Impact of Trucking Network Flow on Preferred Biorefinery Locations in the Southern United States

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The median family income, timberland annual growth-to-removal ratio, and transportation delays were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site locations, and locations not preferential for a mill location. The model predicted that higher probability locations for smaller biomass mills (feedstock capacity, the size of sawmills) were in southern Alabama, southern Georgia, southeast Mississippi, southern Virginia, western Louisiana, western Arkansas, and eastern Texas. The higher probability locations for large capacity mills (feedstock capacity, the size for pulp and paper mills) were in southeastern Alabama, southern Georgia, central North Carolina, and the Mississippi Delta regions

LOGISTIC REGRESSION MODELS OF FACTORS INFLUENCING THE LOCATION OF BIOENERGY AND BIOFUELS PLANTS

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of “thinnings to a basal area of 31.7m2/ha,” “availability of unused mill residues,” and “high density of railroad availability” had positive significant influences on the location of all wood-using faciities. “Median family income,” “population,” “low density of railroad availability,” and “harvesting costs for logging residues” had negative significant influences on the location of all wood-using faciities. For larger woody biomass-using mills (e.g., biopower) availability of “thinnings to a basal area of 79.2m2/ha,” “number of primary and secondary wood-using mills within an 128.8km haul distance,” and “amount of total mill residues,” had positive significant influences on the location of larger wood-using faciities. “Population” and “harvesting costs for logging residues” have negative significant influences on the location of larger wood-using faciities. Based on the logistic models, 25 locations were predicted for bioenergy or biofuels plants for a 13-state study region in the Southern United States

Exact maximum likelihood estimation using masked system data,

Reader Ai dssGeneral purpose: Advance state of the art Special math needed for explanations: Elementary probability Special math needed to use results: Same Results useful to: Reliability analysts & researchers distribution, and installation processes. Because of [these advantages, companies are beginning to implement computer plans designed to track such system life data and generate component reliability estimates [9]. In practice, however, this type of analysis is often confounded by the problem of masking, viz, the exact cause of system failure is unknown. This occurs frequently in complex systems and in field data where the failure cause might be isolated only to some subset of components, such as a circuit card containing many individual components. The quantities observed are then: 1) the lifelength of the system, and 2) partial information on the cause of failure. for the general of a series system of3 exponentid components with independent mask-for a SpeCial-CaSe: l-out-of-3:F System Of exponential COming. Their previous work shows that closed-form MLE are intractable, and they propose an iterative method for the solution of a system of 3 non-linear likelihood equations. They do not, however, prove convergence for their iterative method. As such, we show how this system of non-linear equations can be replaced by a single quartic equation, whose solution is straight-forward. Since it does not depend upon the convergence of numerical solution algorithms, the results are exact. Though the resulting estimators are somewhat lengthy & cumbersome to fund manually, they can be written as a straightforward computer code. The calculations can then be easily performed on a personal computer. This method for reducing the likelihood equations to simpler-to-solve forms can be extended readily to a higher number of components. In many cases for more than 3 components it is easier while for others it is more complicated; even in the more complicated cases, this simplification makes the problems much more tractable

Accepted for the Council:

paper copy of this dissertation for form and content and recommend that it be accepted i

A missense mutation in the previously undescribed gene Tmhs underlies deafness in hurry-scurry (hscy) mice

Mouse deafness mutations provide valuable models of human hearing disorders and entry points into molecular pathways important to the hearing process. A newly discovered mouse mutation named hurry-scurry (hscy) causes deafness and vestibular dysfunction. Scanning electron microscopy of cochleae from 8-day-old mutants revealed disorganized hair bundles, and by 50 days of age, many hair cells are missing. To positionally clone hscy, 1,160 F(2) mice were produced from an intercross of (C57BL/6-hscy × CAST/EiJ) F(1) hybrids, and the mutation was localized to a 182-kb region of chromosome 17. A missense mutation causing a critical cysteine to phenylalanine codon change was discovered in a previously undescribed gene within this candidate interval. The gene is predicted to encode an integral membrane protein with four transmembrane helices. A synthetic peptide designed from the predicted protein was used to produce specific polyclonal antibodies, and strong immunoreactivity was observed on hair bundles of both inner and outer hair cells in cochleae of newborn +/+ controls and +/hscy heterozygotes but was absent in hscy/hscy mutants. Accordingly, the gene was given the name “tetraspan membrane protein of hair cell stereocilia,” symbol Tmhs. Two related proteins (>60% amino acid identity) are encoded by genes on mouse chromosomes 5 and 6 and, together with the Tmhs-encoded protein (TMHS), comprise a distinct tetraspan subfamily. Our localization of TMHS to the apical membrane of inner ear hair cells during the period of stereocilia formation suggests a function in hair bundle morphogenesis