Search

In this brief, stabilization of Boolean networks (BNs) by flipping a subset of nodes is considered, here we call such action state-flipped control. The state-flipped control implies that the logical variables of certain nodes are flipped from 1 to 0 or 0 to 1 as time flows. Under state-flipped control on certain nodes, a state-flipped-transition matrix is defined to describe the impact on the state transition space. Weak stabilization is first defined and then some criteria are presented to judge the same. An algorithm is proposed to find a stabilizing kernel such that BNs can achieve weak stabilization to the desired state with in-degree more than 0. By defining a reachable set, another approach is proposed to verify weak stabilization, and an algorithm is given to obtain a flip sequence steering an initial state to a given target state. Subsequently, the issue of finding flip sequences to steer BNs from weak stabilization to global stabilization is addressed. In addition, a model-free reinforcement algorithm, namely the Q-learning (QL) algorithm, is developed to find flip sequences to achieve global stabilization. Finally, several numerical examples are given to illustrate the obtained theoretical results.

This article aims to stabilize probabilistic Boolean networks (PBNs) via a novel pinning control strategy. In a PBN, the state evolution of each gene switches among a collection of candidate Boolean functions with preassigned probability distributions, which govern the activation frequency of each Boolean function. Due to the existence of stochasticity, the mode-independent pinning controller might be disabled. Thus, both mode-independent and mode-dependent pinning controller are required here. Moreover, a criterion is derived to determine whether mode-independent controllers are applicable while the pinned nodes are given. It is worth pointing out that this pinning control is based on the nx n network structure rather than 2ⁿ x 2ⁿ state transition matrix. Therefore, compared with the existing results, this pinning control strategy is more practicable and has the ability to handle large-scale networks, especially sparsely connected networks. To demonstrate the effectiveness of the designed control scheme, a PBN that describes the mammalian cell-cycle encountering a mutated phenotype is discussed as a simulation.

Prior research has demonstrated that angry participants exhibit biased threat detection whereby they are more likely to misidentify neutral objects as guns. Yet, it is unclear whether independent components of anger, such as conceptual knowledge about anger or the affective features of an anger instance, could lead to altered bias alone. Consistent with constructionist theories of emotion, the present set of two experiments demonstrates that threat-detection bias only differs significantly between participants in an emergent-anger condition, who had engaged both components of anger (i.e., conceptual knowledge of anger and negative, high arousal affect), and participants in a control condition, who had engaged neither. Study 2 demonstrates that this pattern of findings also extends to another threat-relevant emotional state (i.e., fear). Implications for studying anger and fear, and emotions more generally, as constructed mental experiences are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

This paper investigates observability of Boolean control networks(BCNs) and probabilistic Boolean networks(PBNs). First, weak observability of BCNs is discussed via the non-augmented approach. The obtained result is then applied to determine (asymptotic) observability of PBNs. Finally, complexity of algorithms based on new criteria is analyzed. Compared with existing ones, time and space complexity do not get worse, even are improved under some mild conditions.

In this article, we study the finite-time stabilization and the asymptotic stabilization with probability one of Markovian jump Boolean control networks (MJBCNs) by sampled-data state feedback controls (SDSFCs). Based on the semi-tensor product (STP), we introduce an augmented variable multiplied by the vector form of the switching signal and the state of MJBCN. We find that under SDSFC, the sequence of the states of the augmented variable at sampling instants satisfies the Markov property. Based on the convergences of the switching signal and the augmented variable, we obtain the sufficient and necessary criteria for the finite-time stabilization and the asymptotic stabilization of MJBCNs by SDSFCs, respectively. Moreover, for the two kinds of stabilization, the feedback matrices of SDSFCs are constructed, respectively. Finally, the obtained results are applied to an apoptosis network and a model of the lactose operon in the Escherichia Coli.

This paper investigates the event-triggered control problem for switched affine systems. The presence of affine terms brings many difficulties on the exclusion of triggering Zeno behavior when ensuring asymptotic stability. We propose an event-triggered control strategy dynamically updated with triggering to solve this problem, in which the triggering condition is associated with the affine terms and given with a time-varying term that is updated at triggering instants. Based on the triggering strategy, the controllers for subsystems and a switching rule are designed to achieve asymptotic stability of the resulting closed-loop system. Moreover, due to the special feature of event-triggered switched affine systems, a new method for proving the exclusion of Zeno behavior is given. Finally, the developed results are illustrated by an electric circuit example.

Synthetic control methods are a popular tool for measuring the effects of policy interventions on a single treated unit. In practice, researchers create a counterfactual using a linear combination of untreated units that closely mimic the treated unit. Often times, creating a synthetic control is not possible due to untreated units' dynamic characteristics such as integrated processes or a time varying relationship. These are cases in which viewing the counterfactual estimation problem as a cross sectional one fails. In this paper, I propose a new approach to estimate the synthetic control counterfactual by incorporating time varying parameters. This is done using a state space framework and Bayesian shrinkage. The dynamics allow for a closer pre-treatment fit leading to a more accurate counterfactual estimate. Monte Carlo simulations are performed to investigate the usefulness of the proposed model in a synthetic control setting. I then compare the proposed model to two existing approaches in classic synthetic control case studies. Results suggest the proposed model produces lower mean squared forecast error when dynamic relationships are present and better coverage compared to the existing model. In addition, the model performs similar to existing approaches when no dynamics are present.

Adaptive behavior is supported by context-dependent cognitive control that enables stable and flexible sensorimotor transformations. Impairments in this type of control are often attributed to dysfunction in the prefrontal cortex (PFC). However, the underlying circuit principles of PFC function that support cognitive control have remained elusive. While the complex, diverse responses of PFC neurons to cognitive variables have been studied both from the perspective of individual cell activity and overall population dynamics, these two levels have often been investigated separately. This review discusses two specific cell groups, context/brain state responsive interneuron subtypes and output decoder neurons, that might bridge conceptual frameworks derived from these two research approaches. I highlight the unique properties and functions of these cell groups and discuss how future studies leveraging their features are likely to provide a new understanding of PFC dynamics combining single-neuron and network perspectives.

It is well known that the conventional fractional-order neural networks (FONNs) cannot generate nonconstant periodic oscillation. For this point, this article discusses a class of impulsive FONNs with piecewise Caputo derivatives (IPFONNs). By using the differential inclusion theory, the existence of the Filippov solutions for a discontinuous IPFONNs is investigated. Furthermore, some decision theorems are established for the existence and uniqueness of the (periodic) solution, global exponential stability, and impulsive control global stabilization to IPFONNs. This article achieves four key issues that were not solved in the previously existing literature: 1) the existence of at least one Filippov solution in a discontinuous IPFONN; 2) the existence and uniqueness of periodic oscillation in a nonautonomous IPFONN; 3) global exponential stability of IPFONNs; and 4) impulsive control global Mittag-Leffler stabilization for FONNs.

The proportional-integral (PI) control method is long well-known because of its simplicity as well as effectiveness. Although the PI control method overwhelmingly dominates engineering applications, the issue of developing a general PI control for nonlinear systems remains open. This paper considers the PI control problem for a family of unknown nonlinear systems. A structurally simple and computationally inexpensive PI-type control approach with constant gains is provided to solve the problem, which maintains pre-assigned transient as well as steady-state performance and guarantees the global stability of closed-loop systems. The key to possessing such properties comes down to the transformation and modification of tracking errors. The effectiveness of the presented control approach is assessed via the experiment on a pneumatic artificial muscle, and the results corroborate the theoretical findings.

It has been shown that self-triggered control has the ability to reduce computational loads and deal with the cases with constrained resources by properly setting up the rules for updating the system control when necessary. In this paper, self-triggered stabilization of Boolean control networks (BCNs), including deterministic BCNs, probabilistic BCNs and Markovian switching BCNs, is first investigated via semi-tensor product of matrices and Lyapunov theory of Boolean networks. The self-triggered mechanism with the aim to determine when the controller should be updated is given based on the decrease of the corresponding Lyapunov functions between two successive sampling times. We show that the self-triggered controllers can be chosen as the conventional controllers without sampling, and also can be optimally constructed based on the triggering conditions.

This article is devoted to an adaptive tracking control problem for nonlinear systems with input deadzone and saturation, whose virtual control coefficients include the known and unknown terms. A novel smooth function is first introduced to approximate the input nonlinearities. By utilizing an auxiliary variable and the Nussbaum gain technique, an improved real control signal is constructed to handle the uncertainties of the virtual control coefficients and input nonlinearities. Furthermore, an adaptive tracking controller is constructed and applied to the attitude control of a quadrotor, which guarantees the boundedness of all the signals in the resulting closed-loop system. Finally, both stability analysis and simulation results validate the effectiveness of the developed control strategy.

In this article I raise a new problem for quantum mechanics, which I call the control problem. Like the measurement problem, the control problem places a fundamental constraint on quantum t...

The control of underactuated Euler–Lagrange systems with uncertain and switched parameters is an important problem whose solution has many applications. The problem is challenging as standard adaptive control techniques do not extend to this class of systems due to structural constraints that lead to parameterization difficulties. This note proposes an adaptive switched control framework that handles the uncertainty and switched dynamics without imposing structural constraints. A case study inspired by autonomous vessel operations is used to show the effectiveness of the proposed approach.

The fixed-time control problem for a class of interconnected systems is studied in this article via an asynchronous event-triggered control strategy, in which all subsystems are interconnected by discontinuous interactions. Especially, due to the discontinuity of interactions, the existence of solutions for the concerned systems is solved via the framework of differential inclusion. Unlike the common ones, the asynchronous event-triggered mechanism in this article generates samplings, triggering events, and control updates asynchronously among multiple subsystems. More significantly, due to the discontinuity of the interactions, a fixed-time discontinuous control law and a specific event-triggered scheme are designed to stabilize this class of interconnected systems in a fixed time. In addition, the interexecution time is lower bounded by a positive constant, and it is assured that the Zeno behavior will not happen. Finally, the effectiveness of the obtained results is illustrated by an example.

In this paper, the problem of array pattern synthesis is considered by developing two array response control algorithms. The devised two algorithms build on the basis of the accurate array response control (A2RC) approach, and both of them have closed-form solutions and low computational complexities. More specifically, the first proposed algorithm realizes multi-point array response control from an any given weight vector, and the second algorithm modifies the first one by avoiding the possible beam axis shift. The proposed two algorithms are able to control array responses accurately under the conditions of independence on steering vectors. By applying the first and the second array response control algorithm, two effective pattern synthesis methods for focused patterns and shaped patterns are devised, respectively. Representative examples are presented to demonstrate the performance of the proposed algorithms in array response control and pattern synthesis.

Mandarin Chinese is the high language in Taiwan; Taiwanese is the low. Taiwanese does not have the standardized written form. Many laymen even say that Taiwanese does not have the written form. English is the most widely learned foreign language in Taiwan, and Japanese is the second widely learned. Besides the fact that Japanese is the second widely learned foreign language, it was the 'national' language from 1895 to 1945 when Taiwan was under the control of Japan; therefore, the Taiwanese born before 1940 or so could speak either fluent Japanese or a little Japanese. In other words, people over 50 years old may be able to speak Japanese. In modern Taiwan, not only the mixing use of Mandarin and Taiwanese is very prevalent, but the mixing use of Mandarin and English and that of Mandarin and Japanese are also very popular. This phenomenon does not only exist in the educational domain (Liao 1992), but it also exists in the videotape shows made by the most profitable actor Zhu Ge-liang, who earns more than two million US dollars per year. Zhu's videotapes are mainly to entertain the working class in Taiwan. They are often shown on the deluxe tour bus; and intellectuals or university students often consider them as entertainment for the lower classes. It has been a tashion for the past two or three years that there is heavy code-mixing of Mandarin and Taiwanese, Mandarin and English, and sometimes, there is code-mixing of Mandarin and Japanese on TV programs depicting modern lif-e. On these programs, the major language used is still Mandarin. Recently on a TV program depicting modern life, we heard one actor say

Cultural differences have been shown to be social phenomena, arising in a complex reciprocal relationship between social actors and historical context. National character descriptions have also been shown to do ideological work. Language plays a crucial role in the construction of perceived reality, including perceived differences, and in the support of power structures. This study uses critical language analysis to uncover ideological frameworks behind cultural descriptions Americans have constructed of Russians. First, I will argue that American images of Russians became reified during the Cold War forming crucial building blocks in the ideological war between communism and anti-communism. I will show that linguistic strategies known to be used to gain symbolic control over the Other shaped these descriptions. I will then turn to the post-Cold War era and examine whether the change in ideological climate is reflected in current descriptions. The analysis shows the old descriptions and their familiar vocabulary to persist. Underlying reasons for the continued acceptance of the old descriptions are explored.

Fifty-nine Japanese college students of English at two different proficiency levels were evaluated for their ability to produce a speech act of request in a spoken role play task. Learners’ production was analyzed quantitatively by rating performance on a six-point scale for overall appropriateness, as well as qualitatively by identifying the directness levels of the linguistic expressions used to produce requests. Results revealed a significant L2 proficiency influence on overall appropriateness, but only a marginal difference in the types of linguistic expressions used between the two proficiency groups. Moreover, grammatical and discourse control encoded in the rating scale seemed to have affected the quality of speech acts.

Because of its paramount importance in the successful industrial control strategy of a given heat exchanger network (HEN), the control structure designs for providing appropriate manipulated variable (MV) and controlled variable (CV) pairings have received considerable attention. However, quite frequently HENs with such control structures face the problem of hard constraints, typically holding the HENs at less controlled operating space. So both the MV pairings and the above control pairings should be considered to design a control structure. This paper investigates the systematic incorporation of the two pairings, and presents a methodology for designing such two-tier control structure. This is developed based on the sequential strategy, coupling an indirect-tier with direct-tier control structure design, wherein the intention is realized in the former stage and the latter is implemented for further optimization. The MV identification and pairing are achieved through variations in heat load of heat exchangers to design the indirect-tier control structure. Then the direct-tier control structure is followed the relative gain array (RGA) pairing rules. With the proposed methodology, on the one hand, it generates an explicit connection between the MV pairings and the HEN configuration, and the quantitative interaction measure is improved to avoid the multiple solutions to break the relationship among all the control pairings into individuals; on the other hand, a two-tier control structure reveals control potentials and control system design requirements, this may avoid complex and economically unfavourable control and HEN structures. The application of proposed framework is illustrated with two cases involving the dynamic simulation analysis, the quantitative assessment and the random test.

This article is concerned with an issue of fixed time adaptive neural control for a class of uncertain nonlinear systems subject to hysteresis input and immeasurable states. The state observer and neural networks (NNs) are used to estimate the immeasurable states and approximate the unknown nonlinearities, respectively. On this foundation, an adaptive fixed time neural control strategy is developed. Technically, this control strategy is based on a novel fixed-time stability criterion. Different from the research on fixed-time control in the conventional literature, this article designs a new controller with two fractional exponential powers. In the light of the established stability criterion, the fixed-time stability of the systems is guaranteed under the proposed control scheme. Finally, a simulation study is carried out to test the performance of the developed control strategy.

In the analysis and design of a network system, one is often faced with the situation where the network structure is known but the node dynamics is almost unknown. Thus structural analysis problems, in which the prior knowledge is almost about the network structure, have received attention in science and engineering fields. Along this line of research, this paper addresses a structural design problem, that is, to find a sign pattern of the control input such that any constant input with the sign pattern drives the system to a desirable steady state in a qualitative sense. A solution is presented by reducing the problem into a design problem of the so-called sign-solvable equation, which is a linear equation whose qualitative solution can be determined from the prior knowledge of the sign pattern of the coefficients. Our framework is demonstrated by applying to a control problem of the biological network of apoptosis.

Background: Previous researches state vision as vital source of information for movement control and more precisely for accurate hand movement. Further, fine bimanual motor activity may be associated to various oscillatory activities within distinct brain areas and inter-hemispheric interactions. However, neural coordination among the distinct brain areas responsible to enhance motor accuracy is still not adequate.Methods: In the current study we investigated task-dependent modulation by simultaneously measuring high time resolution electroencephalogram (EEG), electromyogram (EMG) and force along with bi-maual and uni-manual motor tasks. The errors were controlled using visual feedback. In order to complete the uni-manual tasks, subject was asked to grip the strain gauge using index finger and thumb of right hand thereby exerting force on connected visual feedback system. Whereas the bi-manual task involved finger abduction of left index finger in two contractions along with visual feedback system and at the same time the right hand gripped using definite force on two conditions that whether visual feedback existed or not for right hand.Results: Primarily, the existence of visual feedback for right hand significantly decreased brain network global and local efficiency in theta and alpha band when compared with the elimination of visual feedback.Conclusions: Brain network activity in theta and alpha band coordinate to facilitate fine hand movement. The findings may provide a new neurological insight on virtual reality auxiliary equipment and subjects with neurological disorders that cause movement errors requiring accuracy motor training.

The musculoskeletal robot is a promising direction of the next-generation robots. However, current control methods of musculoskeletal robots lack multi-tasks learning ability, great generalization, and biological plausibility. In this article, a motor-cortex-like recurrent neural network (RNN) and a reward modulated multi-tasks learning method are proposed. First, inspired by dynamic system hypothesis of motor cortex, the RNN is introduced to transform movement targets into muscle excitations. The condition that makes a RNN generate motor-cortex-like consistent population response is investigated. Second, a reward modulated multi-tasks learning method of such a RNN is proposed. In the experiments, the control of a musculoskeletal system is realized with multi-tasks learning ability, great generalization, and robustness for noises. Furthermore, the RNN and muscle excitations demonstrate motorcortex-like consistent population response and human-like muscle synergies respectively. Therefore, the proposed method has better performance and biological plausibility, and verifies the neural mechanisms in the robotic research.

This paper discusses the exponential stability in mean square of Takagi-Sugeno fuzzy systems (T-SFSs) under the stochastic case. Moreover, stochastic factors are taken into account to make the model more general. Different from traditional time-triggered control, we introduce event-triggered control strategy into intermittent control and then intermittent dynamic event-triggered control (IDE-TC) is developed, which can reduce updates of the controller and save resources. Besides, we eliminate Zeno phenomena, which is independent of mathematical expectation. Furthermore, the minimum interexecution time by the IDE-TC can be obtained directly for T-SFSs under the stochastic case. In addition, in order to illustrate the theoretical results, an application about double-link robot arm model is given. Meanwhile, we exhibit some numerical simulations.

We consider a gradient-like system for real-time implementation of multivariable algebraic loops arising in input-constrained control problems. Using results from mathematical programming, we establish global asymptotic convergence under a less stringent condition as compared to existing techniques. We comment on the application of the gradient-like system in anti-windup control implementation where the closed loop can also be interpreted within the framework of singularly perturbed systems. The proposed system can easily be realized for practical circuit implementation and may be prototyped using fast analog processors.

Vibrational control seeks to stabilize an unstable system by judiciously injecting a state-free high-frequency dither. This paper presents some robustness properties of vibrational control with respect to additive disturbances in a nonlinear system. We assume that, without disturbances, the appropriately averaged system is regionally asymptotically stable. Using perturbation techniques, our first result shows that the stabilization realised through vibrational control is robust with respect to additive disturbances of sufficiently small amplitude. Indeed, the perturbed system of vibrational control has a robustness feature similar to input-to-state stability in a local region. In the case of periodic disturbances, our second result indicates that vibrational control naturally dampens disturbances of sufficiently high frequency, which allows for relatively high amplitude disturbances. A tight bound for the effect of such periodic disturbances on the ultimate deviation of states from the desired equilibrium is presented. Simulation results from a planar manipulator support the theoretic analysis.

This study is conducted to determine the effectiveness of teaching grammar-in-context to minimize students‘ grammatical errors in writing. The design of the study was a quasi-experimental with a non-randomized pretest-posttest control group. The samples of the study were taken from the population of the tenth-grade students. The control group was taught by conventional grammar which was separately given with writing skills. Likewise, the experimental one was treated by teaching grammar-in-context. The results of the study showed that the mean score in the post-test was higher than that in the pretest; and the mean score of experimental group increased 16.20 point after the treatment. This result indicated that teaching grammarin- context is considered to be effective in minimizing students‘ grammatical errors in writing. Keywords: grammar-in-context, grammatical errors