1. Marchiori, Massimo and Latora, Vito (2000) {Harmony in the small-world}. Physica A: Statistical Mechanics and its Applications https://doi.org/10.1016/S0378-4371(00)00311-3, cond-mat, 03784371, 0008357, cond-mat/0008357, arXiv, The small-world phenomenon, popularly known as six degrees of separation, has been mathematically formalized by Watts and Strogatz in a study of the topological properties of a network. Small-world networks are defined in terms of two quantities: they have a high clustering coefficient C like regular lattices and a short characteristic path length L typical of random networks. Physical distances are of fundamental importance in applications to real cases; nevertheless, this basic ingredient is missing in the original formulation. Here, we introduce a new concept, the connectivity length D, that gives harmony to the whole theory. D can be evaluated on a global and on a local scale and plays in turn the role of L and 1/C. Moreover, it can be computed for any metrical network and not only for the topological cases. D has a precise meaning in terms of information propagation and describes in a unified way, both the structural and the dynamical aspects of a network: small-worlds are defined by a small global and local D, i.e., by a high efficiency in propagating information both on a local and global scale. The neural system of the nematode C. elegans, the collaboration graph of film actors, and the oldest US subway system, can now be studied also as metrical networks and are shown to be small-worlds.
2. Morani, Maria Cristina and Carravetta, Armando and {Del Giudice}, Giuseppe and McNabola, Aonghus and Fecarotta, Oreste (2018) {A Comparison of Energy Recovery by PATs against Direct Variable Speed Pumping in Water Distribution Networks}. Fluids 3(2) https://doi.org/10.3390/fluids3020041
3. {Gonz{\'{a}}lez Perea}, R. and {Camacho Poyato}, E. and Montesinos, P. and {Rodr{\'{i}}guez D{\'{i}}az}, J. A. (2018) {Prediction of applied irrigation depths at farm level using artificial intelligence techniques}. Agricultural Water Management https://doi.org/10.1016/j.agwat.2018.05.019, ANFIS,Genetic algorithm,Irrigation scheduling,Optimal input variables,Precision agriculture, 18732283, Irrigation water demand is highly variable and depends on farmer behaviour, which affects the performance of irrigation networks. The irrigation depth applied to each farm also depends on farmer behaviour and is affected by precise and imprecise variables. In this work, a hybrid methodology combining artificial neural networks, fuzzy logic and genetic algorithms was developed to model farmer behaviour and forecast the daily irrigation depth used by each farmer. The models were tested in a real irrigation district located in southwest Spain. Three optimal models for the main crops in the irrigation district were obtained. The representability (R2) and accuracy of the predictions (standard error prediction, SEP) were 0.72, 0.87 and 0.72; and 22.20{%}, 9.80{%} and 23.42{%}, for rice, maize and tomato crop models, respectively.
4. Tung, Yeou-koung and Ph, D {Reliability Assessment and Risk Analysis}. 0071589007
5. Sanz, G and P{\'{e}}reza, R (2014) {Demand pattern calibration in water distribution networks}. 70, 10.1016/j.proeng.2014.02.164, Procedia Engineering, Water distribution network models are widely used by water companies. Consumer demands are one of the main uncertainties in these models, but their calibration is not feasible due to the low number of sensors available in most real networks. However, the behaviour of these individual demands can be also calibrated if some a priori information is available. A methodology for calibrating demand patterns based on singular value decomposition (SVD) is presented. Demand stochastic nature is overcome by using multiple data samples. The methodology is applied to two water distribution systems: an academic network and a real network with synthetic data. ?? 2013 The Authors.