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With the accelerated progress of urbanization, there is an increasing occurrence of buried flexible water distribution pipelines subjected to high internal pressure and heavy loads. The probability of damage to these pipelines is magnified due to traffic load-internal pressure coupling action. Hence, investigating the mechanical performance of buried pipelines under such conditions is a topic of significant research importance. The pipe made of acrylate polymer blended with polyvinyl chloride resin for water supply (ABR pipe) is modified from PVC pipe, exhibiting high toughness, low temperature, and impact resistance. Experimental and numerical studies were conducted to investigate the mechanical performance of buried ABR pipe by applying traffic load, internal pressure, and a combined traffic load-internal pressure on the buried ABR pipe. The experimental and numerical anlyses were aimed to study the mechanical performance and deformation characteristics of the buried ABR pipe under varying loading conditions. The research results indicated that the mechanical performance of buried ABR pipe was superior, with the most critical sections occurring at 90° and 180°. The circumferential stress of the ABR pipe increased with the growth of internal pressure and traffic load, and the influence of internal pressure was significantly higher than that of traffic load. Additionally, with the increased pipe diameter to thickness ratio, the circumferential stress of the ABR pipe was significantly decreased. Furthermore, a theoretical calculation model for the buried ABR pipe under soil pressure, traffic load, and internal pressure was established based on the prism load method and Moore’s method. Finally, the circumferential stress calculation theory for buried ABR pipe was proposed based on safety factors. This theory was crucial for enhancing the safety and reliability of buried flexible water distribution pipelines under various load conditions.

Aqueous zinc batteries are ideal candidates for grid-scale energy storage because of their safety and low-cost aspects. However, the production of large-format aqueous Zn batteries is hindered by electrolyte consumption, hydrogen gas evolution and accumulation, and Zn dendrites growth. To circumvent these issues, here we propose an “open” pouch cell design for large-format production of aqueous Zn batteries, which can release hydrogen gas and allow the refilling of the electrolyte components consumed during cell cycling. The cell uses a gel electrolyte containing crosslinked kappa (k)-carrageenan and chitosan. It bonds water molecules and hinders their side reaction with Zn, preventing electrolyte leakage and fast evaporation. As a proof-of-concept, we report the assembly and testing of a Zn | |Zn x V 2 O 5 ·nH 2 O multi-layer “open” pouch cell using the carrageenan/chitosan gel electrolyte, which delivers an initial discharge capacity of 0.9 Ah and 84% capacity retention after 200 cycles at 200 mA g ‒1 , 370 kPa and 25 °C. The production of large-format aqueous Zn batteries is hindered by electrolyte consumption, hydrogen gas evolution and Zn dendrites growth during cycling. Here, the authors propose a specific pouch cell design capable of releasing hydrogen gas and refilling the electrolyte components.

Background Circular RNAs (circRNAs) represent a novel type of regulatory RNA characterized by high evolutionary conservation and stability. CircRNAs are expected to be potential diagnostic biomarkers and therapeutic targets for a variety of malignancies. However, the regulatory functions and underlying mechanisms of circRNAs in triple-negative breast cancer (TNBC) are largely unknown. Methods By using RNA high-throughput sequencing technology, qRT-PCR and in situ hybridization assays, we screened dysregulated circRNAs in breast cancer and TNBC tissues. Then in vitro assays, animal models and patient-derived organoids (PDOs) were utilized to explore the roles of the candidate circRNA in TNBC. To investigate the underlying mechanisms, RNA pull-down, RNA immunoprecipitation (RIP), co immunoprecipitation (co-IP) and Western blotting assays were carried out. Results In this study, we demonstrated that circRNA-CREIT was aberrantly downregulated in doxorubicin resistant triple-negative breast cancer (TNBC) cells and associated with a poor prognosis. The RNA binding protein DHX9 was responsible for the reduction in circRNA-CREIT by interacting with the flanking inverted repeat Alu (IRAlu) sequences and inhibiting back-splicing. By utilizing in vitro assays, animal models and patient-derived organoids, we revealed that circRNA-CREIT overexpression significantly enhanced the doxorubicin sensitivity of TNBC cells. Mechanistically, circRNA-CREIT acted as a scaffold to facilitate the interaction between PKR and the E3 ligase HACE1 and promoted proteasomal degradation of PKR protein via K48-linked polyubiquitylation. A reduced PKR/eIF2α signaling axis was identified as a critical downstream effector of circRNA-CREIT, which attenuated the assembly of stress granules (SGs) to activate the RACK1/MTK1 apoptosis signaling pathway. Further investigations revealed that a combination of the SG inhibitor ISRIB and doxorubicin synergistically inhibited TNBC tumor growth. Besides, circRNA-CREIT could be packaged into exosomes and disseminate doxorubicin sensitivity among TNBC cells. Conclusions Our study demonstrated that targeting circRNA-CREIT and SGs could serve as promising therapeutic strategies against TNBC chemoresistance.

Olfactory cues are considered a new sensory medium that can enhance learning, but the lack of empirical data has hampered their widespread use in educational practice. This requires empirical research to explore the effects of olfactory cues on learning. To address this research need, an experimental research study was conducted among 87 fourth graders from a Chinese elementary school. It explored the innovative design of adding olfactory cues to text materials by examining their effects on retention and schemata construction as learning outcomes, as well as their influence on learners’ cognitive load and learning experience. In this between-subjects design experiment, the experimental group (n = 44) learned text materials with the introduction of olfactory cues, while the control group (n = 43) only learned text materials. After the learning activity, participants were asked to complete the questionnaires, immediate test, and delayed test. The results revealed that the usage of olfactory cues synchronized with text materials can enhance delayed retention, facilitate schemata construction, and improve learner experience without increasing cognitive load. This study confirms the potential of well-designed olfactory cues in educational practice and provides insights for designing and presenting multimedia learning resources.

Network traffic prediction is essential for intelligent resource management in modern transportation systems, but existing methods struggle to simultaneously capture multi-scale temporal patterns, long-range dependencies, and periodic behaviors while maintaining computational efficiency. This paper presents WMF-Traffic (Wavelet-Mamba-Fourier Traffic prediction framework), a novel traffic forecasting approach that synergistically integrates wavelet decomposition, selective state space modeling, and frequency domain processing. The framework introduces four key components: Multi-scale Wavelet Decomposition for hierarchical temporal pattern extraction, Wavelet Traffic Convolution with scale-adaptive mechanisms, Traffic-aware Mamba for efficient long-range dependency modeling, and Fourier Pattern Adjustment for periodic pattern enhancement. WMF-Traffic employs a comprehensive training objective that balances reconstruction accuracy, temporal consistency, and spectral coherence. Extensive experiments on four real-world traffic datasets (METR-LA, PEMS-BAY, PEMS04, PEMS08) demonstrate consistent improvements over state-of-the-art methods, achieving 1.0-1.3% gains in MAE, 0.6-1.1% in RMSE, and 0.2-1.0% in MAPE across different prediction horizons. Ablation studies reveal that Traffic-aware Mamba provides the largest individual contribution (10.2% MAE reduction), while the complete framework achieves up to 27.1% improvement over baseline approaches. The proposed uncertainty-based fusion mechanism further enhances robustness with 3.2-4.1% additional improvements.

Higher-order Weyl semimetals are a family of recently predicted topological phases simultaneously showcasing unconventional properties derived from Weyl points, such as chiral anomaly, and multidimensional topological phenomena originating from higher-order topology. The higher-order Weyl semimetal phases, with their higher-order topology arising from quantized dipole or quadrupole bulk polarizations, have been demonstrated in phononics and circuits. Here, we experimentally discover a class of higher-order Weyl semimetal phase in a three-dimensional photonic crystal (PhC), exhibiting the concurrence of the surface and hinge Fermi arcs from the nonzero Chern number and the nontrivial generalized real Chern number, respectively, coined a real higher-order Weyl PhC. Notably, the projected two-dimensional subsystem with k z  = 0 is a real Chern insulator, belonging to the Stiefel-Whitney class with real Bloch wavefunctions, which is distinguished fundamentally from the Chern class with complex Bloch wavefunctions. Our work offers an ideal photonic platform for exploring potential applications and material properties associated with the higher-order Weyl points and the Stiefel-Whitney class of topological phases. Here, the authors experimentally discover a class of higher-order Weyl semimetal phase in a three-dimensional photonic crystal, exhibiting the concurrence of the surface and hinge Fermi arcs from the nonzero Chern number and the nontrivial generalized real Chern number, respectively, coined a real higher-order Weyl photonic crystals.

Feature selection (FS) can increase the accuracy of forest aboveground biomass (AGB) prediction from multiple satellite data and identify important predictors, but the role of FS in AGB estimation has not received sufficient attention. Here, we aimed to quantify the degree to which FS can benefit forest AGB prediction. To this end, we extracted a series of features from Landsat, Phased Array L-band Synthetic Aperture Radar (PALSAR), and climatic and topographical information, and evaluated the performance of four state-of-the-art FS methods in selecting predictive features and improving the estimation accuracy with selected features. We then proposed an ensemble FS method that takes inro account the stability of an individual FS algorithm with respect to different training datasets used; the heterogeneity or diversity of different FS methods; the correlations between features and forest AGB; and the multicollinearity between the selected features. We further investigated the performance of the proposed stability-heterogeneity-correlation-based ensemble (SHCE) method for AGB estimation. The results showed that selected features by SHCE provided a more accurate prediction of forest AGB than existing state-of-the-art FS methods, with R2 = 0.66 ± 0.01, RMSE = 14.35 ± 0.12 Mg ha−1, MAE = 9.34 ± 0.09 Mg ha−1, and bias = 1.67 ± 0.11 Mg ha−1 at 90 m resolution. Boruta yielded comparable prediction accuracy of forest AGB, but could not identify the importance of features, which led to a slightly greater bias than the proposed SHCE method. SHCE not only ranked selected features by importance but provided feature subsets that enabled accurate AGB prediction. Moreover, SHCE provides a flexible framework to combine FS results, which will be crucial in many scenarios, particularly the wide-area mapping of land-surface parameters from various satellite datasets.

Tropospheric delay is an important error source in global positioning systems (GPS), and the water vapor retrieved from the tropospheric delay is widely used in meteorological research such as climate analysis and weather forecasting. Most zenith tropospheric delay (ZTD) models are presently used as positioning corrections, and few models are used for the estimation of water vapor, especially in Antarctica. Through two blind source separation algorithms (principal component analysis (PCA) and independent component analysis (ICA)), a back-propagation (BP) neural network and a deep learning technique (long short-term memory (LSTM) network), we establish an hourly high-accuracy ZTD model for GPS meteorology using the GPS-ZTD from 52 GPS stations in West Antarctica. Our results show that under the condition in which the principal components (PCs) and independent components (ICs) remain fixed after decomposition, the mean accuracy of the models for West Antarctica using PCA or ICA are better than 10 mm. Compared with the ZTDs from the nonmodeling stations, the mean root mean square (RMS) of the PCA and ICA models are 9.3 and 8.9 mm, respectively, and the correlation coefficients between the GPS-ZTD and model-ZTDs all exceed 90%. The accuracy of the ICA model is slightly higher than that of the PCA model, and the ICs of the ICA model show more consistent spatial responses. The six-hour forecast is the best among the forecast results, with a mean correlation coefficient of 90.6% and a mean RMS of 7.2 mm using GPS-ZTD. The long-term forecast result is significantly inaccurate, as the correlation coefficient between the 24-h forecast and GPS-ZTD is only 63.2%. Generally modest results have been achieved (HSS ≤ 0.38). Furthermore, the forecast accuracy in coastal areas is lower than that in inland areas. Our study confirms that the combined use of ICA and deep learning in ZTD modeling can effectively restore the original signals, and short-term forecasting can be effectively used in GPS meteorology. However, further development of the technology is necessary.

Cancer immunotherapy, especially immune checkpoint therapy, has revolutionized therapeutic options by reactivating the host immune system. However, the efficacy varies, and only a small portion of patients develop sustained antitumor responses. Hence, illustrating novel strategies that improve the clinical outcome of immune checkpoint therapy is urgently needed. N6-methyladenosine (m 6 A) has been proved to be an efficient and dynamic posttranscriptional modification process. It is involved in numerous RNA processing, such as splicing, trafficking, translation and degradation. Compelling evidence emphasizes the paramount role of m 6 A modification in the regulation of immune response. These findings may provide a foundation for the rational combination of targeting m 6 A modification and immune checkpoints in cancer treatment. In the present review, we summarize the current landscape of m 6 A modification in RNA biology, and highlight the latest findings on the complex mechanisms by which m 6 A modification governs immune checkpoint molecules. Furthermore, given the critical role of m 6 A modification in antitumor immunity, we discuss the clinical significance of targeting m 6 A modification to improve the efficacy of immune checkpoint therapy for cancer control.

The influence of ocean chemistry on Early Triassic biotic recovery is poorly understood in the Chaohu Area. Here, we evaluate the influence of ocean chemistry following the Permian–Triassic crisis using pyrite content, δ13Corg, and S isotopic composition of pyrite. The pyrite content, V/(V + Ni) ratio, and S isotopic composition of pyrite in the Early Triassic from the northern Pingdingshan section of the Chaohu area in eastern China reveal recurrent and long-term ocean anoxia and two episodes of oxic conditions that occurred in the earliest Spathian and the late early Spathian. A positive δ13Corg shift of ~4‰ around the Smithian/Spathian boundary (SSB) in the lowermost Spathian was associated with significant biotic recovery, coincident with a positive δ34S excursion of ~25‰ and a low V/(V + Ni) ratio. The results suggest that the oxic conditions contributed to this recovery. Enhanced global ocean circulation during the SSB climate cooling may also have promoted this recovery. Frequent environmental perturbations may have aborted the biotic recovery, although the second episode of oxic conditions occurred in the late early Spathian. Sustained recovery did not appear in the Early Triassic because of the recurrent and long-term ocean anoxia.