Noise and noise propagation are vital in various biological processes. In a phenotypic transition cascade of colonic cells, there are three compartments: stem cells (SCs), transit-amplifying cells ...(TACs), and fully differentiated cells (FDCs). Instead of pure linear feedback (L) or pure saturation feedback (S) in regulating SCs and TACs differentiation, four coupled feedbacks including LL, LS, SL, and SS are considered to compare the impacts of different feedbacks on noise and noise propagation of cells. Rather than LL feedback and LS feedback, SL feedback and SS feedback have obvious advantages: Firstly, the characteristics of cells steady states changing with the parameter are more consistent with the tumor development. Secondly, as long as the key parameter is adjusted reasonably, the strong correlation between any two fluctuations of cells can be effectively utilized or avoided. Finally, although there is no direct interaction between SCs and FDCs, the noise in SCs can propagate to FDCs by TACs. And the transmitted noise from upstream cells can cause the large total noise of downstream cells even the number of downstream cells is large. The strong correlation and noise propagation between upstream and downstream cells may be the theoretical basis of the targeted therapy, which can achieve dual or even triple-drug targeted anti-tumor therapy by acting on different targets in the upstream or downstream pathways. Therefore, SL feedback or SS feedback may be a better choice for SCs and TACs to adjust their differentiation rather than LL feedback and LS feedback. This work is an extension and application of the elementary fluctuation theory of statistical physics in life system.
•We discuss the influence of different feedbacks on noise and noise propagation.•The elementary fluctuation theory and stochastic dynamics theory are used.•Noise can propagate from upstream cells to downstream cells due to the interaction between cells.•Even if the total number of downstream cells is not small, their noise will be large due to the propagation of noise.
With the rapid increase in the use of optogenetics to investigate the nervous system, there is a high demand for a neural interface that enables 2D mapping of electrophysiological neural signals with ...high precision during simultaneous light stimulation. Here, a gold nanonetwork (Au NN)‐based transparent neural electrocorticogram (ECoG) monitoring system is proposed as implantable neural electronics. The neural interface enables accurate 2D mapping of ECoG neural signals without any photoelectric artifact during light stimulation. By using the Au NN, not only the transmittance of the microelectrodes is increased by 81% but also a low electrochemical impedance of 33.9 kΩ at 1 kHz with improved mechanical stability is achieved. It is demonstrated that the transparent microelectrode array records multichannel in vivo neural activities with no photoelectric artifact and a high signal‐to‐noise ratio. Propagation of neural dynamics of optically driven neural activities is also clearly visualized using the 2D Au NN microelectrode array. This transparent, flexible ECoG microelectrode array is a promising candidate for next‐generation in vitro and in vivo neural interface for 2D mapping of neural dynamics.
A transparent electrocorticogram (ECoG) array based on gold nanonetwork (Au NN) structures is achieved through a typical micro‐electromechanical system process and electrospinning of poly(methyl methacrylate) nanofibers, which allows simple control of the transparency of microelectrodes. The high transmittance of Au NN microelectrodes is capable of not only negligible photoelectric artifact, but also improved mechanical stability compared to those of Au film microelectrodes.
Friction exists in most mechanical systems, and it can have a major influence on their dynamic performance and operating conditions. As a consequence of frictional contact phenomena, energy is ...dissipated and the state of a system can change slowly and rapidly, depending on the nature of the contact, continuous or impact condition. Other effects associated with friction in mechanical systems are the vibration and noise propagation of the system components, nonlinear systems’ behavior and wear. Overall, the knowledge of the friction regimen, as well as the frictional forces developed at the interface of mechanical parts in contact with relative motion, is crucial for the dynamic analysis of mechanical systems, and has consequences in the design process. Thus, this work is a review of the modeling and analysis of frictional effects in multibody systems with the purpose of better understanding and obtaining accurate responses. In this process, pure dry sliding friction, stick–slip effect, viscous friction, Stribeck effect, and frictional lag are some of the main phenomena associated with friction, which are addressed in depth. Overall, the friction models can be divided into two main groups, namely the “static friction models” and the “dynamic friction models”. The static models describe the steady-state behavior of the relation friction-force/relative-velocity, while the dynamic models allow for the capturing of more physical responses and properties by using extra state variables. In a simpler manner, the static and dynamic friction models differ mostly in the modeled frictional effects, implementation complexity, and computational efficiency. Hence, this research is aimed at analyzing in detail the role of friction modeling in the dynamic response of multibody system, as well as addressing the importance of friction models selection for accurately describing the friction related phenomena. Demonstrative application examples, which include friction in ideal mechanical joints and systems involving contact–impact events, including an example of rolling contact will be considered and investigated to illustrate the main assumptions and procedures adopted in this work. The results from this study indicate that in most cases, a static friction model, which accounts for static friction and avoids the discontinuity at zero velocity, is a suitable choice. A more advanced dynamic friction model has to be developed to be utilized for systems containing high variations of normal load, namely with impact conditions.
Potassium ion and sodium ion channels play important roles in the propagation of action potentials along a myelinated axon. The random opening and closing of ion channels can cause the fluctuation of ...action potentials. In this paper, an improved Hodgkin-Huxley chain network model is proposed to study the effects of ion channel blocks, temperature, and ion channel noise on the propagation of action potentials along the myelinated axon. It is found that the chain network has minimum coupling intensity threshold and maximum tolerance temperature threshold that allow the action potentials to pass along the whole axon, and the blockage of ion channels can change these two thresholds. A striking result is that the simulated value of the optimum membrane size (inversely proportional to noise intensity) coincides with the area range of feline thalamocortical relay cells in biological experiments.
Reconfigurable intelligent surfaces (RISs) have attracted enormous interest thanks to their ability to overcome line-of-sight blockages in mmWave systems, enabling in turn accurate localization with ...minimal infrastructure. Less investigated are however the benefits of exploiting RIS with suitably designed beamforming strategies for optimized localization and synchronization performance. In this paper, a novel low-complexity method for joint localization and synchronization based on an optimized design of the base station (BS) active precoding and RIS passive phase profiles is proposed, for the challenging case of a single-antenna receiver. The theoretical position error bound is first derived and used as metric to jointly optimize the BS-RIS beamforming, assuming a priori knowledge of the user position. By exploiting the low-dimensional structure of the solution, a novel codebook-based robust design strategy with optimized beam power allocation is then proposed, which provides low-complexity while taking into account the uncertainty on the user position. Finally, a reduced-complexity maximum-likelihood based estimation procedure is devised to jointly recover the user position and the synchronization offset. Extensive numerical analysis shows that the proposed joint BS-RIS beamforming scheme provides enhanced localization and synchronization performance compared to existing solutions, with the proposed estimator attaining the theoretical bounds even at low signal-to-noise-ratio and in the presence of additional uncontrollable multipath propagation.
We are now at a point where airframes, aeroengines and their integration require radical changes to meet increasingly aggressive environmental targets. Generally, fan noise emitted from rotor-stator ...assemblies would be one of the dominant noise sources for modern and next-generation aircraft engines, which shall direct most research interest into fans in the coming decade and, therefore, the associated measurement constitutes the main focus of the current review. Amongst various approaches, an experimental study of fan noise is usually efficient and of high-fidelity, but the associated cost is expensive. Moreover, the design of a test would be time-consuming due to the adequate choice of testing methods with the adaption to the testing case and rig. In addition, in most cases the experimental setup requires some sort of optimization to achieve high measurement accuracy. This article provides a contemporary review of the most well-known and state-of-the-art testing methods with the focus on fan noise problems. More specifically, the acoustic mode detection and noise source reconstruction methods are most relevant to the understanding of noise generation mechanism and propagating characteristics, which are useful for further noise reduction studies, and therefore are extensively reviewed in this article. As timely guidance to potential interested readers, this paper also provides an overview of recent developments based on the compressive sensing and machine learning techniques that have enabled disruptive innovations by fundamentally changing the testing practices of conventional measurement methods, thus constituting the continued research direction for next-generation aeroengines.
Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and ...strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.
Abstract In experimental structural dynamics, reliable estimation of Frequency Response Functions (FRF) is important to correctly characterize a mechanical system. In Experimental Modal Analysis ...(EMA), the FRFs are used as input to a modal parameter estimation algorithm to obtain the modal characteristics of the system. Errors due to noisy measurements are inevitably present in the FRFs and propagate to the modal parameters. A consistent FRF-estimator with low uncertainty is therefore needed. Different FRF estimators have been proposed with some consistency when certain noise-related assumptions are fulfilled (H1, H2, etc.). To choose the appropriate frequency response function estimator, information about the noise in the experimental setup is desirable. In this work it is shown how to use measurements of noise, to characterize different noise components in the experimental setup and determine the appropriate number of averages needed for the experimental setup. The identified noise components can be used to identify the main source of uncertainty in the experimental setup and which FRF estimator to use.
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