Introducing the Principles of Tendon Transfer for Surgical Trainees to Improve Anatomical Knowledge

This article reviewed the principles and outcomes of tendon transfer procedures described in the literature to restore function following injuries delivered in a workshop as a way of improving basic science and anatomical knowledge in surgical trainees preparing for surgical examinations. Post intervention surveys showed an improvement in trainees’ familiarity with musculoskeletal anatomy and engagement in learning with improved readiness for surgical examinations.

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.

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.

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.

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 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.

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.

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.

Language Assessment Feedback Towards Pedagogical Conversational Agent

Continuous development of technology provides an opportunity to incorporate feedback in online assessments.The mode of online instruction during the pandemic was the most significant survival change.Technology enabled every teacher and student to enter a virtual classroom to make sense of education.Feedback is part of language instruction and is a powerful key to improving students’learning performance.Feedback plays an influential and crucial role in teaching and learning.Feedback is an invaluable,ultimate learning tool for learners that aids them in not committing the same error again and creates impetus.Thus,knowing about formative exam feedback is students’right because quality feedback allures them.Given students’eagerness,providing feedback is considered a good practice to be followed by all the teaching faculty.Apropos of online feedback,the present study attempts to study how pedagogical agents provide online feedback in language assessments.The study also considers the characteristics of pedagogical conversational agents that are suitable for providing feedback in online language assessment.Simply put,the study encapsulates that screen agents play an essential role in students’motivation and acceptability of learning through feedback.

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.

The Effectiveness of Peer Feedback on Second Language Writing

Peer feedback is a widely utilized practice in writing classrooms for second languages(L2)that has drawn a lot of attention.While some support the practice because of the clear advantages that peer feedback offers students,others doubt its efficacy.Comparing various forms of feedback is therefore necessary,as is determining the effects that peer feedback has on students and the variables that might affect how successful peer feedback is.This paper focuses on the usefulness of peer feedback for L2 writing,based on a review of studies on the subject.The findings imply that the functions of peer and teacher feedback are distinct,and that peer feedback is advantageous to both parties,particularly to the supplier.The attitudes and cultural backgrounds of the students are among the other factors that affect how effective peer feedback is.Lastly,some implications for future research and education are presented.

A Study on the Application of Peer Feedback among EFL Students in Blended Teaching

Recent years have witnessed the rise and popularity of blended teaching for EFL students,yet the lack of face-to-face communication and insufficient interaction lead researchers and educators to explore methods of improving the effectiveness of teaching.Peer feedback,a teaching strategy used to raise the bars by exposing instructions to a new teaching perspective to enhance students’online and offline collaboration,has received the lion’s share of attention in recent studies.On the basis of literature review,class records,and interviews,this paper,in hope of supporting advances in teachers’practice and field researches,examines the possible approaches to effectively implementing the application of peer feedback in blended teaching mode with a special focus on teachers’roles.

An Underwater Robot Inspection Anomaly Localization Feedback System Based on Sonar Technology

This article introduces an underwater robot inspection anomaly localization feedback system comprising a real-time water surface tracking,detection,and positioning system located on the water surface,while the underwater robot inspection anomaly feedback system is housed within the underwater robot.The system facilitates the issuance of corresponding mechanical responses based on the water surface’s real-time tracking,detection,and positioning,enabling recognition and feedback of anomaly information.Through sonar technology,the underwater robot inspection anomaly feedback system monitors the underwater robot in real-time,triggering responsive actions upon encountering anomalies.The real-time tracking,detection,and positioning system from the water surface identifies abnormal conditions of underwater robots based on changes in sonar images,subsequently notifying personnel for necessary intervention.

The Role and features of Student Peer Feedback in Interpreting Training——Preliminary Findings of a Survey of both trainers and students

The project delves into the preliminary findings of a survey of both trainers and students on the practice of using student peer feedback in interpreting practice.It first explains the theoretical foundation which justifies the use of peer feedback in interpreting practice,the research methodology and data collection.Then it brings forth specific findings concerning the implementation of peer feedback in the interpreting class followed by discussions of the role and features of student peer feedback as a means to help students ready for the booth.Analysis of the results shows that peer feedback in interpreting practice keeps students on-task,attentive and help them spot their own problems.Trainers and students themselves point to similar features of student peer feedback as focusing on comprehension of the original,word choice and numbers.The preliminary findings of the survey demonstrate the roles and features of student peer feedback in interpreting practice and point to the possible way of enhancing student’s learning curve through more effective peer feedback.

Difficult-to-Treat-Depression—Perceptions of GPs and GP Trainees

Introduction: GPs are often the first contact point for mental health concerns. Training to qualify as a GP involves completing a three-to-four year program. Among other things, the training program may include a six-hour mental health training workshop. The study’s purpose was to compare GPs’ and Trainees’ perceptions of difficult-to-treat-depression (DTTD). Methods: Using a semi-structured interview schedule comprising six questions, 10 GPs and six Trainees participated in focus groups or telephone interview. To understand participants’ perspectives, data were analyzed using the Framework Method. Findings: Trainees were less clear than GPs about the meaning of DTTD and other diagnostic terms. GPs’ diagnosis included querying diagnosis and treatment options. Trainees’ experience was limited but they raised barriers including non-response to various treatment trials. Both groups identified management difficulties including: doctor shopping, suicide risk, patients not being well informed;with management difficulties exacerbated by limited access/referring to health professionals, cost and/or unavailability of bulk billing. While some GPs and Trainees had heard of an illness management model or chronic illness model, few used a model. Most reported limited referring to psychiatrists, mainly because of cost and/or limited availability. GPs were more likely to refer to other health professionals and use pharmacological and complementary therapies. Both groups discussed the impact of external factors including cultural factors, patient compliance, treatment failure, and the importance of the relationship between the patient and the professional. Trainees were more likely to stress the importance local clinical guidelines. Discussion and Conclusion: Despite the small size and limited nature, this research provides insight into some of the similarities and differences of GPs’ and Trainees’ experiences and understanding of DTTD. This may have implications for training providers, clinical supervisors and Trainees, and suggests that enhancement in the role of mental health training may be relevant.

H∞ Output Feedback Control of Constrained Systems via Moving Horizon Strategy

这份报纸与致动器浸透为分离时间的系统处理 H 产量反馈控制问题。开始，一条抑制 H 输出反馈控制途径在线性矩阵不平等(LMI ) 的框架被介绍优化。在骚乱精力界限上的某些假设下面，靠近环的 H 性能被完成。而且，动人的地平线策略被用于控制性能的一个联机管理以便靠近环的系统能在意外大骚乱的情况中满足控制限制。驱散限制被导出完成动人的地平线靠近环的系统消散。模拟结果证明抑制 H 控制器在骚乱假设下面有效地工作并且动人的地平线 H 控制器罐头交易自动地在令人满意的控制限制和提高的性能之间。