Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this ...paper, we theorize a new notion—the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply–demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.
Notwithstanding the many methodological advances made in the field of psychotherapy research, at present a metatheoretical, school-independent framework to explain psychotherapy change processes ...taking into account their dynamic and complex nature is still lacking. Over the last years, several authors have suggested that a dynamic systems (DS) approach might provide such a framework. In the present paper, we review the main characteristics of a DS approach to psychotherapy. After an overview of the general principles of the DS approach, we describe the extent to which psychotherapy can be considered as a self-organizing open complex system, whose developmental change processes are described in terms of a dialectic dynamics between stability and change over time. Empirical evidence in support of this conceptualization is provided and discussed. Finally, we propose a research design strategy for the empirical investigation of psychotherapy from a DS approach, together with a research case example. We conclude that a DS approach may provide a metatheoretical, school-independent framework allowing us to constructively rethink and enhance the way we conceptualize and empirically investigate psychotherapy.
In this paper, we introduce a new division of fuzzy vectors depending on a determinant algorithm and develop a theory of almost periodic fuzzy multidimensional dynamic systems on time scales. ...Moreover, several applications are provided. In particular, a new type of fuzzy dynamic systems called fuzzy q-dynamic systems (i.e., fuzzy quantum dynamic systems) is proposed and studied. Our results are not only effective on classical fuzzy dynamic systems including their continuous and discrete situations (i.e., T=Z or R) but are also valid for other fuzzy multidimensional dynamic systems on various hybrid domains like qZ¯,±N12, ∪k=1+∞2k,2k+1 etc.
•A review of fractional calculus applications to the real world problems from science and engineering fields.•The real world applications of fractional calculus in different science and engineering ...fields are presented.•Fractional calculus provides better description for analyzing the dynamics of complex systems.
Fractional calculus is at this stage an arena where many models are still to be introduced, discussed and applied to real world applications in many branches of science and engineering where nonlocality plays a crucial role. Although researchers have already reported many excellent results in several seminal monographs and review articles, there are still a large number of non-local phenomena unexplored and waiting to be discovered. Therefore, year by year, we can discover new aspects of the fractional modeling and applications. This review article aims to present some short summaries written by distinguished researchers in the field of fractional calculus. We believe this incomplete, but important, information will guide young researchers and help newcomers to see some of the main real-world applications and gain an understanding of this powerful mathematical tool. We expect this collection will also benefit our community.
•A predictive COVID-19 model is considered.•The second wave of COVID-19 in Iran is studied.•Some predictive results of the peak epidemic outbreak are given.•Estimated times of the end of the epidemic ...in Iran in several scenarios are approximated in the plots.•The second wave of COVID-19 is predicated to happen between August and December 2020.
One of the common misconceptions about COVID-19 disease is to assume that we will not see a recurrence after the first wave of the disease has subsided. This completely wrong perception causes people to disregard the necessary protocols and engage in some misbehavior, such as routine socializing or holiday travel. These conditions will put double pressure on the medical staff and endanger the lives of many people around the world. In this research, we are interested in analyzing the existing data to predict the number of infected people in the second wave of out-breaking COVID-19 in Iran. For this purpose, a model is proposed. The mathematical analysis corresponded to the model is also included in this paper. Based on proposed numerical simulations, several scenarios of progress of COVID-19 corresponding to the second wave of the disease in the coming months, will be discussed. We predict that the second wave of will be most severe than the first one. From the results, improving the recovery rate of people with weak immune systems via appropriate medical incentives is resulted as one of the most effective prescriptions to prevent the widespread unbridled outbreak of the second wave of COVID-19.
Synchronization is defined as interdependencies among coupled dynamic systems. In most coupled systems the intrinsic and internal variants, and the interdependencies among their subsystems are not ...accessible. Therefore, in order to quantify the interdependencies among the coupled systems, attempts have been made through measuring the synchronization between their outputs represented mostly as time series. In this paper a new method, called Visibility Graph Similarity (VGS), is presented as a method of measuring Generalized Synchronization. First, each time series is reconstructed as a trajectory in a state space. Next, a Distance Time Series (DTS) is created from a sequence of relative distances of the states to a reference state. Subsequently, a visibility graph (VG) is constructed using DTS. Then, a sequence of degrees of the VG, called Degree Sequence (DS), is obtained. Correlation of the DSs of two coupled systems is called VGS and is presented as a measurement of similarity of dynamics of the coupled systems. The synchronization measurement performance of the VGS is compared with synchronization likelihood (SL) and the classical cross correlation method using two identical and non-identical models of two coupled Henon map over the entire time domain. Also, it is compared with SL for tracing temporal synchronization using both models. It is shown that VGS provides a more accurate measure of the overall synchronization compared with SL. It is more reliable for measuring weak couplings compared with the cross correlation method. Moreover, VGS uses fewer parameters and detects the temporal synchronization sooner than the SL.
► A method, Visibility Graph Similarity, for measuring Generalized Synchronization. ► Performance of the VGS is compared with synchronization likelihood (SL). ► Comparison is made using two coupled Henon map systems. ► VGS provides a more accurate measure of the overall synchronization. ► VGS uses fewer parameters and detects the temporal synchronization sooner than SL.
•A robust data-driven model predictive control algorithm is presented.•Construction of a probabilistic state space model using Gaussian processes.•Back-offs are computed offline using closed-loop ...Monte Carlo simulations.•Independence of samples allows probabilistic guarantees to be derived.•Explicit consideration of online learning and state dependency of the uncertainty.
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Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) present a powerful tool to identify the required plant model and quantify the residual uncertainty of the plant-model mismatch. It is crucial to consider this uncertainty, since it may lead to worse control performance and constraint violations. In this paper we propose a new method to design a GP-based NMPC algorithm for finite horizon control problems. The method generates Monte Carlo samples of the GP offline for constraint tightening using back-offs. The tightened constraints then guarantee the satisfaction of chance constraints online. Advantages of our proposed approach over existing methods include fast online evaluation, consideration of closed-loop behaviour, and the possibility to alleviate conservativeness by considering both online learning and state dependency of the uncertainty. The algorithm is verified on a challenging semi-batch bioprocess case study.
The repeated co‐occurrence of cold spells over Eastern North America and wet or windy extremes over Western Continental Europe during recent winters, has led to hypothesize a link between the two. ...Here, we analyze the interplay between the large‐scale atmospheric circulation and co‐occurring cold spells in North America and wet or windy extremes in Europe. We collectively term these occurrences compound cold–wet–windy extremes. We leverage a recent approach grounded in dynamical systems theory, which provides an analytically and computationally efficient analysis of spatially resolved, multivariate climate extremes. We find that there are specific, recurrent large‐scale atmospheric circulation patterns systematically associated with both the individual extremes and co‐occurring cold–wet–windy anomalies. Evidence for this is also found when focusing on compound cold–wet–windy extremes, although with a weaker signal. This motivates further analyses focusing specifically on the statistics and drivers of these compound extreme occurrences.
Plain Language Summary
In recent winters, very cold weather over the eastern part of North America and stormy weather or heavy rainfall in Europe have often made the news. One may think that these events are independent, since they occur several thousands of kilometres apart. However, researchers have hypothesized that there may be weather patterns that connect these different episodes. Here we test this idea. We find that there is indeed a connection between unusually cold weather in Eastern North America and unusually stormy weather and heavy rainfall in Western Continental Europe. We also find a link, albeit weaker, when focusing specifically on extreme events—namely only the coldest of the cold spells, the windiest of the stormy days and the wettest of the heavy rainfall days. The strongest connection, however, emerges when looking at unusual but not extreme weather episodes.
Key Points
North American cold spells and European wet or windy extremes are very strongly coupled to recurrent large‐scale atmospheric patterns
The compound occurrence of North American cold spells and European wet or windy extremes is associated with common atmospheric patterns
Extremes show weaker evidence of common atmospheric patterns than all temperature, precipitation and wind anomalies
This brief is concerned with the formation control problem of a multi-agent system composed of multiple quadrotors tasked to achieve aggressive trajectory tracking with prescribed formation patterns. ...An underactuated model with six degrees of freedom is considered for each quadrotor, the dynamics of which account for nonlinearities, parameter uncertainties, and external disturbances. A robust control approach is proposed that stems from linear quadratic regulation and robust compensation theory fundamentals. Theoretical analysis and simulation results validate the effectiveness of the presented theoretical framework.
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