Showing papers by "University of Rhode Island published in 2020"

GW190425: Observation of a Compact Binary Coalescence with Total Mass ∼ 3.4 M O

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.

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

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

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M

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}.

Scientists' warning on invasive alien species.

Abstract: Biological invasions are a global consequence of an increasingly connected world and the rise in human population size The numbers of invasive alien species – the subset of alien species that spread widely in areas where they are not native, affecting the environment or human livelihoods – are increasing Synergies with other global changes are exacerbating current invasions and facilitating new ones, thereby escalating the extent and impacts of invaders Invasions have complex and often immense long‐term direct and indirect impacts In many cases, such impacts become apparent or problematic only when invaders are well established and have large ranges Invasive alien species break down biogeographic realms, affect native species richness and abundance, increase the risk of native species extinction, affect the genetic composition of native populations, change native animal behaviour, alter phylogenetic diversity across communities, and modify trophic networks Many invasive alien species also change ecosystem functioning and the delivery of ecosystem services by altering nutrient and contaminant cycling, hydrology, habitat structure, and disturbance regimes These biodiversity and ecosystem impacts are accelerating and will increase further in the future Scientific evidence has identified policy strategies to reduce future invasions, but these strategies are often insufficiently implemented For some nations, notably Australia and New Zealand, biosecurity has become a national priority There have been long‐term successes, such as eradication of rats and cats on increasingly large islands and biological control of weeds across continental areas However, in many countries, invasions receive little attention Improved international cooperation is crucial to reduce the impacts of invasive alien species on biodiversity, ecosystem services, and human livelihoods Countries can strengthen their biosecurity regulations to implement and enforce more effective management strategies that should also address other global changes that interact with invasions

GW190412: Observation of a binary-black-hole coalescence with asymmetric masses

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.

An overview of the uses of per- and polyfluoroalkyl substances (PFAS)

Abstract: Per- and polyfluoroalkyl substances (PFAS) are of concern because of their high persistence (or that of their degradation products) and their impacts on human and environmental health that are known or can be deduced from some well-studied PFAS. Currently, many different PFAS (on the order of several thousands) are used in a wide range of applications, and there is no comprehensive source of information on the many individual substances and their functions in different applications. Here we provide a broad overview of many use categories where PFAS have been employed and for which function; we also specify which PFAS have been used and discuss the magnitude of the uses. Despite being non-exhaustive, our study clearly demonstrates that PFAS are used in almost all industry branches and many consumer products. In total, more than 200 use categories and subcategories are identified for more than 1400 individual PFAS. In addition to well-known categories such as textile impregnation, fire-fighting foam, and electroplating, the identified use categories also include many categories not described in the scientific literature, including PFAS in ammunition, climbing ropes, guitar strings, artificial turf, and soil remediation. We further discuss several use categories that may be prioritised for finding PFAS-free alternatives. Besides the detailed description of use categories, the present study also provides a list of the identified PFAS per use category, including their exact masses for future analytical studies aiming to identify additional PFAS.

Properties and Astrophysical Implications of the 150 M Binary Black Hole Merger GW190521

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.

Association of Obesity with Disease Severity Among Patients with Coronavirus Disease 2019.

Abstract: Objective The aim of this study was to explore the potential association of obesity and other chronic diseases with severe outcomes, such as intensive care unit (ICU) admission and invasive mechanical ventilation (IMV), in patients hospitalized with coronavirus disease 2019 (COVID-19). Methods This study analyzed a retrospective cohort of 103 patients hospitalized with COVID-19. Demographic data, past medical history, and hospital course were collected and analyzed. A multivariate logistic regression analysis was implemented to examine associations. Results From February 17 to April 5, 103 consecutive patients were hospitalized with COVID-19. Among them, 44 patients (42.7%) were admitted to the ICU, and 29 (65.9%) required IMV. The prevalence of obesity was 47.5% (49 of 103). In a multivariate analysis, severe obesity (BMI ≥ 35 kg/m2 ) was associated with ICU admission (adjusted odds ratio [aOR]: 5.39, 95% CI: 1.13-25.64). Moreover, patients who required IMV were more likely to have had heart disease (aOR: 3.41, 95% CI: 1.05-11.06), obesity (BMI = 30-34.9 kg/m2 ; aOR: 6.85, 95% CI: 1.05-44.82), or severe obesity (BMI ≥ 35 kg/m2 ; aOR: 9.99, 95% CI: 1.39-71.69). Conclusions In our analysis, severe obesity (BMI ≥ 35 kg/m2 ) was associated with ICU admission, whereas history of heart disease and obesity (BMI ≥ 30 kg/m2 ) were independently associated with the use of IMV. Increased vigilance and aggressive treatment of patients with obesity and COVID-19 are warranted.

Impact of Power Grid Strength and PLL Parameters on Stability of Grid-Connected DFIG Wind Farm

Abstract: This paper investigates the impact of power grid strength and phase-locked loop (PLL) parameters on small signal stability of grid-connected doubly fed induction generator (DFIG)-based wind farm. Modal analysis of the grid-connected DFIG wind turbine under different operating conditions and various power grid strengths are investigated at first. Modal analysis results reveal that the DFIG connected to a weak grid may easily lose stability under the heavy-duty operating conditions due to PLL oscillation. The object of this paper is to identify the PLL oscillation mechanism as well as influence factors and propose a damping solution for this oscillation mode. A simplified linear system model of the grid-connected DFIG wind turbine is proposed for analyzing the PLL oscillation. Through the complex torque coefficients method and using this model, the oscillation mechanism and influence factors including the power grid strength and the PLL parameters are identified. To suppress this PLL oscillation, a mixed $H_2/H_{\infty }$ robust damping controller is proposed and designed for the DFIG. Electromagnetic transient simulation results of both single-DFIG system and multiply-DFIG system verify the correctness of the analysis results and effectiveness of the proposed damping controller.

A guide to LIGO–Virgo detector noise and extraction of transient gravitational-wave signals

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.

Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning

Abstract: Electric vehicles (EVs) have been popularly adopted and deployed over the past few years because they are environment-friendly. When integrated into smart grids, EVs can operate as flexible loads or energy storage devices to participate in demand response (DR). By taking advantage of time-varying electricity prices in DR, the charging cost can be reduced by optimizing the charging/discharging schedules. However, since there exists randomness in the arrival and departure time of an EV and the electricity price, it is difficult to determine the optimal charging/discharging schedules to guarantee that the EV is fully charged upon departure. To address this issue, we formulate the EV charging/discharging scheduling problem as a constrained Markov Decision Process (CMDP). The aim is to find a constrained charging/discharging scheduling strategy to minimize the charging cost as well as guarantee the EV can be fully charged. To solve the CMDP, a model-free approach based on safe deep reinforcement learning (SDRL) is proposed. The proposed approach does not require any domain knowledge about the randomness. It directly learns to generate the constrained optimal charging/discharging schedules with a deep neural network (DNN). Unlike existing reinforcement learning (RL) or deep RL (DRL) paradigms, the proposed approach does not need to manually design a penalty term or tune a penalty coefficient. Numerical experiments with real-world electricity prices demonstrate the effectiveness of the proposed approach.

National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images

Abstract: Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China’s wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China’s wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China’s wetlands and biodiversity conservation.

Global diversity of microbial communities in marine sediment.

Abstract: Microbial life in marine sediment contributes substantially to global biomass and is a crucial component of the Earth system. Subseafloor sediment includes both aerobic and anaerobic microbial ecosystems, which persist on very low fluxes of bioavailable energy over geologic time. However, the taxonomic diversity of the marine sedimentary microbial biome and the spatial distribution of that diversity have been poorly constrained on a global scale. We investigated 299 globally distributed sediment core samples from 40 different sites at depths of 0.1 to 678 m below the seafloor. We obtained ∼47 million 16S ribosomal RNA (rRNA) gene sequences using consistent clean subsampling and experimental procedures, which enabled accurate and unbiased comparison of all samples. Statistical analysis reveals significant correlations between taxonomic composition, sedimentary organic carbon concentration, and presence or absence of dissolved oxygen. Extrapolation with two fitted species-area relationship models indicates taxonomic richness in marine sediment to be 7.85 × 103 to 6.10 × 105 and 3.28 × 104 to 2.46 × 106 amplicon sequence variants for Archaea and Bacteria, respectively. This richness is comparable to the richness in topsoil and the richness in seawater, indicating that Bacteria are more diverse than Archaea in Earth's global biosphere.

Pornography use in the setting of the COVID-19 pandemic.

Abstract: With the global expansion of the COVID-19 pandemic, social or physical distancing, quarantines, and lockdowns have become more prevalent. Concurrently, Pornhub, one of the largest pornography sites, has reported increased pornography use in multiple countries, with global traffic increasing over 11% from late February to March 17, 2020. While some substantial increases have coincided with Pornhub making its premium services free to countries in lockdowned or quarantined jurisdictions, countries without such free premium access have also reported increases in the range of 4-24%. In addition, pornography searches using the terms "coronavirus", "corona", and "covid" have reached more than 9.1 million. In this letter, we discuss COVID-19-related pornography-use patterns and the impact they may have with respect to problematic pornography use.

Strategies for grouping per- and polyfluoroalkyl substances (PFAS) to protect human and environmental health

Abstract: Grouping strategies are needed for per- and polyfluoroalkyl substances (PFAS), in part, because it would be time and resource intensive to test and evaluate the more than 4700 PFAS on the global market on a chemical-by-chemical basis. In this paper we review various grouping strategies that could be used to inform actions on these chemicals and outline the motivations, advantages and disadvantages for each. Grouping strategies are subdivided into (1) those based on the intrinsic properties of the PFAS (e.g. persistence, bioaccumulation potential, toxicity, mobility, molecular size) and (2) those that inform risk assessment through estimation of cumulative exposure and/or effects. The most precautionary grouping approach of those reviewed within this article suggests phasing out PFAS based on their high persistence alone (the so-called "P-sufficient" approach). The least precautionary grouping approach reviewed advocates only grouping PFAS for risk assessment that have the same toxicological effects, modes and mechanisms of action, and elimination kinetics, which would need to be well documented across different PFAS. It is recognised that, given jurisdictional differences in chemical assessment philosophies and methodologies, no one strategy will be generally acceptable. The guiding question we apply to the reviewed grouping strategies is: grouping for what purpose? The motivation behind the grouping (e.g. determining use in products vs. setting guideline levels for contaminated environments) may lead to different grouping decisions. This assessment provides the necessary context for grouping strategies such that they can be adopted as they are, or built on further, to protect human and environmental health from potential PFAS-related effects.

A conceptual map of invasion biology: Integrating hypotheses into a consensus network

Abstract: BACKGROUND AND AIMS: Since its emergence in the mid‐20th century, invasion biology has matured into a productive research field addressing questions of fundamental and applied importance. Not only has the number of empirical studies increased through time, but also has the number of competing, overlapping and, in some cases, contradictory hypotheses about biological invasions. To make these contradictions and redundancies explicit, and to gain insight into the field’s current theoretical structure, we developed and applied a Delphi approach to create a consensus network of 39 existing invasion hypotheses. RESULTS: The resulting network was analysed with a link‐clustering algorithm that revealed five concept clusters (resource availability, biotic interaction, propagule, trait and Darwin’s clusters) representing complementary areas in the theory of invasion biology. The network also displays hypotheses that link two or more clusters, called connecting hypotheses, which are important in determining network structure. The network indicates hypotheses that are logically linked either positively (77 connections of support) or negatively (that is, they contradict each other; 6 connections). SIGNIFICANCE: The network visually synthesizes how invasion biology’s predominant hypotheses are conceptually related to each other, and thus, reveals an emergent structure – a conceptual map – that can serve as a navigation tool for scholars, practitioners and students, both inside and outside of the field of invasion biology, and guide the development of a more coherent foundation of theory. Additionally, the outlined approach can be more widely applied to create a conceptual map for the larger fields of ecology and biogeography.

Rate and Risk Factors Associated With Prolonged Opioid Use After Surgery: A Systematic Review and Meta-analysis

Abstract: Importance Prolonged opioid use after surgery may be associated with opioid dependency and increased health care use However, published studies have reported varying estimates of the magnitude of prolonged opioid use and risk factors associated with the transition of patients to long-term opioid use Objectives To evaluate the rate and characteristics of patient-level risk factors associated with increased risk of prolonged use of opioids after surgery Data Sources For this systematic review and meta-analysis, a search of MEDLINE, Embase, and Google Scholar from inception to August 30, 2017, was performed, with an updated search performed on June 30, 2019 Key words may includeopioid analgesics, general surgery, surgical procedures, persistent opioid use,andpostoperative pain Study Selection Of 7534 articles reviewed, 33 studies were included Studies were included if they involved participants 18 years or older, evaluated opioid use 3 or more months after surgery, and reported the rate and adjusted risk factors associated with prolonged opioid use after surgery Data Extraction and Synthesis The Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines were followed Two reviewers independently assessed and extracted the relevant data Main Outcomes and Measures The weighted pooled rate and odds ratios (ORs) of risk factors were calculated using the random-effects model Results The 33 studies included 1 922 743 individuals, with 1 854 006 (964%) from the US In studies with available sex and age information, participants were mostly female (1 031 399; 827%) and had a mean (SD) age of 593 (128) years The pooled rate of prolonged opioid use after surgery was 67% (95% CI, 45%-98%) but decreased to 12% (95% CI, 04%-39%) in restricted analyses involving only opioid-naive participants at baseline The risk factors with the strongest associations with prolonged opioid use included preoperative use of opioids (OR, 532; 95% CI, 294-964) or illicit cocaine (OR, 434; 95% CI, 150-1258) and a preoperative diagnosis of back pain (OR, 205; 95% CI, 163-258) No significant differences were observed with various study-level factors, including a comparison of major vs minor surgical procedures (pooled rate: 70%; 95% CI, 49%-99% vs 111%; 95% CI, 60%-194%;P = 20) Across all of our analyses, there was substantial variability because of heterogeneity instead of sampling error Conclusions and Relevance The findings suggest that prolonged opioid use after surgery may be a substantial burden to public health It appears that strategies, such as proactively screening for at-risk individuals, should be prioritized

Real-Time Residential Demand Response

Abstract: This paper presents a real-time demand response (DR) strategy for optimal scheduling of home appliances. The uncertainty of the resident’s behavior, real-time electricity price, and outdoor temperature is considered. An efficient DR scheduling algorithm based on deep reinforcement learning (DRL) is proposed. Unlike traditional model-based approaches, the proposed approach is model-free and does not need to know the distribution of the uncertainty. Besides, unlike conventional RL-based methods, the proposed approach can handle both discrete and continuous actions to jointly optimize the schedules of different types of appliances. In the proposed approach, an approximate optimal policy based on neural network is designed to learn the optimal DR scheduling strategy. The neural network based policy can directly learn from high-dimensional sensory data of the appliance states, real-time electricity price, and outdoor temperature. A policy search algorithm based upon trust region policy optimization (TRPO) is used to train the neural network. The effectiveness of our proposed approach is validated by simulation studies where the real-world electricity price and outdoor temperature are used.

The high persistence of PFAS is sufficient for their management as a chemical class

Abstract: Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic organic substances with diverse structures, properties, uses, bioaccumulation potentials and toxicities. Despite this high diversity, all PFAS are alike in that they contain perfluoroalkyl moieties that are extremely resistant to environmental and metabolic degradation. The vast majority of PFAS are therefore either non-degradable or transform ultimately into stable terminal transformation products (which are still PFAS). Under the European chemicals regulation this classifies PFAS as very persistent substances (vP). We argue that this high persistence is sufficient concern for their management as a chemical class, and for all “non-essential” uses of PFAS to be phased out. The continual release of highly persistent PFAS will result in increasing concentrations and increasing probabilities of the occurrence of known and unknown effects. Once adverse effects are identified, the exposure and associated effects will not be easily reversible. Reversing PFAS contamination will be technically challenging, energy intensive, and costly for society, as is evident in the efforts to remove PFAS from contaminated land and drinking water sources.

Deterministic Policy Gradient With Integral Compensator for Robust Quadrotor Control

Abstract: In this paper, a deep reinforcement learning-based robust control strategy for quadrotor helicopters is proposed. The quadrotor is controlled by a learned neural network which directly maps the system states to control commands in an end-to-end style. The learning algorithm is developed based on the deterministic policy gradient algorithm. By introducing an integral compensator to the actor-critic structure, the tracking accuracy and robustness have been greatly enhanced. Moreover, a two-phase learning protocol which includes both offline and online learning phase is proposed for practical implementation. An offline policy is first learned based on a simplified quadrotor model. Then, the policy is online optimized in actual flight. The proposed approach is evaluated in the flight simulator. The results demonstrate that the offline learned policy is highly robust to model errors and external disturbances. It also shows that the online learning could significantly improve the control performance.

Roadmap for naming uncultivated Archaea and Bacteria

Abstract: The assembly of single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) has led to a surge in genome-based discoveries of members affiliated with Archaea and Bacteria, bringing with it a need to develop guidelines for nomenclature of uncultivated microorganisms. The International Code of Nomenclature of Prokaryotes (ICNP) only recognizes cultures as ‘type material’, thereby preventing the naming of uncultivated organisms. In this Consensus Statement, we propose two potential paths to solve this nomenclatural conundrum. One option is the adoption of previously proposed modifications to the ICNP to recognize DNA sequences as acceptable type material; the other option creates a nomenclatural code for uncultivated Archaea and Bacteria that could eventually be merged with the ICNP in the future. Regardless of the path taken, we believe that action is needed now within the scientific community to develop consistent rules for nomenclature of uncultivated taxa in order to provide clarity and stability, and to effectively communicate microbial diversity.

Global Synchronization of Fuzzy Memristive Neural Networks With Discrete and Distributed Delays

Abstract: This paper investigates the synchronization problem of Takagi–Sugeno fuzzy memristive neural networks (FMNNs) with mixed delays, in which the bounded distributed and unbounded discrete time-varying delays are involved. Then, under the nonsmooth analysis and Lyapunov stability theory, several easily verified algebraic criteria are established to guarantee the global synchronization of FMNNs via a designed fuzzy feedback controller. Moreover, to show the superiority of the theoretical results, several discussions and comparisons with existing work are provided, indicating that derived results in this paper are general and include several existing ones as special cases. Finally, two numerical examples and two applications in psuedorandom number generation and image encryption are presented to show the validity and practicability of the theoretical results.

Drivers of future alien species impacts: An expert-based assessment.

Abstract: Understanding the likely future impacts of biological invasions is crucial yet highly challenging given the multiple relevant environmental, socio-economic and societal contexts and drivers. In the absence of quantitative models, methods based on expert knowledge are the best option for assessing future invasion trajectories. Here, we present an expert assessment of the drivers of potential alien species impacts under contrasting scenarios and socioecological contexts through the mid-21st century. Based on responses from 36 experts in biological invasions, moderate (20%-30%) increases in invasions, compared to the current conditions, are expected to cause major impacts on biodiversity in most socioecological contexts. Three main drivers of biological invasions-transport, climate change and socio-economic change-were predicted to significantly affect future impacts of alien species on biodiversity even under a best-case scenario. Other drivers (e.g. human demography and migration in tropical and subtropical regions) were also of high importance in specific global contexts (e.g. for individual taxonomic groups or biomes). We show that some best-case scenarios can substantially reduce potential future impacts of biological invasions. However, rapid and comprehensive actions are necessary to use this potential and achieve the goals of the Post-2020 Framework of the Convention on Biological Diversity.

A Critical Review of Extraction and Identification Methods of Microplastics in Wastewater and Drinking Water

Abstract: This critical review analyzes methodologies used to collect, quantify, and characterize microplastics in both wastewater and drinking water. Researchers used different techniques at all stages, from collection to characterization, for quantifying microplastics in urban water systems. This represents a barrier to comprehensively assess the current loads of microplastic and to compare the results obtained by such assessments. The compiled studies address microplastic contamination using four types of sample collection techniques, four methods for processing samples, and four techniques for characterizing microplastics. This results in significant discrepancies in each of the following: (1) reported concentrations in both wastewater effluents and drinking water, (2) microplastic characteristics (i.e., size, color, shape, and composition), and (3) quality control and assurance procedures. Finally, this review qualitatively evaluated the reports by the completeness of their data based on a ranking system using five criteria: sample collection, sample processing, quality control, identification technique, and results. The results of this ranking system clarify disparities between the studies.

Dimensionality Reduction of Hyperspectral Imagery Based on Spatial–Spectral Manifold Learning

Abstract: The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm called spatial–spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification. At first, a weighted mean filter (WMF) is employed to preprocess the image, which aims to reduce the influence of background noise. According to the spatial consistency property of HSI, SSMRPE utilizes a new spatial–spectral combined distance (SSCD) to fuse the spatial structure and spectral information for selecting effective spatial–spectral neighbors of HSI pixels. Then, it explores the spatial relationship between each point and its neighbors to adjust the reconstruction weights to improve the efficiency of manifold reconstruction. As a result, the proposed method can extract the discriminant features and subsequently improve the classification performance of HSI. The experimental results on the PaviaU and Salinas hyperspectral data sets indicate that SSMRPE can achieve better classification results in comparison with some state-of-the-art methods.

Model Comparison from LIGO-Virgo Data on GW170817's Binary Components and Consequences for the Merger Remnant

Abstract: GW170817 is the very first observation of gravitational waves originating from the coalescence of two compact objects in the mass range of neutron stars, accompanied by electromagnetic counterparts, and offers an opportunity to directly probe the internal structure of neutron stars. We perform Bayesian model selection on a wide range of theoretical predictions for the neutron star equation of state. For the binary neutron star hypothesis, we find that we cannot rule out the majority of theoretical models considered. In addition, the gravitational-wave data alone does not rule out the possibility that one or both objects were low-mass black holes. We discuss the possible outcomes in the case of a binary neutron star merger, finding that all scenarios from prompt collapse to long-lived or even stable remnants are possible. For long-lived remnants, we place an upper limit of 1.9 kHz on the rotation rate. If a black hole was formed any time after merger and the coalescing stars were slowly rotating, then the maximum baryonic mass of non-rotating neutron stars is at most 3.05M⊙, and three equations of state considered here can be ruled out. We obtain a tighter limit of 2.67M⊙ for the case that the merger results in a hypermassive neutron star.

Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks

Abstract: Plant diseases are a crucial issue in agriculture. An accurate and automatic identification of leaf diseases could help to develop an early response to reduce economic losses. Recent research in plant diseases has adopted deep neural networks. However, such research has used the models as a black-box passing the labeled images through the networks. This letter presents an analysis of the network weights for the automatic recognition of soybean leaf diseases applied to images taken straight from a small and cheap unmanned aerial vehicle (UAV). To achieve high accuracy, we evaluated four deep neural network models trained with different parameters for fine-tuning (FT) and transfer learning. Data augmentation and dropout were used during the network training to avoid overfitting. Our methodology consists of using the SLIC method to segment the plant leaves in the top-view images obtained during the flight. We tested our data set created from real flight inspections in an end-to-end computer vision approach. Results strongly suggest that the FT of parameters substantially improves the identification accuracy.

Opioid use disorder and the COVID 19 pandemic: A call to sustain regulatory easements and further expand access to treatment.

Abstract: We highlight the critical roles that pharmacists have related to sustaining and advancing the changes being made in the face of the current COVID-19 pandemic to ensure that patients have more seamless and less complex access to treatment. Discussed herein is how the current COVID-19 pandemic is impacting persons with substance use disorders, barriers that persist, and the opportunities that arise as regulations around treatments for this population are eased.