Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Managers as drivers of CSR in state-owned enterprises

Corporate social responsibility (CSR) is increasingly required not only of private sector companies, but also of state-owned enterprises (SOEs), whose main function is to provide services to society and not principally to obtain financial reward. The important role played by managers in implementing CSR has been demonstrated by prior research. The objective of this paper is to examine how managers of SOEs perceive the concept of CSR, the reasons for their involvement and how it is integrated into everyday business practices. In addition, we examine whether managers' attitudes towards CSR policies are influenced by their personal profiles and by the sector in which the SOEs work. We find that, although these managers are aware of the importance of the CSR principles underlying their activities and understand the meaning, scope and dimensions of CSR, there remains an evident need to enhance the application of CSR policies among the strategies of SOEs. The manager profile and the sector in which the SOEs work both have a strong influence on their behaviour regarding CSR issues.     

Effects of gas flow rate, inlet concentration and temperature on the biofiltration of toluene vapors

In this work the variation in the elimination capacity of a biofilter as a function of the gas flow and toluene concentration was studied. A bioreactor 0.75 m high x 14.5 cm diameter was used, divided into three equal stages, using compost to support the microorganisms, and sea shells to control the pH. The biofiltration of toluene was evaluated for flows between 0.12 and 0.73 m(3)h(-1) in a concentration range of 1-3.2 gm(-3). It was observed that on increasing the toluene inlet load by 90% (from 37 to 70 gm(3)h(-1)), the conversion by the biofilter varied by only 5% (from 98% to 93%). The biofiltration system used achieved elimination capacities of up to 82 gm(-3)h(-1) for a toluene load of 100 gm(-3)h(-1).     

Expedient Metrics to Describe Plant Community Change Across Gradients of Anthropogenic Influence

Human influence associated with land use may cause considerable biodiversity losses, namely in oceanic islands such as the Azores. Our goal was to identify plant indicator species across two gradients of increasing anthropogenic influence and management (arborescent and herbaceous communities) and determine similarity between plant communities of uncategorized vegetation plots to those in reference gradients using metrics derived from R programming. We intend to test and provide an expedient way to determine the conservation value of a given uncategorized vegetation plot based on the number of native, endemic, introduced, and invasive indicator species present. Using the metric IndVal, plant taxa with a significant indicator value for each community type in the two anthropogenic gradients were determined. A new metric, ComVal, was developed to assess the similarity of an uncategorized vegetation plot toward a reference community type, based on (i) the percentage of pre-defined indicator species from reference communities present in the vegetation plots, and (ii) the percentage of indicator species, specific to a given reference community type, present in the vegetation plot. Using a data resampling approach, the communities were randomly used as training or validation sets to classify vegetation plots based on ComVal. The percentage match with reference community types ranged from 77 to 100 % and from 79 to 100 %, for herbaceous and arborescent vegetation plots, respectively. Both IndVal and ComVal are part of a suite of useful tools characterizing plant communities and plant community change along gradients of anthropogenic influence without a priori knowledge of their biology and ecology.