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    Commissioning and first measurements with LHC collisions of BIS78 RPCs, an innovative detector for ATLAS HL-LHC upgrades
    (Elsevier B.V., 2024) Simsek, S.
    The BIS78 project, BI pilot project, consists of 16 sMDT + RPC chambers installed in the barrel–endcap transition region with the function of helping in the reduction of the fake muons produced upstream of the cryostats. The BIS78 RPCs represents a new generation of RPCs, basing their largely improved performance on a novel and highly performing front-end (FE) electronics, which is able to detect 1-2 fC of induced signals increasing rate capability by a factor of 10 with respect to the present ATLAS RPCs. BIS78 are equipped with a gas gap 1 mm thick, granting a time resolution of 350 ps and less weight and space occupancy. Additionally, the new electronics could make the BIS78 apparatus more easily compatible with the new eco-gas mixtures. The entire BIS78 apparatus has been installed successfully in the ATLAS experimental cavern and its commissioning will be illustrated along with its performance at the beginning of LHC Run-3. © 2024 Elsevier B.V.
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    A machine learning decision support system for determining the primary factors impacting cancer survival and their temporal effect
    (Elsevier Inc., 2023) Dag, A.Z.; Johnson, M.; Kibis, E.; Simsek, S.; Cankaya, B.; Delen, D.
    It is critical for healthcare providers to accurately determine lung cancer patients' prognostics and develop customized treatment plans. However, lung cancer has proven to be a complex disease, and every patient responds differently to treatment options, making survivability predictions highly challenging. This study proposes a holistic machine learning model that can assist healthcare providers in predicting the temporal effects of lung cancer-related factors on one-, five-, and ten-year survival rates. Variable selection algorithms such as genetic algorithm (GA) and Baruta are employed along with data balancing methods to achieve parsimonious models for survival prediction. Classification results are obtained through logistic regression and extreme gradient boosting algorithms followed by an information fusion technique to combine the classification results and identify the temporal effects of lung cancer variables over time. Results demonstrate that the prediction power of the classification models improved as the survival period increased. The models trained using the GA and intersection variable sets generated better average prediction scores. The study contributes to the cancer literature by analyzing the varying temporal impacts of lung cancer variables over varying time periods. Medical professionals can use these findings to understand better the longitudinal characteristics of lung cancer patients’ survival indicators. © 2023 The Authors