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In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy C-means (FCM) clustering and multi-feature fusion. A three-dimensional feature space is constructed using B-channel pixels and fuzzy clustering with c = 3, justified by the distinct distribution patterns of these three regions in the image, enabling effective preliminary segmentation. To enhance accuracy, connected domain labeling combined with a circularity threshold is introduced to differentiate linear cracks from granular noise. Furthermore, a 5 × 5 neighborhood search strategy, based on crack pixel amplitude, is designed to restore the continuity of fragmented cracks. Experimental results on the Concrete Crack and SDNET2018 datasets demonstrate that the proposed algorithm achieves an accuracy of 0.885 and a recall rate of 0.891, outperforming DeepLabv3+ by 4.2%. Notably, with a processing time of only 0.8 s per image, the algorithm balances high accuracy with real-time efficiency, effectively addressing challenges, such as missed fine cracks and misjudged broken cracks in noisy environments by integrating geometric features and pixel distribution characteristics. This study provides an efficient unsupervised solution for bridge damage detection.

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has demonstrated significant potential for soil moisture content (SMC) monitoring due to its high spatiotemporal resolution. However, GNSS-IR inversion experiments are notably influenced by vegetation and meteorological factors. To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. The multi-factor SMC inversion method achieved remarkable results. For instance, at the P038 station, the model attained an R2 of 0.98, with an RMSE of 0.0074 and an MAE of 0.0038. Experimental results indicate that the multi-factor inversion model significantly outperformed the traditional univariate model, whose R2 (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R2 outperforming SVR by 0.11 and CNN by 0.03. Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R2 values above 0.9. Therefore, the accuracy of the multi-factor SMC inversion model was validated, supporting the practical application of GNSS-IR technology in SMC inversion.

Excessive nitrogen application and low nitrogen use efficiency have been major issues in China’s agricultural development, posing significant challenges for field management. Nitrogen is a critical nutrient for crop growth, playing an indispensable role in crop development, yield formation, and quality enhancement. Therefore, precisely controlling nitrogen application rates can reduce environmental pollution caused by excessive fertilization and improve nitrogen use efficiency. This study employs multispectral remote sensing images, combined with field-measured nitrogen content, to develop canopy nitrogen content inversion models for maize using three algorithms: backpropagation neural network (BP), support vector machine (SVM), and partial least squares regression (PLSR). The results reveal that there is a degree of redundancy in the information contained in various spectral indices. Feature selection effectively eliminates correlated and redundant spectral information, thereby improving modeling efficiency. The spectral indices Green Index (GI) and Nitrogen Reflectance Index (NRI) exhibit strong correlations with nitrogen content in the maize canopy, suggesting that the green and red spectral bands are crucial for retrieving maize’s biophysical and biochemical parameters. In studies on nitrogen content inversion in the maize canopy, the random forest (RF) algorithm, coupled with PLSR, demonstrated superior predictive performance. Compared to the standalone PLSR model, accuracy improved by 3.5%–6.5%, providing a scientific foundation and technical support for precise nitrogen diagnosis and fertilizer management in maize cultivation.

In recent years, the deterioration of seismic behavior for reinforced concrete (RC) bridges due to reinforcement corrosion has received increasing attention. Many studies have been performed to explore the seismic capacity of corroded piers by applying uniaxial cyclic loading. However, the uniaxial seismic responses of corroded piers cannot accurately reflect the true structural response influenced by corrosion condition and real multidimensional earthquake action. Pointing at this problem, in this study, the seismic performance of corroded pier columns suffering biaxial lateral cyclic loadings is investigated by conducting comprehensive numerical analyses. First, a fiber-based numerical model is built and verified by the cyclic experiment results of two RC pier columns under different displacement loading patterns and corrosion levels. Then, three corrosion levels and five uniaxial and biaxial displacement protocols are used in the verified numerical models. The simulation results show that the ultimate force, deformation capacity, and dissipated energy of the piers are significantly influenced by corrosion degrees and biaxial loading patterns compared with the uniaxially loaded uncorroded piers. The phase lag between the biaxial displacement trajectory and biaxial force trajectory has three stages: a small-range fluctuation stage, a wide-range fluctuating and rise stage, and a wide-range fluctuating and decrease stage. The RE pattern is the most detrimental pattern, which leads to the fastest damage accumulation of the same-level corroded bridge piers.

Mesenchymal stem cells (MSCs) were treated with bone morphogenetic protein-2 (BMP-2), vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF) dose-dependently and time-dependently. Together they caused a strong synergistic effect on the osteogenic differentiation of MSCs, with lower concentrations of each factor being enough to show the synergistic promotion (50 ng BMP-2/ml, 1 ng VEGF/ml and 10 ng bFGF/ml). When both VEGF and bFGF were added in the early proliferating stage (the first 7 days) and BMP-2 was added in the late differentiation stage (the last 7 days), osteogenic differentiation of MSCs could be enhanced more effectively.

In the present study, the effects of bone morphogenetic protein-2 (BMP-2), vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF) on regulation of rat osteoblast (ROB) maturation in vitro were investigated. It was found that the proliferation, differentiation and mineralization of ROBs were all dose-dependently increased at particular times in the case of treatment with only one growth factor. To investigate the effects of combined treatment, ROBs were treated with either a single application of a relatively high dose of each growth factor, or binary/triple combined applications of relatively low doses of the growth factors. Osteogenic differentiation was significantly promoted in the triple combination treatment of BMP-2, VEGF and bFGF compared with the single or binary combination treatments. The optimal timing of the triple combination to enhance osteogenesis was also tested. When bFGF and VEGF were added in the early stage, and BMP-2 and VEGF were added in the late stage, osteogenic differentiation of ROBs could be enhanced more effectively. These results could be used to construct bone tissue engineering scaffolds that release growth factors sequentially.

Morphine-loaded poly( l -lactide)-poly(ethylene glycol)-poly( l -lactide) (PLLA-PEG-PLLA) microparticles were prepared using solution enhanced dispersion by supercritical CO 2 (SEDS). The effects of process variables on the morphology, particles size, drug loading (DL), encapsulation efficiency and release properties of the microparticles were investigated. All particles showed spherical or ellipsoidal shape with the mean diameter of 2.04–5.73 μm. The highest DL of 17.92 % was obtained when the dosage ratio of morphine to PLLA-PEG-PLLA reached 1:5, and the encapsulation efficiency can be as high as 87.31 % under appropriate conditions. Morphine-loaded PLLA-PEG-PLLA microparticles displayed short-term release with burst release followed by sustained release within days or long-term release lasted for weeks. The degradation test of the particles showed that the degradation rate of PLLA-PEG-PLLA microparticles was faster than that of PLLA microparticles. The results collectively suggest that PLLA-PEG-PLLA can be a promising candidate polymer for the controlled release system.

Surface-porous titanium samples were prepared by anodic oxidation in H 2 SO 4 , H 3 PO 4 and CH 3 COOH electrolytes under various electrochemical conditions. X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) were employed to characterize the structure, morphology and chemical composition of the surface layer, respectively. Closer analysis on the effect of the electrochemical conditions on pore configuration was involved. It can be indicated that porous titania was formed on the surface layer, and the pore configuration was influenced by electrolyte composition and crystal structure of the titania. The fibroblast cells experiment showed that anodic oxidation of titanium surface could promote fibroblast adhesion on Ti substrate. The results suggested that anodic oxidation of Ti in CH 3 COOH was suitable to obtain surface-porous titanium oxides layers, which might be beneficial for better soft tissue ingrowths.

Vanadium carbide (VC) nanoparticles were synthesized by a novel refluxing-derived precursor. The organic/inorganic hybrid precursor was prepared by a two-step refluxing method using hydrous V 2 O 5 as vanadium source and n -dodecane as carbon source. The reaction process, phase composition, microstructure, and element composition of VC were investigated by X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy (XPS). The results showed that VC nanoparticles could be obtained at 900 °C for 1 h in flowing Argon (Ar), which was much lower than those of conventional synthesis methods. XRD pattern indicated that the product was face-centered cubic VC with a lattice constant a  = 4.1626 Å and average crystallite size of 22.3 nm. Raman spectra indicated that long time refluxing resulted in alkane dehydrogenation and the formation of coke on V 2 O 5 nanoparticles. XPS investigations confirmed oxygen presence in VC lattice. Electron microscopy photographs showed the particle size ranged from 20 to 50 nm. All these results confirmed that the two-step refluxing method was a novel and feasible method to synthesize VC nanoparticles.

To develop more biomarkers for diagnosis and therapy of ovarian cancer, a 12-mer phage display library was used to isolate peptides that bound specifically to the human ovarian tumor cell line SK-OV-3. After five rounds of in vitro screening, the recovery rate of phages showed a 69-fold increase over the first round of washings and a group of phage clones capable of binding to SK-OV-3 cells were obtained. A phage clone named Z1 with high affinity and specificity to SK-OV-3 cells was identified in vitro. More importantly, the synthetic biotin-labeled peptide, ZP1 (=SVSVGMKPSPRP), which corresponded to the sequence of the inserted fragment of Z1, demonstrated a high specificity to SK-OV-3 cells especially when compared to other cell lines (A2780 and 3T3). ZP1 might therefore be a biomarker for targeting drug delivery in ovarian cancer therapy.