Combinations of single-top-quark production cross-section measurements and vertical bar f(LV)V(tb)vertical bar determinations at root s=7 and 8 TeV with the ATLAS and CMS experiments

This paper presents the combinations of single-top-quark production cross-section measurements by the ATLAS and CMS Collaborations, using data from LHC proton-proton collisions at = 7 and 8 TeV corresponding to integrated luminosities of 1.17 to 5.1 fb(-1) at = 7 TeV and 12.2 to 20.3 fb(-1) at = 8 TeV. These combinations are performed per centre-of-mass energy and for each production mode: t-channel, tW, and s-channel. The combined t-channel cross-sections are 67.5 +/- 5.7 pb and 87.7 +/- 5.8 pb at = 7 and 8 TeV respectively. The combined tW cross-sections are 16.3 +/- 4.1 pb and 23.1 +/- 3.6 pb at = 7 and 8 TeV respectively. For the s-channel cross-section, the combination yields 4.9 +/- 1.4 pb at = 8 TeV. The square of the magnitude of the CKM matrix element V-tb multiplied by a form factor f(LV) is determined for each production mode and centre-of-mass energy, using the ratio of the measured cross-section to its theoretical prediction. It is assumed that the top-quark-related CKM matrix elements obey the relation |V-td|, |V-ts| << |V-tb|. All the |f(LV)V(tb)|(2) determinations, extracted from individual ratios at = 7 and 8 TeV, are combined, resulting in |f(LV)V(tb)| = 1.02 +/- 0.04 (meas.) +/- 0.02 (theo.). All combined measurements are consistent with their corresponding Standard Model predictions.Peer reviewe

Smart decision support for maintenance logistics

Spare parts demand forecasting has been an interesting area of research over the past years. This area of research is getting increasing attention due to the enormous costs associated with managing and owning spare parts, while spare parts availability is critical in industries where any process blockage will cost thousands up to millions of euros. To this extent, various spare parts forecasting techniques exist to decrease the stock level, without sacrificing the required service availability. These techniques mostly use historical demand data (time series) to forecast future demand. Lately, the fast technological development in sensors and communication technologies has paved the path towards real-time condition monitoring, which in turn enabled Condition-Based maintenance (CBM). In CBM, real-time condition information allows planners to anticipate future component failures, and maintenance actions are planned accordingly. However, few researchers have considered exploiting condition information in spare parts decision-making. Doing so could be beneficial because real-time condition data gives real-time information about future spare parts demand. We propose an inventory policy that exploits the condition information and pooling effect in spare parts decision-making. It anticipates the need for a spare to perform the maintenance action by ordering one when the degradation crosses an Order Threshold smaller than the part replacement threshold. We hypothesize that the proposed policy will exploit both condition information and the pooling effect to reduce the average stock level. The policy's two decision variables are the initial stock level and the Order Threshold. We evaluate this policy using Discrete Event Simulation (DES), and we develop a simulation-based optimization algorithm that explores the search space intelligently to find the optimal parameters. We show that the proposed proactive policy reduces the average stock level by 35\% on average

Smart decision support for maintenance logistics

Spare parts demand forecasting has been an interesting area of research over the past years. This area of research is getting increasing attention due to the enormous costs associated with managing and owning spare parts, while spare parts availability is critical in industries where any process blockage will cost thousands up to millions of euros. To this extent, various spare parts forecasting techniques exist to decrease the stock level, without sacrificing the required service availability. These techniques mostly use historical demand data (time series) to forecast future demand. Lately, the fast technological development in sensors and communication technologies has paved the path towards real-time condition monitoring, which in turn enabled Condition-Based maintenance (CBM). In CBM, real-time condition information allows planners to anticipate future component failures, and maintenance actions are planned accordingly. However, few researchers have considered exploiting condition information in spare parts decision-making. Doing so could be beneficial because real-time condition data gives real-time information about future spare parts demand. We propose an inventory policy that exploits the condition information and pooling effect in spare parts decision-making. It anticipates the need for a spare to perform the maintenance action by ordering one when the degradation crosses an Order Threshold smaller than the part replacement threshold. We hypothesize that the proposed policy will exploit both condition information and the pooling effect to reduce the average stock level. The policy's two decision variables are the initial stock level and the Order Threshold. We evaluate this policy using Discrete Event Simulation (DES), and we develop a simulation-based optimization algorithm that explores the search space intelligently to find the optimal parameters. We show that the proposed proactive policy reduces the average stock level by 35\% on average

Smart Decision Support for Maintenance Logistics

Spare parts demand forecasting has been an interesting area of research over the past years. This area of research is getting increasing attention due to the enormous costs associated with managing and owning spare parts, while spare parts availability is critical in industries where any process blockage will cost thousands up to millions of euros. To this extent, various spare parts forecasting techniques exist to decrease the stock level, without sacrificing the required service availability. These techniques mostly use historical demand data (time series) to forecast future demand. Lately, the fast technological development in sensors and communication technologies has paved the path towards real-time condition monitoring, which in turn enabled Condition-Based maintenance (CBM). In CBM, real-time condition information allows planners to anticipate future component failures, and maintenance actions are planned accordingly. However, few researchers have considered exploiting condition information in spare parts decision-making. Doing so could be beneficial because real-time condition data gives real-time information about future spare parts demand. We propose an inventory policy that exploits the condition information and pooling effect in spare parts decision-making. It anticipates the need for a spare to perform the maintenance action by ordering one when the degradation crosses an Order Threshold smaller than the part replacement threshold. We hypothesize that the proposed policy will exploit both condition information and the pooling effect to reduce the average stock level. The policy's two decision variables are the initial stock level and the Order Threshold. We evaluate this policy using Discrete Event Simulation (DES), and we develop a simulation-based optimization algorithm that explores the search space intelligently to find the optimal parameters. We show that the proposed proactive policy reduces the average stock level by 35\% on average

Global impact of the COVID-19 pandemic on subarachnoid haemorrhage hospitalisations, aneurysm treatment and in-hospital mortality: 1-year follow-up

Background: Prior studies indicated a decrease in the incidences of aneurysmal subarachnoid haemorrhage (aSAH) during the early stages of the COVID-19 pandemic. We evaluated differences in the incidence, severity of aSAH presentation, and ruptured aneurysm treatment modality during the first year of the COVID-19 pandemic compared with the preceding year. Methods: We conducted a cross-sectional study including 49 countries and 187 centres. We recorded volumes for COVID-19 hospitalisations, aSAH hospitalisations, Hunt-Hess grade, coiling, clipping and aSAH in-hospital mortality. Diagnoses were identified by International Classification of Diseases, 10th Revision, codes or stroke databases from January 2019 to May 2021. Results: Over the study period, there were 16 247 aSAH admissions, 344 491 COVID-19 admissions, 8300 ruptured aneurysm coiling and 4240 ruptured aneurysm clipping procedures. Declines were observed in aSAH admissions (-6.4% (95% CI -7.0% to -5.8%), p=0.0001) during the first year of the pandemic compared with the prior year, most pronounced in high-volume SAH and high-volume COVID-19 hospitals. There was a trend towards a decline in mild and moderate presentations of subarachnoid haemorrhage (SAH) (mild: -5% (95% CI -5.9% to -4.3%), p=0.06; moderate: -8.3% (95% CI -10.2% to -6.7%), p=0.06) but no difference in higher SAH severity. The ruptured aneurysm clipping rate remained unchanged (30.7% vs 31.2%, p=0.58), whereas ruptured aneurysm coiling increased (53.97% vs 56.5%, p=0.009). There was no difference in aSAH in-hospital mortality rate (19.1% vs 20.1%, p=0.12). Conclusion: During the first year of the pandemic, there was a decrease in aSAH admissions volume, driven by a decrease in mild to moderate presentation of aSAH. There was an increase in the ruptured aneurysm coiling rate but neither change in the ruptured aneurysm clipping rate nor change in aSAH in-hospital mortality