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The paper aims to investigate what is the best ontological framework of anticipatory systems. Its aim is to argue the thesis that the ontology on which anticipatory systems are based should be a dynamic one: a kind of process ontology. It seeks to include a demonstration of the fruitfulness of such an ontological framework for the investigation of anticipatory systems.

The methodology of the paper is a process ontological one. The objectives are achieved by a comparative analysis of the static and dynamic approaches to the ontological framework.

A process ontological framework is a reliable basis for the substantiation of the thesis that there is no great gap between living and non‐living systems as far as anticipation is concerned.

An example is represented of an anticipatory non‐living system that is artificially created and is programmed as a self‐control system. In this respect the paper has some practical implications.

A new approach is suggested to the investigation of anticipatory systems. It could be of interest not only for philosophers, but also for scientists who work on ontology as technology.

The purpose of this paper is to give broad‐spectrum analysis of general system theory, including the researching approach – dynamic structure approach, the study on general object system by broad‐spectrum analysis, and the study of general cognitive system by broad‐spectrum analysis, etc.

The theoretical basis of broad‐spectrum analysis is dynamic structuralism, which, therefore, leads to the fact that its methodology is dynamic structural methodology, including that from the perspective of generalized structure, that from the dynamic perspective (the fluxional viewpoint of structure and the transforming viewpoint of structure).

Generalized structural models of general object system (including general thing system and general dynamic system, general cognitive system and general value system) are given in this paper. It shows a unique perspective of broad‐spectrum analysis, its close relationship with general system theory and the viewpoints of many questions in general system.

The details of the structural models in this paper have to be worked out.

The methodological principle for us to analyze, study, optimize, and control the general system is provided.

Generalized structural models of this paper originate from pansystem methodology and discrete mathematics. This paper has the value of methodology for those who are studying general systems.

While the dynamic capabilities perspective is the most cited strategic theory in the information systems field of research, little effort has been made to review and integrate the associate literature of this perspective in the field. Accordingly, this paper aims to systematically analyze the information systems literature on dynamic capabilities and provide a holistic understanding of the topical composition and trend of dynamic capabilities studies in information systems research.

Using latent Dirichlet allocation as the text analysis algorithm, the author conducted a topic modeling of the dynamic capabilities corpus in the information systems field of research to quantitatively review, summarize and classify the prior literature. The review covered 191 articles published on dynamic capabilities between 1998 and 2018 in pioneering information systems journals and conference proceedings.

In accordance with the topic modeling results, the topical composition of the dynamic capabilities corpus in information systems research dominantly includes seven themes titled T1. Information systems value, T2. Information systems change, T3. Digitalization, T4. Information systems agility, T5. Big data, T6. Information systems innovation and T7. Information systems alignment. Also, the overall and topical trend of dynamic capabilities studies in the information systems field of research were revealed. The trends indicated that the investigated domain and its prominent sub-domains have generally had positive productivity over the past years.

The current study contributes to the domain by developing knowledge and improving literature on dynamic capabilities in information systems research, discovering the main topics of interest for information systems researchers to deploying the dynamic capabilities perspective in their studies, and prioritizing the future information systems research on dynamic capabilities based on the identified trends of topics.

In this paper, we present a brief study on various paradigms to tackle complexity or in other words manage uncertainty in the context of understanding science, society and nature. Fuzzy real numbers, fuzzy logic, possibility theory, probability theory, Dempster‐Shafer theory, artificial neural nets, neuro‐fuzzy, fractals and multifractals, etc. are some of the paradigms to help us to understand complex systems. We present a very detailed discussion on the mathematical theory of fuzzy dynamical system (FDS), which is the most fundamental theory from the point of view of evolution of any fuzzy system. We have made considerable extension of FDS in this paper, which has great practical value in studying some of the very complex systems in society and nature. The theories of fuzzy controllers, fuzzy pattern recognition and fuzzy computer vision are but some of the most prominent subclasses of FDS. We enunciate the concept of fuzzy differential inclusion (not equation) and fuzzy attractor. We attempt to present this theoretical framework to give an interpretation of cyclogenesis in atmospheric cybernetics as a case study. We also have presented a Dempster‐Shafer's evidence theoretic analysis and a classical probability theoretic analysis (from general system theoretic outlook) of carcinogenesis as other interesting case studies of bio‐cybernetics.

Thinking and research in respect of accounting and finance over the past three decades have been dominated by a methodology that is primarily based on the predictability of accounting data and its relationship to certain phenomena. The magnitude of change in business makes the future unpredictable. Analysts and managers are confronting an entirely new business environment in which traditional approaches are no longer valid. A systems approach provides a new way of looking at financial analysis. The purpose of this study is to focus on the present, on the ability to cope and the capacity to change in a changing environment. The ability to create an own future is being seen as more important than the art of predicting the future. This paper describes an empirically tested dynamic balance model to establish whether entities are able to adapt, survive and prosper.

The purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.

Applications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland^® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.

This paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.

The learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.

The models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.

The method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.

A production‐inventory system based on a model proposed by Axsäter is examined with the purpose of understanding the dynamic properties of the model.

The information flow concept is discussed and a dynamic analysis using a system simplification approach is carried out to achieve an understanding of the dynamic behaviour of the system. Finally, the information flow is examined and analysed from a hierarchical perspective.

The model is extended to include an order decision rule and a production unit and it is shown that the extended model has the capability to represent the dynamics of a number of different system management principles. The three different model instances of base stock, kanban and material requirements planning character are analysed.

Dynamic modelling of production‐inventory and supply chain models are usually analysed at an aggregate level not involving any complex relations of materials or capacities. In this paper, this line of research is merged with an approach based on multiple information channels using matrix representation and it is shown how a system simplification approach can be used for this purpose.

This paper aims to examine the dynamic behaviours of a three-dimensional (3D) rod-fastening rotor bearing system (RFBS) with a crack in a fastening rod.

Based on the 3D finite element method model and stress analysis of a cracked RFBS, a 3D dynamic model of the RFBS with a crack in a fastening rod is established with considering the initial bending and stress redistribution caused by the crack. A combined numerical simulation technology is used to investigate the dynamic behaviours of the system.

The distribution of contact stress between the two disks will be not uniform, and the initial bending of the system will occur due to the presence of a crack. This will lead to the change of system stiffness and the dynamic behaviours such as vibration amplitude, and motion orbits will change significantly.

A 3D finite element method dynamic model is proposed for the study of dynamic characteristics of complex combined rotor bearing system with cracks.

It is helpful and significant to master the dynamic behaviours of cracked RFBS. It is helpful to detect the presence of a crack of the rotor bearing system.

Some of the losses caused by crack failure may be reduced.

The proposed 3D method can provide a useful reference for the study of dynamic characteristics of complex combined rotor bearing system with cracks.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2020-0189

As the transmission component of a locomotive, the traction gear pair system has a direct effect on the stability and reliability of the whole machine. This paper aims to provide a detailed dynamic analysis for the traction system under internal and external excitations by numerical simulation.

A non-linear dynamic model of locomotive traction gear pair system is proposed, where the comprehensive time-varying meshing stiffness is obtained through the Ishikawa formula method and verified by the energy method, and then the sliding friction excitation is analyzed based on the location of the contact line. Meantime, the adhesion torque is constructed as a function of the adhesion-slip feature between wheelset and rail. Through Runge–Kutta numerical method, the system responses are studied with varying bifurcation parameters consisting of exciting frequency, load fluctuation, gear backlash, error fluctuation and friction coefficient. The dynamic behaviors of the system are analyzed and discussed from bifurcation diagram, time history, spectrum plot, phase portrait, Poincaré map and three-dimensional frequency spectrum.

The analysis results reveal that as control parameters vary the system experiences complex transition among a diverse range of motion states such as one-periodic, multi-periodic and chaotic motions. Specifically, the significant difference in system bifurcation characteristics can be observed under different adhesion conditions. The suitable gear backlash and error fluctuation can avoid the chaotic motion, and thus, reduce the vibration amplitude of the system. Similarly, the increasing friction coefficient can also suppress the unstable state and improve the stability of the system.

The numerical results may provide a systemic understanding of dynamic characteristics and present some available information to design and optimize the transmission performance of the locomotive traction system.