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Bio-inspired flapping wings have been extensively studied for their remarkable aerodynamic performance. Recently, their noise emission has attracted growing interest, but a careful analysis of scaling laws for their sound generation is missing. This work presents scaling laws for the sound generation of bio-inspired flapping wings during hovering flight based on the potential flow theory and Ffowcs Williams-Hawkings acoustic analogy. Direct numerical simulations considering a range of parameters including the Reynolds number, Mach number and wing kinematics confirms that the proposed scaling laws capture the major physics involved and their predictions agree well with the numerical results. The scaling laws can be used as a powerful tool for engineers in the design of micro-aerial vehicles considering both aerodynamics and acoustics performances simultaneously.

With the rapid advancement in machine learning, the recognition and analysis of brain activity based on EEG and eye movement signals have attained a high level of sophistication. Utilizing deep learning models for learning EEG and eye movement features proves effective in classifying brain activities. A focused state indicates intense concentration on a task or thought. Distinguishing focused and unfocused states can be achieved through eye movement behaviors, reflecting variations in brain activities. By calculating binocular focusing point disparity in eye movement signals and integrating relevant EEG features, we propose an annotation method for focused states. The resulting comprehensive dataset, derived from raw data processed through a bio-acquisition device, includes both EEG features and focused labels annotated by eye movements. Extensive training and testing on several deep learning models, particularly the Transformer, yielded a 90.16% accuracy on the subject-dependent experiments. The validity of this approach was demonstrated, with cross-subject experiments, key frequency band and brain region analyses confirming its generalizability and providing physiological explanations.

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

This study presents a framework for conducting psychological and linguistic research through simulated conversations using large language models (LLMs). The proposed methodology offers significant advantages, particularly for simulating human interactions involving potential unethical language or behaviors that would be impermissible in traditional experiments with human participants. As a demonstration, we employed LLMs to simulate family conversations across four parenting styles (authoritarian, authoritative, permissive, and uninvolved). In general, we observed that the characteristics of the four parenting styles were portrayed in the simulated conversations. Several strategies could be used to improve the simulation quality, such as including context awareness, employing a few-shot prompting approach or fine-tuning models to cater to specific simulation requirements. Overall, this study introduces a promising methodology for conducting psychological and linguistic research through simulated conversations, while acknowledging the current limitations and proposing potential solutions for future refinement and improvement.

An immersed boundary method for the fluid--structure--thermal interaction in rarefied gas flow is presented. In this method, the slip model is incorporated with the penalty immersed boundary method to address the velocity and temperature jump conditions at the fluid--structure interface in rarefied gas flow within slip regime. In this method, the compressible flow governed by Navier-Stokes equations are solved by using high-order finite difference method; the elastic solid is solved by using finite element method; the fluid and solid are solved independently and the fluid--structure--thermal interaction are achieved by using a penalty method in a partitioned way. Several validations are conducted including Poiseuille flow in a 2D pipe, flow around a 2D NACA airfoil, moving square cylinder in a 2D pipe, flow around a sphere and moving sphere in quiescent flow. The numerical results from present method show good agreement with the previous published data obtained by other methods, and it confirms the the good ability of the proposed method in handling fluid--structure--thermal interaction for both weakly compressible and highly compressible rarefied gas flow. To overcome the incapability of Navier-Stokes equations at high local Knudsen numbers in supersonic flow, an artificial viscosity is introduced to ease the sharp transition at the shock wave front. Inspired by Martian exploration, the application of proposed method to study the aerodynamics of flapping wing in rarefied gas flow is conducted in both 2D and 3D domains, to obtain some insights for the flapping-wing aerial vehicles operating in Martian environment.

The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by parallel computing systems can significantly improve the efficiency and scalability of EC. On the other hand, data are collected and processed in a distributed manner, which brings a novel development direction and new challenges to EC. In this paper, we intend to give a systematic review on distributed EC (DEC). First, a new taxonomy for DEC is proposed from top design mechanism to bottom implementation mechanism. Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment. Second, we clarify two major purposes of DEC, i.e., improving efficiency through parallel processing for centralized optimization and cooperating distributed individuals/sub-populations with partial information to perform distributed optimization. Third, noting that the latter purpose of DEC is an emerging and attractive trend for EC with the booming of spatially distributed paradigms, this paper gives a systematic definition of the distributed optimization and classifies it into dimension distributed-, data distributed-, and objective distributed-optimization problems. Formal formulations for these problems are provided and various DEC studies on these problems are reviewed. We also discuss challenges and potential research directions, aiming to enlighten the design of DEC and pave the way for future developments.

For a fixed integer $r\geqslant 3$, let $\mathbb{H}_r(n,p)$ be a random $r$-uniform hypergraph on the vertex set $[n]$, where each $r$-set is an edge randomly and independently with probability $p$. The random $r$-generalized triadic process starts with a complete bipartite graph $K_{r-2,n-r+2}$ on the same vertex set, chooses two distinct vertices $x$ and $y$ uniformly at random and iteratively adds $\{x,y\}$ as an edge if there is a subset $Z$ with size $r-2$, denoted as $Z=\{z_1,\cdots,z_{r-2}\}$, such that $\{x,z_i\}$ and $\{y,z_i\}$ for $1\leqslant i\leqslant r-2$ are already edges in the graph and $\{x,y, z_1,\cdots,z_{r-2}\}$ is an edge in $\mathbb{H}_r(n,p)$. The random triadic process is an abbreviation for the random $3$-generalized triadic process. Kor\'{a}ndi et al. proved a sharp threshold probability for the propagation of the random triadic process, that is, if $p= cn^{ - \frac 12}$ for some positive constant $c$, with high probability, the triadic process reaches the complete graph when $c> \frac 12$ and stops at $O(n^{\frac 32})$ edges when $c< \frac 12$. In this note, we consider the final size of the random $r$-generalized triadic process when $p=o( n^{- \frac 12}\log^{ \alpha(3-r)} n)$ with a constant $\alpha> \frac 12$. We show that the generated graph of the process essentially behaves like $\mathbb{G}(n,p)$. The final number of added edges in the process, with high probability, equals $ \frac {1}{2}n^{2}p(1\pm o(1))$ provided that $p=\omega(n^{-2})$. The results partially complement the ones on the case of $r=3$., Comment: 10 pages

Preschoolers' repeating patterning knowledge is predictive of their concurrent and later math and numeracy knowledge, but strong experimental evidence is needed to determine if these relations are causal. The purpose of the current Study was to examine the causal effects of repeating patterning and numeracy tutoring on repeating patterning, numeracy, and general mathematics knowledge in the year before kindergarten (i.e., pre-K). Children in pre-K (N = 211) were randomly assigned to receive five sessions of researcher-delivered tutoring (a) on repeating patterns and numeracy or (b) on numeracy (and literacy as an active control), or received no tutoring and business as usual classroom instruction(control). Children who received tutoring in repeating patterning and numeracy improved in their repeating patterning knowledge the most. However, children's general math and numeracy knowledge improved similarly across conditions, and a specific aspect of numeracy emphasized during the tutoring did not improve. Children's repeating patterning knowledge is malleable, but this initial attempt to demonstrate causal links between repeating patterning and math knowledge was not successful. Results parallel mixed success in research training other skills, such as working memory or spatial skills, for improving mathematics knowledge. Findings are discussed in terms of the relations between patterning, numeracy, and general math knowledge in preschoolers. [This paper will be published in "Journal of Educational Psychology."]

For a fixed integer $k\geqslant 2$, let $G\in \mathcal{G}(n,p)$ be a simple connected graph on $n\rightarrow\infty$ vertices with the expected degree $d=np$ satisfying $d\geqslant c$ and $d^{k-1}= o(n)$ for some large enough constant $c$. We show that the asymptotical size of any maximal collection of edges $M$ in $G$ such that no two edges in $M$ are within distance $k$, which is called a distance $k$-matching, is between $ \frac{(k-1)n\log d}{4d^{k-1}}$ and $ \frac{k n \log d}{2d^{k-1}}$. We also design a randomized greedy algorithm to generate one large distance $k$-matching in $G$ with asymptotical size $ \frac{kn\log d}{4d^{k-1}}$. Our results partially generalize the results on the size of the largest distance $k$-matchings from the case $k=2$ or $d=c$ for some large constant $c$.