Browsing by Author "Kumar, A."

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Advanced Oxidation Processes for Degradation of Pharmaceuticals Used During Covid-19 Pandemic
(Elsevier, 2023) Ramirez, I.; Mariam, E.; Kumar, A.; Yanardağ, D.; Villaseñor-Basulto, D.L.; Garcia-Huante, Y.G.; Ordaz, A.
The COVID-19 pandemic impacted public health, the economy, and the environment worldwide. During the pandemic, high demand for prescribed pharmaceuticals to treat COVID-19 and other consequential illnesses was observed, including antiviral, corticosteroids, antidepressants, analgesics, and antibiotics. The excessive use of these pharmaceutical compounds provoked new concerns regarding their presence in water bodies. Although the concentrations of these compounds in water are in trace levels (e.g., ng L−1 in most cases), the scientific community has classified them as emerging contaminants of paramount importance. Wastewater and drinking water systems have been encouraged and, in some cases, required to remove these emerging contaminants. Among various treatment techniques, advanced oxidation processes (AOPs) are potential technologies to degrade and remove these contaminants. AOPs represents a broad group of treatment processes for oxidizing compounds that are typically resistant to conventional redox processes. In this chapter, the impact of COVID-19 on water systems is discussed to understand the current circumstances of associated pharmaceutical compounds. We explore the effectiveness of AOPs from the lens of removing these organic molecules. In addition, we provide an overview of the current methods for the detection and quantification of pharmaceutical compounds against COVID-19 in wastewater. The information presented in this chapter has the potential to help engineers, scientists, and public health professionals navigate how AOPs can be used for degradation of pharmaceuticals in water. © 2024 Elsevier Inc. All rights reserved.
Citation - WoS: 1
Angular Analysis of the B0 → K*(892)0μ+μ- Decay in Proton-Proton Collisions at √s=13 TeV
(Elsevier, 2025) Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Benato, L.; Bergauer, T.; Kumar, A.
A full set of optimized observables is measured in an angular analysis of the decay B-0 -> K*(892)(0)mu(+)mu(-) using a sample of proton-proton collisions at root s = 13 TeV, collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 140 fb(-1). The analysis is performed in six bins of the squared invariant mass of the dimuon system, q(2), over the range 1.1 < q(2) < 16 GeV2. The results are among the most precise experimental measurements of the angular observables for this decay and are compared to a variety of predictions based on the standard model. Some of these predictions exhibit tension with the measurements.
Identification of Low-Momentum Muons in the CMS Detector Using Multivariate Techniques in Proton-Proton Collisions at √s=13.6 TeV
(IOP Publishing Ltd, 2025) Chekhovsky, V.; Hayrapetyan, A.; Makarenko, V.; Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Kumar, A.
Soft muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb(-1).