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Studying the spatiotemporal distribution pattern of carbon storage, balancing land development and utilization with ecological protection, and promoting urban low-carbon sustainable development are important topics under China’s “dual carbon strategy” (Carbon emissions stabilize and harmonize with natural carbon absorption). However, existing research has paid little attention to the impact of land use changes under different spatial policies on the provincial-scale ecosystem carbon storage. In this study, we established a carbon density database for Liaoning Province and obtained the spatial and temporal distribution of carbon storage over the past 20 years. Then, based on 16 driving factors and multiple spatial policies in Liaoning Province, we predicted land use and land cover changes (LUCC) under three scenarios for 2050 and analyzed the spatiotemporal distribution characteristics and response mechanisms of carbon storage under different scenarios. The results showed that (1) LUCC directly affected carbon storage, with a 35.61% increase in construction land and a decrease in carbon storage of 0.51 Tg over the 20-year period. (2) From 2020 to 2050, the carbon storage varied significantly among the natural trend scenario (NTS), ecological restoration scenario (ERS), and economic priority scenario (EPS), with values of 2112.05 Tg, 2164.40 Tg, and 2105.90 Tg, respectively. Carbon storage in the ecological restoration scenario exhibited positive growth, mainly due to a substantial increase in forest area. (3) The spatial pattern of carbon storage in Liaoning Province was characterized by “low in the center, high in the east, and balanced in the west”. Therefore, Liaoning Province can consider rationally formulating and strictly implementing the spatial policy of ecological protection in the future land planning so as to control the disorderly growth of construction land, realize the growth of ecological land area, effectively enhance carbon storage, and ensure the realization of the goal of “dual carbon strategy”.

The DEAD-box helicase family member DDX3X (DBX, DDX3) functions in nearly all stages of RNA metabolism and participates in the progression of many diseases, including virus infection, inflammation, intellectual disabilities and cancer. Over two decades, many studies have gradually unveiled the role of DDX3X in tumorigenesis and tumour progression. In fact, DDX3X possesses numerous functions in cancer biology and is closely related to many well-known molecules. In this review, we describe the function of DDX3X in RNA metabolism, cellular stress response, innate immune response, metabolic stress response in pancreatic β cells and embryo development. Then, we focused on the role of DDX3X in cancer biology and systematically demonstrated its functions in various aspects of tumorigenesis and development. To provide a more intuitive understanding of the role of DDX3X in cancer, we summarized its functions and specific mechanisms in various types of cancer and presented its involvement in cancer-related signalling pathways.

Younger generations actively use social media to access health information. However, research shows that they also avoid obtaining health information online at times when confronted with uncertainty. This study aims to examine the phenomenon of health information avoidance among Generation Z, a representative cohort of active web users in this era. Drawing on the planned risk information avoidance model, we adopted a qualitative approach to explore the factors related to information avoidance within the context of health and risk communication. The researchers recruited 38 participants aged 16 to 25 years for the focus group discussion sessions. In this study, we sought to perform a deductive qualitative analysis of the focus group interview content with open, focused, and theoretical coding. Our findings support several key components of the planned risk information avoidance model while highlighting the underlying influence of cognition on emotions. Specifically, socioculturally, group identity and social norms among peers lead some to avoid health information. Cognitively, mixed levels of risk perception, conflicting values, information overload, and low credibility of information sources elicited their information avoidance behaviors. Affectively, negative emotions such as anxiety, frustration, and the desire to stay positive contributed to avoidance. This study has implications for understanding young users' information avoidance behaviors in both academia and practice.

Macular pathologies can cause significant vision loss. Optical coherence tomography (OCT) images of the retina can assist ophthalmologists in diagnosing macular diseases. Traditional deep learning networks for retinal disease classification cannot extract discriminative features under strong noise conditions in OCT images. To address this issue, we propose a multi-scale-denoising residual convolutional network (MS-DRCN) for classifying retinal diseases. Specifically, the MS-DRCN includes a soft-denoising block (SDB), a multi-scale context block (MCB), and a feature fusion block (FFB). The SDB can determine the threshold for soft thresholding automatically, which removes speckle noise features efficiently. The MCB is designed to capture multi-scale context information and strengthen extracted features. The FFB is dedicated to integrating high-resolution and low-resolution features to precisely identify variable lesion areas. Our approach achieved classification accuracies of 96.4% and 96.5% on the OCT2017 and OCT-C4 public datasets, respectively, outperforming other classification methods. To evaluate the robustness of our method, we introduced Gaussian noise and speckle noise with varying PSNRs into the test set of the OCT2017 dataset. The results of our anti-noise experiments demonstrate that our approach exhibits superior robustness compared with other methods, yielding accuracy improvements ranging from 0.6% to 2.9% when compared with ResNet under various PSNR noise conditions.

Airborne LiDAR bathymetry (ALB) has proven to be an effective technology for shallow water mapping. To collect data with a high point density, a lightweight dual-wavelength LiDAR system mounted on unmanned aerial vehicles (UAVs) was developed. This study presents and evaluates the system using the field data acquired from a flight test in Dazhou Island, China. In the precision and accuracy assessment, the local fitted planes extracted from the water surface points and the multibeam echosounder data are used as a reference for water surface and bottom measurements, respectively. For the bathymetric performance comparison, the study area is also measured with an ALB system installed on the manned aerial platform. The object detection capability of the system is examined with placed small cubes. Results show that the fitting precision of the water surface is 0.1227 m, and the absolute accuracy of the water bottom is 0.1268 m, both of which reach a decimeter level. Compared to the manned ALB system, the UAV-borne system provides higher resolution data with an average point density of 42 points/m2 and maximum detectable depth of 1.7–1.9 Secchi depths. In the point cloud of the water bottom, the existence of a 1-m target cube and the rough shape of a 2-m target cube are clearly observed at a depth of 12 m. The system shows great potential for flexible shallow water mapping and underwater object detection with promising results.

The pastures in China’s pastoral areas have a \"small and scattered\" distribution, which results in overloading and overgrazing, ecological degradation, and other problems. These problems have constrained the sustainable development of grassland animal husbandry. Governments at all levels have implemented measures to promote the transfer of pastureland for herders, which has become a meaningful way to optimize the allocation of pastureland resources and improve the ecological environment in the second instance. In order to deeply explore the influence of pasture turn-out on herders’ traditional lifestyle and to promote the rational utilization of pastureland in pastoral areas, the study is based on 437 interview data of herders in Inner Mongolia and Xinjiang. It adopts the Binary Logit model to analyze the influence and mechanism of herders’ willingness to turn out of pastureland in terms of ethnic sentiment and social differentiation. The results show that (1) Nomadic and mutual aid sentiments significantly and negatively affect herders’ willingness to transfer pasture. The stronger the national sentiment, the lower the willingness to transfer pasture and the more cautious the behaviour of transferring pasture. (2) The proportion of pasture income and the proportion of pasture labour significantly and negatively affect the herders’ willingness to transfer pasture. Specifically, the increase in herders’s family pasture income and the proportion of pasture labour will reduce the willingness to transfer pasture. The conclusion still holds after further robustness checks by introducing instrumental variables, changing the regression model, and replacing the sample size. (3) At the macro level, the government needs to take advantage of the situation and tap the positive role of national sentiment in rural revitalization; at the micro level of herders, it needs to enhance their employability, enrich income channels, stimulate the endogenous dynamics of social differentiation in the development of herders’ livelihoods, and realize the effective matching of pasture resources.

Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is the most successful conducting polymer in terms of practical application. It possesses many unique properties, such as good film forming ability by versatile fabrication techniques, superior optical transparency in visible light range, high electrical conductivity, intrinsically high work function and good physical and chemical stability in air. PEDOT:PSS has wide applications in energy conversion and storage devices. This review summarizes its applications in organic solar cells, dye-sensitized solar cells, supercapacitors, fuel cells, thermoelectric devices and stretchable devices. Approaches to enhance the material/device performances are highlighted.

In order to solve the problem of “attaching importance to the construction while neglecting the evaluation” in construction of continuous rigid frame bridges in China, a post-construction evaluation system was established by taking the elasticity modulus of concrete, frictional loss of prestress and loss of prestress as the indexes, and a three-level fuzzy comprehensive post-design evaluation model was built. The continuous rigid frame bridge on the Chongqing-Hechuan segment of Lanzhou-Haikou Expressway was taken as an example to conduct a comprehensive post-design evaluation. The results showed that the studied bridge was evaluated and categorized into Category II in the bridge design evaluation system, indicating a good design and operation state of this bridge. Suggestions and countermeasures were proposed for the design of continuous rigid frame bridges, with a vision to provide a basis for construction of bridges of the same type.

Early classification of brain tumors is the key to effective treatment. With advances in medical imaging technology, automated classification algorithms face challenges due to tumor diversity. Although Swin Transformer is effective in handling high-resolution images, it encounters difficulties with small datasets and high computational complexity. This study introduces SparseSwinMDT, a novel model that combines sparse token representation with multipath decision trees. Experimental results show that SparseSwinMDT achieves an accuracy of 99.47% in brain tumor classification, significantly outperforming existing methods while reducing computation time, making it particularly suitable for resource-constrained medical environments.

The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate.