Showing papers by "Missouri University of Science and Technology published in 2021"

Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

Abstract: We report on the population of 47 compact binary mergers detected with a false-alarm rate of 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9-+8.614.3 Gpc-3 yr-1 for BBHs and RBNS = 320-+240490 Gpc-3 yr-1 for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier.

Observation of Gravitational Waves from Two Neutron Star–Black Hole Coalescences

Abstract: We report the observation of gravitational waves from two compact binary coalescences in LIGO’s and Virgo’s third observing run with properties consistent with neutron star–black hole (NSBH) binaries. The two events are named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and GW200115; the first was observed by LIGO Livingston and Virgo and the second by all three LIGO–Virgo detectors. The source of GW200105 has component masses 8.9−1.5+1.2 and 1.9−0.2+0.3M⊙ , whereas the source of GW200115 has component masses 5.7−2.1+1.8 and 1.5−0.3+0.7M⊙ (all measurements quoted at the 90% credible level). The probability that the secondary’s mass is below the maximal mass of a neutron star is 89%–96% and 87%–98%, respectively, for GW200105 and GW200115, with the ranges arising from different astrophysical assumptions. The source luminosity distances are 280−110+110 and 300−100+150Mpc , respectively. The magnitude of the primary spin of GW200105 is less than 0.23 at the 90% credible level, and its orientation is unconstrained. For GW200115, the primary spin has a negative spin projection onto the orbital angular momentum at 88% probability. We are unable to constrain the spin or tidal deformation of the secondary component for either event. We infer an NSBH merger rate density of 45−33+75Gpc−3yr−1 when assuming that GW200105 and GW200115 are representative of the NSBH population or 130−69+112Gpc−3yr−1 under the assumption of a broader distribution of component masses.

New development of ultra-high-performance concrete (UHPC)

Abstract: Ultra-high-performance concrete (UHPC) is a type of cement-based composite for new construction and/or restoration of existing structures to extend service life. UHPC features superior workability, mechanical properties, and durability compared with conventional concrete. However, some challenges limit the wider application of UHPC, such as low workability for large-volume production, high autogenous shrinkage, insufficient flexural/tensile properties, and unpredictable durability after concrete cracking. Therefore, this paper reviews the state-of-the-art technologies for developing UHPC mixtures with improved properties. This review covers the following aspects: (1) the existing design methodologies; (2) the typical ingredients (e.g., binders, aggregates, chemical admixtures, and fibers) for preparation of UHPC and the underlying working principals; (3) the technologies for improving and controlling key properties (e.g., workability, autogenous shrinkage, compressive performance, tensile/flexural properties, and durability); and (4) the representative successful applications. This review is expected to advance the fundamental knowledge of UHPC and promote further research and applications of UHPC.

A gravitational-wave measurement of the Hubble constant following the second observing run of Advanced LIGO and Virgo

Abstract: This paper presents the gravitational-wave measurement of the Hubble constant (H 0) using the detections from the first and second observing runs of the Advanced LIGO and Virgo detector network. The presence of the transient electromagnetic counterpart of the binary neutron star GW170817 led to the first standard-siren measurement of H 0. Here we additionally use binary black hole detections in conjunction with galaxy catalogs and report a joint measurement. Our updated measurement is H 0 = km s−1 Mpc−1 (68.3% of the highest density posterior interval with a flat-in-log prior) which is an improvement by a factor of 1.04 (about 4%) over the GW170817-only value of km s−1 Mpc−1. A significant additional contribution currently comes from GW170814, a loud and well-localized detection from a part of the sky thoroughly covered by the Dark Energy Survey. With numerous detections anticipated over the upcoming years, an exhaustive understanding of other systematic effects are also going to become increasingly important. These results establish the path to cosmology using gravitational-wave observations with and without transient electromagnetic counterparts.

Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo’s third observing run

Abstract: We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO’s and Advanced Virgo’s third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density Ω GW ≤ 5.8 × 10 − 9 at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20–76.6 Hz; Ω GW ( f ) ≤ 3.4 × 10 − 9 at 25 Hz for a power-law GWB with a spectral index of 2 / 3 (consistent with expectations for compact binary coalescences), in the band 20–90.6 Hz; and Ω GW ( f ) ≤ 3.9 × 10 − 10 at 25 Hz for a spectral index of 3, in the band 20–291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB, 8.8 for a spectral index of 2 / 3 , and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z ≳ 2 than can be achieved with individually resolved mergers alone.

Reinforcement Learning-Based Load Forecasting of Electric Vehicle Charging Station Using Q -Learning Technique

Abstract: The electric vehicles’ (EVs) rapid growth can potentially lead power grids to face new challenges due to load profile changes. To this end, a new method is presented to forecast the EV charging station loads with machine learning techniques. The plug-in hybrid EVs (PHEVs) charging can be categorized into three main techniques (smart, uncoordinated, and coordinated). To have a good prediction of the future PHEV loads in this article, the Q -learning technique, which is a kind of the reinforcement learning, is used for different charging scenarios. The proposed Q -learning technique improves the forecasting of the conventional artificial intelligence techniques such as the recurrent neural network and the artificial neural network. Results prove that PHEV loads can accurately be forecasted by using the Q -learning technique under three different scenarios (smart, uncoordinated, and coordinated). The simulations of three different scenarios are obtained in the Keras open source software to validate the effectiveness and advantages of the proposed Q -learning technique.

LIGO Detector Characterization in the Second and Third Observing Runs

Abstract: The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO–Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs.

Recent Advances in 3D Printing of Structured Materials for Adsorption and Catalysis Applications.

Abstract: Porous solids in the form of adsorbents and catalysts play a crucial role in various industrially important chemical, energy, and environmental processes. Formulating them into structured configurations is a key step toward their scale up and successful implementation at the industrial level. Additive manufacturing, also known as 3D printing, has emerged as an invaluable platform for shape engineering porous solids and fabricating scalable configurations for use in a wide variety of separation and reaction applications. However, formulating porous materials into self-standing configurations can dramatically affect their performance and consequently the efficiency of the process wherein they operate. Toward this end, various research groups around the world have investigated the formulation of porous adsorbents and catalysts into structured scaffolds with complex geometries that not only exhibit comparable or improved performance to that of their powder parents but also address the pressure drop and attrition issues of traditional configurations. In this comprehensive review, we summarize the recent advances and current challenges in the field of adsorption and catalysis to better guide the future directions in shape engineering solid materials with a better control on composition, structure, and properties of 3D-printed adsorbents and catalysts.

Aridity-driven shift in biodiversity-soil multifunctionality relationships.

Abstract: Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially strong consequences under low and high aridity conditions, respectively, which calls for climate-specific biodiversity conservation strategies to mitigate the effects of aridification.

Guidelines for Responding to COVID-19 Pandemic: Best Practices, Impacts, and Future Research Directions

Abstract: Due to the novelty of coronavirus disease 2019 (COVID-19) and the lack of measurable data, no enough research studies have been conducted to provide guidelines for responding to the coronav...

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

Abstract: The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Mechanisms underlying the strength enhancement of UHPC modified with nano-SiO2 and nano-CaCO3

Abstract: The unique physical and chemical properties of nano-particles can enhance the nature of cement-based materials at the micro-scale and nano-scale levels, leading to improved properties. To uncover the strengthening mechanism associated with various types of nano-particles, a laboratory investigation was undertaken to evaluate and compare the influence of nano-SiO2 and nano-CaCO3 on mechanical properties of ultra-high performance concrete (UHPC) made with 2% steel fibers. Each type of nano-particle was incorporated at four contents, and the mini-slump flow of the UHPC was maintained at 240–260 mm. The microstructure of the matrix and the fiber-matrix interface of UHPC, as well as the features of hydration products were characterized using advanced techniques, such as electron microscopy (SEM), X-ray diffraction (XRD), differential thermal gravimetric (DTG) analyses, 3D micro-tomography, and mercury intrusion porosimetry (MIP). Test results indicate that both the fiber-matrix strength and mechanical strength of UHPC increased with the increase of nano-SiO2 and nano-CaCO3 until threshold limits of 1% and 3.2%, respectively. The 28-d fiber-matrix bond, compressive, and flexural strengths of the optimal UHPC mixtures made with 3.2% nano-CaCO3 were approximately 40%, 10%, and 20%, respectively, greater than those of the reference mixture. These strength values were higher than those of UHPC made with 1% nano-SiO2. When used below these optimal nano-material contents, the filler and nucleation effects related of the nano-SiO2 and nano-CaCO3 promoted the strength development through improved density and homogeneity with optimized structure of hydration products, as indicated by SEM observation and DTG analysis. Beyond these limits, additional use of nano-materials resulted in increased volume of air voids and capillary pores and weak interfacial zones due to the agglomeration of nano-particles, which hindered strength development.

MXene-Graphene Field-Effect Transistor Sensing of Influenza Virus and SARS-CoV-2

Abstract: An MXene-graphene field-effect transistor (FET) sensor for both influenza virus and 2019-nCoV sensing was developed and characterized. The developed sensor combines the high chemical sensitivity of MXene and the continuity of large-area high-quality graphene to form an ultra-sensitive virus-sensing transduction material (VSTM). Through polymer linking, we are able to utilize antibody-antigen binding to achieve electrochemical signal transduction when viruses are deposited onto the VSTM surface. The MXene-graphene VSTM was integrated into a microfluidic channel that can directly receive viruses in solution. The developed sensor was tested with various concentrations of antigens from two viruses: inactivated influenza A (H1N1) HA virus ranging from 125 to 250,000 copies/mL and a recombinant 2019-nCoV spike protein ranging from 1 fg/mL to 10 pg/mL. The average response time was about ∼50 ms, which is significantly faster than the existing real-time reverse transcription-polymerase chain reaction method (>3 h). The low limit of detection (125 copies/mL for the influenza virus and 1 fg/mL for the recombinant 2019-nCoV spike protein) has demonstrated the sensitivity of the MXene-graphene VSTM on the FET platform to virus sensing. Especially, the high signal-to-viral load ratio (∼10% change in source-drain current and gate voltage) also demonstrates the ultra-sensitivity of the developed MXene-graphene FET sensor. In addition, the specificity of the sensor was also demonstrated by depositing the inactivated influenza A (H1N1) HA virus and the recombinant 2019-nCoV spike protein onto microfluidic channels with opposite antibodies, producing signal differences that are about 10 times lower. Thus, we have successfully fabricated a relatively low-cost, ultrasensitive, fast-responding, and specific inactivated influenza A (H1N1) and 2019-nCoV sensor with the MXene-graphene VSTM.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO–Virgo Observing Run

Abstract: We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions A template-based search for short-duration transient signals does not yield a detection We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models Additionally, we develop and test a third model that interpolates between these two models Our results improve upon the previous LIGO-Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10^{-15} In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models

Machine learning to predict properties of fresh and hardened alkali-activated concrete

Abstract: Alkali-activated concrete (AAC) is widely considered to be a sustainable alternative to Portland cement concrete. However, on account of extensive heterogeneity in composition of the aluminosilicates, coupled with the failure of classical materials science approaches to unravel the underlying composition-property linkages, reliable prediction of AAC's properties has remained infeasible. This paper presents a random forest (RF) model to predict two properties of fly ash-based AACs that are important from compliance standpoint – slump flow; and compressive strength – in relation to physiochemical attributes, curing conditions, and mixing procedures of the concretes. Results show that the RF model – once meticulously trained, and after its hyperparameters are rigorously optimized – is able to produce high fidelity predictions of both properties of new AACs. The model is also used to quantitatively assess the influence of physiochemical attributes and process parameters on the AAC's properties. Outcomes of this work present a pathway to optimization of AACs' properties.

Hamiltonian-Driven Hybrid Adaptive Dynamic Programming

Abstract: This article presents a model-based hybrid adaptive dynamic programming (ADP) framework consisting of continuous feedback-based policy evaluation and policy improvement steps as well as an intermittent policy implementation procedure. This results in an intermittent ADP with a quantifiable performance and guaranteed closed-loop stability of the equilibrium point. To investigate the effect of aperiodic sampling on the communication bandwidth and the control performance of the intermittent ADP algorithms, we use a Hamiltonian-driven unified framework. With such a framework, it is shown that there is a tradeoff between the communication burden and the control performance. We finally show that the developed policies exhibit Zeno-free behaviors. Simulation examples show the efficiency of the proposed framework along with quantifiable comparisons of the policies with different intermittent information.

Qualitative analysis of caputo fractional integro-differential equations with constant delays

Abstract: In this paper, a nonlinear Volterra integro-differential equation with Caputo fractional derivative, multiple kernels, and multiple constant delays is considered. The aim of this paper is to investigate qualitative properties of solutions of this equation such as uniform stability, asymptotic stability, and Mittag-Leffler stability of the zero solution as well as boundedness of nonzero solutions. Here, we prove four new theorems on the mentioned properties of the solutions of the considered fractional integro-differential equation. The technique used in the proofs of these theorems includes defining an appropriate Lyapunov function and applying the Lyapunov–Razumikhin method. To illustrate the obtained results, two examples are provided, one of them related to an RLC circuit, to illustrate and show applications of the given results. The obtained results are new, original, and they can be useful for applied researchers in sciences and engineering.

Mobile Edge Cooperation Optimization for Wearable Internet of Things: A Network Representation-Based Framework

Abstract: As a new computing paradigm, edge computing emerges in various fields. Many tasks previously relied on cloud computing are distributed to various edge devices that cooperate to complete the tasks. However, circumstantial factors in the edge network (e.g., functionality, transmission efficiency, and resource limitation) become more complex than those in cloud computing. Consequently, there is instability that cannot be ignored in the cooperation between the edge devices. In this article, we propose a novel framework to optimize edge cooperative network (ECN), called ECN-Opt, to improve the performance of edge computing tasks. Specifically, we first define the evaluation metrics for cooperation. Next, the cooperation of an ECN is optimized to improve the performance of specific tasks. Extensive experiments using real datasets from wearable sensors on the players in soccer teams demonstrate that our ECN-Opt framework performs well, and it also validate the effectiveness of the proposed optimization algorithm.

Achieving superlubricity with 2D transition metal carbides (MXenes) and MXene/graphene coatings

Abstract: Two-dimensional (2D) materials have demonstrated unique friction and antiwear properties unmatched by their bulk (3D) counterparts. A relatively new, large and quickly growing family of two-dimensional early transition metal carbides and nitrides (MXenes) present a great potential in different applications. There is a growing interest in understanding the mechanical and tribological properties of MXenes, however, no report of MXene superlubricity in a solid lubrication process at the macroscale has been presented. Here we investigate the tribological properties of two-dimensional titanium carbide (Ti3C2) MXene deposited on SiO2-coated silicon (Si) substrates subjected to wear by sliding against a diamond-like carbon (DLC)-coated steel ball counterbody using a ball-on-disc tribometer. We have observed that a reduction of the friction coefficient to the superlubric regime (0.0067 ± 0.0017) can be achieved with Ti3C2 MXene in dry nitrogen environment. Moreover, the addition of graphene to Ti3C2 further reduced the friction by 37.3% and wear by the factor of 2 as compared to Ti3C2 alone, while the superlubricity behavior of the MXene remains unchanged. These results open up new possibilities for exploring the family of MXenes in various tribological applications.