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We present a framework for scene understanding from interferometric synthetic aperture radar data that is based on Bayesian machine learning and information extraction and fusion. A generic description of the data in terms of multiple models is automatically generated from the original signals. The obtained feature space is then mapped to user semantics representing urban scene elements in a supervised step. The procedure is applicable at multiple scales. We give examples of urban area classification and building recognition of Shuttle Radar Topography Mission data and of building reconstruction from submetric resolution Intermap data. Index Terms--Bayesian data fusion, interferometric synthetic aperture radar (InSAR), scene understanding.

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm 3 ) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified - ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.

Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the "fourth industrial revolution" that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes.

Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential component for building phenological models. Satellite data and, particularly, Copernicus' ERA5 climate reanalysis data are easily available. Weather stations, on the other hand, provide scattered temperature data, with fragmentary spatial coverage and accessibility, as such being scarcely efficacious as unique source of information for the implementation of predictive models. However, as ERA5 reanalysis data are not real temperature measurements but reanalysis products, it is necessary to verify whether these data can be used as a replacement for weather station temperature measurements. The aims of this study were: (i) to assess the validity of ERA5 data as a substitute for weather station temperature measurements, (ii) to test different machine learning models for the prediction of phenological phases while using different sets of features, and (iii) to optimize the base temperature of olive tree phenological model. The predictive capability of machine learning models and the performance of different feature subsets were assessed when comparing the recorded temperature data, ERA5 data, and a simple growing degree day phenological model as benchmark. Data on olive tree phenology observation, which were collected in Tuscany for three years, provided the phenological phases to be used as target variables. The results show that ERA5 climate reanalysis data can be used for modelling phenological phases and that these models provide better predictions in comparison with the models trained with weather station temperature measurements.

Dermoscopy (dermatoscopy, epiluminescence microscopy) is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions (PSLs), allowing a better visualization of surface and subsurface structures (from the epidermis to the papillary dermis). This diagnostic tool permits the recognition of morphologic structures not visible by the naked eye, thus opening a new dimension in the analysis of the clinical morphologic features of PSLs. In order to reduce the learning-curve of non-expert clinicians and to mitigate problems inherent in the reliability and reproducibility of the diagnostic criteria used in pattern analysis, several indicative methods based on diagnostic algorithms have been introduced in the last few years. Recently, numerous systems designed to provide computer-aided analysis of digital images obtained by dermoscopy have been reported in the literature. The goal of this article is to review these systems, focusing on the most recent approaches based on content-based image retrieval systems (CBIR).

Objectives: The goal of this study was to determine the maximally tolerated doses (MTDs) of carboplatin, paclitaxel (Taxol), etoposide, and cyclophosphamide (CTEC) with granulocyte-colony stimulating factor (G-CSF, Filgrastim) support as first-line chemotherapy in women with advanced epithelial ovarian cancer (EOC)., Methods: Newly diagnosed patients with either stage IV EOC, or stage III EOC and any amount of gross residual tumor after surgical debulking were eligible to receive six cycles of CTEC over five different dose levels in this phase I trial (planned 21-day cycle length). Paclitaxel, carboplatin, and cyclophosphamide were administered intravenously on Day 1, and oral etoposide was administered on Days 1, 2, and 3. G-CSF was administered beginning Day 4., Results: Twenty patients received a total of 98 cycles of CTEC over the five dose levels evaluated. Bone marrow suppression was the major toxic effect, with grade 4 neutropenia and thrombocytopenia being observed in 25 and 23% of cycles, respectively. The overall incidence of febrile neutropenia was 10%, and no toxic deaths occurred. No grade IV thrombocytopenia or febrile neutropenia was observed once the carboplatin dose was reduced from AUC of 7 to 5. Nonhematologic toxicity was generally mild (grade 2 or less). Dose-limiting toxicity was not observed at the highest dose level evaluated in this study, preventing assignment of the MTD. The clinical complete response rate was 92%, although 15 of 16 evaluable patients have progressed with a median progression-free interval of 4 months (range, 2-11 months). One patient remains disease-free 9 months from the completion of CTEC., Conclusions: The CTEC regimen is well tolerated and highly active. Although the MTD was not reached in this study, the short median progression-free interval suggests that this regimen is unlikely to be superior to standard treatment with paclitaxel and carboplatin. Strategies to optimize the development of future combination chemotherapy regimens in the treatment of newly diagnosed ovarian cancer are discussed., (Copyright 2000 Academic Press.)

Oral administration of Ag results in systemic hyporesponsiveness termed oral tolerance. The regulatory cells induced by oral Ag are effective in the suppression of Th1-type autoimmune diseases. We examined the cytokine microenvironment in gut-associated lymphoid tissue in response to orally administered OVA in OVA TCR-transgenic mice. Mice were fed a low (0.5 mg) or high (500 mg) dose of OVA one time or five times. Immunohistochemical analysis demonstrated increased IL-4, IL-10, and TGF-beta in the gut within 6 h of a low-dose feeding and after five low-dose or high-dose feedings. IFN-gamma and IL-2 either decreased or showed no change, with the exception of a small transient increase in IL-2 at 6 h after a low dose. Increases in IL-4 and IL-10 were found in the dome of the Peyer's patch, and increases in TGF-beta were observed in the interfollicular region and the villi. IL-10 was also substantially increased in the villi. IL-4 and IL-10 were produced predominately by CD4+ T cells. TGF-beta was found predominately in macrophages and CD4+ T cells. Peyer's patches had a marked up-regulation of TGF-beta mRNA as measured by RT-PCR. These results demonstrate the differential activation of cytokine production in discrete regions of gut-associated lymphoid tissue. The induction of cytokines known to inhibit autoimmune disease at the site of Ag absorption indicates an important role for the mucosal immune system in the establishment of oral tolerance.