Effects of three coniferous plantation species on plant-soil feedbacks and soil physical and chemical properties in semiarid mountain ecosystems

Background:Large-scale afforestation can significantly change the ground cover and soil physicochemical properties,especially the soil fertility maintenance and water conservation functions of artificial forests,which are very important in semi-arid mountain ecosystems.However,how different tree species affect soil nutrients and soil physicochemical properties after afforestation,and which is the best plantation species for improving soil fertility and water conservation functions remain largely unknown.Methods:This study investigated the soil nutrient contents of three different plantations(Larix principis-rupprechtii,Picea crassifolia,Pinus tabuliformis),soils and plant-soil feedbacks,as well as the interactions between soil physicochemical properties.Results:The results revealed that the leaves and litter layers strongly influenced soil nutrient availability through biogeochemical processes:P.tabuliformis had higher organic carbon,ratio of organic carbon to total nitrogen(C:N)and organic carbon to total phosphorus(C:P)in the leaves and litter layers than L.principis-rupprechtii or P.crassifolia,suggesting that higher C:N and C:P hindered litter decomposition.As a result,the L.principis-rupprechtii and P.crassifolia plantation forests significantly improved soil nutrients and clay components,compared with the P.tabuliformis plantation forest.Furthermore,the L.principis-rupprechtii and P.crassifolia plantation forests significantly improved the soil capacity,soil total porosity,and capillary porosity,decreased soil bulk density,and enhanced water storage capacity,compared with the P.tabuliformis plantation forest.The results of this study showed that,the strong link between plants and soil was tightly coupled to C:N and C:P,and there was a close correlation between soil particle size distribution and soil physicochemical properties.Conclusions:Therefore,our results recommend planting the L.principis-rupprechtii and P.crassifolia as the preferred tree species to enhance the soil fertility and water conservation functions,especially in semi-arid regions mountain forest ecosystems.

The Representation of Soil Moisture−Atmosphere Feedbacks across the Tibetan Plateau in CMIP6

Thermal processes on the Tibetan Plateau(TP)influence atmospheric conditions on regional and global scales.Given this,previous work has shown that soil moisture−driven surface flux variations feed back onto the atmosphere.Whilst soil moisture is a source of atmospheric predictability,no study has evaluated soil moisture−atmosphere coupling on the TP in general circulation models(GCMs).In this study,we use several analysis techniques to assess soil moisture−atmosphere coupling in CMIP6 simulations including:instantaneous coupling indices;analysis of flux and atmospheric behaviour during dry spells;and a quantification of the preference for convection over drier soils.Through these metrics we partition feedbacks into their atmospheric and terrestrial components.Consistent with previous global studies,we conclude substantial inter-model differences in the representation of soil moisture−atmosphere coupling,and that most models underestimate such feedbacks.Focusing on dry spell analysis,most models underestimate increased sensible heat during periods of rainfall deficiency.For example,the model-mean bias in anomalous sensible heat flux is 10 W m−2(≈25%)smaller compared to observations.Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response.We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection.The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture−driven predictability.To improve the representation of land−atmosphere feedbacks requires developments in not only atmospheric modelling,but also surface processes,as we find weak relationships between rainfall biases and coupling indexes.

Evaluating Chinese College Students' English Compositions:Different Writing Corrective Feedbacks between a Chinese Teacher and a Foreign Teacher & the Attitudes of College Students

The study aims to uncover the differences between a Chinese writing teacher and a foreign writing teacher in evaluating Chinese college students' English compositions and find out students' attitudes towards different kinds of feedbacks. The data collection method, sample analysis and questionnaire have been implemented to probe for the results. The results are supposed to give some suggestions to the English writing class in Chinese universities.

The role of oceanic feedbacks in the 2014–2016 El Niño events as derived from ocean reanalysis data

Why did the predicted“super El Niño”fade out in the summer 2014 and the following year develop into one of the three strongest El Niño on record?Although some hypotheses have been proposed in previous studies,the quantitative contribution of oceanic processes to these events remains unclear.We investigated the role of various oceanic feedbacks,especially in response to intra-seasonal westerly wind busts,in the evolution of the 2014–2016 El Niño events,through a detailed heat budget analysis using high temporal resolution Estimating the Circulation and Climate of the Ocean—Phase II(ECCO2)simulation outputs and satellite-based observations.Results show that the Ekman feedback and zonal advective feedback were the two dominant oceanic processes in the developing phase of the warm event in the spring of 2014 and its decay in June.In the 2015–2016 super El Niño event,the zonal advective feedback and thermocline feedback played a signifi cant role in the eastern Pacifi c warming.Moreover,the thermocline feedback tended to weaken in the central Pacifi c where the zonal advection feedback became the dominant positive feedback.

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.

Atmospheric feedbacks on Arctic summer sea-ice anomalies in ensemble simulations of a coupled regional climate model

Ensemble simulations with the Arctic coupled regional climate model HIRHAM-NAOSIM have been analyzed to investigate atmospheric feedbacks to September sea-ice anomalies in the Arctic in autumn and the following winter. Different ＂low- minus high ice＂ composites have been calculated using selected model runs and different periods. This approach allows us to investigate the robustness of the simulated regional atmospheric feedbacks to detected sea-ice anomalies. Since the position and strength of the September sea-ice anomaly varies between the different ＂low- minus high ice＂ composites, the related simulated atmospheric patterns in autumn differ depending on the specific surface heat flux forcing through the oceaaa-atmosphere interface. However, irrespective of those autumn differences, the regional atmospheric feedback in the following winter is rather insensitive to the applied compositing. Neither the selection of simulations nor the considered period impacts the results. The simulated consistent large-scale atmospheric circulation pattern show-s a wave-like pattern with positive pressure anomaly over the region of the Barents/Kara Seas and Scandinavia/western Russia （＂Scandinavian-Ural blocking＂） and negative pressure anomaly over the East Siberian/Laptev Seas.

Reliability Analysis of Repairable System with Multiple Closed-Loop Feedbacks Based on GO Method

In order to solve the problem of reliability modeling and the analysis of complex systems with multiple closed-loop feedbacks,a new reliability analysis method for repairable systems with multiple closed-loop feedbacks is proposed based on the goal-oriented(GO)methodology.Firstly,the basic theories and advantages of GO method are introduced.Secondly,a type-24B multiple closed-loop feedback structure operator is proposed through GO method with its operation formula given,which expands the types of GO method operators and the application scope of their reliability analysis.Finally,taking a certain type of diesel engine fuel supply system an example,the quantitative and qualitative analysis is carried out through GO method,Monte Carlo simulation as well as FTA respectively.The availability results verify the availability of the proposed type-24B operator in the reliability analysis of multiple closed-loop feedback systems.The qualitative analysis results indicate the accuracy and usability of the GO method as a qualitative analysis method.

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Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Particle Swarm Optimization for the Design of H∞ Static Output Feedbacks

The design of H∞ reduced order controllers is known to be a non-convex optimization problem for which no generic solution exists. In this paper, the use of Particle Swarm Optimization （PSO） for the computation of H~ static output feedbacks is investigated. Two approaches are tested. In a first part, a probabilistic-type PSO algorithm is defined for the computation of discrete sets of stabilizing static output feedback controllers. This method relies on a technique for random sample generation in a given domain. It is therefore used for computing a suboptimal Ha static output feedback solution, In a second part, the initial optimization problem is solved by PSO, the decision variables being the feedback gains. Results are compared with standard reduced order problem solvers using the COMPIeib （Constraint Matrix-optimization Problem Library） benchmark examples and appear to be much than satisfactory, proving the great potential of PSO techniques.

AI Enlightens Wireless Communication:A Transformer Backbone for CSI Feedback

This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AIWork Group,where the framework of the eigenvector-based channel state information(CSI)feedback problem is firstly provided.Then a basic Transformer backbone for CSI feedback referred to EVCsiNet-T is proposed.Moreover,a series of potential enhancements for deep learning based(DL-based)CSI feedback including i)data augmentation,ii)loss function design,iii)training strategy,and iv)model ensemble are introduced.The experimental results involving the comparison between EVCsiNet-T and traditional codebook methods over different channels are further provided,which show the advanced performance and a promising prospect of Transformer on DL-based CSI feedback problem.

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.

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.

Amplifying colorectal cancer progression: impact of a PDIA4/SP1 positive feedback loop by circPDIA4 sponging miR-9-5p

Objective:Colorectal cancer(CRC)is a prevalent malignant tumor with a high fatality rate.CircPDIA4 has been shown to have a vital role in cancer development by acting as a facilitator.Nevertheless,the impact of the circPDIA4/miR-9-5p/SP1 axis on development of CRC has not been studied.Methods:Western blot,immunohistochemistry,and reverse transcription-quantitative polymerase chain reaction assays were used to analyze gene expression.The CCK-8 assay was used to assess cell growth.The Transwell assay was used to detect invasion and migration of cells.The luciferase reporter and RNA immunoprecipitation tests were used to determine if miR-9-5p and circPDIA4(or SP1)bind to one another.An in vivo assay was used to measure tumor growth.Results:It was shown that circPDIA4 expression was greater in CRC cell lines and tissues than healthy cell lines and tissues.CircPDIA4 knockdown prevented the invasion,migration,and proliferation of cells in CRC.Additionally,the combination of circPDIA4 and miR-9-5p was confirmed,as well as miR-9-5p binding to SP1.Rescue experiments also showed that the circPDIA4/miR-9-5p/SP1 axis accelerated the development of CRC.In addition,SP1 combined with the promoter region of circPDIA4 and induced circPDIA4 transcription.CircPDIA4 was shown to facilitate tumor growth in an in vivo assay.Conclusions:The circPDIA4/miR-9-5p/SP1 feedback loop was shown to aggravate CRC progression.This finding suggests that the ceRNA axis may be a promising biomarker for CRC patient treatment.

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.

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.

Exponential stabilization of linear systems with feedback optimization and its application to microgrids control

This paper proposes a feedback-optimization-based control method for linear time-invariant systems,which is aimed to exponentially stabilize the system and,meanwhile,drive the system output to an approximate solution of an optimization problem with convex set constraints and affine inequality constraints.To ensure the exponential stability of the closed-loop system,the original optimization problem is first reformulated into a counterpart that has only convex set constraints.It is shown that the optimal solution of the new optimization problem is an approximate optimal solution of the original problem.Then,based on this new optimization problem,the projected primal–dual gradient dynamics algorithm is used to design the controller.By using the singular perturbation method,sufficient conditions are provided to ensure the exponential stability of the closed-loop system.The proposed method is also applied to microgrid control.

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.