Strengtheneddirectivity with higher-order side lobes can be generated by the transducer with a largerradius at a higher frequency. The multi-annular pressure distributions are displayed in the cross-section of the a...
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Strengtheneddirectivity with higher-order side lobes can be generated by the transducer with a largerradius at a higher frequency. The multi-annular pressure distributions are displayed in the cross-section of the acoustic vortices(AVs)which are formed by side lobes. In the near field, particles can be trapped in the valley region between the two annuli of the pressure peak, and cannot be moved to the vortex center. In this paper, a trapping method based on a sector transducer array is proposed, which is characterized by the continuously variable topological charge(CVTC). This acoustic field can not only enlarge the range of particle trapping but also improve the aggregation degree of the trappedparticles. In the experiments, polyethylene particles with a diameter of 0.2 mm are trapped into the multi-annular valleys by the AV with a fixed topological charge. Nevertheless, by applying the CVTC, particles outside the radius of the AV can cross the pressure peak successfully and move to the vortex center. Theoretical studies are also verified by the experimental particles trapping using the AV with the continuous variation of three topological charges, and suggest the potential application of large-scale particle trapping in biomedical engineering.
We propose a mixed precision Jacobi algorithm for computing the singular value decomposition (SVd) of a dense matrix. After appropriate preconditioning, the proposed algorithm computes the SVd in a lower precision as ...
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We propose a mixed precision Jacobi algorithm for computing the singular value decomposition (SVd) of a dense matrix. After appropriate preconditioning, the proposed algorithm computes the SVd in a lower precision as an initial guess and then performs one-sided Jacobi rotations in the working precision as iterative refinement. By carefully transforming a lower precision solution to a higher precision one, our algorithm achieves about 2x speedup on the x86-64 architecture compared to the usual one-sided Jacobi SVd algorithm in LAPACK, without sacrificing the accuracy. CCS Concepts: centerdot Mathematics of computing -> Computations on matrices
The characterization of bio-aviation fuel composition is paramount for assessing biomass conversion processes and its suitability to meet international *** with one-dimensional gas chromatography mass spectrometry(1dG...
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The characterization of bio-aviation fuel composition is paramount for assessing biomass conversion processes and its suitability to meet international *** with one-dimensional gas chromatography mass spectrometry(1dGC-MS),comprehensive two-dimensional gas chromatography with mass spectrometry(GC×GC-MS)emerges as a promising analytical approach for bio-aviation fuel,offering enhanced separation,resolution,selectivity,and *** study addresses the qualitative and quantitative analysis methods for both bulk components and trace fatty acid methyl ester(FAME)in bio-aviation fuel obtained by hydrogenation at 400℃ with Ni-Mo/γ-Al_(2)O_(3)&Meso-SAPO-11 as catalyst using GC×*** bulk composition analysis,C1_(2) concentration was highest at 25.597%.Based on GC×GC-MS analysis platform,the quality control method of FAME in bio-aviation fuel was *** the split ratio of 10:1,limits of detections of six FAMEs were 0.011–0.027 mg·kg^(–1),and limits of quantifications were 0.036–0.090 mg·kg^(–1),and the GC×GC-MS research platform had the ability to detect FAME from 2 to 5 mg·kg^(–1).The results showed that this bio-aviation fuel did not contain FAME.
In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,near...
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In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automatedreinforcement learning(AutorL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.
In recent years,research focusing on synaptic device based on phototransistors has provided a new method for asso-ciative learning and neuromorphic computing.A TiO_(2)/AlGaN/GaN heterostructure-based synaptic phototra...
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In recent years,research focusing on synaptic device based on phototransistors has provided a new method for asso-ciative learning and neuromorphic computing.A TiO_(2)/AlGaN/GaN heterostructure-based synaptic phototransistor is fabricated and measured,integrating a TiO_(2)nanolayer gate and a two-dimensional electron gas(2dEG)channel to mimic the synaptic weight and the synaptic cleft,*** maximum drain to source current is 10 nA,while the device is driven at a reverse bias not exceeding-2.5 V.A excitatory postsynaptic current(EPSC)of 200 nA can be triggered by a 365 nm UVA light spike with the duration of 1 s at light intensity of 1.35μW·cm^(-2).Multiple synaptic neuromorphic functions,including EPSC,short-term/long-term plasticity(STP/LTP)and paried-pulse facilitation(PPF),are effectively mimicked by our GaN-based het-erostructure synaptic *** the typical Pavlov’s dog experiment,we demonstrate that the device can achieve"retraining"process to extend memory time through enhancing the intensity of synaptic weight,which is similar to the working mecha-nism of human brain.
Flooddisasters pose one of the greatest threats to humanity. Effectively addressing this challenge requires improving the accuracy of flood simulation. Taking Xunhe watershed in Shandong Province as the study area, t...
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Flooddisasters pose one of the greatest threats to humanity. Effectively addressing this challenge requires improving the accuracy of flood simulation. Taking Xunhe watershed in Shandong Province as the study area, the random Forest model was utilized to classify historical flood events within the watershed based on rainfall conditions, such as varying rainfall durations, intensities, and total precipitations. Multiple sets of hydrological model parameters were established to conduct flood classification simulation, reducing the error caused by using a single parameter set for the entire watershed. The results indicate that the random Forest model can be applied to flood classification simulation in Xunhe watershed. Compared to unclassified simulations, the method proposed in this study leads to an improvement in the Nash coefficient by 0.06 to 0.14, a reduction in the relative error of peak discharge by 3% to 11.24% and a reduction in the relative error of flood volume by 1.46% to 9.44%. The flood classification simulation method proposed in this study has certain applicability in reducing flood simulation errors underdifferent rainfall scenarios and improving accuracy in the watershed, providing new insights for flood control anddisasterreduction efforts.
Atherosclerosis,as the most prevalent form of cardiovasculardisease,is characterized by oxidized lowdensity lipoprotein(ox-LdL)accumulation in the vascular wall,increased inflammation of the large arteries,dysfunctio...
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Atherosclerosis,as the most prevalent form of cardiovasculardisease,is characterized by oxidized lowdensity lipoprotein(ox-LdL)accumulation in the vascular wall,increased inflammation of the large arteries,dysfunction of the endothelial cells(ECs)and vascular smooth muscle cells(VSMCs),which may eventually lead to the formation of ***,one of the main groups of carotenoids,have been proposed as preventive agents or adjunct therapies to prevent and slow the progression of atherosclerosis due to their cardioprotective ***,the underlying preventive mechanism of action of xanthophylls on the pathogenesis of atherosclerosis remains unclear,and clinical evidence of the effect of xanthophylls on atherosclerosis have not yet been summarized and critically *** this regard,we conducted a comprehensive literature search in four scientific databases(Pub Med,Google Scholar,Science direct and Web of Science)and carefully analyzed the existing evidence to provide meaningful insights on the association between xanthophylls and atherosclerosis from various *** on the evidence from in vitro and in vivo studies,we explored several potential mechanisms,including antioxidant effect,anti-inflammatory effect,regulation of lipid metabolism,and modulation of ECs and VSMCs dysfunction,and we found that a clear picture of regulatory pathways of xanthophylls on atherosclerosis prevention and treatment is still *** addition,epidemiological studies suggested the possible relationship among high dietary intake of xanthophylls,high plasma/serum xanthophylls and a reducedrisk of *** evidence from interventional studies investigating the effect of xanthophylls on atherosclerosis is very sparse,whilst indirect clinical evidence was only limited to astaxanthin and ***,well-designed long-term randomized controlled trials(rCTs)are highly recommended for future studies to investigate the effective dose of different
dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic *** event-based camera is adopted to capture the machine vibration states in the perspe...
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dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic *** event-based camera is adopted to capture the machine vibration states in the perspective of vision.
This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-dOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input *** the mod...
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This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-dOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input *** the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller ***,the control gain of ultra-local model is considered as a *** fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped ***,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is ***,to avoiddisadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown ***,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown ***,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is ***,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov ***,with the co-simulations on virtual prototype of 7-dOF ireHave upper-limb exoskeleton and experiments on 2-dOF upper-limb exoskeleton,the obtained comparedresults illustrate the effectiveness and superiority of the proposed method.
Existing multi-view deep subspace clustering methods aim to learn a unifiedrepresentation from multi-view data,while the learnedrepresentation is difficult to maintain the underlying structure hidden in the origin s...
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Existing multi-view deep subspace clustering methods aim to learn a unifiedrepresentation from multi-view data,while the learnedrepresentation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighborrelationship between *** overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering *** creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion *** this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one *** addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering *** code is available at https://***/songzuolong/MNF-MdSC(accessed on 25 december 2024).
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