A model of deforestation by human population interactions

A differential equation was employed in modelling deforestation by human population interactions to yield an explicit mathematical model. The theoretical relation and many possible models were applied to the grid cell data in Hiroshima Prefecture, and relative appropriateness of each model was evaluated by Akaike's information criterion (AIC) using raw data. Intensive further verification was executed bythe bootstrap method. It was demonstrated that the theoretical relation was in the best agreement among many other models in comparison.     

Novel enzymatic activity of cell free extract from thermophilic Geobacillus sp. UZO 3 catalyzes reductive cleavage of diaryl ether bonds of 2,7-dichlorodibenzo-p-dioxin

We characterized the ability of the cell free extract from polychlorinated dibenzo-p-dioxins degrading bacterium Geobacillus sp. UZO 3 to reduce even highly chlorinated dibenzo-p-dioxins such as octachlorodibenzo-p-dioxins in incineration fly ash. The degradation of 2,7-dichlorodibenzo-p-dioxin (2,7-DCDD) as a model dioxin catalyzed by the cell free extract from this strain implicates that the ether bonds of 2,7-DCDD molecule undergo reductive cleavage, since 4′,5-dichloro-2-hydroxydiphenyl ether and 4-chlorophenol were detected as intermediate products of 2,7-DCDD degradation.     

Modeling and inference of forest coverage ratio using zero-one inflated distributions with spatial dependence

This paper explores statistical modeling of forest area with two covariates. The forest coverage ratio of grid-cell data was modeled by taking human population density and relief energy into account. The likelihood of the forest ratios was decomposed into the product of two likelihoods. The first likelihood was due to trinomial logistic distributions on three categories: the forest ratios take zero, or one, or values between zero and one. The second one was due to a logistic-normal regression model for the ratios between zero and one. This model was applied to real grid-cell data and it fit better than zero-inflated beta regression models.