Mohsen Pourmohammad Shahvar

This paper presents the results of an optical performance monitoring campaign for the primary mirrors installed in fluorescence telescopes at the Coihueco and Loma Amarilla sites at the Pierre Auger Observatory in Argentina. Since the end of 2003, this effort has developed into a unique long-term dataset that addresses the performance of the primary mirrors. We have focused on the scattering characteristics and specular reflectance of mirror segments, as well as their evolution over several years of operation. Despite being housed in climate-controlled buildings, dust accumulation, impurities, and natural aging — such as oxidation or other chemical or physical degradation processes — gradually deteriorate the specular reflectivity of the mirror segments. In this paper, we summarize data collected over eight years of fluorescence telescope operation since the last major cleaning in autumn 2016. During this period, periodic in situ measurements were performed of scattering characteristics using two techniques: (1) specular and diffuse reflectance measurements at a wavelength of 670 nm using a portable scatterometer, and (2) spectral reflectance measurements in the range from 300 nm to 700 nm using an integrating sphere and a spectrometer. This study builds upon a previous monitoring campaign conducted between 2012 and 2017. The measurements show that the primary mirrors remain in acceptable condition after more than 20 years of operation.

Deflections in the propagation of charged ultra-high-energy cosmic rays (UHECRs) caused by magnetic fields make the identification of their sources challenging. On the other hand, the arrival directions at Earth of neutrons point directly to their origin. The emission of UHECRs from a source is expected to be accompanied by the production of neutrons in its vicinity through interactions with ambient matter and radiation. Since free neutrons travel a mean distance d kpc ⁻¹ = 9.2 ( E EeV ⁻¹ ) before decaying, a neutron flux in the EeV range could be detected on Earth from sources of UHECRs in our Galaxy. Using cosmic-ray data from Phase I of the Surface Detector of the Pierre Auger Observatory, we search for neutron fluxes from Galactic candidate sources. We select more than 1000 objects of astrophysical interest, stacking them into target sets. The targets all have decl. within the exposure of the Observatory, ranging from −90° up to +45° for energies above 1 EeV (and up to +20° for energies down to 0.1 EeV). Given that a neutron air shower is indistinguishable from a proton one, there is a significant background due to cosmic rays. A neutron flux from the direction of a candidate source would be identified by a celestial density of events that significantly exceeds the expected density of cosmic rays for that direction. No significant excess is found at any tested target direction, and an upper limit on the neutron flux is calculated for each candidate source.

This study enhances coastal vulnerability assessment by introducing a Modified Fuzzy DEMATEL technique that incorporates socio-ecological and power dynamic considerations. The novelty of the study lies in integrating causal analysis with a participatory vulnerability framework tailored to coastal management. By analyzing multiple natural and socio-economic parameters, the results identify land use and land cover as the most influential factors, highlighting a shift toward socio-economic prioritization. The model also acknowledges limitations due to reliance on expert judgment and the absence of ground-truth validation. Our findings emphasize the need for location-specific vulnerability models rather than universal frameworks, offering insights for future participatory and evidence-based coastal management strategies.

This study presents a comprehensive framework for agro-meteorological prediction, combining stochastic modeling, machine learning techniques, and environmental feature engineering to address challenges in yield prediction and wind behavior modeling. Focused on mango cultivation in the Mediterranean region, the workflow integrates diverse datasets, including satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features like wind speed and direction. Stochastic modeling was employed to capture environmental variability, while a proxy yield was defined using key environmental factors in the absence of direct field yield measurements. Machine learning models, including random forest and multi-layer perceptron (MLP), were hybridized to improve the prediction accuracy for both proxy yield and wind components (U and V that represent the east–west and north–south wind movement). The hybrid model achieved mean squared error (MSE) values of 0.333 for U and 0.181 for V, with corresponding R² values of 0.8939 and 0.9339, respectively, outperforming the individual models and demonstrating reliable generalization in the 2022 test set. Additionally, although NDVI is traditionally important in crop monitoring, its low temporal variability across the observation period resulted in minimal contribution to the final prediction, as confirmed by feature importance analysis. Furthermore, the analysis revealed the significant influence of environmental factors such as LST, precipitable water, and soil moisture on yield dynamics, while wind visualization over digital elevation models (DEMs) highlighted the impact of terrain features on the wind patterns. The results demonstrate the effectiveness of combining stochastic and machine learning approaches in agricultural modeling, offering valuable insights for crop management and climate adaptation strategies.

This study focuses on identifying highly inclined muons in water-Cherenkov detectors similar to those used by the Pierre Auger Observatory using neural networks. Highly inclined muons, which are distinctive signatures of air showers induced by neutrinos or cosmic rays arriving at significant inclinations, offer a lower background rate compared to less inclined atmospheric particles. We explore the transition from conventional statistical approaches to machine learning methodologies to identify highly inclined muons by leveraging their unique signatures in the temporal signal distributions of three photosensors that uniformly observe the volume of a water-Cherenkov detector. By adopting machine learning, particularly neural network techniques, we aim to enhance the identification of highly inclined muons, thus improving triggering schemas designed for detecting neutrino primaries. This study not only advances the identification of highly inclined muons but also investigates the optimization of machine learning models for their efficient recognition within the water-Cherenkov detector setup.

This study introduces a novel hybrid approach for forecasting daily temperatures by integrating satellite imagery from MODIS Terra and Aqua with historical temperature records using a ResNet Convolutional Neural Network (CNN). The focus is on Acquedolci and Caronia, Sicily, regions with extensive mango plantations where accurate temperature forecasts are essential for crop protection and yield optimization. By leveraging the ResNet architecture’s capability to extract spatial features, combined with XGBoost and ARIMA for tabular data integration and residual correction, the model excels at identifying spatial and temporal patterns. The results demonstrate the potential of this approach to improve agricultural forecasting and climate modeling, paving the way for enhanced environmental monitoring and precise agricultural planning.

The coastal environment is vulnerable to natural hazards and human-induced stressors. The assessment and management of coastal risks have become a challenging task, due to many environmental and socio-economic risk factors together with the complex interactions that might arise through natural and human-induced pressures. This work evaluates the combined effect of climate-related stressors on low-lying coastal areas by applying a multi-risk scenario analysis through a Bayesian Network (BN) approach for the Venice coast. Based on the available open-source and remote sensing data for detecting shoreline changes, the developed BN model was trained and validated with oceanographic variables for the 2015–2019 timeframe, allowing us to understand the dynamics of local-scale shoreline erosion and related water quality parameters. Three “what-if” scenarios were carried out to analyze the relationships between oceanographic boundary conditions, shoreline evolution, and water quality parameters. The results demonstrate that changes in sea surface height and significant wave height may significantly increase the probability of high-erosion and high-accretion states. Moreover, by altering the wave direction, the water quality variables show significant changes in the higher-risk class. The outcome of this study allowed us to identify current and future coastal risk scenarios, supporting local authorities in developing adaptation plans.