Analyzing Russia–Ukraine War Patterns Based on Lanchester Model Using SINDy Algorithm

In this paper, we present an effective method for analyzing patterns in the Russia–Ukraine war based on the Lanchester model. Due to the limited availability of information on combat powers of engaging forces, we utilize the loss of armored equipment as the primary data source. To capture the intricate dynamics of modern warfare, we partition the combat loss data into disjoint subsets by examining their geometric properties. Separate systems of ordinary differential equations for these subsets are then identified using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm under a generalized formulation of the historical Lanchester model. We provide simulations of our method to demonstrate its effectiveness and performance in analyzing contemporary warfare dynamics

Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method

Complex coacervates based on recombinant mussel adhesive proteins: their characterization and applications

Complex coacervates are a dense liquid phase of oppositely charged polyions formed by the associative separation of a mixture of polyions. Coacervates have been widely employed in many fields including the pharmaceutical, cosmetic, and food industries due to their intriguing interfacial and bulk material properties. More recently, attempts to develop an effective underwater adhesive have been made using complex coacervates that are based on recombinant mussel adhesive proteins (MAPs) due to the water immiscibility of complex coacervates and the adhesiveness of MAPs. MAP-based complex coacervates contribute to our understanding of the physical nature of complex coacervates and they provide a promising alternative to conventional invasive surgical repairs. Here, this review provides an overview of recombinant MAP-based complex coacervations, with an emphasis on their characterization and the uses of such materials for applications in the fields of biomedicine and tissue engineering.113sciescopu

The position of lysine controls the catechol-mediated surface adhesion and cohesion in underwater mussel adhesion

Intensive studies have found that 3,4-dihydroxyphenylalanine (Dopa) is one of the key molecules for underwater mussel adhesion. Although basic mechanisms of mussel adhesion have been elucidated, little is known about how mussels control the balance between surface adhesion and cohesion, which is critical for successful adhesion without peeling and/or tearing. In this work, we focused on lysine (Lys) molecules which are frequently flanked to Dopa residues in interfacial adhesive proteins, specifically their synergy and anti-synergy on surface adhesion and cohesion. Three model peptides were designed to characterize flanking Lys effects. Through nano-mechanistic analyses, we found that flanking Lys enhanced surface adhesion but disrupted Fe3+-mediated cohesion. Through nuclear magnetic resonance analyses and density functional theory calculations, we corroborated the synergetic effect on surface adhesion and anti-synergetic effect on cohesion. We also confirmed the consistency of flanking Lys effects in the actual protein system. Thus, we, for the first time, discovered that each Dopa molecule in interfacial adhesive proteins is participated in surface adhesion and cohesion differently through controlling the existence of flanking Lys. Our discovery enlightens how nature designs adhesive proteins through according roles of Dopa. (C) 2019 Elsevier Inc. All rights reserved.11Nsciescopu

Sprayable Adhesive Nanotherapeutics: Mussel-Protein-Based Nanoparticles for Highly Efficient Locoregional Cancer Therapy

Following surgical resection for primary treatment of solid tumors, systemic chemotherapy is commonly used to eliminate residual cancer cells to prevent tumor recurrence. However, its clinical outcome is often limited due to insufficient local accumulation and the systemic toxicity of anticancer drugs. Here, we propose a sprayable adhesive nanoparticle (NP)-based drug delivery system using a bioengineered mussel adhesive protein (MAP) for effective locoregional cancer therapy. The MAP NPs could be administered to target surfaces in a surface-independent manner through a simple and easy spray process by virtue of their unique adhesion ability and sufficient dispersion property. Doxorubicin (DOX)-loaded MAP NPs (MAP@DOX NPs) exhibited efficient cellular uptake, endolysosomal trafficking, and subsequent low pH microenvironment-induced DOX release in cancer cells. The locally sprayed MAP@DOX NPs showed a significant inhibition of tumor growth <i>in vivo</i>, resulting from the prolonged retention of the MAP@DOX NPs on the tumor surface. Thus, this adhesive MAP NP-based spray therapeutic system provides a promising approach for topical drug delivery in adjuvant cancer therapy

A 32-Gb/s PAM4-Binary Bridge With Sampler Offset Cancellation for Memory Testing

This brief presents a 32-Gb/s PAM4-Binary bridge for the next-generation memory testing. The bridge incorporates all the required functions to evaluate a high-speed PAM4 memory using a low-speed NRZ tester. The low-speed data transmitted from the NRZ tester to the bridge are converted into high-speed PAM4 data through half-rate clock control and forwarded to the memory, and vice-versa. The ground-terminated PAM4 driver provides the single-ended output by controlling the output current with a 2-tap feed-forward equalizer, achieving a ratio level mismatch (RLM) of 0.95. To minimize the offset at the PAM4 receiver, the offset cancellation circuit with an offset of 2.76mV consisting of a CTLE and sampling latches is employed, and the horizontal margin of the received PAM4 signal is 50% for BER&lt;10(-9). An all-digital PLL integrated in the bridge doubles the 4-GHz WCK used as forwarded clock for the graphic memory. The count-based PAM4 eye-opening monitor is also proposed to find the optimal codes for the maximum eye opening using the PRBS7 data sequence. The bridge fabricated in the 40-nm CMOS technology occupies an active area of 1.6mm(2) and dissipates 132mW.N