Lattice relaxation in α‐iron containing small xenon‐vacancy clusters

Binding energies and atomic configurations for small xenon‐vacancy clusters in α‐iron have been computed using a model based on a two‐body potential between the interacting atoms. Various clusters were placed near the center of a sphere containing about 150 iron atoms initially at regular positions in bcc configuration. The results of these calculations are of importance for the interpretation of hyperfine interaction measurements on implanted radioactive sources

A single-year comparison of two methods of censusing breeding Red Knot and Sanderling in High Arctic Greenland

A uniquely intense field effort at Zackenberg, NE Greenland, in June–July 2003 made it possible, for the first time, to compare two methods of measuring breeding densities of two notoriously difficult-to-census High Arctic breeding shorebirds (Red Knot Calidris canutus and Sanderling Calidris alba): (1) mapping of displays and other activities of birds in a rapid assessment early in the season, and (2) systematic ‘roping’ of potential breeding areas to disturb and then find incubating birds on, or very close to, their nests. The latter method is particularly relevant to species that rely on crypsis to avoid nest detection. During 16 and 19 June an experienced observer, in a standardized way, mapped all visual observations of Red Knot and Sanderling over a 4.0 km2 study area, which consisted mainly of low-angle mountain slopes between altitudes of 100 and 400 m. The observations were interpreted to represent 8–9 ‘pairs’ of Red Knot and 13–17 ‘pairs’ of Sanderling. Observations nearby allowed for a few additional pairs of Red Knot. Between 17 June and 5 July a team of five observers systematically roped the same study area and found two Red Knot nests and 15 Sanderling nests. Most of the study area remained under daily scrutiny until 19 July, and during these visits we encountered two more families of Red Knots and seven more Sanderling families. Thus, the roping effort yielded a few more Sanderling ‘pairs’ than expected from the early-season survey, but fewer Red Knot. This may imply that either: (1) the early-season rapid assessment particularly overestimated the knot population, and/or (2) relative to Sanderlings, knot nests were heavily depredated before roping, and/or (3) incubating birds escaped notice during roping, and/or (4) some of the local Red Knots may not have started a breeding attempt at all. Further work with radio-tagged individuals is necessary to establish whether we need to invoke non-breeding as a cause of the discrepancy

Engaging Storytimes

Two picture book authors will present ways to make storytimes more engaging. We\u27ll each give examples of activities and ideas to make a reading more interactive and encourage participation using a variety of picture books for examples

Modeling Performance of Butterfly Valves Using Machine Learning Methods

Control of airflow of activated sludge systems has significant challenges due to the non-linearity of the control element (butterfly valve). To overcome this challenge, some valve manufacturers developed valves with linear characteristics. However, these valves are 10–100 times more expensive than butterfly valves. By developing models for butterfly valves installed characteristics and utilizing these models for real-time airflow control, the authors of this paper aimed to achieve the same accuracy of control using butterfly valves as achieved using valves with linear characteristics. Several approaches were tested to model the installed valve’s characteristics, such as a formal mathematical model utilizing Simscape/Matlab software, a semi-empirical model, and several machine learning methods (MLM), including regression, support vector machine, Gaussian process, decision tree, and deep learning. Several versions of the airflow-valve position models were developed using each machine learning method listed above. The one with the smallest forecast error was selected for field testing at the 55.5×103 m3/day 12 MGD City of Chico activated sludge system. Field testing of the formal mathematical model, semi-empirical model, and the regularized gradient boosting machine model (the best among MLMs) showed that the regularized gradient boosting machine model (RGBMM) provided the best accuracy. The use of the RGBMMs in airflow control loops since 2019 at the City of Chico wastewater treatment plant showed that these models are robust and accurate (2.9% median error)

Modeling Performance of Butterfly Valves Using Machine Learning Methods

Control of airflow of activated sludge systems has significant challenges due to the non-linearity of the control element (butterfly valve). To overcome this challenge, some valve manufacturers developed valves with linear characteristics. However, these valves are 10–100 times more expensive than butterfly valves. By developing models for butterfly valves installed characteristics and utilizing these models for real-time airflow control, the authors of this paper aimed to achieve the same accuracy of control using butterfly valves as achieved using valves with linear characteristics. Several approaches were tested to model the installed valve’s characteristics, such as a formal mathematical model utilizing Simscape/Matlab software, a semi-empirical model, and several machine learning methods (MLM), including regression, support vector machine, Gaussian process, decision tree, and deep learning. Several versions of the airflow-valve position models were developed using each machine learning method listed above. The one with the smallest forecast error was selected for field testing at the 55.5×103 m3/day 12 MGD City of Chico activated sludge system. Field testing of the formal mathematical model, semi-empirical model, and the regularized gradient boosting machine model (the best among MLMs) showed that the regularized gradient boosting machine model (RGBMM) provided the best accuracy. The use of the RGBMMs in airflow control loops since 2019 at the City of Chico wastewater treatment plant showed that these models are robust and accurate (2.9% median error)