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The aim is to obtain a dense and reliable disparity map from a stereoscopic pair under any luminosity condition. From the disparity map it is possible to obtain some information about the objects appearing in the scene, such as their position and their distance to the camera.

The disparity map is obtained using a correspondence method based on a multiresolution scheme. It also uses the simulated annealing algorithm to minimize the energy function, which integrates information coming from several sources: grey levels, non‐parametric transforms, edges and geometrical constraints. The multiresolution scheme allows us to interpolate the disparity map obtained in each level and to use it as an initial estimation for the simulating annealing process in the following level.

The multiresolution scheme speeds up the convergence. The fact of adding information about the neighbourhood improves the results. A simple and suitable energy function has been proposed. A simple efficient scaling scheme has been used. Their results are as good as other costly techniques such as block‐to‐point.

The use of grey level images has the drawback that some bright areas can be confused. As a solution, some other additional features are proposed.

A new metric is proposed to evaluate the quality of a disparity map. A low‐cost energy function is proposed. It integrates several type information to add robustness to the method. The convergence of the method is dramatically speeded up reusing the results of the algorithm thanks to the multiresolution scheme.

In this paper the authors aim to show the advantages of using the decomposition method introduced by Adomian to solve Emden's equation, a classical non‐linear equation that appears in the study of the thermal behaviour of a spherical cloud and of the gravitational potential of a polytropic fluid at hydrostatic equilibrium.

In their work, the authors first review Emden's equation and its possible solutions using the Frobenius and power series methods; then, Adomian polynomials are introduced. Afterwards, Emden's equation is solved using Adomian's decomposition method and, finally, they conclude with a comparison of the solution given by Adomian's method with the solution obtained by the other methods, for certain cases where the exact solution is known.

Solving Emden's equation for n in the interval [0, 5] is very interesting for several scientific applications, such as astronomy. However, the exact solution is known only for n=0, n=1 and n=5. The experiments show that Adomian's method achieves an approximate solution which overlaps with the exact solution when n=0, and that coincides with the Taylor expansion of the exact solutions for n=1 and n=5. As a result, the authors obtained quite satisfactory results from their proposal.

The main classical methods for obtaining approximate solutions of Emden's equation have serious computational drawbacks. The authors make a new, efficient numerical implementation for solving this equation, constructing iteratively the Adomian polynomials, which leads to a solution of Emden's equation that extends the range of variation of parameter n compared to the solutions given by both the Frobenius and the power series methods.

Segmentation is an important topic in computer vision and image processing. In this paper, we sketch a scheme for a multiscale segmentation algorithm and prove its validity on some real images. We propose an approach to the model based on MRF (Markov Random Field) as a systematic way for integrating constraints for robust image segmentation. To do that, robust features and their integration in the energy function, which directs the process, have been defined. In this approach, the image is first transformed to different scales to determine which one fits better to our purposes. Then, it is segmented into a set of disjoint regions, the adjacent graph (AG) is determined and a MRF model is defined on the corresponding AG. Robust features are incorporated to the energy function by means of clique functions and optimal segmentation is then achieved by finding a labeling configuration that minimizes the energy function using Simulated Annealing.

One of the main goals of vision systems is to recognize objects in real world to perform appropriate actions. This implies the ability of handling objects and, moreover, to know the relations between these objects and their environment in what we call scenes. Most of the time, navigation in unknown environments is difficult due to a lack of easily identifiable landmarks. Hence, in this work, some geometric features to identify objects are considered. Firstly, a Markov random field segmentation approach is implemented. Then, the key factor for the recognition is the calculation of the so‐called distance histograms, which relate the distances between the border points to the mass center for each object in a scene.

This work, first discusses the features to be analyzed in order to create a reliable database for a proper recognition of the objects in a scene. Then, a robust classification system is designed and finally some experiments are completed to show that the recognition system can be utilized in a real‐world operation.

The results of the experiments show that including this distance information improves significantly the final classification process.

This paper describes an object recognition scheme, where a set of histograms is included to the features vector. As is shown, the incorporation of this feature improves the robustness of the system and the recognition rate.

The main intention of this paper is to state the benefits of using online videogames as a research environment, where AI algorithms are improved by means of learning from real‐human‐behaviour examples.

The manner of taking advantage from the flux of real‐human‐behaviour examples inside an online videogame is stated. Then Mad University, a prototype online videogame specifically conceived and developed for this purpose, is explained.

Human‐like AI in artificial algorithms can be boosted by means of a specific kind of online videogame called MMORPGs, used as a research environment.

Mad University is a prototype videogame which has been developed to experiment with AI algorithms that aim to learn strategies in a generalized fashion. The next research step will be to improve Mad University and to put it to work with hundreds of players and then research and test the effectiveness of the AI algorithms.

This paper proposes a new way of testing and experimenting with AI algorithms in order to obtain more human‐like results, and claims to have attempted to develop a generalized learning method.

In this work, we propose an approach to the model based on Markov random field (MRF) as a systematic way for integrating constraints for robust image segmentation. To do that, robust features and their integration in the energy function, which directs the process, have been defined. The suitability of the method has been verified by comparing classic features with the robust ones. In this approach, the image is first segmented into a set of disjoint regions and the adjacent graph (AG) has been determined. This approach is applied by defining an MRF model on the corresponding AG. Robust features are incorporated to the energy function by means of clique functions, and optimal segmentation is then achieved by finding a labelling configuration, which minimizes the energy function using the simulated annealing.

This study aims to investigate the evolutionary pathways adopted by a digital platform to favor the development of an entrepreneurial ecosystem inspired by circular economy behaviors, becoming an enabler in the development of a coevolutionary relationship between entrepreneurial ecosystems and circular economy.

An in-depth single-case study method has been applied, investigating the case of circularity.com, the first and only circular economy industrial symbiosis platform in Italy.

The paper shows how digital platforms can transition towards circular business models, particularly for small and medium enterprises (SMEs). Moreover, the findings show how sustainable platforms' need to revise their business models to effectively engage with stakeholders. The analysis also shows the central role covered by entrepreneurial ecosystems in the transition towards a more circular and sustainable business models.

This paper contributes to theoretical development by offering new and insightful explanations of firms' behavior and coevolution, moving beyond the classic interpretation of industry dynamics and analyzing a unique case study. This study has implications for both practice and research, as it offers a better and more holistic understanding of the enabling role of digital platforms for a circular economy.

The purpose of this study is to investigate the benefits and challenges of implementing circular economy (CE), as well as shed light on the influence of procurement strategy in CE implementation in construction projects.

A qualitative research approach with abductive reasoning was adopted. The empirical data were collected from the construction industry in the United Arab Emirates (UAE).

The results reveal that clients, consultants and contractors have limited awareness, knowledge and motivation to implement CE in construction projects. The absence of incentives to design projects following CE principles, lack of involvement of contractors and suppliers, non-use of materials that use CE principles and current procurement strategies are the main challenges for the implementation of CE in the UAE.

Previous research offers limited knowledge on CE and its implementation in construction projects particularly from a procurement strategy perspective. The findings of the study provide new knowledge of the benefits, challenges and role of procurement strategy for implementing CE. It is suggested that collaborative and partnering-based procurement methods are needed to facilitate the effective implementation of CE.