Feedback Intensity Research Articles

Miniaturized, portable gustation interfaces for VR/AR/MR

Gustation is one of the five innate sensations for humans, distinguishing from vision, auditory, tactile, and olfaction, as which is a close and chemically induced sense. Despite the fact that a handful of gustation display technologies have been developed, the new technologies still pose significant challenges in miniaturization of the overall size for portability, enriching taste options within a limited working area, supporting natural human-device interaction, and achieving precisely controlled taste feedback. To address these issues, here, we report a set of intelligent and portable lollipop-shaped taste interfacing systems covering from 2 to 9 different taste options for establishing an adjustable taste platform in virtual reality (VR), augmented reality (AR), and mixed reality (MR) environments. Tasteful and food-grade chemicals embedded agarose hydrogels serve as taste sources based on iontophoresis operation principle, with an adjustable feedback intensity and independent operation time by tuning the voltage input. To achieve portability and user-friendly operation, the devices are miniaturized into a gustation interface with 9-channel taste generators in the dimension of 8 cm × 3 cm × 1 cm. To realize both gustation and olfaction feedbacks in Metaverse, an olfaction interface based on 7-channel odor generators is also introduced into the gustation interface system. As a result, the demonstrations of our gustation interface systems in intelligent medical gustation assessment, remote shopping, and mixed reality have proven their advances and great progress in various potential application areas, ranging from human-machine interfaces, to biomedical science, and to entertainment.

Weakened large-scale surface heat flux feedback at midlatitudes under global warming.

The surface heat flux feedback, which refers to the response of surface heat flux anomaly to the underlying sea surface temperature anomaly (SSTA), is one of the key processes in air-sea interaction. It plays an important role in regulating various aspects of the climate system, ranging from local SSTA persistence to the global overturning circulation and major climate modes. Yet its change under greenhouse gas-induced warming remains unknown. Here, using an ensemble of global climate simulations under a high radiative forcing scenario, we demonstrate that the intensity of surface heat flux feedback for spatially large-scale SSTA at the midlatitudes is projected to halve by the end of the 21st century, compared to pre-industrial levels. Such weakening is primarily attributed to a more stabilized marine atmospheric boundary layer, which diminishes the air-sea thermal disequilibrium caused by SSTA. In a warming climate, the variance of midlatitude SSTA at large spatial scales is expected to be significantly enhanced in response to the weakened surface heat flux feedback.

Minimalist Design for Multi-Dimensional Pressure-Sensing and Feedback Glove with Variable Perception Communication

Immersive human–machine interaction relies on comprehensive sensing and feedback systems, which enable transmission of multiple pieces of information. However, the integration of increasing numbers of feedback actuators and sensors causes a severe issue in terms of system complexity. In this work, we propose a pressure-sensing and feedback glove that enables multi-dimensional pressure sensing and feedback with a minimalist design of the functional units. The proposed glove consists of modular strain and pressure sensors based on films of liquid metal microchannels and coin vibrators. Strain sensors located at the finger joints can simultaneously project the bending motion of the individual joint into the virtual space or robotic hand. For subsequent tactile interactions, the design of two symmetrically distributed pressure sensors and vibrators at the fingertips possesses capabilities for multi-directional pressure sensing and feedback by evaluating the relationship of the signal variations between two sensors and tuning the feedback intensities of two vibrators. Consequently, both dynamic and static multi-dimensional pressure communication can be realized, and the vibrational actuation can be monitored by a liquid-metal-based sensor via a triboelectric sensing mechanism. A demonstration of object interaction indicates that the proposed glove can effectively detect dynamic force in varied directions at the fingertip while offering the reconstruction of a similar perception via the haptic feedback function. This device introduces an approach that adopts a minimalist design to achieve a multi-functional system, and it can benefit commercial applications in a more cost-effective way.

An analysis of estimation strategies for knock control

Since knock behaves as a random process, all knock controllers regulate some statistical property of this process which must be estimated from the data. The choice of the statistical metric used for feedback, its target value, and the method used for its estimation all have a strong impact on the performance of the closed loop system. In this work, expressions for the asymptotic variance of the estimates of different test statistics are derived and used to compare empirical versus parametric estimation methods. The variance of different metrics and the slope of their response curves (e.g. knock probability vs knock percentile intensity feedback) are also used to compute and compare the efficacy of these different measures for knock control purposes. The efficacy is seen to vary as a function of the chosen knock probability target, and lower knock thresholds/higher knock rate targets are recommended. Finally, the estimator dynamics are shown to dominate the dynamics of the closed loop response, and estimates obtained using a sliding window versus an exponentially weighted moving average are compared and contrasted. In each case, the results are illustrated using closed loop simulations of controller response subject to disturbances.

Dynamical analysis and fault detection application of a time-delayed multi-stable stochastic resonance system driven by white correlated noises.

Bearing fault diagnosis is a crucial means to ensure the normal operation of machinery, reduce maintenance costs, enhance equipments safety and extend the service life of bearings. However, the noise interference in input signals often affects the effective extraction of bearing fault signals. In this paper, a time-delay multi-stable stochastic resonance (SR) system driven by white correlated noises is proposed. Firstly, the expressions of the steady-state probability density (SPD) function and the mean first passage time (MFPT) is derived. Simultaneously, we delve into the influences of various system parameters, including multiplicative noise intensity, additive noise intensity, noise correlated strength, time-delay, and time-delay feedback intensity, on the dynamical behavior exhibited by particles within the system. Then, according to the signal-to-noise ratio (SNR) formula, the paper investigates the impact of system parameters on the SNR. It is found that the ease of SR occurrence is directly related to the system parameters. Finally, the experimental results demonstrate that the proposed method exhibits superior performance in detecting faults compared with the classical bistable SR system and the quad-stable SR system.

Flexible intelligent fabric composed of pinecone-like microstructure Co-cMOFs/CoZnAl-LDH composite nanomaterial for various types of sensor signals to guide personalized health activities

Multifunctional interfacing is a key aspect of flexible sensor development that facilitates enhanced interaction with the natural environment. However, challenges faced by contemporary multifunctional flexible sensors are structural complexity and high cost. Herein, inspired by the specific structure of pinecone in nature and the microstructured neural networks randomly distributed within human skin, we fabricated Co-cMOFs/CoZnAl-LDH composite nanomaterials with pinecone-like microstructure. Through the mutual verification of finite element simulation and experimental data, the mechanism of high sensitivity of the sensor is dynamically revealed from the structural point of view. The prepared pressure sensors exhibit a detection range (0–120 kPa), high sensitivity (2.35 × 109 kPa−1) and fast response. The photoelectric sensor shows enhanced photoelectric energy conversion, which shows great potential in the detection of sunlight intensity (20–100 mW cm−2). Simultaneously, building a hybrid pressure, acoustic and optical sensing platform and validating its superior performance in human movement and health monitoring applications. Furthermore, a strategy of light intensity feedback system to guide personalized health activities is put forward, so that doctors can guide patients/elderly health activities remotely.

Mechanical and biochemical feedback combine to generate complex contractile oscillations in cytokinesis

The actomyosin cortex is an active material that generates force to drive shape changes via cytoskeletal remodeling. Cytokinesis is the essential cell division event during which a cortical actomyosin ring closes to separate two daughter cells. Our active gel theory predicted that actomyosin systems controlled by a biochemical oscillator and experiencing mechanical strain would exhibit complex spatiotemporal behavior. To test whether active materials invivo exhibit spatiotemporally complex kinetics, we imaged the C.elegans embryo with unprecedented temporal resolution and discovered that sections of the cytokinetic cortex undergo periodic phases of acceleration and deceleration. Contractile oscillations exhibited a range of periodicities, including those much longer periods than the timescale of RhoA pulses, which was shorter in cytokinesis than in any other biological context. Modifying mechanical feedback invivo or in silico revealed that the period of contractile oscillation is prolonged as a function of the intensity of mechanical feedback. Fast local ring ingression occurs where speed oscillations have long periods, likely due to increased local stresses and, therefore, mechanical feedback. Fast ingression also occurs where material turnover is high, invivo and in silico. We propose that downstream of initiation by pulsed RhoA activity, mechanical feedback, including but not limited to material advection, extends the timescale of contractility beyond that of biochemical input and, therefore, makes it robust to fluctuations in activation. Circumferential propagation of contractility likely allows for sustained contractility despite cytoskeletal remodeling necessary to recover from compaction. Thus, like biochemical feedback, mechanical feedback affords active materials responsiveness and robustness.

Electric field induced mechanical flapping motors enabling soft robotic and wearable applications

Here we present a new type of soft motors, termed Electric Field Induced Mechanical Flapping (EFIMF) motors, which are robust and present versatile potential applications. The EFIMF motors harness an actuation mechanism that combines two processes: 1) using the electric field to induce charges, and 2) coupling the electromechanical forces to achieve fast and robust actuation. Our EFIMF motor can remain functional even with its electrode punctured by a metal needle. The EFIMF motors also present the fault-tolerant capability, which helps them to survive the dielectric breakdown. We demonstrate a robust crawling soft robot, which is powered by one EFIMF motor and capable of surviving continuous hammering. We also show a wearable haptic glove powered by five EFIMF motors, generating rich notification signals with a wide frequency range and different mechanical feedback intensities. The robustness of the soft EFIMF motors enhances their lifetime and performances, and represents an important step towards realistic soft mobile machines and wearable human machine interfaces.

Displacement Sensing for Laser Self-Mixing Interferometry by Amplitude Modulation and Integral Reconstruction.

To robustly and adaptively reconstruct displacement, we propose the amplitude modulation integral reconstruction method (AM-IRM) for displacement sensing in a self-mixing interferometry (SMI) system. By algebraically multiplying the SMI signal with a high-frequency sinusoidal carrier, the frequency spectrum of the signal is shifted to that of the carrier. This operation overcomes the issue of frequency blurring in low-frequency signals associated with continuous wavelet transform (CWT), enabling the precise extraction of the Doppler frequency of the SMI signal. Furthermore, the synchrosqueezing wavelet transform (SSWT) is utilized to enhance the frequency resolution of the Doppler signal. Our experimental results demonstrate that the proposed method achieves a displacement reconstruction accuracy of 21.1 nm (0.89%). Additionally, our simulations demonstrated that this method can accurately reconstruct target displacement under the conditions of time-varying optical feedback intensity or a signal-to-noise ratio (SNR) of 0 dB, with a maximum root mean square (RMS) error of 22.2 nm. These results highlight its applicability in real-world environments. This method eliminates the need to manually determine the window length for time-frequency conversion, calculate the parameters of the SMI system, or add additional optical devices, making it easy to implement.

Dynamic mechanisms of delay formation and propagation in neuronal astrocytic network with electromagnetic induction

A model of neuro-astrocytic coupling with electromagnetic induction has been developed to deeply investigate the genesis and propagation of Delay within networks. Delay is defined as the time neurons continue to fire after external stimulation has ceased until they reach a resting state. The findings reveal that the primary cause of Delay in the neuron-astrocyte coupling system is the role of astrocytes. We observe that within a certain range, the feedback intensity causes a fractal structure of Delay, and as the feedback from astrocytes intensifies, Delay increases indefinitely. Neurons can continue firing indefinitely post-stimulation, and we have identified the feedback threshold for this infinite Delay. Further, we observed the transmission of Delay in neuron-astrocyte networks. Subsequent neurons, which would not normally fire, begin to discharge during the transmission of Delay, a phenomenon induced by either synaptic or astrocytic coupling. Finally, with electromagnetic induction, we noted the cumulative effect of Delay in the network, leading to longer sustained firing in more distant neurons as the network extends and propagates. Our research elucidates the precise mechanisms of generation and propagation of Delay, offering insights akin to mechanisms of memory in the nervous system, potentially providing novel understandings in the field of cognitive science.

Performance feedback duration and strategic change in USA manufacturing firms

PurposeThis paper seeks to explore the effect of performance duration (rather than intensity) on the subsequent initiation of strategic change by firms. Specifically, the effect of outperformance and underperformance duration on strategic change, as well as the moderating effect of environmental dynamism, is studied.Design/methodology/approachUsing a fixed-effects model, analyzing a sample of 34,907 firm-year observations from 1980 to 2018 across 112 industries mostly supported proposed hypotheses.FindingsResults revealed a U-shaped relationship between outperformance duration and strategic change and an inverted U-shaped relationship between underperformance duration and strategic change. The moderation role of environmental dynamism was only partially supported.Originality/valueThis study examines a new dimension of performance feedback, namely duration, rather than the widely used intensity of performance feedback, to enhance our understanding of the behavioral theory of the firm.

Nonlinear Dynamic Behavior of Two Distinct Chaotic Systems

The nonlinear dynamics of two chaotic systems, namely the optoelectronic feedback (OEFB) and the Lu-Chen electronic chaotic system, are simulated and implemented in this work using the Berkeley Madonna software. Control parameters and initial conditions have been adjusted to demonstrate transitions from one state to another. Upon modifying certain regulating elements, both systems exhibited excessive sensitivity to initial conditions and displayed dynamic nonlinear behavior. The OEFB system reveals a homoclinic condition with a Shilnikov attractor as the feedback intensity increases. In contrast, the Lu-Chen system exhibits sensitivity to parameters a, b, and c, accompanied by multiscroll behavior, as evidenced by time series, the Fast Fourier Transform (FFT), and attractor analysis. These results offer potential applications, including data encoding, secure communications, and image processing. This research studied the properties of two different chaotic dynamical systems. These two chaotic systems are optoelectronic feedback and Chua systems. The results are analyzed, and it is found that the behavior of the Chua system changes in the time series, which in turn causes the attractor to change. The results showed a significant increase in the Chua system's bandwidth. Studying the different characteristics opens a broad scope for many applications, the most important of which is secure communications.

Implementation of Er-doped random fiber laser self-mixing sensor with ultra-limit sensitivity

This study first demonstrates that the random distributed feedback fiber laser (RDFL) can be implemented for sensing detection by using the self-mixing effect as a sensing mechanism. By constructing a compact self-mixing velocimeter based on Er-doped RDFL with the integration of a laser, sensing element, and transmission platform, we successfully measured the minimum detectable feedback intensity of 38.65 fW for the velocity signal, corresponding to 0.55 photons per Doppler cycle, exhibiting ultra-high sensitivity dynamics characteristics. In addition, the velocity measurement of a non-cooperative target at a single-channel distance of 100 km is accomplished because of the natural feature of long-distance transmission for the random distributed feedback fiber lasers, which greatly improves the ultra-long detection range in the field of self-mixing sensing. The proposed sensing scheme not only unveils a fresh perspective on the exploration of random fiber laser sensing but also showcases its diverse and wide-ranging applications within the realm of remote sensing measurements.

Harmonic active mode-locking optoelectronic oscillator with suppressed supermode noise based on pulse intensity feedback.

A harmonic active mode-locking optoelectronic oscillator (HAML-OEO) with pulse intensity feedback is proposed and experimentally demonstrated. It is capable of generating microwave pulses characterized by suppressed supermode noise, uniform intensity, and tunable repetition rates. Unlike traditional HAML-OEOs, active mode-locking and pulse intensity feedback are simultaneously achieved through the use of a dual-drive Mach-Zehnder modulator (DDMZM). By synchronously feeding back the generated microwave pulses to the DDMZM, each pulse undergoes a loss proportional to its intensity, facilitating pulse intensity equalization and supermode noise suppression. In the experiment, intensity-equalized microwave pulse trains with repetition rates of 499 kHz and 998 kHz are generated by the 5th- and 10th-order HAML-OEOs, respectively, with the measured supermode noise suppression ratios exceeding 40 dB.

Uncertainty in the Past and Future Changes of Tropical Pacific SST Zonal Gradient: Internal Variability versus Model Spread

Abstract The zonal sea surface temperature (SST) gradient across the tropical Pacific is a pacemaker of the variable rates of global warming. In both historical simulations and future projections, the current state-of-the-art climate models show evident spread in the changes of zonal SST gradient, but the reasons remain unknown. Here, we quantify the contributions of internal variability and model spread to the uncertainty of zonal SST gradient changes by analyzing 342 realizations from CMIP5 and CMIP6 models and several sets of large-ensemble simulations. We found that the internal variability dominates the total uncertainty at multidecadal time scales (∼31-yr trends). Although the ratio of internal uncertainty to the total uncertainty declines along with higher emission of greenhouse gases under global warming, it is still over 80% at the multidecadal time scales in the future. The Pacific decadal oscillation is identified as the key internal mode responsible for the multidecadal uncertainty. For the future projections at centurial time scales, the uncertainty of zonal SST gradient changes is mainly from the intermodel spread in response to external forcing, accounting for about 70% of the uncertainty based on the difference between 2070–99 and 1970–99. The model spread in the cloud–shortwave radiation–SST feedback over the tropical Pacific is important in the uncertainty of zonal SST gradient changes. In particular, the intensity of negative convective cloud feedback in the western Pacific dominates the spread in CMIP5 models, while the intensity of stratocumulus cloud feedback over the southeastern Pacific is the primary process influencing the uncertainty in CMIP6 models.

Attributing interdecadal variations of southern tropical Indian Ocean dipole mode to rhythms of Bjerknes feedback intensity

Attributing interdecadal variations of southern tropical Indian Ocean dipole mode to rhythms of Bjerknes feedback intensity

Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction

Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.

Some qualitative analyses on a vegetation-water model with cross-diffusion and internal competition

This paper is concerned with a vegetation-water model with cross-diffusion and intra-plant competitive feedback under Neumann boundary conditions. First, we found that the equilibrium with small vegetation density is always unstable, and if the cross-diffusion coefficient is suitably large, the equilibrium with relatively large vegetation density loses its stability, and Turing instability occurs. A priori estimates of positive steady-state solutions are also established by the maximum principle of elliptic equations. Moreover, some qualitative analyses on the steady-state bifurcations for both simple and double eigenvalues are conducted in detail. Space decomposition and the implicit function theorem are used for double eigenvalues. In particular, the global continuation is obtained, and the result shows that there is at least one non-constant positive steady-state solution when cross-diffusion is large. Finally, numerical simulations are provided to prove and supplement theoretic research results, and some vegetation patterns with the increase of the soil water diffusion feedback intensity are formed, where the transition appears: gap [Formula: see text] stripe [Formula: see text] spot.

Burning characteristic and ceiling temperature of moving fires in a tunnel: A comparative study

Moving fire is a very important fire scenario in highway tunnel and subway tunnel. Compared to fixed fire sources, there is still limited research on moving fires in tunnels. In order to reveal the burning characteristics and evolutionary mechanisms of moving fires in tunnels, this study conducted reduced scale tunnel fire experiments on moving fires under natural ventilation conditions (Movefire) at travelling speeds ranging from 0.5 m/s to 2.0 m/s, as well as fixed fires under longitudinal ventilation (Ventfire) velocities ranging from 0.5 m/s to 2.0 m/s. The results demonstrate that it is unreasonable to solely use Ventfire experiments as a substitute for Movefire experiments to study fire development and ceiling smoke temperature distributions. When the travelling speed of fire and the ventilation velocity were equal, the HRRs of Movefires were less than those of Ventfires. At the same relative moving velocity of 0.5 m/s, Ventfire received more fresh air to increase burning intensity, while Movefire experienced decreases in thermal feedback and burning intensity. The Movefire smoke temperature rise was small and short in duration, while the Ventfire smoke temperature rise was large and long in duration. Furthermore, the dimensionless maximum ceiling temperature rise of the Movefire experiment was lower than that of the Ventfire experiment. And then a modified ceiling temperature model applicable to the Movefire experiment was proposed by introducing a speed correction factor.

Research on intensity–difference squeezing enhancement of phase-sensitive amplifier based on coherent feedback

The intensity-difference squeezed state is an important concept in quantum optics, which is not only of great significance for fundamental research in quantum physics, but also an important quantum resource in the fields of quantum communication, quantum computing, and quantum precision measurement. The optical parametric amplifier based on atomic four-wave mixing is one of the most effective means to achieve intensity-difference squeezed light. However, due to the absorption loss of atomic vapor in the light field, the output squeezing still needs improving. By feeding the non-classical optical field from the optical parametric amplifier back to the input port, the quantum characteristics of its output optical field can be enhanced. However, the intensity-difference squeezing enhancement from a phase-insensitive amplifier is experimentally realized based on coherent feedback control. The intensity-difference squeezing enhancement of the phase-sensitive amplifier has not been discussed. In this work, a two-port coherent feedback-controlled phase-sensitive amplifier is analyzed theoretically. The dependence of the intensity-difference squeezing, respectively, on the feedback intensity, the intensity gain of the optical parametric amplifier, and the losses of the system are investigated. For the ideal case in which the losses of the system are ignored, infinite squeezing can be achieved by adjusting the strength and phase of feedback. Considering the actual atomic absorption losses, squeezing enhancement can also be achieved over a wide range of intensity gains within a certain feedback intensity range. In addition, the squeezing enhancement is quite efficient for the medium intensity gain range. The intensity-difference squeezing enhancement strongly depends on the absorption loss of atomic vapor. The smaller the absorption loss, the more significant the squeezing enhancement effect is. Furthermore, the experimental feasibility of this scheme is also considered in detail. Our research can provide useful references for achieving high-quality non classical light fields in experiment, which may find applications in quantum information processing and quantum precise measurement.