Feedback Model Research Articles

A closed-loop negative feedback model for the pancreas: A new paradigm and pathway to a cure.

To develop a model that describes how the pancreas functions, how the rate of synthesis of digestive enzymes is regulated, and finally what puts the pancreas to rest between meals. We applied the principals of control theory to previously published canine data to develop a model for how the canine pancreas functions. Using this model, we then describe the steps needed to apply this model to the human pancreas. This new closed-loop negative feedback model describes what regulates digestive enzyme synthesis. This model is based on basolateral exocytosis of butyrylcholinesterase (BCHE) into the interstitial space. It is this level of BCHE * BCHE activity that controls the rate of canine pancreas digestive enzyme synthesis, and in the absence of stimulation from the vagus nerve, puts the pancreas to rest between meals. Finding secretagogue-specific inhibitory enzymes in the human pancreas that are analogous to BCHE in the canine, and blocking its associated receptors, may lead to a cure for human pancreatitis.

Fostering key competency growth: a feedback model for university English teaching

ABSTRACT Purpose To explore a theoretical model for providing effective feedback combined with key competencies in China’s university-level English education. Design/methodology/approach Drawing from Hattie and Timperley’s feedback model regarding English key competence, we construct a feedback model and conduct teaching experiments over three semesters using mixed-methods. Quantitative data from the questionnaires were examined using variance and regression analyses in SPSS (17.0), while qualitative data from the focus group interviews were analyzed using NVivo. Findings and originality/value The results both verified and expanded Hattie and Timperley’s feedback model. The four feedback levels and factors promoted each other, thus validating the key competency-based feedback model for university English teaching. This model included the four feedback levels as its main structure, the dual promotion effect as the feedback mechanism, and the leading and subjective roles of teachers and students, respectively, as the principle. A teaching design according to this model can provide personalized, formative, and effective feedback. Additionally, the teaching environment can be optimized, and the gap between learning objectives and current practice can be effectively shortened. This will lead to the attainment of the teaching and feedback goals, eventually promoting the comprehensive development of students as well as the professional development of teachers.

Position control of a soft pneumatic actuator based on the pressure parameter feedback model (PPFM)

Abstract. Soft pneumatic actuators have been one of the cores of soft robotics research and play a key role in driving the development of soft robots. Due to its high degree of internal nonlinearity and unpredictable deformation caused by environmental influences, the control model established for soft robots is still a difficult problem in terms of improving accuracy. This paper proposes a new positional control method for soft pneumatic actuators that are suitable for independent 3D deformation at any position and are the core units of continuous robots. The pressure parameter feedback model (PPFM) of the airbag is obtained by adjusting the pressure input through a proportional valve, collecting the air pressure inside the airbag and obtaining the airbag expansion height. The pressure input signal is changed according to the PPFM of the airbag to control the position of the soft pneumatic actuator. A modular experimental platform is built to validate the PPFM-based control strategy, which is able to adjust the position of the end center point of the soft pneumatic actuator in space with the discussed characteristics. It is demonstrated that the theoretical model can significantly improve the stability and accuracy of the soft pneumatic actuator motion.

Bounded rationality consensus reaching process with prospect theory and preventing individual weight manipulation for multi-attribute group decision making

Owing to the development and popularization of social media, multi-attribute group decision-making (MAGDM) adopting social network analysis (SNA) has appealed to widespread focus for a few years. However, most existing research on SNA has not simultaneously considered the expert's weight manipulation behavior and bounded rationality during the consensus-reaching process (CRP). To overcome this limitation, an original consensus model predicated on prospect theory (PT) and preventing individual weight manipulation is proposed in this paper. First, the experts’ behavior under certain risks is characterized by using PT, where a dynamic reference point is proposed to provide a more objective description of the experts’ psychological behavior. Then, the PT is integrated into the CRP, and the three-level prospect consensus measurement and a three-stage feedback identification mechanism are investigated. Subsequently, to safeguard against individual weight manipulation and obtain the appropriate expert weights, a feedback model incorporating minimum adjustment and maximum entropy rooted in PT is developed. This model is established from the perspectives of “efficiency” and “equity.” At last, the proposed method is applied to evaluate low-carbon investment projects in a power generation enterprise to verify its effectiveness and applicability. The superiority and robustness of the proposed method are also demonstrated through comparative analysis and sensitivity analysis.

Exploring the effects of negative supervisory feedback on creativity among research and development personnel: challenge or threat?

Supervisory feedback to stimulate research and development (R&D) employee creativity is a management issue that concerns scholars and practitioners. However, there are divergences and contradictions regarding whether negative feedback promotes or hinders employee creativity. Integrating the feedback intervention and cognitive appraisal theories, we developed a double-edged sword model for negative supervisory feedback's influence on creativity. We tested the proposed model using a field sample of 513 R&D employees from seven science and technology enterprises. The results indicated that R&D employee challenge and threat appraisal moderated negative supervisory feedback's effect on prevention focus and the distal consequences for creativity. Individuals with high (low) levels of challenge (threat) appraisal have decreased prevention focus, thereby increasing their creativity when receiving negative supervisory feedback. In contrast, individuals with low (high) challenge (threat) appraisal have increased prevention focus, thereby decreasing their creativity when receiving negative supervisory feedback. These findings offer interesting implications for research on negative feedback and stimulation of science and technology R&D employee creativity in organizations.

Robust Trajectory Planning of Gliding-Guided Projectiles with Weak Maneuverability

Due to constraints in launch platforms and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and state deviation at the control start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Our simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy; compared with parallel MCS, the computation time is reduced by 96.8%. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties (the standard deviations of terminal altitude and lateral deviations are reduced by 23.6% and 35.3%, respectively, compared to deterministic planning) but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

Unravelling jet quenching criteria across L* galaxies and massive cluster ellipticals

ABSTRACT In the absence of supplementary heat, the radiative cooling of halo gas around massive galaxies (Milky Way mass and above) leads to an excess of cold gas or stars beyond observed levels. Active galactic nucleus jet-induced heating is likely essential, but the specific properties of the jets remain unclear. Our previous work concludes from simulations of a halo with $10^{14} \,\mathrm{ M}_\odot$ that a successful jet model should have an energy flux comparable to the free-fall energy flux at the cooling radius and should inflate a sufficiently wide cocoon with a long enough cooling time. In this paper, we investigate three jet modes with constant fluxes satisfying the criteria, including high-temperature thermal jets, cosmic ray (CR)-dominant jets, and widely precessing kinetic jets in $10^{12}-10^{15}\, {\rm M}_{\odot }$ haloes using high-resolution, non-cosmological magnetohydrodynamic simulations with the FIRE-2 (Feedback In Realistic Environments) stellar feedback model, conduction, and viscosity. We find that scaling the jet energy according to the free-fall energy at the cooling radius can successfully suppress the cooling flows and quench galaxies without violating observational constraints. On the contrary, if we scale the energy flux based on the total cooling rate within the cooling radius, strong interstellar medium cooling dominates this scaling, resulting in a jet flux exceeding what is needed. Among the three jet types, the CR-dominant jet is most effective in suppressing cooling flows across all surveyed halo masses due to enhanced CR pressure support. We confirm that the criteria for a successful jet model work across a wider range, encompassing halo masses of $10^{12}-10^{15} {\rm M_\odot }$.

Bursting with Feedback: The Relationship between Feedback Model and Bursty Star Formation Histories in Dwarf Galaxies

Due to their inability to self-regulate, ultrafaint dwarfs are sensitive to prescriptions in subgrid physics models that converge and regulate at higher masses. We use high-resolution cosmological simulations to compare the effect of bursty star formation histories (SFHs) on dwarf galaxy structure for two different subgrid supernova (SN) feedback models, superbubble and blastwave, in dwarf galaxies with stellar masses from 5000 < M */M ⊙ < 109. We find that in the “MARVEL-ous Dwarfs” suite both feedback models produce cored galaxies and reproduce observed scaling relations for luminosity, mass, and size. Our sample accurately predicts the average stellar metallicity at higher masses, however low-mass dwarfs are metal poor relative to observed galaxies in the Local Group. We show that continuous bursty star formation and the resulting stellar feedback are able to create dark matter (DM) cores in the higher dwarf galaxy mass regime, while the majority of ultrafaint and classical dwarfs retain cuspy central DM density profiles. We find that the effective core formation peaks at M */M halo ≃ 5 × 10−3 for both feedback models. Both subgrid SN models yield bursty SFHs at higher masses; however, galaxies simulated with superbubble feedback reach maximum mean burstiness values at lower stellar mass fractions relative to blastwave feedback. As a result, core formation may be better predicted by stellar mass fraction than the burstiness of SFHs.

Exploring the Evolution of a Dwarf Spheroidal Galaxy with Smoothed-particle Hydrodynamics Simulations. I. Stellar Feedback

A fundamental question regarding the evolution of dwarf spheroidal galaxies is the identification of the key physical mechanisms responsible for gas depletion. Here, we focus on the study of stellar feedback in isolated dwarf spheroidal galaxies by performing numerical simulations using a modified version of the smoothed-particle hydrodynamics code GADGET-3. The Milky Way satellite Leo II (PGC 34176) in the Local Group was considered as our default model dwarf galaxy. The parameter space for the stellar feedback models was explored to match observational constraints of Leo II, such as residual gas mass, total mass within the tidal radius, star formation history, final stellar mass, stellar ages, and metallicity. Additionally, we examined the impact of the binary fraction of stars, initial mass function, dark matter halo mass, and initial gas reservoir. Many simulations revealed recent star formation quenching due to stellar feedback. In general, the gas depletion, expected star formation history, total mass of stars, and total mass within the tidal radius were adequately reproduced in the simulations when compared to observational estimates. However, there were discrepancies in the distribution of stellar ages and metallicities, which suggested that the cosmic gas infall would play a more complex role in our dwarf spheroidal galaxy than captured by a monolithic infall scenario. Our results suggest that currently quenched dwarf galaxies may not necessarily need to evolve within clusters or groups and that stellar feedback alone could be a sufficient factor in shaping at least some of these galaxies as we observe them today.

Violent Starbursts and Quiescence Induced by Far-ultraviolet Radiation Feedback in Metal-poor Galaxies at High Redshift

JWST observations of galaxies at z ≳ 8 suggest that they are more luminous and clumpier than predicted by most models, prompting several proposals on the physics of star formation and feedback in the first galaxies. In this paper, we focus on the role of ultraviolet (UV) radiation in regulating star formation by performing a set of cosmological radiation hydrodynamics simulations of one galaxy at subparsec resolution with different radiative feedback models. We find that the suppression of cooling by far-UV (FUV) radiation (i.e., H2 dissociating radiation) from Population II stars is the main physical process triggering the formation of compact and massive star clusters and is responsible for the bursty star formation observed in metal-poor galaxies at z ≳ 10. Indeed, artificially suppressing FUV radiation leads to a less intense continuous mode of star formation distributed into numerous but low-mass open star clusters. Due to the intense FUV field, low-metallicity clouds remain warm (∼104 K) until they reach a relatively high density (≳103 cm−3), before becoming self-shielded and transitioning to a colder (∼100 K), partially molecular phase. As a result, star formation is delayed until the clouds accumulate enough mass to become gravitationally unstable. At this point, the clouds undergo rapid star formation, converting gas into stars with high efficiency. We therefore observe exceptionally bright galaxies (10 times brighter than for continuous star formation) and subsequent quenched “dead” galaxies that did not form stars for tens of Myr.

Uncertainty-aware output feedback model predictive combustion control of RCCI engines

Accurate model development is essential for effective model-based control of Reactivity Controlled Compression Ignition (RCCI) engines. However, due to the intricate nature of engine combustion process, achieving a precise model that can capture the complex dynamic behavior and ensure high control performance poses a significant challenge. In this paper, we propose an uncertainty-aware output feedback model predictive control approach for efficient combustion management in RCCI engines. In contrast to the previously developed approaches, this method adopts a data-driven approach within the linear parameter-varying (LPV) framework for model development. To address the model mismatch between the LPV model and the real system/data, Bayesian Neural Networks (BNNs) are employed which provide the probability distribution of the uncertainties. The BNNs enable the formation of a scenario tree, effectively characterizing the range of potential uncertainties in the system. Through the implementation of scenario-based model predictive control, our approach ensures high tracking performance for the RCCI engine in the presence of modeling uncertainties and measurement noise. Extensive simulations and experimental validations demonstrate the superiority of our uncertainty-aware model predictive control over traditional control strategies.

Special Issue: Selected papers from the AIxIA 2023 Workshops

The 2023 edition of the AIxIA Conference, held in Rome, brought together a large number of researchers and practitioners to discuss the most recent and important advancements in Artificial Intelligence (AI). The conference featured 19 workshops, organized by 77 experts, attracting 248 submissions and resulting in 16 proceedings. This special issue presents extended versions of selected papers initially showcased at these workshops. Each paper underwent rigorous review and represents a diverse array of topics, reflecting the multifaceted nature of the Italian AI community. The topics covered include ethical foundations to symbiotic AI, symbolic knowledge extraction from black-box models, creative influence prediction using graph theory, AI approaches to multidimensional poverty prediction, an assessment of AI-based supports for informal caregivers, deep learning-based EEG denoising, AI-assisted board-game-based learning, large language models for assessment and feedback in higher education, geometric reasoning in the Traveling Salesperson Problem, defeasible reasoning in weighted knowledge bases, and conditional computation in neural networks. These contributions demonstrate the innovative and interdisciplinary research within the AI community, offering valuable insights and advancing the field.

The nature of diffuse ionized gas in star-forming galaxies

ABSTRACT We present an analysis of the diffuse ionized gas (DIG) in a high-resolution simulation of an isolated Milky Way-like galaxy, incorporating on-the-fly radiative transfer and non-equilibrium thermochemistry. We utilize the Monte-Carlo radiative transfer code colt to self-consistently obtain ionization states and line emission in post-processing. We find a clear bimodal distribution in the electron densities of ionized gas ($n_{\rm e}$), allowing us to define a threshold of $n_{\rm e}=10\, \mathrm{cm}^{-3}$ to differentiate DIG from ${\rm H\, {\small II}}$ regions. The DIG is primarily ionized by stars aged 5 – 25 Myr, which become exposed directly to low-density gas after ${\rm H\, {\small II}}$ regions have been cleared. Leakage from recently formed stars ($\lt 5$ Myr) is only moderately important for DIG ionization. We forward model local observations and validate our simulated DIG against observed line ratios in [${\rm S\, {\small II}}$]/H$\alpha$, [${\rm N\, {\small II}}$]/H$\alpha$, [${\rm O\, {\small I}}$]/H$\alpha$, and [${\rm O\, {\small III}}$]/H$\beta$ against $\Sigma _{\rm H\alpha }$. The mock observations not only reproduce observed correlations, but also demonstrate that such trends are related to an increasing temperature and hardening ionizing radiation field with decreasing $n_{\rm e}$. The hardening of radiation within the DIG is caused by the gradual transition of the dominant ionizing source with decreasing $n_{\rm e}$ from 0 to 25 Myr stars, which have progressively harder intrinsic ionizing spectra primarily due to the extended Wolf–Rayet phase caused by binary interactions. Consequently, the DIG line ratio trends can be attributed to ongoing star formation, rather than secondary ionization sources, and therefore present a potent test for stellar feedback and stellar population models.

A tolerance index based non-cooperative behaviour managing method with minimum cost in social network group decision making

This paper introduces a novel consensus theoretical framework designed to effectively manage non-cooperative behaviour in social network group decision making (SNGDM). It addresses the challenge by considering both individuals’ willingness to adjust preferences and the associated costs of achieving consensus. To deal with this issue, the personalised individual semantics (PIS) model is employed to handle original evaluation matrices by converting linguistic terms into numerical values based on experts’ personalised opinions. Subsequently, a tolerance index (TI) is defined to reflect the willingness of experts to adjust their preferences. An improved minimum cost (MC) feedback model based on TI is established. The novelty of the proposed approach is that its integration of individual preference adjustment willingness and consensus efficiency, effectively preventing groupthink. In addition, a maximum group consensus degree optimisation model is proposed to detect non-cooperative behaviour of experts. To ensure an optimal solution for the minimum cost feedback model, a weight update method is proposed, considering the trust relationship between experts. A detailed analysis regarding the selection of tolerance thresholds to prevent over-penalisation of weights of non-collaborators is reported. Finally, comprehensive numerical and comparative analyses are presented to validate the proposed method.

Simba-C: the evolution of the thermal and chemical properties in the intragroup medium

ABSTRACT The newly updated GIZMO and Simba based simulation, Simba-C, with its new stellar feedback, chemical enrichment, and recalibrated AGN feedback, allows for a detailed study of the intragroup medium X-ray properties. We discuss the impact of various physical mechanisms, e.g. stellar and AGN feedback, and chemical enrichment, on the composition and the global scaling relations of nearby galaxy groups. We also study the evolution (z = 2 to 0) of the global properties for the $1\, \mathrm{keV}$ temperature groups. Simba-C shows improved consistent matching with the observations of all X-ray scaling relations compared to Simba. It is well known that AGN feedback has a significant influence on LX, 0.5–2.0–Tspec, corr, S500/2500–Tspec, corr, and gas mass fractions, with our Simba-C results consistent with it. Our recalibrated AGN feedback strength also showed an additional improvement in gas entropy, which now aligns with CLoGS observations. The updated stellar feedback and chemical enrichment model is shown to play an important role in our understanding of the chemical abundance ratios and their evolution within galaxy groups. In particular, we find that Simba-C produces an increase in the amount of heavier elements (specifically Si and Fe) relative to O, compared to Simba.

Combined translational and rotational perturbations of standing balance reveal contributions of reduced reciprocal inhibition to balance impairments in children with cerebral palsy.

Balance impairments are common in cerebral palsy. When balance is perturbed by backward support surface translations, children with cerebral palsy have increased co-activation of the plantar flexors and tibialis anterior muscle as compared to typically developing children. However, it is unclear whether increased muscle co-activation is a compensation strategy to improve balance control or is a consequence of reduced reciprocal inhibition. During translational perturbations, increased joint stiffness due to co-activation might aid balance control by resisting movement of the body with respect to the feet. In contrast, during rotational perturbations, increased joint stiffness will hinder balance control as it couples body to platform rotation. Therefore, we expect increased muscle co-activation in response to rotational perturbations if co-activation is caused by reduced reciprocal inhibition but not if it is merely a compensation strategy. We perturbed standing balance by combined backward translational and toe-up rotational perturbations in 20 children with cerebral palsy and 20 typically developing children. Perturbations induced forward followed by backward movement of the center of mass. We evaluated reactive muscle activity and the relation between center of mass movement and reactive muscle activity using a linear feedback model based on center of mass kinematics. In typically developing children, perturbations induced plantar flexor balance correcting muscle activity followed by tibialis anterior balance correcting muscle activity, which was driven by center of mass movement. In children with cerebral palsy, the switch from plantar flexor to tibialis anterior activity was less pronounced than in typically developing children due to increased muscle co-activation of the plantar flexors and tibialis anterior throughout the response. Our results thus suggest that a reduction in reciprocal inhibition causes muscle co-activation in reactive standing balance in children with cerebral palsy.

Implementation models for teacher peer feedback: A systematic review

ABSTRACT Although the effects of teacher peer feedback have been previously examined, peer feedback is implemented differently in continuous professional development (CPD) programmes. Thus, a review of the implementation of teacher peer feedback can provide us with an integrated perspective of this activity, and it may further improve the practices of teacher CPD in the future. This study systematically reviewed 29 empirical articles in the last 20 years. Four implementation models of teacher peer feedback are distinguished: lesson study-based peer feedback, research-initiated peer feedback, supervisor-guided peer feedback, and self-regulated peer feedback. Additionally, five influential factors are found to affect teacher peer feedback: characteristics of participants, training and supervision, schedule and duration, support and tools, and characteristics of feedback. These findings imply that programme organisers should design peer feedback based on the precise analysis of the peer feedback model, influential factors, training goals, participants’ competencies, and learning needs.

Origami manipulation by robot hand utilizing electroadhesion

This study presents strategies for the three fundamental origami operations of grasping, bending, and folding using a novel robot hand and simple motions. These operations are executed using a simple geometric model and without any visual feedback or physical modeling not to restrict the motions. With a few applications in the field of paper manipulation, the electroadhesion technology is employed to perform single hand grasping. Bending is realized by a single hand utilizing the elasticity of origami and friction. Folding is performed by holding an origami with more than two points to fix it at any moment for preciseness. In addition to the design of hardware and motions, operations are demonstrated with general criteria for the crease precision evaluation.

Supporting the Nurse Preceptor Role: Implementing the R2C2 Feedback Model.

Nurse preceptors are key to the successful transition of graduate nurses to practice and experienced nurses to a new organization. Providing ongoing preceptor development is essential to support nurses in this vital role. The evidence-based R2C2 (relationship, reaction, content, coach) feedback and coaching model was implemented to facilitate the delivery of constructive feedback from nurse preceptors to their orientees. Post-intervention survey results suggest an overall positive response to the use of the feedback model in practice.

Learner perceptions of the feedback process in the online component of a blended course

Despite extensive research on feedback models, there is still sparse empirical evidence of their validity and application in higher education learning settings, whether online, hybrid, or face-to-face. Understanding how a feedback framework, integrated in the instructional cycle, is perceived by the learners can provide empirical support about its intended purpose and effectiveness. Recent reviews of research on student perceptions of feedback in different learning environments have revealed the need to research feedback as a process with the use of a more solid methodological approach. The aim of the present descriptive survey study is to investigate learners’ perceptions of an established feedback model, a Matrix of Feedback for Learning (Brooks et al., 2019), in the asynchronous online component of an undergraduate blended course. More specifically, the impact of its application on learners’ encounters with three types of feedback (feed-up, feed-back and feed-forward) and three levels of feedback (task, process and self-regulatory) is explored. A 36-item survey, previously piloted and preliminarily validated, was used to explore learners’ perceptions (N=135) of the three feedback types and levels. Approximately 68% of responses showed that students recognized feedback and the possibilities to use and act on feedback as helpful to their learning. This present survey supports a line of research on feedback model validation in online learning environments while offering meaningful insights from the learners that can inform both course design and interventions to support engagement with feedback.