MARKOV INTERTEMPORAL LAND USE SIMULATION MODEL

Land Economics/Use,

Comparing compact binary parameter distributions I: Methods

Being able to measure each merger's sky location, distance, component masses, and conceivably spins, ground-based gravitational-wave detectors will provide a extensive and detailed sample of coalescing compact binaries (CCBs) in the local and, with third-generation detectors, distant universe. These measurements will distinguish between competing progenitor formation models. In this paper we develop practical tools to characterize the amount of experimentally accessible information available, to distinguish between two a priori progenitor models. Using a simple time-independent model, we demonstrate the information content scales strongly with the number of observations. The exact scaling depends on how significantly mass distributions change between similar models. We develop phenomenological diagnostics to estimate how many models can be distinguished, using first-generation and future instruments. Finally, we emphasize that multi-observable distributions can be fully exploited only with very precisely calibrated detectors, search pipelines, parameter estimation, and Bayesian model inference

Pitfalls associated with the use of molecular diagnostic panels in the diagnosis of cryptococcal meningitis

Abstract We report the case of a kidney transplantation patient on chronic immunosuppressive therapy presenting with subacute meningitis. The final diagnosis of cryptococcal meningitis was delayed due to 2 false-negative cryptococcal results on a molecular diagnostic panel. Caution with such platforms in suspected cryptococcal meningitis is needed.</jats:p

Information criteria for efficient quantum state estimation

Recently several more efficient versions of quantum state tomography have been proposed, with the purpose of making tomography feasible even for many-qubit states. The number of state parameters to be estimated is reduced by tentatively introducing certain simplifying assumptions on the form of the quantum state, and subsequently using the data to rigorously verify these assumptions. The simplifying assumptions considered so far were (i) the state can be well approximated to be of low rank, or (ii) the state can be well approximated as a matrix product state. We add one more method in that same spirit: we allow in principle any model for the state, using any (small) number of parameters (which can, e.g., be chosen to have a clear physical meaning), and the data are used to verify the model. The proof that this method is valid cannot be as strict as in above-mentioned cases, but is based on well-established statistical methods that go under the name of "information criteria." We exploit here, in particular, the Akaike Information Criterion (AIC). We illustrate the method by simulating experiments on (noisy) Dicke states

Getting to the Root of Bacterial Hairs: What is “s”?

An atomic force microscope (AFM) was used to measure the steric forces of lipopolysaccharides (LPS) on the biofilm-forming bacteria, Pseudomonas aeruginosa. It is well known that LPS play a vital role in biofilm formation. These forces were characterized with a modified version of the Alexander and de Gennes (AdG) model for polymers, which is a function of equilibrium brush length, L, probe radius, R, temperature, T, separation distance, D, and an indefinite density variable, s. This last parameter was originally distinguished by de Gennes as the root spacing or mesh spacing depending upon the type of polymer adhesion; however since then it has been commonly thought of as the root spacing. This study aims to clarify the ambiguity of this parameter as a first step in characterizing biofilm formation. Varying the temperature and pH at which the steric forces of the LPS are measured and then analyzing the produced force curves with Matlab, should allow us to measure s. The Matlab program has been written to crop large numbers of force curves in accordance with the Alexander and de Gennes polymer model objectively and quickly. If s is the root spacing it should remain constant regardless of the changing polymer lengths, on the other hand if it is the mesh spacing it will be proportional to the temperature and pH. Preliminary data suggest that the LPS vary with temperature and pH. The data also suggest that s represents the mesh spacing. Once s has been described, further studies can be done to determine how environmental changes influence L, and s and consequently biofilm formation

Shiny app to predict agricultural tire dimensions

The main objective of this project, carried out in an industrial context, was to apply a multivariate analysis to variables related to the specifications required for the production of an agricultural tire and the dimensional test results. With the exploratory data analysis, it was possible to identify strong correlations between predictor variables and with the response variables of each test. In this project, the principal component analysis (PCA) serves to eliminate the effects of multicollinearity. The use of regression analysis was intended to predict the behavior of the agricultural tire considering the selected variables of each test. In the case of Test 1, when applying the Stepwise methods to select the variables, the model with the lowest value of Akaike Information Criterion (AIC) was achieved with the technique “Both”. However, the lowest value of AIC for Test 2 was achieved with “Backward”. Regarding the validation of assumptions, both Test 1 and Test 2 were validated. Therefore, all the quantitative variables are important, both in Test 1 and Test 2, because they are a linear combination that determines the principal components. In order to make it easier to compute predictions for future agricultural tires, an application that was developed in Shiny allows the company to know the behavior of the tire before it was produced. Using the application, it is possible to reduce the industrialization time, materials and resources, thus increasing efficiency and profits.This work has been supported by FCT – Fundação para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Epidemiological analysis of spatially misaligned data: a case of highly pathogenic avian influenza virus outbreak in Nigeria

This research is focused on the epidemiological analysis of the transmission of the highly pathogenic avian influenza (HPAI) H5N1 virus outbreak in Nigeria. The data included 145 outbreaks together with the locations of the infected farms and the date of confirmation of infection. In order to investigate the environmental conditions that favoured the transmission and spread of the virus, weather stations were realigned with the locations of the infected farms. The spatial Kolmogorov–Smirnov test for complete spatial randomness rejects the null hypothesis of constant intensity (P < 0·0001). Preliminary exploratory analysis showed an increase in the incidence of H5N1 virus at farms located at high altitude. Results from the Poisson log-linear conditional intensity function identified temperature (−0·9601) and wind speed (0·6239) as the ecological factors that influence the intensity of transmission of the H5N1 virus. The model also includes distance from the first outbreak (−0·9175) with an Akaike’s Information Criterion of −103·87. Our analysis using a point process model showed that geographical heterogeneity, seasonal effects, temperature, wind as well as proximity to the first outbreak are very important components of spread and transmission of HPAI H5N1.Web of Scienc

Spatial models of carbon, nitrogen, and sulfur stable isotope distributions (isoscapes) across a shelf sea: an INLA approach

Spatial models of variation in the isotopic composition of structural nutrients across habitats (isoscapes) offer information on physical, biogeochemical and anthropogenic processes occurring across space, and provide a tool for retrospective assignment of animals or animal products to their foraging area or geographic origin. The isotopic differences among reference samples used to construct isoscapes may vary spatially and according to non‐spatial terms (e.g. sampling date, or among individual or species effects). Partitioning variance between spatially dependent and spatially independent terms is a critical but overlooked aspect of isoscape creation with important consequences for the design of studies collecting reference data for isoscape creation and the accuracy and precision of isoscape models. We introduce the use of integrated nested Laplace approximation (INLA) to construct isoscape models. Integrated nested Laplace approximation provides a computationally efficient framework to construct spatial models of isotopic variability explicitly addressing additional variation introduced by including multiple reference species (or other recognized sources of variance). We present carbon, nitrogen and sulphur isoscape models extending over c. 1 million km2 of the UK shelf seas. Models were built using seven different species of jellyfish as spatial reference data and a suite of environmental correlates. Compared to alternative isoscape prediction methods, INLA‐spatial isotope models show high spatial precision and reduced variance. We briefly discuss the likely biogeochemical explanations for the observed spatial isotope distributions. We show for the first time that sulphur isotopes display systematic spatial variation across open marine shelf seas and may therefore be a useful additional tool for marine spatial ecology. The INLA technique provides a promising tool for generating isoscape models and associated uncertainty surfaces where reference data are accompanied by multiple, quantifiable sources of uncertainty