Numerical Solution of Piecewise Constant Delay Systems Based on a Hybrid Framework

An efficient numerical scheme for solving delay differential equations with a piecewise constant delay function is developed in this paper. The proposed approach is based on a hybrid of block-pulse functions and Taylor’s polynomials. The operational matrix of delay corresponding to the proposed hybrid functions is introduced. The sparsity of this matrix significantly reduces the computation time and memory requirement. The operational matrices of integration, delay, and product are employed to transform the problem under consideration into a system of algebraic equations. It is shown that the developed approach is also applicable to a special class of nonlinear piecewise constant delay differential equations. Several numerical experiments are examined to verify the validity and applicability of the presented technique

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient’s survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient’s likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran.Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey.Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis.Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis

Tornado Detection with Support Vector Machines

Abstract. The National Weather Service (NWS) Mesocyclone Detec-tion Algorithms (MDA) use empirical rules to process velocity data from the Weather Surveillance Radar 1988 Doppler (WSR-88D). In this study Support Vector Machines (SVM) are applied to mesocyclone detection. Comparison with other classification methods like neural networks and radial basis function networks show that SVM are more effective in meso-cyclone/tornado detection.

Methods and pharmaceutical compositions for the cardioprotection

The present invention relates to methods and pharmaceutical compositions for cardioprotection of subjects who experienced a myocardial infarction. In particular, the present invention relates to a ligand of the sonic hedgehog signaling pathway for use in the cardioprotection of a subject who experienced a myocardial infarction

Design of a high-speed germanium-tin absorption modulator at mid-infrared wavelengths

We propose a high-speed electro-absorption modulator based on a direct bandgap Ge0.875Sn0.125 alloy operating at mid-infrared wavelengths. Enhancement of the Franz-Keldysh-effect by confinement of the applied electric field to GeSn in a reverse-biased junction results in 3.2dB insertion losses, a 35GHz bandwidth and a 6dB extinction ratio for a 2Vpp drive signal

Biological and reproduction behaviour of Eleuthronema tetradactylum

In this study spawning season, brood stocks live place and biological behaviour of Eleutheronema tetradactylum (rashgoo) was identified in the coastal waters of Bushehr province, Persian Gulf from July. 2005 to Sep. 2006. During this time fish samples were collected by the gillnet or set net. With the mesh size of 9 and 25 Cm. A total of 93 fishes were examined in the fishery Research Centre laboratory, Standard length, total weight, gonad and hepatic weight were measured and histological studies on gonads were conducted in this period. Gonadosomatic index was calculated in the different seasons we showed significant differences between research seasons (P<0.05) the Maximum GSI was in winter and spring with (1-1/2 %) and minimum was in summer and autumn (/15-/22 %). In histological study of ovary had observed five stages in sections: IMMATURE, INITIAL MATURATION, ADVANCED MATURATION, MATURED and SPAWNED. And testicular tissues were showed three stages of the sperm development: Spermatogonia, Spermatocytes and Spermatides or sperm. This study had showed that Higher quantities of brood stock were caught in DAYER region of Bushehr province. The stomach contents observed that small fishes, shrimp and crab were feed by the caught fishes, it means that Eleutheronema tetradactylum (rashgoo).is a carnivorous fish. The results were obtained from this research showed that reproductive season in Four finger thread fine (Eleutheronema tetradactylum) (rashgoo) in the coastal waters of Bushehr was in the could seasons

One-loop Vilkovisky-DeWitt Counterterms for 2D Gravity plus Scalar Field Theory

The divergent part of the one-loop off-shell effective action is computed for a single scalar field coupled to the Ricci curvature of 2D gravity ($c \phi R$), and self interacting by an arbitrary potential term $V(\phi)$. The Vilkovisky-DeWitt effective action is used to compute gauge-fixing independent results. In our background field/covariant gauge we find that the Liouville theory is finite on shell. Off-shell, we find a large class of renormalizable potentials which include the Liouville potential. We also find that for backgrounds satisfying $R=0$, the Liouville theory is finite off shell, as well.Comment: 19 pages, OKHEP 92-00