Simulation of the transport of marine microplastic particles in the Ionian Archipelago (NE Ionian Sea) using a Lagrangian model and the control mechanisms affecting their transport.

The Mediterranean Sea is among the most affected areas of our planet by microplastic (MP) pollution. However, some regions are still underrepresented in the current literature. This work studied the fate of microplastics (MPs) released from major populated areas within the NE Ionian Sea, an area that contains highly significant biodiversity. This was accomplished by incorporating oceanographic data into a Lagrangian particle-tracking numerical model that simulated the transport of MP particles for the interval of 27 months. The findings report a high possibility of beaching within the first weeks of the simulation for most locations, where 63 % of MPs were beached and 37 % were still floating at the end of the simulation. Seaward transport and eddy diffusivity are the controlling mechanisms of the MP transport, with diffusion being the primary force controlling the movement of MP particles in 1/3 of the simulated regions. This is highly significant, because in areas where diffusion is the main mechanism controlling MP transport, accumulation of floating MP particles is occurring, as reported in previous studies. The MPs' transport and beaching behavior, as well as the observed residence times, were used to determine the threat level that MPs pose to the biodiversity of specific areas.

Utilization of New Technologies in the Production of Pharmaceutical Olive Oil.

Olive oil is a key ingredient in the Mediterranean diet and offers many health benefits. However, many factors affect the quality and quantity of olive oil such as olive tree diseases and olive-related pests. Unfortunately, the procedure of identifying pests or the outbreak of a disease is time-consuming, and it depends heavily on the size of the olive grove. Through the use of ICT, remote monitoring of the olive grove can be achieved, by collecting environment-related data and having an overview of the olive grove's overall health. In this paper we propose a low-cost dense network of sensors that collects daily data regarding the olive grove, thus, providing the possibility to prevent infestation of olive fruit fly and/or the outbreak of olive tree-related disease.

Effective Stochastic Algorithm in Disease Prediction.

Traditionally, the main process for olive fruit fly population monitoring is trap measurements. Although the above procedure is time-consuming, it gives important information about when there is an outbreak of the population and how the insect is spatially distributed in the olive grove. Most studies in the literature are based on the combination of trap and environmental data measurements. Strictly speaking, the dynamics of olive fruit fly population is a complex system affected by a variety of factors. However, the collection of environmental data is costly, and sensor data often require additional processing and cleaning. In order to study the volatility of correlation in trap counts and how it is connected with population outbreaks, a stochastic algorithm, based on a stochastic differential model, is experimentally applied. The results allow us to predict early population outbreaks allowing for more efficient and targeted spraying.

Self-Optimized Computational Method Calculating Robust b Values for Earthquake Catalogs Containing Small Number of Events.

In the Gutenberg-Richter relation that describes the frequency-magnitude distribution of earthquakes, the b value represents the distribution's slope. Since b values can be used for mapping the dynamic response of earthquake source, methodologies for calculating robust b values are of great importance. Although nowadays software which is meant for statistical analysis of earthquake data can determine b values with high accuracy, in occasions where catalogs that contain small number of earthquake events, the produced results are not satisfactory. In this paper we present a new self-optimized algorithm for a more efficient calculation of the b value. The algorithm's results are compared with two widely known software for statistical analysis of earthquake data, showing a better performance in evaluating b values for earthquake catalogs containing small number of events.