Assessing the Potential of Intra-specific Biodiversity towards Adaptation of Irrigated and Rain-fed Italian Production Systems to Future Climate

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Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

MODIFICA DELLA POTENZA OTTICA ASSORBITA IN FILM SOTTILI PER EFFETTO DI RETICOLI PLASMONICI 2D,

Optical absorption enhancement in thin Si films induced by 2D periodic patterns of metallic nanodisks is discussed. The critical dimensions and conditions for spectrally resonant enhancement are evaluated. In case of SOI films, absorption peaks can exceed 450%

Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification

Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a new stepwise sequence of unsupervised and supervised classification methods using both SAR and MS data. The MS and SAR signatures of land and water targets in the study area were evaluated prior to the classification to identify the land and water classes that could be delineated. The delineation based on a simple thresholding method provided a satisfactory estimate of the total flooded area but did not perform well on heterogeneous surface water. To deal with the heterogeneity and fragmentation of water patches, a new unsupervised classification approach based on a combination of thresholding and segmentation (CThS) was developed. Since sandy areas and emergent vegetation could not be classified by the SAR-based unsupervised methods, supervised random forest (RF) classification was applied to a time series of SAR and co-event MS data, both combined and separated. The new stepwise approach was tested for determining the flood extent of two events in Italy. The results showed that all the classification methods applied to MS data outperformed the ones applied to SAR data. Although the supervised RF classification may lead to better accuracies, the CThS (unsupervised) method achieved precision and accuracy comparable to the RF, making it more appropriate for rapid flood mapping due to its ease of implementation

UN MODELLO MATEMATICO PER LA SIMULAZIONE DEL PROCESSO DI PRODUZIONE DEL PERCOLATO IN DISCARICA CONTROLLATA

L’impatto ambientale, anche a lungo termine, di una discarica controllata adibita allo stoccaggio dei rifiuti solidi urbani è essenzialmente legato alla dispersione dei prodotti di degradazione della componente organica dei rifiuti: percolato e biogas. In questo lavoro viene descritto un modello matematico sviluppato in ambiente MATLAB in grado di fornire simulazioni quantitative del processo di produzione del percolato in discarica. Una valutazione dell’attendibilità del modello proposto è stata effettuata mediante la sua applicazione alla discarica di Montegrosso (Comune di Potenza). Le simulazioni generate dal modello sono risultate discretamente accurate ed il confronto con i dati sperimentali forniti dal gestore dell’impianto ha consentito la validazione dei risultati

Characterising fire hazard from temporal sequences of thermal infrared modis measurements

The objective of the present research was the characterisation of fire hazard using temporal sequences of land surface temperature (LST) derived from Terra-MODIS measurements. The investigation was based on a complete sequence of MODIS LST data from 2000 to 2006 on Campania (Italy) and on a data set of fires officially recorded in the area in the same period. Missing and/or cloudy LST data were reconstructed by means of the HANTS (Harmonic ANalysis of Time Series) algorithm applied to annual sequences of daily observations. The coefficients of the Fourier analysis were then assessed against spatial patterns of fire occurrence. The HANTS algorithm was also used on the complete LST data set to construct daily reference temperature maps against which to evaluate temperature anomalies and cumulated temperature anomalies. Results show that fires tend to occur in areas characterised by specific values of several Fourier coefficients with high significance, and to avoid the other areas. The amplitude of the second harmonic is the only Fourier coefficient dictating mean fire size. The mean fire size and the proportion of large fires correlate with both daily and cumulated thermal anomalies. However, the dynamic range of the predictions from cumulated anomalies is much larger, and thus maps of the latter are more effective in predicting fire hazard.Geoscience & Remote SensingCivil Engineering and Geoscience

Stima della pericolosita d'incendio in Campania mediante serie temporali di misure MODIS nell'infrarosso termico

Geoscience & Remote SensingCivil Engineering and Geoscience

Comparing thresholding with machine learning classifiers for mapping complex water

Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polarisation and the S2 NDWI, respectively, provided better overall accuracies than when thresholding was applied to any of the individual features considered. The accuracies of this dual thresholding technique were comparable to those of machine learning and may thus offer a viable solution for automatic mapping of waterbodies.</p