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A nonlinear finite element model has been developed to numerically simulate the hysteretic behavior of steel tube-reinforced concrete columns after exposure to fire on two adjacent faces under reciprocating loads. This model considers various parameters, including fire duration, slenderness ratio, section size, core area ratio, external concrete strength, and reinforcement ratio. The study systematically investigates and analyzes characteristics such as the skeleton curve, ductility coefficient, stiffness degradation, and hysteretic energy dissipation of columns post-fire. Results indicate that member stiffness decreases with increasing displacement loading, and the equivalent viscous damping coefficient of the member escalates with increased horizontal displacement. Moreover, as fire duration and slenderness ratio increase, there is a corresponding decrease in member stiffness, leading to a reduction in the equivalent viscous damping coefficient. In contrast, increases in section size and core area ratio enhance member stiffness and decrease the equivalent viscous damping coefficient. Furthermore, enhancements in external concrete strength and reinforcement ratio elevate member stiffness, which subsequently increases the equivalent viscous damping coefficient.

Under the blast loads, the energy-absorbing structure significantly influences the anti-explosion performance of the concrete-filled bilateral steel plate composite shear walls with the replaceable protective structure (RPS-SCS). This paper employs finite element numerical simulation methods to investigate the impact of the replaceable polyurethane foam energy-absorbing structure (PUF) on the anti-explosion performance of composite shear walls. The numerical models were established based on LS-DYNA. The arbitrary Lagrange Euler (ALE) algorithm is adopted to analyze the models under blast loads, and the accuracy of the models is verified based on existing experiments. The structural response and failure modes of concrete-filled bilateral steel plate composite shear walls with the replaceable energy absorbing and protective structure (PUF-RPS-SCS) are obtained from the numerical results. The displacement histories and energy absorption capacity of composite shear walls are obtained and discussed by conducting numerical calculations on parameters such as the thickness of concrete protection structure, the axial load rate, the thickness of the replaceable PUF energy-absorbing structure, and the mass of explosive charges. Numerical results indicate that the blast resistance performance of the shear wall increases with the increase of the replaceable protective structure thickness. The replaceable PUF energy-absorbing structure could effectively absorb the energy generated by blast loads. The set of the replaceable PUF energy-absorbing structure could greatly decrease the deformation of steel plates and core concrete, enhance the structure’s integrity after the explosion and effectively improve the blast resistance performance of the shear wall.

The RHT concrete strength model and failure model are employed for the numerical simulations of composite projectile penetrating concrete targets. The simulations show that RHT parameters, specially the failure parameters, greatly influence the penetration depth, exit velocity. The failure mode of concrete should be paid great attentions in numerical analysis. If the crushing failure is the primary mechanism, RHT model seems effective. For the cracking process of concrete, the model should be tensile failure description. Reasonable material parameters are important in penetration simulations. Comparison between numerical analysis and experimental date is made for discussion about the parameter effects.

The flow structures of jet impingement dominate heat and mass transfer process, even the whole thermal performance. In this study, we have inspected the flow structures and mechanism of nanofluid jet impingement onto a dimpled target surface with different design parameters. Investigations are performed for the relative depth of dimple ($\delta /D$), the jet-to-plate spacing ($H/d$), nanoparticle volume concentration ($\varphi$), and Reynolds number (Re) ranging to explore the mechanism of flow structure variations. Results indicate that these parameters have a significant effect on the flow structure of nanofluid jet impingement near the dimpled target surface. The flow begins to separate after passing the edge of the dimple along with the curvature of a dimple. $\delta /D$ will affect the form and location of flow separation and reattachment, and $\varphi$ will affect the intensity of separation flow. The length of the flow separation bubble varies in different $H/d$ cases. When $H/d$ increases, the impinging energy and the velocity near the dimple edge decreases. The different Re has little effect on the length of the flow separation bubble and the tendency of the pressure coefficient (Cp). These results can provide further mechanism inspiration for the design of the flow structure of nanofluid jet impingement.

In this contribution, a design of a synthetic calibration genetic circuit to characterize the relative strength of different sensing promoters is proposed and its specifications and performance are analyzed via an effective mathematical model. Our calibrator device possesses certain novel and useful features like modularity (and thus the possibility of being used in many different biological contexts), simplicity, being based on a single cell, high sensitivity and fast response. To uncover the critical model parameters and the corresponding parameter domain at which the calibrator performance will be optimal, a sensitivity analysis of the model parameters was carried out over a given range of sensing protein concentrations (acting as input). Our analysis suggests that the half saturation constants for repression, sensing and difference in binding cooperativity (Hill coefficients) for repression are the key to the performance of the proposed device. They furthermore are determinant for the sensing speed of the device, showing that it is possible to produce detectable differences in the repression protein concentrations and in turn in the corresponding fluorescence in less than two hours. This analysis paves the way for the design, experimental construction and validation of a new family of functional genetic circuits for the purpose of calibrating promoters.

The current cold-formed steel (CFS) wall is limited to the low-rise cold-formed thin-walled steel structure, which is inappropriate for multi-story constructions due to its low shear resistance and poor corrosion resistance of outer sheathing board. Therefore, a new composite wall of CFS frame sheathing with concrete and plasterboard was proposed, the cast-in situ concrete layer was used for the outer surface in response to the needs of corrosion resistance, and the plasterboard was utilized for the inner surface to reduce weight. This paper presented a quasi-static test on the seismic behavior of one CFS wall and three composite-walls’ sheathings with concrete and plasterboard to obtain different failure modes and investigate the influence of different sheathing and opening sizes. Combined with the finite element model, a large parametric study of composite walls was conducted to analyze the impact of load ratio, reinforcement ratio, sheathing board, and concrete and steel strength. Test and finite element analysis (FEA) results demonstrate that the seismic performance of composite walls with concrete and plasterboard is satisfactory considering their shear strength, ductility, and energy dissipation. Load ratio, opening size, and reinforcement ratio significantly affect the seismic performance of the composite wall, while the concrete and steel strength and sheathing board have minimal effects. The seismic design suggestions of composite walls are given based on this study.

The traditional damping structure usually transfers the vibration energy into heat, dissipated and without harvesting. But for harsh environments, such as military activities and lunar probe patrols, the vibration energy can be extracted, and realize the purpose of self-power condition monitoring. In this paper, a bionic suspension with energy-harvesting property is proposed based on bionic configurations. The bionic suspension adopts a symmetrical three-link arrangement to simulate the arrangement of bones, and a horizontal spring to simulate muscles. The micro-generator embedded in the knee joint provides the main damping force for the overall structure and also plays a role in harvesting energy. The dynamic equation and virtual prototype model are established, and the effects of parameter changes on the performance of vibration reduction and energy harvesting are analyzed, and the relationship between the two is revealed. The results show that the parameters to achieve their respective optimal performance are not consistent in the trend, though it is difficult to obtain the best performance of both, it can be designed according to the specific functions to be satisfied. This structure can be utilized as a general model for vibration reduction and energy harvesting scenarios, providing new ideas for self-powered sensing and condition monitoring configurations.

Adrenal lesions refer to abnormalities or growths that occur in the adrenal glands, which are located on top of each kidney. These lesions can be benign or malignant and can affect the function of the adrenal glands. This paper presents a study on adrenal tumor segmentation using a modified U-Net model with various parameter selection strategies. The study investigates the effect of fine-tuning parameters, including k-fold values and batch sizes, on segmentation performance. Additionally, the study evaluates the effectiveness of different preprocessing techniques, such as Discrete Wavelet Transform (DWT), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Image Fusion, in enhancing segmentation accuracy. The results show that the proposed model outperforms the original U-Net model, achieving the highest scores for Dice, Jaccard, sensitivity, and specificity scores of 0.631, 0.533, 0.579, and 0.998, respectively, on the T1-weighted dataset with DWT applied. These results highlight the importance of parameter selection and preprocessing techniques in improving the accuracy of adrenal tumor segmentation using deep learning.

Acoustic Emission (AE) is a non-destructive testing technique which can be used to not only monitor the process of failure in material under both laboratory and field tests, but classify the damage level as well as the nature of that damage such as tensile cracks and shear cracks within the material by parameter analysis. This paper presents a series results of unloading rock tests conducted by the true triaxial test system. The AE parameters of AE hit, amplitude, frequency, rise time and energy were used in the tests. The damage level and the classification of micro cracks were identified by frequency-amplitude distribution of AE and the relationship of average frequency (AF) and RA value. In the loading stage, AE waves with the feature of low-medium amplitude and low energy were formed. Closure and slip of the original microcracks in the rock specimen were generated. When the rock sample is relatively stable, less AE waves characterized by low amplitude and low energy are formed, which stand for microcracks slip split in local zone correspondingly. However, the amplitudes of AE waves increase significantly in all frequency range during unloading stage, which implies high energy releasing and macrocracks generated by shear cracks propagation with plenty of tensile cracks. The analysis of AE parameters was successfully used to determine the type of crack movements in different damage levels in accordance with the SEM observations of specimens.