Search Results (11,198)

In the aerospace field, the BT25y titanium alloy is recommended as a candidate material for manufacturing compressor discs and rotor blades of aircraft engines. The influence of hot deformation parameters on the microstructural evolution, recrystallization softening, and globularization mechanism of the BT25y alloy with an initial lamellar structure was studied. Furthermore, the coupling relationship between dynamic recrystallization and lamellar globularization was explored by means of EBSD, SEM, and TEM techniques. The experiment results indicate that the characteristics of initial lamellar α, α/α sub-grain boundaries within α lamellae, and the α/β phase boundary show significant variations due to the formation of equiaxed α grains during hot deformation. As the strain rate increases, the recrystallization mechanism of α phase gradually shifts from CDRX softening characterized by sub-grain evolution and lamellae fracture, to DDRX softening characterized by grain boundary arching and sub-grain boundary bridging. As the deformation temperature increases, the intense thermal activation promotes the accumulation of distortion storage energy, providing enhanced driving force for the occurrence of dynamic recrystallization. The research results will contribute to a deeper understanding of the relationship between dynamic recrystallization and lamellar globularization, providing theoretical guidance for the deformation process optimization and mechanical property control of the BT25y alloy. Full article

This study investigated the distribution patterns of zooplankton species composition and functional groups, their correlations with aquatic environmental factors, and the mechanisms underlying community stability under the influence of regional barriers in arid areas of Xinjiang, China. The aim was to elucidate the ecological processes driving zooplankton communities in artificial aquatic ecosystems in Central Asia. A systematic survey was conducted on water environmental parameters and zooplankton community structures across 10 artificial water bodies, including the southern foot of the Altai Mountains and both northern and southern slopes of the Tianshan Mountains. The survey encompassed physical and nutrient indicators, and the results revealed significant spatial variation among water bodies across regions. Artificial water bodies in the southern Altai Mountains and northern Tianshan Mountains exhibited substantial fluctuations in temperature, dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). In contrast, water bodies in the southern Tianshan Mountains showed less variation in nutrient indicators. Zooplankton identification results indicated marked differences in zooplankton communities across regions, which were further confirmed by cluster analysis and non-metric multidimensional scaling (NMDS). A total of 19 dominant zooplankton species were identified across the three basins, classified into 6 functional groups. The composition of zooplankton functional groups also varied considerably, which may be closely associated with significant fluctuations in nutrient indicators of aquatic environmental factors across regional barriers. Additionally, there were specific differences in zooplankton diversity among the three basins: the SA region ranged from α-mesosaprobic to polysaprobic and β-mesosaprobic; the NT region was classified as β-mesosaprobic; and the ST region ranged between β-mesosaprobic and lightly polluted. These results may be attributed to differences in regional barriers and glacial meltwater conditions. Canonical Correspondence Analysis (CCA) showed that environmental factors collectively explained 71.1% of the variation in species distribution. Exploring the zooplankton species composition and their relationships with aquatic environmental factors under different regional barriers provides a scientific basis for regional water resource management and environmental protection. Full article

Cystic fibrosis (CF) is caused by loss-of-function variants in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride and bicarbonate channel and affects multiple organs, with pancreatic involvement showing very high penetrance. In pancreatic ducts, CFTR drives secretion of alkaline, bicarbonate-rich fluid that maintains intraductal patency, neutralises gastric acid and permits safe delivery of digestive enzymes. Selective impairment of CFTR-dependent bicarbonate transport, even in the presence of residual chloride conductance, is strongly associated with exocrine pancreatic insufficiency, recurrent pancreatitis and cystic-fibrosis-related diabetes. These clinical manifestations are captured by pharmacodynamic anchors such as faecal elastase-1, steatorrhoea, pancreatitis burden and glycaemic control, providing clinically meaningful benchmarks for CFTR-targeted therapies. In this review, we summarise the principal mechanisms underlying pancreatic pathophysiology and the current approaches to clinical management. We then examine in vitro pancreatic duct models that are used to evaluate small molecules and emerging therapeutics targeting CFTR. These experimental systems include native tissue, primary cultures, organoids, co-cultures and microfluidic devices, each of which has its own advantages and limitations. Intact micro-perfused ducts provide the physiological benchmark for studying luminal pH control and bicarbonate (HCO₃⁻) secretion. Primary pancreatic duct epithelial cells (PDECs) and pancreatic ductal organoids (PDO) preserve ductal identity, patient-specific genotype and key regulatory networks. Immortalised ductal cell lines grown on permeable supports enable scalable screening and structure activity analyses. Co-culture models and organ-on-chip devices incorporate inflammatory, stromal and endocrine components together with flow and shear and provide system-level readouts, including duct-islet communication. Across this complementary toolkit, we prioritise bicarbonate-relevant endpoints, including luminal and intracellular pH and direct measures of HCO₃⁻ flux, to improve alignment between in vitro pharmacology and clinical pancreatic outcomes. The systematic use of complementary models should facilitate the discovery of next-generation CFTR modulators and adjunctive strategies with the greatest potential to protect both exocrine and endocrine pancreatic function in people with CF. Full article

To investigate the hydraulic instability mechanisms of low-specific-speed pump–turbines operating in turbine mode, this study experimentally characterized the pressure distribution and pulsation evolution on the guide vanes of a model unit (ns = 28) using an embedded sensor technique. By overcoming the accessibility limitations of traditional measurement methods, this research reveals the distinct pressure response mechanisms on the guide vane Front Side (upstream-facing) and Back Side (runner-facing). The results demonstrate that the time-averaged pressure distribution is highly sensitive to the Guide Vane Opening (GVO). Specifically, pressure on the Front Side increases with GVO, dominated by the improvement of flow pattern and stagnation effect, whereas pressure on the Back Side decreases monotonically, governed by the Bernoulli effect. Increasing the GVO significantly improves pressure uniformity, reducing the surface pressure gradient by 55%. Regarding dynamic characteristics, pressure fluctuation intensity on the Back Side is significantly higher than that on the Front Side. Furthermore, fluctuations are notably amplified near the tongue, confirming that flow distortion induced by the tongue is a key factor driving circumferential non-uniformity. Spectral analysis identifies the Blade Passing Frequency (BPF) as the dominant frequency, verifying Rotor–Stator Interaction (RSI) as the primary excitation source, while the guide vane channel exhibits a significant low-pass filtering effect on high-order harmonics. These findings provide a solid theoretical foundation and data support for the optimal design and stability control of pump–turbine guide vanes. Full article

The synergistic development of digitalization and green transition has become a key driver for promoting China’s high-quality economic development. To elucidate the impact and mechanism of digital–green policy synergy on urban carbon emissions, this paper utilizes the intersection of the “National Big Data Comprehensive Pilot Zones” (BDPZ) and “Low-Carbon City Pilot” (LCCP) programs as a quasi-natural experiment. Based on panel data from 300 prefecture-level cities in China from 2005 to 2023, a multi-period DID model is constructed for empirical research. The empirical results indicate the following: (1) The synergy between digital and green policies significantly curbs urban carbon emissions, and this conclusion remains robust after parallel trend tests and a series of robustness checks. (2) Compared with single digital or green policies, the digital–green synergy exhibits a significantly superior carbon reduction effect. (3) Mechanism analysis reveals that digital–green synergy promotes low-carbon transition primarily through three pathways: driving green technology innovation, promoting the agglomeration of scientific and technological talent, and optimizing the allocation efficiency of capital factors. (4) Heterogeneity analysis reveals stronger emission reduction effects in non-resource-based, eastern, and developed cities, highlighting how structural rigidities and the digital divide constrain the policy’s effectiveness. We suggest strengthening policy integration and adopting differentiated strategies to break path dependence and achieve “Dual Carbon” goals. Full article

HIV-1 entry into host cells culminates in integration of the reverse transcribed double-stranded viral DNA into host genes. Several lines of evidence suggest that intact, or nearly intact, HIV-1 cores—large, ~60 nm-wide structures—pass through the nuclear pore complex (NPC), and that this passage is associated with pore remodeling. Cryo-electron tomography studies support the dynamic nature of NPCs and their regulation by cytoskeleton and ATP-dependent processes. To explore NPC remodeling, we used super-resolution Stochastic Optical Reconstruction Microscopy (STORM) of U2OS cells endogenously expressing nucleoporin 96 tagged with SNAP. Single-molecule localization imaging and computational averaging resolved 8-fold symmetric nuclear pores with an average radius of ~51 nm. Depletion of cellular ATP using sodium azide or antimycin A, previously reported to reduce the size of yeast NPCs, did not significantly alter the nuclear pore radius in U2OS cells. Similarly, stressing the nuclear envelope by hypotonic or hypertonic conditions failed to induce detectable expansion or contraction of NPCs. These results indicate that the NPCs in U2OS cells do not respond to ATP depletion nor mechanical stresses on changes in pore morphology that can be resolved by STORM. Since these cells are infectable by HIV-1, we surmise that direct multivalent interactions between HIV-1 capsid and phenylalanine-glycine nucleoporins lining the pore’s interior drive the core penetration into the nucleus and the associated changes in the pore structure. Full article

With the rapid advancement of autonomous driving technology, Traffic Sign Detection and Recognition (TSDR) has become a critical component for ensuring vehicle safety. However, existing TSDR systems still face significant challenges in accurately detecting partially occluded traffic signs, which poses a substantial risk in real-world applications. To address this issue, this study proposes a comprehensive solution from three perspectives: data augmentation, model architecture enhancement, and dataset construction. We propose an innovative network framework tailored for occluded traffic sign detection. The framework enhances feature representation through a dual-path convolutional mechanism (DualConv) that preserves information flow even when parts of the sign are blocked, and employs a spatial attention module (SEAM) that helps the model focus on visible sign regions while ignoring occluded areas. Finally, we construct the Jinzhou Traffic Sign (JZTS) occlusion dataset to provide targeted training and evaluation samples. Extensive experiments on the public Tsinghua-Tencent 100K (TT-100K) dataset and our JZTS dataset demonstrate the superior performance and strong generalisation capability of our model under occlusion conditions. This work not only advances the robustness of TSDR systems for autonomous driving but also provides a valuable benchmark for future research. Full article

Myosin XI-K plays an important role in cell expansion and polarized growth, acting as a motor protein that drives organelle trafficking and cytoplasmic streaming. To elucidate the molecular mechanisms of myosin XI-K’s role in the polarized growth of cotton fiber, we investigated the interactions between GhXI-K and Rab GTPases in cotton (Gossypium hirsutum). Protein docking analyses based on AlphaFold3 predicted that GhXI-K interacted with eight Rab GTPases. A total of 37 interaction residues were identified in GhXI-K, of which 5 crucial contact residues were located in the globular tail domain (GTD) and 2 were located in the motor domain. Key interaction residues in the Rab GTPases were also found to be located in conserved regions: switch-I and switch-II. Yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays confirmed the predictions and showed that these interactions occur primarily in the GTD and the motor domain. Our findings reveal that GhXI-K interacts with Rab GTPases through both the motor and tail domains, suggesting a synergistic mechanism that facilitates polarized vesicle trafficking in cotton fiber cells. Full article

With the large-scale integration of power electronic converters, non-linear loads, and renewable energy generation, voltage and current waveform distortion in modern power systems has become increasingly severe, making harmonic resonance amplification and non-stationary distortion more prominent. Accurate and robust harmonic-level prediction and detection have become essential foundations for power quality monitoring and operational protection. However, traditional harmonic analysis methods remain highly dependent on pre-designed time–frequency transformations and manual feature extraction. They are sensitive to noise interference and operational variations, often exhibiting performance degradation under complex operating conditions. To address these challenges, a Unified Physics-Transformer-based harmonic detection scheme is proposed to accurately forecast harmonic levels in offshore wind farms (OWFs). This framework utilizes real-world wind speed data from Bozcaada, Turkey, to drive a high-fidelity electromagnetic transient simulation, constructing a benchmark dataset without reliance on generative data expansion. The proposed model features a Feature Tokenizer to project continuous physical quantities (e.g., wind speed, active power) into high-dimensional latent spaces and employs a Multi-Head Self-Attention mechanism to explicitly capture the complex, non-linear couplings between meteorological inputs and electrical states. Crucially, a Multi-Task Learning (MTL) strategy is implemented to simultaneously regress the Total Harmonic Distortion (THD) and the characteristic 5th Harmonic (H5), effectively leveraging shared representations to improve generalization. Comparative experiments with Random Forest, LSTM, and GRU systematically evaluate the predictive performance using metrics such as root mean square error (RMSE) and mean absolute percentage error (MAPE). Results demonstrate that the Physics-Transformer significantly outperforms baseline methods in prediction accuracy, robustness to operational variations, and the ability to capture transient resonance events. This study provides a data-efficient, high-precision approach for harmonic forecasting, offering valuable insights for future renewable grid integration and stability analysis. Full article

In this paper, a new type of actuator was presented. The actuator has two modes, stepping and scanning modes. The stepping mode can achieve large stroke through motion accumulation, while the scanning mode can enable high-resolution driving. The mechanical structure and the operating principles of the actuator were described. The structure and the model of the clamping units and driving unit were designed by theoretical modeling and finite element simulation analysis. Finally, a prototype was fabricated, and the experiment results revealed that the actuator achieves a maximum speed of 367 μm/s and a resolution of 0.02 μm. Full article

Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and combinatorial state spaces, where observation-level density estimation and prediction-error heuristics often become unreliable. In this work, we propose an information-theoretic framework for intrinsically motivated reinforcement learning grounded in the Information Bottleneck principle. Our approach learns compact latent state representations by explicitly balancing the compression of observations and the preservation of predictive information about future state transitions. Within this bottlenecked latent space, intrinsic rewards are defined through information-theoretic quantities that characterize the novelty of state–action transitions in terms of mutual information, rather than raw observation dissimilarity. To enable scalable estimation in continuous and high-dimensional settings, we employ neural mutual information estimators that avoid explicit density modeling and contrastive objectives based on the construction of positive–negative pairs. We evaluate the proposed method on two representative combinatorial routing problems, the Travelling Salesman Problem and the Split Delivery Vehicle Routing Problem, formulated as Markov decision processes with sparse terminal rewards. These problems serve as controlled testbeds for studying exploration and representation learning under long-horizon decision making. Experimental results demonstrate that the proposed information bottleneck-driven intrinsic motivation improves exploration efficiency, training stability, and solution quality compared to standard reinforcement learning baselines. Full article

Oxidative stress from environmental pollutants is linked to chronic degenerative diseases. Research indicates that specific phytochemicals in our diets can reduce and mitigate the harmful effects of pro-oxidant insults on health. However, limited randomized clinical trials show the protective effects of these compounds. This lack of in vivo evidence is partly due to the low bioavailability of these compounds, which can obscure their actual benefits. The present work investigates whether selected dietary phytochemicals are equally effective in activating cellular defense against oxidative stress at low doses. In a previous study, we found that Curcumin (Curc) at a concentration of 1 μM protected human myeloid cells from cytotoxicity induced by pro-oxidant species by activating the expression of Nrf2/ARE-dependent transcripts, including NADPH: quinone oxidoreductase-1 (NQO-1) and heme oxygenase-1 (HO-1). Now, we aim to extend our observation to other natural activators of the Nrf2 pathway, such as Sulforaphane (SFN) and three structurally related molecules belonging to the flavonoid family: Quercetin (Q), Catechin (C), and Fisetin (F). These compounds were applied at low concentrations (1 μM) to assess their antioxidant activity against H₂O₂-induced oxidative stress, their effects on cellular viability, and the capacity to drive the expression of NQO-1/HO-1 in various cellular models. Our findings indicate that low-dose phytochemicals differ in their cytoprotective efficacy, which depends on both dosage and intracellular uptake or metabolism. We propose that only specific natural antioxidants can protect cells from oxidative stress, underscoring the need to clarify the mechanisms behind this selectivity to better design nutraceuticals and functional foods. Full article

Accurately quantifying the sensitivity of alpine vegetation to climate change is a key prerequisite for formulating regional climate change adaptation policies. The sensitivity of the fragile alpine grasslands on the Tibetan Plateau to climate change has received widespread attention. However, the spatiotemporal dynamics and driving mechanisms of this sensitivity are still unclear under continuous warming and wetting. This study, based on MODIS_NDVI and meteorological data from 2000 to 2023, constructed a dynamic Vegetation Sensitivity Index (VSI) framework and integrated Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models with Shapley Additive exPlanations (SHAP) attribution analysis to reveal the spatiotemporal evolution characteristics and driving mechanisms of vegetation sensitivity on the Tibetan Plateau. The results show that (1) the VSI of alpine grasslands exhibited a spatial pattern of higher values in the southwest and lower values in the northeast, with an overall upward trend. Specifically, 56.31% of the region showed an increase in the VSI, with the upward trend being more pronounced in the northern plateau. (2) The dominant role of different climate factors varied regionally; vegetation sensitivity to precipitation increased in the northern plateau, and temperature sensitivity decreased in the central plateau, while sensitivity to solar radiation significantly increased in the central plateau. (3) SHAP attribution analysis indicated that elevation was the core factor driving VSI differentiation, showing a higher sensitivity at higher elevations, with lower growth rates. These findings reveal the dynamic evolution of vegetation sensitivity under the warming and wetting climate trend and its elevation-regulated mechanism, providing important scientific insights for regional ecological adaptation management. Full article

IgA nephropathy (IgAN) is the most common primary chronic glomerular disease worldwide. Its clinical features include proteinuria and complement pathway activation, which are the strongest predictors of progression to renal failure. This disease can occur at any age. Approximately 30–40% of IgAN patients progress to end-stage renal disease (ESRD) within 20–25 years after diagnosis, making it one of the major causes of ESRD. As understanding of the autoimmune development of IgA nephropathy (IgAN) grows, research shows that BAFF and APRIL promote B-cell activation by binding to the receptors TACI, BCMA, and BAFF-R. This results in the overproduction of galactose-deficient IgA1 (Gd-IgA1), which helps drive the progression of IgA nephropathy. B-cell and plasma cell-targeted therapies, such as biologics against BAFF/APRIL, can precisely and effectively improve patient symptoms. Corresponding agents have now been successfully developed and are administered via subcutaneous or intravenous injection. Clinical trials have demonstrated the significant effectiveness of this approach, especially in reducing proteinuria, stabilizing eGFR, and lowering Gd-IgA1 levels. Although current trial data for BAFF/APRIL-targeted biologics in IgA nephropathy are promising, these new treatments need ongoing clinical monitoring for long-term infection risks and potential drug resistance. This article focuses on the application of BAFF/APRIL biologics in the treatment of IgA nephropathy, addressing gaps in existing literature. While prior studies have emphasized the mechanisms of action of these drugs in IgA nephropathy, they have lacked a comprehensive summary of the current status of specific drug research and clinical progress. Full article

As an important industrial organizational form in the era of the digital economy, digital industry agglomeration exerts a profound impact on urban ecological resilience. Using panel data of 281 prefecture-level cities in China from 2011 to 2021, this study measures the level of digital industry agglomeration by means of the location entropy method, and constructs an urban ecological resilience evaluation system based on the “Pressure-State-Response (PSR)” model. It systematically examines the impact effects and action mechanisms of digital industry agglomeration on urban ecological resilience. The results show that: (1) The spatio-temporal evolution of the two presents a gradient pattern of “eastern leadership and central-western catch-up”, and their spatial correlation deepens over time, with the synergy maturity in the eastern region being significantly higher than that in the central and western regions. (2) Digital industry agglomeration significantly promotes the improvement in urban ecological resilience, and this conclusion remains valid after endogeneity treatment and robustness tests. (3) The promotional effect is more prominent in central cities, coastal cities, and key environmental protection cities, whose advantages stem from digital infrastructure and innovation endowments, industrial synergy and an open environment, and the adaptability of green technologies under strict environmental regulations, respectively. (4) Digital industry agglomeration empowers ecological resilience by driving green innovation and improving the efficiency of land resource allocation, while the construction of digital infrastructure plays a positive regulatory role. Full article