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To investigate the interfacial bonding performance between high-early-strength high-ductility concrete (HES-HDC) and existing concrete, 108 bonding specimens were used to study the effects of concrete substrate roughness, the content of silica fume, hydroxypropyl methylcellulose (HPMC), and polyethylene (PE) fiber in HES-HDC, as well as curing age and testing methods on the interface failure mode, load-slip curve, and interfacial bonding strength between HES-HDC and concrete. The results show that the interfacial bonding strength at 2 h of all bonding specimens exceeded 1.2 MPa, with the interfacial bonding strength at 1 day reaching 60% of that at 28 days, demonstrating significant high-early-strength properties, meeting the requirements for rapid repairs. The concrete substrate roughness significantly influenced the interface failure mode and the characteristics of the shear load-slip curve. The interfacial shear strength increases with increasing concrete substrate roughness, HPMC content, fiber content, and curing age. HES-HDC with 6% silica fume exhibits higher interfacial shear strength with existing concrete. Based on the experimental results, a formula for the interfacial bonding strength between HES-HDC and concrete was proposed, considering interface properties and material strength, which could be applicable for predicting bonding strength using different interface testing methods. [ABSTRACT FROM AUTHOR]

The prevalence of non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers, with the Wnt/β-catenin signaling pathway exhibiting robust activation in this particular subtype. The expression of FAM83A (family with sequence similarity 83, member A) has been found to be significantly upregulated in lung cancer, leading to the stabilization of β-catenin and activation of the Wnt signaling pathway. In this study, we conducted a screening of down-regulated miRNAs in lung cancer with FAM83A as the target. Ultimately, we identified miR-1 as a negative regulator of FAM83A and confirmed that FAM83A is a direct target gene of miR-1 through dual luciferase reporter assays. The overexpression of miR-1 significantly attenuated the expression level of FAM83A and suppressed the Wnt signaling pathway, leading to a reduction in the expression levels of downstream target genes AXIN2, CyclinD1, and C-MYC. Additionally, it decreased the nuclear translocation of β-catenin. In addition, overexpression of miR-1 accelerated the degradation of β-catenin by inhibiting FAM83A, promoted the assembly of β-catenin degradation complex, and inhibited the proliferation, migration and invasion of NSCLC cells. In summary, miR-1 may be a potential candidate miRNA for the treatment of NSCLC. [ABSTRACT FROM AUTHOR]

Specimens of gradient IF steel produced as a result of severe plastic deformations often display strong textures that cannot be completely eliminated by annealing. Both the gradient in grain size and the resulting texture contribute to the mechanical behavior of gradient IF steel. A dislocation-based multiscale model is employed to simulate the tensile behavior of gradient interstitial free (IF) steel with various initial shearing textures. It is found that strong {110} textures in the transit region can enhance the material strength and result in late-stage strain/stress redistribution. This unveils the importance of the synergetic effect of both the initial texture in the transit region and the gradient microstructure. [ABSTRACT FROM AUTHOR]

Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved. [ABSTRACT FROM AUTHOR]

Coal to ethylene glycol still lacks algorithm optimization achievements for distillation sequencing due to high-dimension and strong nonconvexity characteristics, although there are numerous reports on horizontal comparisons and process revamping. This scenario triggers the navigation in this paper into the simultaneous optimization of parameters and heat integration of the coal to ethylene glycol distillation scheme and double-effect superstructure by the self-adapting dynamic differential evolution algorithm. To mitigate the influence of the strong nonconvexity, a redistribution strategy is adopted that forcibly expands the population search domain by exerting external influence and then shrinks it again to judge the global optimal solution. After two redistributive operations under the parallel framework, the total annual cost and CO2 emissions are 0.61%/1.85% better for the optimized process and 3.74%/14.84% better for the superstructure than the sequential optimization. However, the thermodynamic efficiency of sequential optimization is 11.63% and 10.34% higher than that of simultaneous optimization. This study discloses the unexpected great energy-saving potential for the coal to ethylene glycol process that has long been unknown, as well as the strong ability of the self-adapting dynamic differential evolution algorithm to optimize processes described by the high-dimensional mathematical model. [ABSTRACT FROM AUTHOR]

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Accurate vehicle trajectory prediction is a fundamental prerequisite for downstream tasks like safety analysis and trajectory planning. Despite extensive research efforts for trajectory prediction, challenges still persist in fusing driving intentions and deeply exploring interaction features among vehicles in complex traffic environments. In this paper, an Attention-based Spatial–Temporal Non-Autoregressive Transformer (ASTNAT) network to capture complex features in historical information for vehicle trajectory prediction is proposed. To begin, we present the Historical Trajectory Time Encoder (HTTE) module, designed to capture the dependencies of different timestamps of vehicles. Secondly, interaction features between the target vehicle and surrounding vehicles at timestamp t\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$t$$\end{document} are captured by the Spatial Social Interaction (SSI) module, while the Spatial–Temporal Social Dependence (STSD) module captures interaction features across different timestamps. Thirdly, the Driving Intention Feature Fusion (DIFF) module is used to capture driving intention features. Finally, in the Decoder Output module, future trajectories are generated all at once in a non-autoregressive manner, avoiding the impact of error accumulation in single timestamp iterative prediction output. Experimental results on the NGSIM and HighD datasets indicate that compared to state-of-the-art models, the proposed model exhibits better performance. The Root Mean Square Error (RMSE) of the prediction trajectory at 5 s time horizon is 3.23 m on NGSIM dataset and 1.31 m on the HighD dataset. Furthermore, ablation experiments are conducted to evaluate the performance of each module. [ABSTRACT FROM AUTHOR]

Grain size gradient materials exhibit superior strength and ductility due to their multiscale microstructures. A dislocation-based multiscale model was applied to study the effect of grain size gradient on the flow stress and deformation of gradient Aluminum. It is found that the grain orientation has a slight effect on the macroscopic behavior of gradient Aluminum, but without a strong texture fiber, such effect is neglectable. The strength depends not only on the average grain size but also on the grain size gradient. By varying the grain size gradient in the region connecting finest 200 nm grains and coarse grains in tens of microns, we found that the plastic flow stress is inverse proportional to the grain size gradient. A smooth transit could lead to a redistribution of the stress concentration and strain localization. The results on the deformation of different grain size gradients Aluminum shed light on processing gradient materials with an improved combination of strength–ductility. [ABSTRACT FROM AUTHOR]

The identification of structural damages takes a more and more important role within the modern economy, where often the monitoring of an infrastructure is the last approach to keep it under public use. Conventional monitoring methods require specialized engineers and are mainly time-consuming. This research paper considers the ability of neural networks to recognize the initial or alteration of structural properties based on the training processes. The presented model, a spatially asymmetric encoder–decoder network, is based on 1D-Convolutional Neural Networks (CNN) for wave field pattern recognition, or more specifically the wave field change recognition. The proposed model is used to identify the change within propagating wave fields after a crack initiation within the structure. The paper describes the implemented method and the required training procedure to get a successful crack detection accuracy, where the training data are based on the dynamic lattice model. Although the training of the model is still time-consuming, the proposed new method has an enormous potential to become a new crack detection or structural health monitoring approach within the conventional monitoring methods. [ABSTRACT FROM AUTHOR]

Neuroinflammation plays a critical role in the pathogenesis of subarachnoid hemorrhage (SAH). Microglia, as the resident immune cells, orchestrate neuroinflammation distinctly in neurological diseases with different polarization statuses. However, microglial polarizations in the neuroinflammatory responses after SAH are not fully understood. In this study, we investigated the dynamics of microglial reaction in an endovascular perforated SAH model. By using the Cx3cr1GFP/GFP Ccr2RFP/RFP transgenic mice, we found that the reactive immune cells were largely from resident microglia pool rather than infiltrating macrophages. Immunostaining and real-time PCR were employed to analyze the temporal microglial polarization and the resulting inflammatory responses. Our results showed that microglia accumulated immediately after SAH with a centrifugal spreading through the Cortex Adjacent to the Perforated Site (CAPS) to the remote motor cortex. Microglia polarized dynamically from M1 to M2 phenotype along with the morphological transformation from ramified to amoeboid shapes. The ramified microglia demonstrated the M1 property, which suggested the function-related microglial polarization occurred prior to morphological transformation after SAH. Bipolar-shaped microglia appeared as the intermediate and transitional status with the capacity of bidirectional polarization. The microglial polarization status is distinct in molecular inflammatory responses; M1-related pro-inflammation was predominant in the early phase and subsequently transited to the M2-related anti-inflammation. The systematic characterization of the dynamics of microglial polarization in this study contributes to the understanding of the origin of neuroinflammatory responses after SAH and provides key foundation for further investigations to develop target treatment. [ABSTRACT FROM AUTHOR]

A novel ultra-wideband multiple-input-multiple-output (UWB-MIMO) antenna with quintuple notched band characteristics is introduced in the paper. Inserting four upside-down L-slots to the radiator and placing a pair of C-shaped resonator symmetrically around the feeder line, five stop bands of 3.5 GHz/5.2 GHz/5.8 GHz/7.4 GHz/8.4 GHz are produced to alleviate interference from WiMAX/lower WLAN/upper WLAN/downlink of X-band/uplink of X-band. Adopting a T-stepped ground stub not only improves impedance matching but also reduces port coupling. The results exhibit that the quintuple notched-band UWB-MIMO antenna works from 3.1 to 11 GHz except five rejected bands. Low coupling of − 15 dB, preferable radiation pattern and stable gain illustrate that the introduced antenna is meaningful. The proposed antenna is compact, only occupies 21 × 27 mm2. [ABSTRACT FROM AUTHOR]

The heterogeneous microstructure has a predominant effect on the mechanical behavior of polycrystalline material. In most instances, a homogenized parameter such as mean grain size is used to describe and to represent the microstructure. However, these models do not account for a measure of heterogeneity in the grain size and grain shape distributions. In this work, we introduce the grain size distribution into a multi-scale stress-strain-gradient model using a controlled Poisson Voronoi tessellation. The correlation between grain size distribution and strength is studied with various cases of grain size distribution with a fixed grid area and mean grain size. In addition, the effect of the spatial distribution of second phases and grain size on the material strength and ductility is also investigated. The results show that introducing heterogeneity into the microstructure can enhance the strength and ductility of the material compared with its equivalent homogeneous microstructure. In addition, different spatial distributions of phases can also lead to different mechanical responses. [ABSTRACT FROM AUTHOR]