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The microstructure of EUHPC under steam curing was denser than that under standard curing, and the interfacial transition zones under both curing regimes were compact.The aim of this research is to recommend a set of criteria for estimating the compressive strength of concrete under marine environment with various saturation and salinity conditions. Cylindrical specimens from three different design mixtures are used as concrete samples. The specimens are subjected to different saturation levels (oven-dry, saturated-surface dry and three partially dry conditions 25%, 50% and 75%) on water and water-NaCl solutions. Three parameters (P- and S-wave velocities and electrical resistivity) of concrete are measured using two NDT equipment in the laboratory while two parameters (density and water-to-binder ratio) are obtained from the design documents of the concrete cylinders. Three different machine learning methods, which include, artificial neural network (ANN), support vector machine (SVM) and Gaussian process regression (GPR), are used to obtain multivariate prediction models for compressive strength from multiple parameters. Based on the R-squared value, ANN results in the highest accuracy of estimation while GPR gives the lowest root-mean-squared error (RMSE). Considering both the data analysis and practicality of the method, the prediction model based on two NDE parameters (P-wave velocity measurement and electrical resistivity) and one design parameter (water-to-binder ratio) is recommended for assessing compressive strength under marine environment.In this study, a WC-reinforced Ni-based surfacing layer was prepared on Q235 steel plate by plasma arc welding. The effects of nano-Y2O3 with different contents (0 wt.%, 0.4 wt.%, 0.8 wt.%, 1.2 wt.%, and 1.6 wt.%) on the microstructure, phase composition, microhardness, and wear resistance of the surfacing layer were studied by scanning electron microscope (SEM), energy dispersive spectrometer (EDS), X-ray diffraction (XRD), microhardness test, and pin-on-disk test. The results show that the phase composition of the surfacing layer was γ-Ni, FeNi3 solid solution, WC, W2C, M23C6, M6C, Cr7C3, and other carbides. When the addition of nano-Y2O3 was 1.2 wt.%, it has a good improvement on microstructure grain refinement and carbide hard-phase increase. Compared with other contents, 1.2 wt.% nano-Y2O3 surfacing layer has the highest microhardness and the lowest friction coefficient and wear loss. At this time, the wear mechanism is abrasive wear accompanied by slight adhesive wear.Wearable electronic skin (e-skin) has provided a revolutionized way to intelligently sense environmental stimuli, which shows prospective applications in health monitoring, artificial intelligence and prosthetics fields. Drawn inspiration from biological skins, developing e-skin with multiple stimuli perception and self-healing abilities not only enrich their bionic multifunctionality, but also greatly improve their sensory performance and functional stability. In this review, we highlight recent important developments in the material structure design strategy to imitate the fascinating functionalities of biological skins, including molecular synthesis, physical structure design, and special biomimicry engineering. Moreover, their specific structure-property relationships, multifunctional application, and existing challenges are also critically analyzed with representative examples. Furthermore, a summary and perspective on future directions and challenges of biomimetic electronic skins regarding function construction will be briefly discussed. We believe that this review will provide valuable guidance for readers to fabricate superior e-skin materials or devices with skin-like multifunctionalities and disparate characteristics.Medical science technology has improved tremendously over the decades with the invention of robotic surgery, gene editing, immune therapy, etc. However, scientists are now recognizing the significance of 'biological circuits' i.e., bodily innate electrical systems for the healthy functioning of the body or for any disease conditions. Therefore, the current trend in the medical field is to understand the role of these biological circuits and exploit their advantages for therapeutic purposes. Bioelectronics, devised with these aims, work by resetting, stimulating, or blocking the electrical pathways. Bioelectronics are also used to monitor the biological cues to assess the homeostasis of the body. In a way, they bridge the gap between drug-based interventions and medical devices. With this in mind, scientists are now working towards developing flexible and stretchable miniaturized bioelectronics that can easily conform to the tissue topology, are non-toxic, elicit no immune reaction, and address the issues that drugs are unable to solve. Since the bioelectronic devices that come in contact with the body or body organs need to establish an unobstructed interface with the respective site, it is crucial that those bioelectronics are not only flexible but also stretchable for constant monitoring of the biological signals. Understanding the challenges of fabricating soft stretchable devices, we review several flexible and stretchable materials used as substrate, stretchable electrical conduits and encapsulation, design modifications for stretchability, fabrication techniques, methods of signal transmission and monitoring, and the power sources for these stretchable bioelectronics. Ultimately, these bioelectronic devices can be used for wide range of applications from skin bioelectronics and biosensing devices, to neural implants for diagnostic or therapeutic purposes.Under strong earthquakes, steel structures are prone to undergoing ultra-low cycle fatigue (ULCF) fracture after sustaining cyclic large-strain loading, leading to severe earthquake-induced damage. Thus, establishing a prediction method for ULCF plays a significant role in the seismic design of steel structures. However, a simple and feasible model for predicting the ULCF life of steel structures has not been recognized yet. Among existing models, the ductile fracture model based on ductility capacity consumption has the advantage of strong adaptability, while the loading history effect in the damage process can also be considered. Nevertheless, such models have too many parameters and are inconvenient for calibration and application. To this end, focusing on the prediction methods for ULCF damage in steel structures, with the fragile parts being in moderate and high stress triaxiality, this paper proposes a simplified uncoupled prediction model that considers the effect of stress triaxiality on damage and introduces a new historical-effect related variable function reducing the calibration work of model parameters. Finally, cyclic loading test results of circular notched specimens verify that the proposed model has the advantages of a small dispersion of parameters for calibration, being handy for application, and possessing reliable results, providing a prediction method for ULCF damage of structural steels.The design of modern construction materials with heterogeneous microstructures requires a numerical model that can predict the distribution of microstructural features instead of average values. The accuracy and reliability of such models depend on the proper identification of the coefficients for a particular material. This work was motivated by the need for advanced experimental data to identify stochastic material models. Extensive experiments were performed to supply data to identify a model of austenite microstructure evolution in steels during hot deformation and during the interpass times between deformations. Two sets of tests were performed. The first set involved hot compressions with a nominal strain of 1. The second set involved hot compressions with lower nominal strains, followed by holding at the deformation temperature for different times. Histograms of austenite grain size after each test were measured and used in the identification procedure. The stochastic model, which was developed elsewhere, was identified. Inverse analysis with the objective function based on the distance between the measured and calculated histograms was applied. Validation of the model was performed for the experiments, which were not used in the identification. The distance between the measured and calculated histograms was determined for each test using the Bhattacharyya metric and very low values were obtained. As a case study, the model with the optimal coefficients was applied to the simulation of the selected industrial hot-forming process.In order to improve the initial viscosity and stability of Camellia oleifera cake-protein adhesive, Camellia oleifera cake-protein was blended with defatted soybean protein (DSP), soybean protein isolate (SPI), and casein, followed by adhesive preparation through degradation and crosslinking methods. The performance of Camellia oleifera cake-protein adhesive was investigated by Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), scanning electron microscopic (SEM), and thermogravimetric (TG) and X-ray diffraction (XRD). check details The results showed that DSP, SPI, and casein likely promoted the effective degradation of Camellia oleifera cake-protein, and, thus, more active groups were formed in the system, accompanied by more reactivity sites. The prepared adhesive had a lower curing temperature, and higher initial viscosity and stability, but the storage time was shortened. Moreover, DSP, SPI, and casein, themselves, were degraded into peptide chains with lower molecular weights; thus, improving the overall flexibility of the adhesive, facilitating a better elastic contact and regular array between crosslinking products, and further strengthening the crosslinked structure and density of the products. After curing, a compact and coherent reticular structure was formed in the adhesive layer, with both bonding strength and water resistance being significantly improved. According to the results obtained, the next step will be to study the DSP-modified Camellia oleifera cake-protein adhesive in depth.With the application of Selective Laser Melting (SLM) technology becoming more and more widespread, it is important to note the process parameters that have a very important effect on the forming quality. Key process parameters such as laser power (P), scan speed (s), and scanning strategy (μ) were investigated by determining the correlation between the microstructure and residual stress in this paper. A total of 10 group 316L specimens were fabricated using SLM for comprehensive analysis. The results show that the key process parameters directly affect the morphology and size of the molten pool in the SLM deposition, and the big molten pool width has a direct effect on the larger grain size and crystal orientation distribution. In addition, the larger grain size and misorientation angle also affect the size of the residual stress. Therefore, better additive manufacturing grain crystallization can be obtained by reasonably adjusting the process parameter combinations. The transfer energy density can synthesize the influence of four key process parameters (P, v, the hatching distance (δ), and the layer thickness (h)).