The efficient use of water is an issue that has captured the attention of scientists, technicians, and the community at large. The sustainability of water resources has been threatened by the current imbalance between water supply and demand. Intelligent consumption of water would contribute to the balance and reduce the waste in applications such as the agriculture. This paper shows the design of a water consumption monitoring system based on the Internet of Things (IoT). With the implementation of this system could be known in real time the consumption of water in a crop. In addition, the user of the system may take corrective actions that optimize their water consumption; this is achieved by applying the SEMMA methodology to evaluate the data obtained by the system using two cluster algorithms, Simple K-means and GenClus++. With the application of SEMMA it was possible to determine periods of water consumption that were considered as waste in the irrigation of crops, applying data analysis with both algorithms.

Models based on self-excluded walks have been widely used to generate random linear chains. In this work, we present an algorithm capable of generating linear strings in two and three dimensions, in a simple and efficient way. The discrete growth process of the chains takes place in a finite time, in a network without pre-established boundary conditions and without the need to explore the entire configurational space. The computational processing time and the length of the strings depending on the number of trials N′ . This number is always less than the real number of steps in the chain, N. From the statistical analysis of the characteristic distances, the radius of gyration ( Rg ), and the end-to-end distance ( Ree ), we make a morphological description of the chains and we study the dependence of this quantities on the number of steps, N. The universal critical exponent obtained are in very good agreement with previous values reported in literature. We also study fractal characteristics of the chains using two different methods, Box-Counting Dimension or Capacity Dimension and Correlation Dimension. The studies revealed essential differences between chains of different dimensions, for the two methods used, showing that three-dimensional chains are more correlated than twodimensional chains.

For this study, articles from the fields of Social Sciences; Economics; Econometrics and Finance; Business and Management; and Accounting have been monitored for the period from 1979 to 2019. VOSviewer® was used to create, visualize and explore bibliometric information from Scopus scientific database, for the identification of trends about indicators that have not been frequently considered, compared to competitiveness regional indexes territorial competitiveness indicators. It has generated preliminary conclusions using citation relationships between journals, collaborative relationships between researchers, and coexistence relationships between scientific terms from the identified target literature. The identified trends are related to Renewable energy, Sustainability, Higher Education, Destination Competitiveness, Knowledge Management, Cross Borders Regions and Small and Medium-sized Entrepreneurship.

In this work, a generalization of the study of the human gait was made from already existent models in the literature, like models of Keller and Kockshenev. In this hybrid model, a strategy of metabolic energy minimization is combined in a race process, with a non-linear description of the movement of the mass center's libration, trying to reproduce the behavior of the walk-run transition. The results of the experimental data, for different speed regimes, indicate that the perimeter of the trajectory of the mass center is a relevant quantity in the quantification of this dynamic. An experimental procedure was put into practice in collaboration with the

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work.