Predicting Post-Fire Change in West Virginia, USA from Remotely-Sensed Data

Prescribed burning is used in West Virginia, USA to return the important disturbance process of fire to oak and oak-pine forests. Species composition and structure are often the main goals for re-establishing fire with less emphasis on fuel reduction or reducing catastrophic wildfire. In planning prescribed fires land managers could benefit from the ability to predict mortality to overstory trees. In this study, wildfires and prescribed fires in West Virginia were examined to determine if specific landscape and terrain characteristics were associated with patches of high/moderate post-fire change. Using the ensemble machine learning approach of Random Forest, we determined that linear aspect was the most important variable associated with high/moderate post-fire change patches, followed by hillshade, aspect as class, heat load index, slope/aspect ratio (sine transformed), average roughness, and slope in degrees. These findings were then applied to a statewide spatial model for predicting post-fire change. Our results will help land managers contemplating the use of prescribed fire to spatially target landscape planning and restoration sites and better estimate potential post-fire effects

Combining Models of Approximation with Partial Learning

In Gold's framework of inductive inference, the model of partial learning requires the learner to output exactly one correct index for the target object and only the target object infinitely often. Since infinitely many of the learner's hypotheses may be incorrect, it is not obvious whether a partial learner can be modifed to "approximate" the target object. Fulk and Jain (Approximate inference and scientific method. Information and Computation 114(2):179--191, 1994) introduced a model of approximate learning of recursive functions. The present work extends their research and solves an open problem of Fulk and Jain by showing that there is a learner which approximates and partially identifies every recursive function by outputting a sequence of hypotheses which, in addition, are also almost all finite variants of the target function. The subsequent study is dedicated to the question how these findings generalise to the learning of r.e. languages from positive data. Here three variants of approximate learning will be introduced and investigated with respect to the question whether they can be combined with partial learning. Following the line of Fulk and Jain's research, further investigations provide conditions under which partial language learners can eventually output only finite variants of the target language. The combinabilities of other partial learning criteria will also be briefly studied.Comment: 28 page

Preliminary Studies on the fluctuation of the biomass of sizefractionated zooplankton in sea grass bed of Pulau Tinggi, Malaysia

Zooplanktons biomass was extensively studied in the sea grass bed of Pulau Tinggi, Malaysia for six months. In 2015, sampling months were April, June, October, whereas in 2016, April, June, August were the sampling months. A cone shaped plankton net was used with 0.30 m mouth, 1.00 m length and 100 μm mesh size. The fractionation of zooplankton size was carried out in to >2000 μm (large), 501-2000 μm (medium) and <500 μm (small). Zooplankton was classified as copepods, larvaceans, chaetognaths, cnidarians, ctenophores, decapods and polychaetes. Copepods were categorized as Calanoida, Poecilostomatoida, Cyclopoida and Harpacticoida but identified as a total of 54 species, 26 genera and 19 families. We conclude that among the biomass of 3 size fractions; medium (36%) was dominant followed by large and small (32% each) throughout the study period

Best practice, best teaching

Keynote address discussing examples from my own, colleagues, and attendees practice. Conference participants worked in groups to share and build upon their existing teaching and learning strategies

Practical application of e-Learning

A 3 hour presentation to a group of 20 hairdressing tutors. Examples of technology used in hairdressing education from colleagues and my own practice were given. Participants discussed how they could incorporate technology into their own practice

‘Economics with training wheels’: Using blogs in teaching and assessing introductory economics

Blogs provide a dynamic interactive medium for online discussion, consistent with communal constructivist pedagogy. This paper explores the use of blogs in the teaching and assessment of a small (40-60 students) introductory economics paper. The role of blogs as a teaching, learning and assessment tool are discussed. Using qualitative and quantitative data collected across four semesters, students’ participation in the blog assessment is found to be associated with student ability, gender, and whether they are distance learners. Importantly, students with past economics experience do not appear to crowd out novice economics students. Student performance in tests and examinations does not appear to be associated with blog participation after controlling for student ability. However, students generally report overall positive experiences with the blog assessment

Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis:A Brief Review

Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.Publisher PDFPeer reviewe

Cortical Networks for Control of Voluntary Arm Movements Under Variable Force Conditions

A neural model of voluntary movement and proprioception functionally interprets and simulates cell types in movement related areas of primate cortex. The model circuit maintains accurate proprioception while controlling voluntary reaches to spatial targets, exertion of force against obstacles, posture maintenance despite perturbations, compliance with an imposed movement, and static and inertial load compensations. Computer simulations show that model cell properties mimic cell properties in areas 4 and 5. These include delay period activation, response profiles during movement, kinematic and kinetic sensitivities, and latency of activity onset. Model area 4 phasic and tonic cells compute velocity and position commands which activate alpha and gamma motor neurons, thereby shifting the mechanical equilibrium point. Anterior area 5 cells compute limb position using corollary discharges from area 4 and muscle spindle feedback. Posterior area 5 cells use the perceived position and target position signals to compute a desired movement vector. The cortical loop is closed by a volition-gated projection of this movement vector to area 4 phasic cells. Phasic-tonic cells in area 4 incorporate force command components to compensate for static and inertial loads. Predictions are made for both motor and parietal cell types under novel experimental protocols.Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N00014-95-l-0409, N00014-92-J-4015); National Science Foundation (IRI-90-24877, IRI-90-00530

Buprenorphine in Neonatal Abstinence Syndrome.

Infants exposed in utero to opioids will demonstrate a withdrawal syndrome known as neonatal abstinence syndrome (NAS). Buprenorphine is a long-acting opioid with therapeutic use in medication-assisted treatment of opioid dependency in adults and adolescents. Emerging data from clinical trials and treatment cohorts demonstrate the efficacy and safety of sublingual buprenorphine for those infants with NAS who require pharmacologic treatment. Pharmacometric modeling will assist in defining the exposure-response relationships and facilitate dose optimization