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The progression of Chronic gastritis seems to follow a pattern from chronic non-atrophic gastritis (CNAG) to chronic atrophic gastritis (CAG) to cancer, so it is particularly important to block key targets in disease progression. A gene that synthesizes secreted phospholipase A2, attracted our attention. To study whether phospholipase A2 group 10 (PLA2G10) in epithelial cells promote the progression of CNAG to CAG through the transforming growth factor-β (TGF-β) signaling. We used RNA microarray and single-cell RNA sequencing datasets for bioinformatics analysis. The effects of PLA2G10 were verified by in vivo and in vitro experiments. The in vivo experiments used SD rats to establish a CNAG model for PLA2G10 and TGF-β intervention to observe the effects on gastric mucosal inflammation. In vitro experiments were conducted using human gastric mucosal epithelial cells (GES-1) for similar interventions. PLA2G10 inhibition led to the downregulation of TGF-β expression and attenuated the inflammatory response of the gastric mucosa. And the blockade of TGF-β signalling delayed the progression of CNAG to CAG, as evidenced by a reduction in inflammatory cell infiltration, a more regular cellular arrangement, and a reduction in collagen deposition. Our study shows that PLA2G10 plays a key role in the progression of chronic gastritis and highlights the important role played by the TGF-β signalling pathway in this process.

The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

Precise tuning of dielectric constants (εr) in oxide glasses is critical for high‐frequency devices in 5G/6G systems, where εr directly governs signal propagation efficiency. A machine learning framework combining data augmentation and physicochemical descriptor integration is developed to address data scarcity. Validated pseudo‐labels are generated via ensemble learning, expanding the dataset from 1503 to 11,029 compositions without distributional shift. The XGBoost model trained on the augmented dataset achieved superior accuracy, with an R2 of 0.96 and an MSE of 0.14. For prediction tasks on unseen data, it reduced the error rate by 48% compared to the non‐augmented model and improved generalization performance by 43% over GlassNet. B2O3 and SiO2 are identified as εr suppressors and BaO and TiO2 as enhancers through SHAP analysis, aligning with network former/modifier roles. Cation‐specific polarizabilities are derived via Clausius–Mossotti regression (R2 = 0.909). Integration of physicochemical descriptors (coordination number and bond strength) enables transferable predictions for Y2O3 and La2O3 containing glasses, with mean deviation 2.46%–4.76%. Crucially, structural descriptors dominate polarizability with 69.9% feature importance, establishing network engineering as the optimal design paradigm. A data‐driven pathway for rational dielectric glass development is thus established. Data augmentation and physicochemical descriptors achieve precise dielectric constant prediction in oxide glasses, reducing error by 72%. Structural descriptors (69.9% importance) establish glass network engineering as the key optimization pathway, validated in novel Y2O3/La2O3 glasses.

The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations of complex control architectures and low positioning accuracy of conventional offset actuators for rotary steering drilling tools, a novel three hydraulic cylinder synchronous steering offset actuator driven by a drilling fluid rotary valve distributor, along with its dedicated control strategy, is proposed. Laboratory experiments and numerical simulations are performed to analyze the piston displacement characteristics of the three hydraulic cylinder under different drilling fluid flow rates and rotary valve rotational speeds. The results demonstrate that the proposed actuator exhibits controllable piston displacement behavior. The simulated and experimental data show consistent variation tendencies with a relative error of less than 8%, thus validating the reliability of the proposed numerical model. Increasing the flow rate from 1 to 1.5 L/s increases the cycle-averaged peak-to-peak piston displacement by 14.5 mm, while raising the rotational speed from 60 rpm to 120 rpm reduces it by 25.3 mm, corresponding to a dogleg severity variation of approximately 1.9–3.1°/30 m. Piston displacement deviations are mainly attributed to valve port machining tolerance, drilling fluid compressibility, pipeline pressure loss, and internal leakage, and these discrepancies are exacerbated as the rotary valve speed or flow rate increases. Finally, optimization strategies for improving synchronization performance are proposed, thereby providing theoretical and technical support for the engineering implementation and parameter optimization of the proposed actuator.

Robots complete tasks in dangerous and extreme environments can significantly enhance people's life safety. Given the complexity and variability of indoor fire accidents, the traditional artificial potential field (APF) method faces challenges in executing the task of path planning. As two types of deep reinforcement learning (DRL) with APF methods, in this paper we leverage an on‐value method deep Q‐learning network (DQN–APF) and an on‐policy method deep deterministic policy gradient (DDPG‐APF) to address the path planning problem. For the purpose of validation upon both the algorithm's efficiency and generalisation, we take Harbin Geographic Information Industrial Park in Heilongjiang Province of China as our research area and incorporate Microsoft HoloLens devices as APF builders for simulating potential fields. Experiments show that both optimisation algorithms can significantly enhance the capabilities on path planning in complex environments. In six random obstacles environments, the DDPG‐APF is better than the DQN–APF method, with a 14.4% higher efficiency. Furthermore, for the generalisation of the algorithm, DDPG‐APF requires less time to plan a more efficient path than DQN–APF, respectively. The experimental results indicate that both optimisation algorithms effectively enable path planning for robots in indoor fire accidents and demonstrate good generalisation abilities. For the context of indoor fire accidents, this paper presents two optimised algorithms on DRL with APF, including an on‐policy method DDPG‐APF algorithm and an on‐value method DQN–APF algorithm, leveraging reinforcement learning's reward and punishment mechanism to address the local optimisation problem of the APF method in complex environments.

This study aimed to investigate whether the beneficial effects of PCA on chondrocyte senescence are mediated through the regulation of mitophagy. Chondrocyte senescence plays a significant role in the development and progression of knee osteoarthritis (OA). The compound protocatechuic aldehyde (PCA), which is abundant in the roots of Salvia miltiorrhiza, has been reported to have antioxidant properties and the ability to protect against cellular senescence. To achieve this goal, a destabilization of the medial meniscus (DMM)-induced mouse OA model and a lipopolysaccharide (LPS)-induced chondrocyte senescence model were used, in combination with PINK1 gene knockdown or overexpression. After treatment with PCA, cellular senescence was assessed using Senescence-Associated β-Galactosidase (SA-β-Gal) staining, DNA damage was evaluated using Hosphorylation of the Ser-139 (γH2AX) staining, reactive oxygen species (ROS) levels were measured using Dichlorodihydrofluorescein diacetate (DCFH-DA) staining, mitochondrial membrane potential was determined using a 5,5',6,6'-TETRACHLORO-1,1',3,3'-*. TETRAETHYBENZIMIDA (JC-1) kit, and mitochondrial autophagy was examined using Mitophagy staining. Western blot analysis was also performed to detect changes in senescence-related proteins, PINK1/Parkin pathway proteins, and mitophagy-related proteins. Our results demonstrated that PCA effectively reduced chondrocyte senescence, increased the mitochondrial membrane potential, facilitated mitochondrial autophagy, and upregulated the PINK1/Parkin pathway. Furthermore, silencing PINK1 weakened the protective effects of PCA, whereas PINK1 overexpression enhanced the effects of PCA on LPS-induced chondrocytes. PCA attenuates chondrocyte senescence by regulating PINK1/Parkin-mediated mitochondrial autophagy, ultimately reducing cartilage degeneration. Graphical Abstract

Silicate glass-ceramics were prepared by adding 40 wt% granite wastes. The effects of CaO/MgO (C/M) molar ratio on microstructure and mechanical properties of glass-ceramics were investigated. With C/M ratio increasing, the crystallization behavior changed from bulk crystallization to surface crystallization with heat treatment at 800 °C. However, bulk crystallization occurred in all samples when crystallized at both 850 and 900 °C. The content of forsterite and tainiolite initially increased and then decreased, while diopside and kalsilite increased when heated at 850 °C. For 900 °C, the increase of C/M ratio promoted the precipitation of diopside rather than forsterite and tainiolite, and interlocked plate crystals abundantly appeared with C/M ratio ≥ 0.14. The values of Vickers hardness for samples crystallized at 850 and 900 °C increased initially followed by a decrease, while the values of fracture toughness showed the opposite trend. The glass-ceramic with C/M ratio 0.065 heated at 900 °C showed relatively high Vickers hardness ((5.7 ± 0.14) GPa) and excellent fracture toughness ((3.55 ± 0.14) MPa·m 1/2 ).

In this work, the structure, viscosity and ion-exchange process of Na 2 O-MgO-Al 2 O 3 -SiO 2 glasses with different Al 2 O 3 /SiO 2 molar ratios were investigated. The results showed that, with increasing Al 2 O 3 /SiO 2 ratio, the simple structural units Q 1 and Q 2 transformed into highly aggregated structural units Q 3 and Q 4 , indicating the increase of polymerization degree of glass network. Meanwhile, the coefficient of thermal expansion decreased from 9.23×10 −6 °C −1 to 8.88×10 −6 °C −1 . The characteristic temperatures such as melting, forming, softening and glass transition temperatures increased with the increase of Al 2 O 3 /SiO 2 ratio, while the glasses working temperature range became narrow. The increasing Al 2 O 3 /SiO 2 ratio and prolonging ion-exchange time enhanced the surface compressive stress (CS) and depth of stress layer (DOL). However, the increase of ion exchange temperature increased the DOL and decreased the CS affected by stress relaxation. There was a good linear relationship between stress relaxation and surface compressive stress. Chemical strengthening significantly improved the hardness of glasses, which reached the maximum value of (622.1 ° 10) MPa for sample with Al 2 O 3 /SiO 2 ratio of 0.27 after heat treated at 410 °C for 2 h.

Cordierite-based glass–ceramics for LTCC applications were prepared by traditional sintering method. And the effects of BaO addition on the crystallization, structure and dielectric properties were investigated by differential scanning calorimetry (DSC), scanning electron microscopy (SEM), X-ray diffractometer (XRD), dilatometer and impedance instrument. The DSC curves displayed that the glass transition temperature ( T g ) slowly decreased with the BaO content increasing from 0 to 4 mol%. However, the onset of crystallization temperature ( T x ) and crystallization peak temperature ( T c ) showed the opposite trend. XRD analysis revealed that μ-cordierite was the major crystal phase for all the glass–ceramic samples, while α-cordierite precipitated as the minor crystal phase with BaO addition. As more BaO was added, the bulk density gradually increased, while the porosity decreased, indicating that BaO improved the sinterability of the glass samples. The dielectric constant showed minimum value with the addition of 2 mol% BaO, and the dielectric loss reached the minimum value when the content of BaO was 3 mol%. Wherein, after heated at 950 °C, glass–ceramics doped with 2 mol% BaO showed a dense structure, a relatively low dielectric constant (4.53), a low dielectric loss (2 × 10 −3 ) at 1 MHz, and a proper CTE value (3.74 × 10 –6 /°C), which can be used to prepare LTCC materials.

The slit down draw method is the main manufacturing process of flexible glass. In this study, Flow3DTM software was used to simulate the process of drawing and thinning glass slits during the slit down draw process. The influence of glass viscosity, initial plate thickness and initial plate speed on the glass spreading process was studied. The maximum pull-down force that the root can bear is linearly proportional to the viscosity, the initial thickness of 1.3837 power and the initial plate speed, respectively. The best way to improve the tensile strength of flexible glass is to increase the viscosity. Flexible glass was more easily to obtain with low viscosity, low thickness and low drawing speed.