Pseudo-LIDAR data analysis and feed-forward wind turbine control design

To investigate potential improvement in wind turbine control employing LIDAR measurement, pseudo-LIDAR wind speed data is produced with Bladed using a designed sampling strategy, and assessed with preliminary frequency-domain analysis. A model-inverse feed-forward controller is adapted to combine with feedback control so as to enhance pitch control performance at high wind speed. This controller is applied to an industrial-scale 5MW wind turbine model and the control performance is compared with a baseline feedback controller. Simulation study demonstrates that the combined feed-forward/feedback control scheme has improvements in reducing pitch angle variation and reduction of load relevant metrics

LIDAR-based wind speed modelling and control system design

Abstract—The main objective of this work is to explore the feasibility of using LIght Detection And Ranging (LIDAR) measurement and develop feedforward control strategy to improve wind turbine operation. Firstly the Pseudo LIDAR measurement data is produced using software package GH Bladed across a distance from the turbine to the wind measurement points. Next the transfer function representing the evolution of wind speed is developed. Based on this wind evolution model, a model-inverse feedforward control strategy is employed for the pitch control at above-rated wind conditions, in which LIDAR measured wind speed is fed into the feedforward. Finally the baseline feedback controller is augmented by the developed feedforward control. This control system is developed based on a Supergen 5MW wind turbine model linearised at the operating point, but tested with the nonlinear model of the same system. The system performances with and without the feedforward control channel are compared. Simulation results suggest that with LIDAR information, the added feedforward control has the potential to reduce blade and tower loads in comparison to a baseline feedback control alone

An empirical comparison of several recent epistatic interactions detection methods

ABSTRACT Motivation: Many new methods have recently been proposed for detecting epistatic interactions in GWAS data. There is however no in-depth independent comparison of these methods yet. Results: Five recent methods-TEAM, BOOST, SNPHarvester, SNPRuler, and Screen and Clean (SC)-are evaluated here in terms of power, type-1 error rate, scalability, and completeness. In terms of power, TEAM performs best on data with main effect and BOOST performs best on data without main effect. In terms of type-1 error rate, TEAM and BOOST have higher type-1 error rates than SNPRuler and SNPHarvester. SC does not control type-1 error rate well. In terms of scalability, we tested the five methods using a dataset with 100,000 SNPs on a 64-bit Ubuntu system, with Intel (R) Xeon(R) CPU 2.66GHz, 16G memory. TEAM takes ∼36 days to finish and SNPRuler reports heap allocation problems. BOOST scales up to 100,000 SNPs and the cost is much lower than that of TEAM. SC and SNPHarvester are the most scalable. In terms of completeness, we study how frequently the pruning techniques employed by these methods incorrectly prune away the most significant epistatic interactions. We find that, on average, 20% of datasets without main effect and 60% of datasets with main effect are pruned incorrectly by BOOST, SNPRuler, and SNPHarvester

Towards exploratory hypothesis testing and analysis

10.1109/ICDE.2011.5767907Proceedings - International Conference on Data Engineering745-75

Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function

Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings

Hydrogen Sulfide Mitigates Kidney Injury in High Fat Diet-Induced Obese Mice

Obesity is prevalent worldwide and is a major risk factor for the development and progression of kidney disease. Hydrogen sulfide (H2S) plays an important role in renal physiological and pathophysiological processes. However, whether H2S is able to mitigate kidney injury induced by obesity in mice remains unclear. In this study, we demonstrated that H2S significantly reduced the accumulation of lipids in the kidneys of high fat diet- (HFD-) induced obese mice. The results of hematoxylin and eosin, periodic acid-Schiff, and Masson’s trichrome staining showed that H2S ameliorated the kidney structure, decreased the extent of interstitial injury, and reduced the degree of kidney fibrosis in HFD-induced obese mice. We found that H2S decreased the expression levels of tumor necrosis factor-α, interleukin- (IL-) 6, and monocyte chemoattractant protein-1 but increased the expression level of IL-10. Furthermore, H2S treatment decreased the protein expression of p50, p65, and p-p65 in the kidney of HFD-induced obese mice. In conclusion, H2S is able to mitigate renal injury in HFD-induced obese mice through the reduction of kidney inflammation by downregulating the expression of nuclear factor-kappa B. H2S or its releasing compounds may serve as a potential therapeutic molecule for obesity-induced kidney injury

Mechanism of Regulation of Big-Conductance Ca2+-Activated K+ Channels by mTOR Complex 2 in Podocytes

Podocytes, dynamic polarized cells wrapped around glomerular capillaries, are an essential component of the glomerular filtration barrier. BK channels consist of one of the slit diaphragm (SD) proteins in podocytes, interact with the actin cytoskeleton, and play vital roles in glomerular filtration. Mechanistic target of rapamycin (mTOR) complexes regulate expression of SD proteins, as well as cytoskeleton structure, in podocytes. However, whether mTOR complexes regulate podocyte BK channels is still unclear. Here, we investigated the mechanism of mTOR complex regulation of BK channels via real-time PCR, western blot, immunofluorescence, and patch clamping. Inhibiting mTORC1 with rapamycin or downregulating Raptor had no significant effect on BK channel mRNA and protein levels and bioactivity. However, the dual inhibitor of mTORC1 and mTORC2 AZD8055 and short hairpin RNA targeting Rictor downregulated BK channel mRNA and protein levels and bioactivity. In addition, MK2206, GF109203X, and GSK650394, which are inhibitors of Akt, PKCα, and SGK1, respectively, were employed to test the downstream signaling pathway of mTORC2. MK2206 and GF109203X had no effect on BK channel protein levels. MK2206 caused an obvious decrease in the current density of the BK channels. Moreover, GSK650394 downregulated the BK channel protein and mRNA levels. These results indicate mTORC2 not only regulates the distribution of BK channels through Akt, but also modulates BK channel protein expression via SGK1 in podocytes

Association between Arsenic Exposure from Drinking Water and Longitudinal Change in Blood Pressure among HEALS Cohort Participants

Background: Cross-sectional studies have shown associations between arsenic exposure and prevalence of high blood pressure; however, studies examining the relationship of arsenic exposure with longitudinal changes in blood pressure are lacking. Method: We evaluated associations of arsenic exposure in relation to longitudinal change in blood pressure in 10,853 participants in the Health Effects of Arsenic Longitudinal Study (HEALS). Arsenic was measured in well water and in urine samples at baseline and in urine samples every 2 years after baseline. Mixed-effect models were used to estimate the association of baseline well and urinary creatinine-adjusted arsenic with annual change in blood pressure during follow-up (median, 6.7 years). Result: In the HEALS population, the median water arsenic concentration at baseline was 62 μg/L. Individuals in the highest quartile of baseline water arsenic or urinary creatinine-adjusted arsenic had a greater annual increase in systolic blood pressure compared with those in the reference group (β = 0.48 mmHg/year; 95% CI: 0.35, 0.61, and β = 0.43 mmHg/year; 95% CI: 0.29, 0.56 for water arsenic and urinary creatinine-adjusted arsenic, respectively) in fully adjusted models. Likewise, individuals in the highest quartile of baseline arsenic exposure had a greater annual increase in diastolic blood pressure for water arsenic and urinary creatinine-adjusted arsenic, (β = 0.39 mmHg/year; 95% CI: 0.30, 0.49, and β = 0.45 mmHg/year; 95% CI: 0.36, 0.55, respectively) compared with those in the lowest quartile. Conclusion: Our findings suggest that long-term arsenic exposure may accelerate age-related increases in blood pressure. These findings may help explain associations between arsenic exposure and cardiovascular disease