Gravitational waves from BH-NS binaries: Effective Fisher matrices and parameter estimation using higher harmonics

Inspiralling black hole-neutron star (BH-NS) binaries emit a complicated gravitational wave signature, produced by multiple harmonics sourced by their strong local gravitational field and further modulated by the orbital plane's precession. Some features of this complex signal are easily accessible to ground-based interferometers (e.g., the rate of change of frequency); others less so (e.g., the polarization content); and others unavailable (e.g., features of the signal out of band). For this reason, an ambiguity function (a diagnostic of dissimilarity) between two such signals varies on many parameter scales and ranges. In this paper, we present a method for computing an approximate, effective Fisher matrix from variations in the ambiguity function on physically pertinent scales which depend on the relevant signal to noise ratio. As a concrete example, we explore how higher harmonics improve parameter measurement accuracy. As previous studies suggest, for our fiducial BH-NS binaries and for plausible signal amplitudes, we see that higher harmonics at best marginally improve our ability to measure parameters. For non-precessing binaries, these Fisher matrices separate into intrinsic (mass, spin) and extrinsic (geometrical) parameters; higher harmonics principally improve our knowledge about the line of sight. For the precessing binaries, the extra information provided by higher harmonics is distributed across several parameters. We provide concrete estimates for measurement accuracy, using coordinates adapted to the precession cone in the detector's sensitive band.Comment: 19 pages, 11 figure

Deep Semi-supervised Anomaly Detection with Metapath-based Context Knowledge

Graph anomaly detection has attracted considerable attention in recent years. This paper introduces a novel approach that leverages metapath-based semi-supervised learning, addressing the limitations of previous methods. We present a new framework, Metapath-based Semi-supervised Anomaly Detection (MSAD), incorporating GCN layers in both the encoder and decoder to efficiently propagate context information between abnormal and normal nodes. The design of metapath-based context information and a specifically crafted anomaly community enhance the process of learning differences in structures and attributes, both globally and locally. Through a comprehensive set of experiments conducted on seven real-world networks, this paper demonstrates the superiority of the MSAD method compared to state-of-the-art techniques. The promising results of this study pave the way for future investigations, focusing on the optimization and analysis of metapath patterns to further enhance the effectiveness of anomaly detection on attributed networks

Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.Comment: 9 pages, 2 figures, 1 tables; to appear in the IEEE Access (Please cite our journal version.

In-silico based redesign of CO-dehydrogenase catalyzing the oxidation of toxic waste CO gas for improved O2 resistance and mediator affinity

Carbon monoxide (CO) harmful to most creatures, is largely discharged by industrial processes in steel mill and thermal power plant. Conversion of toxic waste CO gas to safe gas or more valuable chemicals will be a great worth at this point. Interestingly, carbons and high potential electrons from CO-oxidation can be resourced as essential core parts for the chemical products by using CO-dehydrogenase (CODH) and artificial mediator. For industrial application of the enzymatic CO-oxidation, however, key issues remain that most CODHs show oxygen (O2) sensitivity and low-affinity for artificial mediator. Because steel mill waste gas such as blast furnace gas (BFG) commonly contains a little O2 and higher affinity is required to achieve higher reaction rate. In this research, in-silico based approach was used to redesign Carboxydothermus hydrogenoformans CODH (ChCODH) II, capable of increasing O2 resistance and affinity to ethyl viologen (EV) mediator. ChCODHs belong to a group of Ni-Fe containing CODH. Among five known ChCODHs (ChCODH I-V), ChCODH II shows the highest activity toward CO but more O2 sensitive than ChCODH IV. The artificial mediator of EV functions as an electron acceptor for ChCODH II but the affinity of ChCODH II to EV mediator is known poor. As our result, more than 10 folds increase of O2 resistance was achieved for the redesigned ChCODH II enzyme, which will be definitely a working horse in the conversion of waste CO gas into value-added chemicals

Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Acute dystonia by droperidol during intravenous patient-controlled analgesia in young patients.

Patient-controlled analgesia (PCA) is an important means for postoperative analgesia with parenteral opioid. However, postoperative nausea and vomiting (PONV) remains a major problem with a PCA system. Droperidol is used in PCA to prevent PONV. Extrapyramidal reactions by droperidol are, however, occasionally induced. We describe two cases of severe extrapyramidal hypertonic syndrome with an intravenous administration of droperidol in PCA in young patients, following orthopedic surgery

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described

Simultaneous VLBI Astrometry of H2O and SiO Masers toward the Semiregular Variable R Crateris

We obtained, for the first time, astrometrically registered maps of the 22.2 GHz H2O and 42.8, 43.1, and 86.2 GHz SiO maser emission toward the semiregular b-type variable (SRb) R Crateris, at three epochs (2015 May 21, and 2016 January 7 and 26) using the Korean Very-long-baseline Interferometry Network. The SiO masers show a ring-like spatial structure, while the H2O maser shows a very asymmetric one-side outflow structure, which is located at the southern part of the ring-like SiO maser feature. We also found that the 86.2 GHz SiO maser spots are distributed in an inner region, compared to those of the 43.1 GHz SiO maser, which is different from all previously known distributions of the 86.2 GHz SiO masers in variable stars. The different distribution of the 86.2 GHz SiO maser seems to be related to the complex dynamics caused by the overtone pulsation mode of the SRb R Crateris. Furthermore, we estimated the position of the central star based on the ring fitting of the SiO masers, which is essential for interpreting the morphology and kinematics of a circumstellar envelope. The estimated stellar coordinate corresponds well to the position measured by Gaia

Enzyme immunoassay for the rapid detection of Escherichia coli O157

An enzyme immunoassay(EIA) to detect Escherichia(E.) coli 0157 in pork was developed by using a sandwich-type assay on the 96-well microplates. All E. coli O157 strains and Citrobacter amalonaticus reacted strongly, however 29 E. coli serotypes and 15 non-E. coli bacterial strains showed negative in E. coli O157 EIA. E. coli 0157 in pork could be detected with in 13 h including 10 h in enrichment broth and 3 h in EIA. As few as 1.8 CFU of E. coli O157 per g of pork could be detected after enrichment, whereas above 1.8 \u3e. 1 o5 CFU of E. coli O157 per g of pork could be detected without enrichment. The E. coli 0157 EIA was a sensitive, easy-to-perform and efficient method for the screening of E. coliO 157 in pork

Transcatheter Arterial Chemoembolization of Hepatocellular Carcinoma: Prevalence and Causative Factors of Extrahepatic Collateral Arteries in 479 Patients

OBJECTIVE: We wanted to investigate the prevalence and causative factors of extrahepatic arterial blood supply to hepatocellular carcinoma (HCC) at its initial presentation and during chemoembolization. MATERIALS AND METHODS: Between February 1998 and April 2000, consecutive 479 patients with newly diagnosed HCC were prospectively enrolled into this study. A total of 1629 sessions of transcatheter arterial chemoembolization (TACE) were performed in these patients (range: 1-15 sessions; mean: 3.4 sessions) until April 2004. For each TACE procedure, we determined the potential extrahepatic collateral arteries (ExCAs) depending on the location of the tumor, and we performed selective angiography of all suspected collaterals that could supply the tumor. The prevalence of ExCAs and the causative factors were analyzed. RESULTS: At initial presentation, 82 (17%) of these 479 patients showed 108 ExCAs supplying tumors. Univariate analysis showed that tumor size (p or =5 cm) was significantly higher than that for those patients with a small tumor (< 5 cm) (p < 0.01). CONCLUSION: The presence of ExCAs supplying HCC is rather common, and the tumor size is a significant causative factor for the development of these collateral arteries.This study was supported by a grant (0620220-1) from the National R & D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea