Explore the vast range of titles available.

To discuss the coupling coordination relationship among tourism carbon emissions, economic development and regional innovation it is not only necessary to realize the green development of tourism economy, but also great significance for the tourism industry to take a low-carbon path. Taking the 30 provinces of China for example, this paper calculated the tourism carbon emission efficiency based on the super-efficiency Slacks based measure and Data envelope analyse (SBM-DEA) model from 2007 to 2017, and on this basis, defined a compound system that consists of tourism carbon emissions, tourism economic development and tourism regional innovation. Further, the coupling coordination degree model and dynamic degree model were used to explore its spatiotemporal evolution characteristics of balanced development, and this paper distinguished the core influencing factors by Geodetector model. The results showed that (1) during the study period, the tourism carbon emission efficiency showed a reciprocating trend of first rising and then falling, mainly due to the change of pure technical efficiency. (2) The coupling coordination degree developed towards a good trend, while there were significant differences among provinces, showing a gradient distribution pattern of decreasing from east to west. Additionally, (3) the core driving factors varied over time, however, in general, the influence from high to low were as follows: technological innovation, economic development, urbanization, environmental pollution control, and industrial structure. Finally, some policy recommendations were put forward to further promote the coupling coordination degree.

Aiming at solving the effective data delivery and energy hole problem in multi-hop cognitive radio sensor networks (CRSNs), a weighted energy consumption minimization-based uneven clustering (ECMUC) routing protocol is proposed in this paper. For the first time, the impact of control overhead on the network performance is taken into consideration, to be specific, the energy consumption of control overhead is integrated with that of data communication to model the network energy consumption. Through effective transformation and theoretical analysis, cluster radius of each ring is derived by minimizing the network energy consumption and balancing the residual energy among nodes in different rings. Distributed cluster heads (CHs) selection and cluster formation are carried out within this range to control the cluster size and the corresponding energy cost. Expected times for being CHs metric is defined to measure nodes’ energy and spectral potential and help select powerful CHs. Simulation results show that ECMUC protocol is superior to most clustering protocols designed for CRSNs in terms of network surveillance capability and network lifetime, and it is also demonstrated that taking control overhead into consideration is beneficial for improving the network performance.

In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an R2 value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications.

Background and Object. There is a growing body of evidence highlighting the significant role of gut microbiota in various neurological and psychiatric disorders. We performed an evidence mapping to review the association between different microbiota and these disorders and assessed the strength of evidence for these associations. Methods. We searched PubMed, Cochrane Library, and Epistemonikos to identify systematic reviews and meta-analysis (SRs). We searched for neurological diseases and psychiatric disorders, including Alzheimer’s disease (AD), attention deficit hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), autism spectrum disorder (ASD), anorexia nervosa (AN), bipolar disorder (BD), eating disorder (ED), generalized anxiety disorder (GAD), major depressive disorder (MDD), multiple sclerosis (MS), obsessive compulsive disorder (OCD), Parkinson’s disease (PD), posttraumatic stress disorder (PTSD), spinal cord injury (SCI), schizophrenia, and stroke. We used A Measurement Tool to Assess Systematic Reviews (AMSTAR-2) to evaluate the quality of included SRs. We also created an evidence map showing the role of gut microbiota in neurological diseases and the certainty of the evidence. Results. In total, 42 studies were included in this evidence mapping. Most findings were obtained from observational studies. According to the AMSTAR-2 assessment, 21 SRs scored “critically low” in terms of methodological quality, 16 SR scored “low,” and 5 SR scored “moderate.” A total of 15 diseases have been investigated for the potential association between gut microbiome alpha diversity and disease, with the Shannon index and Simpson index being the most widely studied. A total of 12 diseases were investigated for potential link between beta diversity and disease. At the phylum level, Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, and Verrucomicrobia were more researched. At the genus level, Prevotella, Coprococcus, Parabacteroides, Phascolarctobacterium, Escherichia Shigella, Alistipes, Sutteralla, Veillonella, Odoribacter, Faecalibacterium, Bacteroides, Bifidobacterium, Dialister, and Blautia were more researched. Some diseases have been found to have specific flora changes, and some diseases have been found to have common intestinal microbiological changes. Conclusion. We found varied levels of evidence for the associations between gut microbiota and neurological diseases; some gut microbiota increased the risk of neurological diseases, whereas others showed evidence of benefit that gut microbiota might be promising therapeutic targets for such diseases.

Accurate prediction of the Remaining Useful Life (RUL) of Li-ion batteries is essential for safe and efficient energy systems, but it is difficult because sensor data are multivariate and irregularly sampled. Many state-of-the-art deep learning methods are domain-agnostic. For example, T-PATCHGNN uses generic patching that cuts charge–discharge cycles into fragments with little physical meaning, weakening the input signal and limiting learning of true degradation. We propose Bat-T-GNN, which injects domain knowledge at both the input and objective levels. First, Cycle-Aware Patching segments the time series by actual charge–discharge cycles, giving the model coherent, physically meaningful inputs. Second, a Physics-Informed Consistency Loss (PINN-RUL) regularizes training by requiring the final RUL prediction to be consistent with a physically plausible degradation curve learned from the data. This method experimented on public benchmarks show that proposed method significantly outperforms prior methods, including T-PATCHGNN. Ablation studies confirm that both proposed components drive the performance gains, establishing a new state of the art in battery RUL prediction.

Fibrous dysplasia is a benign bone disease characterized by the replacement of normal bone tissue with fibrous tissue, resulting in irregular bone structure. Cases of craniofacial fibrous dysplasia in children associated with Chiari type I malformation and syringomyelia are extremely rare. This case illustrates the complex clinical manifestations of craniofacial fibrous dysplasia along with Chiari type I malformation and syringomyelia, in which surgical intervention significantly improved the prognosis, and follow-up revealed near-complete resolution of the syringomyelia. It offers valuable insights for managing similar cases in the future.

Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations impractical. Few-shot semantic segmentation, which enables effective generalization from limited labeled samples, thus becomes essential for coastal region analysis. In this work, we propose an optimized few-shot segmentation method based on the Segment Anything Model (SAM) with a frozen-parameter segmentation backbone to improve generalization. To address the high visual similarity among coastal vegetation classes, we design a cross-matching module integrated with a hyper-correlation pyramid to enhance fine-grained visual correspondence. Additionally, a self-matching module is introduced to mitigate scale variations caused by UAV altitude changes. Furthermore, we construct a novel few-shot segmentation dataset, OUC-UAV-SEG-2i, based on the OUC-UAV-SEG dataset, to alleviate data scarcity. In quantitative experiments, the suggested approach outperforms existing models in mIoU and FB-IoU under ResNet50/101 (e.g., ResNet50’s 1-shot/5-shot mIoU rises by 4.69% and 4.50% vs. SOTA), and an ablation study shows adding CMM, SMM, and SAM boosts Mean mIoU by 4.69% over the original HSNet, significantly improving few-shot semantic segmentation performance.

Reverse circulation impact drilling has the advantages of high drilling efficiency and less dust, which can effectively form holes in hard rock and gravel layer. As integral reverse circulation drill bits used in the conventional down-the-hole (DTH) hammers are only suitable for specific formations, the whole set of DTH hammer needs to be replaced when drilling different formations. In this paper, several types of split drill bits for different drilling technologies are designed. The flow field characteristics of one of the split drill bits is analyzed based on the computational fluid dynamics (CFD) method, with four technic parameters considered, which are input flow rate, number of inlet holes, angle of injection exhaust holes, and diameter of injection exhaust holes, respectively. Three parameters are selected as indicators to evaluate the rationality and performance of the split drill bit, which are injection exhaust hole outlet mass flow rate, ratio of the mass flow rate out of injection exhaust holes to the whole inlet mass flow rate, and maximum pressure at the upper end of the split drill bit. According to the CFD analysis results, the above four technic parameters influence the flow rate and pressure in different rules. Considering the injection capacity, pressure loss, and bit strength, inlet holes of 10, injection exhaust holes with an angle of 50°, and injection exhaust holes with a diameter of 12 mm are recommended to obtain ideal reverse circulation. Different types of split drill bits were manufactured, and drilling experiments were carried out in unconsolidated formations. The maximum drilling rate can reach 1.5 m/min in the drilling experiments. The split drill bit proposed in this paper exhibits excellent adaptability for reverse circulation drilling in loose formations.

Cancer-associated fibroblasts (CAFs) critically regulate tumor progression, angiogenesis, metastasis, and therapeutic resistance. This study investigated the characteristics of CAFs in glioblastoma (GBM) and developed a CAF-based risk signature to predict patient prognosis. The single-cell RNA sequencing (scRNA-seq) data were sourced from the Gene Expression Omnibus (GEO) database, whereas the bulk RNA-seq datasets were retrieved from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. The Seurat R package processed scRNA-seq data to identify CAF clusters using established markers. Prognostic genes were screened through univariate Cox regression, with Lasso regression constructing the final risk model. A nomogram incorporating clinical parameters was subsequently developed. Immunohistochemical validation was performed using the Human Protein Atlas (HPA) for the signature genes. The qRT-PCR validation was conducted in U251 and HA cells. ScRNA-seq analysis revealed five CAF clusters in GBM, including three prognostically relevant subtypes. Three key genes were refined to construct a risk signature functionally enriched in the the IL6_JAK_STAT3 signaling, P53 pathway, and inflammatory response. The signature correlated strongly with stromal and immune scores. Multivariate analysis confirmed risk signature independent prognostic value ( P  < 0.0001), followed by age ( P  = 0.005). The CAF-derived nomogram demonstrated superior predictive accuracy for 1-/2-year survival compared to clinical factors alone. The signature genes were validated in the HPA database and experimental validation. This study proposes CAF-derived molecular signatures as potential predictors of glioblastoma prognosis worthy of clinical validation. Systematic characterization of CAF heterogeneity may offer insights for interpreting GBM immunotherapy responses, providing a foundation for future exploration of stroma-targeted therapeutic strategies.

Spontaneous coal combustion accounts for more than 90% of mine fires, and at the same time, the ‘dual carbon’ strategy requires fire prevention and extinguishing materials to have both low-carbon and environmentally friendly functions. To meet on-site application needs, a composite gel with fast injection, flame retardant, and CO2 adsorption functions was developed. PVA-PEI-PAC materials were selected as the gel raw materials, and an orthogonal test with three factors and three levels was used to optimize the gelation time parameters to identify the optimal formulation. The microstructure of the gel, CO2 adsorption performance, as well as its inhibition rate of CO, a marker gas of coal spontaneous combustion, and its effect on activation energy were systematically characterized through SEM, isothermal/temperature-programmed/cyclic adsorption experiments, and temperature-programmed gas chromatography. The results show that the optimal gel formulation is 14% PVA, 7% PEI, and 5.5% PAC. The gel microstructure is continuous, dense, and rich in pores, with a CO2 adsorption capacity at 30 °C and atmospheric pressure of 0.86 cm3/g, maintaining over 76% efficiency after five cycles. Compared with raw coal, a 10% gel addition reduces CO release at 170 °C by 25.97%, and the temperature-programmed experiment shows an average CO inhibition rate of 25% throughout, with apparent activation energy increased by 14.96%. The gel prepared exhibited controllable gelation time, can deeply encapsulate coal, and can efficiently adsorb CO2, significantly raising the coal–oxygen reaction energy barrier, providing an integrated technical solution for mine fire prevention and extinguishing with both safety and carbon reduction functions.