Network Behavior in Thin Film Growth Dynamics

We present a new network modeling approach for various thin film growth techniques that incorporates re-emitted particles due to the non-unity sticking coefficients. We model re-emission of a particle from one surface site to another one as a network link, and generate a network model corresponding to the thin film growth. Monte Carlo simulations are used to grow films and dynamically track the trajectories of re-emitted particles. We performed simulations for normal incidence, oblique angle, and chemical vapor deposition (CVD) techniques. Each deposition method leads to a different dynamic evolution of surface morphology due to different sticking coefficients involved and different strength of shadowing effect originating from the obliquely incident particles. Traditional dynamic scaling analysis on surface morphology cannot point to any universal behavior. On the other hand, our detailed network analysis reveals that there exist universal behaviors in degree distributions, weighted average degree versus degree, and distance distributions independent of the sticking coefficient used and sometimes even independent of the growth technique. We also observe that network traffic during high sticking coefficient CVD and oblique angle deposition occurs mainly among edges of the columnar structures formed, while it is more uniform and short-range among hills and valleys of small sticking coefficient CVD and normal angle depositions that produce smoother surfaces.Comment: 11 pages, 9 figures, revtex

Motivated learning with digital learning tasks: what about autonomy and structure?

Article about Motivated learning with digital learning tasks: what about autonomy and structure

Texas Business Review, November 1959

The Business Situation in Texas; The Prestressed Concrete Industry in Texas; Building Construction: September Permits Up Less Than Expected; Agriculture: Texas Cabbage Production: Prospects for Improvement; Industrial Production: The Texas Shoe Manufacturing Industry; Retail Trade: September Sales Down from August but Above a Year AgoBureau of Business Researc

Catching Up on Health Outcomes: The Texas Medication Algorithm Project

OBJECTIVE: To develop a statistic measuring the impact of algorithm-driven disease management programs on outcomes for patients with chronic mental illness that allowed for treatment-as-usual controls to “catch up” to early gains of treated patients. DATA SOURCES/STUDY SETTING: Statistical power was estimated from simulated samples representing effect sizes that grew, remained constant, or declined following an initial improvement. Estimates were based on the Texas Medication Algorithm Project on adult patients (age≥18) with bipolar disorder (n=267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas. STUDY DESIGN: Study patients were assessed at baseline and three-month follow-up for a minimum of one year. Program tracks were assigned by clinic. DATA COLLECTION/EXTRACTION METHODS: Hierarchical linear modeling was modified to account for declining-effects. Outcomes were based on 30-item Inventory for Depression Symptomatology—Clinician Version. PRINCIPAL FINDINGS: Declining-effect analyses had significantly greater power detecting program differences than traditional growth models in constant and declining-effects cases. Bipolar patients with severe depressive symptoms in an algorithm-driven, disease management program reported fewer symptoms after three months, with treatment-as-usual controls “catching up” within one year. CONCLUSIONS: In addition to psychometric properties, data collection design, and power, investigators should consider how outcomes unfold over time when selecting an appropriate statistic to evaluate service interventions. Declining-effect analyses may be applicable to a wide range of treatment and intervention trials