Feedback Model Research Articles

MODEL TO STUDY INTERDEPENDENT CALCIUM AND IP3 DISTRIBUTION REGULATING NFAT PRODUCTION IN T LYMPHOCYTE

The cooperative system of calcium (Ca[Formula: see text]) and inositol 1,4,5-trisphosphate (IP3) plays a crucial role in maintaining structure and functions of T lymphocyte cells including the production of nuclear factor of activated T cells (NFAT) as an immunological response. NFAT regulates several mechanisms and dysregulation in this process can lead to various disorders like inflammatory bowel diseases, etc. In this study, the two-way feedback model of Ca[Formula: see text] and IP3 dynamics regulating NFAT production in T lymphocytes has been constructed. A system of nonlinear reaction-diffusion equations for calcium and IP3 for a one-dimensional case has been employed in this proposed model. The numerical simulation is performed using finite element and the Crank–Nicholson method. The effects of the parameters like Ryanodine Receptor (RyR), source amplitude and buffers on active NFAT production have been analyzed. The novel insights about regulatory and dysregulation events and mechanisms of Ca[Formula: see text], IP3 and NFAT are concluded from these results.

The Application of Game Mechanics and Technological Trend in Game-Based Learning: A Review of the Research

The rapid development of information technology affects numerous aspects of human life, including education. An example of an IT application in education is game-based learning. Game-based learning has been implemented in various fields or subjects on various platforms. This is due to the potential of game-based learning to enhance student engagement in the learning process. However, the effectiveness of this method needs to be further studied. This systematic review of the literature aimed to explore the mechanics of games that are applied in current research on game-based learning, accompanied by the trend of technological use in research papers published in this domain. This study covered 30 journal and conference proceedings papers published from 2012-2022. The review was conducted using the Kitchenham method. The selected articles were then analyzed to determine the engagement model used in each paper (Feedback Model, Incentive and achievement model, and Progression Model). Findings included the trend of research in this field (technology applied to each research, on-line feature, study majors/subject) are displayed based on the time paper were published. The study result indicated that all previous research used at least one of the engagement models, with 12 articles using the three models. In terms of technology, it was found that the adoption of web-based technology has been increasing in recent years, including online features that have also increased, along with the study subjects who implemented game-based learning. In summary, game-based learning can be applied in a wide range of subjects and platforms with the support of its characteristic, making learning more flexible.

The LDL receptor is regulated by membrane cholesterol as revealed by fluorescence fluctuation analysis

Membrane cholesterol-rich domains have been shown to be important for regulating a range of membrane protein activities. Low-density lipoprotein receptor (LDLR)-mediated internalization of cholesterol-rich LDL particles is tightly regulated by feedback mechanisms involving intracellular sterol sensors. Since LDLR plays a role in maintaining cellular cholesterol homeostasis, we explore the role that membrane domains may have in regulating LDLR activity. We expressed a fluorescent LDLR-mEGFP construct in HEK293T cells and imaged the unligated receptor or bound to an LDL/DiI fluorescent ligand using total internal reflection fluorescence microscopy. We studied the receptor’s spatiotemporal dynamics using fluorescence fluctuation analysis methods. Image cross correlation spectroscopy reveals a lower LDL-to-LDLR binding fraction when membrane cholesterol concentrations are augmented using cholesterol esterase, and a higher binding fraction when the cells are treated with methyl-β-cyclodextrin) to lower membrane cholesterol. This suggests that LDLR’s ability to metabolize LDL particles is negatively correlated to membrane cholesterol concentrations. We then tested if a change in activity is accompanied by a change in membrane localization. Image mean-square displacement analysis reveals that unligated LDLR-mEGFP and ligated LDLR-mEGFP/LDL-DiI constructs are transiently confined on the cell membrane, and the size of their confinement domains increases with augmented cholesterol concentrations. Receptor diffusion within the domains and their domain-escape probabilities decrease upon treatment with methyl-β-cyclodextrin, consistent with a change in receptor populations to more confined domains, likely clathrin-coated pits. We propose a feedback model to account for regulation of LDLR within the cell membrane: when membrane cholesterol concentrations are high, LDLR is sequestered in cholesterol-rich domains. These LDLR populations are attenuated in their efficacy to bind and internalize LDL. However, when membrane cholesterol levels drop, LDL has a higher binding affinity to its receptor and the LDLR transits to nascent clathrin-coated domains, where it diffuses at a slower rate while awaiting internalization.

SIMBA-C: an updated chemical enrichment model for galactic chemical evolution in the SIMBA simulation

ABSTRACT We introduce a new chemical enrichment and stellar feedback model into GIZMO, using the SIMBA sub-grid models as a base. Based on the state-of-the-art chemical evolution model of Kobayashi et al., SIMBA-C tracks 34 elements from H→Ge and removes SIMBA’s instantaneous recycling approximation. Furthermore, we make some minor improvements to SIMBA’s base feedback models. SIMBA-C provides significant improvements on key diagnostics such as the knee of the z = 0 galaxy stellar mass function, the faint end of the main sequence, and the ability to track black holes in dwarf galaxies. SIMBA-C also matches better with recent observations of the mass–metallicity relation at z = 0, 2. By not assuming instantaneous recycling, SIMBA-C provides a much better match to galactic abundance ratio measures such as [O/Fe] and [N/O]. SIMBA-C thus opens up new avenues to constrain feedback models using detailed chemical abundance measures across cosmic time.

Emergence of ultradiscrete states due to phase lock caused by saddle-node bifurcation in discrete limit cycles

Abstract Dynamical properties of limit cycles for a tropically discretized negative feedback model are numerically investigated. This model has a controlling parameter τ, which corresponds to the time interval for the time evolution of phase in the limit cycles. By considering τ as a bifurcation parameter, we find that an ultradiscrete state emerges due to phase lock caused by saddle-node bifurcation. Furthermore, focusing on limit cycles for the max-plus negative feedback model, it is found that the unstable limit cycle in the max-plus model corresponds to the unstable fixed points emerging by the saddle-node bifurcation in the tropically discretized model.

IIoT based framework for data communication and prediction using augmented reality for legacy machine artifacts

Machine data automation, process feedback, and visualization are critical elements in monitoring and improving system performance. Legacy machines constitute a large part of the factory floor assets, especially in small and medium enterprises (SMEs). Digitalization enables them to seamlessly connect within the smart factory framework to exchange product and process information. This paper extends our work on extracting digitized data from legacy machines using computer vision in the Industrial Internet of things (IIoT) environment. The proposed framework includes data communication through the cloud for process automation, performing data analytics, interpretation, and visualization using Augmented Reality (AR) based smart devices through human-in-the-loop feedback. Real-time process recommendations are provided on an AR app by implementing a Bayesian Network to enable the user to input desired quality metrics. A case study of capturing injection molding data represented through different process parameters is presented to evaluate the performance. Further, its extension to using other machine learning (ML) models for process feedback, the latency of real-time data presented, communication with different IoT protocols, and authentication are discussed. This AR-based Digital Thread framework has promising applications for visualizing machine process parameters and performance, real-time feedback, troubleshooting, and maintenance activities for legacy and smart machines.

Quantifying the energetics of molecular superbubbles in PHANGS galaxies

Context. Star formation and stellar feedback are interlinked processes that redistribute energy, turbulence, and material throughout galaxies. Because young and massive stars form in spatially clustered environments, they create pockets of expanding gas termed superbubbles, which retain information about the physical processes that drive them. As these processes play a critical role in shaping galaxy discs and regulating the baryon cycle, measuring the properties of superbubbles provides important input for galaxy evolution models. Aims. With the wide coverage and high angular resolution (∼50–150 pc) of the PHANGS–ALMA 12CO (J = 2−1) survey, we can now resolve, identify and characterise a statistically representative number of superbubbles using molecular gas in nearby galaxies. Methods. We identify superbubbles by requiring spatial correspondence between shells in CO with stellar populations identified in PHANGS–HST. Then, by combining the properties of the stellar populations with the CO, we quantify the energetics of the stars and constrain feedback models. We visually find 325 cavities across 18 PHANGS–ALMA galaxies, 88 of which have clear superbubble signatures (unbroken shells, central clusters, kinematic signatures of expansion). We measure their radii and expansion velocities using CO (2–1) to dynamically derive their ages and the mechanical power driving the bubbles, which we use to compute the expected properties of the parent stellar populations driving the bubbles. Results. We find consistency between the predicted and derived stellar ages and masses of the stellar populations if we use a supernova (SN) model that injects energy with a coupling efficiency of ∼10%. Not only does this confirm that molecular gas accurately traces superbubble properties, but it also provides key observational constraints for superbubble models. We also find evidence that the bubbles are sweeping up gas as they expand, and speculate that these sites have the potential to host new generations of stars. Conclusions. This work demonstrates that molecular superbubbles provide novel quantitative constraints on SNe feedback efficiencies and gas clearing times, and represent a promising environment to search for the propagation of star formation, all of which are needed to understand what sets the observed star formation rates in galaxies.

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)

Appreciative Inquiry Utilizing Online Platforms

This experiential exercise teaches students what constitutes Appreciative Inquiry and how it is different from other feedback models, along with giving students the opportunity to apply their knowledge. Students practice giving Appreciative Inquiry to their peers’ presentations through the use of an online platform that allows for written words along with memes, GIFs, and photos to express their feedback. The activity is suitable for face-to-face undergraduate and graduate-level organizational behavior and management classes who have at least one, ideally two teams-based presentations, and are looking to provide strengths-based, positive feedback to produce high-performing teams. Information is presented about how to set up, run, and debrief this activity to meet learning objectives.

System identification and simulation of soft tissue force feedback in a spine surgical simulator

Surgical simulators are being introduced as training modalities for surgeons. This paper aims to evaluate dynamic models used to convey force feedback from puncturing the soft tissue during a spine surgical simulation. The force feedback of the tissue is treated as a dynamic system. This is done by performing classical system identification across a bandwidth of frequencies on a tissue analogue and fitting that behaviour to dynamic viscoelastic models. The models that are tested are an inverted linear model, the Maxwell model, the Kelvin–Boltzmann (KB) model, and a higher-order blackbox (HO) model. Several error metrics such as percent variance accounted for (%VAF) are determined to measure solution accuracy. The force feedback models are programmed into a surgical simulator and tested with study participants who rated them based on how well the identified models match the behaviour of the rubber tissue analogue. The highest %VAF is 82.64% when the tissue is modelled as the HO model. Statistically significant differences (p < 0.05) are found between all model ratings from participants except between the HO model and the KB model. However, the HO model has the highest percentage (37.8%) of participants who rank its performance as the closest to the tissue analogue compared to the other force feedback models. The more accurately the dynamic behaviour resembles the tissue analogue, the higher the model was rated by study participants. This study highlights the importance of utilizing dynamic signals to generate dynamic models of soft tissue for spine surgical simulators.

Galactic coronae in Milky Way-like galaxies: the role of stellar feedback in gas accretion

ABSTRACT Star-forming galaxies like the Milky Way are surrounded by a hot gaseous halo at the virial temperature – the so-called galactic corona – that plays a fundamental role in their evolution. The interaction between the disc and the corona has been shown to have a direct impact on accretion of coronal gas onto the disc with major implications for galaxy evolution. In this work, we study the gas circulation between the disc and the corona of star-forming galaxies like the Milky Way. We use high-resolution hydrodynamical N-body simulations of a Milky Way-like galaxy with the inclusion of an observationally motivated galactic corona. In doing so, we use SMUGGLE, an explicit interstellar medium (ISM), and stellar feedback model coupled with the moving-mesh code arepo. We find that the reservoir of gas in the galactic corona is sustaining star formation: the gas accreted from the corona is the primary fuel for the formation of new stars, helping in maintaining a nearly constant level of cold gas mass in the galactic disc. Stellar feedback generates a gas circulation between the disc and the corona (the so-called galactic fountain) by ejecting different gas phases that are eventually re-accreted onto the disc. The accretion of coronal gas is promoted by its mixing with the galactic fountains at the disc–corona interface, causing the formation of intermediate temperature gas that enhances the cooling of the hot corona. We find that this process acts as a positive feedback mechanism, increasing the accretion rate of coronal gas onto the galaxy.

Dynamic simulation of the water-energy-food nexus (WEFN) based on a new nexus in arid zone: A case study in Ningxia, China

‘Quantity-type’ water shortages and ‘quality-type’ water shortages are important factors that constrain the security and coordinated development of regional water-energy-food nexus (WEFN) system, especially in arid areas where water is scarce. Therefore, it is of great significance to accurately identify the coupling and mutual feedback effects of the WEFN on different types of water resources demand. This information can be used to understand the regional WEFN and alleviate pressure on regional water resources. In this study, a new relationship diagram of the WEFN system was formed with the incorporation of the water footprint (WF), which can characterize a system's water demand, water source type, water pollution amount and pollution type. Based on this nexus, a WEFN system feedback model suitable for arid regions was established using the system dynamics approach. Taking Ningxia, China, as a case study, six future scenarios were designed, and the development trends of the WEFN system under different development scenarios were simulated to explore the impact of different policies on the WEFN. Finally, practical suggestions to promote the synergistic development of WEFN systems were proposed. The results indicate that the rational distinction between ‘quantity-type’ and ‘quality-type’ water resources can effectively alleviate the regional water stress and promote the coordinated development of water, energy and food. And the water security is the main factor that constrains the coordinated development of the WEFN system in Ningxia. Incorporating water environmental pollution policies into the water subsystem is necessary. Furthermore, the resource saving scenario and energy production restructuring adjustment scenario can effectively alleviate the energy security problems that has resulted from rapid economic development. In addition, Ningxia urgently needs to optimize its food production structure to address a continuous reduction in the food security index.

X-ray emission from the interstellar and circumgalactic medium of elliptical galaxies based on macer simulations

ABSTRACT Interstellar medium (ISM) and circumgalactic medium (CGM) around galaxies are linked to several physical processes that drive galaxy evolution. For example, the X-ray emission from the CGM gas around ellipticals has been linked to the active galactic nucleus (AGN) feedback occurring in the host. Upcoming telescopes, such as Hot Universe Baryon Survey (HUBS) with ∼2 eV resolution, can provide us with deep insights about the hot gas properties of such galaxies that constrain these processes. In this project, we discuss X-ray emission of the ISM and CGM of elliptical galaxies simulated using macer code. We generate X-ray emission data from the macer simulations with various feedback models and produce mock observations for an instrument with high spectral resolution, which is a necessary step of selecting sources for the future observations with planned mission such as HUBS. More importantly, we establish connections between the physics of AGN and stellar feedback with the emission spectra from the ISM and CGM to investigate the possibility of using observations to constrain feedback models. We fit the X-ray spectra from these simulations with standard fitting procedures and compare the retrieved physical properties with their counterparts from the simulations to understand whether the future high-resolution observations can reliably reveal the properties of the gas in the galaxies.

The Study of Evolution and Application Related to the Chat-GPT

Chat-GPT has become increasingly popular and has provided tremendous help to people in their daily life. The fundamental working principle of Chat-GPT involves the incorporation of various methods such as Natural Language Processing (NLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Transformer and Reinforcement Learning from Human Feedback model (RLHF). These methods assist Chat-GPT in understanding, predicting and computing the desired outcomes for the users. NLP is used to help machines understand and process human language. RNN is employed to facilitate the machine in comprehending the input's logic. LSTM enables the control of memory elements, allowing the machine to combine unrelated elements in memory. RLHF serves as a switch button, controlling the machine's output and improving the accuracy of results. Chat-GPT can be used for image editing, coding, translation, paper editing and other tasks. However, it is critical to exercise caution while using Chat-GPT and not to become overly reliant on it, given the potential problems it may create.

Exploitation and depredation rates determine viability of depredation-impacted fisheries

Depredation can broadly affect marine socio-ecological systems, yet it has been little studied via modelling approaches. Here we used qualitative mathematical modelling of system feedback (Puccia and Levins’ loop analysis) and a system of ordinary differential equations to represent major interactions between a fishery, an exploited stock and a depredating species. We then assess response of depredation-impacted fisheries to three scenarios of sustained changes: i) increased fishing effort, ii) increased abundance of depredating species and iii) increased abundance of the exploited stock. Persistence of depredation-impacted fisheries depends on (1) the fishers’ success in retrieving fish onboard being relatively greater than the ability of depredating species to remove the catch from the gear and (2) sustainable management of the exploited and depredated stock. Moreover, predicted responses of depredation-impacted systems can be summarised according to four broad categories, which partially depend on (1) the population dynamics of the stock and its exploitation status, and (2) the ecology of the depredating species and the magnitude of its competition with fishers. In all categories, an increase in the rate of growth of the depredating species reduces fishery yield, which is likely to intensify fishing effort as fishers try and compensate for losses. However, such an increase in effort is only sustainable for well-managed stocks harvested by a fishery that is sufficiently profitable to support extra costs. We identify several processes that determine the co-existence of fishers, exploited stocks and depredating species and propose a suite of recommendations to better represent depredation in marine ecosystem and fisheries models.

Leveraging WiFi network logs to infer student collocation and its relationship with academic performance

A comprehensive understanding of collocated social interactions can help campuses and organizations better support their community. Universities could determine new ways to conduct classes and design programs by studying how students have collocated in the past. However, this needs data that describe large groups over a long period. Harnessing user devices to infer collocation, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors across the entire campus is expensive. Instead, we investigate an easily accessible data source that can retroactively depict multiple users on campus over a semester, a managed WiFi network. Despite the coarse approximations of collocation provided by WiFi network logs, we demonstrate that leveraging such data can express meaningful outcomes of collocated social interaction. Since a known outcome of collocating with peers is improved performance, we inspected if automatically–inferred collocation behaviors can indicate the individual performance of project group members on a campus. We studied 163 students (in 54 project groups) over 14 weeks. After describing how we determine collocation with the WiFi logs, we present a study to analyze how collocation within groups relates to a student’s final score. We found that modeling collocation behaviors showed a significant correlation (Pearson’sr =0.24) with performance (better than models of peer feedback or individual behaviors). These findings emphasize that it is feasible and valuable to characterize collocated social interactions with archived WiFi network logs. We conclude the paper with a discussion of applications for repurposing WiFi logs to describe collocation, along with privacy considerations, and directions for future work.

Event-triggered robust distributed output feedback model predictive control for nonlinear MASs against false data injection attacks

In this paper, an event-triggered distributed output feedback model predictive control scheme for the nonlinear disturbed multiagent systems with sensor–controller channel false data injection attacks is proposed. To provide valid system states to the controller in the event of cyber attacks, a robust multivariate observer is designed to realize the estimation and separation of uncompromised system states, false data injection attacks, and measurement disturbances, simultaneously. Based on these reconstructed signals and a newly-designed linear robustness constraint, the distributed predictive controller is established to achieve smooth cooperative stabilization among agents. Meanwhile, an event-triggered mechanism is applied to save computing resources, and it restricts the error of predictive states and estimated states to guarantee the feasibility of the optimization control problem. Theoretical analyses on robustness and security for the nonlinear multiagent systems under event-triggered distributed output feedback model predictive control are presented. Finally, a simulation on two pairs of one-link flexible joint manipulator systems verifies the theoretical results.

Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. Using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (−0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (−0.12 ± 0.08 °C/10a), despite some day-night process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

Redshift evolution of the baryon and gas fraction in simulated groups and clusters of galaxies

We study the redshift evolution of the baryon budget in a large set of galaxy clusters from the Magneticum suite of smoothed particle hydrodynamical cosmological simulations. At high redshifts (z ≳ 1), we obtain ‘closed-box’ (i.e. baryon mass fraction fbar = Ωbar/Ωtot) systems independently of the mass of the systems on radii greater than 3R500, c, whereas at lower redshifts, only the most massive halos can be considered closed box. Furthermore, in the innermost regions (r &lt; R500, c), the baryon fraction shows a general decrease with redshift, and for less massive objects we observe a much more prominent decrease than for massive halos (fbar × Ωtot/Ωbar = Ybar decreases by ∼4% from z ∼ 2.8 to z ∼ 0.2 for massive systems and by ∼15% for less massive objects in the same redshift range). The gas depletion parameter Ygas = fgas/(Ωbar/Ωtot) shows a steeper and highly scattered radial distribution in the central regions (0.5R500, c ≤ r ≤ 2R500, c) of less massive halos with respect to massive objects at all redshifts, while on larger radii (r ≥ 2R500, c) the gas fraction distributions are independent of the masses or the redshifts. We divide the gas content of halos into the hot and cold phases. The hot, X-ray-observable component of the gas accurately traces the total amount of gas at low redshifts (e.g., for z ∼ 0.2 at R500, c, in the most massive subsample, that is, 4.6 × 1014 ≤ M500, c/M⊙ ≤ 7.5 × 1014 and least massive subsample, that is, 6.0 × 1013 ≤ M500, c/M⊙ ≤ 1.9 × 1014, we obtain Ygas ∼ 0.75 and 0.67, Yhot ∼ 0.73 and 0.64, and Ycold ∼ 0.02 and 0.02, respectively). On the other hand, at higher redshifts, the cold component provides a non-negligible contribution to the total amount of baryons in our simulated systems, especially in less massive objects (e.g., for z ∼ 2.8 at R500, c, in the most massive subsample, that is, 2.5 × 1013 ≤ M500, c/M⊙ ≤ 5.0 × 1013 and least massive subsample, that is, 5.8 × 1012 ≤ M500, c/M⊙ ≤ 9.7 × 1012, we obtain Ygas ∼ 0.63 and 0.64, Yhot ∼ 0.50 and 0.45, and Ycold ∼ 0.13 and 0.18, respectively). Moreover, the behaviour of the baryonic, entire-gas, and hot-gas-phase depletion parameters as functions of radius, mass, and redshift are described by some functional forms for which we provide the best-fit parametrization. The evolution of metallicity and stellar mass in halos suggests that the early (z &gt; 2) enrichment process is dominant, while more recent star-formation processes make negligible contributions to the enrichment of the gas metallicity. In addition, active galactic nuclei (AGN) play an important role in the evolution of the baryon content of galaxy clusters. Therefore, we investigate possible correlations between the time evolution of AGN feedback and the depletion parameters in our numerical simulations. Interestingly, we demonstrate that the energy injected by the AGN activity shows a particularly strong positive correlation with Ybar, Ycold, and Ystar and a negative correlation with Yhot and ZTot. On the other hand, Ygas shows a less prominent level of negative correlation, a result which is highly dependent on the mass of the halos. These trends are consistent with previous theoretical and numerical works, meaning that our results, combined with findings derived from current and future X-ray observations, represent possible proxies with which to test the AGN feedback models used in different suites of numerical simulations.

Codesign of Quantized Dynamic Output Feedback MPC for the Takagi–Sugeno Model

We present a co-design of measurement quantized dynamic output feedback model predictive control (DOFMPC) for Takagi-Sugeno model with bounded disturbance. The system output is quantized by a dynamic quantizer before it is transmitted to the DOFMPC controller. Hence, we utilize the dynamic output feedback control law with quantized output signal and consider the mixed input and quantized output constraint for the controller design. By optimizing the quantizer and controller paramters on-line, the control performance is enhanced. Moreover, we formulate a 2-leveled optimizations, with the upper level optimizing the performance index and the lower level optimizing the soft constraint in a lexicographic order, for the co-design of the DOFMPC controller and dynamic quantizer. Thus, there are more degrees of freedom for tighting the soft constraints. The recursive feasibility and stability of the proposed approaches are guaranteed. The applicability of the proposed approach is illustrated by a simulation example.