ABSTRACT
The attenuated total reflection (ATR) apparatus, with an added partial reflection/partial transmission mode, was used to demonstrate a novel way of characterizing water-based substances at 0.7 to 10.0 THz at the Australian Synchrotron THz-far infrared beamline. The technique utilized a diamond-crystal-equipped ATR to track temperature-dependent changes in reflectance. A "crossover flare" feature in the spectral scan was noted, which appeared to be a characteristic of water and water-dominated compounds. A "quiet zone" feature was also seen, where no temperature-dependent variation in reflectance exists. The variation in these spectral features can be used as a signature for the presence of bound and bulk water. The method can also potentially identify the presence of fats and oils in a biological specimen. The technique requires minimal sample preparation and is non-destructive. The presented method has the promise to provide a novel, real-time, low-preparation, analytical method for investigating biological material, which offers avenues for rapid medical diagnosis and industrial analysis.
Subject(s)
Plant Oils , Synchrotrons , Spectroscopy, Fourier Transform Infrared/methods , Australia , WaterABSTRACT
Surfactant-enhanced aquifer remediation (SEAR) is an appropriate method for DNAPL-contaminated aquifer remediation; However, due to the high cost of the SEAR method, finding the optimal remediation scenario is usually essential. Embedding numerical simulation models of DNAPL remediation within the optimization routines are computationally expensive, and in this situation, using surrogate models instead of numerical models is a proper alternative. Ensemble methods are also utilized to enhance the accuracy of surrogate models, and in this study, the Stacking ensemble method was applied and compared with conventional methods. First, Six machine learning methods were used as surrogate models, and various feature scaling techniques were employed, and their impact on the models' performance was evaluated. Also, Bagging and Boosting homogeneous ensemble methods were used to improve the base models' accuracy. A total of six stand-alone surrogate models and 12 homogeneous ensemble models were used as the base input models of the Stacking ensemble model. Due to the large size of the Stacking model, Bayesian hyper-parameter optimization method was used to find its optimal hyper-parameters. The results showed that the Bayesian hyper-parameter optimization method had better performance than common methods such as random search and grid search. The artificial neural network model, whose input data was scaled by the power transformer method, had the best performance with a cross-validation RMSE of 0.065. The Boosting method increased the base models' accuracy more than other homogeneous methods, and the best Boosting model had a test RMSE of 0.039. The Stacking ensemble method significantly increased the base models' accuracy and performed better than other ensemble methods. The best ensemble surrogate model constructed with Stacking had a cross-validation RMSE of 0.016. Finally, a differential evolution optimization model was used by substituting the Stacking ensemble model with the numerical model, and the optimal remediation strategy was obtained at a total cost of $ 72,706.
Subject(s)
Groundwater , Models, Theoretical , Bayes Theorem , Computer Simulation , Surface-Active AgentsABSTRACT
OBJECTIVE: To evaluate in real conditions the role of drugs in heat-related adverse effects. METHODS: We performed a multicentric case-control study in 3 university hospitals in France, including 36 cases (patients older than 65 years hospitalized with hyperthermia or dehydration between 1st July and 31st August 2007) and 51 controls. We compared drugs and changes in drug dosage according to the values of renal function. RESULTS: At inclusion, clearance creatinine was available for only 12 patients (mean = 31.6 ml/min), and three-fold higher in controls (p < 0.001). Cases took more drugs than controls (4.3 vs. 3.9; p < 0.001), particularly neuroleptic drugs (3.6% vs. 0.5%; p = 0.007). CONCLUSION: Despite its small size, our study shows that patients suffering from dehydration in summer are those with more severe renal impairment, taking more drugs (particularly neuroleptics). This pilot work could allow to improve methodology of further studies on drugs during heat waves.
