DNA nanotechnology-enabled chiral plasmonics: from static to dynamic

In this Account, we discuss a variety of static and dynamic chiral plasmonic nanostructures enabled by DNA nanotechnology. In the category of static plasmonic systems, we first show chiral plasmonic nanostructures based on spherical AuNPs, including plasmonic helices, toroids, and tetramers. To enhance the CD responses, anisotropic gold nanorods with larger extinction coefficients are utilized to create chiral plasmonic crosses and helical superstructures. Next, we highlight the inevitable evolution from static to dynamic plasmonic systems along with the fast development of this interdisciplinary field. Several dynamic plasmonic systems are reviewed according to their working mechanisms.Comment: 7 figure

Urban biodiversity conservation planning integrating the analysis of green space structure and functional connection

The process of urbanization and population growth lead to the fragmentation of biological habitat and the loss of biodiversity. It is of great significance to use effective models and indicators to evaluate landscape connectivity and construct green space network for habitat restoration and biodiversity conservation. Taking Fengtai District of Beijing as an example, firstly, the optimal distance threshold of green space construction suitable for biological migration is discussed by using the connectivity index based on graph theory, and the source patches are selected according to the evaluation results of landscape connectivity. Secondly, the resistance surface is constructed by using the minimum cost path model, and the potential connection path of species migration is determined by Linkage Mapper tool. Finally, according to the relative importance of patch and corridor in the quantitative source of current density, the “pinch” area, which is very important to species migration, is identified, and the model recognition results are compared with the empirical observation results of remote sensing satellite map and bird abundance. The results showed that the ecological base of green space in the western part of the study area was good, which provided the main habitat for species, and the patch fragmentation of green space in the central and eastern regions was serious, so it was necessary to increase urban green space as a stepping stone for species migration in pinch areas. The circuit model focusing on species diffusion is introduced in the study, which makes up for the lack of urban green space network construction method at the level of biodiversity conservation, clarifies the present situation of habitat quality and the future development of green space network in Fengtai District of Beijing, and provides scientific reference for the optimization of regional green space pattern and biodiversity conservation planning strategy

NONLINEAR ADAPTIVE HEADING CONTROL FOR AN UNDERACTUATED SURFACE VESSEL WITH CONSTRAINED INPUT AND SIDESLIP ANGLE COMPENSATION

In this paper, a nonlinear adaptive heading controller is developed for an underactuated surface vessel with constrained input and sideslip angle compensation. The controller design is accomplished in a framework of backstepping technique. First, to amend the irrationality of the traditional definition of the desired heading, the desired heading is compensated by the sideslip angle. Considering the actuator physical constrain, a hyperbolic tangent function and a Nussbaum function are introduced to handle the nonlinear part of control input. The error and the disturbance are estimated and compensated by an adaptive control law. In addition, to avoid the complicated calculation of time derivatives of the virtual control, the command filter is introduced to integrate with the control law. It is analysed by the Lyapunov theory that the closed loop system is guaranteed to be uniformly ultimately bounded stability. Finally, the simulation studies illustrate the effectiveness of the proposed control method

THE RESEARCH ON ISOKINETIC STRENGTH TESTING OF KNEE JOINT MUSCLE OF SHI DONGPENG, THE INTERNATIONALLY MASTER OF SPORTS

Since over 10 years ago, there have been many reports conceming tests of muscle strength and evaluation of muscle function, both of which adopted 'Isokinetic test system. Yet, in contrast, there have been fewer reports devoted to analysis and research on individual athletes. With Kinitech system, this paper will test and evaluate the knee joint muscle of Shi Dongpeng, the internationally top-notch 110m hurdle athlete, using the following 3 indexes: Relative Peak Torque (PT/SW), Time to Peak Torque (TPT) and Flexors/Extensors Values (FIE). And all the 3 indexes will be correspondingly contrasted with those of common young men's knee joint muscle. And the purpose of this paper is to prOVide scientific referent basis for the athlete to choose suitable training ways, to have specific strength training and to have great achievement

ADVANCED INTRAVASCULAR MAGNETIC RESONANCE IMAGING WITH INTERACTION

Intravascular (IV) Magnetic Resonance Imaging (MRI) is a specialized class of interventional MRI (iMRI) techniques that acquire MRI images through blood vessels to guide, identify and/or treat pathologies inside the human body which are otherwise difficult to locate and treat precisely. Here, interactions based on real-time computations and feedback are explored to improve the accuracy and efficiency of IVMRI procedures. First, an IV MRI-guided high-intensity focused ultrasound (HIFU) ablation method is developed for targeting perivascular pathology with minimal injury to the vessel wall. To take advantage of real-time feedback, a software interface is developed for monitoring thermal dose with real-time MRI thermometry, and an MRI-guided ablation protocol developed and tested on muscle and liver tissue ex vivo. It is shown that, with cumulative thermal dose monitored with MRI thermometry, lesion location and dimensions can be estimated consistently, and desirable thermal lesions can be achieved in animals in vivo. Second, to achieve fully interactive IV MRI, high-resolution real-time 10 frames-per-second (fps) MRI endoscopy is developed as an advance over prior methods of MRI endoscopy. Intravascular transmit-receive MRI endoscopes are fabricated for highly under-sampled radial-projection MRI in a clinical 3Tesla MRI scanner. Iterative nonlinear reconstruction is accelerated using graphics processor units (GPU) to achieve true real-time endoscopy visualization at the scanner. The results of high-speed MRI endoscopy at 6-10 fps are consistent with fully-sampled MRI endoscopy and histology, with feasibility demonstrated in vivo in a large animal model. Last, a general framework for automatic imaging contrast tuning over MRI protocol parameters is explored. The framework reveals typical signal patterns over different protocol parameters from calibration imaging data and applies this knowledge to design efficient acquisition strategies and predicts contrasts under unacquired protocols. An external computer in real-time communication with the MRI console is utilized for online processing and controlling MRI acquisitions. This workflow enables machine learning for optimizing acquisition strategies in general, and provides a foundation for efficiently tuning MRI protocol parameters to perform interventional MRI in the highly varying and interactive environments commonly in play. This work is loosely inspired by prior research on extremely accelerated MRI relaxometry using the minimal-acquisition linear algebraic modeling (SLAM) method

Experimental Studies on Two-Layer Corium Heat Transfer in Light Water Reactor Lower Head in LIVE2D Facility

In-vessel melt retention (IVMR) is a promising strategy in severe accident management for light water reactors. This strategy is not only adopted in the VVER 440 or AP600 reactors, but also included in higher-power reactors around 1000 MW(electric), like the AP1000 and Chinese CPR 1000. There is still a large uncertainty of IVMR by external cooling at powers higher than 1000 MW(electric), and especially where a thin metallic layer appears on the top of a heat-generating oxide layer. Less knowledge based on large-scale experiments is available until now of the interactive physical, chemical, and thermohydraulic processes between the oxide layer and the metallic layer. A test series of naturally separated two liquid layers was conducted in the upgraded LIVE2D test facility in Karlsruhe Institute of Technology using a nitrate salt mixture and high-temperature oil as the lower layer and upper layer simulant, respectively. The transparent front wall of the test vessel enables direct observation of global convection patterns of the melts and the response of the crust at the layer interface. The experiment reveals major thermohydraulic characteristics of the metallic layer during the transient and steady states. The intensity of the heat flux focusing effect in dependence of layer thickness can be clearly identified

Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation

Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the assorted intensities of user interests in different clicks. In this paper, we aim to achieve multi-granularity Click confidence Learning via Self-Distillation in recommendation (CLSD). Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method. After that, we define a local confidence function to adapt confidence scores at the user group level, since the confidence distributions can be varied among user groups. With the combination of multi-granularity confidence learning, we can distinguish the quality of clicks and model user interests more accurately without involving extra data and model structures. The significant improvements over different backbones on industrial offline and online experiments in a real-world recommender system prove the effectiveness of our model. Recently, CLSD has been deployed on a large-scale recommender system, affecting over 400 million users

FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer

Federated Learning (FL) has been widely concerned for it enables decentralized learning while ensuring data privacy. However, most existing methods unrealistically assume that the classes encountered by local clients are fixed over time. After learning new classes, this assumption will make the model's catastrophic forgetting of old classes significantly severe. Moreover, due to the limitation of communication cost, it is challenging to use large-scale models in FL, which will affect the prediction accuracy. To address these challenges, we propose a novel framework, Federated Enhanced Transformer (FedET), which simultaneously achieves high accuracy and low communication cost. Specifically, FedET uses Enhancer, a tiny module, to absorb and communicate new knowledge, and applies pre-trained Transformers combined with different Enhancers to ensure high precision on various tasks. To address local forgetting caused by new classes of new tasks and global forgetting brought by non-i.i.d (non-independent and identically distributed) class imbalance across different local clients, we proposed an Enhancer distillation method to modify the imbalance between old and new knowledge and repair the non-i.i.d. problem. Experimental results demonstrate that FedET's average accuracy on representative benchmark datasets is 14.1% higher than the state-of-the-art method, while FedET saves 90% of the communication cost compared to the previous method.Comment: Accepted by 2023 International Joint Conference on Artificial Intelligence (IJCAI2023