Search Results (1,254)

Hydrogels, especially Polyvinyl alcohols, have received extensive attention as alternative materials for articular cartilage. Aiming at the problems such as low strength and poor toughness of polyvinyl alcohol hydrogels in practical applications, an enhancement and modification strategy is proposed. Sodium alginate fibers were introduced into polyvinyl alcohol hydrogel network through physical blending and freezing/thawing methods. The prepared composite hydrogels exhibited a three-dimensional porous network structure similar to that of human articular cartilage. The mechanical and tribological properties of hydrogels have been significantly improved, due to the multiple hydrogen bonding interaction between sodium alginate fibers and polyvinyl alcohol. Most importantly, under a load of 2 N, the friction coefficient of the PVA/0.4SA hydrogel can remain stable at 0.02 when lubricated in PBS buffer for 1 h. This work provides a novel design strategy for the development of high-performance polyvinyl alcohol hydrogels. Full article

This study takes four parallel axial-flow pumps (three in operation + one on standby) as the research object. Using a 1D–3D coupling method, it explores the flow characteristics of axial-flow pumps under different startup strategies during multi-pump parallel operation. Through comparative analysis, the following conclusions are drawn: when all three pumps start simultaneously, the internal pressure exceeds the rated head by 23.43%, and the reverse flow reaches 10.57% of the rated flow. When starting the pumps sequentially with 5 s intervals, the pressure can be reduced to 11.41% above the rated head, but the reverse flow increases to 13.87%. Further extending the startup interval to 15 s results in only minimal improvements compared to 5 s intervals: the maximum internal pressure and maximum reverse flow decrease by just 0.97% and 0.05%, respectively. When valve coordination is added to the 5 s sequential startup strategy (pre-opening the valve to 60% before pump startup), the pressure exceeds the rated head by 10.49%, and the reverse flow exceeds the rated flow by 6.04%. In this scenario, the high-pressure areas and high-turbulence zones on the blade back surfaces are significantly reduced, achieving optimal flow stability. Therefore, the parallel system startup should adopt a coordinated strategy combining moderate time intervals with 60% valve pre-opening. This approach can both avoid excessive pressure impact and effectively control reverse flow phenomena, providing an important basis for optimizing the startup of multi-pump parallel systems. Full article

With the growing use of deep learning in medical image analysis, automated classification of fundus images is crucial for the early detection of fundus diseases. However, the complexity of fluorescein fundus angiography (FFA) images poses challenges in the accurate identification of lesions. To address these issues, we propose the Enhanced Feature Fusion ConvNeXt (EFF-ConvNeXt) model, a novel architecture combining VGG16 and an enhanced ConvNeXt for FFA image classification. VGG16 is employed to extract edge features, while an improved ConvNeXt incorporates the Context-Aware Feature Fusion (CAFF) strategy to enhance global contextual understanding. CAFF integrates an Improved Global Context (IGC) module with multi-scale feature fusion to jointly capture local and global features. Furthermore, an SKNet module is used in the final stages to adaptively recalibrate channel-wise features. The model demonstrates improved classification accuracy and robustness, achieving 92.50% accuracy and 92.30% F1 score on the APTOS2023 dataset—surpassing the baseline ConvNeXt-T by 3.12% in accuracy and 4.01% in F1 score. These results highlight the model’s ability to better recognize complex disease features, providing significant support for more accurate diagnosis of fundus diseases. Full article

Asynchrony between the movement of water and sediment in a reservoir will affect long-term maintenance of the reservoir’s capacity to a certain extent. Based on water and sediment data on the Three Gorges Reservoir (TGR) measured over the years and a river network model, optimization of the dispatching mode of the reservoir’s sand peak process was studied, and the corresponding water and sediment dispatching indicators were provided. The results show that (1) sand peak discharge dispatching of the TGR can be divided roughly into three stages, namely the flood detention period, the sediment transport period, and the sediment discharge period. (2) According to the process of the flood peak and the sand peak, a division method for each period is proposed. (3) A corresponding scheduling index is proposed according to the characteristics of the sand peak process and the needs of flood control scheduling. This research can provide operational indicators for the operation and management of the sediment load in the TGR and also provide technical support for sustainable reservoirs similar to TGR. Full article

Tuberous sclerosis complex (TSC) is a systemic disease caused by mutations in either the TSC1 (encoding hamartin) or TSC2 (encoding tuberin) gene, with mutations in the TSC2 gene potentially leading to more severe clinical symptoms. Neurological symptoms are a common clinical manifestation of TSC, and neuroinflammation is thought to play an important role. Glial cells are a major source of neuroinflammation, but whether microglia are involved in the activation of the NOD-like receptor protein 3 (NLRP3) inflammasome and the expression of interleukin-1β (IL-1β) in TSC patients remains unclear. We used a transcriptome sequencing dataset for bioinformatics analysis to explore the differences in the expression of microglial inflammasome-associated hub genes. TSC2 knockdown (TSC2 KD) microglia (HMC3 cell line) were generated by lentivirus, and the expression of inflammasome-associated hub genes, microglial activation, and NLRP3 inflammasome activation were verified. In addition, experiments were performed to explore the regulatory effects of rapamycin. Bioinformatics analysis identified a total of eight inflammasome-associated hub genes. By detecting GFP fluorescence, TSC2 mRNA, TSC2 protein expression, and the phosphorylation of the mammalian target of rapamycin (p-mTOR)/mTOR, we confirmed that the TSC2 KD microglia model was successfully established. Compared with the control group, the TSC2 KD group presented higher mRNA levels and fluorescence intensities of microglia AIF1 and CD68, as well as greater reactive oxygen species (ROS) production. Eight inflammasome-associated hub gene mRNA assays revealed that the expression of the NLRP3 and IL1B genes was increased. Compared with the control group, the TSC2 KD group presented increased levels of NLRP3 and Pro-IL-1β proteins in cells and Cleaved-Caspase 1 and Cleaved-IL-1β proteins in the supernatant, suggesting NLRP3 inflammasome activation. Rapamycin intervention alleviated these changes, demonstrating that the TSC2 gene regulation of microglial activation and NLRP3 inflammasome activation are correlated with mTOR phosphorylation. In conclusion, microglia are activated in TSC patients and participate in the NLRP3 inflammasome-associated neuroinflammatory response, and rapamycin treatment can alleviate these changes. Full article

The simulation analysis of a novel stay-in-place formwork (SIPF) beam reinforced with perforated steel pipe skeleton was conducted. The SIPF beam consists of a modified magnesium oxysulfide mortar (MMOM) formwork, a square steel pipe skeleton with holes dug on the sides and top, and cast-in-place concrete. The finite element (FE) analysis model of the SIPF beam was established by using the ABAQUS CAE 2021 software, and simulation analysis was conducted with the shear span ratio (SSR), the distance between the remaining steel strips, and the strength of concrete as the variation parameters. The results show that the stiffness and shear capacity of the SIPF beam decrease with the increase in SSR and increase with the decrease in strip spacing. Under the same conditions, when the concrete strength grade is increased from C30 to C50, the shear bearing capacity of the SIPF beam increases by 11.8% to 16.2%. When the spacing of the steel strips is reduced from 200 mm to 150 mm, the shear bearing capacity can be increased by 12.7% to 31.5%. When the SSR increases from 1.5 to 3.0, the shear bearing capacity decreases by 26.9% to 37.3%. Moreover, with the increase in the SSR, the influence of the steel strip spacing on the shear bearing capacity of the SIPF beam improves, while the influence of the concrete strength on the shear bearing capacity decreases. Taking parameters such as SSR, steel strip spacing, and concrete strength as variables, the influence of steel pipe constraining the core concrete on the shear bearing capacity was considered. The calculation formula for the shear bearing capacity of the SIPF beam with perforated steel pipe skeleton was established. The calculation results fit well with the laboratory test and simulation test results and can be used for the design and calculation of engineering structures. Full article

Background: Measuring frequencies of antigen-specific memory B cells (B_mem), their immunoglobulin (Ig) class and subclass usage, cross-reactivity, and affinity can provide insights into the efficacy of future antibody responses in case of antigen re-encounter. B cell ImmunoSpot^® assays can provide such information; however, like most cell-based tests, they require considerable amounts of blood to be drawn from the donor and this has hindered their inclusion in clinical trials and routine immune diagnostics. Methods: We introduce strategies for reducing the cell numbers required to 2–3 million peripheral blood mononuclear cells (PBMCs) per antigen, obtainable from 2–3 mL of blood from healthy adult donors. Results: Except when B_mem frequencies were very low, we found that testing PBMCs in singlet wells, but in serial dilution, enables as reliable B_mem frequency assessments as when testing replicate wells at a single fixed cell number. Additionally, B cell ImmunoSpot^® assays can be multiplexed for detecting four Ig classes, or IgG subclasses, simultaneously and without loss of sensitivity. The requirement for low cell numbers and the retention of B cell functionality by cryopreserved PBMCs equivalent to freshly isolated material implies that fewer than the standard 10 million PBMCs per vial can be frozen. This would reduce the number of individuals who could not be tested for B_mem due to insufficient availability of PBMCs, a common problem with such assays. Conclusions: The predictable need for and recovery of cryopreserved PBMCs facilitates planning of and optimal cell utilization in B cell ImmunoSpot^® assays and increases the practical feasibility of extensive B_mem characterization in larger cohorts. Full article

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

Precisely deciding potential suppliers enables companies to engage with high-caliber partners that fulfill their strategic development requirements, bolster their core competitiveness, and foster sustainable market growth. To mitigate the challenges enterprises face in selecting appropriate suppliers, a recommendation method for potential suppliers tailored to a small-sized dataset is proposed. This approach employs an enhanced Graph Convolutional Neural Network (GCNN) to resolve the accuracy deficiencies in supplier recommendations within a limited dataset. Initially, a supply preference network is created to ascertain the topological relationship between the company and its suppliers. Subsequently, the GCNN is enhanced through dual-path refinements in network structure and loss function, culminating in the adaptive feature perception model. Thereafter, the adaptive feature perception model is employed to adaptively learn the topological relationship and extract the company’s procurement preference vector from the trained model. A matching approach is employed to produce a recommended supplier list for the company. A case study involving 143 publicly listed companies is presented, revealing that the proposed method markedly enhances the accuracy of potential supplier recommendations on a small-sized dataset, thereby offering a dependable and efficient approach for enterprises to effectively evaluate potential suppliers with limited data. Full article

The mechanical characteristics of soil–rock mixtures (S-RMs) are essential for ensuring geotechnical engineering stability and are significantly influenced by the microstructure’s contact network configuration. Due to the irregularity of particle shapes and the variability in particle grading with S-RMs, their macro-mechanical characteristics and mesoscopic contact skeleton distribution exhibit increased complexity. To further elucidate the macro-mesoscopic mechanical behavior of S-RMs, this study employed the DEM to develop a model incorporating irregular specimens representing various states, based on CT scan outlines, and applied flexible boundary conditions. A main skeleton system of contact force chains is an effective methodology for characterizing the dominant structural features that govern the mechanical behavior of soil–rock mixture specimens. The results demonstrate that the strength of S-RMs was significantly influenced by gravel content and consolidation state; however, the relationship is not merely linear but rather intricately associated with the strength and distinctiveness of the contact force chain skeleton. In the critical state, the mechanical behavior of S-RMs was predominantly governed by the characteristics of the principal contact force skeleton: the contact force skeleton formed by gravel–gravel, despite having fewer contact forces, exhibits strong contact characteristics and an exceptionally high-density distribution of weak contacts, conferring the highest shear strength to the specimens. Conversely, the principal skeleton formed through gravel–sand exhibits contact characteristics that are less distinct compared to those associated with strong contacts. Simultaneously, the probability density distribution of weak contacts diminishes, resulting in reduced shear strength. The contact skeleton dominated by sand–sand contact forces displays similar micro-mechanical characteristics yet possesses the weakest macroscopic behavior strength. Consequently, the concept of the main skeleton of contact force chains utilized in this study presents a novel research approach for elucidating the macro- and micro-mechanical characteristics of multiphase media. Full article

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance. Full article

Mitotic figures in tumor tissues are an important criterion for diagnosing malignant lesions, and physicians often search for the presence of mitosis in whole slide imaging (WSI). However, prolonged visual inspection by doctors may increase the likelihood of human error. With the advancement of deep learning, AI-based automatic cytopathological diagnosis has been increasingly applied in clinical settings. Nevertheless, existing diagnostic models often suffer from high computational costs and suboptimal detection accuracy. More importantly, when assessing cellular abnormalities, doctors frequently compare target cells with their surrounding cells—an aspect that current models fail to capture due to their lack of intercellular information modeling, leading to the loss of critical medical insights. To address these limitations, we conducted an in-depth analysis of existing models and propose an Inter–Intra Hypergraph Neural Network (II-HGNN). Our model introduces a block-based feature extraction mechanism to efficiently capture deep representations. Additionally, we leverage hypergraph convolutional networks to process both intracellular and intercellular information, leading to more precise diagnostic outcomes. We evaluate our model on publicly available datasets under varying imaging conditions, and experimental results demonstrate that our approach consistently outperforms baseline models in terms of accuracy. Full article

The (1−x)Ba_0.96Ca_0.04TiO₃-xBa(Mg_1/3Nb_2/3)O₃ (x = 0–0.20) lead-free ceramics were prepared through the chemical-furnace-assisted combustion synthesis (abbreviated as CFACS). The phase structure, microstructure, dielectric, and piezoelectric properties were systematically investigated. Phase analysis revealed the coexistence of orthorhombic and tetragonal phases in the vicinity of x = 0.07. More importantly, the composition with x = 0.07 exhibited optimal overall electrical properties, including a high piezoelectric coefficient (d₃₃) of 495 pC/N, the planar electromechanical coupling factor (K_p) of 41.9%, and the Curie temperature (T_c) of 123.7 °C. In addition, the average grain size was observed to progressively decrease with increasing x. Full article

This research conducts a comparative study of the economic growth and income distribution effects of consumption and comprehensive income taxes by introducing them into an endogenous economic growth model with human capital formation. We obtained the following results. First, consumption tax does not directly suppress economic growth. Instead, it promotes physical capital accumulation, which causes favorable income distribution effects for capital income earners. Second, comprehensive income tax has the direct effects of suppressing economic growth, restraining physical capital accumulation, and increasing labor supply. Third, comprehensive income tax promotes human capital accumulation, which causes a more favorable income distribution for workers. Finally, by conducting an empirical study using international panel data, we show the growth effects of human capital and educational investment and the differentiated growth effects of income and consumption taxes. Full article