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Finite element buckling analysis for micro structures based on the couple stress theory
No full textMechanical behavior at the micro- and nano-scale exhibits size-dependent effects, such as increased stiffness, which are not captured by classical continuum mechanics. These effects become particularly important in thin structures, where buckling is a critical failure mode; however, previous studies have been limited to simple geometries due to the complexity of the governing equations. This study develops a finite element framework for buckling analysis based on the modified couple stress theory (MCST), applicable to arbitrary geometries. Beam and shell elements are formulated to incorporate size effects, enabling buckling analysis of general structures. The framework is demonstrated on various configurations, including thin membranes under residual stress, stiffened plates, and tensile bars, highlighting its versatility. Numerical results show that accounting for size effects consistently increases the predicted critical buckling loads compared to classical continuum mechanics. The proposed approach offers a broadly applicable tool for stability assessment in micro-electromechanical systems (MEMS), flexible electronics, and thin-film micro sensors and actuators.
Large eddy simulation of wake mixing control strategies in offshore wind farms
No full textWake-turbine interaction reduces power output in wind farms, especially in aligned layouts where downstream turbines operate in the wake of upstream units. This study evaluates established wake mixing control strategies, including dynamic induction control (DIC), baseline dynamic individual pitch control (DIPC), and a new higher-order DIPC method for enhancing wake recovery in floating offshore wind turbines. Wall-modeled large eddy simulation, coupled with an actuator line model, is employed to simulate a two-turbine wind farm under a neutrally stratified turbulent atmospheric boundary layer. The flow fields are analyzed using proper orthogonal decomposition, mean kinetic energy flux, and Fourier transformation techniques, which revealed that the DIPC-based methods significantly enhance wake mixing through a higher energy entrainment and tip-vortex breakdown, especially in below-rated wind region. The higher-order DIPC method, which combines two sinusoidal blade pitch signals, exhibits greater energy flux as well as a notable wake deflection that can improve downstream turbine performance. Compared to the conventional Greedy method, the wake mixing control methods generally increase the growth rates of mutual inductance instability modes that accelerate tip-vortex breakdown. In terms of wind turbine performance, the higher-order DIPC method achieves a 7.9% increase in total power relative to the Greedy case and 2.3% more than the baseline DIPC, while having slightly higher structural load and comparable platform motions. These findings demonstrate the potential of wake forcings to increase wake recovery and power yield in offshore wind farms, showing the dominant modes in the blade pitching frequency and their effect on exciting floating platform motions.
Microbial production of propionic acid through a novel β-alanine route
No full textPropionic acid is a key three carbon platform chemical with broad applications in food preservation, pharmaceuticals, and polymer production. Traditional microbial production of propionic acid employing Propionibacterium species is constrained by slow growth, and limited genetic engineering tools, thereby restricting its industrial use. Here, we report the development of a novel biosynthetic pathway for propionic acid production via the beta-alanine route. This pathway was engineered into two modules: an upstream beta-alanine-forming module and a downstream propionic acid-forming module. The downstream pathway was first constructed and validated in Escherichia coli W3110. Subsequently, co-expression of the upstream module enabled de novo propionic acid production from glucose. Through enzyme screening, precursor flux enhancement, and optimization of phosphoenolpyruvate carboxylase (PPC) flux, the final engineered E. coli strain achieved 14.8 g/L of propionic acid in fed-batch fermentation. Furthermore, we explored Corynebacterium glutamicum ATCC 13032 as an alternative host due to its superior tolerance to propionic acid. The same downstream pathway was introduced into a previously developed beta-alanine-overproducing C. glutamicum strain to enable propionic acid production from glucose. Additional engineering strategies, such as enzyme screening, disruption of competing pathways (ackpta), and elimination of propionic acid catabolic pathways (prpD2B2C2), led to the production of 47.4 g/L of propionic acid in fed-batch fermentation, representing the highest reported titer of heterologous propionic acid production. This work establishes a novel and vitamin B12-independent strategy for bio-based propionic acid production, offering a sustainable alternative to conventional processes.
Bounding quantum uncommon information with quantum neural estimators
No full textIn classical information theory, uncommon information refers to the amount of information that is not shared between two messages, and it admits an operational interpretation as the minimum communication cost required to exchange the messages. Extending this notion to the quantum setting, quantum uncommon information is defined as the amount of quantum information necessary to exchange two quantum states. While the value of uncommon information can be computed exactly in the classical case, no direct method is currently known for calculating its quantum analogue. Prior work has primarily focused on deriving upper and lower bounds for quantum uncommon information. In this work, we propose a new approach for estimating these bounds by utilizing the quantum Donsker-Varadhan representation and implementing a gradient-based optimization method. Our results suggest a pathway toward efficient approximation of quantum uncommon information using variational techniques grounded in quantum neural architectures.
A novel low-fidelity-guided design of experiments for multi-fidelity surrogate modeling
No full textThis paper presents a novel low-fidelity-guided design of experiments (LFGD) strategy to enhance multi-fidelity (MF) surrogate models under a limited high-fidelity (HF) data budget. The key idea is to perform a design of experiments tailored to MF surrogate modeling by leveraging inexpensive yet less accurate low-fidelity (LF) data as prior information. The proposed method aims to allocate more HF samples to high-variation regions, while distributing the remaining samples evenly across low-variation regions, as guided by LF information. Based on this idea, the proposed LFGD method comprises three main stages: (1) sample quantity allocation, (2) high-variation exploitation sampling, and (3) low-variation exploration sampling. The first stage analyzes the LF surrogate to determine how many of the given HF samples should be assigned to high-variation exploitation and low-variation exploration. The second stage selects HF sample locations for high-variation exploitation using the Hessian of the LF surrogate. The third stage places the remaining HF samples to maximize the minimum distance between input points, ensuring space-filling. During the two sampling stages, a Latin hypercube sampling constraint is applied to prevent excessive concentration in specific input regions. Numerical results demonstrate that the proposed method outperforms existing ones in terms of accuracy and robustness under the same HF budget.
MicGraphNet: Microphone graph network for cross-correlation-based time delay estimation for accurate sound source localization
No full textSound source localization (SSL) has gained widespread attention due to its critical role in various applications. As a representative SSL algorithm, the time difference of arrival (TDOA) method estimates the direction of arrival (DOA) of the sound source by calculating the cross-correlation (CC) of measured signals from multiple microphones. For TDOA-based DOA estimation, noisy environments and finite sampling rates complicate precise DOA measurements, while inaccuracies in microphone array (MA) configurations further challenge SSL accuracy. To address these challenges, we propose the microphone graph network (MicGraphNet), a novel deep learning framework leveraging graph neural networks (GNNs) for TDOA-based SSL. MicGraphNet takes advantage of the inductive bias between microphone-array configurations and CC features, enabling efficient learning that compensates for noise-and reverberation-induced errors and thereby improves DOA accuracy. MicGraphNet also directly regresses continuous TDOAs rather than relying on grid-based CC peaks, which mitigates quantization errors and enhances precision. Finally, the model incorporates a position-uncertainty injector, enhancing robustness to practical sensor placement inaccuracies. Experimental validations conducted in free-field, anechoic chamber, reverberant rooms, as well as conversational speech scenarios, demonstrate that MicGraphNet consistently improves DOA accuracy across multiple tetrahedral sub-array configurations. In particular, average DOA error reductions of approximately 14 degrees, 3 degrees, 8 degrees, and 4 degrees were obtained in these respective environments, confirming the model's effectiveness in real-world applications.
PACMAN: Rapid identification of keypoint patch-based fiducial marker in occluded environments
No full textFiducial marker systems are widely used in image-based localization methods due to their high robustness and low computational latency. However, occlusions caused by dynamic environmental factors, such as shadows and unexpected objects, significantly hinder the detection of fiducial markers, as partially visible patterns and significant image degradation often contradict the fundamental assumptions of marker detection systems. To address this challenge, we propose a keypoint-based fiducial marker and a deep-learning-based detector that jointly handle occlusion and image degradation with minimal computational latency. First, we design four distinct keypoint patches that account for occlusions and maintain essential functionalities. A fiducial marker is then constructed by assembling six identical patches under geometric constraints. Second, a widely-used interest point detector network is optimized for the proposed marker design, resulting in robust keypoint detection under various types of image deformation and degradation. A geometric consistency check is subsequently applied to map imperfect keypoint detections to 6D marker poses in the image, effectively rejecting occlusions and potential network failures. Third, neural network quantization and parallel CPU processing are applied to minimize computational latency. Our experimental results demonstrate higher detection rates then other types of single marker in occluded environments, with improvements ranging from 17% to 40%. The proposed system is also evaluated under motion blur, dimming effects, and variations in scale and rotation. Additionally, the efficient computational design enables end-to-end processing at up to 749 FPS on a desktop PC and 138 FPS on an edge device, for 640 x 480 resolution images containing a single marker. Our code is available at:
Structural distortion-driven design of cobalt-free high-entropy perovskite electrodes for high-performance solid oxide cells
No full textHigh-entropy oxides (HEOs) offer a promising platform for advanced air electrodes in solid oxide electrochemical cells (SOCs), yet the fundamental mechanisms underpinning their enhanced catalytic performance remain elusive. Here, we systematically engineer cobalt-free HEOs of the form (Pr0.2Bi0.2Sr0.2La0.2X0.2)MnO3-delta (X = Ba, Ca, Nd, Gd) by modulating the Goldschmidt tolerance factor to control structural distortion. Fourier electron density analysis reveals distinct octahedral tilting and lattice asymmetry across the series. We uncover a strong correlation between lattice asymmetry, oxygen-ion diffusion characteristics, defect formation, and electrochemical kinetics. Among the compositions, the Nd-substituted variant (PBSLNM) achieves an optimal distortion profile and exhibits outstanding performance, delivering a peak power density of 1.59 W/cm2 in fuel cell mode and a current density of 0.73 A/cm2 at 1.3 V in electrolysis mode at 700 degrees C, with excellent durability over 500 h. Density functional theory calculations reveal that structural distortion lowers the oxygen vacancy formation energy, elevates the O 2p band center, and induces heterogeneous electronic distributions that promote both oxygen reduction and evolution reactions. Our findings establish structural distortion as a critical descriptor for HEO performance and provide a rational design strategy for high-performance SOC air electrodes.
Optimizing PES microfiltration membranes for sterile filtration: A comparative study of polydopamine coating in acidic and basic conditions to minimize protein adsorption
No full textThe increasing need for effective sterile filtration in the pharmaceutical industry calls for advancements in microfiltration (MF) membrane technology that can effectively reduce fouling and preserve essential protein products. Our study highlights the crucial role of improving the hydrophilicity throughout the entire membrane, not just on the surface, to decrease both protein adsorption and fouling. We have developed a facile but effective method for producing hydrophilic porous polyethersulfone (PES) MF membranes using vapor-induced phase separation (VIPS) followed by a hydrophilic coating. By meticulously adjusting the vapor exposure time, we fabricated two specific types of symmetric membranes: a standard one with a mean pore size of 0.22 mu m and another designed with slightly larger pores for the subsequent polydopamine (PDA) coating. All membranes developed in this study showed an outstanding sterilization performance (over 99.99999 % bacteria rejection) due to the absence of defects. Our comparative studies of PDA coating in both acidic and basic environments revealed that the PDA-modified membranes in a Tris-HCl buffer (pH 8.0) (PDA-b-PES2M) outperform others in performance of protein microfiltration. While the membrane modified under acidic conditions underwent a gradual degradation of PDA leading to a poor filtration performance, the PDA-b-PES2M membranes showcased remarkable anti-fouling properties, greatly minimizing the adsorption of bovine serum albumin (BSA) and simplifying the removal of adsorbed materials. Our optimized membrane has a great potential for sterile filtration applications where minimizing protein loss is crucial.
A note on an effective bound for the gonality conjecture
No full textThe gonality conjecture, proved by Ein-Lazarsfeld, asserts that the gonality of a nonsingular projective curve of genus g can be detected from its syzygies in the embedding given by a line bundle of sufficiently large degree. An effective result obtained by Rathmann says that any line bundle of degree at least 4g - 3 would work in the gonality theorem. In this note, we develop a new method to improve the degree bound to 4g - 4 with two exceptional cases. Published by Elsevier B.V.