The non-bipartite integral graphs with spectral radius three

In this paper, we classify the connected non-bipartite integral graphs with spectral radius three.Comment: 18 pages, 5 figures, 2 table

Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes

Relationship between time of emergency department admission and adherence to the Surviving Sepsis Campaign bundle in patients with septic shock

Abstract Background Nighttime hospital admission is often associated with increased mortality risk in various diseases. This study investigated compliance rates with the Surviving Sepsis Campaign (SSC) 3-h bundle for daytime and nighttime emergency department (ED) admissions and the clinical impact of compliance on mortality in patients with septic shock. Methods We conducted an observational study using data from a prospective, multicenter registry for septic shock provided by the Korean Shock Society from 11 institutions from November 2015 to December 2017. The outcome was the compliance rate with the SSC 3-h bundle according to the time of arrival in the ED. Results A total of 2049 patients were enrolled. Compared with daytime admission, nighttime admission was associated with higher compliance with the administration of antibiotics within 3 h (adjusted odds ratio (adjOR), 1.326; 95% confidence interval (95% CI), 1.088–1.617, p = 0.005) and with the complete SSC bundle (adjOR, 1.368; 95% CI, 1.115–1.678; p = 0.003), likely to result from the increased volume of all patients and sepsis patients admitted during daytime hours. The hazard ratios of the completion of SSC bundle for 28-day mortality and in-hospital mortality were 0.750 (95% CI 0.590–0.952, p = 0.018) and 0.714 (95% CI 0.564–0.904, p = 0.005), respectively. Conclusion Septic shock patients admitted to the ED during the daytime exhibited lower sepsis bundle compliance than those admitted at night. Both the higher number of admitted patients and the higher patients to medical staff ratio during daytime may be factors that are responsible for lowering the compliance

Combination therapy of vitamin C and thiamine for septic shock in a multicentre, double-blind, randomized, controlled study (ATESS): study protocol for a randomized controlled trial

Background Septic shock is a life-threatening condition with underlying circulatory and cellular/metabolic abnormalities. Vitamin C and thiamine are potential candidates for adjunctive therapy; they are expected to improve outcomes based on recent experimental and clinical research. The aim of the Ascorbic Acid and Thiamine Effect in Septic Shock (ATESS) trial is to evaluate the effects of early combination therapy with intravenous vitamin C and thiamine on recovery from organ failure in patients with septic shock. Methods This study is a randomized, double-blind, placebo-controlled, multicentre trial in adult patients with septic shock recruited from six emergency departments in South Korea. Patients will be randomly allocated into the treatment or control group (1:1 ratio), and we will recruit 116 septic shock patients (58 per group). For the treatment group, vitamin C (50 mg/kg) and thiamine (200 mg) will be mixed in 50 ml of 0.9% saline and administered intravenously every 12 h for a total of 48 h. For the placebo group, an identical volume of 0.9% saline will be administered in the same manner. The primary outcome is the delta Sequential Organ Failure Assessment (SOFA) score (ΔSOFA = initial SOFA at enrolment – follow-up SOFA after 72 h). Discussion This trial will provide valuable evidence about the effectiveness of vitamin C and thiamine therapy for septic shock. If effective, this therapy might improve survival and become one of the main therapeutic adjuncts for patients with septic shock. Trial registration ClinicalTrials.gov, NCT03756220. Registered on 5 December 2018.This work was supported by a National Research Foundation of Korea grant funded by the Korean government (No. 2018R1C1B6006821). The government did not have any role in the study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication

Valuation of interest rate swap default risk.

This thesis presents a valuation model for the default risk of an interest rate swap to a riskless swap dealer. Previous studies evaluated the default risk of an interest rate swap by assuming that swap default probability is independent of interest rate movement. As this assumption is questionable, the present thesis evaluates the swap default risk by endogenously specifying the swap default probability in terms of the value of the firm which, in turn, is contingent on the interest rate. The default risk of a swap is measured in terms of the bid-ask spread of a matched interest rate swap, ignoring transaction costs. The bid-ask spread is computed by combining the default premiums of two separate swaps in a matched interest rate swap: one swap between the swap dealer and the fixed-rate paying end-user, and the other between the swap dealer and the floating-rate paying end-user. The default premium in a swap between the swap dealer and the fixed-rate paying end-user is computed by subtracting the fixed rate of a default-free swap from that of a comparable risky swap. The default premium in a swap between the swap dealer and the floating-rate paying end-user is computed by subtracting the fixed rate of a risky swap from that of a default-free swap. A path-dependent binomial model is used for computing the swap default premium in this thesis. The short-term interest rate is used as the underlying state variable on which the value of an interest rate swap is contingent. For a reasonable set of input values, the model developed in this thesis gives the numerical results which are close to the observed bid-ask spread in the market. It is found that the bid-ask spread is a decreasing function of the firm's cash flows, mean reversion speed of interest rates, and instantaneous term premium, and an increasing function of initial interest rates and interest rate volatility. The numerical results also show that as a firm enters into a swap contract, a wealth transfer occurs between equity holders and debtholders while the total value of the firm still remains the same.Ph.D.Business AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/105508/1/9135573.pdfDescription of 9135573.pdf : Restricted to UM users only

Waveform inversion using a back-propagation algorithm and a Huber function

Waveform inversion faces difficulties when applied to real seismic data, including the existence of many kinds of noise. The l1-norm is more robust to noise with outliers than the least-squares method. Nevertheless, the least-squares method is preferred as an objective function in many algorithms because the gradient of the l1-norm has a singularity when the residual becomes zero. We propose a complex-valued Huber function for frequency-domain waveform inversion that combines the l2-norm (for small residuals) with the l1-norm (for large residuals). We also derive a discretized formula for the gradient of the Huber function. Through numerical tests on simple synthetic models and Marmousi data, we find the Huber function is more robust to outliers and coherent noise. We apply our waveform-inversion algorithm to field data taken from the continental shelf under the East Sea in Korea. In this setting, we obtain a velocity model whose synthetic shot profiles are similar to the real seismic data.This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2007-314-D00320), the energy technology innovation (ETI) project funded by the Ministry of Knowledge Economy, and by the Brain Korea 21 project of the Ministry of Education. The work of T. Ha was supported by the National Institute of Mathematical Sciences (NIMS). We are grateful to our reviewers for their comments and encouragement, which greatly improved the paper

Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department

Abstract This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148,958 1 h-units. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care