Recent Deforestation Pattern Changes (2000-2017) in the Central Carpathians:A Gray-Level Co-Occurrence Matrix and Fractal Analysis Approach

The paper explores the distribution of tree cover and deforested areas in the Central Carpathians in the central-east part of Romania, in the context of the anthropogenic forest disturbances and sustainable forest management. The study aims to evaluate the spatiotemporal changes in deforested areas due to human pressure in the Carpathian Mountains, a sensitive biodiverse European ecosystem. We used an analysis of satellite imagery with Landsat-7 Enhanced Thematic Mapper Plus (Landsat-7 ETM+) from the University of Maryland (UMD) Global Forest Change (GFC) dataset. The workflow started with the determination of tree cover and deforested areas from 2000–2017, with an overall accuracy of 97%. For the monitoring of forest dynamics, a Gray-Level Co-occurrence Matrix analysis (Entropy) and fractal analysis (Fractal Fragmentation-Compaction Index and Tug-of-War Lacunarity) were utilized. The increased fragmentation of tree cover (annually 2000–2017) was demonstrated by the highest values of the Fractal Fragmentation-Compaction Index, a measure of the degree of disorder (Entropy) and heterogeneity (Lacunarity). The principal outcome of the research reveals the dynamics of disturbance of tree cover and deforested areas expressed by the textural and fractal analysis. The results obtained can be used in the future development and adaptation of forestry management policies to ensure sustainable management of exploited forest areas

Kolmogorov compression complexity may differentiate different schools of Orthodox iconography

The complexity in the styles of 1200 Byzantine icons painted between 13th and 16th from Greece, Russia and Romania was investigated through the Kolmogorov algorithmic information theory. The aim was to identify specific quantitative patterns which define the key characteristics of the three different painting schools. Our novel approach using the artificial surface images generated with Inverse FFT and the Midpoint Displacement (MD) algorithms, was validated by comparison of results with eight fractal and non-fractal indices. From the analyzes performed, normalized Kolmogorov compression complexity (KC) proved to be the best solution because it had the best complexity pattern differentiations, is not sensitive to the image size and the least affected by noise. We conclude that normalized KC methodology does offer capability to differentiate the icons within a School and amongst the three Schools

Correction: Gruia, M.-I. et al. The Antioxidant Response Induced by Lonicera caerulaea Berry Extracts in Animals Bearing Experimental Solid Tumors. Molecules 2008, 13, 1195-1206

In the original published version of this paper [1] we omitted to acknowledge that the work was supported by a grant-in-aid. The acknowledgment is hereby published as follows. [...

Acknowledgments

molecule

Selected Papers from the 1st ACIS International Workshop on Self-Assembling Wireless Networks

Selected Papers from the 1st ACIS International Workshop on Self-Assembling Wireless Network

The Antioxidant Response Induced by Lonicera caerulaea Berry Extracts in Animals Bearing Experimental Solid Tumors

Lonicera caerulea is a species of bush native to the Kamchatka Peninsula (Russian Far East) whose berries have been extensively studied due to their potential high antioxidant activity. The aim of our work was to investigate the in vivo effects of the antioxidant action of Lonicera caerulea berry extracts on the dynamics of experimentallyinduced tumors. Our data showed that aqueous Lonicera caerulaea extracts reduced the tumor volume when administered continuously during the tumor growth and development stages, but augmented the tumor growth when the administration of extracts started three weeks before tumor grafting. Prolonged administration of Lonicera caerulaea berry extracts induced the antioxidant defense mechanism in the tumor tissues, while surprisingly amplifying the peripheral oxidative stress