60 research outputs found
sj-pdf-1-tar-10.1177_17534666231155744 – Supplemental material for Ventilatory ratio and mechanical power in prolonged mechanically ventilated COVID-19 patients versus respiratory failures of other etiologies
No full textSupplemental material, sj-pdf-1-tar-10.1177_17534666231155744 for Ventilatory ratio and mechanical power in prolonged mechanically ventilated COVID-19 patients versus respiratory failures of other etiologies by Alessandro Ghiani, Konstantinos Tsitouras, Joanna Paderewska, Kathrin Kahnert, Swenja Walcher, Lukas Gernhold, Claus Neurohr and Nikolaus Kneidinger in Therapeutic Advances in Respiratory Disease</p
sj-docx-1-taj-10.1177_20406223221109655 – Supplemental material for Incidence, causes, and predictors of unsuccessful decannulation following prolonged weaning
No full textSupplemental material, sj-docx-1-taj-10.1177_20406223221109655 for Incidence, causes, and predictors of unsuccessful decannulation following prolonged weaning by Alessandro Ghiani, Konstantinos Tsitouras, Joanna Paderewska, Katrin Milger, Swenja Walcher, Mareike Weiffenbach, Claus Neurohr and Nikolaus Kneidinger in Therapeutic Advances in Chronic Disease</p
A physically based rain attenuation model for terrestrial links
No full textThis contribution presents a novel physically based approach to modeling the rain attenuation affecting terrestrial links. The model is devised by investigating the path reduction factor (PF) on terrestrial links, a typical element of rain attenuation prediction models introduced to take into due account the spatial inhomogeneity of rainfall. To this aim, a large number of PF values are analyzed by simulating the interaction of a hypothetical terrestrial link with a set of realistic synthetic rain fields. The dependence of PF on different parameters such as the path length, the operational frequency, and the rain rate measured at the transmitter is addressed, and the results are exploited to devise analytical expressions aiming to provide an accurate yet simple approach to predicting rain attenuation on terrestrial links. Finally, the prediction accuracy of the proposed method is discussed (and compared to the one of other two models) by considering experimental data collected worldwide
Is storage a business? A test on the Italian day-ahead electricity market
No full textEnergy storage systems emerge as key elements in optimizing sustainable resource use, ensuring a steady flow of energy even when primary sources, such as sun or wind, are intermittent. In this scenario, the techno-economic integration of storage technologies becomes a key building block in the transition to a more resilient and efficient energy system. In this paper, we considered the possibility of using the electricity storage as a business, buying electric energy from the Italian electric dayahead market when prices are low, and selling it back when prices are high. The theoretical modeling and the resulting simulations show that the use of storage in the day-ahead electricity market can actually be a profitable business, by quantifying the possible revenue on each of the last five years, showing that it is highly variable over time. In particular, the recent gas prices crisis changed the framework, showing very interesting revenue opportunities. About forecasting methods, we show that simple forecasting methods (e.g., daily averages) perform adequately, while optimization-based approaches outperform heuristic methods
Multi-Energy Planning of Urban District Retrofitting
No full textIn city contexts it is common to encounter entire urban districts with poor energy performances buildings, where urban and building retrofitting can be carried out ranging from light to radical transformation. Taking into account that the Clean energy package for All Europeans, which defines European climate and energy policy beyond 2020, encourages the Member States to develop policies, financial measures, and other tools for the promotion of strategies that can lead to building renovation, this paper wants to provide a multi-energy planning methodology for an entire urban neighborhood. The development of a district's multi-energy smart grid has been planned with the objective of increasing the local production from local renewable thermal and electrical energy to decrease the gap between energy production and consumption of the district. The investment cost is considered as objective for multi-energy system optimization. The proposed methodology is applied to an existing urban district in the City of Cagliari (Italy) considering different retrofitting scenarios. The simulation results show the effectiveness of the proposed methodology
Artichoke deep learning detection network for site-specific agrochemicals UAS spraying
No full textAbstract
Input optimization is a distinguishing characteristic of Precision Agriculture approaches, helping reduce the environmental impact and costs and increase vegetable production quality. Thanks to the high automation evolution of Unmanned Aerial Systems (UAS), a new approach derived from their combination with Deep Learning techniques is leading to significant improvements in agricultural management practices. The study aims at artichoke plants detection and georeferencing as a first step for an on-the-fly, real time, UAS spraying system, and use the gathered information to monitor crop development through a multi-temporal approach. A commercial UAS, equipped with an RGB sensor, acquired images of the artichoke field located in Sardinia (Italy) during the 2021–2022 season in different crop growth stages. The FPN (Feature Pyramid Network), trained and compared with the YOLOv5 (You Only Look Once) network, showed a high detection level with an average F1 score of around 90%, and satisfactory off-line performances on the Nvidia Jetson Nano board. YOLOv5 achieved the best overall result. The FPN recorded a lower recall, which is desirable to achieve a minimum number of detection errors and limit the leakage of agrochemicals on false-positive targets. The multi-temporal approach influenced detection performances, with an inverse response of precision and recall metrics. The growing index trend showed a distinct value in October, peaking at the beginning of December as expected. The proposed approach contributes to designing future automatic and reliable site-specific UAS agrochemicals application and the classification of management zones
Integrating UAVs and Canopy Height Models in Vineyard Management: A Time-Space Approach
No full textThe present study illustrates an operational approach estimating individual and aggregate vineyards’ canopy volume estimation through three years Tree-Row-Volume (TRV) measurements and remotely sensed imagery acquired with unmanned aerial vehicle (UAV) Red-Green-Blue (RGB) digital camera, processed with MATLAB scripts, and validated through ArcGIS tools. The TRV methodology was applied by sampling a different number of rows and plants (per row) each year with the aim of evaluating reliability and accuracy of this technique compared with a remote approach. The empirical results indicate that the estimated tree-row-volumes derived from a UAV Canopy Height Model (CHM) are up to 50% different from those measured on the field using the routinary technique of TRV in 2019. The difference is even much higher in the two 2016 dates. These empirical findings outline the importance of data integration among techniques that mix proximal and remote sensing in routine vineyards’ agronomic practices, helping to reduce management costs and increase the environmental sustainability of traditional cultivation systems
Paramagnetic Solid Lipid Nanoparticles as a novel platform for the development of molecular MRI probes
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