Accurate Multi-physics Numerical Analysis of Particle Preconcentration Based on Ion Concentration Polarization

This paper studies mechanism of preconcentration of charged particles in a straight micro-channel embedded with permselective membranes, by numerically solving coupled transport equations of ions, charged particles and solvent fluid without any simplifying assumptions. It is demonstrated that trapping and preconcentration of charged particles are determined by the interplay between drag force from the electroosmotic fluid flow and the electrophoretic force applied trough the electric field. Several insightful characteristics are revealed, including the diverse dynamics of co-ions and counter ions, replacement of co-ions by focused particles, lowered ion concentrations in particle enriched zone, and enhanced electroosmotic pumping effect etc. Conditions for particles that may be concentrated are identified in terms of charges, sizes and electrophoretic mobilities of particles and co-ions. Dependences of enrichment factor on cross-membrane voltage, initial particle concentration and buffer ion concentrations are analyzed and the underlying reasons are elaborated. Finally, post priori a condition for validity of decoupled simulation model is given based on charges carried by focused charge particles and that by buffer co-ions. These results provide important guidance in the design and optimization of nanofluidic preconcentration and other related devices.Comment: 18 pages, 11 firgure

On the C4C_4-isolation number of a graph

Let $C_k$ be the cycle of length $k$. For any graph $G$, a subset $D \subseteq V(G)$ is a $C_k$-isolating set of $G$ if the graph obtained from $G$ by deleting the closed neighbourhood of $D$ contains no $C_k$ as a subgraph. The $C_k$-isolation number of $G$, denoted by $\iota(G,C_k)$, is the cardinality of a smallest $C_k$-isolating set of $G$. Borg (2020) and Borg et al. (2022) proved that if $G \ncong C_3$ is a connected graph of order $n$ and size $m$, then $\iota(G,C_3) \leq \frac{n}{4}$ and $\iota(G,C_3) \leq \frac{m+1}{5}$. Very recently, Bartolo, Borg and Scicluna showed that if $G$ is a connected graph of order $n$ that is not one of the determined nine graphs, then $\iota(G,C_4) \leq \frac{n}{5}$. In this paper, we prove that if $G \ncong C_4$ is a connected graph of size $m$, then $\iota(G,C_4) \leq \frac{m+1}{6}$, and we characterize the graphs that attain the bound. Moreover, we conjecture that if $G \ncong C_k$ is a connected graph of size $m$, then $\iota(G,C_k) \leq \frac{m+1}{k+2}$.Comment: 15 pages, 2 figure

Joint Power and Multiple Access Control for Wireless Mesh Network with Rose Projection Method

This paper investigates the utility maximization problem for the downlink of the multi-interface multichannel wireless mesh network with orthogonal frequency division multiple access. A cross-layer joint power and multiple access control algorithm are proposed. Rosen projection matrix is combined with Solodov projection techniques to build a three-memory gradient Rosen projection method, which is applied to solve this optimization problem. The convergence analysis is given and simulations show that the proposed solution achieves significant throughput compared with existing approaches

A Generalized Method for Calculating Atmospheric Ionization by Energetic Electron Precipitation

Accurate specification of ionization production by energetic electron precipitation is critical for atmospheric chemistry models to assess the resultant atmospheric effects. Recent model-observation comparison studies have increasingly highlighted the importance of considering precipitation fluxes in the full range of electron energy and pitch angle. However, previous parameterization methods were mostly proposed for isotropically precipitation electrons with energies up to 1 MeV, and the pitch angle dependence has not yet been parameterized. In this paper, we first characterize and tabulate the atmospheric ionization response to monoenergetic electrons with different pitch angles and energies between ∼3 keV and ∼33 MeV. A generalized method that fully accounts for the dependence of ionization production on background atmospheric conditions, electron energy, and pitch angle has been developed based on the parameterization method of Fang et al. (2010, https://doi.org/10.1029/2010GL045406). Moreover, we validate this method using 100 random atmospheric profiles and precipitation fluxes with monoenergetic and exponential energy distributions, and isotropic and sine pitch angle distributions. In a suite of 6,100 validation tests, the error in peak ionization altitude is found to be within 1 km in 91% of all the tests with a mean error of 2.7% in peak ionization rate and 1.9% in total ionization. This method therefore provides a reliable means to convert space-measured precipitation energy and pitch angle distributions into ionization inputs for atmospheric chemistry models.publishedVersio

Analytic study on the foundation of shaker based on AIR spring

In view of the limitation of the traditional installation method of the shaker placed on the floor structure, a method of foundation isolation based on the AIR spring is proposed. According to the dynamic characteristics of the AIR spring, the relationship between the natural frequency and the parameters, such as the air pressure and the weight of the load, are analyzed. In order to evaluate the coupling properties between the vibration isolation system and the vibration test system, the factors affecting the vibration isolation transfer function of the system and the response of the foundation under vibration excitation were analyzed. Test results revealed that: it is feasible to adjust the natural frequency of system, to obtain the good isolation performance and stationary dynamic response, by reasonably choosing the structural parameters of the AIR spring and adjusting the internal inflation pressure of spring

ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation

Recent sparse detectors with multiple, e.g. six, decoder layers achieve promising performance but much inference time due to complex heads. Previous works have explored using dense priors as initialization and built one-decoder-layer detectors. Although they gain remarkable acceleration, their performance still lags behind their six-decoder-layer counterparts by a large margin. In this work, we aim to bridge this performance gap while retaining fast speed. We find that the architecture discrepancy between dense and sparse detectors leads to feature conflict, hampering the performance of one-decoder-layer detectors. Thus we propose Adaptive Sparse Anchor Generator (ASAG) which predicts dynamic anchors on patches rather than grids in a sparse way so that it alleviates the feature conflict problem. For each image, ASAG dynamically selects which feature maps and which locations to predict, forming a fully adaptive way to generate image-specific anchors. Further, a simple and effective Query Weighting method eases the training instability from adaptiveness. Extensive experiments show that our method outperforms dense-initialized ones and achieves a better speed-accuracy trade-off. The code is available at \url{https://github.com/iSEE-Laboratory/ASAG}.Comment: Accepted to ICCV 202