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Although only 23 people on average have been killed per year by terrorist attacks in the United States since 2001, American citizens and politicians consistently rank terrorism as a top security threat, leading to costly wars abroad and the repression of civil liberties at home. To what extent can education about terrorism alter perceptions of the threat? Much existing scholarship—and consistent polling over the past two decades—suggests that it cannot, but we disagree. Evidence gathered from an extensive series of experimental and observational surveys involving students in 31 terrorism and non-terrorism related courses at 12 universities—including massive open online courses (MOOC) and online surveys—reveals that the more individuals learn about terrorism, the smaller they perceive the threat to be to themselves and to the U.S. In the fight against terrorism and the fear it inspires, knowing really is half the battle.

The vehicle structure is a highly complex system as it is subject to different requirements of many engineering disciplines. Multidisciplinary optimization (MDO) is a simulation-based approach for capturing this complexity and achieving the best possible compromise by integrating all relevant CAE-based disciplines. However, to enable operative application of MDO even under consideration of crash, various adjustments to reduce the high numerical resource requirements and to integrate all disciplines in a target way must be carried out. They can be grouped as follows: The use of efficient optimization strategies, the identification of relevant load cases and sensitive variables as well as the reduction of CAE calculation time of costly crash load cases by so-called finite element (FE) submodels. By assembling these components in a clever way, a novel, adaptively controllable MDO process based on metamodels is developed. There are essentially three special features presented within the scope of this paper: First, a module named global sensitivity matrix which helps with targeted planning and implementation of a MDO by structuring the multitude of variables and disciplines. Second, a local, heuristic and thus on all metamodel types computable prediction uncertainty measure that is further used in the definition of the optimization problem. And third, a module called adaptive complexity control which progressively reduces the complexity and dimensionality of the optimization problem. The reduction of resource requirements and the increase in the quality of results are significant, compared to the standard MDO procedure. This statement is confirmed by providing results for a FE full vehicle example in six load cases (five crash load cases and one frequency analysis).

The objective of our study was to investigate the association of safety-net hospital (SNH) status with the use of premium technologies in total hip arthroplasty (THA) using the American Academy of Orthopaedic Surgeons American Joint Replacement Registry.Premium technology was defined as having one or more of the following three characteristics: ceramic femoral head, dual mobility (DM) bearing, or surgery conducted with robotic assistance (RA). Patients of all ages were included and subdivided into ceramic femoral head, DM, and RA cohorts. SNH status (based on disproportionate share data), patient demographics, geographical region, hospital size, and teaching affiliation were assessed. Multivariate regression analysis was conducted to analyze any notable associations.A total of 624,933 THAs between SNHs and non-SNHs were available for analysis. Based on the three different premium technology categories, there were 551,838 THAs for ceramic femoral head utilization analysis, 601,223 THAs for DM utilization analysis, and 199,250 THAs for RA utilization analysis. SNHs were associated with less use of DM and RA (odds ratio [OR] 0.53 P0.0001, 0.39 P0.0001, respectively). No difference was observed in ceramic femoral head utilization between SNHs and non-SNHs. Patient age was significantly associated with less utilization of all three premium THA technologies (ceramic: OR 0.43 P0.0001; DM: OR 0.93 P0.0001, RA: OR 0.89 P0.001). Teaching hospitals were significantly associated with increased utilization of premium THA technologies (ceramic: OR 1.23 P0.0001, DM: OR 1.62 P0.0001, RA: OR 5.33 P0.001).Premium THA technologies are becoming increasingly used across the US healthcare system; however, that growth is not equal in hospitals with marginalized patient populations. The utilization of ceramic femoral heads is becoming increasingly common across healthcare systems suggesting that ceramic femoral heads may no longer be considered premium technology but rather standard THA care.Level III.

Snow is a crucial component of the hydrological cycle in the Western Himalaya, where the warming climate is already impacting precipitation and melt runoff patterns. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment (2,210 km2) of Afghanistan. Located in northern Afghanistan, the Panjshir catchment of the Kabul river basin is the westernmost catchment of the transboundary Indus river system. The climate in Panjshir catchment is characterised by warm-dry summer and cold-wet winter with a large spatial and temporal heterogeneity. Water from snowmelt is used in various sectors in downstream regions, and thus plays a critical role in securing the livelihood of millions of people. In order to analyse the future evolution of snow-related processes under climate change, a few global climate model simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5 which showed reasonable performance when compared with ERA5 data for the historic period (1981-2010) were selected. The selected models were then segregated into two groups: those projecting a cold-wet climate with a 13-28% and 2.5-4.9oC increase in precipitation and temperature respectively, and those projecting a warm-dry climate with a 26-40% decrease in precipitation and a 4.3-7.8oC increase in temperature by the end of the 21st century. These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. Results of our analysis show that the J2000 model captures the snow cover dynamics well in the historical period (2003-2018) compared to the improved MODIS-derived snow cover with a coefficient of determination of 0.94. The model was then forced by climate projections from the selected GCMs to quantify the future changes in snow cover area, snow storage and snowmelt. A consistent decrease in decadal snow cover is projected in which the warm-dry models showed a higher decrease than cold-wet models. A 10-18 % reduction in annual snow cover is projected by the cold-wet models whereas a 22-36% reduction is expected from the warm-dry models. At the seasonal scale, across all models and scenarios, the snow cover in autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources in the Panjshir region have direct implications for the water-dependent sectors in the downstream regions and highlight a need for a better understanding of current water usage and future adaptation practices in the Western Himalaya.

Snow is a crucial component of the hydrological cycle in the Western Himalaya. Water from snowmelt is used in various sectors in downstream regions, thus playing a critical role in securing the livelihoods of millions of people. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment of Afghanistan, a sub-basin of the Indus, in the Western Himalaya. We applied a three-step approach to select a few global climate model (GCM) simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5, which showed reasonable performance with ERA5-Land dataset for the chosen historical period (1981–2010). The selected model simulations were then segregated into two groups: those projecting a cold-wet climate and those projecting a warm-dry climate by the end of the 21st century (2071–2100). These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. The results indicate that the model captures well the snow cover dynamics for the historical period when compared with the daily MODIS-derived snow cover. The J2000 model was then forced by climate projections from the selected GCMs to quantify future changes in snow cover area, snow storage and snowmelt. While a 10–18% reduction in annual snow cover area is projected in the cold-wet models, a 22–36% reduction is projected in the warm-dry models. Similarly, the snow cover area is projected to decrease in all elevation bands under climate change. At the seasonal scale, across all models and scenarios, the snow cover in the autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources are likely to have direct implications for water-dependent sectors in the region and call for a better understanding of water usage and future adaptation practices.

For production of sheet metal parts for car bodies, an adjustment of process parameters is required to maintain the desired part quality in presence of scattering blank properties. The digital transformation enables the application of data-driven methods for finding process parameters instead of a time-consuming experience-driven trial-and-error approach. However, due to cost and technical limitations, it is still hard to measure quality for every part. Removing data points of low-quality parts helps recommending proper process parameters. In this paper, we propose classification-based solution for recommending process parameters. In data preprocessing, the solution utilizes anomaly detection and knowledge-based methods to remove potential data points of low-quality parts without quality measures. On the processed data, a classification model is trained to predict process parameters according to blank properties. Our solution detects 30% low-quality parts and gives competitive performance (92.26% prediction accuracy) compared to a model trained on data comprising quality measures.

This reflection article presents insights on conducting fieldwork during and after COVID-19 from a diverse collection of political scientists-from department heads to graduate students based at public and private universities in the United States and abroad Many of them contributed to a newly published volume, Stories from the Field: A Guide to Navigating Fieldwork in Political Science (Krause and Szekely 2020) As in the book, these contributors draw on their years of experience in the field to identify the unique ethical and logistical challenges posed by COVID-19 and offer suggestions for how to adjust and continue research in the face of the pandemic's disruptions Key themes include how contingency planning must now be a central part of our research designs;how cyberspace has increasingly become "the field"for the time being;and how scholars can build lasting, mutually beneficial partnerships with "field citizens,"now and in the future © The Author(s), 2021 Published by Cambridge University Press on behalf of the American Political Science Association