Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence–based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives

Insertion Detection System Employing Neural Network MLP and Detection Trees Using Different Techniques

by addressing intruder attacks, network security experts work to maintain services available at all times. The Intrusion Detection System (IDS) is one of the available mechanisms for detecting and classifying any abnormal behavior. As a result, the IDS must always be up to date with the most recent intruder attack signatures to maintain the confidentiality, integrity, and availability of the services. This paper shows how the NSL-KDD dataset may be used to test and evaluate various Machine Learning techniques. It focuses mostly on the NLS-KDD pre-processing step to create an acceptable and balanced experimental data set to improve accuracy and minimize false positives. For this study, the approaches J48 and MLP were employed. The Decision Trees classifier has been demonstrated to have the highest accuracy rate for detecting and categorizing all NSL-KDD dataset attacks

Domain walls at the spin density wave endpoint of the organic superconductor (TMTSF)2PF6 under pressure

We report the first comprehensive investigation of the organic superconductor (TMTSF)2PF6 in the vicinity of the endpoint of the spin density wave - metal phase transition where phase coexistence occurs. At low temperature, the transition of metallic domains towards superconductivity is used to reveal the various textures. In particular, we demonstrate experimentally the existence of 1D and 2D metallic domains with a cross-over from a filamentary superconductivity mostly along the c?-axis to a 2D superconductivity in the b?c-plane perpendicular to the most conducting direction. The formation of these domain walls may be related to the proposal of a soliton phase in the vicinity of the critical pressure of the (TMTSF)2PF6 phase diagram.Comment: 5 page

Upper critical field divergence induced by mesoscopic phase separation in the organic superconductor (TMTSF)2ReO4

Due to the competition of two anion orders, (TMTSF)2ReO4, presents a phase coexistence between semiconducting and metallic (superconducting) regions (filaments or droplets) in a wide range of pressure. In this regime, the superconducting upper critical field for H parallel to both c* and b' axes present a linear part at low fields followed by a divergence above a cross-over field. This cross-over corresponds to the 3D-2D decoupling transition expected in filamentary or granular superconductors. The sharpness of the transition also demonstrates that all filaments are of similar sizes and self organize in a very ordered way. The distance between the filaments and their cross-section are estimated.Comment: 4 pages, 4 figure

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods

HACK: Hierarchical ACKs for Efficient Wireless Medium Utilization

WiFi’s physical layer has increased in speed from 802.11b’s 11 Mbps to the Gbps rates of emerging 802.11ac. Despite these gains, WiFi’s inefficient MAC layer limits achievable end-to-end throughput. The culprit is 802.11’s mandatory idle period before each medium acquisition, which has come to dwarf the duration of a packet’s transmission. This overhead is especially punishing for TCP traffic, whose every two data packets elicit a short TCP ACK. Even frame aggregation and block link-layer ACKs (introduced in 802.11n) leave signifi- cant medium acquisition overhead for TCP ACKs. In this paper, we propose TCP/HACK (Hierarchical ACKnowledgment), a system that applies cross-layer optimization to TCP traffic on WiFi networks by carrying TCP ACKs within WiFi’s link-layer acknowledgments. By eliminating all medium acquisitions for TCP ACKs in unidirectional TCP flows, TCP/HACK significantly improves medium utilization, and thus significantly increases achievable capacity for TCP workloads. Our measurements of a real-time, line-speed implementation for 802.11a on the SoRa software-defined radio platform and simulations of 802.11n networks at scale demonstrate that TCP/HACK significantly improves TCP throughput on WiFi networks

Case-finding of chronic obstructive pulmonary disease with questionnaire, peak flow measurements and spirometry : a cross-sectional study

Peer reviewedPublisher PD

The variability of methane, nitrous oxide and sulfur hexafluoride in Northeast India

High-frequency atmospheric measurements of methane (CH[subscript 4]), nitrous oxide (N[subscript 2]O) and sulfur hexafluoride (SF[subscript 6]) from Darjeeling, India are presented from December 2011 (CH[subscript 4])/March 2012 (N[subscript 2]O and SF[subscript 6]) through February 2013. These measurements were made on a gas chromatograph equipped with a flame ionization detector and electron capture detector, and were calibrated on the Tohoku University, the Scripps Institution of Oceanography (SIO)-98 and SIO-2005 scales for CH[subscript 4], N[subscript 2]O and SF[subscript 6], respectively. The observations show large variability and frequent pollution events in CH[subscript 4] and N[subscript 2]O mole fractions, suggesting significant sources in the regions sampled by Darjeeling throughout the year. By contrast, SF[subscript 6] mole fractions show little variability and only occasional pollution episodes, likely due to weak sources in the region. Simulations using the Numerical Atmospheric dispersion Modelling Environment (NAME) particle dispersion model suggest that many of the enhancements in the three gases result from the transport of pollutants from the densely populated Indo-Gangetic Plains of India to Darjeeling. The meteorology of the region varies considerably throughout the year from Himalayan flows in the winter to the strong south Asian summer monsoon. The model is consistent in simulating a diurnal cycle in CH[subscript 4] and N[subscript 2]O mole fractions that is present during the winter but absent in the summer and suggests that the signals measured at Darjeeling are dominated by large-scale (~100 km) flows rather than local (<10 km) flows.Massachusetts Institute of Technology. Center for Global Change Science (Director's Fund)Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global ChangeMartin Family Society of Fellows for SustainabilityMIT Energy InitiativeMIT International Science and Technology InitiativeUnited States. National Aeronautics and Space Administration (Grant NNX11AF17G)United States. National Oceanic and Atmospheric Administration (Contract RA133R09CN0062