Effects of Different Feedback Conditions on Sensorimotor Adaptation Revealed in a Mirror Reversal Paradigm

Humans are able to overcome sensory perturbations imposed on their movements through motor learning. One of the key mechanisms to accomplish this is sensorimotor adaptation, an implicit, error-driven learning mechanism. Past work on sensorimotor adaptation focused mainly on adaptation to rotated visual feedback—A paradigm known as visuomotor rotation. Recent studies have shown that sensorimotor adaptation can also occur under mirror-reversed visual feedback. In visuomotor rotation, sensorimotor adaptation can be driven by both endpoint and online feedback [1] [2]. However, it’s not been clear whether both kinds of feedback can similarly drive adaptation under a mirror reversed perturbation. We performed a study to establish what kinds of feedback can drive adaptation under mirror reversal. In the first two conditions, the participants were asked to ignore visual feedback. In the first condition, we provided mirror reversed online feedback and endpoint feedback. We reproduced previous findings showing that online feedback elicited adaptation under mirror reversal. In a second condition, we provided mirror reversed endpoint feedback. However, in the second condition, we found that endpoint feedback alone failed to elicit adaptation. In a third condition, we provided both types of feedback at the same time, but in a conflicting way: endpoint feedback was non-reversed while online feedback was mirror reversed. The participants were asked to ignore online visual feedback and try to hit the target with help from veridical endpoint feedback. In the third condition, in which veridical endpoint feedback and mirror reversed online feedback were provided, adaptation still occurred. Our results showed that endpoint feedback did not elicit adaptation under mirror reversal but online feedback did. This dissociation between effects of endpoint feedback and online feedback on adaptation under mirror reversal suggests that adaptation under these different kinds of feedback might in fact operate via distinct mechanisms.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Cumulative effects of excess high-normal alanine aminotransferase levels in relation to new-onset metabolic dysfunction-associated fatty liver disease in China

BACKGROUND Within the normal range,elevated alanine aminotransferase(ALT)levels are associated with an increased risk of metabolic dysfunction-associated fatty liver disease(MAFLD).AIM To investigate the associations between repeated high-normal ALT measurements and the risk of new-onset MAFLD prospectively.METHODS A cohort of 3553 participants followed for four consecutive health examinations over 4 years was selected.The incidence rate,cumulative times,and equally and unequally weighted cumulative effects of excess high-normal ALT levels(ehALT)were measured.Cox proportional hazards regression was used to analyse the association between the cumulative effects of ehALT and the risk of new-onset MAFLD.RESULTS A total of 83.13%of participants with MAFLD had normal ALT levels.The incidence rate of MAFLD showed a linear increasing trend in the cumulative ehALT group.Compared with those in the low-normal ALT group,the multivariate adjusted hazard ratios of the equally and unequally weighted cumulative effects of ehALT were 1.651[95%confidence interval(CI):1.199-2.273]and 1.535(95%CI:1.119-2.106)in the third quartile and 1.616(95%CI:1.162-2.246)and 1.580(95%CI:1.155-2.162)in the fourth quartile,respectively.CONCLUSION Most participants with MAFLD had normal ALT levels.Long-term high-normal ALT levels were associated with a cumulative increased risk of new-onset MAFLD.

Carbon Emission Effects Driven by Evolution of Chinese Dietary Structure from 1987 to 2020

Exploring carbon emission effects based on the evolution of residents’ dietary structure to achieve the carbon neutrality goal and mitigate climate change is an important task.This study took China as the research object(data excluding Hong Kong,Macao and Taiwan) and used the carbon emission coefficient method to quantitatively measure the food carbon emissions from 1987–2020,then analyzed the carbon emission effects under the evolution of dietary structure.The results showed that during the study period,the Chinese dietary structure gradually changed to a high-carbon consumption pattern.The dietary structure of urban residents developed to a balanced one,while that of rural residents developed to a high-quality one.During the study period,the per capita food carbon emissions and total food consumption of Chinese showed an increasing trend.The per capita food carbon emissions of residents in urban and rural showed an overall upward trend.The total food carbon emissions in urban increased significantly,while that in rural increased first and then decreased.The influence of beef and mutton on carbon emissions is the highest in dietary structure.Compared with the balanced dietary pattern,the food carbon emissions of Chinese residents had not yet reached the peak,but were evolving to a high-carbon consumption pattern.

Simulation of the Ecosystem Productivity Responses to Aerosol Diffuse Radiation Fertilization Effects over the Pan-Arctic during 2001–19

The pan-Arctic is confronted with air pollution transported from lower latitudes.Observations have shown that aerosols help increase plant photosynthesis through the diffuse radiation fertilization effects(DRFEs).While such DRFEs have been explored at low to middle latitudes,the aerosol impacts on pan-Arctic ecosystems and the contributions by anthropogenic and natural emission sources remain less quantified.Here,we perform regional simulations at 0.2o×0.2ousing a well-validated vegetation model(Yale Interactive terrestrial Biosphere,YIBs)in combination with multi-source of observations to quantify the impacts of aerosol DRFEs on the net primary productivity(NPP)in the pan-Arctic during 2001-19.Results show that aerosol DRFEs increase pan-Arctic NPP by 2.19 Pg C(12.8%)yr^(-1)under clear-sky conditions,in which natural and anthropogenic sources contribute to 8.9% and 3.9%,respectively.Under all-sky conditions,such DRFEs are largely dampened by cloud to only 0.26 Pg C(1.24%)yr^(-1),with contributions of 0.65% by natural and 0.59% by anthropogenic species.Natural aerosols cause a positive NPP trend of 0.022% yr^(-1)following the increased fire activities in the pan-Arctic.In contrast,anthropogenic aerosols induce a negative trend of-0.01% yr^(-1)due to reduced emissions from the middle latitudes.Such trends in aerosol DRFEs show a turning point in the year of 2007 with more positive NPP trends by natural aerosols but negative NPP trends by anthropogenic aerosols thereafter.Though affected by modeling uncertainties,this study suggests a likely increasing impact of aerosols on terrestrial ecosystems in the pan-Arctic under global warming.

Feedback linearization and equivalent-disturbance compensation control strategy for piezoelectric stage

Piezoelectric stages use piezoelectric actuators and flexure hinges as driving and amplifying mechanisms,respectively.These systems have high positioning accuracy and high-frequency responses,and they are widely used in various precision/ultra-precision positioning fields.However,the main challenge with these devices is the inherent hysteresis nonlinearity of piezoelectric actuators,which seriously affects the tracking accuracy of a piezoelectric stage.Inspired by this challenge,in this work,we developed a Hammerstein model to describe the hysteresis nonlinearity of a piezoelectric stage.In particular,in our proposed scheme,a feedback-linearization algorithm is used to eliminate the static hysteresis nonlinearity.In addition,a composite controller based on equivalent-disturbance compensation was designed to counteract model uncertainties and external disturbances.An analysis of the stability of a closed-loop system based on this feedback-linearization algorithm and composite controller was performed,and this was followed by extensive comparative experiments using a piezoelectric stage developed in the laboratory.The experimental results confirmed that the feedback-linearization algorithm and the composite controller offer improved linearization and trajectory-tracking performance.

Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators

The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace(BF)system.In this work,a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed.The data on raw and fuel materials,process op-eration,smelting state,and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed.A novel method for the delay analysis of furnace heat indicators was established.The extracted delay variables were found to play an important role in modeling.The method that combined the genetic algorithm and stacking efficiently im-proved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model.The hit ratio for predicting the temperature of hot metal in the error range of±10℃ was 92.4%,and that for the chemical heat of hot metal in the error range of±0.1wt%was 93.3%.On the basis of the furnace heat prediction model and expert experience,a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels.These sugges-tions were highly accepted by BF operators.Finally,the comprehensive and dynamic model proposed in this work was successfully ap-plied in a practical BF system.It improved the BF temperature level remarkably,increasing the furnace temperature stability rate from 54.9%to 84.9%.This improvement achieved considerable economic benefits.

Effects of urbanization and forest type on species composition and diversity,forest characteristics,biomass carbon sink,and their associations in Changchun,Northeast China:implications for urban carbon stock improvement

Differences in forest attributes and carbon sequestration of each organ and layer between broadleaved and conifer forests of central and outer urban areas are not well-defined,hindering the precise management of urban forests and improvement of function.To clarify the effect of two forest types with different urbanization intensities,we determined differences in vegetation composition and diversity,structural traits,and carbon stocks of 152 plots(20 m×20 m)in urban park forests in Changchun,which had the largest green quantity and carbon density effectiveness.We found that 1.1-fold thicker and healthier trees,and 1.6-to 2.0-fold higher,healthier,denser,and more various shrubs but with sparser trees and herbs occurred in the central urban forests(p<0.05)than in the outer forests.The conifer forests exhibited 30–70%obviously higher tree aboveground carbon sequestration(including stem and leaf)and 20%bigger trees,especially in the outer forests(p<0.05).In contrast,1.1-to 1.5-fold higher branch stocks,healthier and more diverse trees were found in broadleaved forests of both the inner and outer forests(p<0.05).Plant size and dominant species had similarly important roles in carbon stock improvement,especially big-sized woody plants and Pinus tabuliformis.In addition,a higher number of deciduous or needle species positively affected the broadleaved forest of the central urban area and conifer forest of the outer urban area,respectively.These findings can be used to guide precise management and accelerate the improvement of urban carbon function in Northeast China in the future.

Temperature-feedback two-photon-responsive metal-organic frameworks for efficient photothermal therapy

The realization of real-time thermal feedback for monitoring photothermal therapy(PTT)under near-infrared(NIR)light irradiation is of great interest and challenge for antitumor therapy.Herein,by assembling highly efficient photothermal conversion gold nanorods and a temperature-responsive probe((E)-4-(4-(diethylamino)styryl)-1-methylpyridin-1-ium,PyS)within MOF-199,an intelligent nanoplatform(AMPP)was fabricated for simultaneous chemodynamic therapy and NIR light-induced temperature-feedback PTT.The fluorescence intensity and temperature of the PyS probe are linearly related due to the restriction of the rotation of the characteristic monomethine bridge.Moreover,the copper ions resulting from the degradation of MOF-199 in an acidic microenvironment can convert H_(2)O_(2)into•OH,resulting in tumor ablation through a Fenton-like reaction,and this process can be accelerated by increasing the temperature.This study establishes a feasible platform for fabricating highly sensitive temperature sensors for efficient temperature-feedback PTT.

A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

Health Effects of Extreme Low Temperatures and Cold Waves on Respiratory Diseases

Climate change is the most significant threat to public health and exerts myriad influences on health,including the occurrence of extreme temperature events.Studies have demonstrated that populations will experience significantly severe cold waves in the future^([1]),increasing the risk of respiratory diseases.

Tilted Perpendicular Anisotropy-Induced Spin-Orbit Ratchet Effects

Using micromagnetic simulations, we demonstrate the tilted perpendicular anisotropy-induced spin-orbit ratchet effect. In spin-orbit torque(SOT)-induced magnetization switching, the critical currents required to switch between the two magnetization states(upward and downward magnetization) are asymmetric. In addition, in the nanowire structure, tilted anisotropy induces formation of tilted domain walls(DWs). The tilted DWs exhibit a ratchet behavior during motion. The ratchet effect during switching and DW motions can be tuned by changing the current direction with respect to the tilting direction of anisotropy. The ratchet motion of the DWs can be used to mimic the leaky-integrate-fire function of a biological neuron, especially the asymmetric property of the “potential” and “reset” processes. Our results provide a full understanding of the influence of tilted perpendicular anisotropy on SOT-induced magnetization switching and DW motion, and are beneficial for designs of further SOT-based devices.

Performance enhancement of a viscoelastic bistable energy harvester using time-delayed feedback control

This paper focuses on the stochastic analysis of a viscoelastic bistable energy harvesting system under colored noise and harmonic excitation, and adopts the time-delayed feedback control to improve its harvesting efficiency. Firstly, to obtain the dimensionless governing equation of the system, the original bistable system is approximated as a system without viscoelastic term by using the stochastic averaging method of energy envelope, and then is further decoupled to derive an equivalent system. The credibility of the proposed method is validated by contrasting the consistency between the numerical and the analytical results of the equivalent system under different noise conditions. The influence of system parameters on average output power is analyzed, and the control effect of the time-delayed feedback control on system performance is compared. The output performance of the system is improved with the occurrence of stochastic resonance(SR). Therefore, the signal-to-noise ratio expression for measuring SR is derived, and the dependence of its SR behavior on different parameters is explored.

A Tutorial on Quantized Feedback Control

In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation,achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.

Towards engineering a portable platform for laparoscopic pre-training in virtual reality with haptic feedback

Background Laparoscopic surgery is a surgical technique in which special instruments are inserted through small incision holes inside the body.For some time,efforts have been made to improve surgical pre training through practical exercises on abstracted and reduced models.Methods The authors strive for a portable,easy to use and cost-effective Virtual Reality-based(VR)laparoscopic pre-training platform and therefore address the question of how such a system has to be designed to achieve the quality of today's gold standard using real tissue specimens.Current VR controllers are limited regarding haptic feedback.Since haptic feedback is necessary or at least beneficial for laparoscopic surgery training,the platform to be developed consists of a newly designed prototype laparoscopic VR controller with haptic feedback,a commercially available head-mounted display,a VR environment for simulating a laparoscopic surgery,and a training concept.Results To take full advantage of benefits such as repeatability and cost-effectiveness of VR-based training,the system shall not require a tissue sample for haptic feedback.It is currently calculated and visually displayed to the user in the VR environment.On the prototype controller,a first axis was provided with perceptible feedback for test purposes.Two of the prototype VR controllers can be combined to simulate a typical both-handed use case,e.g.,laparoscopic suturing.A Unity based VR prototype allows the execution of simple standard pre-trainings.Conclusions The first prototype enables full operation of a virtual laparoscopic instrument in VR.In addition,the simulation can compute simple interaction forces.Major challenges lie in a realistic real-time tissue simulation and calculation of forces for the haptic feedback.Mechanical weaknesses were identified in the first hardware prototype,which will be improved in subsequent versions.All degrees of freedom of the controller are to be provided with haptic feedback.To make forces tangible in the simulation,characteristic values need to be determined using real tissue samples.The system has yet to be validated by cross-comparing real and VR haptics with surgeons.

A NOTE ON THE GENERAL STABILIZATION OF DISCRETE FEEDBACK CONTROL FOR NON-AUTONOMOUS HYBRID NEUTRAL STOCHASTIC SYSTEMS WITH A DELAY

Discrete feedback control was designed to stabilize an unstable hybrid neutral stochastic differential delay system(HNSDDS) under a highly nonlinear constraint in the H_∞ and exponential forms.Nevertheless,the existing work just adapted to autonomous cases,and the obtained results were mainly on exponential stabilization.In comparison with autonomous cases,non-autonomous systems are of great interest and represent an important challenge.Accordingly,discrete feedback control has here been adjusted with a time factor to stabilize an unstable non-autonomous HNSDDS,in which new Lyapunov-Krasovskii functionals and some novel technologies are adopted.It should be noted,in particular,that the stabilization can be achieved not only in the routine H_∞ and exponential forms,but also the polynomial form and even a general form.

Faster split-based feedback network for image super-resolution

Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.

A Study on Negative Feedback in English Extensive Reading Course

Negative feedback serves as a means of correcting and rectifying students’utterances.While content feedback is commonly used in extensive reading course,indirect feedback dominates in written assignments,providing students with opportunities for second thoughts and self-correction.For oral limited-response and open-ended questions,recasts are predominantly used to give students more time to reflect and reduce teacher talk,thereby mitigating anxiety and enhancing learning motivation.

Effects of Collocational Congruency,Frequency and L2 Proficiency on the Processing of English Adjective-Noun Collocations

The present study investigates the effects of congruency and frequency on adjective-noun collocational processing for Chinese learners of English at two proficiency levels based on the data obtained in an online acceptability judgment task.The subject pool of this research included 60 English majors studying at a university in China;30 were selected as a higher-proficiency group and 30 as a lower-proficiency group according to their Vocabulary Levels Test(Schmitt et al.,2001)scores and their self-reported proficiency in English.The experimental materials were programmed to E-prime 2.0 and included six types of collocations:(1)15 high-frequency congruent collocations,(2)15 low-frequency congruent collocations,(3)15 high-frequency incongruent collocations,(4)15 low-frequency incongruent collocations,(5)15 Chinese-only items,and(6)75 unrelated items for baseline data.The collected response times(RTs)and accuracy rates data were statistically analyzed by the use of an ANOVA test and pairwise comparisons through SPSS 16.0 software.The results revealed that:(1)the adjective-noun collocational processing of Chinese English learners is influenced by collocational frequency,congruency and L2 proficiency;(2)the processing time is affected by the interaction of congruency and frequency;and(3)the interactive effect of L2 proficiency in conjunction with congruency and frequency also influences the processing quality.