Acceleration of Plasma Flows Due to Reverse Dynamo Mechanism

The "reverse-dynamo" mechanism - the amplification/generation of fast plasma flows by micro scale (turbulent) magnetic fields via magneto-fluid coupling is recognized and explored. It is shown that macro-scale magnetic fields and flows are generated simultaneously and proportionately from micro scale fields and flows. The stronger the micro-scale driver, the stronger are the macro-scale products. Stellar and astrophysical applications are suggested.Comment: 16 pages including 3 figures. The Astrophys. J. (accepted); additional material is given for clarification; terminology is change

Schéma de niveaux de 223Ra. I. Rayonnements γ émis dans la désintégration 227Th → 223Ra

Les transitions γ dans 223Ra ont été étudiées à l'aide de diodes Ge-Li en faisant appel à différents procédés de purification et de mesure afin de minimiser l'importance des six descendants radioactifs. A peu près 180 transitions parmi celles observées ont pu être placées dans le schéma de niveaux

MLS: Airplane system modeling

Analysis, modeling, and simulations were conducted as part of a multiyear investigation of the more important airplane-system-related items of the microwave landing system (MLS). Particular emphasis was placed upon the airplane RF system, including the antenna radiation distribution, the cabling options from the antenna to the receiver, and the overall impact of the airborne system gains and losses upon the direct-path signal structure. In addition, effort was expended toward determining the impact of the MLS upon the airplane flight management system and developing the initial stages of a fast-time MLS automatic control system simulation model. Results ot these studies are presented

Creative Interventions to Increase Counselor-in-Training Wellness

Wellness is a broad term that emphasizes a person’s current state of mental, physical, emotional, spiritual, and professional wellness. Professional counseling was built upon the foundations of wellness and practitioner self-care. While counseling programs understand the importance of wellness and counseling ethics emphasize the necessity of professional wellness, students frequently do not feel prepared or trained enough in wellness dimensions. This manuscript provides a rationale for a program wellness model and outlines a proposed series of interventions aimed at increasing students’ understanding, ability to self-assess, and strategies related to self-care and wellness

Long-Range Displacive Reconstruction of Au(110) Triggered by Low Coverage of Sulfur

We propose a new model for the c(4 × 2) phase of sulfur adsorbed on Au(110). This is a reconstruction achieved by short-range rearrangements of Au atoms that create a pseudo-4-fold-hollow (p4fh) site for adsorbed sulfur. The model is based partly upon the agreement between experimental STM images and those predicted from DFT, both within c(4 × 2) domains and at a boundary between two domains. It is also based on the stability of this structure in DFT, where it is not only favored over the chemisorbed phase at its ideal coverage of 0.25 ML, but also at lower coverage (at T = 0 K). This is compatible with the fact that in experiments, it coexists with 0.06 ± 0.03 ML of sulfur chemisorbed on the (1 × 2) surface. The relative stability of the c(4 × 2) phase at 0.25 ML has been verified for a variety of functionals in DFT. In the chemisorbed phase, sulfur adsorbs at a pseudo-3-fold-hollow (p3fh) site near the tops of rows in the (1 × 2) reconstruction. This is similar to the fcc site on an extended (111) surface. Sulfur causes a slight separation between the two topmost Au atoms, which is apparent both in STM images and in DFT-optimized structures. The second-most stable site is also a p3fh site, similar to an hcp site. DFT is used to construct a simple lattice gas model based on pairs of excluded sites. The set of excluded sites is in good qualitative agreement with our STM data. From DFT, the diffusion barrier of a sulfur atom is 0.61 eV parallel to the Au row, and 0.78 eV perpendicular to the Au row. For the two components of the perpendicular diffusion path, that is, crossing a trough and hopping over a row, the former is considerably more difficult than the latter

Internal Dictionary Matching

We introduce data structures answering queries concerning the occurrences of patterns from a given dictionary D in fragments of a given string T of length n. The dictionary is internal in the sense that each pattern in D is given as a fragment of T. This way, D takes space proportional to the number of patterns d=|D| rather than their total length, which could be Theta(n * d). In particular, we consider the following types of queries: reporting and counting all occurrences of patterns from D in a fragment T[i..j] (operations Report(i,j) and Count(i,j) below, as well as operation Exists(i,j) that returns true iff Count(i,j)>0) and reporting distinct patterns from D that occur in T[i..j] (operation ReportDistinct(i,j)). We show how to construct, in O((n+d) log^{O(1)} n) time, a data structure that answers each of these queries in time O(log^{O(1)} n+|output|) - see the table below for specific time and space complexities. Query | Preprocessing time | Space | Query time Exists(i,j) | O(n+d) | O(n) | O(1) Report(i,j) | O(n+d) | O(n+d) | O(1+|output|) ReportDistinct(i,j) | O(n log n+d) | O(n+d) | O(log n+|output|) Count(i,j) | O({n log n}/{log log n} + d log^{3/2} n) | O(n+d log n) | O({log^2n}/{log log n}) The case of counting patterns is much more involved and needs a combination of a locally consistent parsing with orthogonal range searching. Reporting distinct patterns, on the other hand, uses the structure of maximal repetitions in strings. Finally, we provide tight - up to subpolynomial factors - upper and lower bounds for the case of a dynamic dictionary

String Periods in the Order-Preserving Model

The order-preserving model (op-model, in short) was introduced quite recently but has already attracted significant attention because of its applications in data analysis. We introduce several types of periods in this setting (op-periods). Then we give algorithms to compute these periods in time O(n), O(n log log n), O(n log^2 log n/log log log n), O(n log n) depending on the type of periodicity. In the most general variant the number of different periods can be as big as Omega(n^2), and a compact representation is needed. Our algorithms require novel combinatorial insight into the properties of such periods