作者：
Zhang, YueweiJia, YunlongZhu, ShoujunJilin Univ
First Hosp Jilin Univ Joint Lab Optofunct Theranost Med & Chem Changchun Peoples R China Jilin Inst Chem Technol
Sch Chem & Pharmaceut Engn Jilin Peoples R China Jilin Univ
Coll Chem Ctr Supramol Chem Biol State Key Lab Supramol Struct & Mat Changchun Peoples R China Jilin Univ
First Hosp Jilin Univ Joint Lab Optofunct Theranost Med & Chem Changchun 130021 Peoples R China
The near-infrared (NIR)-II bioimaging technique is highly important for both diagnosing and treating life-threatening diseases due to its exceptional imaging capabilities. However, the lack of suitable NIR-II fluoresc...
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The near-infrared (NIR)-II bioimaging technique is highly important for both diagnosing and treating life-threatening diseases due to its exceptional imaging capabilities. However, the lack of suitable NIR-II fluorescent probes has hindered their widespread clinical application. To address this issue, the binding of albumin to cyanine dyes has emerged as a practical and efficient method for developing high-performance NIR-II probes. Cyanine dyes can bind with exogenous and endogenous albumin through either covalent or noncovalent interactions, serving various purposes. The resulting cyanine@albumin (or albumin@cyanine) fluorophores offer significant advantages, including strong brightness, excellent photostability, good biosafety, and a long-term, high-resolution imaging window. Cyanine dye in situ binding with endogenous albumin can also enhance the targeting imaging capability. This review provides a summary of the interaction mechanism, performance enhancement, tumor-targeting feature, and in vivo imaging applications of the cyanine@albumin fluorophores. These advancements not only highlight the unique characteristics of cyanine@albumin fluorophores in preclinical research but also emphasize their potential for clinical diagnosis.
Space exploration plays a pivotal role in advancing human technological progress and deepening our understanding of the universe [1]. Satellites, as key instruments in space exploration, not only play an essential rol...
Space exploration plays a pivotal role in advancing human technological progress and deepening our understanding of the universe [1]. Satellites, as key instruments in space exploration, not only play an essential role in communication, navigation and meteorological monitoring,but also provide indispensable data for scientific research, environmental monitoring and national security.
In the past decade, autonomous driving has witnessed significant advancements, largely attributable to the evolution of precise algorithms and efficient computing platforms. Nevertheless, the open-pit mine, a typical ...
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In the past decade, autonomous driving has witnessed significant advancements, largely attributable to the evolution of precise algorithms and efficient computing platforms. Nevertheless, the open-pit mine, a typical scenario within closed-field environments, has garnered limited attention in autonomous driving, primarily owing to the scarcity of data and experimental benchmarks. This work presents original data collected from five platforms, comprising one passenger vehicle, three wide-body trucks, and one mining truck, across eight different mining sites. We provide a comprehensive elucidation of platform types, sensors, calibration methodologies, synchronization techniques, data collection approaches, and a thorough analysis of the data characteristics. In addition, we offer a detailed benchmark comparison of short and long odometry and navigation performance across multiple vehicles in open-pit mines. With comprehensive data characteristics, experimental performance evaluations, and thorough analysis, we believe that this work establishes a robust research foundation for navigation and fusion methods in open-pit mines, thereby constituting a significant contribution to the autonomous driving and field robotics communities.
Developing reasonable and effective adaptive under frequency load shedding (AUFLS) schemes is crucial for preventing system frequency drops. In order to overcome the mismatching of event-driven load shedding scheme in...
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Developing reasonable and effective adaptive under frequency load shedding (AUFLS) schemes is crucial for preventing system frequency drops. In order to overcome the mismatching of event-driven load shedding scheme in the traditional second line of defense, this paper proposes a response-driven AUFLS scheme based on energy model. In this scheme, the energy model is established by the kinetic energy theorem, which represents the equivalent conversion relationship between rotor kinetic energy deviation (KED) and the unbalanced power does work (UPDW) of each component. Furthermore, a kinetic energy margin indicator (KEMI) reflecting the system frequency is introduced, and the minimum load shedding amount (MLSA) corresponding to the critical KEMI is calculated. The comprehensive load shedding allocation weight is established by combining the UPDW of load and shedding costs. The combination of MLSA and comprehensive load allocation weights enables precise control with minimal cost. Modified IEEE 10-generator 39-bus test system and CSEE-SSFS 197-bus real system are used to verify the performance of the proposed AUFLS scheme.
Developing high-performance aerogels has long been a hot topic in the fields of insulation and thermal protection. Nanofiber aerogels with ultralight weight and high porosity have recently emerged as promising candida...
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Developing high-performance aerogels has long been a hot topic in the fields of insulation and thermal protection. Nanofiber aerogels with ultralight weight and high porosity have recently emerged as promising candidates. However, the weak inter-fiber interaction hampers the robustness of the three-dimensional network, resulting in poor overall mechanical properties that hinder their wide adoption. Herein, we propose a novel template-anchored strategy for constructing polyimide hybrid nanofiber aerogels. By utilizing self-supporting chitosan as a sacrificial template, polyimide (PI) nanofibers are directionally interconnected by chemical pre-anchoring and heat treatment, which endows the three-dimensional fiber network with good structural stability. These directly assembled nanofiber aerogels exhibit an adjustable low-density range (12.3-31.5 mg/cm3), excellent compressive resilience and fatigue resistance (with only 7.2% permanent deformation after 100 cycles at 60% strain), demonstrating good shape recovery. Moreover, the complex nanofiber pathway and porous network structure contribute to superior thermal insulation performance with low thermal conductivity (28.5-31.8 mW m-1 K-1). Furthermore, the incorporation of polyimide and silica (SiO2) imparts these hybrid aerogels with remarkable high-temperature resistance and flame retardancy. This study introduces and validates a novel approach for obtaining superelastic and lightweight aerogels, highlighting its promising potential in the realm of high-temperature thermal insulation.
Autonomous driving requires path planning to choose an optimal path to destination. Most works on path planning focus on the shortest travelling time, but neglect positioning condition of a path while positioning accu...
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Autonomous driving requires path planning to choose an optimal path to destination. Most works on path planning focus on the shortest travelling time, but neglect positioning condition of a path while positioning accuracy is significant for autonomous driving. In this work, we predict GNSS protection level (PL), which is positioning uncertainty upper bound, over planned paths to help choose the optimal path. We propose an improved stochastic model that considers the signal attenuation caused by trees and buildings using laser scanning point cloud. An adaptive strategy is proposed to linearly correlate the improved model and the carrierto-noise-density ratio to make our stochastic model more practical. Finally, pseudorange-based RTD PL is calculated by advanced receiver autonomous integrity monitoring (ARAIM) using the planned path information and GNSS ephemeris. The experiments demonstrate that our method reduces the misleading rate by over 90% both in horizontal and vertical directions compared to the original stochastic model, with the lowest false alarm rates.
As a fundamental image characteristic, edge features encapsulate a wealth of information, serving as a crucial foundation in image segmentation networks for accurately delineating and partitioning object edges. Convol...
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As a fundamental image characteristic, edge features encapsulate a wealth of information, serving as a crucial foundation in image segmentation networks for accurately delineating and partitioning object edges. Convolutional neural networks (CNNs) have gained prominence recently, finding extensive utility in edge detection. Previous methods primarily emphasized edge prediction accuracy, ignoring edge refinement. In this work, we introduce a novel encoder-decoder architecture that effectively harnesses hierarchical features. By extending the decoder horizontally, we progressively enhance resolution to preserve intricate details from the original image, thereby producing sharp edges. Additionally, we propose a novel loss function named the Pixel-Patch Combination Loss (P2CL), which employs distinct detection strategies in edge and non-edge regions to bolster network accuracy and yield crisp edges. Furthermore, considering the practicality of the algorithm, our method strikes a fine balance between accuracy and model size. It delivers precise and sharp edges while ensuring efficient model operation, thereby laying a robust foundation for advancements deployed on mobile devices or embedded systems. Our method was evaluated on three publicly available datasets, including BSDS500, Multicue, and BIPED. The experimental results show the superiority of our approach, achieving a competitive ODS F-score of 0.832 on the BSDS500 benchmark and significantly enhancing edge detection accuracy.
One of the biggest problems for orthogonal frequency division multiplexing (OFDM) systems is the high peak- to-average power ratio (PAPR), which breaks the orthogonality among subcarriers and leads to the nonlinear di...
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One of the biggest problems for orthogonal frequency division multiplexing (OFDM) systems is the high peak- to-average power ratio (PAPR), which breaks the orthogonality among subcarriers and leads to the nonlinear distortion of transmitted signals after being processed by the power amplifier (PA). The iterative clipping and filtering (ICF) method is one of the well known and applied existing PAPR reduction techniques at the transmitter and the modified iterative receiver (MIR) is an effective existing method for signal recovery with the ICF method in its iterative process at the receiver. However, the ICF method, as well as the MIR, suffers from the high computational complexity due to the oversampling and high-order inverse fast Fourier transform/fast Fourier transform (IFFT/FFT) operators. Besides, the performance of MIR is limited by the number of iterations. In this paper, to reduce the computational complexity of ICF method, the phase rotation iterative clipping and filtering (PRICF) method is proposed, which performs padding, phase rotation and low- order IFFT/FFT operators. Meanwhile, the computational complexity of MIR is also reduced because the ICF method is replaced by the PRICF method in its iterative process. Furthermore, to accelerate the iteration or improve the performance, the modified iterative network receiver (MIR-Net) is proposed by introducing trainable parameters based on the method of model-driven deep learning. Comparing with the combination of ICF and MIR, the simulation results demonstrate the advantages of our proposed methods, which is the combination of PRICF and MIR-Net, in terms of the computational complexity and performance.
The traditional chemical reduction method for preparing metal nanoparticles requires organic solvents, which seriously affects the ecological environment. Meanwhile, polysaccharide-based packaging films' brittlene...
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The traditional chemical reduction method for preparing metal nanoparticles requires organic solvents, which seriously affects the ecological environment. Meanwhile, polysaccharide-based packaging films' brittleness limits their application scope. In contrast, protein-based packaging films (PBPFs) have excellent flexibility and ductility. Therefore, this study synthesized novel PBPFs loaded with silver oxide nanoparticles (Ag2O NPs) in situ and green by microwave technology. The results showed that the Ag2O NPs reduced up to 90.03 % in the reduction system of this study. With increased silver nitrate concentration, Ag2O NP reduction rates and the degree of intermolecular interaction of the film-forming fluids decreased, and PBPF crystallinity and thermal stability increased. PBPFs exhibit increased interfacial interaction when Ag2O NPs generated by reduction are uniformly distributed on their surfaces. The maximum tensile strength of PBPFs was 8.87 +/- 0.71 MPa, and the maximum WCA was 97.58 +/- 4.05 degrees. All PBPFs have UV-blocking properties and antimicrobial activity, extending grape storage life.
The collection and transportation of underwater bubbles has attracted significant attention due to their wide range of applications in the mining, petroleum, and chemical industries. In this study, robust superhydroph...
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The collection and transportation of underwater bubbles has attracted significant attention due to their wide range of applications in the mining, petroleum, and chemical industries. In this study, robust superhydrophobic tapered needles were successfully fabricated by spraying a superhydrophobic coating prepared by an organic-inorganic hybrid method. The prepared tapered needles present excellent surface stability and good superhydrophobicity with a contact angle (CA) of about 156 degrees. The fabricated tapered needles demonstrate excellent performance in collection and transportation of underwater bubbles and the working mechanism was also thoroughly studied. The prepared robust superhydrophobic tapered needles provide a simple, efficient and economical way for collection and transportation of underwater bubbles.
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