Showing papers by "Pusan National University published in 2020"
TL;DR: In 2019, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9 and the Virgo detector was also taking data that did not contribute to detection due to a low SINR but were used for subsequent parameter estimation as discussed by the authors.
Abstract: On 2019 April 25, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9. The Virgo detector was also taking data that did not contribute to detection due to a low signal-to-noise ratio, but were used for subsequent parameter estimation. The 90% credible intervals for the component masses range from to if we restrict the dimensionless component spin magnitudes to be smaller than 0.05). These mass parameters are consistent with the individual binary components being neutron stars. However, both the source-frame chirp mass and the total mass of this system are significantly larger than those of any other known binary neutron star (BNS) system. The possibility that one or both binary components of the system are black holes cannot be ruled out from gravitational-wave data. We discuss possible origins of the system based on its inconsistency with the known Galactic BNS population. Under the assumption that the signal was produced by a BNS coalescence, the local rate of neutron star mergers is updated to 250-2810.
1,189 citations
TL;DR: In this paper, the authors reported the observation of a compact binary coalescence involving a 222 −243 M ⊙ black hole and a compact object with a mass of 250 −267 M ⋆ (all measurements quoted at the 90% credible level) The gravitational-wave signal, GW190814, was observed during LIGO's and Virgo's third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network.
Abstract: We report the observation of a compact binary coalescence involving a 222–243 M ⊙ black hole and a compact object with a mass of 250–267 M ⊙ (all measurements quoted at the 90% credible level) The gravitational-wave signal, GW190814, was observed during LIGO's and Virgo's third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network The source was localized to 185 deg2 at a distance of ${241}_{-45}^{+41}$ Mpc; no electromagnetic counterpart has been confirmed to date The source has the most unequal mass ratio yet measured with gravitational waves, ${0112}_{-0009}^{+0008}$, and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system The dimensionless spin of the primary black hole is tightly constrained to ≤007 Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is confirmed at high confidence We estimate a merger rate density of 1–23 Gpc−3 yr−1 for the new class of binary coalescence sources that GW190814 represents Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries
913 citations
TL;DR: It is inferred that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M⊙, which can be considered an intermediate mass black hole (IMBH).
Abstract: On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85_{-14}^{+21} M_{⊙} and 66_{-18}^{+17} M_{⊙} (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M_{⊙}. We calculate the mass of the remnant to be 142_{-16}^{+28} M_{⊙}, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3_{-2.6}^{+2.4} Gpc, corresponding to a redshift of 0.82_{-0.34}^{+0.28}. The inferred rate of mergers similar to GW190521 is 0.13_{-0.11}^{+0.30} Gpc^{-3} yr^{-1}.
876 citations
TL;DR: In this article, the authors reported the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO and Virgo's third observing run.
Abstract: We report the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO’s and Virgo’s third observing run. The signal was recorded on April 12, 2019 at 05∶30∶44 UTC with a network signal-to-noise ratio of 19. The binary is different from observations during the first two observing runs most notably due to its asymmetric masses: a ∼30 M⊙ black hole merged with a ∼8 M⊙ black hole companion. The more massive black hole rotated with a dimensionless spin magnitude between 0.22 and 0.60 (90% probability). Asymmetric systems are predicted to emit gravitational waves with stronger contributions from higher multipoles, and indeed we find strong evidence for gravitational radiation beyond the leading quadrupolar order in the observed signal. A suite of tests performed on GW190412 indicates consistency with Einstein’s general theory of relativity. While the mass ratio of this system differs from all previous detections, we show that it is consistent with the population model of stellar binary black holes inferred from the first two observing runs.
507 citations
National Center for Atmospheric Research1, Pusan National University2, Cornell University3, Geophysical Fluid Dynamics Laboratory4, University of Texas at Austin5, Lamont–Doherty Earth Observatory6, University of Paris7, University of Victoria8, Northwestern University9, University of Colorado Boulder10, Cooperative Institute for Research in Environmental Sciences11, ETH Zurich12, Institute of Arctic and Alpine Research13, Max Planck Society14, University of California, Los Angeles15, Hokkaido University16, University of California, Irvine17, University of Exeter18
TL;DR: In this article, a collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences.
Abstract: Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed. Climate change detection is confounded by internal variability, but recent initial-condition large ensembles (LEs) have begun addressing this issue. This Perspective discusses the value of multi-model LEs, the challenges of providing them and their role in future climate change research.
426 citations
TL;DR: In this article, the authors provide a comprehensive overview of mobile edge computing (MEC) and its potential use cases and applications, as well as discuss challenges and potential future directions for MEC research.
Abstract: Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.
402 citations
TL;DR: In this paper, a physically sound circuit model based on the characteristics of the battery cell system is presented. And the relationship between the obtained resistances of the bulk (Rb), charge transfer reaction (Rct), interface layer (RSEI), diffusion process (W) and battery characteristics, such as the state of charge (SOC), temperature, and state of health (SOH), is introduced.
Abstract: As research on secondary batteries becomes important, interest in analytical methods to examine the condition of secondary batteries is also increasing. Among these methods, the electrochemical impedance spectroscopy (EIS) method is one of the most attractive diagnostic techniques due to its convenience, quickness, accuracy, and low cost. However, since the obtained spectra are complicated signals representing several impedance elements, it is necessary to understand the whole electrochemical environment for a meaningful analysis. Based on the understanding of the whole system, the circuit elements constituting the cell can be obtained through construction of a physically sound circuit model. Therefore, this mini-review will explain how to construct a physically sound circuit model according to the characteristics of the battery cell system and then introduce the relationship between the obtained resistances of the bulk (Rb), charge transfer reaction (Rct), interface layer (RSEI), diffusion process (W) and battery characteristics, such as the state of charge (SOC), temperature, and state of health (SOH).
391 citations
TL;DR: The GW190521 signal is consistent with a binary black hole (BBH) merger source at redshift 0.13-0.30 Gpc-3 yr-1.8 as discussed by the authors.
Abstract: The gravitational-wave signal GW190521 is consistent with a binary black hole (BBH) merger source at redshift 0.8 with unusually high component masses, 85-14+21 M o˙ and 66-18+17 M o˙, compared to previously reported events, and shows mild evidence for spin-induced orbital precession. The primary falls in the mass gap predicted by (pulsational) pair-instability supernova theory, in the approximate range 65-120 M o˙. The probability that at least one of the black holes in GW190521 is in that range is 99.0%. The final mass of the merger (142-16+28 M o˙) classifies it as an intermediate-mass black hole. Under the assumption of a quasi-circular BBH coalescence, we detail the physical properties of GW190521's source binary and its post-merger remnant, including component masses and spin vectors. Three different waveform models, as well as direct comparison to numerical solutions of general relativity, yield consistent estimates of these properties. Tests of strong-field general relativity targeting the merger-ringdown stages of the coalescence indicate consistency of the observed signal with theoretical predictions. We estimate the merger rate of similar systems to be 0.13-0.11+0.30 Gpc-3 yr-1. We discuss the astrophysical implications of GW190521 for stellar collapse and for the possible formation of black holes in the pair-instability mass gap through various channels: via (multiple) stellar coalescences, or via hierarchical mergers of lower-mass black holes in star clusters or in active galactic nuclei. We find it to be unlikely that GW190521 is a strongly lensed signal of a lower-mass black hole binary merger. We also discuss more exotic possible sources for GW190521, including a highly eccentric black hole binary, or a primordial black hole binary.
347 citations
TL;DR: Density functional theory and X-ray absorption fine structure analysis computations reveal that W2 N/WC interfaces synergistically facilitate transport and separation of charge, thus accelerating the electrochemical ORR, OER, and HER.
Abstract: To meet the practical demand of overall water splitting and regenerative metal-air batteries, highly efficient, low-cost, and durable electrocatalysts for the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are required to displace noble metal catalysts. In this work, a facile solid-state synthesis strategy is developed to construct the interfacial engineering of W2 N/WC heterostructures, in which abundant interfaces are formed. Under high temperature (800 °C), volatile CNx species from dicyanodiamide are trapped by WO3 nanorods, followed by simultaneous nitridation and carbonization, to form W2 N/WC heterostructure catalysts. The resultant W2 N/WC heterostructure catalysts exhibit an efficient and stable electrocatalytic performance toward the ORR, OER, and HER, including a half-wave potential of 0.81 V (ORR) and a low overpotential at 10 mA cm-2 for the OER (320 mV) and HER (148.5 mV). Furthermore, a W2 N/WC-based Zn-air battery shows outstanding high power density (172 mW cm-2 ). Density functional theory and X-ray absorption fine structure analysis computations reveal that W2 N/WC interfaces synergistically facilitate transport and separation of charge, thus accelerating the electrochemical ORR, OER, and HER. This work paves a novel avenue for constructing efficient and low-cost electrocatalysts for electrochemical energy devices.
344 citations
TL;DR: In this article, an overview of the fabrication techniques for super-hydrophobic coating and self-cleaning (SC) applications in various fields is presented, along with the critical conclusions, forthcoming views, and obstacles on the field of the durability of SCT are discussed in the presented survey.
299 citations
TL;DR: In this article, the authors used a simple hydrothermal method from biowaste, dwarf banana peel (HN-CDs) for the detection of metal ion (Fe3+ ion) in aqueous solution by the fluorometric method.
TL;DR: An overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the CO VID-19 situation.
Abstract: The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.
TL;DR: A dense architecture is incorporated into YOLOv3 to facilitate the reuse of features and help to learn a more compact and accurate model for tomato detection, and it had the best detection performance.
Abstract: Automatic fruit detection is a very important benefit of harvesting robots. However, complicated environment conditions, such as illumination variation, branch, and leaf occlusion as well as tomato overlap, have made fruit detection very challenging. In this study, an improved tomato detection model called YOLO-Tomato is proposed for dealing with these problems, based on YOLOv3. A dense architecture is incorporated into YOLOv3 to facilitate the reuse of features and help to learn a more compact and accurate model. Moreover, the model replaces the traditional rectangular bounding box (R-Bbox) with a circular bounding box (C-Bbox) for tomato localization. The new bounding boxes can then match the tomatoes more precisely, and thus improve the Intersection-over-Union (IoU) calculation for the Non-Maximum Suppression (NMS). They also reduce prediction coordinates. An ablation study demonstrated the efficacy of these modifications. The YOLO-Tomato was compared to several state-of-the-art detection methods and it had the best detection performance.
TL;DR: A new paradigm for highly sensitive, flexible, negative temperature coefficient (NTC) thermistor-based artificial skin is reported, with the highest temperature sensing ability reported to date among previously reported NTC thermistors.
Abstract: Accurate temperature field measurement provides critical information in many scientific problems. Herein, a new paradigm for highly sensitive, flexible, negative temperature coefficient (NTC) thermistor-based artificial skin is reported, with the highest temperature sensing ability reported to date among previously reported NTC thermistors. This artificial skin is achieved through the development of a novel monolithic laser-induced reductive sintering scheme and unique monolithic structures. The unique seamless monolithic structure simultaneously integrates two different components (a metal electrode and metal oxide sensing channel) from the same material at ambient pressure, which cannot be achieved by conventional heterogeneous integration through multiple, complex steps of photolithography or vacuum deposition. In addition to superior performance, electronic skin with high temperature sensitivity can be fabricated on heat-sensitive polymer substrates due to the low-temperature requirements of the process. As a proof of concept, temperature-sensitive artificial skin is tested with conformally attachable physiological temperature sensor arrays in the measurement of the temperatures of exhaled breath for the early detection of pathogenic progression in the respiratory system. The proposed highly sensitive flexible temperature sensor and monolithic selective laser reductive sintering are expected to greatly contribute to the development of essential components in various emerging research fields, including soft robotics and healthcare systems.
TL;DR: Gold and oil markets have been inefficient, particularly during the COVID-19 outbreak, and it is found that gold (oil) is more inefficient during upward (downward) trends.
TL;DR: This review presents an overview of recent advances in catalytic co-pyrolysis of biomass and plastic from the perspective of chemistry, catalyst, and feedstock pretreatment and introduces not only recent research results of acid catalysts for catalyticcolysis, but also recent approaches that utilize base catalysts.
TL;DR: A botnet detection system based on a two-level deep learning framework for semantically discriminating botnets and legitimate behaviors at the application layer of the domain name system (DNS) services is proposed.
Abstract: Internet of Things applications for smart cities have currently become a primary target for advanced persistent threats of botnets. This article proposes a botnet detection system based on a two-level deep learning framework for semantically discriminating botnets and legitimate behaviors at the application layer of the domain name system (DNS) services. In the first level of the framework, the similarity measures of DNS queries are estimated using siamese networks based on a predefined threshold for selecting the most frequent DNS information across Ethernet connections. In the second level of the framework, a domain generation algorithm based on deep learning architectures is suggested for categorizing normal and abnormal domain names. The framework is highly scalable on a commodity hardware server due to its potential design of analyzing DNS data. The proposed framework was evaluated using two datasets and was compared with recent deep learning models. Various visualization methods were also employed to understand the characteristics of the dataset and to visualize the embedding features. The experimental results revealed substantial improvements in terms of F 1-score, speed of detection, and false alarm rate.
TL;DR: The rational design and synthesis of novel nanostructured electrode materials on various flexible-based substrates and the latest representative techniques and active materials of recently developed wearable supercapacitors with superior performance are summarized.
Abstract: Wearable electronic devices, such as electrical sensors, flexible displays, and health monitors have gained considerable attention and experienced rapid progress. Recently, the progress of flexible and wearable supercapacitors (SCs) have received considerable attention due to their ease of fabrication, low cost, flexible integration into textiles, long cycle life, fast charging/discharging, high efficiency, and capability to bridge the energy/power gap between conventional capacitors and batteries/fuel cells. In this context, the recent progress and achievements of flexible and wearable supercapacitors are presented, especially, the rational design and synthesis of novel nanostructured electrode materials on various flexible-based substrates, such as, carbon cloth, graphene coated fabric, silver coated fabric, nickel coated fabric, copper/nickel coated polyester fabric (CNF), etc. are summarized. The latest representative techniques and active materials of recently developed wearable supercapacitors with superior performance are summarized. Lastly, the current challenges, and future research directions and perspective in optimizing and developing the energy storage performance and function of flexible and wearable supercapacitors for their practical applications are addressed.
TL;DR: The frequency analysis of herbs, Glycyrrhizae Radix et Rhizoma was found to be the herb with the highest frequency of usage in the Chinese guidelines, and this review can be used as guidance for the traditional medicine treatment of COVID-19.
Abstract: Background Coronavirus disease 2019 (COVID-19) is pandemic and has caused illness to many people worldwide. This review aimed to summarize and analyze the herbal formulae provided by the guidelines for their pattern identifications (PIs) and compositions of herbs to treat patients with COVID-19. Methods We searched 7 data sources for eligible traditional medicine guidelines up to March 6, 2020 and found a total of 28 traditional medicine guidelines that provide treatment measures for COVID-19. Results Of the 28 guidelines, there were 26 government-issued Chinese guidelines and 2 Korean guidelines. After standardizing the terminology of the PIs and herbal formulae, there were 8 PIs and 23 herbal formulae for the mild stage, 11 PIs and 31 herbal formulae for the moderate stage, 8 PIs and 21 herbal formulae for the severe stage, and 6 PIs and 23 herbal formulae for the recovery stage in the Chinese guidelines. In the Korean guidelines, there were 4 PIs and 15 herbal formulae for the mild stage, 3 PIs and 3 herbal formulae for the severe stage, and 2 PIs and 2 herbal formulae for the recovery stage. In the frequency analysis of herbs, Glycyrrhizae Radix et Rhizoma, Armeniacae Semen Amarum, Ephedrae Herba, and Gypsum Fibrosum were found to be the herbs with the high frequency of usage in the Chinese guidelines. Conclusion This review can be used as guidance for the traditional medicine treatment of COVID-19. Clinical evidence is needed in the future to evaluate the efficacy of traditional medicine.
TL;DR: The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era as discussed by the authors.
Abstract: The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era. All eleven events were consistent with being from well-modeled mergers between compact stellar-mass objects: black holes or neutron stars. The data around the time of each of these events have been made publicly available through the gravitational-wave open science center. The entirety of the gravitational-wave strain data from the first and second observing runs have also now been made publicly available. There is considerable interest among the broad scientific community in understanding the data and methods used in the analyses. In this paper, we provide an overview of the detector noise properties and the data analysis techniques used to detect gravitational-wave signals and infer the source properties. We describe some of the checks that are performed to validate the analyses and results from the observations of gravitational-wave events. We also address concerns that have been raised about various properties of LIGO–Virgo detector noise and the correctness of our analyses as applied to the resulting data.
TL;DR: This article presents the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints and presents the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.
Abstract: Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches, such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based approaches. Whale optimization algorithm (WOA) has recently gained the attention of the research community as an efficient method to solve a variety of optimization problems. As an alternative to the existing methods, our main goal in this article is to study the applicability of WOA to solve resource allocation problems in wireless networks. First, we present the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints. Then, we demonstrate three examples of WOA to resource allocation in wireless networks, including power allocation for energy-and-spectral efficiency tradeoff in wireless interference networks, power allocation for secure throughput maximization, and mobile edge computation offloading. Lastly, we present the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.
Yonsei University1, University of Maryland, College Park2, Ewha Womans University3, Pusan National University4, Seoul National University5, Pukyong National University6, Gwangju Institute of Science and Technology7, Gangneung–Wonju National University8, Kyungpook National University9, Ulsan National Institute of Science and Technology10, Goddard Space Flight Center11, Harvard University12, Langley Research Center13, European Space Research and Technology Centre14, Korea Aerospace Research Institute15, Konkuk University16, National Institute of Environmental Research17, Royal Netherlands Meteorological Institute18, National Center for Atmospheric Research19, Chiba University20, Nara Women's University21, Japan Agency for Marine-Earth Science and Technology22, University of Science and Technology of China23, Peking University24, University of Iowa25, University of Alabama in Huntsville26, NASA Headquarters27, University of Maryland, Baltimore County28, Hong Kong University of Science and Technology29, California Institute of Technology30, EUMETSAT31, York University32, University of Houston33
TL;DR: The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a...
Abstract: The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a ...
TL;DR: This review provides a reference point on the SCs for researchers and designers working in the field of energy storage devices and applications.
Abstract: Supercapacitors (SCs) possess paramount importance and are a promising solution among the class of energy storage devices since they have appreciable features like higher specific power, longer life span, and eco-friendly nature. They replace the difference of energy/power between the high-power traditional capacitors and high energy fuel cells/ batteries. In the current review, comprehensive research on the recent literature of SCs investigates and elucidates with great consideration. The global market analysis, manufacturing firms, challenges, and recent advances present the fundamental, as well as application perspective of SCs. An elaborate analysis of the classifications of SCs, fabrication of different electrode materials from the very beginning of their evolution gives the lucid idea. The evaluation of SC nanocomposites with different parameters such as specific capacitance, energy density, power density, cycling performance, and capacity retention depicts the performance. Detail description of the selection of electrolytes and various synthesis methods of the SCs explains comprehensively. In the end, future scope and challenges mention briefly and consider for the next-generation SC's design and applications. This review provides a reference point on the SCs for researchers and designers working in the field of energy storage devices and applications.
TL;DR: A cost-efficient convolutional neural network for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems and achieves the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.
Abstract: This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.
TL;DR: In this article, a reagent-assisted composite of the ZnCo2O4 (ZC-UAH) electrode material showed a higher specific capacity of 462.5C g−1 at a current density of 1'A'g−1 within the potential range of 0.0-0.4'V in the 2'M KOH electrolyte solution.
TL;DR: It is argued that the applicability of these underlying assumptions in theorising BPM needs to be re-examined in the context of digital transformation, and proposed new managerial approaches for BPM in digital transformation contexts are proposed.
Abstract: Business process management (BPM) research emphasises three important logics – modelling (process), infrastructural alignment (infrastructure) and procedural actor (agency) logics. These logics cap...
TL;DR: In this paper, the authors conducted a systematic review and network meta-analysis (NMA) to evaluate the comparative efficacy and safety of pharmacological interventions and the level of evidence behind each treatment regimen in different clinical settings.
Abstract: Background Numerous clinical trials and observational studies have investigated various pharmacological agents as potential treatment for Coronavirus Disease 2019 (COVID-19), but the results are heterogeneous and sometimes even contradictory to one another, making it difficult for clinicians to determine which treatments are truly effective. Methods and findings We carried out a systematic review and network meta-analysis (NMA) to systematically evaluate the comparative efficacy and safety of pharmacological interventions and the level of evidence behind each treatment regimen in different clinical settings. Both published and unpublished randomized controlled trials (RCTs) and confounding-adjusted observational studies which met our predefined eligibility criteria were collected. We included studies investigating the effect of pharmacological management of patients hospitalized for COVID-19 management. Mild patients who do not require hospitalization or have self-limiting disease courses were not eligible for our NMA. A total of 110 studies (40 RCTs and 70 observational studies) were included. PubMed, Google Scholar, MEDLINE, the Cochrane Library, medRxiv, SSRN, WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov were searched from the beginning of 2020 to August 24, 2020. Studies from Asia (41 countries, 37.2%), Europe (28 countries, 25.4%), North America (24 countries, 21.8%), South America (5 countries, 4.5%), and Middle East (6 countries, 5.4%), and additional 6 multinational studies (5.4%) were included in our analyses. The outcomes of interest were mortality, progression to severe disease (severe pneumonia, admission to intensive care unit (ICU), and/or mechanical ventilation), viral clearance rate, QT prolongation, fatal cardiac complications, and noncardiac serious adverse events. Based on RCTs, the risk of progression to severe course and mortality was significantly reduced with corticosteroids (odds ratio (OR) 0.23, 95% confidence interval (CI) 0.06 to 0.86, p = 0.032, and OR 0.78, 95% CI 0.66 to 0.91, p = 0.002, respectively) and remdesivir (OR 0.29, 95% CI 0.17 to 0.50, p < 0.001, and OR 0.62, 95% CI 0.39 to 0.98, p = 0.041, respectively) compared to standard care for moderate to severe COVID-19 patients in non-ICU; corticosteroids were also shown to reduce mortality rate (OR 0.54, 95% CI 0.40 to 0.73, p < 0.001) for critically ill patients in ICU. In analyses including observational studies, interferon-alpha (OR 0.05, 95% CI 0.01 to 0.39, p = 0.004), itolizumab (OR 0.10, 95% CI 0.01 to 0.92, p = 0.042), sofosbuvir plus daclatasvir (OR 0.26, 95% CI 0.07 to 0.88, p = 0.030), anakinra (OR 0.30, 95% CI 0.11 to 0.82, p = 0.019), tocilizumab (OR 0.43, 95% CI 0.30 to 0.60, p < 0.001), and convalescent plasma (OR 0.48, 95% CI 0.24 to 0.96, p = 0.038) were associated with reduced mortality rate in non-ICU setting, while high-dose intravenous immunoglobulin (IVIG) (OR 0.13, 95% CI 0.03 to 0.49, p = 0.003), ivermectin (OR 0.15, 95% CI 0.04 to 0.57, p = 0.005), and tocilizumab (OR 0.62, 95% CI 0.42 to 0.90, p = 0.012) were associated with reduced mortality rate in critically ill patients. Convalescent plasma was the only treatment option that was associated with improved viral clearance rate at 2 weeks compared to standard care (OR 11.39, 95% CI 3.91 to 33.18, p < 0.001). The combination of hydroxychloroquine and azithromycin was shown to be associated with increased QT prolongation incidence (OR 2.01, 95% CI 1.26 to 3.20, p = 0.003) and fatal cardiac complications in cardiac-impaired populations (OR 2.23, 95% CI 1.24 to 4.00, p = 0.007). No drug was significantly associated with increased noncardiac serious adverse events compared to standard care. The quality of evidence of collective outcomes were estimated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework. The major limitation of the present study is the overall low level of evidence that reduces the certainty of recommendations. Besides, the risk of bias (RoB) measured by RoB2 and ROBINS-I framework for individual studies was generally low to moderate. The outcomes deducted from observational studies could not infer causality and can only imply associations. The study protocol is publicly available on PROSPERO (CRD42020186527). Conclusions In this NMA, we found that anti-inflammatory agents (corticosteroids, tocilizumab, anakinra, and IVIG), convalescent plasma, and remdesivir were associated with improved outcomes of hospitalized COVID-19 patients. Hydroxychloroquine did not provide clinical benefits while posing cardiac safety risks when combined with azithromycin, especially in the vulnerable population. Only 29% of current evidence on pharmacological management of COVID-19 is supported by moderate or high certainty and can be translated to practice and policy; the remaining 71% are of low or very low certainty and warrant further studies to establish firm conclusions.
TL;DR: In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network and experimental results prove that the MLSTM approach outperforms the other ML approaches.
Abstract: The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches.
TL;DR: In this paper, the authors developed a multicomponent integration of hierarchical Co9S8-Ni3S2 nanoparticles anchored on CuMn2O4-NiMn 2O4 nanosheet arrays (CoNi-S NPs/Cu-Ni-Mn-O NSAs) and rhombus-like shaped MnFe2O 4-ZnFe-O/G-ink nanosheets, which were effectively applied as a superior binder free cathode and anode electrodes for ASCs.
University of Hamburg1, University of Toulouse2, University of Miami3, Cooperative Institute for Research in Environmental Sciences4, National Institute for Space Research5, National Center for Atmospheric Research6, George Mason University7, Barcelona Supercomputing Center8, ETH Zurich9, European Centre for Medium-Range Weather Forecasts10, Max Planck Society11, National Oceanic and Atmospheric Administration12, World Climate Research Programme13, Columbia University14, Met Office15, Japan Meteorological Agency16, University of Strathclyde17, University of Buenos Aires18, Embry-Riddle Aeronautical University, Daytona Beach19, Colorado State University20, University of Pretoria21, M-Systems22, University of Reading23, University of California, San Diego24, International Centre for Theoretical Physics25, University of Mainz26, University of Leeds27, Scripps Institution of Oceanography28, Office of Oceanic and Atmospheric Research29, Deutscher Wetterdienst30, Japan Agency for Marine-Earth Science and Technology31, Pusan National University32, Monash University33, University of Southampton34, University of Illinois at Urbana–Champaign35, Princeton University36, University of Graz37, Escuela Superior Politecnica del Litoral38, Leibniz Institute of Marine Sciences39, Dalhousie University40, Hobart Corporation41
TL;DR: In this article, the authors surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services.
Abstract: Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.