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Groundwater recharge by faults is often ignored in current hydrothermal potential assessment approaches. This omission may cause a significant discrepancy between the estimated potential and the actual potential in areas adjacent to faults. To address this gap, this study proposes a novel approach for assessing hydrothermal potential that incorporates the influence of faults. This approach was applied to investigate the hydrothermal potential in the main urban area of Yichang, China. The data obtained from the traditional methods and the novel approach were evaluated through comparison with field pumping test data. The findings indicate that the estimates from both methods generally align with the actual potential in most areas. However, significant discrepancies emerge between the estimated and actual potential in zones proximate to faults. Specifically, the traditional method yielded an estimate 60–75% lower than the field measurements. In contrast, the novel approach reduced this deviation by 20%, demonstrating its superior accuracy in fault zones. This study demonstrates the superior performance of the proposed approach over traditional methods for characterizing the heterogeneous distribution of hydrothermal resources in fault-developed zones.

We report on comprehensive experimental studies of turbulence spreading in edge plasmas. These studies demonstrate the relation of turbulence spreading and entrainment to intermittent convective density fluctuation events or bursts (i.e. blobs and holes). The non-diffusive character of turbulence spreading is thus elucidated. The turbulence spreading velocity (or mean jet velocity) manifests a linear correlation with the skewness of density fluctuations, and increases with the auto-correlation time of density fluctuations. Turbulence spreading by positive density fluctuations is outward, while spreading by negative density fluctuations is inward. The degree of symmetry breaking between outward propagating blobs and inward propagating holes increases with the amplitude of density fluctuations. Thus, blob-hole asymmetry emerges as crucial to turbulence spreading. These results highlight the important role of intermittent convective events in conveying the spreading of turbulence, and constitute a fundamental challenge to existing diffusive models of spreading.

Experimental studies of the dynamics of shear flow and turbulence spreading at the edge of tokamak plasmas are reported. Scans of line-averaged density and plasma current are carried out while approaching the Greenwald density limit on the J-TEXT tokamak. In all scans, when the Greenwald fraction fG=n¯/nG=n¯/(Ip/πa2) increases, a common feature of enhanced turbulence spreading and edge cooling is found. The result suggests that turbulence spreading is a good indicator of edge cooling, indeed better than turbulent particle transport is. The normalized turbulence spreading power increases significantly when the normalized E×B shearing rate decreases. This indicates that turbulence spreading becomes prominent when the shearing rate is weaker than the turbulence scattering rate. The asymmetry between positive/negative (blobs/holes) spreading events, turbulence spreading power and shear flow are discussed. These results elucidate the important effects of interaction between shear flow and turbulence spreading on plasma edge cooling.

As an admixture of cement-based materials, the reaction of fly ash (FA) usually takes place in the late age of curing, so FA will affect the self-healing ability of long-age cement-based materials. The self-healing potential and the characteristics of self-healing products of cementitious materials before and after crack healing were analyzed by microscopic tests, and the mechanism of the effect of fly ash on the self-healing performance of cementitious materials was revealed. The results showed that the increase in fly ash content promoted the improvement of the self-repair performance of cracked specimens at 28 d, especially when the fly ash dosage was 40%, the crack opened after 30 d of healing in water was completely closed, the UPV value after recovery was close to 3000 m/s, the self-repair efficiency of maximum amplitude and main frequency amplitude was up to more than 60%, and the recovery rate of compressive strength was increased to more than 30%. However, the increase in fly ash content was not conducive to the self-repair of cracked samples at 210 d, and with the increase in fly ash content, the crack closure effect weakened, the UPV value after recovery decreased, the crack repair rate based on ultrasonic transmission decreased to about 20%, and the compressive strength recovery rate increased slightly. In addition, calcium carbonate precipitation was the main repair product of crack filling and healing, including calcite and spherulite. With the increase in fly ash content, the content of element C in the self-repair products of 28-day-old specimens gradually increased, and the size of calcium carbonate crystals gradually decreased, but the filling was denser, whereas the calcium carbonate crystals in the self-repair products of 210 d specimens gradually became fine and loose.

Bladder cancer (BLCA) is one of the most common tumors of the urinary tract. The diagnosis of BLCA is mostly by invasive tests, which are damaging and unsuitable for early screening. Current non‐invasive diagnostic modalities are insufficient in sensitivity and specificity. Therefore, novel diagnostic markers are urgently needed to facilitate early detection of bladder cancer. tRNA‐derived small RNAs (tsRNAs) are considered to be novel and potentially biologically functional non‐coding RNAs (ncRNAs). tsRNAs have been used to help early diagnosis of a variety of tumors. However, whether tsRNAs in BLCA are altered or involved in BLCA progression or regulation remains unclear. Here, we identified a group of up‐regulated tsRNAs in BLCA by sequencing tsRNAs in the plasma of BLCA patients and normal controls and further screened two highly correlated tsRNAs with BLCA in the training set and validation set, which were named as tRF‐1:28‐chrM.Ser‐TGA and tiRNA‐1:34‐Glu‐CTC‐1‐M2. ROC analyses of the expression profiles of these two tsRNAs by the validation set identified a high diagnostic value. We also found that circulating tRF‐1:28‐chrM.Ser‐TGA and tiRNA‐1:34‐Glu‐CTC‐1‐M2 were specifically expressed and released by BLCA cells and were positively correlated with the degree of disease malignancy. In vitro and in vivo experiments revealed that the two tsRNAs exacerbated BLCA progression and played a role in promoting tumor lipid metabolism. Our study screened two plasma tsRNAs that could serve as valuable early screening and diagnostic biomarkers for BLCA and is also expected to provide potential novel molecular targets for the treatment of BLCA. We found that circulating tRF‐1:28‐chrM.Ser‐TGA and tiRNA‐1:34‐Glu‐CTC‐1‐M2 were specifically expressed and released by bladder tumor cells and were positively correlated with the degree of disease malignancy. In vitro and in vivo experiments revealed that the two tsRNAs exacerbated bladder cancer progression and played a role in promoting tumor lipid metabolism. Our study screened two plasma tsRNAs that could serve as valuable early screening and diagnostic biomarkers for BLCA and is also expected to provide potential novel molecular targets for the treatment of BLCA.

Remote sensing images (RSIs) are widely used in various fields due to their versatility, accuracy, and capacity for earth observation. Direct application of RSIs to harvest optimal results is generally difficult, especially for weak information features in the images. Thus, extracting the weak information in RSIs is reasonable to promote further applications. However, the current techniques for weak information extraction mainly focus on spectral features in hyperspectral images (HSIs), and a universal weak information extraction technology for RSI is lacking. Therefore, this study focused on mining the weak information from RSIs and proposed the deep multi-order spatial–spectral residual feature extractor (DMSRE). The DMSRE considers the global information and three-dimensional cube structures by combining low-rank representation, high-order residual quantization, and multi-granularity spectral segmentation theories. This extractor obtains spatial–spectral features from two derived sequences (deep spatial–spectral residual feature (DMSR) and deep spatial–spectral coding feature (DMSC)), and three RSI datasets (i.e., Chikusei, ZY1-02D, and Pasture datasets) were employed to validate the DMSRE method. Comparative results of the weak information extraction-based classifications (including DMSR and DMSC) and the raw image-based classifications showed the following: (i) the DMSRs can improve the classification accuracy of individual classes in fine classification applications (e.g., Asphalt class in the Chikusei dataset, from 89.12% to 95.99%); (ii) the DMSC improved the overall accuracy in rough classification applications (from 92.07% to 92.78%); and (iii) the DMSC improved the overall accuracy in RGB classification applications (from 63.25% to 63.6%), whereas DMSR improved the classification accuracy of individual classes on the RGB image (e.g., Plantain classes in the Pasture dataset, from 32.49% to 39.86%). This study demonstrates the practicality and capability of the DMSRE method to promote target recognition on RSIs and presents an alternative technique for weak information mining on RSIs, indicating the potential to extend weak information-based applications of RSIs.

Overgrinding of Portland cement brings excessive shrinkage and poor self-healing ability to concrete. In this paper, through the ultrasonic test and optical micrograph observation, the self-healing properties of concrete prepared by cement with different particle size distributions were studied. Besides, the effect of carbonation and continued hydration on self-healing of concrete was analyzed. Results show that, for the Portland cement containing more particles with the size 30~60 μm, the concrete could achieve a better self-healing ability of concrete at 28 days. For the two methods to characterize the self-healing properties of concrete, the ultrasonic test is more accurate in characterizing the self-healing of internal crack than optical micrograph observation. The autogenous self-healing of concrete is jointly affected by the continued hydration and carbonation. At 7 days and 30 days, the autogenous self-healing of concrete is mainly controlled by the continued hydration and carbonation, respectively. The cement particle size could affect the continued hydration by affecting un-hydrated cement content and the carbonation by affecting the Ca(OH)2 content. Therefore, a proper distribution of cement particle size, which brings a suitable amount of Ca(OH)2 and un-hydrated cement, could improve the self-healing ability of concrete.

Ultra-high performance concrete (UHPC) is a type of cementitious material that has been specifically engineered to achieve exceptional mechanical properties and durability through optimized particle filling. However, the addition of steel fibers to the UHPC matrix creates a transitional region at the interface. Previous research has indicated that the use of silane coupling agents (SCA) on the surface of steel fibers is a promising approach for improving the bonding properties between the fibers and the matrix. This study aims to explore the impact of varying amounts of untreated steel fibers versus those that have undergone SCA treatment on the durability of UHPC. The findings indicate that treating steel fibers with SCA significantly narrows the pore space between the matrix and steel fibers, as well as enhances the production of hydration products on the steel fiber surface. Furthermore, this treatment facilitates the formation of a compact transition zone between the UHPC matrix and steel fibers. The electrochemical corrosion resistance, chloride ion penetration resistance, frost resistance, and sulfate erosion resistance of UHPC are all enhanced by this method. As a result, the durability of UHPC is significantly improved, making it an extremely promising avenue of research.

Implants with high coefficients of friction reduce tightening torque requirements while mitigating fracture risks at bone–implant interfaces. This study engineered flexible nanowire textures on titanium surfaces to significantly increase the coefficient of friction without accelerating surface wear. Results demonstrate that these textures maintain a friction coefficient exceeding 0.8 during reciprocating sliding tests under both dry and water conditions. Our analysis reveals that this friction enhancement stems not from surface roughness but from increased tangential resistance during nanowire‐textured deformation. Implementing such high‐friction nanostructures on the implant surface is critical for enhancing preload and improving connection reliability.