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Forecasting the future levels of air pollution provides valuable information that holds importance for the general public, vulnerable populations, and policymakers. High-quality data are essential for precise and reliable forecasts and investigations of air pollution. Missing observations arise when the sensors utilized for assessing air quality parameters experience malfunctions, which result in erroneous measurements or gaps in the dataset and hinder the data quality. This research paper presents a novel approach for imputing missing values in air quality data in a univariate approach. The algorithm employs the random forest (RF) algorithm to impute missing observations in a bi-directional (forward and reverse in time) manner for air quality (particulate matter less than 2.5 μm (PM2.5)) data from the Republic of Serbia. The algorithm was evaluated against simple methods, such as the mean and median imputation methods, for missing observations over durations of 24, 48, and 72 h. The results indicate that our algorithm yielded comparable error rates to the median imputation method for all periods when imputing the PM2.5 data. Ultimately, the algorithm's higher computational complexity proved itself as not justified considering the minimal error decrease it achieved compared with the simpler methods. However, for future improvement, additional research is needed, such as utilizing low-code machine learning libraries and time-series forecasting techniques. [ABSTRACT FROM AUTHOR]

This document is a summary of a collection of research papers in the fields of astrophysics and geophysics. The papers cover a wide range of topics, including big data, climate change, and remote sensing. They provide datasets and analyses related to landslides, detonation craters, coastal change, and spectroscopy. The research has potential applications in fields such as climate change and astronomy. The papers underwent peer review and are considered valuable contributions to the scientific community. The authors express their gratitude to the contributors and reviewers for their efforts. [Extracted from the article]

Atomic and molecular (A&M) databases that contain information about species, their identities and radiative/collisional processes are essential and helpful tools that are utilized in many fields of physics, chemistry, and chem/phys-informatics. Errors or inconsistencies in the datasets are a serious issue since they can lead to inaccurate predictions and generate problems with the modeling. This demonstrates that data curation efforts around A&M databases are still indispensable and that in the curation process studious attention is required. Therefore, we herein present research activities around Belgrade "nodes" – datasets of collision/radiative cross-sections and rates needed for spectroscopy analysis in various A&M, optical and plasma physics fields. Methodologies of our research and both present and future aspects of the applications are explained. We explored the possibility to extend our nodes towards building a new database on Judd–Ofelt parameters by using machine learning in order to predict optical properties of luminescence materials. In addition, we hope that public availability of our datasets and their graphical representations will also motivate others to investigate the potential of these data. [ABSTRACT FROM AUTHOR]

On the basis of calculated values for the conductivity in an external electric field, we determined the high-frequency characteristics of plasmas under extreme conditions (e. g. dense plasma focus device). The examined range of frequencies covers the IR, visible, UV, XUV up to X regions and the considered electronic number density and temperature are in the ranges of 1021 cm-3 ≤ Ne ≤ 5×1024 cm-3 and 2×104 K ≤ T ≤ 106 K, respectively. The data obtained using this method are important for plasma focus research, laboratory plasma research, investigation of atmosphere plasmas of astrophysical objects like white dwarfs with different atmospheric compositions. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 176002 Grant no. III44002]

We have investigated the formation of laser‐dressed states in HF components of sodium atoms and their manifestations in Autler–Townes spectra. An explicit procedure is proposed for constructing a specific Morris–Shore wave function basis, within which two‐photon excitation schemes are reduced to sets of mutually orthogonal 3‐ and 2‐level excitation sequences, and single uncoupled states. It has been demonstrated that the Morris–Shore basis states correspond to bright and dark states, along with chameleon states introduced in our recent work. Numerical simulations of the Autler–Townes spectra reveal that chameleon states can be further categorized as "fast" or "slow." Our extended classification of dressed states enables more accurate reconstruction of excited state properties in optical methods for atomic media diagnostics. [ABSTRACT FROM AUTHOR]

The study of solar-influenced space weather and, consequently, extreme weather events, climate change, protection, and preservation are of particular interest in modern science because they are acknowledged as being important for sustainable development [[1], [3], [5]]. The authors of [[8]] have developed an empirical model of the lower ionosphere plasmas density, and have provided a simple approximative formula for electron density. [Extracted from the article]