Relative Output Research Articles

Fully Distributed Event-Triggered Bipartite Output Formation Control for Heterogeneous MASs With Directed Graphs

This brief considers the bipartite output time-varying formation control problem for heterogeneous multi-agent systems over signed directed graphs under an adaptive event-triggered mechanism. A fully distributed bipartite controller is designed to estimate the state of the leader and form the desired antagonistic formation shape only using the relative output information. With the help of the event-triggered mechanism, the proposed protocol prevents continuous communication among neighbor agents under the directed topology. Also, Zeno behavior is verified to be ruled out. Furthermore, the proposed protocol not only avoids using the eigenvalues of the Laplacian matrix but also does not need to continuously monitor the information of neighbor agents. Finally, the effectiveness of the designed protocol is illustrated by a numerical simulation.

A similarity-based Bayesian mixture-of-experts model

We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k-nearest neighbors algorithm. Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point, yielding predictive distributions represented by Gaussian mixtures. Posterior inference is performed on the parameters of the mixture components as well as the distance metric using a mean-field variational Bayes algorithm accompanied with a stochastic gradient-based optimization procedure. The proposed method is especially advantageous in settings where inputs are of relatively high dimension in comparison to the data size, where input–output relationships are complex, and where predictive distributions may be skewed or multimodal. Computational studies on five datasets, of which two are synthetically generated, illustrate clear advantages of our mixture-of-experts method for high-dimensional inputs, outperforming competitor models both in terms of validation metrics and visual inspection.

Reinforcement learning for process control with application in semiconductor manufacturing

Process control is widely discussed in the manufacturing process, especially in semiconductor manufacturing. Due to unavoidable disturbances in manufacturing, different process controllers are proposed to realize variation reduction. Since Reinforcement Learning (RL) has shown great advantages in learning actions from interactions with a dynamic system, we introduce RL methods for process control and propose a new controller called RL-based controller. Considering the fact that most existing run-to-run (R2R) controllers mainly rely on a linear model assumption for the process input–output relationship, we first discuss theoretical properties of RL-based controllers based on the linear model assumption. Then the performance of RL-based controllers and traditional R2R controllers (e.g., Exponentially Weighted Moving Average (EWMA), double EWMA, adaptive EWMA, and general harmonic rule controllers) are compared for linear processes. Furthermore, we find that the RL-based controllers have potential advantages to deal with other complicated nonlinear processes. The intensive numerical studies validate the advantages of the proposed RL-based controllers.

Employee and customer perspectives in discovering the antecedent and output relationships for hotel branding development

PurposeThe purpose of this study is to propose and develop an antecedent model from star hotel employee viewpoint and a consequence model for star hotel customers’ evaluation of the brand equity development process.Design/methodology/approachIn terms of the study setting, structural equation modelling was conducted. Study 1 used the views of 323 star hotels’ employees on the mutual relationships among employee-based brand equity, and Study 2 used 516 star hotels’ customer perspectives concerning the mutual relationships among customer-based brand equity.FindingsIn the antecedent model of Study 1 from an employee viewpoint, service quality is a foundational attribute of the indirect influences of brand equity development through brand promise, values and message. A moderating role of brand identity is also found. In the consequence model of Study 2 from the perspective of customers’ evaluation, mutual relationships of brand equity are identified, and social interactions are established that enhance the positive relationships among the different critical attributes of brand equity.Originality/valueAlthough numerous hotel branding studies have identified the consequences of hotels for employees, few have simultaneously considered employees and customer feedback in terms of star hotel branding. This research considers the bidirectional effects of such a comprehensive perspective.

A machine learning method for distinguishing detrital zircon provenance

Zircon geochemistry provides a sensitive monitor of its parental magma composition. However, due to the complexity of the uptake of trace elements during zircon growth, identifying source magmas remains challenging, particularly for detrital grains whose petrological context is lost. We use a machine learning-based approach to explore the classifiers for zircon provenance, based on 3794 published, high-quality zircon trace element analyses compiled from I-, S-, and A-type granites. Three supervised machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) were used and trained with 11 features, including 7 trace elements (Ce, Eu, Ho, Nb, Ta, Th, and U) and 4 derived trace element ratios (Th/U, U/Yb, Ce/Ce*, and Eu/Eu*). Our results show that all three trained machine learning methods perform very well with accuracy varying from 0.86 to 0.89, and that input–output relationships captured by different ML methods are nearly consistent and can be explained by the known petrological processes. The application of our trained machine learning classifiers to detrital zircon studies will enhance the interpretability of zircon assemblages of different origins. It also helps develop interpretations, approaches, and tools that will benefit, for example, the study of continental crust evolution and mineral exploration.

Distributed Disturbance Observer-Based Containment Control of Multi-Agent Systems via an Event-Triggered Approach

This paper studies the containment control problem of linear multi-agent systems (MASs) subject to external disturbances, where the communication graph is a directed graph with the followers being undirected connections. In order to save communication costs and energy consumption, a distributed disturbance observer-based event-triggered controller is employed based on the relative outputs of neighboring followers. Compared with conventional controllers, our observer-based controller utilizes the relative outputs of neighboring followers at the same triggered instant. Furthermore, it is shown that Zeno behavior can be avoided. Finally, the validity of our proposed methodology is demonstrated by a simulation example.

Reaction time as a libido indicator and its relation to pre-freeze and post-thaw sperm quality in bulls.

Libido and sperm quality output relationship is already not clear in farm animals. The present study compared reaction time (RT) as a libido indicator and the pre-freeze and post-thaw sperm quality of AI bulls. Before the collection of ejaculates (n = 53, from 22 AI bulls [4.2 ± 1 years of age]), RTs were collected using a chronometer as the interval between the bull's arrival at the semen collection area and his first false mount (FM) on another male. The ejaculates were examined for their volume, concentration and motility. Subsequently, all aliquots were diluted with a commercial semen extender and equilibrated for 3 h before freezing. Frozen semen samples were thawed and examined for sperm kinematics using CASA, plasma membrane and acrosome integrity of sperm (PMAI) by flow cytometry. Additionally, the temperature humidity index (THI) values were assessed during the study. Multiple linear regression analysis was used to analyse the data. The results indicated that THI had a significant effect on libido (p < .001). However, libido had no effect on either pre- or post-thaw sperm quality parameters except for the velocity of the average pathway (VAP) (p < .05). Therefore, relying solely on RT -libido- as an indicator of bull sperm quality at AI stations may not be reliable, as it is a complex behavioural assessment.

A domain-region based evaluation of ML performance robustness to covariate shift

Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpected performance of the learned model in deployment. The issue in which the training and test data inputs follow different probability distributions while the input–output relationship remains unchanged is referred to as covariate shift. In this paper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariate shift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function of the input data to assess the classifier’s performance per domain region. Distributional changes were simulated in a two-dimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on the experimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing the lowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the results reveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that the models exhibit high bias toward the region with high density in the input space domain of the training samples.

Event‐triggered cooperative control for uncertain multi‐agent systems and applications

AbstractThis paper investigates the event‐triggered cooperative output regulation problem for uncertain multi‐agent systems, demonstrating that the output regulation error of the uncertain multi‐agent system can converge to a small neighborhood of the origin. The proposed method has several notable features. Firstly, both the leader and follower systems contain unknown parameters, and follower systems can be heterogeneous. Secondly, the controller only requires the relative output of each agent, rather than the absolute output or state. Thirdly, the controller is fully distributed, which implies that it does not rely on any global information. In order to achieve the above purpose, a fully distributed reduced‐order observer is proposed with new adaptive laws. Furthermore, new adaptive event‐triggered mechanisms are designed on the basis of the neighborhood regulation error (NRE) and the observational regulation error (ORE). Finally, the proposed approach is validated through simulation and experiment on a multi‐joint robot manipulator.

A Structured Sparse Bayesian Channel Estimation Approach for Orthogonal Time-Frequency Space Modulation.

Orthogonal time-frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. In OTFS modulation-based ISAC systems, accurate channel acquisition is critical for both communication reception and sensing parameter estimation. However, the existence of the fractional Doppler frequency shift spreads the effective channels of the OTFS signal significantly, making efficient channel acquisition very challenging. In this paper, we first derive the sparse structure of the channel in the delay Doppler (DD) domain according to the input and output relationship of OTFS signals. On this basis, a new structured Bayesian learning approach is proposed for accurate channel estimation, which includes a novel structured prior model for the delay-Doppler channel and a successive majorization-minimization (SMM) algorithm for efficient posterior channel estimate computation. Simulation results show that the proposed approach significantly outperforms the reference schemes, especially in the low signal-to-noise ratio (SNR) region.

Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives

Process Analytical Technology (PAT) is a systematic approach for monitoring of process parameters and product quality attributes and nowadays is considered for continuous processing of many industrial products. It is a mechanism to design, analyse and control manufacturing processes through on-line, in-line, at-line and off-line configurations for monitoring Critical Quality Attributes (CQAs). PAT systems include a combination of reliable in-line sensors, spectroscopic instruments and Multivariate Statistical Methods (MSMs) to provide informative knowledge for quality assessment of powdered and granule products. Nevertheless, monitoring programs of advanced manufacturing processes based on PAT systems typically provide large sets of data which are complex to interpret. The application of appropriate data-driven modelling techniques could assist in the interpretation of complex data matrices to better control of processes. Data fusion is a data-driven approach that could increase performance and robustness of models used for data interpretation to generate more accurate knowledge about process conditions and performance by merging related outputs collected from several instruments and considering synergies from multiple sources. This paper aims at presenting the current state of the art regarding the application of multi-sensors data fusion for powdered and granule manufacturing processes and making a critical review of recent progress and future possible perspectives in this field.

Performance Research of Dual Power Flow Continuous Differential Steering System for Tracked Vehicle

The tracked vehicle has good passability and adaptability for different complex environments, and also can maintain a stable posture with the ability to regulate flexible posture. Therefore, it is necessary to design a steering system with continuous and accurate steering radius and high transmission efficiency. The dynamic model of differential steering system was established including input rotate speed and output rotate speed. The dynamic model between input rotate speed and steering radius was obtained according to the relationship of output rotate speed and steering radius. The dynamic model of steady steering including the length, gauge, ground instantaneous center offset and so on of tracked vehicle was built, which could be used to predict the steady state steering characteristic of tracked vehicle.

Inefficient international risk-sharing

Complete financial markets are widely believed to be beneficial for the international economy, since they enable cross-country risk-sharing. Using a two-country New Keynesian model, we show that this is not the case if the source of income fluctuations is a country-specific markup shock. When preferences involve the wealth effect on labor supply and imply that Home and Foreign goods are Edgeworth substitutes, the absence of risk-sharing in autarky acts like a favorable markup shock, reducing the variability of relative inflation and output gaps created by the shock. Thus, welfare can be higher under autarky than under complete financial markets, indicating that international risk-sharing can be undesirable. However, if the wealth effect on labor supply is absent or Home and Foreign goods are Edgeworth complements, welfare reversals do not occur.

Increased maximal oxygen uptake after sprint-interval training is mediated by central haemodynamic factors as determined by right heart catheterization.

There is a lack of knowledge regarding the contribution of central and peripheral factors to the increases in VO2max following sprint-interval training (SIT). This study investigated the importance of maximal cardiac output (Qmax ) in relation to VO2max improvements following SIT and the relative importance of the hypervolemic response on Qmax and VO2max . We also investigated whether systemic O2 extraction increased with SIT as has been previously suggested. Healthy men and women (n=9) performed 6weeks of SIT. State-of-the-art measurements: right heart catheterization, carbon monoxide rebreathing and respiratory gas exchange analysis were used to assess Qmax , arterial O2 content (ca O2 ), mixed venous O2 content (cv O2 ), blood volume (BV) and VO2max before and after the intervention. In order to assess the relative contribution of the hypervolemic response to the increases in VO2max , BV was re-established to pre-training levels by phlebotomy. Following the intervention, VO2max , BV and Qmax increased by 11% (P < 0.001), 5.4% (P=0.013) and 8.8% (P=0.004), respectively. cv O2 decreased by 12.4% (P=0.011) and systemic O2 extraction increased by 4.0% (P=0.009) during the same period, both variables were unaffected by phlebotomy (P=0.589 and P=0.548, respectively). After phlebotomy, VO2max and Qmax reverted back to pre-intervention values (P=0.064 and P=0.838, respectively) and were significantly lower compared with post-intervention (P=0.016 and P=0.018, respectively). The decline in VO2max after phlebotomy was linear to the amount of blood removed (P=0.007, R=-0.82). The causal relationship between BV, Qmax and VO2max shows that the hypervolemic response is a key mediator of the increases in VO2max following SIT. KEY POINTS: Sprint-interval training (SIT) is an exercise model involving supramaximal bouts of exercise interspersed with periods of rest known for its efficiency in improving maximal oxygen uptake (VO2max ). In contrast to the commonly accepted view where central haemodynamic adaptations are considered to be the key mediators of increases in VO2max there have been propositions highlighting peripheral adaptations as the main mediators in the context of SIT-induced changes in VO2max . By combining right heart catheterization, carbon monoxide rebreathing and phlebotomy, this study shows that increases in maximal cardiac output due to the expansion of the total blood volume is a major explanatory factor for the improvement in VO2max following SIT, with a smaller contribution from improved systemic oxygen extraction. The present work not only clarifies a controversy in the field by using state-of-the-art methods, but also encourages future research to investigate regulatory mechanisms that could explain how SIT can lead to improvements in VO2max and maximal cardiac output similar to those that have previously been reported for traditional endurance exercise.

Consensus of input-constrained periodic linear multi-agent systems by fully distributed protocols

This paper studies consensus for input-constrained linear periodic multi-agent systems under undirected graphs. Assume that only the relative output information among agents is known, two types of system dynamics are considered. The first type of system dynamics satisfies that the open-loop characteristic multipliers are within the closed unit circle, while the second type is neutrally stable. With the first type of system dynamics, a novel linear protocol is proposed to solve the semi-global consensus problem. With the second type of system dynamics, another simple linear protocol is proposed to solve the global consensus problem. The most outstanding advantages of the proposed protocols are that the considered system dynamics are time-varying and the proposed protocols do not need the a priori global information of the network topology and can thus achieve consensus for arbitrary undirected communication graph.

Using linear regression metamodels for evaluating interventions in an individual-based influenza epidemic model

Agent-based simulation modeling is frequently used to model and simulate the spread of transmissible diseases such as influenza, COVID-19, and HIV/AIDS in communities. Besides incorporating disease-specific parameters, these models include a set of parameters to observe the effect of different intervention combinations on the course of an epidemic, bringing the opportunity to use these models as virtual laboratories for decision-making. However, these models are primarily large-scale and complex, increasing the runtime of experimentation. As a solution, metamodeling approaches are frequently employed to represent input–output relationships of simulation models. Instead of running the time-consuming agent-based model, policymakers use the metamodel to obtain predicted outcomes in a comparatively short time. In addition to time-saving advantages, metamodels can provide insights into how disease-specific and intervention parameters affect the outcome of interest. In this regard, this study uses an influenza epidemic model, FluTE, as the experimental platform. Instead of using the original agent-based model, we fit linear regression metamodels to quantify the effect of interventions, such as vaccination, quarantine, and school closure, on the influenza attack rate. After validating the metamodel, we observe that the day on which interventions start, ascertainment delay, the daily number of vaccinations administered, isolation and quarantine compliance probabilities, and the number of school closure days stand as the significant intervention policies.

Uncovering the spatio-temporal dynamics of crop-specific nutrient budgets in China

Nitrogen (N) and phosphorus (P) are two critical nutrients for agroecosystems. In meeting food demands, human use of both nutrients has crossed planetary boundaries for sustainability. Further, there has been a dramatic shift in their relative inputs and outputs, which may generate strong N:P imbalances. Despite enormous efforts on agronomic N and P budgets, the spatio-temporal characteristics of different crop types in using nutrients are unknown as are patterns in the stoichiometric coupling of these nutrients. Thus, we analyzed the annual crop-specific N and P budgets and their stoichiometric relations for producing ten major crops at the provincial level of China during 2004–2018. Results show that, China has generally witnessed excessive N and P input over the past 15 years, with the N balance remaining stable while the P balance increasing by more than 170%, thus resulting in a decline in the N:P mass ratios from 10.9 in 2004 to 3.8 in 2018. Crop-aggregated nutrient use efficiency (NUE) of N has increased by 10% in these years while most crops have shown a decreasing trend of this indicator for P, which reduced NUE of P from 75% to 61% during this period. At the provincial level, the nutrient fluxes of Beijing and Shanghai have obviously declined, while the nutrient fluxes of provinces such as Xinjiang and Inner Mongolia have increased significantly. Although N management has made progress, P management should be further explored in the future due to eutrophication concerns. More importantly, N and P management strategies for sustainable agriculture in China should take account of not only the absolute nutrient use, but also their stoichiometric balance for different crops in different locations.

Quantification of CO2 degassing and atmospheric dispersion at Caldeiras da Ribeira Grande (São Miguel Island, Azores)

Caldeiras da Ribeira Grande is one of the degassing areas of Fogo, a trachytic central volcano located at São Miguel Island (Azores archipelago). Recently, new steam emissions, soil CO2 and temperature anomalies developed towards the inhabited area, causing high indoor CO2 values and affecting the vegetation and several small animals that were found dead in depressions and low-ventilated zones. During July–August 2021, a soil CO2 flux survey was carried out on the north flank of the volcano, estimating a soil gas release of at least 40 t d−1 (excluding the contribution of the fumaroles) over an area of ∼0.27 km2. Two populations for the CO2 released were found, highlighting the biogenic and volcanic-hydrothermal origins. General NW-SE diffuse degassing structures (DDS) were identified, in agreement with the tectonic lineaments previously recognized in the area. In this regard, we investigated the passive gas dispersion in the atmosphere at Caldeiras da Ribeira Grande performing a model validation aimed to estimate the fumarolic gas flux at source and the potential hazard for human and animal lives posed by CO2. Numerical simulations were carried out with the DISGAS-2.3, a 3D Eulerian advection-diffusion model, and the relative outputs processed through the VIGIL-1.3 workflow able to provide probabilistic long-term CO2 concentration maps, considering a meteorological variability over the last 30 years (1991–2020) taken from the ECMWF ERA5 reanalysis dataset. A best-fit between observed and simulated CO2 concentrations allowed us to estimate the total gas flux of the area (∼209 t d−1) obtained by scaling the soil CO2 gas flux by a factor 30. Such an estimate is composed of ∼174 t d−1 as unknown fumarolic and ∼ 35 td−1 as diffuse contribution, in a good agreement with measurements.Although the present-day CO2 concentration at 0.3 m height cannot be considered to raise serious concerns for human health, we reasonably infer that the death of small animals may be due to local conditions of CO2 accumulation or to the presence of H2S. The current study highlights the relevance of coupling gas flux maps, concentration data, and gas dispersion modeling to obtain robust estimation of gas fluxes, including the fumarolic contribution, and identify zones potentially impacted by dangerous concentrations of volcanic gases, which are relevant for land-use planning and hazard assessment in case of renewed escalations of volcanic activity.

A chemometrics-based approach for the chemical prediction of lead (Pb) levels in surface soil, Dammam, Saudi Arabia

High levels of trace metals in top soil may impose serious health problems to humans and the environment. Thus, there is a need to assess the geochemical conditions of surface soils where various human activities are intensified. This study aims to evaluate contaminations associated with trace metals within Dammam, Saudi Arabia. The study also aims to hyphenate the results into a chemometrics-based approach for predicting the concentration of lead (Pb) due to its toxicity. No previous work was found on Pb prediction in top soils using chemometrics-based approach. Surface soil samples were collected from the different zones and analyzed in the laboratory for their trace metals’ constituents. According to the study’s findings, all trace metals were below international allowable metals with the highest mean concentration of Ba (309.7 mg/kg), Zn (7.9 mg/kg), and Cr (10.2 mg/kg) in industrial, agriculture and residential areas, respectively. The prediction steps involve the application of two AI-based techniques; Gaussian process regression (GPR) and Least-square (L-Boost), as well as a linear Step-Wise-Linear Regression (SWLR). The modelling was featured in two scenarios, M1 and M2, based on the input–output relationship designated according to the correlational feature extraction approach. The performance results of the models indicate that the second scenario (M2) showed higher performance skills than the first scenario (M1) in all three approaches. Overall, the performance accuracy of the models showed that the non-linear GPR-M2 showed higher performance accuracy than all the model combinations applied in the current work with 99% accuracy and an MSE of 0.012.

Q-Markov Covariance equivalent realizations for unstable and marginally stable systems

An observer-based q-Markov Covariance equivalent realization (QMC) formulation is presented in the paper. It is shown that by inserting an observer in the input–output relationship associated with the dynamical system, a QMC approach can be developed that is applicable to systems with marginally stable or unstable equilibrium points. A system of equations governing all linear state-space realizations, along with the corresponding observers, is derived from matching a set of Markov and Covariance parameters. The solution to this equation system is shown to parameterize all state-space realizations that match the pre-specified correlation functions. Four numerical examples are used to show the utility of the proposed approach. The numerical results show that the observer-based QMC presented in the paper can identify linear SISO and MIMO systems with stable, marginally stable, and unstable characteristics.