Winter habitat use of fishes in the Ohio River

Winter is a critical period during which fishes may suffer increased mortality. To identify the habitats that fishes use in large rivers during winter conditions, we electrofished six habitat types in the Belleville Pool, Ohio River. We collected the greatest diversity and numbers of fishes in low-velocity tributary confluences when water temperatures were \u3e4°C. When water temperatures were \u3c4°C, certain species were collected in greater abundance in faster-velocity main channel and back channel habitats while other species continued to associate with lower flows in tributary mouths. Differing habitat use between species obscures broad generalizations about when and how fishes use overwintering refuges. In an additional habitat sampled, an embayment, 85% of all fishes collected were juveniles. Centrarchids, rarely collected in the mainstem portion of the river, were one of the dominant fishes collected in the embayment. Protecting large river embayments may prove important for managing recreational sunfish fisheries

Oxygen vacancies at the origin of pinned moments in oxide interfaces: the example of tetragonal CuO/SrTiO3_3

Obtaining an accurate theoretical description of the emergent phenomena in oxide heterostructures is a major challenge. Recently, intriguing paramagnetic spin and pinned orbital moments have been discovered by x-ray magnetic circular dichro\"ism measurements at the Cu $L_{2,3}$-edge of a tetragonal CuO/SrTiO$_3$ heterostructure. Using first principles calculations, we propose a scenario that explains both types of moments, based on the formation of oxygen vacancies in the TiO$_2$ interface layer. We show the emergence of a paramagnetic 2D electron gas hosted in the interface CuO layer. It is invisible at the Ti $L_{2,3}$-edge since the valence of the Ti atoms remains unchanged. Strong structural distortions breaking both the local and global fourfold rotation $C_4$ symmetries at the interface lead to the in-plane pinning of the Cu orbital moment close to the vacancy. Our results, and in particular the pinning of the orbital moment, may have implications for other systems, especially monoxide/dioxide interfaces with similar metal-oxygen bond length and weak spin-orbit coupling.Comment: 11 pages, 9 figure

Far-infrared excess emission as a tracer of disk-halo interaction

Given the current and past star-formation in the Milky Way in combination with the limited gas supply, the re-fuelling of the reservoir of cool gas is an important aspect of Galactic astrophysics. The infall of \ion{H}{i} halo clouds can, among other mechanisms, contribute to solving this problem. We study the intermediate-velocity cloud IVC135+54 and its spatially associated high-velocity counterpart to look for signs of a past or ongoing interaction. Using the Effelsberg-Bonn \ion{H}{i} Survey data, we investigated the interplay of gas at different velocities. In combination with far-infrared Planck and IRIS data, we extended this study to interstellar dust and used the correlation of the data sets to infer information on the dark gas. The velocity structure indicates a strong compression and deceleration of the infalling high-velocity cloud (HVC), associated with far-infrared excess emission in the intermediate-velocity cloud. This excess emission traces molecular hydrogen, confirming that IVC135+54 is one of the very few molecular halo clouds. The high dust emissivity of IVC135+54 with respect to the local gas implies that it consists of disk material and does not, unlike the HVC, have an extragalactic origin. Based on the velocity structure of the HVC and the dust content of the IVC, a physical connection between them appears to be the logical conclusion. Since this is not compatible with the distance difference between the two objects, we conclude that this particular HVC might be much closer to us than complex C. Alternatively, the indicators for an interaction are misleading and have another origin.Comment: 11 pages, 10 figures, accepted for publication in A&

Smart Feature Selection to enable Advanced Virtual Metrology

The present dissertation enhances the research in computer science, especially state of the art Machine Learning (ML), in the field of process development in Semiconductor Manufacturing (SM) by the invention of a new Feature Selection (FS) algorithm to discover the most important equipment and context parameters for highest performance of predicting process results in a newly developed advanced Virtual Metrology (VM) system. In complex high-mixture-low-volume SM, chips or rather silicon wafers for numerous products and technologies are manufactured on the same equipment. Process stability and control are key factors for the production of highest quality semiconductors. Advanced Process Control (APC) monitors manufacturing equipment and intervenes in the equipment control if critical states occur. Besides Run-To-Run (R2R) control and Fault Detection and Classification (FDC) new process control development activities focus on VM which predicts metrology results based on productive equipment and context data. More precisely, physical equipment parameters combined with logistical information about the manufactured product are used to predict the process result. The compulsory need for a reliable and most accurate VM system arises to imperatively reduce time and cost expensive physical metrology as well as to increase yield and stability of the manufacturing processes while concurrently minimizing economic expenditures and associated data flow. The four challenges of (1) efficiency of development and deployment of a corporate-wide VM system, (2) scalability of enterprise data storage, data traffic and computational effort, (3) knowledge discovery out of available data for future enhancements and process developments as well as (4) highest accuracy including reliability and reproducibility of the prediction results are so far not successfully mastered at the same time by any other approach. Many ML techniques have already been investigated to build prediction models based on historical data. The outcomes are only partially satisfying in order to achieve the ambitious objectives in terms of highest accuracy resulting in tight control limits which tolerate almost no deviation from the intended process result. For optimization of prediction performance state of the art process engineering requirements lead to three criteria for assessment of the ML algorithm for the VM: outlier detection, model robustness with respect to equipment degradation over time and ever-changing manufacturing processes adapted for further development of products and technologies and finally highest prediction accuracy. It has been shown that simple regression methods fail in terms of prediction accuracy, outlier detection and model robustness while higher-sophisticated regression methods are almost able to constantly achieve these goals. Due to quite similar but still not optimal prediction performance as well as limited computational feasibility in case of numerous input parameters, the choice of superior ML regression methods does not ultimately resolve the problem. Considering the entire cycle of Knowledge Discovery in Databases including Data Mining (DM) another task appears to be crucial: FS. An optimal selection of the decisive parameters and hence reduction of the input space dimension boosts the model performance by omitting redundant as well as spurious information. Various FS algorithms exist to deal with correlated and noisy features, but each of its own is not capable to ensure that the ambitious targets for VM can be achieved in prevalent high-mixture-low-volume SM. The objective of the present doctoral thesis is the development of a smart FS algorithm to enable a by this advanced and also newly developed VM system to comply with all imperative requirements for improved process stability and control. At first, a new Evolutionary Repetitive Backward Elimination (ERBE) FS algorithm is implemented combining the advantages of a Genetic Algorithm (GA) with Leave-One-Out (LOO) Backward Elimination as wrapper for Support Vector Regression (SVR). At second, a new high performance VM system is realized in the productive environment of High Density Plasma (HDP) Chemical Vapor Deposition (CVD) at the Infineon frontend manufacturing site Regensburg. The advanced VM system performs predictions based on three state of the art ML methods (i.e. Neural Network (NN), Decision Tree M5’ (M5’) & SVR) and can be deployed on many other process areas due to its generic approach and the adaptive design of the ERBE FS algorithm. The developed ERBE algorithm for smart FS enhances the new advanced VM system by revealing evidentially the crucial features for multivariate nonlinear regression. Enabling most capable VM turns statistical sampling metrology with typically 10% coverage of process results into a 100% metrological process monitoring and control. Hence, misprocessed wafers can be detected instantly. Subsequent rework or earliest scrap of those wafers result in significantly increased stability of subsequent process steps and thus higher yield. An additional remarkable benefit is the reduction of production cycle time due to the possible saving of time consuming physical metrology resulting in an increase of production volume output up to 10% in case of fab-wide implementation of the new VM system

Parametrization of the Coulomb interaction matrix with point-group symmetry

Coulomb integrals, i.e., matrix elements of bare or screened Coulomb interaction between one-electron orbitals, are fundamental objects in many approaches developed to tackle the challenging problem of calculating the electronic structure of strongly correlated materials. In this paper, Coulomb integrals are analyzed by considering both the point group symmetry of the site occupied by the atom in the crystal or molecule and the permutation symmetries of the orbitals in the integrals. In particular, the case where one-electron orbitals form the basis of a general (i.e. a real, complex or pseudo-complex) irreducible representation is considered. Explicit formulas are provided to calculate all integrals of the interaction tensor in terms of a minimum set of independent ones. The effect of a symmetry breaking is also investigated by describing Coulomb integrals of a group in terms of those of one of its subgroups. We develope the specific example of O(3) as the larger group which can therefore be used to quantify the deviation of a specific system from the spherical symmetry. Possible applications of the presented framework include the calculation of solid-state and molecular spectroscopies via multiplet techniques, dynamical mean-field theory or the GW approximation.Comment: 14 pages, 1 figur

Assessment of LED fluorescence microscopy for the diagnosis of Plasmodium falciparum infections in Gabon

<p>Abstract</p> <p>Background</p> <p>Rapid and accurate diagnosis of malaria is central to clinical management and the prevention of drug-overuse, which may lead to resistance development, toxicity and economic losses. So far, light microscopy (LM) of Giemsa-stained thick blood smears is the gold standard. Under optimal conditions the procedure is fast and reliable; nevertheless a gain in speed would be a great advantage. Rapid diagnosis tests are an alternative, although they cost more and give qualitative instead of quantitative results. Light-emitting diode (LED) fluorescence microscopy (ledFM 400 ×, 1000 ×) may offer a reliable and cheap alternative, which can be used at the point of care.</p> <p>Methods</p> <p>LedFM and conventional fluorescence microscopy (uvFM) were compared to LM in 210 samples from patients with history of fever in the last 24 hours admitted to the Albert Schweitzer Hospital in Lambaréné, Gabon.</p> <p>Results</p> <p>Sensitivities were 99.1% for ledFM and 97.0% for uvFM, specificities 90.7% for ledFM 400 × and 92.6% for ledFM 1000 × and uvFM. High agreement was found in Bland-Altman-plot and Kappa coefficient (ledFM 1000 ×: 0.914, ledFM 400 × and uvFM: 0.895). The time to diagnosis for both FM methods was shorter compared to LM (LM: 43 min, uvFM: 16 min, ledFM 1000 ×: 14 min, ledFM 400 ×: 10 min).</p> <p>Conclusion</p> <p>ledFM is a reliable, accurate, fast and inexpensive tool for daily routine malaria diagnosis and may be used as a point of care diagnostic tool.</p

Two-dimensional fluctuations and competing phases in the stripe-like antiferromagnet BaCoS2_2

By means of a combined x-ray diffraction, magnetic susceptibility and specific heat study, we investigate the interplay between orthorhombic distortion and stripe-like antiferromagnetic (AFM) order in the Mott insulator BaCoS$_{2}$ at $T_N=290$ K. The data give evidence of a purely electronic AFM transition with no participation of the lattice. The observation of large thermal fluctuations in the vicinity of $T_N$ and a Schottky anomaly unveils competing ground states within a minute $\sim$1 meV energy range that differ in the orbital and spin configurations of the Co ions. This interpretation suggests that the stripe-like order results from a spontaneous symmetry breaking of the geometrically frustrated pristine tetragonal phase, which offers an ideal playground to study the driving force of multi-orbital Mott transitions without the participation of the lattice.Comment: 5 pages, 4 figures, Supplemental Informatio

Soil carbon and nitrogen stocks in Arctic river deltas: New data for three Northwest Alaskan deltas

Arctic river deltas are dynamic and rapidly changing permafrost environments in a warming Arctic. Our study presents new data on permafrost carbon and nitrogen stocks from 26 soil permafrost cores collected from the Noatak, Kobuk and Selawik river deltas in Western Alaska. We analyzed 318 samples for total carbon (TC) and total nitrogen (TN). Average landscape-scale carbon storage is 50.1 ± 7.8 kg C (both organic and inorganic) and 2.4 ± 0.3 kg N m-2 (0-200 cm). This totals 67 ± 11 Mt C and 3.3 ± 0.6 Mt N in the first two meters of soil in the Noatak, Kobuk and Selawik deltas combined. Our findings demonstrate that Arctic river deltas are important regions of permafrost soil carbon storage and need to be considered in panarctic permafrost carbon estimations