Soil health and productivity in riparian grass buffers: A re-evaluation after 13 years

In 2001, soil health and productivity were surveyed in riparian grassland buffers adjacent to Bear Creek in northern Story County, Iowa. The investigators resampled these 24 plots in 2014 using the same techniques to see what changes had resulted from the conservation practices applied in the intervening years

Algorithmic Trading and Cryptocurrency- a literature review and key findings

Algorithmic and high-frequency trading gained significant popularity in traditional financial market transactions in the early 2000s. While cryptocurrency was introduced in 2009, it was not until 2015 that cryptocurrency trading experienced explosive growth due to advancements in technologies supporting the cryptocurrency ecosystem and economic uncertainties. Algorithmic trading strategies and high-frequency automated trading have been used in cryptocurrency trading. However, the lack of historical data and the volatility of the cryptocurrency market create unique challenges and impact the performance of these models and strategies. Additionally, cryptocurrency is an unregistered security, and cryptocurrency exchanges remain unregulated, which has generated significant concerns for global securities governing bodies. This research provides a literature review and document analysis of peer-reviewed journal articles and professional literature and identifies themes regarding algorithmic trading and the cryptocurrency ecosystem

Communication interventions in adult and pediatric oncology: A scoping review and analysis of behavioral targets

BackgroundImproving communication requires that clinicians and patients change their behaviors. Interventions might be more successful if they incorporate principles from behavioral change theories. We aimed to determine which behavioral domains are targeted by communication interventions in oncology.MethodsSystematic search of literature indexed in Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Clinicaltrials.gov (2000-October 2018) for intervention studies targeting communication behaviors of clinicians and/or patients in oncology. Two authors extracted the following information: population, number of participants, country, number of sites, intervention target, type and context, study design. All included studies were coded based on which behavioral domains were targeted, as defined by Theoretical Domains Framework.FindingsEighty-eight studies met inclusion criteria. Interventions varied widely in which behavioral domains were engaged. Knowledge and skills were engaged most frequently (85%, 75/88 and 73%, 64/88, respectively). Fewer than 5% of studies engaged social influences (3%, 3/88) or environmental context/resources (5%, 4/88). No studies engaged reinforcement. Overall, 7/12 behavioral domains were engaged by fewer than 30% of included studies. We identified methodological concerns in many studies. These 88 studies reported 188 different outcome measures, of which 156 measures were reported by individual studies.ConclusionsMost communication interventions target few behavioral domains. Increased engagement of behavioral domains in future studies could support communication needs in feasible, specific, and sustainable ways. This study is limited by only including interventions that directly facilitated communication interactions, which excluded stand-alone educational interventions and decision-aids. Also, we applied stringent coding criteria to allow for reproducible, consistent coding, potentially leading to underrepresentation of behavioral domains

New developments in event generator tuning techniques

Data analyses in hadron collider physics depend on background simulations performed by Monte Carlo (MC) event generators. However, calculational limitations and non-perturbative effects require approximate models with adjustable parameters. In fact, we need to simultaneously tune many phenomenological parameters in a high-dimensional parameter-space in order to make the MC generator predictions fit the data. It is desirable to achieve this goal without spending too much time or computing resources iterating parameter settings and comparing the same set of plots over and over again. We present extensions and improvements to the MC tuning system, Professor, which addresses the aforementioned problems by constructing a fast analytic model of a MC generator which can then be easily fitted to data. Using this procedure it is for the first time possible to get a robust estimate of the uncertainty of generator tunings. Furthermore, we can use these uncertainty estimates to study the effect of new (pseudo-) data on the quality of tunings and therefore decide if a measurement is worthwhile in the prospect of generator tuning. The potential of the Professor method outside the MC tuning area is presented as well.Comment: To appear in the proceedings of the 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT2010, Jaipur, India, February 22-27, 201