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Highway slopes are susceptible to various geohazards, including landslides, rockfalls, and soil creep, necessitating early detection to minimize disruptions, prevent collisions, and ensure road safety. Conventional methods, such as visual inspections and periodic surveys, may overlook subtle changes or fail to provide timely alerts. This research aims to enhance slope movement and instability detection by leveraging advanced remote-sensing technologies such as interferometric synthetic aperture radar (InSAR), light detection and ranging (LiDAR), and uncrewed aerial vehicles (UAV). The primary objective is to develop an integrated approach combining multiple data sources to detect and predict highway slope movement effectively. InSAR offers surface deformation measurements over time, capturing nuanced slope movements, while LiDAR and UAVs provide high-resolution elevation information, including slope angles, curvature, and vegetation cover. This study explores methods to integrate these complementary data sets to validate the slope movement detection from InSAR. The research involves establishing a baseline ground motion scenario using historical open-access Sentinel-1 satellite data spanning 10 years (2018􀀐2024) for the central Mississippi region, characterized by expansive clay prone to volume changes, then comparing the ground motions with those observed from near-surface remote sensing. The baseline ground motion scenario is compared with ground truthing from near-surface remote sensing surveys conducted by LiDAR and UAV photogrammetry. The point cloud and imagery obtained from LiDAR and UAVs facilitated cross-verification and validation of the InSAR ground displacements. This study provides a comprehensive and innovative methodology for monitoring highway infrastructure using InSAR and near-surface remote sensing techniques such as LiDAR and UAV. Continuous ground motion analysis provides immediate feedback on slope performance, helping to prevent potential failures. LiDAR change detection allows for detailed evaluation of highway slopes and precise identification of potential failure locations. Integrating remote sensing techniques into geotechnical asset management programs is crucial for proactively assessing risks and enhancing highway safety and resilience. Future studies will use this data set to create finite-element-based numerical models, aiding in developing surrogate models for highway embankments based on observed InSAR ground motion patterns. This study will also serve as a foundation for future machine-learning classification models for detecting vulnerable geo-infrastructure assets. 

Context. With a rapidly rising number of transients detected in astronomy, classification methods based on machine learning are increasingly being employed. Their goals are typically to obtain a definitive classification of transients, and for good performance they usually require the presence of a large set of observations. However, well-designed, targeted models can reach their classification goals with fewer computing resources. Aims. The aim of this study is to assist in the observational astronomy task of deciding whether a newly detected transient warrants follow-up observations. Methods. This paper presents SNGuess, a model designed to find young extragalactic nearby transients with high purity. SNGuess works with a set of features that can be efficiently calculated from astronomical alert data. Some of these features are static and associated with the alert metadata, while others must be calculated from the photometric observations contained in the alert. Most of the features are simple enough to be obtained or to be calculated already at the early stages in the lifetime of a transient after its detection. We calculate these features for a set of labeled public alert data obtained over a time span of 15 months from the Zwicky Transient Facility (ZTF). The core model of SNGuess consists of an ensemble of decision trees, which are trained via gradient boosting. Results. Approximately 88% of the candidates suggested by SNGuess from a set of alerts from ZTF spanning from April 2020 to August 2021 were found to be true relevant supernovae (SNe). For alerts with bright detections, this number ranges between 92% and 98%. Since April 2020, transients identified by SNGuess as potential young SNe in the ZTF alert stream are being published to the Transient Name Server (TNS) under the AMPEL_ZTF_NEW group identifier. SNGuess scores for any transient observed by ZTF can be accessed via a web service https://ampel.zeuthen.desy.de/api/live/docs . The source code of SNGuess is publicly available https://github.com/nmiranda/SNGuess . Conclusions. SNGuess is a lightweight, portable, and easily re-trainable model that can effectively suggest transients for follow-up. These properties make it a useful tool for optimizing follow-up observation strategies and for assisting humans in the process of selecting candidate transients. 

Abstract Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set. 

Abstract The production of $$\pi ^{\pm }$$ π ± , $$\mathrm{K}^{\pm }$$ K ± , $$\mathrm{K}^{0}_{S}$$ K S 0 , $$\mathrm{K}^{*}(892)^{0}$$ K ∗ ( 892 ) 0 , $$\mathrm{p}$$ p , $$\phi (1020)$$ ϕ ( 1020 ) , $$\Lambda $$ Λ , $$\Xi ^{-}$$ Ξ - , $$\Omega ^{-}$$ Ω - , and their antiparticles was measured in inelastic proton–proton (pp) collisions at a center-of-mass energy of $$\sqrt{s}$$ s = 13 TeV at midrapidity ( $$|y|<0.5$$ | y | < 0.5 ) as a function of transverse momentum ( $$p_{\mathrm{T}}$$ p T ) using the ALICE detector at the CERN LHC. Furthermore, the single-particle $$p_{\mathrm{T}}$$ p T distributions of $$\mathrm{K}^{0}_{S}$$ K S 0 , $$\Lambda $$ Λ , and $$\overline{\Lambda }$$ Λ ¯ in inelastic pp collisions at $$\sqrt{s} = 7$$ s = 7  TeV are reported here for the first time. The $$p_{\mathrm{T}}$$ p T distributions are studied at midrapidity within the transverse momentum range $$0\le p_{\mathrm{T}}\le 20$$ 0 ≤ p T ≤ 20 GeV/ c , depending on the particle species. The $$p_{\mathrm{T}}$$ p T spectra, integrated yields, and particle yield ratios are discussed as a function of collision energy and compared with measurements at lower $$\sqrt{s}$$ s and with results from various general-purpose QCD-inspired Monte Carlo models. A hardening of the spectra at high $$p_{\mathrm{T}}$$ p T with increasing collision energy is observed, which is similar for all particle species under study. The transverse mass and $$x_{\mathrm{T}}\equiv 2p_{\mathrm{T}}/\sqrt{s}$$ x T ≡ 2 p T / s scaling properties of hadron production are also studied. As the collision energy increases from $$\sqrt{s}$$ s = 7–13 TeV, the yields of non- and single-strange hadrons normalized to the pion yields remain approximately constant as a function of $$\sqrt{s}$$ s , while ratios for multi-strange hadrons indicate enhancements. The $$p_\mathrm{{T}}$$ p T -differential cross sections of $$\pi ^{\pm }$$ π ± , $$\mathrm {K}^{\pm }$$ K ± and $$\mathrm {p}$$ p ( $$\overline{\mathrm{p}}$$ p ¯ ) are compared with next-to-leading order perturbative QCD calculations, which are found to overestimate the cross sections for $$\pi ^{\pm }$$ π ± and $$\mathrm{p}$$ p ( $$\overline{\mathrm{p}}$$ p ¯ ) at high $$p_\mathrm{{T}}$$ p T .