In this paper, to bridge the gap between physical knowledge and learning approaches, we propose an induced current learning method (ICLM) by incorporating merits in traditional iterative algorithms ...into the architecture of convolutional neural network (CNN). The main contributions of the proposed method are threefold. First, to the best of our knowledge, it is the first time that the contrast source is learned to solve full-wave inverse scattering problems (ISPs). Second, inspired by the basis-expansion strategy in the traditional iterative approach for solving ISPs, a combined loss function with multiple labels is defined in a cascaded end-to-end CNN (CEE-CNN) architecture to decrease the nonlinearity of objective function, where no additional computational cost is introduced in generating extra labels. Third, to accelerate the convergence speed and decrease the difficulties of the learning process, the proposed CEE-CNN is designed to focus on learning the minor part of the induced current by introducing several skip connections and to bypass the major part of induced current to the output layers. The proposed method is compared with the state-of-the-art of deep learning scheme and a well-known iterative ISP solver, where numerical and experimental tests are conducted to verify the proposed ICLM.
This paper investigates a modified version of the subspace-based optimization method for solving inverse-scattering problems. The method is found to share several properties with the ...contrast-source-inversion method. The essence of the subspace-based optimization method is that part of the contrast source is determined from the spectrum analysis without using any optimization, whereas the rest is determined by optimization method. This feature significantly speeds up the convergence of the algorithm. There is a great flexibility in partitioning the space of induced current into two orthogonal complementary subspaces: the signal subspace and the noise subspace. This flexibility enables the algorithm to perform robustly against noise. Numerical simulations validate the efficacy of the proposed method: fast convergent and robust against noise.
Liquid–liquid phase separation (LLPS) facilitates the formation of condensed biological assemblies with well-delineated physical boundaries, but without lipid membrane barriers. LLPS is increasingly ...recognized as a common mechanism for cells to organize and maintain different cellular compartments in addition to classical membrane-delimited organelles. Membraneless condensates have many distinct features that are not present in membrane-delimited organelles and that are likely indispensable for the viability and function of living cells. Malformation of membraneless condensates is increasingly linked to human diseases. In this review, we summarize commonly used methods to investigate various forms of LLPS occurring both in 3D aqueous solution and on 2D membrane bilayers, such as LLPS condensates arising from intrinsically disordered proteins or structured modular protein domains. We then discuss, in the context of comparisons with membrane-delimited organelles, the potential functional implications of membraneless condensate formation in cells. We close by highlighting some challenges in the field devoted to studying LLPS-mediated membraneless condensate formation.
Gradient descent methods have been widely used for organizing multi-agent systems, in which they can provide decentralized control laws with provable convergence. Often, the control laws are designed ...so that two neighboring agents repel/attract each other at a short/long distance of separation. When the interactions between neighboring agents are moreover nonfading, the potential function from which they are derived is radially unbounded. Hence, the LaSalle's principle is sufficient to establish the system convergence. This technical note investigates, in contrast, a more realistic scenario where interactions between neighboring agents have fading attractions. In such setting, the LaSalle type arguments may not be sufficient. To tackle the problem, we introduce a class of partitions, termed dilute partitions, of formations which cluster agents according to the inter- and intra-cluster interaction strengths. We then apply dilute partitions to trajectories of formations generated by the multi-agent system, and show that each of the trajectories remains in a compact subset along the evolution, and converges to the set of equilibria.
► Porosity and hydration degree of mortar increased with increasing w/c. ► Total porosity of mortar decreased with increasing curing period. ► Existing models for pore size distribution of mortar ...were reviewed. ► A single lognormal distribution may not be adequate for pore size distribution. ► Compound lognormal distribution could be used to model pore size distribution.
The effect of water-to-cement ratio (w/c) and age on the pore structure of cement mortar was determined through mercury intrusion porosimetry (MIP). The cement mortar specimens were prepared with w/c of 0.4, 0.5 and 0.6, and were tested at different curing ages (14, 28, 180days). The degree of hydration of the cement in cement mortar was obtained by determining the non-evaporable water content. Test results have shown that, the degree of hydration increased with increasing curing time and water-to-cement ratio of the cement mortar for the ages of cement mortar varying between 14 and 180days. An increase in the water-to-cement ratio increases the total porosity. In addition, the existing models of pore size distribution of cement-based materials has been reviewed and compared with test results in this investigation.
Chemical vapor deposition (CVD) on catalytic metal surfaces is considered to be the most effective way to obtain large‐area, high‐quality graphene films. For practical applications, a transfer ...process from metal catalysts to target substrates (e.g., poly(ethylene terephthalate) (PET), glass, and SiO2/Si) is unavoidable and severely degrades the quality of graphene. In particular, the direct growth of graphene on glass can avoid the tedious transfer process and endow traditional glass with prominent electrical and thermal conductivities. Such a combination of graphene and glass creates a new type of glass, the so‐called “super graphene glass,” which has attracted great interest from the viewpoints of both fundamental research and daily‐life applications. In the last few years, great progress has been achieved in pursuit of this goal. Here, these growth methods as well as the specific growth mechanisms of graphene on glass surfaces are summarized. The typical techniques developed include direct thermal CVD growth, molten‐bed CVD growth, metal‐catalyst‐assisted growth, and plasma‐enhanced growth. Emphasis is placed on the strategy of growth corresponding to the different natures of glass substrates. A comprehensive understanding of graphene growth on nonmetal glass substrates and the latest status of “super graphene glass” production are provided.
A summary of the chemical vapor deposition (CVD) growth techniques of graphene on traditional glass as well as the growth mechanisms is provided. Direct thermal CVD growth, molten‐bed CVD growth, metal‐catalyst‐assisted growth, and plasma‐enhanced growth are covered. Emphasis is laid on the strategy of growth corresponding to the different natures of glass substrates.
The electromagnetic inverse scattering problem can be effectively handled by means of the so-called distorted-Born iterative method (DBIM). A new method, denoted as the subspace-based DBIM (S-DBIM), ...is proposed. It updates the Green's function for the inhomogeneous background at each step of the iterative procedure, like DBIM. By linearly retrieving the deterministic subspace of the induced current, the S-DBIM estimates the total electric field more accurately than the DBIM does and thus exhibits a faster convergence speed. To avoid the computationally demanding and arguably hard choice of the optimal Tikhonov regularization term involved in the procedure, a second version of S-DBIM, which is more robust against noise and has faster convergence, is also proposed. Thorough numerical simulations show that both versions of S-DBIM achieve super-resolved retrievals of the benchmark "Austria" profile.
► Strength and porosity of cement mortar has been measured. ► Strength decreases with increasing porosity. ► Suitability of existing expressions relating strength and porosity is assessed. ► Extended ...Zheng model is good representation of experimental data. ► Compressive/tensile strength ratio decreases with increase porosity.
The compressive, flexural and splitting tensile strength of cement mortar has been measured and interpreted in terms of its porosity. The authors first reviewed the existing porosity–strength relationships (Ryshkewithch, Schiller, Balshin and Hasselman model) and assessed the suitability of existing relationships. The Zheng model for porous materials has been used to evaluate the porosity–strength relationship of cement mortar. Over the porosity ranges examined, the extended Zheng model is good representation of the experimental data on the strength of cement mortar. Based on the generality of the assumptions used in the derivation of the extended Zheng model, this model for cement mortar can be applied for other cement-based materials. The experimental data also show that the ratio between compressive strength and indirect tensile (splitting tensile and flexural) strength of cement mortar is not constant, but is porosity dependent. The ratio decreases with increase porosity values of cement mortar.
Synapses are semi-membraneless, protein-dense, sub-micron chemical reaction compartments responsible for signal processing in each and every neuron. Proper formation and dynamic responses to ...stimulations of synapses, both during development and in adult, are fundamental to functions of mammalian brains, although the molecular basis governing formation and modulation of compartmentalized synaptic assemblies is unclear. Here, we used a biochemical reconstitution approach to show that, both in solution and on supported membrane bilayers, multivalent interaction networks formed by major excitatory postsynaptic density (PSD) scaffold proteins led to formation of PSD-like assemblies via phase separation. The reconstituted PSD-like assemblies can cluster receptors, selectively concentrate enzymes, promote actin bundle formation, and expel inhibitory postsynaptic proteins. Additionally, the condensed phase PSD assemblies have features that are distinct from those in homogeneous solutions and fit for synaptic functions. Thus, we have built a molecular platform for understanding how neuronal synapses are formed and dynamically regulated.
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•Biochemical reconstitution reveals PSD assembly formation via phase separation•The ePSD condensates cluster NMDA receptor and promote actin bundle formation•The ePSD condensates selectively enrich SynGAP and actively exclude gephyrin•The ePSD condensates can be modulated by activity-dependent protein modifications
Phase transition-mediated formation of excitatory postsynaptic density condensates revealed by biochemical reconstitutions.
A signal-subspace approach to reconstruct the permittivities of extended scatterers in two-dimensional settings is proposed. A portion of the scatterers' information is retrieved by the ...signal-subspace method, and the remaining part is obtained by solving a nonlinear least-squares problem. The method exhibits several strengths, including robustness against noise, fast convergence, less scattering data, high resolution, and the ability to deal with scatterers of special shapes.
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