Feedback Component Research Articles

CLEFT: Contextualised Unified Learning of User Engagement in Video Lectures With Feedback

Predicting contextualised engagement in videos is a long-standing problem that has been popularly attempted by exploiting the number of views or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">likes</i> using different computational methods. The recent decade has seen a boom in online learning resources, and during the pandemic, there has been an exponential rise of online teaching videos without much quality control. As a result, we are faced with two key challenges. First, how to decide which lecture videos are engaging to intrigue the listener and increase productivity, and second, how to automatically provide feedback to the content creator using which they could improve the content. The quality of the content could be improved if the creators could automatically get constructive feedback on their content. On the other hand, there has been a steep rise in developing computational methods to predict a user engagement score. In this paper, we have proposed a new unified model, CLEFT, that means “Contextualised unified Learning of user Engagement in video lectures with Feedback” that learns from the features extracted from freely available public online teaching videos and provides feedback on the video along with the user engagement score. Given the complexity of the task, our unified framework employs different pre-trained models working together as an ensemble of classifiers. Our model exploits a range of multi-modal features to model the complexity of language, context agnostic information, textual emotion of the delivered content, animation, speaker’s pitch, and speech emotions. Our results support hypothesis that proposed model can detect engagement reliably and the feedback component gives useful insights to the content creator to further help improve the content.

Cadmium Telluride (CdTe) Compound and Silicon (Si) Semiconductor Materials Detectors for X-Ray

There are different approaches and also materials to construct a detector for x-rays. the available semiconductor materials, Silicon (Si) is mainly used for charged particle detectors and soft X-ray detectors. Cadmium Telluride (CdTe) compound semiconductors for x-ray detectors have experienced a rather rapid development in the last few years, due to their appealing performance. In this paper we review the physical properties of semiconductor detectors for x-ray. In particular, we focus on compound semiconductor detectors. In addition, Si detector has better energy resolution achievement. However, it does not have good detection efficiency for higher energy above (40Kev). While CdTe detector supplies higher detection efficiency for x -rays about 100% for up to 100 keV in energy. The response functions of Si detector to reduce small x-ray, which has limited sensitivity for energy about 30keV. The XR-100T is high performance for x-ray and gamma ray detector by using 241Am (59.5keV), so the cooler are mounted the input FET a feedback components to the Amptek to charge sensitive preamplifier. Both the Si and CdTe detectors are connected to the PX2 combined amplifier and power supply, that was provide the voltage needed to operate XR-100T CdTe. The PX2 T CdTe amplifier has a Rise Time Discrimination circuit, measure the FWHM energy resolution of 55Fe (5.89) the peak in each spectrum hence a graph of energy resolution. Moreover, the Rise Time Discrimination So the escape peak of 55Fe was determined with Si detector was 11.66 keV, also k- sell x-ray energy was calculated to 5.77keV and the Rise Time Discrimination effect was studied on the spectrum when is it switched on and off. Its influence was found in decreasing the noise of the spectrum.

Supporting inference-making during COVID-19 through individualized scaffolding and feedback: a natural experiment.

The purpose of this study is to evaluate the role of the Early Language Comprehension Individualized Instruction (ELCII) program in supporting kindergarteners' learning of inference-making during the COVID-19 pandemic. Two different cohorts of pre- and in-pandemic students completed the ELCII program, which was designed to teach them how to make inferences. Results suggest that kindergarteners during COVID-19 made slower growth over the course of the intervention compared to their counterparts who completed the intervention before the pandemic. However, when growth rates between the two cohorts were compared accounting for the scaffolding and feedback provided by the ELCII program, the growth rates were similar. These findings suggest that the individualized scaffolding and feedback component of ELCII may have supported kindergarteners' learning of inference-making during the pandemic.

Evaluating postgraduate family medicine supervisor feedback in registrars’ learning portfolios

Postgraduate supervision forms a vital component of decentralised family medicine training. While the components of effective supervisory feedback have been explored in high-income countries, how this construct is delivered in resource-constrained low- to middle-income countries has not been investigated adequately. This article evaluated supervisory feedback in family medicine registrars' learning portfolios (LPs) as captured in their learning plans and mini-Clinical Evaluation Exercise (mini-CEX) forms and whether the training district or the year of training affected the nature of the feedback. Registrars' LPs from 2020 across five decentralised sites affiliated with the University of the Witwatersrand in South Africa were analysed. Two modified tools were used to evaluate the quantity of the written feedback in 38 learning plans and 57 mini-CEX forms. Descriptive statistics, Fisher's exact and Wilcoxon rank-sum tests were used for analysis. Content analysis was used to derive counts of areas of feedback. Most learning plans (61.2%) did not refer to registrars' clinical knowledge or offer an improvement strategy (86.1%). The 'extent of supervisors' feedback' was rated as 'poor' (63.2%), with only 14.0% rated as 'good.' The 'some' and 'no' feedback categories in the mini-CEX competencies (p 0.001 top= 0.014) and the 'extent of supervisors' feedback' (p 0.001) were significantly associated with training district. Feedback focused less on clinical reasoning and negotiation skills. Supervisors should provide specific and constructive narrative feedback and an action plan to improve registrars' future performance.Contribution:Supervisory feedback in postgraduate family medicine training needs overall improvement to develop skilled family physicians.

A simplified approach to develop fuzzy logic controller with disturbance estimator for control of a planar parallel (2PRP-PPR) motion platform

This work presents, a robust position tracking control of a 3-degrees of freedom 2PRP-PPR (P: Active Prismatic joint, P: Passive prismatic joint, R; Revolute joint) parallel motion mechanism. For controlling the motion of the defined platform, a simple method to develop a fuzzy logic controller called as robust single input fuzzy logic control (RSIFLC) algorithm is presented and used for the complex workspace trajectory tracking control problem with the existence of outside disturbances, variable uncertainties, and inner noises. The suggested control method comprises two sections: The first section considers a feed-forward expression to increase control action and a disturbance estimator to account for anonymous factors like outer disturbances and un-modeled dynamics. The second section employs single input fuzzy logic control as a feedback component to improve the system’s overall closed-loop stability. The experimental results demonstrate the superiority of the suggested control algorithm over traditional control schemes.

Use of didactic training and feedback to improve quality and acceptability of progress notes written by direct‐care staff on a psychiatric inpatient unit

AbstractWriting progress notes represent a critical activity of practicing clinicians in a variety of settings. They provide a way for medical practitioners, insurance companies, and others to communicate in a timely fashion regarding ongoing clinical care. Previous research showed that intervention components like didactic training, using note templates, and feedback improved the quality of progress notes. At least two questions remain despite several studies already addressing progress note writing. First, previous research most often used multiple intervention components to improve progress notes. Thus, the relative impact of two common components of interventions, such as didactic training and feedback, is unclear. Second, previous research has not evaluated the acceptability of improved progress notes for the practitioners that actually utilize them. Thus, the purpose of the current study evaluated the components of didactic training and feedback on improved progress note writing for four direct staff employed by a psychiatric inpatient unit. A second purpose of this study was to evaluate the acceptability of the training procedures by both (a) the direct‐care staff participating in this study and (b) four members of the psychiatric treatment team that used direct‐care staff progress notes to inform their clinical care. Results showed that feedback was necessary to improve the accuracy of progress notes for three of four participants. The direct‐care staff reported the training procedures as acceptable and the treatment team noted improvements in the quality of the progress notes after intervention. These data will be discussed in terms of ways to arrange effective training programs to improve direct‐care staff's progress notes.

A Digital Observer-Based Repetitive Learning Composite Control Method for Large Range Piezo-Driven Nanopositioning Systems

In this study, a novel digital compound compensation method is proposed to compensate for the hysteresis nonlinearity and the drift disturbance of a piezoelectric nanopositioning system with a large range. The overall hysteresis behaviors can be divided into the static amplitude-dependent behavior and the dynamic rate-dependent behavior, where the static hysteresis is compensated for by a novel discrete feedforward controller, while the dynamic hysteresis and the drift disturbance are compensated for by a novel discrete composite feedback controller composed of a drift observer-based state feedback controller and a repetitive learning controller. Compared with traditional control strategies, the proposed compound control strategy, including feedforward and feedback components, can eliminate system errors more effectively when tracking large range signals with obvious hysteresis. Moreover, the proposed online drift observer is superior over a traditional offline drift compensator both in response speed and compensation accuracy. Sufficient simulation tests and convincing tracking experiments, with large range periodic signals up to 90 μm, are carried out. And comparisons with the two classical control algorithms are performed. The tracking results show that the mean absolute error of the proposed control method is minor compared with the other two algorithms, which validates that the proposed strategy can efficiently compensate for the hysteresis nonlinearity and the drift disturbance.

Improved Linear Quadratic Regulator Lateral Path Tracking Approach Based on a Real-Time Updated Algorithm with Fuzzy Control and Cosine Similarity for Autonomous Vehicles

Path tracking plays a crucial role in autonomous driving. In order to ensure the real-time performance of the controller and at the same time improve the stability and adaptability of the path tracking controller, a lateral path control strategy based on the improved LQR algorithm is proposed in this paper. To begin with, a discrete LQR controller with feedforward and feedback components is designed based on the error model of vehicle lateral dynamics constructed by the natural coordinate system. Then, a fuzzy control method is applied to adjust the weight coefficients of the LQR in real time according to the state of the vehicle. Furthermore, an update mechanism based on cosine similarity is designed to reduce the computational effort of the controller. The improved LQR controller is tested on a joint Simulink–Carsim simulation platform for a two-lane shift maneuver. The results show that the control algorithm improves tracking accuracy, steering stability and computational efficiency.

Changes in Poleward Atmospheric Energy Transport over a Wide Range of Climates: Energetic and Diffusive Perspectives and A Priori Theories

Abstract The midlatitude poleward atmospheric energy transport increases in radiatively forced simulations of warmed climates across a range of models from comprehensive coupled general circulation models (GCMs) to idealized aquaplanet moist GCMs to diffusive moist energy balance models. These increases have been rationalized from two perspectives. The energetic (or radiative) perspective takes the atmospheric energy budget and decomposes energy flux changes (radiative forcing, feedbacks, or surface fluxes) to determine the energy transport changes required by the budget. The diffusive perspective takes the net effect of atmospheric macroturbulence to be a diffusive energy transport down-gradient, so transport changes can arise from changes in mean energy gradients or turbulent diffusivity. Here, we compare these perspectives in idealized moist, gray-radiation GCM simulations over a wide range of climates. The energetic perspective has a dominant role for radiative forcing in this GCM, with cancellation between the temperature feedback components that account for the GCM’s nonmonotonic energy transport changes in response to warming. Comprehensive CMIP5 simulations have similarities in the Northern Hemisphere to the idealized GCM, although a comprehensive GCM over several CO2 doublings has a distinctly different feedback evolution structure. The diffusive perspective requires a non-constant diffusivity to account for the idealized GCM-simulated changes, with important roles for the eddy velocity, dry static stability, and horizontal energy gradients. Beyond diagnostic analysis, GCM-independent a priori theories for components of the temperature feedback are presented that account for changes without knowledge of a perturbed climate state, suggesting that the energetic perspective is the more parsimonious one.

Robust Sensorless Control of Interior Permanent Magnet Synchronous Motor Using Deadbeat Extended Electromotive Force Observer

This paper proposes a novel and robust method of sensorless speed control using a deadbeat observer for an interior permanent magnet synchronous motor (IPMSM). The proposed sensorless speed control method uses a deadbeat observer to estimate the extended electromotive force (EEMF) in a rotational coordinate system. The estimated EEMF is used in the IPMSM velocity estimation algorithm. The deadbeat EEMF observer (DEEMFO) shows greater robustness compared to the reconstructor, which estimates the EEMF by simply recalculating the voltage equation. Unlike a reconstructor, DEEMFO has a feedback component, so it can compensate for errors due to uncertainty in motor parameters and errors due to parameter fluctuations that may occur during use. By simulating and experimenting with speed, load torque, and parameter fluctuations, it is proved to be more robust and precise than the reconstructor. The simulation is performed with MATLAB/Simulink, and the experiments were carried out using a DSP TMS320F28335 and a motor-generator set (M-G Set). The simulation and experiment results show the reliability and precision of the proposed sensorless control method.

Influence of acousto-optic frequency shifter's thermal-induced birefringence on laser frequency-shifted feedback system

The acousto-optic frequency shifter (AOFS) is the key component of the laser frequency-shifted feedback (LFSF) system, and its thermal-induced birefringence (TIB) seriously affects the stability of the system. In this paper, the influence of the AOFS's TIB on LFSF system is analyzed, and the method of suppressing this influence is proposed. The theoretical model of LFSF considering the AOFS's TIB is established, and the influence on both amplitude and phase of frequency-shifted signal is analyzed. When the AOFS starts working, its TIB effect is obvious due to the accumulation of working heat, which makes the polarization state of its diffraction light change dynamically in most cases. The laser feedback imaging system and laser feedback displacement measurement system are built separately to evaluate the amplitude and phase stability. By principal axis alignment or quasi-common-path compensation, the amplitude variation, phase fluctuation, phase jump and phase temperature drift caused by the AOFS's TIB can be eliminated or suppressed. This study improves the startup stability and anti-ambient temperature interference ability of the LFSF system, which also has guiding significance to other heterodyne interference systems.

Metaverse Virtual Learning Management Based on Gamification Techniques Model to Enhance Total Experience

This research aimed to develop metaverse virtual learning management based on gamification techniques model (MVLM-Gt model) and to evaluate the appropriateness of the model. The research methodology was divided into two parts in accordance with these aims. The first part was the design of the learning step using metaverse virtual learning management and gamification techniques based on the gamification process and active learning theory. The second part consisted of an evaluation of the appropriateness of the model. The MVLM-Gt model was submitted to seven experts followed by an appropriateness questionnaire. The MVLM-Gt model had four core components: the inputs, the learning process, the evaluation, and the feedback. The learning process had five steps: motivation and setting goals, constructing content, discussion and interaction, practice and mission, and summarizing and feedback. After evaluation of the appropriateness of the MVLM-Gt model, the experts said that it was excellent (Mean=4.82, S.D.=0.38). After considering each component, the feedback component had the highest appropriateness value (Mean=5.00, S.D.=0.00), followed by the learning process component (Mean= 4.86, S.D.=0.38), and the evaluation component (Mean=4.71, S.D.=0.49). The results showed that this MVLM-Gt model could be adopted to enhance total experience of students.

Experimental study on interaction mechanism on the mass burning rate in two n-heptane fires with cross airflows

This work aims to interpret the flame interaction mechanisms of the mass burning rates of two pool fires in a cross-airflow. The flame morphology, mass burning rate, and heat feedback components are measured. The results show that the mass burning rates of the two pool fires increase rapidly, then decrease, and finally increase slowly with wind speed. Owing to the blockage and air entrainment restriction effect of the upstream flame on cross airflow, the critical transition (from increasing to decaying) of the mass burning rate of the downstream flame corresponds to a relatively larger cross airflow speed under the same experimental conditions. In general, with increasing fire spacing, the dominant mechanism of flame interaction transitions from interactive blockage and air entrainment restriction to heat feedback enhancement to monopolar blockage and air entrainment restriction. The process of the primary control mechanism from radiation dominance to convection dominance with the increase of airflow speed is revealed. Finally, based on the stagnant layer theory, a model of the mass burning rates is developed for the convection-dominated multiple pool fires in the non-merging stage.

Preview repetitive learning control for islanded microgrids based on equivalent input disturbance approach

Due to the rapid drop in peak dc-link voltage (PDV) caused by environmental changes, as well as the majority of loads being naturally nonlinear, the output voltage of the microgrid inverters is always distorted. To boost the PDV at a low cost and reduce the total harmonic distortion (THD) of output voltage, a strategy is proposed in this paper to synchronously control ac- and dc-sides voltage. A double closed-loop PI controller is used to indirectly control the PDV of Z-source inverter (ZSI). Sinusoidal reference voltage generated by the primary power layer is merged to construct an augmented dynamic system, and an integrated optimal controller on the ac-side is obtained. Preview repetitive control (PRC) and error compensation can improve output voltage tracking accuracy. The state feedback component effectively suppresses the resonance of the filter, lowering the system’s sensitivity to parameter perturbation. Combined with this, the equivalent input disturbance (EID) is used to eliminate the potential interference in microgrids, which can shorten the recovery time of the system. Both simulations and experiments are provided to demonstrate the better performance of the proposed approach in comparison to other different repetitive controllers.

Fault Diagnosis to Nuclear Power Plant System Based on Time-Series Convolution Neural Network

Nuclear power plant (NPP) is a highly complex engineering system which has typical internal feedback and strong component coupling. With these features, most NPP systems have high risk of radioactive release, which makes it essential to perform fault detection (FD) to the NPP systems. To address this challenge, this paper proposes a FD mechanism named characteristic time-series convolutional neural network (CT-CNN) based on principal component analysis (PCA), time-series analysis, and convolutional neural network (CNN) mechanisms. First, the models of NPP FD system are formulated. Then, the PCA mechanism is applied to extract the features of the NPP system. Next, the time-series analysis and CNN approaches are applied to realize FD to the NPP system. With the above mechanisms, the proposed approach has not only shown strong stability and become adaptive to different data set, but also preserves both time and state characteristics of the NPP system. In experiment, it shows the proposed approach can achieve better performance in both detection accuracy and variance than the classic back propagation, LSTM method, and standard CNN algorithms. More significantly, its optimal accuracy can be as high as 99.8%.

Customer Satisfaction with Customer Care Service Quality at Baoviet Life Corporation, Vietnam

Based on relevant theoretical bases and through conducting qualitative research to build a research model with a customer care service quality scale including 22 descriptive variables for 5 components of service quality. Customer service, that is: physical facilities, reliability, assurance, response, empathy; customer satisfaction scale for customer service. The result has 159 valid samples. The results of the linear regression analysis between the independent variable which is 5 components of customer service quality and the dependent variable which is customer satisfaction show that all 5 components have a positive relationship. Accordingly, the tangible component has the strongest impact, followed by the trust component and finally the feedback component. Based on this result, the research team has identified the shortcomings and proposed solutions such as: Building uniform policies, remuneration as well as sanctions for agents; Develop customer care regulations for agents; improve the capacity of customer service staff or improve customer support.

Observational Constraints on Southern Ocean Cloud-Phase Feedback

Abstract Shortwave radiative feedbacks from Southern Ocean clouds are a major source of uncertainty in climate projections. Much of this uncertainty arises from changes in cloud scattering properties and lifetimes that are caused by changes in cloud thermodynamic phase. Here we use satellite observations to infer the scattering component of the cloud-phase feedback mechanism and determine its relative importance by comparing it with an estimate of the overall temperature-driven cloud feedback. The overall feedback is dominated by an optical thinning of low-level clouds. In contrast, the scattering component of cloud-phase feedback is an order of magnitude smaller and is primarily confined to free-tropospheric clouds. The small magnitude of this feedback component is a consequence of counteracting changes in albedo from cloud optical thickening and enhanced forward scattering by cloud particles. These results indicate that shortwave cloud feedback is likely positive over the Southern Ocean and that changes in cloud scattering properties arising from phase changes make a small contribution to the overall feedback. The feedback constraints shift the projected 66% confidence range for the global equilibrium temperature response to doubling atmospheric CO2 by about +0.1 K relative to a recent consensus estimate of cloud feedback. Significance Statement Understanding how clouds respond to global warming is a key challenge of climate science. One particularly uncertain aspect of the cloud response involves a conversion of ice particles to liquid droplets in extratropical clouds. Here we use satellite data to infer how cloud-phase conversions affect climate by changing cloud albedo. We find that ice-to-liquid conversions increase cloud optical thickness and shift the scattering angles of cloud particles toward the forward direction. These changes in optical properties have offsetting effects on cloud albedo. This finding provides new insight about how changes in cloud phase affect climate change.

Evaluating the Use of Video Modeling With Voiceover Instructions to Train Therapists to Deliver Caregiver Training Through Telehealth.

Caregiver training is an important component of behavioral intervention; however, many barriers exist for in-person training. Alternatively, behavioral therapists may use telehealth as a service delivery method. To effectively train caregivers through telehealth, therapists should receive explicit training, but there has been limited research on effective methods for teaching this skill. The purpose of the current study was to evaluate video modeling with voice-over instruction (VMVO) to train therapists to implement 11 component skills of caregiver training through telehealth to teach confederate caregivers to implement a guided compliance procedure. We measured the therapist's implementation of the component skills during a scripted role-play before and after video-model training within a multiple baseline design across participants. We also conducted maintenance and generalization probes to a novel skill. All seven therapists learned the skill, but three therapists required a feedback component in addition to the VMVO. The results suggest that VMVO may be an efficient and effective method for training therapists to conduct caregiver training via telehealth. Furthermore, results indicate that component skill analyses may be valuable to monitor skills that require remediation.

A Lyapunov based robust control of the EV gearshift process

This paper presents a novel Lyapunov based robust control algorithm for gearshift system of automated manual transmission (AMT) in electric vehicle (EV). Gearshift process is analyzed and modeled considering a two-gear powertrain. Trajectory-tracking control is designed regarding the engagement rule of the shifting fork as the desired trajectory. Model uncertainties and external disturbance are lumped into a function with the assumed upper bound. The control algorithm consists of a PD feedback component and a piecewise function component, which is error-based and model-based inheriting from conventional PID control and robust control. Furthermore, this proposed control is based on Lyapunov approach, and is theoretical proved to render uniform boundedness and uniform ultimate boundedness. The proposed control can guarantee the deterministic performance of the gearshift system, and its effectiveness is illustrated by numerical simulations and sample vehicle test.

The construction of the Chinese image from the perspective of others in the post-truth era——A Frame and Semiotic analysis of BBC’s reports of the Beijing Winter Olympics

News reports are significant for constructing a country’s image in the international community. The frame analysis of Western media coverage of China is quite popular. With the arrival of the Beijing Winter Olympics, which stands as another historical moment in the processing of China’s external image, disputed issues in China have surfaced again in the Western media. In the context of the new media, there still exists a research gap on the new characteristics of Western media when reporting China and the resulting recent changes in China's image. To fill this gap in the literature, based on the semiotic theory and framing theory, this dissertation has selected the 16 reports and their corresponding Twitter comments on the Beijing Winter Olympics which were published by the British news agency BBC in January - February 2022. By utilizing the AntConc to establish corpus and text analysis, it shall investigate from the perspective of others the discourse construction characteristics of Western media and the Chinese image from the three components of communication theme, text content, and audience feedback. The research results have presented that the Western media represented by the BBC cannot eliminate ideological stereotypes in the discourse construction of the "other" (China). The intention of the report is tendentious. Its reporting news frame is based on inherent prejudice and the opinion tendency to incite Public Opinion. Thus, it has resulted in the formation of a Chinese image of “high-pressure coercion” and “ignoring human rights” from the perspective of the other. Therefore, this dissertation adopts China as an example to propose the media cross-cultural international communication strategy of expanding the discourse subject, updating the symbol system and absorbing the folk discourse.