Energy Saving and Scavenging in Stand-alone and Large Scale Distributed Systems.

This thesis focuses on energy management techniques for distributed systems such as hand-held mobile devices, sensor nodes, and data center servers. One of the major design problems in multiple application domains is the mismatch between workloads and resources. Sub-optimal assignment of workloads to resources can cause underloaded or overloaded resources, resulting in performance degradation or energy waste. This work specifically focuses on the heterogeneity in system hardware components and workloads. It includes energy management solutions for unregulated or batteryless embedded systems; and data center servers with heterogeneous workloads, machines, and processor wear states. This thesis describes four major contributions: (1) This thesis describes a battery test and energy delivery system design process to maintain battery life in embedded systems without voltage regulators. (2) In battery-less sensor nodes, this thesis demonstrates a routing protocol to maintain reliable transmission through the sensor network. (3) This thesis has characterized typical workloads and developed two models to capture the heterogeneity of data center tasks and machines: a task performance model and a machine resource utilization model. These models allow users to predict task finish time on individual machines. It then integrates these two models into a task scheduler based on the Hadoop framework for MapReduce tasks, and uses this scheduler for server energy minimization using task concentration. (4) In addition to saving server energy consumption, this thesis describes a method of reducing data center cooling energy by maintaining optimal server processor temperature setpoints through a task assignment algorithm. This algorithm considers the reliability impact of processor wear states. It records processor wear states through automatic timing slack tests on a cluster of machines with varying core temperatures, voltages, and frequencies. These optimal temperature setpoints are used in a task scheduling algorithm that saves both server and cooling energy.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116746/1/xjhe_1.pd

Glycerol Carbonate: A Novel Biosolvent with Strong Ionizing and Dissociating Powers

The activity of biocatalysts in nonaqueous solvents is related to the interaction of organic solvents with cells or enzymes. The behavior of proteins is strongly dependent on the protonation state of their ionizable groups, which ionization constants are greatly affected by the solvent. Due to the weak ionizing and dissociating powers of common organic solvents, the charge of the protein will change significantly when the protein is transferred from water to common organic solvents, resulting in protein denaturation. In this work, glycerol carbonate (GC) was synthesized, which ionizing and dissociating abilities were very close to those of water. Transesterification activities of Candida antarctica lipase B (CALB) in GC were comparable to those in water and remained constant during 4-week storage. Bacillus subtilis and Saccharomyecs cerevisiae were cultured in liquid media containing GC with test tubes. In the medium containing low GC concentration, Bacillus subtilis and Saccharomyecs cerevisiae grew well as in a medium containing no organic solvent, but, in the medium containing high GC concentration, the growth of Bacillus subtilis and Saccharomyecs cerevisiae was suppressed. The results suggested that GC is a potential biosolvent, which has great significance to biocatalysis in nonaqueous solvents

Prediction of ESRD in IgA Nephropathy Patients from an Asian Cohort: A Random Forest Model

Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model

Multiparametric Cardiovascular Magnetic Resonance in Acute Myocarditis: Comparison of 2009 and 2018 Lake Louise Criteria With Endomyocardial Biopsy Confirmation.

Background: Cardiac magnetic resonance (CMR) has been shown to improve the diagnosis of myocarditis, but no systematic comparison of this technique is currently available. The purpose of this study was to compare the 2009 and 2018 Lake Louise Criteria (LLC) for the diagnosis of acute myocarditis using 3.0 T MRI with endomyocardial biopsy (EMB) as a reference and to provide the cutoff values for multiparametric CMR techniques. Methods: A total of 73 patients (32 ± 14 years, 71.2% men) with clinically suspected myocarditis undergoing EMB and CMR with 3.0 T were enrolled in the study. Patients were divided into two groups according to EMB results (EMB-positive and -negative groups). The CMR protocol consisted of cine-SSFP, T2 STIR, T2 mapping, early and late gadolinium enhancement (EGE, LGE), and pre- and post-contrast T1 mapping. Their potential diagnostic ability was assessed with receiver operating characteristic curves. Results: The myocardial T1 and T2 relaxation times were significantly higher in the EMB-positive group than in the EMB-negative group. Optimal cutoff values were 1,228 ms for T1 relaxation times and 58.5 ms for T2 relaxation times with sensitivities of 86.0 and 83.7% and specificities of 93.3 and 93.3%, respectively. The 2018 LLC had a better diagnostic performance than the 2009 LLC in terms of sensitivity, specificity, positive predictive value, and negative predictive value. T1 mapping + T2 mapping had the largest area under the curve (0.95) compared to other single or combined parameters (2018 LLC: 0.91; 2009 LLC: 0.76; T2 ratio: 0.71; EGEr: 0.67; LGE: 0.73; ). The diagnostic accuracy for the 2018 LLC was the highest (91.8%), followed by T1 mapping (89.0%) and T2 mapping (87.7%). Conclusion: Emerging technologies such as T1/ T2 mapping have significantly improved the diagnostic performance of CMR for the diagnosis of acute myocarditis. The 2018 LLC provided the overall best diagnostic performance in acute myocarditis compared to other single standard CMR parameters or combined parameters. There was no significant gain when 2018LLC is combined with the EGE sequence