Search for: All records

This work investigates the steady-state nonlinear dynamics of a large-deformation flexible beam model under oscillatory flow. A flexible beam dynamics model combined with hydrodynamic loading is employed using large deformation beam theory. The equations of motion discretised using the high-order finite element method (FEM) are solved in the time domain using the efficient Galerkin averaging-incremental harmonic balance (EGA-IHB) method. The arc-length continuation method and Hsu’s method trace stable and unstable solutions. The numerical results are in accordance with the physical experimental results and reveal multiple resonance phenomena. Low-order resonances exhibit hardening due to geometric nonlinearity, while higher-order resonances transition from softening to hardening influenced by inertia and geometric nonlinearity. A strong coupling between tensile and bending deformation is observed. The axial deformation is dominated by inertia, while bending resonance is influenced by an interplay between inertia, structure stiffness, and fluid drag. Finally, the effects of two dimensionless parameters, Keulegan and Carpenter number (KC) and Cauchy number (Ca), on the response of the flexible beam are discussed. 

This study analyzed 281 lesson plans collected from the producers’ websites of 12 educational physical computing and robotics (ePCR) devices. We extracted and coded five variables from each lesson. They were ePCR functionality, coding skills, computational thinking skills, math knowledge, and activity design. First, a two-step cluster analysis was administered to find how three ePCR-related knowledge: ePCR functionality, coding skills, and computational thinking skills, were integrated to teach students ePCR technology in middle-grade math lessons. Results showed three types of lesson plans, including lessons to use basic ePCR functionality to teach students lower-level CT skills, lessons to teach students basic to intermediate coding skills, and lessons to use the technology at the advanced level. Next, we applied the Technological Pedagogical Content Knowledge (TPACK) framework and conducted a second two-step cluster analysis to identify how the technology (ePCR technology), content (math knowledge), and pedagogy (activity design) were integrated into those lesson plans. Results suggested ten clusters of lesson plans with distinct features. We summarized those ten lesson clusters into five categories: 1) ePCR technology lessons, 2) transdisciplinary problem-based learning lessons, 3) technology-assisted lessons, 4) lessons without real-world connections, and 5) lessons integrating middle-grade math learning into ePCR projects. Implications for educators and researchers were discussed at the end of the article. 

The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. Note to Practitioners —Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies. 

Combining experimental and computational studies of nanocomposite interfaces is highly needed to gain insight into their performance. However, there are very few literature reports, combining well-controlled atomic force microscopy experiments with molecular dynamic simulations, which explore the role of polymer chemistry and assembly on interface adhesion and shear strength. In this work, we investigate graphene oxide (GO)-polymer interfaces prevalent in nanocomposites based on a nacre-like architectures. We examine the interfacial strength resulting from van der Waals and hydrogen bonding interactions by comparing the out-of-plane separation and in-plane shear deformations of GO-polyethylene glycol (PEG) and GO-polyvinyl alcohol (PVA). The investigation reveals an overall better mechanical performance for the anhydrous GO-PVA system in both out-of-plane and in-plane deformation modes, highlighting the benefits of the donor-acceptor hydrogen bond formation present in GO-PVA. Such bond formation results in interchain hydrogen bond networks leading to stronger interfaces. By contrast, PEG, a hydrogen bond acceptor only, relies primarily on van der Waals inter-chain interactions, typically resulting in weaker interactions. The study also predicts that water addition increases the adhesion of GOPEG but decreases the adhesion of GO-PVA, and slightly increases the shear strength in both systems. Furthermore, by comparing simulations and experiments, we show that the CHARMM force field has enough accuracy to capture the effect of polymer content, water distribution, and to provide quantitative guidance for achieving optimum interfacial properties. Therefore, the study demonstrates an effective methodology, in the Materials Genome spirit, toward the design of 2D materials-polymer nanocomposites system for applications demanding mechanical robustness. 

We report measurements of production cross sections for ${\rho }^{+}$, ${\rho }^0$, $\omega$, $K^{*+}$, $K^{*0}$, $\varphi$, $\eta$, $K_S^0$, $f_0\left(980\right)$, $D^{+}$, $D^0$, $D_s^{+}$, $D^{*+}$, $D^{*0}$, and $D_s^{*+}$in $e^{+}{e}^{-}$collisions at a center-of-mass energy near 10.58 GeV. The data were recorded by the Belle experiment, consisting of $571{\mathrm{fb}}^{-1}$at 10.58 GeV and $74{\mathrm{fb}}^{-1}$at 10.52 GeV. Production cross sections are extracted as a function of the fractional hadron momentum $x_p$. The measurements are compared to Monte Carlo generator predictions with various fragmentation settings, including those that have increased fragmentation into vector mesons over pseudoscalar mesons. The cross sections measured for light hadrons are consistent with no additional increase of vector over pseudoscalar mesons. The charmed-meson cross sections are compared to earlier measurements—when available—including older Belle results, which they supersede. They are in agreement before application of an improved initial-state radiation correction procedure that causes slight changes in their $x_p$shapes. Published by the American Physical Society2025