Miracle-dz/UniFAR-FTUs huggingface dataset:
Swimming of Undulating Filaments in Viscous Fluids

Swimming of microorganisms in viscous fluids is a complex problem involving many degrees of freedom. In order to gain insight to this problem we investigate resistive force theory as a model for thin undulating filaments at low Reynolds number. The filaments are sufficiently thin so that resistive force theory can be used. We compare four major waveforms, which have one adjustable parameter; they are the square wave, sawtooth, cartesian sine, and curvature sine. The main quantities of interest are the swimming speed and swimming efficiency. For the infinitely long filaments motion is unidirectional along the center-line and the sawtooth is found to be the fastest and most efficient method. The swimming behavior of short filaments is far more complex, because there is cyclic pitching, and the velocity is periodic. Arguments about the symmetry are presented and it is noted that there is an optimal pitching state around half-integer wavelengths. We perform numerical parametric studies in terms of the filament length and actuation parameter and again find the sawtooth strategy to be the fastest and most efficient. We also provide Taylor-series solutions for the swimming of short filaments.
Instability Waves in High-Speed Jets: Near-and Far-Field DNS/LES Data Analysis

Solutions from inviscid linear stability theory (LST) and linear parabolized stability equations (PSE) were used to quantify the extent to which instability waves represent actual near-field dynamics in high-speed forced turbulent jets. The mean and unsteady flow data were taken from a well-validated large-eddy simulation (LES) database (Bodony and Lele, 2005). The jets and their operating conditions were heated high subsonic (M j = 0.97, T j /T∞ = 2.3, Re D = 84,000) and unheated supersonic (M j = 1.95, T j /T∞ = 0.56, Re D = 336,000) jets with the same jet exit velocities. The coherent components of the turbulent jet flow fields were extracted using a filter based on proper orthogonal decomposition (POD). To examine the effects of nonlinear interactions and to test the decomposition method, forced laminar jets were also considered; the flow data were obtained from a direct numerical simulation (DNS) database for an unheated M j = 0.90 and Re D = 4,100 jet (Ryu, 2010). Two methods were used to extract instability waves from the nonlinear flow fields: first, bi-orthogonal relations between regular and adjoint instability waves were formulated from linearized Euler equations to determine the amplitude coefficients of the instability waves in nonlinear flows; then, a least-squares fit was applied to solutions from the PSE, POD, and LES data for the same purpose. The noise radiated by the deduced instability wave modes, POD, and LES were computed using the Kirchhoff surface integral method. Frequencies St = fD j /U j = 0.1 ∼ 0.5 at azimuthal modes m = 0, 1, and 2 were compared. The adjoint decomposition method showed promising results only for the laminar jet. The eigenfunction shape in the radial direction and the growth and decay of energy in the streamwise direction were well captured. However, this method produced errors in analyses of turbulent jets due to sensitivity in regions where the mode had saturated and was decaying. The results of PSE and POD showed reasonably good agreement in the near field for the first helical mode (m = 1). However, there was only limited agreement in the far field, and among other modes, in contrast to the good agreements described in recent studies for supersonic unforced jets. This is due to the strong nonlinear effects in forced turbulent jets considered in the present study. This finding confirms a limitation of linear stability theories when predicting the wavepacket dynamics of forced turbulent jets. The PSE solution for the subsonic jet showed very weak acoustic radiation, and the far-field noise displayed strong sensitivity to the location of the Kirchhoff surface. This suggests a limitation of direct propagation approaches for the PSE solutions of moderately heated subsonic jets.
Analysis of Mock Impoliteness and Its Pragmatic Functions: Taking Crosstalks in CCTV’s Spring Festival Gala from 1983 to 2023 as an Example

Politeness is the basic social etiquette people follow in their daily interactions. The current scholars pay less attention to the study of mock impoliteness. Mock impolite discourse acts account for a large part of crosstalk which is a folk rap art. This paper takes all the crosstalks in CCTV’s Spring Festival Gala (from 1983 to 2023) as the corpus and analyzes the implementation strategies of mock impoliteness, which are joking, teasing, and humor. And summarizes their corresponding three pragmatic functions: solidarity reinforcement, cloaked coercion, and exploitative entertainment. This paper aims to expand readers’ understanding of mock impoliteness in interpersonal communication.
Standardized Operational Protocol of Human Brain Banking for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease for which there is currently no reliable treatment. Existing clinical diagnostic methods include neurophysiological examination, neuroimaging, and genetic testing, etc. In contrast, there is a relative insufficiency of data in neuropathology research, and the pathogenesis is still poorly understood. The neuropathological evaluation results obtained from autopsies of ALS patients and various organ and tissue samples are crucial resources for the study of this disease. The ALS Human Brain Bank is urgently needed for the basic and clinical research on this disease. However, currently there is no standardized protocol for such an ALS Brain Bank. Based on the "Standardized Operational Protocol for the Human Brain Banking in China", this Standardized Operational Protocol is drafted to provide a guideline for the construction and operation of ALS Brain Bank to ensure the quality and homogeneity in China and worldwide. This article focused on the collection of anti-mortem information, donation process, the harvesting, preservation and pathological evaluation of the brain, spinal cord, and other organ/tissue samples of ALS donors in the human brain bank. The establishment of ALS Brain Banks may foster the relevant research works and pave the way for the treatment of this disease.
Type Theorie and Decision Procedures

The goal of this thesis is to develop a logical system in which formal proofs of mathematical statements can be carried out in a way which is close to mathematical practice. Our main contribution is the de nition and study of a new calculus, the Calculus of Congruent and Inductive Constructions, an extension of the Calculus of Inductive Constructions (CIC) integrating in its computational part the entailment relation via decision procedures of a rst order theory over equality. A major technical innovation of this work lies in the computational mechanism: goals are sent to the decision procedure together with a set of user hypotheses available from the current context. Our main results show that this extension of CIC does not compromise its main properties: con uence, strong normalization, consistency and decidability of proof checking are all preserved (as soon as the incorporated theory is itself decidable). As such, our calculus can be seen as a decidable restriction of the Extensional Calculus of Constructions. It can therefore serve as the basis for an extension of the Coq proof assistant.
Testing exogeneity in nonparametric instrumental variables identified by conditional quantile restrictions

This paper presents a test for exogeneity of explanatory variables in a nonparametric instrumental variables (IV) model whose structural function is identified through a conditional quantile restriction. Quantile regression models are increasingly important in applied econometrics. As with mean-regression models, an erroneous assumption that the explanatory variables in a quantile regression model are exogenous can lead to highly misleading results. In addition, a test of exogeneity based on an incorrectly specified parametric model can produce misleading results. This paper presents a test of exogeneity that does not assume the structural function belongs to a known finite-dimensional parametric family and does not require nonparametric estimation of this function. The latter property is important because, owing to the ill-posed inverse problem, a test based on a nonparametric estimator of the structural function has low power. The test presented here is consistent whenever the structural function differs from the conditional quantile function on a set of non-zero probability. The test has non-trivial power uniformly over a large class of structural functions that differ from the conditional quantile function by O(n-1/2) . The results of Monte Carlo experiments illustrate the usefulness of the test.
Bioinformatics-based study of the ceRNA network and the sub-network of the Ziyin Buyang Formula in premature ovarian failure due to ovarian tumour therapy

Ovarian tumour therapy has been known to cause ovary ageing. The competitive endogenous RNA (ceRNA) network plays an indispensable role in various diseases. We constructed the ceRNA network to examine the role of circular RNA (circRNA) in the ovaries of older women and elucidate the intervention mechanism of the Ziyin Buyang Formula. Ovarian tissue datasets were obtained from the Gene Expression Omnibus (GEO) database and screened for differentially expressed circRNAs (DECs), differentially expressed miRNA (DEMis) and differentially expressed mRNAs (DEMs). The circBase and Cancer-Specific CircRNA Databse 2.0 (CSCD) database were used to predict the miRNA response elements (MREs) of DECs. Interacting miRNAs were obtained by intersecting MREs and DEMis. Based on the interaction between circRNA-miRNA and miRNA-mRNA, the node circRNA, miRNA and mRNA were obtained. The ceRNA network was constructed using the Cytoscape software. The targets of the Ziyin Buyang Formula were obtained from the Traditional Chinese Medicine Systems Pharmacology database and related literature. The sub-networks of the interventions of the Ziyin Buyang Formula were constructed according to the target genes and the corresponding relationships in the ceRNA network. A total of seven circRNAs, six miRNAs and 224 mRNAs were distinguished as nodes from 226 DECs, 35 DEMis, 1697 DEMs, 2320 MREs, 19 intersecting miRNAs and 765 intersecting mRNAs. Moreover, 96 chemical components, 239 targets and four target genes were identified in the ceRNA network of the Ziyin Buyang Formula. This study constructed a ceRNA network for ovary ageing due to ovarian tumour therapy, and the sub-network of the interventions of Ziyin Buyang Formula was predicted, providing a novel perspective for the effective treatment of ovary ageing due to ovarian tumour therapy.
The Effects of Physical Activity, Exercise, and Sports Programs on Depressive Symptoms in Individuals with Disabilities: A Systematic Review with Meta-Analysis

Studies show that physical activity, exercise, or sport reduces depressive symptoms in the general population. However, little is known about its effects on individuals with disabilities. Thus, this systematic review with meta-analysis aims to verify the effects of this practice on depressive symptoms in individuals with disabilities. The Pubmed, Web of Science, Scopus, and SportDiscus databases were used, with several descriptors and Boolean operators. A total of 1509 studies were identified through searching the databases. Studies that met the eligibility criteria were subsequently assessed for their methodological quality (Downs and Black scale), and a meta-analysis was performed. The Z-values that were obtained to test the null hypothesis, which states that there is no difference in means, showed Z = −2.294 and a corresponding p-value = 0.022. We can, therefore, reject the null hypothesis in the sense that exercise seems to reduce depressive symptoms in individuals with disabilities. In sum, participants from the intervention group presented more probability of reducing depressive symptoms when compared to the control group (approximately −1.4 standard differences in means; 95% CI −2.602 to −0.204).
Physical Structure of DC Diode Sputtered ZnO Films and its Influence on the Effective Electromechanical Coupling Factors

A number of ZnO polycrystalline films with various degrees of structural ordering of the crystallites were obtained by dc diode sputtering. A quantitative description was obtained by rocking curve studies and pole-figure analysis for the spatial distribution of c-axis orientations of the crystallites in the deposited films. The distribution was found to be expressed by a Gaussian distribution for most of the specimens. The physical structure of the films can be represented by a mean m (an inclination angle of mean c-axis orientation) and a standard deviation σ. Measurements of the effective electromechanical coupling factors kt (m=0°∼6°) and ks (m=7°∼12°) were made for the films of σ=2°∼8°. It was found that the coupling factors kt and ks were not affected remarkably by the standard deviation until σ=6°.
A Fourth-Order Nonlinear Conjugate Gradient Method in Equality Constrained Optimization

This paper presents a fourth-order nonlinear conjugate gradient method in equality constrained optimization. The idea is to transform the constrained problem into unconstrained type through the Lagrange multipliers scheme. Using four terms of Taylor series development, we approximate the transformed function (augmented Lagrange function). Lastly, we employ the new fourth-order nonlinear conjugate gradient method in equality constrained optimization to solve the optimization problem. We present the algorithm in steps and some properties of the gradients are proved, using classical results. Also, the convergence analysis has been proved under classical and known assumptions. Furthermore, we present the obtained numerical results and compare them to some existing results. The analysis of results confirms that the new method is accurate.
Food security and agriculture value-added: Do they asymmetrically matter for Korean environmental sustainability?

Abstract Limited research has previously probed the interplay between Korea’s food security, agricultural sector, and environmental ramifications. This study delves into the asymmetric influence of food security and agricultural value-added on Korea’s carbon dioxide emissions (an environmental sustainability proxy) from 1970 to 2020, incorporating non-renewable energy consumption and foreign direct investment. Employing a nonlinear auto-regressive distributed lag methodology, our findings unveil long-term asymmetric effects of food security and agricultural value-added on carbon dioxide emissions. Specifically, carbon dioxide emissions rise with positive shocks to food security and agricultural value-added but decline with negative shocks. Moreover, our analysis reveals that non-renewable energy consumption positively impacts carbon dioxide emissions, whereas foreign direct investment exerts a negative effect. These insights may underpin policy recommendations for the Korean government to bolster environmental sustainability.
SOME DESIGN CONCEPTS OF NONLINEAR STATE FEEDBACK CONTROL SYSTEM BASED ON IFO-K△x

The increment function observer (IFO-K△x) can be able to used to design state feedback control system which controlled object has nonlinear characteristics and can be described by model of Hammerstein. It was certified by mathematical analysis that all of Luenberger function observer, full dimension state observer and increment state observer is special case of increment function observer under the different premises conditions. In this paper, starting from the close loop system of nonlinear control system effected under the state feedback control law based on increment function observer, several important concepts have been developed. Thus it is circumstantiated that controller and observer can be designed independently when using increment function observer to design state feedback control system which is nonlinear system based on model of Hammerstein. That is separate principle be the same with nonlinear system described in this paper. The theoretic foundation is established for using IFO- K△x in actual engineering of thermal control field.
Extraction of optical properties in the sub-diffuse regime by spatially resolved reflectance spectroscopy

In this paper, the commonly used inverse Monte Carlo model based on absorption and reduced scattering coefficients is extended by a well-known similarity parameter γ (gamma), which carries additional information on the phase function. Sub-diffuse reflectance measurements at five source-detector separations were used to extract the absorption and reduced scattering coefficients and phase function information encapsulated in γ. The performance of the extended inverse Monte Carlo model was evaluated by simulated and experimental reflectance spectra of turbid phantoms. A three-fold increase in the accuracy of the extended inverse Monte Carlo model that incorporates γ was observed.
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining

How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself. Einstein said that we should"make everything as simple as possible, but not simpler. "We rephrase it into the careful simplicity principle: a carefully-designed simple model can surpass sophisticated ones in real-world graphs. Based on the principle, we propose SlimG for semi-supervised node classification, which exhibits four desirable properties: It is (a) accurate, winning or tying on 10 out of 13 real-world datasets; (b) robust, being the only one that handles all scenarios of graph data (homophily, heterophily, random structure, noisy features, etc.); (c) fast and scalable, showing up to 18 times faster training in million-scale graphs; and (d) interpretable, thanks to the linearity and sparsity. We explain the success of SlimG through a systematic study of the designs of existing GNNs, sanity checks, and comprehensive ablation studies.
HiNeRV: Video Compression with Hierarchical Encoding based Neural Representation

Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs. In this context, Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively high decoding speed compared to other methods. However, existing INR-based methods have failed to deliver rate quality performance comparable with the state of the art in video compression. This is mainly due to the simplicity of the employed network architectures, which limit their representation capability. In this paper, we propose HiNeRV, an INR that combines light weight layers with novel hierarchical positional encodings. We employs depth-wise convolutional, MLP and interpolation layers to build the deep and wide network architecture with high capacity. HiNeRV is also a unified representation encoding videos in both frames and patches at the same time, which offers higher performance and flexibility than existing methods. We further build a video codec based on HiNeRV and a refined pipeline for training, pruning and quantization that can better preserve HiNeRV's performance during lossy model compression. The proposed method has been evaluated on both UVG and MCL-JCV datasets for video compression, demonstrating significant improvement over all existing INRs baselines and competitive performance when compared to learning-based codecs (72.3% overall bit rate saving over HNeRV and 43.4% over DCVC on the UVG dataset, measured in PSNR).
Enabling BIM-Driven Robotic Construction Workflows with Closed-Loop Digital Twins

The introduction of assistive construction robots can significantly alleviate physical demands on construction workers. Leveraging a Building Information Model (BIM) offers a natural and promising approach to driving a robotic construction workflow. However, because of uncertainties inherent in construction sites, such as discrepancies between the as-designed and as-built components, robots cannot solely rely on a BIM to plan and perform field construction work. Human workers are adept at improvising alternative plans with their creativity and experience and thus can assist robots in overcoming uncertainties and performing construction work successfully. In such scenarios, it is critical to continuously update the BIM as work processes unfold so that it includes as-built information for the ensuing construction and maintenance tasks. This research introduces an interactive closed-loop digital twin framework that integrates a BIM into human-robot collaborative construction workflows. The robot's functions are primarily driven by the BIM, but it adaptively adjusts its plans based on actual site conditions, while the human co-worker oversees and supervises the process. When necessary, the human co-worker intervenes in the robot's plan by changing the task sequence or workspace geometry or requesting a new motion plan to help the robot overcome the encountered uncertainties. Experiments involving block pick-and-place tasks are carried out to verify system performance using an industrial robotic arm in a research laboratory setting that mimics a construction site. In addition, a drywall installation case study is conducted to validate the system. Integrating the flexibility of human workers and the autonomy and accuracy afforded by BIMs, the proposed framework offers significant promise of increasing the robustness of construction robots in the performance of field construction work.
Granger-Causal Hierarchical Skill Discovery

Reinforcement Learning (RL) has shown promising results learning policies for complex tasks, but can often suffer from low sample efficiency and limited transfer. We introduce the Hierarchy of Interaction Skills (HIntS) algorithm, which uses learned interaction detectors to discover and train a hierarchy of skills that manipulate factors in factored environments. Inspired by Granger causality, these unsupervised detectors capture key events between factors to sample efficiently learn useful skills and transfer those skills to other related tasks—tasks where many reinforcement learning techniques struggle. We evaluate HIntS on a robotic pushing task with obstacles—a challenging domain where other RL and HRL methods fall short. The learned skills not only demonstrate transfer using variants of Breakout, a common RL benchmark, but also show 2-3x improvement in both sample efficiency and final performance compared to comparable RL baselines. Together, HIntS demonstrates a proof of concept for using Granger-causal relationships for skill discovery