Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network

Thermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot-wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and a predictive model of thermal conductivity was developed with an artificial neural network (ANN) technology. The results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increases sharply in the transition stage. The difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. The effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. The change tendency of the thermal conductivity calculated by the established ANN model is basically consistent with that of the laboratory-measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in the freezing process

Postharvest Treatments with Three Yeast Strains and Their Combinations to Control Botrytis cinerea of Snap Beans

Three yeast strains, namely Cryptococcus albidus (Ca63), Cryptococcus albidus (Ca64), and Candida parapsilosis (Yett1006), and their combinations, including single yeast agent, two combined yeast strains, single yeast agent + NaHCO3, single yeast agent + chitosan, single yeast agent + ascorbic acid, and single yeast agent + konjac powder, were evaluated for their activity against Botrytis cinerea, the most economically important fungal pathogens causing postharvest disease of snap beans. In in vitro tests, no inhibition zone was observed in dual cultures of three yeast strains and B. cinerea. The mycelial growth inhibition rates of B. cinerea for Ca63, Ca64, and Yett1006 were 97%, 95%, and 97%, respectively. In in vivo tests, the optimal combination of the lowest disease index of snap beans with B. cinerea was Ca63 + Ca64, with a preventing effect of 75%. The decay rate and rust spots index of Ca64 + ascorbic acid combination were 25% and 20%, respectively, which were the lowest. The activities of defense-related enzymes increased, while malondialdehyde (MDA) content was suppressed in snap beans after different treatments. Our results highlight the potential of the three yeast strains and their combinations as new nonpolluting agents for the integrated control of B. cinerea on snap beans