1,720,988 research outputs found
Novel post-processing applications in weather science
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NOVEL 4H-CHROMEN-4-ONE, 2H-CHROMENE AND CHROMAN DERIVATIVES: DESIGN, SYNTHESIS AND BIOLOGICAL EVALUATION
Full text linkLa progettazione e sintesi di nuovi antivirali strutturalmente correlati a flavanoidi e flavonoidi sia naturali che sintetici e lo studio della relativa attività anti-picornavirus dei 4H-cromen-4-oni e 2H-cromeni ha portato ad identificare l’(E)-3-stiril-2H-cromene come un inibitore potente, selettivo e ad ampio spettro d’azione nei confronti dei rhinovirus umani (HRV). l’(E)-3-stiril-2H-cromene è stato perciò selezionato come hit compound sul quale effettuare uno studio sistematico di ottimizzazione della struttura. Sono stati quindi progettati e sintetizzati un ampio numero di arilalchil cromeni, cromanoni e cromoni che sono stati saggiati in vitro nei confronti dei sierotipi 14 e 1B di HRV, scelti come rappresentati rispettivamente dei gruppi A e B di HRV. L’estensione dello screening ad altri virus ad RNA conferma la bassa citotossicità e la selettività dell’azione anti-HRV. Sono stati così selezionati i composti più potenti, ad ampio spettro d’azione anti-HRV e con alto indice terapeutico per valutarne il meccanismo d’azione. I risultati ottenuti sia sulla particella virale che sulla moltiplicazione virale suggeriscono che tutti i composti selezionati si comportano da capsid-binder, interferendo con le prime fasi dell’infezione virale, ma mentre l’(E)-3-stiril-2H-cromene agisce sull’adsorbimento del virus al recettore cellulare gli altri composti studiati non interferiscono in questa fase ma solo sul processo di uncoating. Il risultato è di notevole interesse dal momento che l’utilizzo di combinazioni di farmaci che agiscono su fasi successive della replicazione virale potrebbe essere utile per superare il problema delle mutazioni virali che rendono rapidamente inefficace la monoterapia
Towards a machine learning based multimodel for precipitation forecast over the italian peninsula
Full text linkDirect model output forecasts by Numerical Weather Prediction models (NWPs) present some limitations caused by errors mostly due to sensitivity to initial conditions, sensitivity to boundary conditions and deficiencies in parametrization schemes (i.e. orography).
These sources of error are unavoidable, and atmospheric chaotic dynamics make prediction errors spread rapidly in time in the course of the forecast, inducing both systematic and random errors.
Nonetheless, in the last 50 years, NWPs had a significant decrease in the impact of these sources of errors, even in the long-term forecast, thanks for instance to an ever-increasing computational capability, but their relevance is still not neglectable.
Moreover, different NWPs present specific different pros and cons which are findable empirically. For instance, in the case of precipitation forecast in north-west Italy, low-resolution models (e.g. ECMWF-IFS) are more reliable in terms of space and time in predicting the average precipitation, while high-resolution models (e.g. COSMO-2I) tend to forecast better the maximum precipitation. Research purposes apart, actual limitations must be seen in an operational context, where weather forecasts’ skillfulness and associated uncertainty are information of the utmost importance to the forecaster and in general to the user of a certain forecasts system.
To tackle these limitations of NWPs and the need for an uncertainty-quantified meteorological forecast, we propose a machine learning-based multimodel post-processing technique for precipitation forecast. We focus on precipitation since it is the most important variable in the issue of spatially localized weather alert notice by the Italian Civil Protection system and at the same time it is one of the most challenging variables to forecast.
We use a Convolutional Neural Network (CNN) to obtain deterministic and probabilistic forecast grids over 24h up to 48h focusing on North-West Italy, using several high and low-resolution deterministic NWPs as input and using high-resolution rain-gauge corrected radar observations for the training. The effect of the usage of different convolutional parameters (e.g. stride, padding) is taken into account. The deterministic output grid is chosen as the grid with the lowest mean square error obtained during the training, and it is compared with the linear regression of the input NWPs and with every single model. The probabilistic output grid is generated by considering the statistical ensemble of the twenty grids with the lowest mean square error obtained during the training, and it is compared with the logistic regression of the input NWPs and with ECMWF-EPS as a benchmark, both at different precipitation thresholds
Precipitation forecast post-processing: blending deterministic NWPs with machine learning
Full text linkDirect model output forecasts by Numerical Weather Prediction models (NWPs) present some limitations caused by errors mostly due to sensitivity to initial conditions, sensitivity to boundary conditions and deficiencies in parametrization schemes (i.e. orography).
These sources of error are unavoidable, and atmosphere chaotic dynamics makes prediction errors to spread rapidly in time in the course of the forecast, inducing both systematic and random errors.
Nonetheless, in the last 50 years NWPs had a significant decrease in the impact of these source of errors, even in the long-term forecast, thanks for instance to an ever-increasing computational capability, but still their relevance is not neglectable.
Moreover, different NWPs present specific different pros and cons which are findable empirically. For instance, in the case of precipitation forecast in the north-west Italy, low spatial resolution models (e.g. ECMWF-IFS) tend to be more reliable in terms of space and time in predicting the average precipitation, while high resolution models (e.g. COSMO-2I) tend to forecasts the maximum precipitation better. Research purposes apart, actual limitations must be seen in an operational context, where weather forecasts’ skillfulness and associated uncertainty are information of the utmost importance to the forecaster and in general to the user of a certain forecasts system.
In order to tackle the limitations of NWPs and the need of an uncertainty-quantified meteorological forecast, we propose a machine learning based multimodel post-processing technique for precipitation forecast. We focus on precipitation since it is the most important variable in the issue of spatially localized weather alert notice by the Italian Civil Protection’ system and at the same time it is one of the most challenging variables to forecast.
We use different Convolutional Neural Networks (CNNs) to obtain both deterministic and probabilistic forecast grids over 24h up to 48h focusing in the North-West Italy, using different high and low resolution deterministic NWPs as input and using high resolution rain-gauge corrected radar observations as ground truth for the training. We use constrainted linear regressions as a mean of deterministic benchmark, and ECMWF-EPS as a mean of probabilistic benchmark. The test phase show decent improvements in terms of RMSE for every season
Synthesis, anti-picornavirus activity and mechanism of action of new 4H-chromen-4-one and 2H-chromene derivatives.
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Design, synthesis and in vitro evaluation of novel chroman-4-one, chroman, and 2H-chromene derivatives as human rhinovirus capsid-binding inhibitors
No full textAs part of an effort to generate broad-spectrum inhibitors of rhinovirus replication, novel series of (E)-3-[(E)-3-phenylallylidene]chroman-4-ones 1a-e, (E)-3-(3-phenylprop-2-yn-1-ylidene)chroman-4-ones 2a and 2b, (Z)-3-[(E)-3-phenylallylidene]chromans 3a-e, and (E)-3-(3-phenylprop-1-en-1-yl)-2H-chromenes 4a-d were designed and synthesized. All the compounds were tested in vitro for their efficacy against infection by human rhinovirus (HRV) 1B and 14, two representative serotypes for rhinovirus group B and A, respectively. Most of the analogues were found to be potent and selective inhibitors of both HRVs, although HRV 1B was generally more susceptible than HRV 14. Mechanism of action studies of (E)-6-chloro-3-(3-phenylprop-1-en-1-yl)-2H-chromene 4b, the most potent compound on HRV 1B infection, suggested that 4b behaves as a capsid-binder probably acting at the uncoating level. (C) 2011 Elsevier Ltd. All rights reserved
Uncertainty-aware methods for enhancing rainfall prediction with deep-learning based post-processing segmentation
Full text linkPrecipitation forecast is critical in flood management, agricultural planning, water resource allocation, and weather warnings. Despite significant advancements in Numerical Weather Prediction (NWP) models, these systems often exhibit substantial biases and errors, particularly at high spatial and temporal resolutions. To address these limitations, we develop and evaluate uncertainty-aware deep learning ensemble architectures, focusing on characterizing forecast uncertainties while achieving high accuracy and an optimal balance between sharpness and reliability. This study presents SDE U-Net, a novel adaptation of SDE-Net designed specifically for segmentation tasks in precipitation forecasting. We conduct a comprehensive evaluation of state-of-the-art ensemble architectures, including SDE U-Net, and compare their forecast uncertainty against that of a Poor Man's Ensemble (PME, i.e. NWPs forecast average) across diverse meteorological conditions, ranging from non-intense precipitation patterns to intense weather events. As an example case, we focus on predicting daily cumulative precipitation in northwest Italy, though our approach is broadly generalizable. Our findings demonstrate that all the evaluated probabilistic deep learning models outperform the PME benchmark in terms of median RMSE for both non-intense and intense precipitation events. Among them, SDE U-Net achieves the best overall performance, delivering the lowest RMSE for intense events (2.637 * 10-2) and demonstrating a more stable error distribution compared to other models. For non-intense events, SDE U-Net perform comparably to other deep learning models, still notably surpassing the baselines. Moreover, SDE U-Net effectively balances sharpness and reliability, making it particularly suitable for operational forecasting of extreme weather. Integrating uncertainty-aware models like SDE U-Net into forecasting workflows can enhance decision-making and preparedness for weather-related hazards
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