In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is consider...
In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is considered as one of the most fundamental models to explain pattern formation in a devel-oping embryo. Controlling pattern formation artificially has gained increasing attention in the field of development biology, which motivates us to investigate this problem mathematically. In this work, the two-dimensional Turing's reaction-diffusion model is discretized into square grids. The minimal control placement problem for the diffusion system is investigated first. The symmetric control sets are defined based on the symmetry of the network structure. A necessary condition is provided to guarantee controllability. Under certain circumstances, we prove that this condition is also sufficient. Then we show that the necessary condition can also be applied to the reaction-diffusion system by means of suitable extension of the symmetric control sets. Under similar circumstances, a sufficient condition is given to place the minimal control for the reaction-diffusion system.
Compounded plane wave imaging (CPWI) allows high-frame-rate measurement benefiting real-time brain imaging. The quality of transcranial brain imaging remains to be improved with the presence of skulls because of phase...
Compounded plane wave imaging (CPWI) allows high-frame-rate measurement benefiting real-time brain imaging. The quality of transcranial brain imaging remains to be improved with the presence of skulls because of phase aberration. This study introduced methods of calculating the transmitting time-delays, and their accuracy was compared with numerical simulation using a transcranial model. Experiments were carried out on a 3D-printed skull phantom. High-quality intracranial images could be achieved using the right transmitting and receiving time-delays. This study suggested that the proposed methods might have the potential for real-time and non-invasive transcranial aberration-corrected imaging.
Named entity recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities in sentences into pre-defined types. It plays a crucial role in various research f...
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Armchair graphene nanoribbons(AGNRs)with sub-nanometer width are potential materials for the fabrication of novel nanodevices thanks to their moderate direct band *** are usually synthesized by polymerizing precursor ...
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Armchair graphene nanoribbons(AGNRs)with sub-nanometer width are potential materials for the fabrication of novel nanodevices thanks to their moderate direct band *** are usually synthesized by polymerizing precursor molecules on substrate ***,it is time-consuming and not suitable for large-scale *** can also be grown by transforming precursor molecules inside single-walled carbon nanotubes(SWCNTs)via furnace annealing,but the obtained AGNRs are normally *** this work,microwave heating is applied for transforming precursor molecules into *** fast heating process allows synthesizing the AGNRs in *** different molecules were successfully transformed into AGNRs,suggesting that it is a universal *** importantly,as demonstrated by Raman spectroscopy,aberrationcorrected high-resolution transmission electron microscopy and theoretical calculations,less twisted AGNRs are synthesized by the microwave heating than the furnace *** results reveal a route for rapid production of AGNRs in large scale,which would benefit future applications in novel AGNRs-based semiconductor devices.
Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pic...
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Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies to ensure seamless connectivity across different altitudes and platforms. Thi...
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Recent studies show that Graph Neural Networks (GNNs) are vulnerable to structure adversarial attacks, which draws attention to adversarial defenses in graph data. Previous defenses designed heuristic defense strategi...
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Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of o...
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Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.
Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do n...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.
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