A Classification of Value for Software Architecture Decisions

This paper introduces a classification for decisions originating from work performed by architects. With the creation of a new architecture, all observed decisions were documented using an existing taxonomy extended with the introduced classification. In the first four months, 80 decisions were documented. Not all decisions have the same value for the architecture and one needed a classification to reason about importance of decisions. After realization of the first increment of the architecture a sanity check was performed: The architects showed how the six most important design artefacts and the fifteen most important architectural constraints and prerequisites were related. The relationship was via decisions and the classification helps to reduce the work to make and maintain this connection over time. The classification is dynamic and over time decisions can be classified differently. This enables architectural learning by pointing out which decisions were taken too early or had little impact

Improving Diatom Enumeration Methods for Use in Predictive Bioassessment Models

Diatoms are routinely sampled in biological assessments of water quality, but the method traditionally used to characterize diatom communities does not adequately capture species richness for use in most assessment applications. The traditional enumeration method of 300 cell (or 600 valve) fixed counts was designed to characterize the relative abundance only of dominant taxa, making it inappropriate for common bioassessment applications such as observed/expected (O/E) models, which rely on species richness. We analyzed the nature of diatom communities in reference sites of varying diversity using a measure of counting efficiency, which revealed that 600 valve fixed counts did not consistently characterize high diversity sites compared to low diversity sites. To address this problem, we compared the fixed count method to a stratified method, which captures both abundance and richness, and a timed presence method, which captures richness. The stratified and timed presence methods captured greater species richness compared to fixed counts. We then evaluated the performance of these methods in O/E models using genus and species-level data. The timed presence method produced more sensitive and precise models than the fixed method at both the genus- and species-level. A timed presence method could thus improve measurements of stream health while expediting analyses and saving effort

Collecting Modern Papers: Some Inherent Challenges

This paper attempts to highlight some of the challenges commonly encountered by archivists working with modern papers. For the purpose of the study, modern papers were defined as ones where the creator of the papers is still living or has died relatively recently. Five separate case studies of modern papers in the women's history, political, family, professional association, and literary genres were based on personal interviews with six practicing archivists. Findings indicate that modern papers, as compared to more historical ones, often translate to increased complexity in donor relations; issues of privacy, trust between archivist and donor, and ego are common. Physical concerns include a general increase in the size of collections, the likelihood of multiple accessions, and problematic special formats. Finally, the modern market for manuscript materials in general has become quite pricey, at times prohibitively so

Reduction of Taxonomic Bias in Diatom Species Data

Inconsistency in taxonomic identification and analyst bias impede the effective use of diatom data in regional and national stream and lake surveys. In this study, we evaluated the effect of existing protocols and a revised protocol on the precision of diatom species counts. The revised protocol adjusts four elements of sample preparation, taxon identification and enumeration, and quality control (QC). We used six independent data sets to assess the effect of the adjustments on analytical outcomes. The first data set was produced by three laboratories with a total of five analysts following established protocols (Charles et al., Protocols for the analysis of algal samples collected as part of the U.S. Geological Survey National Water-Quality Assessment, 2002) or their slight variations. The remaining data sets were produced by one to three laboratories with a total of two to three analysts following a revised protocol. The revised protocol included the following modifications: (1) development of coordinated precount voucher floras based on morphological operational taxonomic units, (2) random assignment of samples to analysts, (3) postcount identification and documentation of taxa (as opposed to an approach in which analysts assign names while they enumerate), and (4) increased use of QC samples. The revised protocol reduced taxonomic bias, as measured by reduction in analyst signal, and improved similarity among QC samples. Reduced taxonomic bias improves the performance of biological assessments, facilitates transparency across studies, and refines estimates of diatom species distributions

Alumina and Synthesis Intermediates Derived from Diethylkaluminum Amide, Benzaldehyde and Water

The reaction of diethylaluminum amide [Et2AINH2] with benzaldehyde in toluene produces a solution of ethylaluminoxane polymer [EtAlO] and hydrobenzamide [PhCH=NCH(Ph)N=CHPh]. Alumina then is precipitated by the addition of water. Transition aluminas that may be useful in heterogeneous catalyst applications are obtained after calcining. Details of the chemistry of solution intermediates according to 1H NMR and the properties of the alumina product according to surface area analyses and powder x-ray diffraction are described

The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (\u3e60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California\u27s mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (\u3e60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions

Probabilistic estimates of future changes in California temperature and precipitation usingstatistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling

Robonaut Mobile Autonomy: Initial Experiments

A mobile version of the NASA/DARPA Robonaut humanoid recently completed initial autonomy trials working directly with humans in cluttered environments. This compact robot combines the upper body of the Robonaut system with a Segway Robotic Mobility Platform yielding a dexterous, maneuverable humanoid ideal for interacting with human co-workers in a range of environments. This system uses stereovision to locate human teammates and tools and a navigation system that uses laser range and vision data to follow humans while avoiding obstacles. Tactile sensors provide information to grasping algorithms for efficient tool exchanges. The autonomous architecture utilizes these pre-programmed skills to form complex behaviors. The initial behavior demonstrates a robust capability to assist a human by acquiring a tool from a remotely located individual and then following the human in a cluttered environment with the tool for future use