Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small graphic processing unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE 13-node test feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications. © 2013 State Grid Electric Power Research Institute

KLIC-score for predicting early failure in prosthetic joint infections treated with debridement, implant retention and antibiotics

AbstractDebridement, irrigation and antibiotic treatment form the current approach in early prosthetic joint infection (PJI). Our aim was to design a score to predict patients with a higher risk of failure. From 1999 to 2014 early PJIs were prospectively collected and retrospectively reviewed. The primary end-point was early failure defined as: 1) the need for unscheduled surgery, 2) death-related infection within the first 60 days after debridement or 3) the need for suppressive antibiotic treatment. A score was built-up according to the logistic regression coefficients of variables available before debridement. A total of 222 patients met the inclusion criteria. The most frequently isolated microorganisms were coagulase-negative staphylococci (95 cases, 42.8%) and Staphylococcus aureus (81 cases, 36.5%). Treatment of 52 (23.4%) cases failed. Independent predictors of failure were: chronic renal failure (OR 5.92, 95% CI 1.47–23.85), liver cirrhosis (OR 4.46, 95% CI 1.15–17.24), revision surgery (OR 4.34, 95% CI 1.34–14.04) or femoral neck fracture (OR 4.39, 95% CI1.16–16.62) compared with primary arthroplasty, C reactive protein >11.5 mg/dL (OR 12.308, 95% CI 4.56–33.19), cemented prosthesis (OR 8.71, 95% CI 1.95–38.97) and when all intraoperative cultures were positive (OR 6.30, 95% CI 1.84–21.53). A score for predicting the risk of failure was designed using preoperative factors (KLIC-score: Kidney, Liver, Index surgery, Cemented prosthesis and C-reactive protein value) and it ranged between 0 and 9.5 points. Patients with scores of ≤2, >2–3.5, 4–5, >5–6.5 and ≥7 had failure rates of 4.5%, 19.4%, 55%, 71.4% and 100%, respectively. The KLIC-score was highly predictive of early failure after debridement. In the future, it would be necessary to validate our score using cohorts from other institutions

Simulations of the solar orbiter spacecraft interactions with the solar wind: effects on RPW and SWA/EAS measurements

International audienceWe present numerical simulations of the future Solar Orbiter spacecraft/plasma interactions performed with the Spacecraft Plasma Interaction System (SPIS) software. This spacecraft, to be launched in October 2018, is dedicated to the Sun observation with in-situ and remote sensing instruments, brought as close as 0.28 A.U. from our star. In this hot and dense environment, the entire satellite will be submitted to high radiations and temperatures (up to 10 Solar constants). Material responses to environment constraints (heat, U.V. flux, photoemission, secondary electron emission under electron impact – SEEE – or under proton impact - SEEP) might bias the scientific instrument measurements. Our interest is focused on two instruments: the Radio and Plasma Waves (RPW) and the Electron Analyzer System (EAS)

Detecting a stochastic gravitational wave background with the Laser Interferometer Space Antenna

The random superposition of many weak sources will produce a stochastic background of gravitational waves that may dominate the response of the LISA (Laser Interferometer Space Antenna) gravitational wave observatory. Unless something can be done to distinguish between a stochastic background and detector noise, the two will combine to form an effective noise floor for the detector. Two methods have been proposed to solve this problem. The first is to cross-correlate the output of two independent interferometers. The second is an ingenious scheme for monitoring the instrument noise by operating LISA as a Sagnac interferometer. Here we derive the optimal orbital alignment for cross-correlating a pair of LISA detectors, and provide the first analytic derivation of the Sagnac sensitivity curve.Comment: 9 pages, 11 figures. Significant changes to the noise estimate

Sensitization of cervix cancer cells to Adriamycin by Pentoxifylline induces an increase in apoptosis and decrease senescence

<p>Abstract</p> <p>Background</p> <p>Chemotherapeutic drugs like Adriamycin (ADR) induces apoptosis or senescence in cancer cells but these cells often develop resistance and generate responses of short duration or complete failure. The methylxantine drug Pentoxifylline (PTX) used routinely in the clinics setting for circulatory diseases has been recently described to have antitumor properties. We evaluated whether pretreatment with PTX modifies apoptosis and senescence induced by ADR in cervix cancer cells.</p> <p>Methods</p> <p>HeLa (HPV 18+), SiHa (HPV 16+) cervix cancer cells and non-tumorigenic immortalized HaCaT cells (control) were treated with PTX, ADR or PTX + ADR. The cellular toxicity of PTX and survival fraction were determinated by WST-1 and clonogenic assay respectively. Apoptosis, caspase activation and ADR efflux rate were measured by flow cytometry, senescence by microscopy. IκBα and DNA fragmentation were determinated by ELISA. Proapoptotic, antiapoptotic and senescence genes, as well as HPV-E6/E7 mRNA expression, were detected by time real RT-PCR. p53 protein levels were assayed by Western blot.</p> <p>Results</p> <p>PTX is toxic (WST-1), affects survival (clonogenic assay) and induces apoptosis in cervix cancer cells. Additionally, the combination of this drug with ADR diminished the survival fraction and significantly increased apoptosis of HeLa and SiHa cervix cancer cells. Treatments were less effective in HaCaT cells. We found caspase participation in the induction of apoptosis by PTX, ADR or its combination. Surprisingly, in spite of the antitumor activity displayed by PTX, our results indicate that methylxantine, <it>per se </it>does not induce senescence; however it inhibits senescence induced by ADR and at the same time increases apoptosis. PTX elevates IκBα levels. Such sensitization is achieved through the up-regulation of proapoptotic factors such as <it>caspase </it>and <it>bcl </it>family gene expression. PTX and PTX + ADR also decrease E6 and E7 expression in SiHa cells, but not in HeLa cells. p53 was detected only in SiHa cells treated with ADR.</p> <p>Conclusion</p> <p>PTX is a good inducer of apoptosis but does not induce senescence. Furthermore, PTX reduced the ADR-induced senescence and increased apoptosis in cervix cancer cells.</p

A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores(PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.Results: The integrated polygenic prediction improved the average performance (pseudo-R2)for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity","type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension",and "sleep apnea" reaching phenome-wide significance.Conclusions: Our study has expanded the methodological utility of PRS in patient stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICDCM, International Classification of Diseases, Clinical Modification; LD, linkage disequilibrium;MA, multi-ancestry; MAF, minor allele frequency; NIH, National Institutes of Health; PCA,principal component analysis; PheWAS, phenome-wide association study; PCOS, polycysticovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk sc

Blended Clustering for Health Data Mining

Exploratory data analysis using data mining techniques is becoming more popular for investigating subtle relationships in health data, for which direct data collection trials would not be possible. Health data mining involving clustering for large complex data sets in such cases is often limited by insufficient key indicative variables. When a conventional clustering technique is then applied, the results may be too imprecise, or may be inappropriately clustered according to expectations. This paper suggests an approach which can offer greater range of choice for generating potential clusters of interest, from which a better outcome might in turn be obtained by aggregating the results. An example use case based on health services utilization characterization according to socio-demographic background is discussed and the blended clustering approach being taken for it is described