Are protected areas covering important biodiversity sites? An assessment of the nature protection network in Sicily (Italy)

GIS spatial analysis of three indicators (vegetation value, faunal richness and landscape heterogeneity) was used to detect and map High-Value Biodiversity Areas (HVBAs), estimate the coverage of biodiversity in the Sicilian protected areas network, and identify new priority areas that could improve long-term biodiversity conservation outcomes. Findings indicated that only 32% of HVBAs are currently covered by the protected areas network. Hotspot analysis revealed that a modest expansion (less than 1%) in the current extent of protected areas would include a disproportionate amount (56%) of biodiversity hotspots, and identified prioritized candidates HVBAs for designation of new protected areas. © 2018 Elsevier Lt

ADVANCED DEEP LEARNING COMPARISONS FOR NON-INVASIVE TUNNEL LINING ASSESSMENT FROM GROUND PENETRATING RADAR PROFILES

Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process for a worthwhile maintenance schedule. Nowadays, machine learning tools are in the spotlight because of their outstanding capabilities to deal with data coming from even heterogeneous sources and their ability to extract information from the structural systems, providing highly effective, reliable, and efficient damage classification tools. In the current study, a supervised multi-level damage classification strategy has been developed regarding Ground Penetrating Radar (GPR) profiles for the assessment of tunnel lining conditions. In previous research, the authors firstly considered a convolutional neural network (CNN), adopting the quite popular ResNet-50, initialized through transfer learning. In the present work, further enhancements have been attempted by adopting two configurations of the newest state-of-art advanced neural architectures: the neural transformers. The foremost is the original Vision Transformer (ViT), whose core is an encoder entirely based on the innovative self-attention mechanism and does not rely on convolution at all. The second is an improvement of ViT which merges convolution and self-attention, the Compact Convolution Transformer (CCT). In conclusion, a critical discussion of the different pros and cons of adopting the above-mentioned different architectures is finally provided, highlighting the actual powerfulness of these technologies in the future civil engineering paradigm nevertheless

In the lycophyte Selaginella martensii is the "extra-qT" related to energy spillover? Insights into photoprotection in ancestral vascular plants

Lycophytes are early diverging vascular plants, representing a minor group as compared to the dominating euphyllophytes, mostly angiosperms. Having maximally developed in a CO2-rich atmosphere, extant lycophytes are characterized by a low carbon fixing capacity, which is compensated by a marked ability to induce the non-photochemical quenching of chlorophyll fluorescence (NPQ). Different kinetic components contribute to NPQ, in particular the fast relaxing high-energy quenching qE, the middle relaxing qT, and the slowly relaxing qI. Unlike angiosperms, lycophytes enhance the qT component under high light, originating from an "extra-qT". In this research, we analyze whether in Selaginella martensii the extra-qT can reflect a photosystem (PS) I-based quenching mechanism activated upon saturation of qE capacity. From comparative analyses of fluorescence quenching parameters, carbon fixation, in vivo low- and room-temperature fluorescence spectroscopy, and thylakoid protein phosphorylation, it is proposed that the extra-qT is not mechanistically separate from the ordinary qT. The results suggest a relationship between qT and photoprotective energy spillover to PSI, which is activated upon sensing the excitation energy pressure inside PSII and is possibly facilitated by phosphorylation of Lhcb6, a minor antenna protein of PSII. Energy spillover emphasizes 77K fluorescence emission from PSI core (F714) and becomes more relevant at irradiance levels corresponding to the CO2-limited, potentially photoinhibiting phase of photosynthesis. At the highest irradiances, when Lhcb6 phosphorylation potential has been saturated, the major LHCII increases in turn its phosphorylation level, probably leading to the full exploitation of PSI as a safe excitation sink. It is suggested that the low photosynthetic capacity of lycophytes could allow an easier experimental access to the use of PSI as a safe excitation quencher for PSII, a debated, emerging issue about thylakoid photoprotection in angiosperms.</p

Trajectories in Argentine children’s literature: Constancio C. Vigil and Horacio Quiroga

Children's author and publishing entrepreneur Constancio C. Vigil was a Uruguayan who spent most of his working life in Argentina. He was best known for his children's magazine Billiken (1919 to present). Vigil's contemporary and compatriot Horacio Quiroga also made the move across the River Plate and went on to have a transformative impact on Argentine literary culture, in part through his Jungle Tales for Children (1924). Both Quiroga and Vigil aspired to have their works for children accepted as school reading books, recognising the role of school authorities in the formation of the national canon. Vigil and Quiroga's trajectories of inclusion and exclusion, and their extraordinary contribution to the Argentine and Latin American cultural landscape in the first half of the twentieth century, provide a window onto the curation of an Argentine national children's literature at the same time as challenging the very nature of such a category

Effective recycling solutions for the production of high-quality PET flakes based on hyperspectral imaging and variable selection

In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level

Recycling-oriented characterization of the PET waste stream by SWIR hyperspectral imaging and variable selection methods

The proposed study was carried out to develop a fast and efficient strategy for plastic waste sensor-based sorting in recycling plants, based on hyperspectral imaging (HSI), combined with variable selection methods, to produce a high-quality recycled polyethylene terephthalate (PET) flakes stream. Variable selection techniques were applied in order to identify a limited number of spectral bands useful to recognize the presence of other plastic materials, considered as contaminant, inside a stream of recycled PET flakes, reducing processing time as requested by sorting online applications. Post-consumer plastic samples were acquired by HSI working in the short-wave infrared (SWIR) range (1000-2500 nm). As a first step, the hypercubes were processed applying chemometric logics to build a partial least squares dis-criminant analysis (PLS-DA) classification model using the full investigated spectral range, able to identify PET and contaminant classes. As a second step, two different variable selection methods were then applied, i.e., interval PLS-DA (i-PLSDA) and variable importance in projection (VIP) scores, in order to identify a limited number of spectral bands useful to recognize the two classes and to evaluate the best meth-od, showing efficiency values close to those obtained by the full spectrum model. The best result was achieved by the VIP score method with an average efficiency value of 0.98. The obtained results suggested that the variables selection method can represent a powerful approach for the sensor-based sorting online, decreasing the amount of data to be processed and thus enabling faster recognition compared to the full spectrum model