Block designs for experiments with non-normal response

Many experiments measure a response that cannot be adequately described by a linear model withnormally distributed errors and are often run in blocks of homogeneous experimental units. Wedevelop the first methods of obtaining efficient block designs for experiments with an exponentialfamily response described by a marginal model fitted via Generalized Estimating Equations. Thismethodology is appropriate when the blocking factor is a nuisance variable as, for example, occursin industrial experiments. A D-optimality criterion is developed for finding designs robust to thevalues of the marginal model parameters and applied using three strategies: unrestricted algorithmicsearch, use of minimum-support designs, and blocking of an optimal design for the correspondingGeneralized Linear Model. Designs obtained from each strategy are critically compared and shownto be much more efficient than designs that ignore the blocking structure. The designs are comparedfor a range of values of the intra-block working correlation and for exchangeable, autoregressive andnearest neighbor structures. An analysis strategy is developed for a binomial response that allows es-timation from experiments with sparse data, and its efectiveness demonstrated. The design strategiesare motivated and demonstrated through the planning of an experiment from the aeronautics industr

A Framework for Near Real-Time AFL Match Outcome Prediction

Sports analysis has always been a real talking point amongst both statisticians and sports personnel. However, the complexity of creating an efficient and accurate model coupled with the difficulties in acquiring in-game statistics have resulted in most research being focused on ex-ante result prediction. This research will present a framework for the real-time prediction of match outcomes at various strategic points within an Australian Football League (AFL) match

Novel concept to guide systolic heart failure medication by repeated biomarker testing-results from TIME-CHF in context of predictive, preventive, and personalized medicine

Background It is uncertain whether repeated measurements of a multi-target biomarker panel may help to personalize medical heart failure (HF) therapy to improve outcome in chronic HF.Methods This analysis included 499 patients from the Trial of Intensified versus standard Medical therapy in Elderly patients with Congestive Heart Failure (TIME-CHF), aged &gt;= 60 years, LVEF = II, who had repeated clinical visits within 19 months follow-up. The interaction between repeated measurements of biomarkers and treatment effects of loop diuretics, spironolactone, beta-blockers, and renin-angiotensin system (RAS) inhibitors on risk of HF hospitalization or death was investigated in a hypothesis-generating analysis. Generalized estimating equation (GEE) models were used to account for the correlation between recurrences of events in a patient.Results One hundred patients (20%) had just one event (HF hospitalization or death) and 87 (17.4%) had at least two events. Loop diuretic up-titration had a beneficial effect for patients with high interleukin-6 (IL6) or high high-sensitivity C-reactive protein (hsCRP) (interaction, P = 0.013 and P = 0.001), whereas the opposite was the case with low hsCRP (interaction, P = 0.013). Higher dosage of loop diuretics was associated with poor outcome in patients with high blood urea nitrogen (BUN) or prealbumin (interaction, P = 0.006 and P = 0.001), but not in those with low levels of these biomarkers. Spironolactone up-titration was associated with lower risk of HF hospitalization or death in patients with high cystatin C (CysC) (interaction, P = 0.021). beta-Blockers up-titration might have a beneficial effect in patients with low soluble fms-like tyrosine kinase-1 (sFlt) (interaction, P = 0.021). No treatment biomarker interactions were found for RAS inhibition.Conclusion The data of this post hoc analysis suggest that decision-making using repeated biomarker measurements may be very promising in bringing treatment of heart failure to a new level in the context of predictive, preventive, and personalized medicine. Clearly, prospective testing is needed before this novel concept can be adopted.</p

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts

A GIS MODEL FOR APIARY SITE SELECTION BASED ON PROXIMITY TO NECTAR SOURCES UTILIZED IN VARIETAL HONEY PRODUCTION ON FORMER MINE SITES IN APPALACHIA

Beekeepers in Appalachia market varietal honeys derived from particular species of deciduous trees; however, finding places in a mountainous landscape to locate new beeyards is difficult. Site selection is hindered by the high up-front costs of negotiating access to remote areas with limited knowledge of the available forage. Remotely sensed data and species distribution modeling (SDM) of trees important to beekeepers could aid in locating apiary sites at the landscape scale. The objectives of this study are i) using publicly available forest inventory data, to model the spatial distribution of three native tree species that are important to honey producers in eastern Kentucky: American Basswood, Sourwood and Tulip Poplar, and to assess the accuracy of the models, ii) to incorporate a method for discounting the value of a nectar resource as a function of distance based on an energetic model of honeybee foraging, and iii) to provide an example by ranking potential apiary locations around the perimeter of a mine site in the study area based on their proximity to probable species habitat using a GIS model. Logistic regression models were trained using presence-absence records from 1,059 USFS Forest Inventory and Analysis (FIA) sub-plots distributed throughout a 9,000 km2 portion of the Kentucky River watershed. The models were evaluated by applying them to a separate dataset, 950 forest inventory sub-plots distributed over a 40.5 km2 research forest maintained by the University of Kentucky. Weights derived from an energic model of honeybee foraging were then applied to the probabilities of tree species occurrence predicted by the SDM. As an example, 24 potential apiary locations around the perimeter of a reclaimed mine site were selected and then ranked according to a site suitability index. Three tributary areas corresponding to different honeybee flight ranges were considered: 500m, 700m, and 1,200m. Results confirm that rankings are dependent on the foraging range considered, suggesting that the number of colonies at an apiary location would be an important factor to consider when choosing a site. However, the methodology makes assumptions that are only anecdotally supported, notably i) that colonies will forage preferentially at the target species when it is in bloom and, ii) that foragers will exhaust resources closest to the hive first, regardless of patch size. Additional study of how bees deplete the nectar resources surrounding an apiary is needed to verify the usefulness of SDM in site selection for varietal honey production