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This paper improved the self‐adaptive filter‐forecasting model that is one of deterministic constant parameter forecasting models. An attenuation/gain function is drawn into a direct iteration search method that belongs to the multi‐variables extreme value research based on the optimization theory, so that it combines the quantitative methods with the qualitative analysis by the experts and increases the explanatory ability and simulation level of the original model. As an empirical study, the authors applied the improved method to the forecasting of the middle‐term demand for cellular phones in China.

This paper aims to examine the association between environmental disclosure and waste performance.

This study is based on a sample of S&P 500 firms over a nine-year period from 2010 to 2018. The pooled ordinary least squares (OLS), logistic, propensity score matching (PSM) and instrumental variable-generalized method of moments regressions analyses have been used to examine the data.

The findings show a significant positive relationship between waste performance and environmental disclosure, suggesting that firms with superior waste performance tend to disclose more environmental information. Further, the authors distinguish between “hard” and “soft” environmental disclosures and find that the effect of waste performance is consistently positive and significant for each type. The observed positive and significant association of waste performance with environmental disclosure remains unchanged, regardless of the industry affiliation of firms, although firms from industries that are less environmentally sensitive provide a slightly higher level of environmental disclosure. The authors also explore possible channels that may explain the association between waste performance and environmental disclosure and find that litigation risk and cash holdings positively moderate the association. The finding remains robust to a number of alternative estimation approaches.

Overall, the authors present important evidence that waste performance is an important indicator of environmental disclosure. The findings are useful for corporations and stakeholders and have important implications around the globe as the authors continue to grapple with the ongoing issue of waste.

Studies have documented the distinctive advantage of innovation ecosystems in integrating and recombining heterogeneous knowledge resources across firms' boundaries. However, hierarchical governance in the form of vertical integration is still preferred in many industries for organizing innovation, and the current literature touches little on the relative performance of different organizational structures (integrated firms vs innovation ecosystem) and the factors that lead firms to choose one over the other. The authors conjecture that structure of technological interdependence is one of such important factors. Using a computational experiment, the authors compare the innovation performance of ecosystems with integrated firms under different interdependency structures.

Using the NKC model, the authors incorporate non-generic complementarities and modularity into the technological interdependence between different components. The authors compare four different types of technological interdependence (modular, hierarchical, nearly modular, and random).

The results show that integrated firms with centralized search demonstrate stable and consistent performance that is robust to the structure of technological interdependencies, but an ecosystem significantly outperforms integrated firms with centralized or decentralized when the products exhibit modular or nearly-modular structures.

This study sheds light on why an ecosystem often exhibits modular structures while vertical integration is prevalent in industries with complex technological interdependence. In addition, it shows the evolutionary nature of ecosystems and indicates how the cooperation and competition between actors shape the interdependence structure of ecosystems.

The purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified simultaneously in the visual servoing process.

By using reference, desired and current images, system errors are constructed by measurable signals that are obtained by decomposing Euclidean homographies. Subsequently, by taking the advantage of the concurrent learning framework, both historical and current system data are used to construct an adaptive updating mechanism for recovering the unknown feature depth. Then, the kinematic controller is designed for the mobile robot to achieve the visual servo trajectory tracking task. Lyapunov techniques and LaSalle’s invariance principle are used to prove that system errors and the depth estimation error converge to zero synchronously.

The concurrent learning-based visual servo tracking and identification technology is found to be reliable, accurate and efficient with both simulation and comparative experimental results. Both trajectory tracking and depth estimation errors converge to zero successfully.

On the basis of the concurrent learning framework, an adaptive control strategy is developed for the mobile robot to successfully identify the unknown scene depth while accomplishing the visual servo trajectory tracking task.