This paper explores finite-time formation control of multi-agent systems (MASs) with high-order nonaffine nonlinear dynamics and saturated input. Based on active disturbance rejection control theory, extended state observer is employed to identify unknown nonaffine nonlinear functions in MASs. The proposed control law consisting of backstepping control, tracking differentiator, and finite-time performance function is adopted for MASs to achieve the desired formation while reaching performance requirements. An auxiliary dynamic compensator is introduced to correct the control deviation caused by input saturation. Lyapunov stability theory is utilized to analyze the stability of the closed-loop system, which guarantees that the formation tracking error can asymptotically converge to an arbitrarily small neighborhood around zero in finite time. Finally, the simulation results show that compared to the adaptive, cooperative learning, and virtual structure methods, the proposed control algorithm has stronger tracking ability and faster setting time (1.8 s) under the influence of nonaffine nonlinear uncertainties. The integral square error for the formation control strategy in this paper is 0.16, which is much smaller than the abovementioned methods and is therefore provided to manifest the validity and feasibility of the proposed control strategy.

A left orderable monster is a finitely generated left orderable group all of whose fixed point-free actions on the line are proximal: the action is semiconjugate to a minimal action so that for every bounded interval I and open interval J, there is a group element that sends I into J. In his 2018 ICM address, Navas asked about the existence of left orderable monsters. By now there are several examples, all of which are finitely generated but not finitely presentable. We provide the first examples of left orderable monsters that are finitely presentable, and even of type $F_\infty $. These groups satisfy several additional properties separating them from the previous examples: they are not simple, they act minimally on the circle, and they have an infinite-dimensional space of homogeneous quasimorphisms. Our construction is flexible enough that it produces infinitely many isomorphism classes of finitely presented (and type $F_{\infty }$) left orderable monsters.

In this paper, a super-twisting disturbance observer (STDO)-based adaptive reinforcement learning control scheme is proposed for the straight air compound missile system with aerodynamic uncertainties and unmodeled dynamics. Firstly, neural network (NN)-based adaptive reinforcement learning control scheme with actor-critic design is investigated to deal with the tracking problems for the straight gas compound system. The actor NN and the critic NN are utilised to cope with the unmodeled dynamics and approximate the cost function that are related to control input and tracking error, respectively. In other words, the actor NN is used to perform the tracking control behaviours, and the critic NN aims to evaluate the tracking performance and give feedback to actor NN. Moreover, with the aid of the STDO disturbance observer, the problem of the control signal fluctuation caused by the mismatched disturbance can be solved well. Based on the proposed adaptive law and the Lyapunov direct method, the eventually consistent boundedness of the straight gas compound system is proved. Finally, numerical simulations are carried out to demonstrate the feasibility and superiority of the proposed reinforcement learning-based STDO control algorithm.

Recently, Indrek Reiland proposed a new version of the act-type theory of propositions (ATT) in which predication is still committal. However, the Frege-Geach problem can be addressed without resorting to Peter Hanks's cancellation manoeuvre. In this article, I argue that if we take predication as a committal act, we will then have to tackle another problem: non-committal representational acts. I argue that Reiland still needs a notion of cancellation to deal with the latter problem. On this account, he cannot avoid the major flaw he attributes to Hanks's version.

Air route networks can no longer meet operational efficiency requirements because of the rapid growth of complex traffic flows. Machine learning is employed to investigate the evolutionary mechanism of congestion in such networks in view of their high complexity and high density, and a reasonable network optimisation scheme is presented. First, deviations between nominal and actual routes are investigated with reference to radar track data, and a network reflecting actual route operations is constructed using adversarial neural networks. Second, flight time is used to characterise congestion in route networks. Actual network operations are considered, and congestion is defined from the perspective of road traffic engineering. The effects of the operational properties of traffic flows on flight times are analysed to establish various congestion indicators. A gradient boosting model is used to select indicator characteristics and analyse patterns in the variations of indicator values for each flight segment in distinct periods. The indicator–time relationship is leveraged to explore the evolutionary mechanism of congestion in the route network. Third, on the basis of this mechanism, a multiobjective optimisation model of congestion is formulated, and a particle swarm optimisation algorithm is executed to adjust the route passage structure, thereby solving the optimisation model. Finally, calculation validation is conducted using radar track data from the control sector of the Yunnan region. The average flight time in a route segment is 10% shorter in the optimised route network than in the nonoptimised route network, which confirms that the optimisation solution is practicable.

Since the birth of the first baby by in vitro fertilization in 1978, more than 9 million children have been born worldwide using medically assisted reproductive treatments. Fertilization naturally takes place in the maternal oviduct where unique physiological conditions enable the early healthy development of the embryo. During this dynamic period of early development major waves of epigenetic reprogramming, crucial for the normal fate of the embryo, take place. Increasingly, over the past 20 years concerns relating to the increased incidence of epigenetic anomalies in general, and genomic-imprinting disorders in particular, have been raised following assisted reproduction technology (ART) treatments. Epigenetic reprogramming is particularly susceptible to environmental conditions during the periconceptional period and non-physiological conditions such as ovarian stimulation, in vitro fertilization and embryo culture, as well as cryopreservation procedure, might have the potential to independently or collectively contribute to epigenetic dysregulation. Therefore, this narrative review offers a critical reappraisal of the evidence relating to the association between embryo cryopreservation and potential epigenetic regulation and the consequences on gene expression together with long-term consequences for offspring health and wellbeing. Current literature suggests that epigenetic and transcriptomic profiles are sensitive to the stress induced by vitrification, in terms of osmotic shock, temperature and pH changes, and toxicity of cryoprotectants, it is therefore, critical to have a more comprehensive understanding and recognition of potential unanticipated iatrogenic-induced perturbations of epigenetic modifications that may or may not be a consequence of vitrification.

This synthetic commentary offers a handful of observations. First, it highlights structural differences between the advanced market/capitalist economy that forms the theoretical scaffolding of Keynesian theory, as against the diverse range of structural and institutional configurations that characterise contemporary developing economies. Second, bearing these distinctions in mind, how far does the notion of “full employment” hold relevance in the context of developing economies? Third, the focus shifts to the central Keynesian policy prescription: reflating economic activity through injecting additional demand into the system, in extremis through pump-priming, digging and filling trenches – would this perform in a poor agrarian economy? Would the multiplier work and deliver in the realities of developing economies? Fourth, the central policy agenda in the South was that of launching industrialisation, leading to a sustained structural transformation of the economy – a la the Kaldorian industrialisation paradigm, which is scarcely visible in the (post-)Keynesianism template. Fifth, it queries the investment function and the role of state investment, and/or of “animal spirits” of capitalist entrepreneurs and agents, whether of domestic or foreign origins. Sixth, there is need to widen the focus, as well argued by Hans Singer, Amiya Bagchi and others, from Keynes-I of The General Theory, to Keynes-II of Bretton Woods, thereby substantially widening the interface with the agenda of development. Finally, there is the inevitable question concerning the nature and role of the state in the contrasting developed-vs-developing-economy, then-and-now scenarios. This discursive commentary is largely a Cambridge dialogue, not inappropriate in a tribute to Geoff Harcourt.