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An air-backed diaphragm is the key structure of most dynamic pressure sensors and plays a critical role in determining the sensor performance. Our previous analytical model investigated the influence of air cavity length on the sensitivity and bandwidth. The model found that as the cavity length decreases, the static sensitivity monotonically decreases, and the fundamental natural frequency shows a three-stage trend: increasing in the long-cavity-length range, reaching a plateau value in the medium-cavity-length range, and decreasing in the short-cavity-length range, which cannot be captured by the widely used lumped model. In this study, we conducted the first experimental measurements to validate these findings. Pressure sensors with a circular polyimide diaphragm and a backing air cavity with an adjustable length were designed, fabricated, and characterized, from which the static sensitivities and fundamental natural frequencies were obtained as a function of the cavity length. A further parametric study was conducted by changing the in-plane tension in the diaphragm. A finite element model was developed in COMSOL to investigate the effects of thermoviscous damping and provide validation for the experimental study. Along with the analytical model, this study provides a new understanding and important design guidelines for dynamic pressure sensors with air-backed diaphragms.

This paper studies the effect of soil–structural interaction (SSI) on gravity‐based wind turbine towers equipped with tuned mass dampers (TMDs) subjected to earthquake loading. A small‐scale shaking table test of wind turbine towers with TMD was conducted, and the results showed that using TMD designed considering SSI resulted in larger vibration suppression. A simplified analytical numerical model was developed for SSI analysis considering TMD. The effect of soil site class and the earthquake intensity on the response reduction efficiency of the TMD was also discussed using the simplified model. It is concluded that the TMD efficiency depends not only on the soil stiffness but also on the characteristics of the applied ground motions, both of which are affected by the site classes and earthquake intensity levels. Moreover, the peak acceleration ratio (PAR), the root mean square acceleration ratio (RAR), the peak displacement ratio (PDR), and the root mean square displacement ratio (RDR) of the top of the wind turbine tower were obtained with and without TMD for different earthquake intensities and sites. These parameters can be used as references for the rational selection of TMD parameters considering SSI.

Organic/quantum dot light-emitting diode displays have recently been manufactured using inkjet printers, which require stable ink drops and strict control during the printing process. Combining Ansys Fluent and Ansys Mechanical simulations, this research establishes the conditions that stabilize the ink droplets. The feasibility of this approach is verified through a jetting simulation of Newtonian fluids with almost constant viscosity and a comparison of the simulation and experimental results. Then, a commercial non-Newtonian ink with a shear-rate-dependent viscosity is simulated, and the simulation and experimental results are again compared. The study then evaluates why the experimental and simulation outcomes of non-Newtonian fluids differ under the same voltage conditions. Besides finding the stable drop conditions, the performed inkjet simulations reveal the pressure changes in the inkjet nozzle.

This study addresses the free vibration analysis of nonlinear structural-acoustic system with non-rigid boundaries. In practice, the boundaries of a panel–cavity system are usually imperfectly rigid. Therefore, this study examines the effect of cavity boundary on the resonant frequencies of the nonlinear system. It is the first work of employing the elliptic integral approach for solving this problem, which is involved with the nonlinear multi-mode governing equations of a large amplitude panel coupled with a cavity. The main advantage of this approach is that less nonlinear algebraic equations are generated in the solution steps. The present elliptic integral solution agrees reasonably well with the results obtained from a finite element harmonic balance method. The effects of other parameters such as vibration amplitude, cavity depth, aspect ratio, etc., are also investigated.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

Highly nutritious lentil proteins (LP) have recently attracted interest in the food industry. However, due to their low solubility, extensive application of LP is severely limited. This study describes a new and successful method for overcoming this challenge by improving the nutritional–functional properties of LP, particularly their solubility and protein quality. By combining protein complexation with water kefir-assisted fermentation, the water solubility of native LP (~58%) increases to over 86% upon the formation of lentil–casein protein complexes (LCPC). Meanwhile, the surface charge increases to over −40 mV, accompanied by alterations in secondary and tertiary structures, as shown by Fourier-transform infrared and UV-vis spectra, respectively. In addition, subjecting the novel LCPC to fermentation increases the protein digestibility from 76% to over 86%, due to the reduction in micronutrients that have some degree of restriction with respect to protein digestibility. This approach could be an effective and practical way of altering plant-based proteins.

The common iliac artery supplies blood to the lower extremities, and stenosis in this region severely impacts hemodynamics. This study investigates the effects of 25%, 50%, and 75% iliac artery stenosis on key hemodynamic parameters using a fluid–structure interaction (FSI) approach. Semi-idealized geometries reconstructed from patient-specific data modeled realistic arterial behavior. Parameters such as wall displacement, time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), high oscillatory low shear magnitude (HOLMES) index, and endothelial cell activation potential (ECAP) were evaluated. Results showed peak wall displacement of 2.85 mm in the bifurcation zone under 75% stenosis. TAWSS increased with stenosis severity, peaking in stenotic regions and decreasing significantly downstream. OSI was highest in non-stenosed right branches and bifurcation areas, indicating multidirectional shear forces. HOLMES values were lowest downstream of stenoses, indicating disturbed flow. ECAP exceeded the thrombosis risk threshold (1.4 Pa−1) in post-stenotic zones under 75% stenosis, suggesting a higher risk of clot formation. These results demonstrate that stenosis disrupts local flow and causes hemodynamic changes downstream, emphasizing the need for comprehensive clinical assessment beyond the stenotic site. Regions with elevated ECAP and low HOLMES downstream may be prone to thrombosis, highlighting the importance of careful hemodynamic monitoring for treatment strategies.

In addition to having the most stability, the first task that every building has to do is having the economic factor, which is one of the concerns of the builders. One of the tools for the advent of architectural form is the structure. This is despite the fact that the limitless artistic thinking has very little unity with numerical and enclosed numerical thinking in the framework of structural engineering math. The date of the interaction between the architecture and the structure implies that the industrial revolution and the consequences are considered as a major event contributed to the further disruption of the relationship between architecture and structural engineering. In many studies, the form of architecture, structure, and nature have been distinctly examined, but in the present study, it was tried to link these two relatives, structures and architectures from the form in nature using technology. First, the evolution of structural and architectural harmony in different historical periods was studied. Then, we focused on natural patterns such as human, plant, and animal structures and finally, works by the Spanish architect, Gullart, was analyzed as an external case study. Regarding the above, this study has achieved a model and a strategy to enhance the quality of construction and interaction of structure and architecture using the structural structure in the existing forms in nature. Keywords: Architecture, Structures, Nature, Architectural and Structural Interaction

Group excavations are composed of several individual excavations adjacent to each other with simultaneous or successive construction sequences (CS), which are distinctive from individual excavation in terms of the performance of excavation. In this study, a hyper-scale 3D finite element model was established to investigate the deformation behavior of a diaphragm wall system retaining a deep and oversized group excavation (DOGE) in Shanghai soft clay deposits. The numerical model simulated the practical construction stages and sequences, and it was verified by a series of comparisons with field measurements. Based on the numerical model, the spatial effect of the performance of DOGE in the process of excavation stages was investigated in this study, which cannot be addressed by limited field measurements. Furthermore, the effects of partition walls and CS on the deformation control were discussed to provide practical suggestions for oversized and deep excavations. The results indicate that the employment of bi-partition walls to divide the oversized excavation into several small pits and mono-partition walls and cross walls to further divide the pits near the metro lines into smaller ones, was proved to have significant effectiveness in controlling the wall deflection and protecting the adjacent metro line. For the partition wall, the magnitude and direction of the wall deflection primarily depended on the initial excavation, while the influence of subsequent excavation activities proved insignificant. Thus, it should be noted that the effect of the initial excavation should be especially concentrated. The findings can help optimize similar DOGE engineering.