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In the context of modeling spectral energy distributions (SEDs) for blazars, we extend the method that uses a convolutional neural network (CNN) to include external inverse Compton processes. The model assumes that relativistic electrons within the emitting region can interact with and up-scatter external photons originating from the accretion disk, the broad-line region, and the torus, to produce the observed high-energy emission. We trained the CNN on a numerical model that accounts for the injection of electrons, their self-consistent cooling, and pair creation-annihilation processes, considering both internal and all external photon fields. Despite the larger number of parameters compared to the synchrotron self-Compton model and the greater diversity in spectral shapes, the CNN enables an accurate computation of the SED for a specified set of parameters. The performance of the CNN is demonstrated by fitting the SED of two flat-spectrum radio quasars, namely 3C 454.3 and CTA 102, and obtaining their parameter posterior distributions. For the first source, the available data in the low-energy band allowed us to constrain the minimum Lorentz factor of the electrons, ${\gamma }_{\min }$ , while for the second source, due to the lack of these data, ${\gamma }_{\min }={10}^{2}$ was set. We used the obtained parameters to investigate the energetics of the system. The model developed here, along with one from Bégué et al., enables self-consistent, in-depth modeling of blazar broadband emissions within a leptonic scenario.

Modeling the multiwavelength spectral energy distributions (SEDs) of blazars provides key insights into the underlying physical processes responsible for the emission. While SED modeling with self-consistent models is computationally demanding, it is essential for a comprehensive understanding of these astrophysical objects. We introduce a novel, efficient method for modeling the SEDs of blazars by the mean of a convolutional neural network (CNN). In this paper, we trained the CNN on a leptonic model that incorporates synchrotron and inverse Compton emissions, as well as self-consistent electron cooling and pair creation–annihilation processes. The CNN is capable of reproducing the radiative signatures of blazars with high accuracy. This approach significantly reduces the computational time, thereby enabling real-time fitting to multiwavelength data sets. As a demonstration, we used the trained CNN with MultiNest to fit the broadband SEDs of Mrk 421 and 1ES 1959+650, successfully obtaining their parameter posterior distributions. This novel framework for fitting the SEDs of blazars will be further extended to incorporate more sophisticated models based on external Compton and hadronic scenarios, allowing for multimessenger constraints in the analysis. The models will be made publicly available via a web interface at the Markarian Multiwavelength Data Center to facilitate self-consistent modeling of multimessenger data from blazar observations.

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winter. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e. MO-OOA + LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major SOA component during the daytime, with a 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during the nighttime. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during the daytime and 36.1 % of total SOA mass (11.2 % of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass (11.7 % and 8.5 % of total OA mass), respectively, during the daytime and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass), respectively, during the nighttime. A simple dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the daytime suggests an increased OA toxicity and health-related consequences for the general public.

Abstract Background The aim of this work was to evaluate the impact of the restrictions put in place to control the COVID‐19 pandemic on new diagnoses of malignant mesothelioma (MM) in Italy. Methods Twelve of the 21 Italian malignant mesothelioma CORs (regional operating centres) participated. The study included all cases of MM with microscopic confirmation; cases without microscopic confirmation and death certificate only (DCO) were excluded. For each case, information on sex, date of birth, tumor site, morphology, and date of diagnosis was retrieved. We compared the number of incident cases in 2020 with 2019, looking at the overall picture and for four periods: pre‐pandemic (January–February), first wave (March–May), low incidence (June–September), and second wave (October–December). Results A total of 604 cases were registered: 307 in 2019 and 297 in 2020. In the 2020 pre‐pandemic period, the incidence was higher than in the same months in 2019 (+45%); there was no significant change during the first wave (+1%) or in the low‐incidence period (−3%), while a decrease was observed during the second wave (−32%). However, the data were not homogeneous across the country: the increase in the pre‐pandemic period concerned mostly the regions of northern (+61.5%) and central Italy (+43.5%); during the first wave, MM diagnoses increased in the northern (+38.5%) and central (+11.4%) regions but decreased in the southern regions (−52.9%). All these differences are compatible with random fluctuations. Conclusion The COVID‐19 pandemic had little or no impact on new MM diagnoses, and variations were not homogeneous throughout the country.

Air pollution studies are still scarce in some areas in Europe like around the Adriatic Sea. Source apportionment of the fine particulate (PM2.5) organic aerosol (OA) was conducted near Rogoznica, a small touristic settlement on the Eastern Adriatic coast of Croatia, near Lake Rogoznica (43.53° N, 15.95° E). Filter-based offline analyses of PM by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) were used to apportion OA to its sources. We quantified the contributions of fresh biomass burning OA (BBOA) and three oxygenated OA (OOA), denoted as background OOA (bkgOOA), summer OOA (SOOA), and sulfur-containing OOA (SC-OOA). The bkgOOA component correlates with anthropogenically influenced secondary inorganic aerosol constituents (e.g. sulfate) and dominates OA both during the warm and cold seasons (44%). It exhibits characteristics of regional SOA and there are indications that during the warm season SOA from wildfires could be a substantial contributor. EESI-TOF measurements of the levoglucosan related ion showed a high correlation with the BBOA factor. Secondary OA has a similar molecular composition during the cold and warm seasons – in line with the large contribution of bkgOOA throughout the investigated seasons. SOOA comprises 19% of total OA and increases exponentially with the local temperature, consistent with SOA production by oxidation of biogenic emissions. In addition to biogenic precursors, other precursors (alkanes and aromatic) may contribute to the OOA enhancement during the warm season. SC-OOA is a minor contributor to OA (6%) and is most likely linked to emissions from a close-by marine lake.

In GNSS-based navigation onboard Low Earth Orbit (LEO) satellites, typical accuracy requirements are 10 cm and 0.1 mm/s for 3D position and velocity, respectively. Previous works have shown that such performance is achieved by including Galileo measurements in the estimation process. Here, we aim to evaluate the impact of employing State Space Representation (SSR) corrections, i.e., GNSS satellite orbit, clock, and biases, to be applied to the broadcast ephemerides. In this framework, the Precise Onboard Orbit Determination (P2OD) software (SW) tool developed at the University of Padua (UNIPD) is used to investigate the needs of onboard navigation. The UNIPD SW employs an Extended Kalman Filter (EKF) using a reduced-dynamics approach. The force model implemented is adapted to onboard processing, and empirical accelerations are included to take into account residual force mismodeling. Actual observation data from the LEO Sentinel-6A satellite are processed along with SSR corrections from the CNES service. Galileo-based solutions are compared to ground-based POD reference orbits. The analysis suggests that the use of SSR corrections provides sub-decimeter and below 0.1 mm/s accuracies in 3D position and velocity, respectively. Such results indicate a P2OD solution quality close to that achievable by adopting precise orbits and clocks.

Background: Recent studies supported the association between occupational exposure to asbestos and risk of cholangiocarcinoma (CC). Aim of the present study is to investigate this association using an update of mortality data from the Italian pooled asbestos cohort study and to test record linkage to Cancer Registries to distinguish between hepatocellular carcinoma (HCC) and intrahepatic/extrahepatic forms of CC. Methods: The update of a large cohort study pooling 52 Italian industrial cohorts of workers formerly exposed to asbestos was carried out. Causes of death were coded according to ICD. Linkage was carried out for those subjects who died for liver or bile duct cancer with data on histological subtype provided by Cancer Registries. Results: 47 cohorts took part in the study (57,227 subjects). We identified 639 causes of death for liver and bile duct cancer in the 44 cohorts covered by Cancer Registry. Of these 639, 240 cases were linked to Cancer Registry, namely 14 CC, 83 HCC, 117 cases with unspecified histology, 25 other carcinomas, and one case of cirrhosis (likely precancerous condition). Of the 14 CC, 12 occurred in 2010-2019, two in 2000-2009, and none before 2000. Conclusion: Further studies are needed to explore the association between occupational exposure to asbestos and CC. Record linkage was hampered due to incomplete coverage of the study areas and periods by Cancer Registries. The identification of CC among unspecific histology cases is fundamental to establish more effective and targeted liver cancer screening strategies. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20–40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. Methods: PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50–75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. Conclusion: The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems. [ABSTRACT FROM AUTHOR]

Background and Aims: The objective of this study was to assess the impact of the COVID-19 pandemic and direct-acting antiviral (DAA) agents on mortality related to chronic liver diseases (CLD). Methods: Age-standardized mortality rates were computed based on CLD as the underlying cause of death (UCOD) and as any mention in death certificates (multiple causes of death-MCOD). Time trends in age-standardized mortality rates were investigated using generalized estimation equation models. Additionally, we conducted age, period, and birth cohort (APC) analyses on CLD-related mortality associated with alcohol and hepatitis C virus (HCV). Results: Between 2008 and 2021, among residents in the Veneto region (Northeastern Italy) aged ≥35 years, there were 20 409 deaths based on the UCOD and 30 069 deaths based on MCOD from all CLD. We observed a 4% annual decline in age-standardized MCOD-based mortality throughout 2008--2021, with minor peaks corresponding to COVID-19 epidemic waves. Starting in 2016, the decline in HCV-related mortality accelerated further (p < .001). A peak in HCV-related mortality in the 1963--1967 birth cohort was observed, which levelled off by the end of the study period. Mortality related to alcoholic liver disease declined at a slower pace, becoming the most common aetiology mentioned in death certificates. Conclusions: The study demonstrates a significant decrease in HCV-related mortality at the population level in Italy with the introduction of DAAs. Continuous monitoring of MCOD data is warranted to determine if this favourable trend will continue. Further studies utilizing additional health records are needed to clarify the role of other CLD etiologies. [ABSTRACT FROM AUTHOR]

The demand for advanced nonlinear time‐history and seismic collapse simulations of old unreinforced masonry (URM) constructions to support informed risk evaluation and mitigation plans is rapidly increasing in the structural engineering profession. On one hand, offering cutting‐edge solutions based on the latest advances is challenging for practitioners, given the reduced timeframe usually available for projects and the specialized knowledge required. On the other hand, researchers frequently face difficulties in accessing old buildings and gathering key data required in complex numerical collapse analysis strategies. In this work, a pragmatic approach for evaluation of the earthquake collapse response of vulnerable old URM churches typical of the Northern Netherlands, now exposed to low‐magnitude induced seismicity due to gas extraction, is presented. To bridge the gap between academic and industry applications, an integrated framework is proposed that combines archival and onsite research, code‐based prescriptions, geometrical characterization and simplified discrete element modeling. Main outcomes include the identification of recurrent damage patterns for five old URM churches erected during the 11th, 13th, 14th, and 19th centuries, representative of key traditional multi‐leaf and cavity‐wall structural types, as well as relevant failure mechanisms and collapsed debris distributions for seismic signals of varying intensities. Produced results constitute a solid foundation of data on which to base the design of ad‐hoc retrofits and development of tailored risk assessment models. This study opens a new line of inquiry while discussing practical challenges and research questions which arose, to be of interest to both applied researchers and structural engineering professionals. [ABSTRACT FROM AUTHOR]

Background: Asbestos is a known human carcinogen and is causally associated with malignant mesothelioma, lung, larynx and ovarian cancers. Methods: Cancer risk was studied among a pool of formerly asbestos‐exposed workers in Italy. Fifty‐two Italian asbestos cohorts (asbestos‐cement, rolling‐stock, shipbuilding, and other) were pooled and their mortality follow‐up was updated to 2018. Standardized mortality ratios (SMRs) were computed for major causes of death considering duration of exposure and time since first exposure (TSFE), using reference rates by region, age and calendar period. Results: The study included 63,502 subjects (57,156 men and 6346 women): 40% who were alive, 58% who died (cause known for 92%), and 2% lost to follow‐up. Mortality was increased for all causes (SMR: men = 1.04, 95% confidence interval [CI] 1.03−1.05; women = 1.15, 95% CI 1.11−1.18), all malignancies (SMR: men = 1.21, 95% CI 1.18−1.23; women = 1.29, 95% CI 1.22−1.37), pleural and peritoneal malignancies (men: SMR = 10.46, 95% CI 9.86−11.09 and 4.29, 95% CI 3.66−5.00; women: SMR = 27.13, 95% CI 23.29−31.42 and 7.51, 95% CI 5.52−9.98), lung (SMR: men = 1.28, 95% CI 1.24−1.32; women = 1.26, 95% CI 1.02−1.53), and ovarian cancer (SMR = 1.42, 95% CI 1.08−1.84). Pleural cancer mortality increased during the first 40 years of TSFE (latency), reaching a plateau thereafter. Conclusions: Analyses by time‐dependent variables showed that the risk for pleural neoplasms increased with latency and no longer increases at long TSFE, consistent with with asbestos clearance from the lungs. Peritoneal neoplasm risk increased over all observation time. [ABSTRACT FROM AUTHOR]