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.     

Stabilization/solidification of ashes in clays used in the manufacturing of ceramic bricks.

This paper presents the results of the lixiviation of metals from different mixtures of fly and bottom ashes that have been stabilized and solidified in clays used in the manufacture of bricks. The ashes used for this study were obtained from a Hoffmann-type brick furnace adapted for the incineration of municipal solid waste during the manufacturing of ceramic bricks. The ashes were stabilized in clay in different proportions of clay:ash mix (99:1, 95:5, 90:10, 80:20 and 60:40). Such mixes were used to manufacture bricks that were calcined at a temperature ranging from 50 to 1100 degrees C. The clay, ashes and manufactured bricks were characterized using X-ray diffraction, fluorescent X-ray, thermogravimetry, differential thermal analysis, atomic absorption spectroscopy and scanning electronic microscopy. In addition, toxicity characteristic leaching procedure lixiviation tests were performed according to the EPA 1311 method for the determination of heavy metals. The results showed an affinity between clay and ash, and also that the bricks manufactured with these mixtures present low lixiviation levels. The tests also showed the highest decrease in the concentration of arsenic, nickel, chromium, zinc and silver for 99:1 mixtures. The 95:5 mixture was found to be the most favourable for the stabilization (greater concentration decrease) of lead and cadmium. Selenium was the metal with the lowest concentration change whereas arsenic, nickel, chromium, zinc and cadmium showed the greatest concentration change in all mixtures, with the exception of cadmium in the mixture 99:1.     

25 years of higher‐order confirmatory factor analysis in the organizational sciences: A critical review and development of reporting recommendations

We discuss how confirmatory factor analysis results should be used to examine potential higher‐order constructs and advocate that researchers present five types of evidence, which are as follows: (1) the ability of the higher‐order model to reproduce the observed covariation among manifest variables; (2) the ability of the higher‐order model to reproduce the observed covariation among manifest variables better than more parsimonious alternative models—and no less well than less parsimonious alternative models; (3) the ability of the higher‐order model to reproduce the observed covariation among lower‐order factors; (4) the ability of the higher‐order factor to explain variation in lower‐order factors; and (5) the ability of the higher‐order factor to explain variation in manifest variables. We illustrate how this type of evidence could be presented with a worked example and contrast our recommendations with the manner in which higher‐order confirmatory factor analysis has been used in the organizational sciences over the past 25 years to support claims regarding higher‐order constructs such as core self‐evaluations and transformational leadership. Our review shows that a substantial proportion of the 44 examined articles failed to present enough evidence to allow readers to understand the size and importance of higher‐order factors. Copyright © 2015 John Wiley & Sons, Ltd.     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

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.