Hongyi Xiao

Particles governed by many-body interactions exhibit remarkably complex structures and dynamics. We experimentally investigate a monolayer of pentagon particles subjected to an up-lifting air flow which induces many-body aerodynamic interactions and stochastic motion akin to a thermal bath. To minimize air flow resistance, particles move collectively with interactions dictated by their geometry: hollow particles exhibit effective attraction, whereas solid particles repel each other. Under sufficiently large air flow, sparsely packed hollow pentagons overcome substrate friction and undergo long-time diffusive motion. Under lower air flow, we see a coexistence of isolated, static pentagons and densely packed, "active" clusters, whose particles display super-diffusivity. This "emergent activity" arises collectively when locally disordered structures interact with the air flow, resulting in correlated motion across broad temporal and spatial scales. Using Langevin dynamics simulations of two-dimensional attractive active pentagons, whose activity is an effective result of the local packing density, we further unravel the basic features of this emergent activity.

Abstract The mechanisms that steer behavioural plasticity in complex environments remain poorly understood. Here, we introduce Peb2les, a quasi-two-dimensional granular arena, for the behavioural assessment of Caenorhabditis elegans nematodes, as they interact with deformable, dynamic terrains. Using customized deep-learning-based methods, we track both nematodes and particles to characterize coupled animal-environment dynamics. We find that nematodes’ locomotor performance depends on particle density and surface properties, and is largely independent of mechanosensation, whereas functional touch sense is required for the initial decision to enter rough-textured arenas. In addition, we identify a previously undescribed touch-seeking behaviour that partially depends on mechanosensation, in which C. elegans actively return to the granular arena after exiting. In parallel, locomoting nematodes rearrange and stack surrounding particles, continuously modifying the granular terrain. Together, these findings establish Peb2les as a versatile platform for probing the mechanism and limits of behavioural plasticity and locomotory adaptability of nematodes in sensory-enriched, soil-like environments, while also providing insight into the potential evolutionary origins of adaptive touch-seeking behaviours in animals.

We experimentally study a scallop-like swimmer with reciprocally flapping wings in a nearly frictionless, cohesive granular medium consisting of hydrogel spheres. Significant locomotion is found when the swimmer’s flapping frequency matches the inverse relaxation time of the material. Remarkably, the swimmer moves in the opposite direction compared with its motion in a cohesion-free granular material of hard plastic spheres. At higher or lower frequencies, we observe no motion of the swimmer, apart from a short initial transient phase. X-ray radiograms reveal that the wing motions create low-density zones, which in turn give rise to a hysteresis in drag and propulsion forces. This time-dependent effect, combined with the swimmer’s inertia, accounts for locomotion at intermediate frequencies.

Abstract Mechanical metamaterials with engineered failure properties typically rely on periodic unit cell geometries or bespoke microstructures to achieve their unique properties. We demonstrate that intelligent use of disorder in metamaterials leads to distributed damage during failure, resulting in enhanced fracture toughness with minimal losses of strength. Toughness depends on the level of disorder, not a specific geometry, and the confined lattices studied exhibit a maximum toughness enhancement at an optimal level of disorder. A mechanics model that relates disorder to toughness without knowledge of the crack path is presented. The model is verified through finite element simulations and experiments utilizing photoelasticity to visualize damage during failure. At the optimal level of disorder, the toughness is more than 2.6× of an ordered lattice of equivalent density.

Abstract The packing behavior of powders is significantly influenced by various types of inter-particle attractive forces, including adhesion and non-bonded van der Waals forces [4, 7, 8, 19, 41, 43]. Alongside particle size and shape distributions, the inter-particle interactions, particularly frictional and adhesive forces, play a crucial role in determining the flow behavior and consequently the packing density of the powder layer. The impact of various types of attractive forces on the packing density of powders with different materials and particle size distributions remains largely unexplored and requires further investigation. Accurately comprehending these effects through experiments while considering specific particle size distributions and material properties poses significant challenges.

This paper aims to develop an optical fiber vibration identification system based on big data analysis to realize the real-time monitoring and data analysis of the running state of optical cable. The goal is to reduce the impact of sudden failures on the network by accurately analyzing fiber vibration data. Big data technology is adopted to process and analyze fiber vibration data, and data-driven method is used to provide decision support, including collecting large amounts of fiber vibration data, applying data mining technology to identify vibration patterns, and using to make fault prediction with machine learning algorithms. The implementation effect of the system shows that through real-time data monitoring and analysis, the stability of the network can be effectively improved, the ledger management and query method can be improved, the network panorama presentation and real-time monitoring can be realized, and the impact of sudden faults on the network operation can be significantly reduced. Through these measures, the system not only improves the reliability of the network, but also optimizes the fault management and early warning mechanism, providing strong support for the network maintenance and operation.

Understanding swimming in soft yielding media is challenging due to their complex deformation response to the swimmer's motion. We experimentally show that a scallop-inspired swimmer with reciprocally flapping wings generates locomotion in granular matter. This disagrees with the scallop theorem prohibiting reciprocal swimming in a liquid when its inertia is negligible. We use X-ray tomography and laser profilometry to show that the propulsion is created by the combined effects of jamming and convection of particles near the wings, which break the symmetry in packing density, surface deformation, and kinematics of the granular medium between an opening and a closing stroke.

The thermal and mechanical behaviors of powders are crucial for additive manufacturing. In powder bed fusion, capturing temperature profiles and packing structures before melting is challenging due to diverse heat transfer pathways and powder properties. This study tackles this challenge with a discrete element model simulating non-spherical particles with thermal properties during powder spreading. Thermal conduction and radiation are integrated into a multisphere particle formulation to model heat transfer among irregular-shaped powders with temperature-dependent elastic properties. The model is utilized to simulate the spreading of pre-heated PA12 powder over a hot substrate representing the part under manufacturing. Variances in temperature profiles are observed in the spreading cases based on particle shapes, spreading speed, and temperature-dependent elastic modulus. Particle temperature beneath the spreading blade is influenced by the kinematics of the particle heap and temperature-dependent properties.

Abstract Producing dense and homogeneous powder layers with smooth free surface is challenging in additive manufacturing, as interparticle cohesion can strongly affect the powder packing structure and therefore influence the quality of the end product. We use the Discrete Element Method to simulate the spreading process of spherical powders and examine how cohesion influences the characteristics of the packing structure with a focus on the fluctuation of the local morphology. As cohesion increases, the overall packing density decreases, and the free surface roughness increases, which is calculated from digitized surface height distributions. Local structural fluctuations for both quantities are examined through the local packing anisotropy on the particle scale, obtained from Voronoï tessellation. The distributions of these particle-level metrics quantify the increasingly heterogeneous packing structure with clustering and changing surface morphology.

The thermal and mechanical behaviors of powders are important for various additive manufacturing technologies. For powder bed fusion, capturing the temperature profile and the packing structure of the powders prior to melting is challenging due to both the various pathways of heat transfer and the complicated properties of powder system. Furthermore, these two effects can be coupled due to the temperature dependence of particle properties. This study addresses this challenge using a discrete element model that simulates non-spherical particles with thermal properties in powder spreading. Thermal conduction and radiation are introduced to a multi-sphere particle formulation for capturing the heat transfer among irregular-shaped powders, which have temperature-dependent elastic properties. The model is utilized to simulate the spreading of pre-heated PA12 powder through a hot substrate representing the part under manufacturing. Differences in the temperature profiles were found in the spreading cases with different particle shapes, spreading speed, and temperature dependence of the elastic moduli. The temperature of particles below the spreading blade is found to be dependent on the kinematics of the heap of particles in front, which eventually is influenced by the temperature-dependent properties of the particles.

The packing behavior of powders is significantly influenced by various types of inter-particle attractive forces, including adhesion and non-bonded van der Waals forces [1, 2, 3, 4, 5, 6]. Alongside particle size and shape distributions, the inter-particle interactions, in particular frictional and adhesive forces, play a crucial role in determining the flow behavior and consequently the packing density of the powder layer. The impact of various types of attractive forces on the packing density of powders with different materials and particle size distributions remains largely unexplored and requires further investigation. Accurately comprehending these effects through experiments while considering specific particle size distributions and material properties poses significant challenges. To address these challenges, we employ Discrete Element Method (DEM) simulations to characterize the packing behavior of fine powders. We can demonstrate quantitative agreement with experimental results by incorporating the appropriate particle size distribution and using an adequate model of attractive particle interactions. Furthermore, our findings indicate that both adhesion, which is modeled using the Johnson-Kendall-Roberts (JKR) model [7], and van der Waals interactions are crucial factors that must be taken into account in DEM simulations.

Producing dense and homogeneous powder layers with smooth free surface is challenging in additive manufacturing, as interparticle cohesion can strongly affect the powder packing structure and therefore influence the quality of the end product. We use the Discrete Element Method to simulate the spreading process of spherical powders and examine how cohesion influences the characteristics of the packing structure with a focus on the fluctuation of the local morphology. As cohesion increases, the overall packing density decreases, and the free surface roughness increases, which is calculated from digitized surface height distributions. Local structural fluctuations for both quantities are examined through the local packing anisotropy on the particle scale, obtained from Vorono\"ı tessellation. The distributions of these particle-level metrics quantify the increasingly heterogeneous packing structure with clustering and changing surface morphology.

There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress-strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity.

We present a continuum approach to model segregation of size-bidisperse granular materials in unsteady bounded heap flow as a prototype for modeling segregation in other time varying flows. In experiments, a periodically modulated feed rate produces stratified segregation like that which occurs due to intermittent avalanching, except with greater layer-uniformity and higher average feed rates. Using an advection-diffusion-segregation equation and characterizing transient changes in deposition and erosion after a feed rate change, we model stratification for varying feed rates and periods. Feed rate modulation in heap flows can create well-segregated layers, which effectively mix the deposited material normal to the free surface at lengths greater than the combined layer-thickness. This mitigates the strong streamwise segregation that would otherwise occur at larger particle-size ratios and equivalent steady feed rates and can significantly reduce concentration variation during hopper discharge. Coupling segregation, deposition and erosion is challenging but has many potential applications.

BACKGROUD: ciprofol is a new type of intravenous anesthetic, which is a tautomer of propofol, with the characteristics of less injection pain, less respiratory depression and higher potency, but little clinical experience. The aim of this study was to observe the efficacy and safety of the application of ciprofol in ambulatory surgery anesthesia in gynecology. METHODS: 128 patients were selected to undergo gynecological day surgery under general anesthesia, and the patients were randomly divided into the ciprofol group and the propofol group, with 64 cases in each group. During anesthesia induction, the ciprofol group was infused at a time limit of 0.5 mg/kg for one minute, and the propofol group was infused at a time limit of 2 mg/kg for 1 min. The overall incidence of adverse events was the primary outcome for this study, while secondary outcomes included the success rate of anesthesia induction, the time of loss of consciousness, the time of awakening,top-up dose and frequency of use of rescue drugs. RESULTS: The overall incidence of adverse events was significantly lower in the ciprofol group compared with the propofol group (56.2% vs. 92.2%,P < 0.05). The success rate of anesthesia induction of ciprofol and propofol group was 100.0%. The time of loss of consciousness of the ciprofol group was longer than that of the propofol group (1.6 ± 0.4 min vs. 1.4 ± 0.2 min, P < 0.05). The time of awakening was not statistically significant (5.4 ± 2.8 min vs. 4.6 ± 1.6 min, P > 0.05). The number of drug additions and resuscitation drugs used were not statistically significant. CONCLUSIONS: Compared with propofol, ciprofol had a similar anesthetic effect in gynecological ambulatory surgery, and the incidence of adverse events in the ciprofol group was lower.

All solids yield under sufficiently high mechanical loads. Below yield, the mechanical responses of all disordered solids are nearly alike, but above yield every different disordered solid responds in its own way. Brittle systems can shatter without warning, like ordinary window glass, or exhibit strain localization prior to fracture, like metallic or polymeric glasses. Ductile systems, e.g. foams like shaving cream or emulsions like mayonnaise, can flow indefinitely with no strain localization. While there are empirical strategies for tuning the degree of strain localization, there is no framework that explains their effectiveness or limitations. We show that Structuro-Elastoplastic (StEP) models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity and elasticity.