MONITORING THE POLLUTION OF GROUNDWATER IN THE AREA OF INDUSTRIAL WASTE

Monitoring of the underground water pollution in the deposit of waste inindustrial area. The paper presents the monitoring of the pollution phenomenon ofunderground water in the industrial landfill area. Industrial landfill causes pronouncedunderground water pollution in the operation phase, but also in the conservation phase.The pollution monitoring is carried out on all environmental components: air, soil andunderground water. Pollution phenomenon is analyzed in time by using a tracking anddata reception characteristic control section. The data taken is processed and interpreted toachieve the best environmental measures in the area of the landfill site. By usingsimulation models provides a forecast of the pollution in different periods of time. Thesimulation model is applicable to the operating period taking into account the change inquantities and concentrations of pollutants. This paper presents remediation measuresappropriate to the type of industrial landfill analyzed. The results obtained allow modelingof environmental protection measures and especially the subsoil and groundwater

Linkage mapping of the Mediterranean cypress, Cupressus sempervirens, based on molecular and morphological markers

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THE MANAGEMENT AND ANALYSIS OF POWER QUALITY IN POWER DISTRIBUTION GRIDS BY USING PQVIEW SOFTWARE SYSTEM

Nowadays the power quality is considered one of the most important aspect regarding the performance of a power grid operator, concerning equally all the consumers categories, too. Since a low level of the power quality has negative consequences on the technical-economic indices of the power networks’operation, there are perfectly justified the permanent efforts of the power grids operators in finding the best methods and tools able to assist them in managing and analyzing a huge volume of power quality data. The paper presents the capabilities of an intelligent system for the management and analysis of power quality data, PQView. This system is used by power grid operators in their operational activity, as well as within Smart Grid Laboratory of University of Craiova’s INCESA for research and testing purposes. This software system was used for an extensive power quality analysis of real operation a PV power plant interconnected to MV power distribution grid

Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and non-invasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to non-invasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate non-invasive cell therapy characterization can be achieved with QBAM and machine learning