Development and implementation of new technologies in the steel industry

At each successive stage in the R&D cycle leading to the commercialization of a new technology, management in any industry must reevaluate prospects and make decisions accordingly. However, for the steel industry the difficulties of this kind of decision-making throughout the usually lengthy R&D cycle are heightened by the very nature of the industry. For example, the large scale of its processes and the high capital costs for plants for new processes mean that the progressive scale-ups during a given development may themselves cost millions. From Bethlehem Steel's experience the author cites one new product and two major process developments to illustrate technical and economic considerations that enter into decision-making as R&D moves step by step toward the commercialization of new technology.     

SYNSEG – Eine Methode zur <Emphasis Type="Italic">syn</Emphasis>taxgeleiteten <Emphasis Type="Italic">Seg</Emphasis>mentierung von Kodiereinheiten für die Analyse von Gruppenprozessen

Observing group processes allows for obtaining insights into what successful groups do differently than less successful groups. In doing so, observational data is typically transcribed or integrated into coding software, coding units are identified, and coding systems are applied to code these units with regard to the respective content. While there are systems available for transcribing and coding observational group data, the segmentation of coding units is mostly left to the coders’ intuition. Standardized and tested procedures for identifying coding units are not available for group research, limiting the reliability of coding group data. We introduce a method which aims at systematically identifying and segmenting coding units to enhance coding reliability. SYNSEG – syntax-based segmentation of coding units – suggests ten rules to segment coding units based on German grammar. To test for reliability, two coders applied SYNSEG for segmenting a 60-minute group discussion. A normalised Levensthein Distance of nD = 0,19 indicated satisfying coder agreement. We discuss the relevance and applicability of SYNSEG in applied group research.     

World Elder Abuse Awareness Day: The Concept,the Reality,and the Promise

This commentary traces the origins and evolution of the World Elder Abuse Awareness Day (WEAAD) initiative and provides recommendations for increasing WEAAD’s impact as a catalyst for a coordinated movement within the United States.     

Subgroup analyses in cost‐effectiveness analyses to support health technology assessments

‘Success’ in drug development is bringing to patients a new medicine that has an acceptable benefit–risk profile and that is also cost‐effective. Cost‐effectiveness means that the incremental clinical benefit is deemed worth paying for by a healthcare system, and it has an important role in enabling manufacturers to obtain new medicines to patients as soon as possible following regulatory approval. Subgroup analyses are increasingly being utilised by decision‐makers in the determination of the cost‐effectiveness of new medicines when making recommendations. This paper highlights the statistical considerations when using subgroup analyses to support cost‐effectiveness for a health technology assessment. The key principles recommended for subgroup analyses supporting clinical effectiveness published by Paget et al. are evaluated with respect to subgroup analyses supporting cost‐effectiveness. A health technology assessment case study is included to highlight the importance of subgroup analyses when incorporated into cost‐effectiveness analyses. In summary, we recommend planning subgroup analyses for cost‐effectiveness analyses early in the drug development process and adhering to good statistical principles when using subgroup analyses in this context. In particular, we consider it important to provide transparency in how subgroups are defined, be able to demonstrate the robustness of the subgroup results and be able to quantify the uncertainty in the subgroup analyses of cost‐effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.     

Who Evacuates When Hurricanes Approach? The Role of Risk,Information, and Location*

Objective. This article offers an expanded perspective on evacuation decision making during severe weather. In particular, this work focuses on uncovering determinants of individual evacuation decisions. Methods. We draw on a survey conducted in 2005 of residents in the eight‐county Houston metropolitan area after Hurricane Rita made landfall on September 24, 2005. Results. We find that evacuation decisions are influenced by a heterogeneous set of parameters, including perceived risk from wind, influence of media and neighbors, and awareness of evacuation zone, that are often at variance with one of the primary measures of risk used by public officials to order or recommend an evacuation (i.e., storm surge). We further find that perceived risk and its influence on evacuation behavior is a local phenomenon more readily communicated by and among individuals who share the same geography, as is the case with residents living inside and outside official risk areas. Conclusions. Who evacuates and why is partially dependent on where one lives because perceptions of risk are not uniformly shared across the area threatened by an approaching hurricane and the same sources and content of information do not have the same effect on evacuation behavior. Hence, efforts to persuade residential populations about risk and when, where, and how to evacuate or shelter in place should originate in the neighborhood rather than emanating from blanket statements from the media or public officials. Our findings also raise important policy questions (included in the discussion section) that require further study and consideration by those responsible with organizing and implementing evacuation plans.     

Statistical comparisons of methods for interpolating the output of a numerical air quality model

This paper compares Models-3/Community Multiscale Air Quality (CMAQ) outputs at multiple resolutions by interpolating from coarse resolution to fine resolution and analyzing the interpolation difference. Spatial variograms provide a convenient way to investigate the spatial character of interpolation differences and, importantly, to distinguish between naive (nearest neighbor) interpolation and bilinear interpolation, which takes a weighted average of four neighboring cells. For example, when the higher resolution is three times the lower, the variogram of the difference between naive interpolation of the lower resolution output and the higher resolution output shows a depression at every third lag. This phenomenon is related to the blocky nature of naive interpolation and demonstrates the inferiority of naive interpolation to bilinear interpolation in a way that pixelwise comparisons cannot. Theoretical investigations show when one can expect to observe this periodic depression in the variogram of interpolation differences. Naive interpolation is in fact used widely in a number of settings; our results suggest that it should be routinely replaced by bilinear interpolation.