Marker studies of nickel silicide formation

Includes bibliographical references.Atomic diffusion during the solid state formation of thin films of nickel silicides (Ni2Si and NiSi) from nickel and amorphous silicon has been investigated using 31Si radioactive tracer and inert marker techniques. Samples were prepared by vacuum deposition of thin films of nickel and silicon, followed by thermal annealing to effect silicide growth. The radioactive tracer investigation of Ni2Si showed nickel to be the diffusing species during silicide growth. Sharply defined Ni2si* profiles of 100% radioactive concentration at the sample surface were - obtained. The results are compared with previous results in which the profiles were more spread out and of lower surface concentration. The radioactive tracer investigation of NiSi formation showed that nickel is also the diffusing species during second phase growth. The NiSi * layer was found to be of 100% concentration. Some spreading of the activity profile near the NiSi/NiSi* interface was observed. The results were consistent with previous 31Si tracer work on NiSi formation and also with the present Ni * 2Si results. The inert marker investigation used an ultra-thin (5-10 A) continuous layer of Mo or Ta to monitor atomic movement during silicide growth. The results confirmed nickel to be the diffusing species during the growth of both phases. These results are in excellent agreement with previous inert marker studies of nickel silicide growth

Edward Harriman Correspondence

Entry is a letter to Harriman

Edward Hudon Correspondence

Entries include brief biographical information and letters of correspondence

Alfalfa Weevil Control in South Dakota

Alfalfa weevil, Hypera postico (Gyllenhal), is a significant pest of alfalfa in some areas of South Dakota each year. Feeding by larval stages results in reductions in yield and quality of the harvested forage. Although it generally is a problem of first-cutting alfalfa in South Dakota, regrowth of the second crop is occasionally hindered by alfalfa weevil as well

Potato Leafhopper Control in South Dakota Alfalfa

Potato leafhoppers migrate into South Dakota each year and are capable of causing extensive damage to first-year and established alfalfa stands. This pest is difficult to detect, and it often damages alfalfa before producers are aware of its presence. Potato leafhoppers can be especially damaging to newly seeded alfalfa stands

Leslie E. Decker Correspondence

Entries include a typed letter on University of Maine, Department of History, stationery

Combining Slaughterhouse Surveillance Data with Cattle Tracing Scheme and Environmental Data to Quantify Environmental Risk Factors for Liver Fluke in Cattle.

Liver fluke infection causes serious disease (fasciolosis) in cattle and sheep in many regions of the world, resulting in production losses and additional economic consequences due to condemnation of the liver at slaughter. Liver fluke depends on mud snails as an intermediate host and infect livestock when ingested through grazing. Therefore, environmental factors play important roles in infection risk and climate change is likely to modify this. Here, we demonstrate how slaughterhouse data can be integrated with other data, including animal movement and climate variables to identify environmental risk factors for liver fluke in cattle in Scotland. We fitted a generalized linear mixed model to the data, with exposure-weighted random and fixed effects, an approach which takes into account the amount of time cattle spent at different locations, exposed to different levels of risk. This enabled us to identify an increased risk of liver fluke with increased animal age, rainfall, and temperature and for farms located further to the West, in excess of the risk associated with a warmer, wetter climate. This model explained 45% of the variability in liver fluke between farms, suggesting that the unexplained 55% was due to factors not included in the model, such as differences in on-farm management and presence of wet habitats. This approach demonstrates the value of statistically integrating routinely recorded slaughterhouse data with other pre-existing data, creating a powerful approach to quantify disease risks in production animals. Furthermore, this approach can be used to better quantify the impact of projected climate change on liver fluke risk for future studies

Edward D. Ives Correspondence

Entries include a letter typed on University of Maine stationery, correspondence with Folkways Records, and a photocopy of a newspaper article

Models of Emergency Departments for Reducing Patient Waiting Times

In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation. The current models implement relatively simple general scenarios, and rely on a combination of simulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency departments. In addition, several concepts from telecommunications engineering are translated into this modeling context. The framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine learning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively. The models' utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input parameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they continue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with more data and real data relative to physical (spatial–topographical) and social inputs (staffing, patient care models, etc.). Real data obtained through proximity location and tracking system technologies is one example discussed