Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit” (DIRT), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario.

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A non‐singular gradient elasticity solution for the dislocation self‐energy is employed and compared to previous results for 1/6〈20$ \bar 2 $3〉 partial dislocations fault in wurtzite GaN obtained by empirical potential calculations. Twenty‐four previously obtained stable dislocation cores, twelve for each GaN polarity, are considered. It is shown that the gradient elasticity solution can be fitted to the atomistic simulations with good agreement and can be employed to trace the energetically favourable cores without the requirement of defining a core radius. As a byproduct of this continuum approach, a dislocation self‐energy quantity is derived as a constant material property in terms of a gradient coefficient. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Realistic models for worm propagation in the Internet

Spring-block models, such as the Olami-Feder-Christensen (OFC) model, were introduced several years ago to describe earthquake dynamics in the context of selforganized criticality. With the aim to address the dependency of the seismicity style on source's material properties we present an analytical enrichment of a 2D OFC model. We concluded with an analytical expression which introduces, through an appropriate constitutive equation, an effective dissipation parameter eff a related analytically not only with the elastic properties of the fault plane, but also with stochastic structural heterogeneities and structural processes of the source through a gradient coefficient.Moreover, within the proposed formulation the low b-values experimentally observed in foreshock sequences can be modeled by a process of material softening in the seismogenic volume. To check our analytical findings a cellular automaton was build-up whereas simulation results have verified model's predictions for the evolution of b in macroscopic records.