane in situ stress heterogeneity, which in turn will affect the gas well productivity. We also calculated the integrated rupture rate (IF) to characterize the degree of tectonic fracture development in the target coal reservoir. The appropriate fracture development degree can improve the petrophysical properties of the coal reservoirs while maintaining good storage conditions, such that the gas wells can achieve a higher production capacity. This study is of great significance for the enrichment of the geomechanical theory of oil and gas exploration.

In this study, the PM 2.5 and PM 10 concentrations from 367 cities in China were integrated with MODIS-retrieved aerosol optical depth (AOD) and Angstrom exponent (AE) data to explore the relationship between ground-measured surface particle concentrations and remote-sensing aerosol parameters. The impact of meteorological and topographical factors and seasonality were also taken into consideration and the partial least squares (PLS) regression model was adopted to evaluate the effects of surface particulate matter (PM) concentration and meteorological factors on the variation of aerosol parameters. PM concentrations and aerosol parameters all presented strong spatial disparity and seasonal patterns in China. After implementation of stringent clean air actions and policies, both the ground-measured and satellite-retrieved aerosol parameters revealed that the concentrations of suspended particles in China’s cities declined dramatically from 2015 to 2018. The PM/AOD ratio showed conspicuous south–north and west–east differences. The ratio was strongly correlated to meteorological and topographic factors, and it tended to be higher in arid and less polluted regions. Moreover, the dominant factors affecting seasonal PM/AOD ratios varied among China’s five regions. The correlations of daily PM-AOD were always strong in southwest China and in basin terrain (e.g., Sichuan Basin and Tarim Basin). In contrast, the PM-AOD correlation was found to be negative in some cities on the Tibetan Plateau because local relative humidity makes a greater contribution to AOD variation. Since the climate is arid and the ratio of coarse particles (e.g., PM 10 ) is much higher, PM tended to have a significantly negative correlation with AE in northwestern cities. Whereas in many southern cities, PM was positively correlated with AE because of the area’s high relative humidity and aerosol hygroscopic properties.

Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.

Design of catalyst layers (CLs) with high proton conductivity in membrane electrode assemblies (MEAs) is an important issue for proton exchange membrane fuel cells (PEMFCs). Herein, an ultrathin catalyst layer was constructed based on Pt-decorated nanoporous gold (NPG-Pt) with sub-Debye-length thickness for proton transfer. In the absence of ionomer incorporation in the CLs, these integrated carbon-free electrodes can deliver maximum mass-specific power density of 198.21 and 25.91 kW·g Pt −1 when serving individually as the anode and cathode, at a Pt loading of 5.6 and 22.0 µg·cm −2 , respectively, comparable to the best reported nano-catalysts for PEMFCs. In-depth quantitative experimental measurements and finite-element analyses indicate that improved proton conduction plays a critical role in activation, ohmic and mass transfer polarizations.

Small-target detection in Unmanned Aerial Vehicle (UAV) aerial images remains a significant and unresolved challenge in aerial image analysis, hampered by low target resolution, dense object clustering, and complex, cluttered backgrounds. In order to cope with these problems, we present AeroLight, a novel and efficient detection architecture that achieves high-fidelity performance in resource-constrained environments. AeroLight is built upon three key innovations. First, we have optimized the feature pyramid at the architectural level by integrating a high-resolution head specifically designed for minute object detection. This design enhances sensitivity to fine-grained spatial details while streamlining redundant and computationally expensive network layers. Second, a Dynamic Feature Fusion (DFF) module is proposed to adaptively recalibrate and merge multi-scale feature maps, mitigating information loss during integration and strengthening object representation across diverse scales. Finally, we enhance the localization precision of irregular-shaped objects by refining bounding box regression using a Shape-IoU loss function. AeroLight is shown to improve mAP50 and mAP50-95 by 7.5% and 3.3%, respectively, on the VisDrone2019 dataset, while reducing the parameter count by 28.8% when compared with the baseline model. Further validation on the RSOD dataset and Huaxing Farm Drone dataset confirms its superior performance and generalization capabilities. AeroLight provides a powerful and efficient solution for real-world UAV applications, setting a new standard for lightweight, high-precision object recognition in aerial imaging scenarios.

Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2–5 mm) and higher resolution (~ 10 μm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.

Design and fabrication of highly efficient and stable electrocatalysts remain key challenges in green energy technologies such as low-temperature direct liquid fuel cells. Based on in-depth theoretical calculations, here we demonstrate that surface Pd atoms with high coordination numbers (HCNs) can effectively modulate their adsorption energies for CO and OH, and thus achieve very high performance for formic acid electro-oxidation reaction (FAOR). Based on epitaxial coating Pd atomic layers onto nanoporous gold (NPG) thin membranes and a slight further decoration of Au clusters on top, the resulted core-shell structured NPG-Pd-Au electrocatalyst can demonstrate Pd intrinsic and mass activities of 8.62 mA·cm −2 and 27.25 A·mg −1 respectively at the peak potential around 0.33 V versus saturated calomel electrode toward FAOR, which are far better than those of commercial Pd/C catalysts (1.09 mA·cm −2 and 0.32 A·mg −1 ) tested under the same conditions. Moreover, the membrane electrode assemblies based on these low precious metal loading electrodes can achieve an anode Pd power efficiency over 10 W·mg −1 in a direct formic acid fuel cell, which is two orders of magnitude higher than that of the commercial Pd/C. These results provide new inspirations for the development of revolutionary electrodes for energy technologies in a rational manner.

The limited electrical performance of microelectronic devices caused by low inter-particle connectivity and inferior printing quality is still the greatest hurdle to overcome for Aerosol jet printing (AJP) technology. Despite the incorporation of carbon nanotubes (CNTs) and specified solvents into functional inks can improve inter-particle connectivity and ink printability respectively, it is still challenging to consider multiple conflicting properties in mixture design simultaneously. This research proposes a novel hybrid multi-objective optimization method to determine the optimal functional ink composition to achieve low electrical resistivity and high printed line quality. In the proposed approach, silver ink, CNTs ink and ethanol are blended according to mixture design, and two response surface models (ReSMs) are developed based on the Analysis of Variance. Then a desirability function method is employed to identify a 2D optimal operating material window to balance the conflicting responses. Following that, the conflicting objectives are optimized in a more robust manner in the 3D mixture design space through the integration of a non-dominated sorting genetic algorithm III (NSGA-III) with the developed ReSMs and the corresponding statistical uncertainty. Experiments are conducted to validate the effectiveness of the proposed approach, which extends the methodology of designing materials with multi-component and multi-property in AJP technology.

Background The aberrant expression of microRNA-140-5p (miR-140-5p) has been described in gastric cancer (GC). However, the role of miR-140-5p in GC remains unclear. In this study, the prognostic relevance of miR-140-5p in GC was investigated and YES1 was identified as a novel target of miR-140-5p in regulating tumor progression. Methods miR-140-5p level was determined in 20 paired frozen specimens through quantitative real-time PCR, and analyzed in tissue microarrays through in situ hybridization. The target of miR-140-5p was verified through a dual luciferase reporter assay, and the effects of miR-140-5p on phenotypic changes in GC cells were investigated in vitro and in vivo. Results Compared with that in adjacent normal tissues, miR-140-5p expression decreased in cancerous tissues. The downregulated miR-140-5p in 144 patients with GC was significantly correlated with the reduced overall survival of these patients. miR-140-5p could inhibit GC cell proliferation, migration and invasion by directly targeting 3′–untranlated region of YES1. miR-140-5p could also remarkably reduce the tumor size in GC xenograft mice. Conclusions miR-140-5p serves as a potential prognostic factor in patients with GC, and miR-140-5p mediated YES1 inhibition is a novel mechanism behind the suppressive effects of miR-140-5p in GC.

Climate change has a significant impact on vegetation development. While existing studies provide some insights, long-term trend analysis and multifactor driver assessments for China are still lacking. At the same time, research on the future vegetation development under different climate change scenarios needs further strengthening. In response to these issues, this study analyzed China’s normalized difference vegetation index (NDVI) data from 2001 to 2023, exploring vegetation cover trends, driving factors, and predicting the impact of future climate change. Firstly, this study decomposed the time series data into seasonal, trend, and residual components using the Seasonal–Trend decomposition using Loess (STL) decomposition method, quantifying vegetation changes across different climate zones. Partial least squares (PLS) regression analysis was then used to examine the relationship between NDVI and driving factors, and the contribution of these factors to NDVI variation was determined through the variable importance in projection (VIP) score. The results show that NDVI has significantly increased over the past two decades, especially since 2010. Further analysis revealed that vegetation growth is primarily influenced by soil moisture, shortwave radiation, and total precipitation (VIP scores > 0.8). Utilizing machine learning with Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel data, this study predicts NDVI trends from 2023 to 2100 under four emission scenarios (SSP126, SSP245, SSP370, SSP585), quantifying future meteorological factors such as temperature, precipitation, and radiation to NDVI. Findings indicate that under high-emission scenarios, the vegetation greenness in some regions may experience improved vegetation conditions despite global warming challenges. Future land management strategies must consider climate change impacts on ecosystems to ensure sustainability and enhance ecosystem services.