Robust SMC-PSS and AVR design: A grid connected solar concentrated OTEC system application.

This work analyzes the stability and performance of an offshore solar-concentrated ocean thermal energy conversion system (SC-OTEC) tied to an onshore AC grid. The OTEC is a system where electricity is generated using small temperature differences between the warm surface and deep cold ocean water. Existing control methods for SC-OTEC systems lack coordination, hindering dynamic stability and effective damping for the synchronous generator (SG). These methods struggle to quickly adapt to sudden disturbances and lack the capability to adequately reject or compensate for such disturbances due to complex control constraints and computational demands. To this regard, a control strategy combining sliding mode control (SMC) and a power system stabilizer (PSS) to improve the SC-OTEC dynamic stability and damping features for the SG. Moreover, an auxiliary secondary automatic voltage regulator is assembled with a non-linear exciter system to provide damping features. The proposed PID-PSS and secondary AVR controller gains are adaptively tuned using a modified whale optimization algorithm with the balloon effect modulation. The studied SC-OTEC is tested through MATLAB/Simulink under a severe 3ϕ short-circuit fault, solar radiation variations, and a change in surface seawater temperature as well as changes in local loads. The final findings approved that the proposed control strategy preserves a strong performance and can mimic effectively the proposed SC-OTEC damping compared to the conventional system.

Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C.

IL28B single nucleotide polymorphism (rs12979860) is an etiology-independent predictor of hepatitis C virus (HCV)-related hepatic fibrosis. Data mining is a method of predictive analysis which can explore tremendous volumes of information from health records to discover hidden patterns and relationships. The current study aims to evaluate and compare the prediction accuracy of scoring system like aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) index versus data mining for the prediction of HCV-related advanced fibrosis. This retrospective study included 427 patients with chronic hepatitis C. We used data mining analysis to construct a decision tree by reduced error (REP) technique, followed by Auto-WEKA tool to select the best classifier out of 39 algorithms to predict advanced fibrosis. APRI and FIB-4 had sensitivity-specificity parameters of 0.523-0.831 and 0.415-0.917, respectively. REPTree algorithm was able to predict advanced fibrosis with sensitivity of 0.749, specificity of 0.729, and receiver operating characteristic (ROC) area of 0.796. Out of the 16 attributes, IL28B genotype was selected by the REPTree as the best predictor for advanced fibrosis. Using Auto-WEKA, the multilayer perceptron (MLP) neural model was selected as the best predictive algorithm with sensitivity of 0.825, specificity of 0.811, and ROC area of 0.880. Thus, MLP is better than APRI, FIB-4, and REPTree for predicting advanced fibrosis for patients with chronic hepatitis C.

Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease.

BACKGROUND: Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. METHODS: This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. RESULTS: The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ≥50.3 ng/ml was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. CONCLUSION: Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (≥50.3 ng/ml). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.