Showing papers by "University of Texas at Arlington published in 2021"

IceCube-Gen2: the window to the extreme Universe

Abstract: The observation of electromagnetic radiation from radio to γ-ray wavelengths has provided a wealth of information about the Universe. However, at PeV (1015 eV) energies and above, most of the Universe is impenetrable to photons. New messengers, namely cosmic neutrinos, are needed to explore the most extreme environments of the Universe where black holes, neutron stars, and stellar explosions transform gravitational energy into non-thermal cosmic rays. These energetic particles havemillions of times higher energies than those produced in the most powerful particle accelerators on Earth. As neutrinos can escape from regions otherwise opaque to radiation, they allow an unique view deep into exploding stars and the vicinity of the event horizons of black holes. The discovery of cosmic neutrinos with IceCube has opened this new window on the Universe. IceCube has been successful in finding first evidence for cosmic particle acceleration in the jet of an active galactic nucleus. Yet, ultimately, its sensitivity is too limited to detect even the brightest neutrino sources with high significance, or to detect populations of less luminous sources. In thiswhite paper, we present an overview of a next-generation instrument, IceCube-Gen2, which will sharpen our understanding of the processes and environments that govern the Universe at the highest energies. IceCube-Gen2 is designed to: (a) Resolve the high-energy neutrino sky from TeV to EeV energies (b) Investigate cosmic particle acceleration through multi-messenger observations (c) Reveal the sources and propagation of the highest energy particles in the Universe (d) Probe fundamental physics with high-energy neutrinos IceCube-Gen2 will enhance the existing IceCube detector at the South Pole. It will increase the annual rate of observed cosmic neutrinos by a factor of ten compared to IceCube, and will be able to detect sources five times fainter than its predecessor. Furthermore, through the addition of a radio array, IceCube- Gen2 will extend the energy range by several orders of magnitude compared to IceCube. Construction will take 8 years and cost about $350M. The goal is to have IceCube-Gen2 fully operational by 2033. IceCube-Gen2 will play an essential role in shaping the new era of multimessenger astronomy, fundamentally advancing our knowledge of the highenergy Universe. This challenging mission can be fully addressed only through the combination of the information from the neutrino, electromagnetic, and gravitational wave emission of high-energy sources, in concert with the new survey instruments across the electromagnetic spectrum and gravitational wave detectors which will be available in the coming years.

Jet energy scale and resolution measured in proton-proton collisions at s =13 TeV with the ATLAS detector

Abstract: Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36-81 fb$^{-1}$ of proton-proton collision data with a centre-of-mass energy of $\sqrt{s}=13$ TeV collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-$k_t$ jet algorithm with radius parameter $R=0.4$ is the primary jet definition used for both jet types. Jets are initially calibrated using a sequence of simulation-based corrections. Next, several $\textit{in situ}$ techniques are employed to correct for differences between data and simulation and to measure the resolution of jets. The systematic uncertainties in the jet energy scale for central jets ($|\eta| 2.5$ TeV). The relative jet energy resolution is measured and ranges from ($24 \pm 1.5$)% at 20 GeV to ($6 \pm 0.5$)% at 300 GeV.

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration.

Abstract: The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE’s sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.

A review of artificial neural networks in the constitutive modeling of composite materials

Abstract: Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. The most popular machine learning model in recent years is artificial neural networks (ANN). Although many ANN models are used in the constitutive modeling of composite materials, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posing new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling. This review focuses on the general frameworks, benefits, and challenges and opportunities of ANN models to the constitutive modeling of composite materials. Moreover, potential applications of ANN-based constitutive models in composite materials and structures are also discussed. This review is intended to initiate discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures.

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at √ s = 13 TeV

Abstract: This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 $$\hbox {fb}^{-1}$$ fb - 1 of pp collision data at $$\sqrt{s}=13$$ s = 13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of $$Z\rightarrow \mu \mu $$ Z → μ μ and $$J/\psi \rightarrow \mu \mu $$ J / ψ → μ μ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of $$|\eta |<2.7$$ | η | < 2.7 .

Mobility-Aware Charging Scheduling for Shared On-Demand Electric Vehicle Fleet Using Deep Reinforcement Learning

Abstract: With the emerging concept of sharing-economy, shared electric vehicles (EVs) are playing a more and more important role in future mobility-on-demand traffic system. This article considers joint charging scheduling, order dispatching, and vehicle rebalancing for large-scale shared EV fleet operator. To maximize the welfare of fleet operator, we model the joint decision making as a partially observable Markov decision process (POMDP) and apply deep reinforcement learning (DRL) combined with binary linear programming (BLP) to develop a near-optimal solution. The neural network is used to evaluate the state value of EVs at different times, locations, and states of charge. Based on the state value, dynamic electricity prices and order information, the online scheduling is modeled as a BLP problem where the decision variables representing whether an EV will 1) take an order, 2) rebalance to a position, or 3) charge. We also propose a constrained rebalancing method to improve the exploration efficiency of training. Moreover, we provide a tabular method with proved convergence as a fallback option to demonstrate the near-optimal characteristics of the proposed approach. Simulation experiments with real-world data from Haikou City verify the effectiveness of the proposed method.

A Review of Developments and Challenges for UHPC in Structural Engineering: Behavior, Analysis, and Design

Abstract: The number of studies on ultrahigh-performance concrete (UHPC) or ultrahigh-performance fiber-reinforced concrete (UHP-FRC) for more resilient and sustainable reinforced concrete (RC) struc...

Hospital nurses' moral distress and mental health during COVID-19.

Abstract: Aims To explore factors associated with nurses' moral distress during the first COVID-19 surge and their longer-term mental health. Design Cross-sectional, correlational survey study. Methods Registered nurses were surveyed in September 2020 about their experiences during the first peak month of COVID-19 using the new, validated, COVID-19 Moral Distress Scale for Nurses. Nurses' mental health was measured by recently experienced symptoms. Analyses included descriptive statistics and regression analysis. Outcome variables were moral distress and mental health. Explanatory variables were frequency of COVID-19 patients, leadership communication and personal protective equipment/cleaning supplies access. The sample comprised 307 nurses (43% response rate) from two academic medical centres. Results Many respondents had difficulty accessing personal protective equipment. Most nurses reported that hospital leadership communication was transparent, effective and timely. The most distressing situations were the transmission risk to nurses' family members, caring for patients without family members present, and caring for patients dying without family or clergy present. These occurred occasionally with moderate distress. Nurses reported 2.5 days each in the past week of feeling anxiety, withdrawn and having difficulty sleeping. Moral distress decreased with effective communication and access to personal protective equipment. Moral distress was associated with longer-term mental health. Conclusion Pandemic patient care situations are the greatest sources of nurses' moral distress. Effective leadership communication, fewer COVID-19 patients, and access to protective equipment decrease moral distress, which influences longer-term mental health. Impact Little was known about the impact of COVID-19 on nurses' moral distress. We found that nurses' moral distress was associated with the volume of care for infected patients, access to personal protective equipment, and communication from leaders. We found that moral distress was associated with longer-term mental health. Leaders should communicate transparently to decrease nurses' moral distress and the negative effects of global crises on nurses' longer-term mental health.

Vulnerable road users and the coming wave of automated vehicles : Expert perspectives

Abstract: Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.

COVID-19, Social Determinants Past, Present, and Future, and African Americans' Health.

Abstract: As the COVID-19 pandemic progresses, more African Americans than whites are falling ill and dying from the virus and more are losing livelihoods from the accompanying recession. The virus thereby exploits structural disadvantages, rooted partly in historical and contemporary anti-Black sentiments, working against African Americans. These include higher rates of comorbid illness and more limited health care access, higher rates of disadvantageous labor market positioning and community and housing conditions, greater exposure to long-term care residence, and higher incarceration rates. COVID-19 also exposes African Americans' greater vulnerability to recession, and possibly greater susceptibility to accompanying behavioral health problems. If they are left unaddressed, the very vulnerabilities COVID-19 exploits may perpetuate themselves. However, continuing and supplementing health and economic COVID mitigation policies can disproportionately benefit African Americans and reduce short- and long-term adverse effects. The greater impact of COVID-19 on African Americans demonstrates the consequences of pervasive social and economic inequality and marks this as a critical time to prevent further compounding of adverse effects.

Impact of Covid-19 on field and office workforce in construction industry

Abstract: The COVID-19 outbreak is the greatest global health crisis in many years. It has had a dramatic effect on workforces and workplaces all around the world, as it has spawned a massive change in the working atmosphere and raised the level of employees’ concerns about their mental health and physical wellbeing. The construction industry has been significantly affected by the COVID-19 pandemic and has been challenged to improve the safety and wellbeing of its workforce. The objectives of this study are to identify the health and safety issues that construction workers have encountered during the pandemic and to recommend management strategies to combat them. A thorough literature search on recently published literature, industry experiences, reports, and other related documents was performed to collect and categorize the required data. Seventeen COVID-19 challenges were identified and classified into five categories, and the results revealed that the lack of a safe environment in the workplace, heavy workloads, home situations, and concerns about job stability often contribute to anxiety, depression, and even suicide. Eleven strategies were identified to overcome these challenges, and the results demonstrated that redefining worksite safety by placing signs, ensuring a safe distance between workers, providing sanitizers and washing stations in the fields, and utilizing effective technologies would enhance project productivity while keeping workers safe. The findings of this study will help the project managers and authorities in the construction industry understand the challenges of the pandemic and adopt effective strategies that will improve the health and safety of their workforce.

Robust Distributed Formation Controller Design for a Group of Unmanned Underwater Vehicles

Abstract: The formation of unmanned underwater vehicles (UUVs) has wide potential for applications in various marine activities. This paper studies the robust formation protocol design problem for multiple UUVs, whose dynamics are subject to nonlinearity, parametric uncertainties, and external disturbances. A robust distributed formation control scheme is proposed, which yields a control structure involving a position loop and an attitude loop to govern the translational motion and rotational motion, respectively. Theoretical analysis is given to show the robustness properties of the global closed-loop control system. Simulation results are provided to validate the effectiveness of the proposed formation control method.

Higgs boson production cross-section measurements and their EFT interpretation in the 4 ℓ decay channel at √s= 13 TeV with the ATLAS detector

Abstract: Higgs boson properties are studied in the fourlepton decay channel (where lepton = e, mu) using 139 fb(-1) of proton-proton collision data recorded at v s =13 TeV by the ATLAS experiment at the Lar ...

Supernova neutrino burst detection with the Deep Underground Neutrino Experiment

Abstract: The Deep Underground Neutrino Experiment (DUNE), a 40-kton underground liquid argon time projection chamber experiment, will be sensitive to the electron-neutrino flavor component of the burst of neutrinos expected from the next Galactic core-collapse supernova. Such an observation will bring unique insight into the astrophysics of core collapse as well as into the properties of neutrinos. The general capabilities of DUNE for neutrino detection in the relevant few- to few-tens-of-MeV neutrino energy range will be described. As an example, DUNE's ability to constrain the $ u_e$ spectral parameters of the neutrino burst will be considered.

Discrimination-aware Network Pruning for Deep Model Compression.

Abstract: We study network pruning which aims to remove redundant channels/kernels and accelerate the inference of deep networks. Existing pruning methods either train from scratch with sparsity constraints or minimize the reconstruction error between the feature maps of the pre-trained models and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, while the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. In this paper, we propose a discrimination-aware channel pruning (DCP) method to choose the channels that actually contribute to the discriminative power. Based on DCP, we further propose several techniques to improve the optimization efficiency. Note that the parameters of a channel (3D tensor) may contain redundant kernels (each with a 2D matrix). To solve this issue, we propose a discrimination-aware kernel pruning (DKP) method to select the kernels with promising discriminative power. Experiments on image classification and face recognition demonstrate the effectiveness of our methods. For example, on ILSVRC-12, the resultant ResNet-50 with 30% reduction of channels even outperforms the baseline model by 0.36% on Top-1 accuracy. The pruned MobileNetV1 and MobileNetV2 achieve 1.93x and 1.42x inference acceleration on a mobile device, respectively, with negligible performance degradation.

Edge Computing Resource Allocation for Unmanned Aerial Vehicle Assisted Mobile Network With Blockchain Applications

Abstract: Mobile edge computing is becoming a major trend in providing computation capacities at the edge of mobile networks. Meanwhile, unmanned aerial vehicles (UAVs) have been considered as distinctly important integrated components to extend services coverage. In order to provide users with higher and satisfied quality of services, edge computing resources need to be allocated between edge computing stations (ECSs) and UAVs in mobile networks. However, there are significant security and privacy problems due to the open environments of ECSs and UAVs. In this paper, we propose a resource pricing and trading scheme based on Stackelberg dynamic game to optimally allocate edge computing resources between ECSs and UAVs, and blockchain technology is applied to record the entire resources trading process to protect the security and privacy. The ECSs control the resources price of the allocated edge computing resources, where the UAVs follow the price announced by the ECSs and make optimal decisions on the edge computing resources demands. Blockchain is integrated in the resource trading process to ensure the security and privacy. Numerical simulations are given to show the effectiveness of the proposed scheme.

Comorbidities and the COVID-19 pandemic dynamics in Africa.

Abstract: The debate around the COVID-19 response in Africa has mostly focused on effects and implications of public health measures, in light of the socio-economic peculiarities of the continent. However, there has been limited exploration of the impact of differences in epidemiology of key comorbidities, and related healthcare factors, on the course and parameters of the pandemic. We summarise what is known about (a) the pathophysiological processes underlying the interaction of coinfections and comorbidities in shaping prognosis of COVID-19 patients, (b) the epidemiology of key coinfections and comorbidities, and the state of related healthcare infrastructure that might shape the course of the pandemic, and (c) implications of (a) and (b) for pandemic management and post-pandemic priorities. There is a critical need to generate empirical data on clinical profiles and the predictors of morbidity and mortality from COVID-19. Improved protocols for acute febrile illness and access to diagnostic facilities, not just for SARS-CoV-2 but also other viral infections, are of urgent importance. The role of malaria, HIV/TB and chronic malnutrition on pandemic dynamics should be further investigated. Although chronic non-communicable diseases account for a relatively lighter burden, they have a significant effect on COVID-19 prognosis, and the fragility of care delivery systems implies that adjustments to clinical procedures and re-organisation of care delivery that have been useful in other regions are unlikely to be feasible. Africa is a large region with local variations in factors that can shape pandemic dynamics. A one-size-fits-all response is not optimal, but there are broad lessons relating to differences in epidemiology and healthcare delivery factors, that should be considered as part of a regional COVID-19 response framework.

Axionlike Particles at Future Neutrino Experiments: Closing the Cosmological Triangle

Abstract: U.S. Department of Energy (DOE)United States Department of Energy (DOE) [DE-SC0010813, DE-SC0011686, DE-FG02-13ER41976/DE-SC0009913/DE-SC0010813, DE-SC0020250, DE-SC0020262]; United States Department of EnergyUnited States Department of Energy (DOE) [DE-AC02-07CH11359]

Compared to What? A Meta-Analysis of Batterer Intervention Studies Using Nontreated Controls or Comparisons

Abstract: This meta-analysis updates the literature on the effectiveness of batterer intervention programs (BIPs) in decreasing recidivism of domestic violence (DV) by focusing on studies with nontreated comparison groups (N = 17). Included studies were published between 1986 and 2016, and 14 of the 17 provided sufficient information for the meta-analysis. Analysis focused on three reported outcomes: DV recidivism reported by the criminal justice system, intimate partner violence (IPV) perpetration assessed by the survivor, and general offense recidivism reported by the criminal justice system. Results of meta-analysis indicated that BIPs were effective in decreasing DV recidivism and general offense recidivism when reported by the criminal justice system, but not when assessed by the survivor. BIP participants were about 3 times less likely to have DV recidivism and about 2.5 times less likely to have general offense recidivism, compared to nontreated control/comparison groups. The pooled effect size varied, however, by research design. Specifically, results indicated a nonsignificant pooled effect size for randomized controlled trials but a significant pooled effect size for quasi-experimental design studies. Implications for future practice and research are discussed.

Robust Formation Control for Cooperative Underactuated Quadrotors via Reinforcement Learning

Abstract: In this article, the model-free robust formation control problem is addressed for cooperative underactuated quadrotors involving unknown nonlinear dynamics and disturbances. Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowledge of each quadrotor dynamics, consisting of a distributed observer to estimate the position state of the leader, a position controller to achieve the desired formation, and an attitude controller to control the rotational motion. Simulation results on the multiquadrotor system confirm the effectiveness of the proposed model-free robust formation control method.

Realizing High Comprehensive Energy Storage and Ultrahigh Hardness in Lead-Free Ceramics.

Abstract: Due to the presence of pores and low density, a high recoverable energy density (Wrec) value is usually obtained at the cost of energy storage efficiency (η) in lead-free potassium sodium niobate [(K, Na)NbO3, KNN] based ceramics, which also affects the hardness of ceramics, finally limiting the further development of practical applications. A high Wrec (∼3.60 J/cm3 ) and a high η (∼74.2%) are obtained in 0.975K0.5Na0.5NbO3-0.025LaBiO3 (0.975KNN-0.025LB) ceramics simultaneously under a high dielectric breakdown strength (DBS) of 340 kV/cm, together with a fast discharge rate (t0.9 ∼ 46 ns) and high power density (PD ∼ 49.4 MW/cm3). Further analysis of the intrinsic electronic structure is carried out via the first-principles calculation based on the density functional theory (DFT). An ultrahigh hardness (H) of 6.63 GPa can be accordingly obtained. This work combines excellent energy storage properties and ultrahigh hardness, which provides significant guidelines for applications in pulsed-power systems.

Search for doubly and singly charged Higgs bosons decaying into vector bosons in multi-lepton final states with the ATLAS detector using proton-proton collisions at √s = 13 TeV

Abstract: A search for charged Higgs bosons decaying into W±W± or W±Z bosons is performed, involving experimental signatures with two leptons of the same charge, or three or four leptons with a variety of charge combinations, missing transverse momentum and jets. A data sample of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider between 2015 and 2018 is used. The data correspond to a total integrated luminosity of 139 fb−1. The search is guided by a type-II seesaw model that extends the scalar sector of the Standard Model with a scalar triplet, leading to a phenomenology that includes doubly and singly charged Higgs bosons. Two scenarios are explored, corresponding to the pair production of doubly charged H±± bosons, or the associated production of a doubly charged H±± boson and a singly charged H± boson. No significant deviations from the Standard Model predictions are observed. H±± bosons are excluded at 95% confidence level up to 350 GeV and 230 GeV for the pair and associated production modes, respectively. [Figure not available: see fulltext.]

Multifunctional cementitious composites with integrated self-sensing and hydrophobic capacities toward smart structural health monitoring

Abstract: In this study, multifunctional cementitious composites with integrated self-sensing and hydrophobicity capacities were developed and investigated using conductive graphene nanoplate (GNP) and silicone hydrophobic powder (SHP) The mechanical properties, permeability, water contact angle, microstructure and piezoresistivity were studied and compared under different contents of GNP and SHP The highest compressive and flexural strengths with 1% SHP and 2% GNP reached 626 MPa and 89 MPa, respectively The water absorption significantly was decreased with the content of SHP, but was minorly affected by GNP The water contact angle firstly increased but then decreased with the dosages of GNP and SHP SHP and GNP could reduce the microscale pores and enhance the density of microstructures The piezoresistivity under compression firstly exhibited low gauge factor, but then gradually increased to a constant value under high-stress magnitude Moreover, compared to the conventional cement-based sensors, this piezoresistive cementitious composites containing SHP and GNP as novel cement-based sensors are less sensitive to water content and humidity The outcomes can provide an insight into promoting the application of multifunctional cement-based sensors toward structural health monitoring under various ambient conditions