Search results

The objective of this study is to formulate a model for forecasting the performance of firms in terms of trends in turnover, investments, exports, employment and flexibility, and to identify the principal correlations with selected dependent variables, such as the level of computerization, the extent of collaboration with competitors and the characteristics of the product.

This paper analyses data, which refers to a survey conducted on a sample of 89 firms from the Treviso province in the north east of Italy by using the Logit model.

From the application of the logit models it emerges that the most important variables contributing to the economic success of the firms are technological flexibility, collaboration, with competitors, and investments in certain areas such as research and development, marketing and fixed technology. Moreover, the findings show that the factors which contribute most significantly to technological flexibility (a key factor for the growth of the firm) are flexibility to demand, the level of computerization and staff training.

From the application of logit models it emerges that the most important variables influencing the good performance of firms are flexibility in keeping pace with technology, collaboration with competitors, and the choice of certain types of investment. Moreover, the variables which contribute most to greater flexibility are investments in human capital and in information technology, as well website use, the technological characteristics of the product and the firm's flexibility in following the demand trend.

In this study logit models are analysed from both a theoretical and applied point of view.

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Brain‐like structures have evolved by performing signal processing initially by minimizing “tracking errors” on a competitive basis. Such systems are highly complex and at the same time notoriously “disordered”. The functional trace of the cerebral cortex of the (human) brain is a good example. The Electroencephalogram (E.E.G) appears particularly fragmented during the execution of mental tasks, as well as during the recurrent episodes of R.E.M. sleep. A stochastically regular or a highly synchronized E.E.G on the other hand, characterises a drowsy (relaxing) or epileptic subject respectively and indicates—in both cases—a very incompetent information processor. We suggest that such behavioral changeovers are produced via bifurcations which trigger the thalamocortical non‐linear pacemaking oscillator to switch from an unstable limit cycle to a strange attractor regime (i.e. to chaos), or vice versa. Our analysis aims to show that the E.E.G's characteristics are not accidental but inevitable and even necessary and, therefore, functionally significant.

The human mind possesses the unique capability of “mapping” the external (as well as part of the organism's internal) world i.e. it “compresses” long and complex strings of impinging environmental stimuli (“observations”) and then uses these “minimal length algorithms” in order to simulate physical phenomena‐thereby revealing the “laws of nature”. In this paper we theorize that this process of “Self”‐organization and category formation is implimented via a set of coexisting (strange) attractors in the cognizant apparatus each one of which attracts (and therefore compresses) whole subsets of “initial conditions” the sum‐total of which constitute the set of external stimuli. This set of the initial conditions forms the “Basin” of the attractors and the processes of partition and category formation in the mind involves the topology of the separatrixes amongst the individual subsets of the Basin. We examine in particular how the information processing is mediated by the thalamocortical pacemaker of the brain and, therefore, what might be the role of E.E.G (which is measurable on a routine basis) in Cognition.

One important issue that modern communication theory does not deal with is the physical substratum of information. We advance here the thesis that information is generated by the ever increasing complexity of a “self”‐organizing hierarchical system—which evolves via cascading bifurcations giving rise to broken symmetry. We call “Language” the process which reveals that information, namely the cognitive gadget which “compresses” the complexity generated by broken symmetry‐thereby providing “minimal length” algorithms for triggering an “internal representation” or the replication of the physical system involved. This compressibility has an obvious “survival value” since it allows the possessor of language to reduce and predict a rapidly changing environment. In characterizing language, like any open system far from equilibrium, the usual concept of free energy mediating a conflict between internal energy and entropy—is not only irrelevant but also wrong. The concepts of “complexity” and organization seem here more pertinent.

The purpose of this paper is to illustrate the many aspects of Poincare recurrence time theorem for an archetype of a complex system, the logistic map.

At the beginning of the twentieth century, Poincare's recurrence theorem had revolutionized modern mechanics and statistical physics. However, this theorem did not attract considerable attention, at least from a numerical and computational point of view. In a series of relatively recent papers, Balakrishnan, Nicolis and Nicolis have addressed the recurrence time problem in a firm basis, introducing notation, theory, and numerical studies. Motivated by this call, the paper proposes to illustrate the many aspects of Poincare recurrence time theorem for an archetype of a complex system, the logistic map. The authors propose here in different tests and computations, each one illuminating the many aspects of the problem of recurrence. The paper ends up with a short discussion and conclusions.

In this paper, the authors obtain new results on computations, each one illuminating the many aspects of the problem of recurrence. One striking aspect of this detailed work, is that when the sizes of the cells in the phase space became considerable, then the recurrence times assume ordinary values.

The paper extends previous results on chaotic maps to the logistic map, enhancing comprehension, making possible connections with number theory, combinatorics and cryptography.

Relations in university settings are becoming more heterogeneous in terms of race, ethnicity, religion, nationality, class, and gender. In South Africa, transformation imperatives have radically changed the complexion of the country’s university campuses but have also entrenched political imperatives in its universities. As a consequence, the university is a highly politicised space. This is not new. What is new is a communication environment characterised by real-time, global networked digital communication and the uptake of digital media platforms (including social media platforms). We explore the effects of politicisation and new modes of communication using the case of a controversial article published in a South Africa journal and the ensuing polemic. Drawing on both institutional theory and Castells’ description of the network society, we conceptualise collegiality along two dimensions: horizontal collegial relations which exist for the purpose of knowledge creation and transfer which, in turn, depends on self-governance according to a taken-for-granted code of conduct; and vertical collegiality which describes collegial relations between academic staff and university management, and which is necessary for the governance of the university as a complex organisation. We conclude that the highly personal nature of communication that is propelled by digital communication has a direct impact on collegial relations within the university. The motivations of both university academic staff and management, as well as the public, extend beyond stimulating collective debate in the service of knowledge production to serving individual and/or ideological agendas as the communication of science becomes politicised. While issues pertaining to collegiality in South Africa may at first glance appear to be unique to the country, we believe that in a globally transforming academy, the South African case may offer novel insights and useful lessons for other highly politicised university systems.

Global and interconnected supply chains are increasingly exposed to systemic risks, whereby individual failures propagate across firms, sectors and borders. Systemic risks have emerged from the decisions of individual firms, e.g., outsourcing and buffer reduction, and are now beyond their control. This paper aims to identify appropriate approaches to mitigating those risks.

Systemic risks require analyzing supply chains beyond a dyadic perspective. This study approaches the problem through the lenses of complex systems and network theories. Drawing on the lessons learned from other systemic-risk-prone systems, e.g. energy and financial networks, both in research and practice, this study analyzes the adequate level of governance to monitor and manage systemic risks in supply chains.

The authors argue that governance institutions should be mandated to overview and reduce systemic risks in supply chains from the top down, as central bankers do for the financial system. Using firm-level data and tools from network analysis and system dynamics, they could quantify systemic risks, identify risk-prone interconnections in supply chains and design mitigating measures. This top-down approach would complement the bottom-up supply chain management approach and could help insurers design policies for contingent business interruptions.

Instead of looking at supply chains purely from the firms’ angle, the perspective of insurers and governments is brought in to reflect on the governance of risks.

The evolution of the conflict of “blackmail” between two individuals is dealt with—both for symmetric and asymmetric contests. State—space diagrams are presented illustrating the dynamical coevolution of the cooperathe propensities of the partners when the games are played inductively—and learning takes place via storing the result of the previous outcome. By changing the three parameters of the game α, č, k (the probability of yield— “chicken”—the tempting factor and the coefficient of mutual loss, respectively) we can modify drastically the probability of “locking‐in” at the cooperative state as well as the dynamical repertoire for each contestant (i.e. the number of states between which his strategy undergoes transitions as well as the probabilities of these transitions). Finally, we study the result of additive white noise on the trajectories of the cooperative propensities, both in the symmetric and the asymmetric case.