Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors.

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid "close-in", and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed.

Cardiovascular comorbidities and their prognostic value in small cell lung cancer patients with chemoradiotherapy.

BACKGROUND: Small cell lung cancer (SCLC) is an extremely malignant subtype of lung cancer because of its high potential for metastases. Cardiac invasion of SCLC is a serious concern that may lead to systemic embolism or tract obstruction. It has aroused much concern that cardiovascular comorbidities may significantly affect the survival of SCLC patients and their treatment decisions. METHODS: We consecutively recruited 772 small cell lung cancer (SCLC) patients between January 2011 and December 2018 from 4 cancer specialty hospitals in China. Only newly diagnosed primary cancer inpatients were included. Univariable and multivariable adjusted Cox proportional hazard models were conducted to evaluate the risk factors associated with mortality. Hazard ratios (HRs) for mortality and corresponding 95% confidence intervals (95% CIs) were calculated. RESULTS: The prevalence of cardiovascular diseases (CVDs) was 34.6% in all SCLC patients. Log-rank analysis presented statistically significant differences in median survival time (MST) between patients with CVD and without CVD in all SCLC patients (9.0 months vs. 15.0 months, P = 0.005) and patients with chemotherapy only (12.0 months vs. 18.0 months, P = 0.048). Pericardial effusion (HR 1.671, 95% CI 1.082-2.580, P = 0.021) and heart failure (HR 1.752, 95% CI 1.290-2.379, P < 0.001) were independent risk factors associated with mortality in all SCLC patients. VTE is related to poorer prognosis in patients with chemotherapy only (HR 5.558, 95% CI 1.335-23.135, P = 0.018) and chemoradiotherapy (HR 3.057, 95% CI 1.270-7.539, P = 0.013). CONCLUSIONS: Comprehensive management of CVD comorbidities is of vital importance for the long-term prognosis of SCLC patients.

Development and validation of a risk score nomogram model to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension: A study based on NHANES data.

Background: The present study aimed to develop and validate a prediction nomogram model for 5-year all-cause mortality in diabetic patients with hypertension. Methods: Data were extracted from the National Health and Nutrition Examination Survey (NHANES). A total of 3291 diabetic patients with hypertension in the NHANES cycles for 1999-2014 were selected and randomly assigned at a ratio of 8:2 to the training cohort (n = 2633) and validation cohort (n = 658). Multivariable Cox regression was conducted to establish a visual nomogram model for predicting the risk of 5-year all-cause mortality. Receiver operating characteristic curves and C-indexes were used to evaluate the discriminant ability of the prediction nomogram model for all-cause mortality. Survival curves were created using the Kaplan-Meier method and compared by the log-rank test. Results: The nomogram model included eight independent predictors: age, sex, education status, marital status, smoking, serum albumin, blood urea nitrogen, and previous cardiovascular disease. The C-indexes for the model in the training and validation cohorts were 0.76 (95% confidence interval: 0.73-0.79, p < 0.001) and 0.75 (95% confidence interval: 0.69-0.81, p < 0.001), respectively. The calibration curves indicated that the model had satisfactory consistency in the two cohorts. The risk of all-cause mortality gradually increased as the tertiles of the nomogram model score increased (log-rank test, p < 0.001). Conclusion: The newly developed nomogram model, a readily useable and efficient tool to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension, provides a novel risk stratification method for individualized intervention.

Pre-existing cardiometabolic comorbidities and survival of middle-aged and elderly non-small cell lung cancer patients.

BACKGROUND: Both lung cancer and cardiometabolic diseases are leading causes of death in China, and they share some common risk factors. However, the prevalence and long-term effect of pre-existing cardiometabolic comorbidities (CMCs) on the survival of middle-aged and elderly lung cancer patients are still not clear. METHODS: We consecutively recruited 3477 non-small cell lung cancer (NSCLC) patients between January 2011 and December 2018 from four cancer specialty hospitals in China. Univariable and multivariable adjusted Cox proportional hazard models were conducted to evaluate the risk factors associated with mortality. Hazard ratio (HR) for mortality and corresponding 95% CI were calculated. RESULTS: The prevalence of CMCs was 30.0% in middle-aged NSCLC patients and 45.5% in elderly NSCLC patients. Log-rank analysis presented statistically significant differences in median survival time between patients with CMCs and without CMCs in both the middle-aged group (21.0 months vs. 32.0 months, P < 0.01) and the elderly group (13.0 months vs. 17.0 months, P = 0.01). Heart failure (HR = 1.754, 95% CI: 1.436-2.144, P < 0.001) and venous thrombus embolism (HR = 2.196, 95% CI: 1.691-2.853, P < 0.001) were independent risk factors for the survival of middle-aged NSCLC patients, while heart failure (HR = 1.709, 95% CI: 1.371-2.130, P < 0.001) continued to decrease overall survival in the elderly group. Hyperlipidemia may be a protective factor for survival in middle-aged group (HR = 0.741, 95% CI: 0.566-0.971, P = 0.030). CONCLUSIONS: Our findings demonstrate for the first time the prevalence and prognostic value of pre-existing CMCs in Chinese middle-aged and elderly NSCLC patients.

A Novel Equivalent Model of Active Distribution Networks Based on LSTM.

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

Structure revision of aspergicin by the crystal structure of aspergicine, a co-occurring isomer produced by co-culture of two mangrove epiphytic fungi.

The structure of aspergicin (1), an antibacterial alkaloid produced by co-culture of two marine-derived mangrove epiphytic fungi, were revised by the co-occurring isomer named as aspergicine (2), whose structure was determined on the basis of spectroscopic analysis and X-ray crystallography.

A novel high-density power energy harvesting methodology for transmission line online monitoring devices.

This paper presents a novel energy-harvesting model which takes the primary current, secondary turns, dimension, the magnitude of magnetic flux density B, and the core loss resistance into consideration systematically. The relationship among the potential maximum output power, the dimension of energy harvesting coil (EHC), the load type of EHC, and the secondary turns is predicted by theoretical analysis and further verified by experiments. A high power density harvester is also developed and tested. It is shown that the power density of this novel harvester is 0.7 mW/g at 10 A, which is more than 2 times powerful than the traditional ones. Hence, it could lighten the half weight of the harvester at the same conditions.