Showing papers by "University of Houston published in 2020"

Current status and future directions of multivalent metal-ion batteries

Abstract: Batteries based on multivalent metals have the potential to meet the future needs of large-scale energy storage, due to the relatively high abundance of elements such as magnesium, calcium, aluminium and zinc in the Earth’s crust. However, the complexity of multivalent metal-ion chemistries has led to rampant confusions, technical challenges, and eventually doubts and uncertainties about the future of these technologies. In this Review, we clarify the key strengths as well as common misconceptions of multivalent metal-based batteries. We then examine the growth behaviour of metal anodes, which is crucial for their safety promises but hitherto unestablished. We further discuss scrutiny of anode efficiency and cathode storage mechanism pertaining to complications arising from electrolyte solutions. Finally, we critically review existing cathode materials and discuss design strategies to enable genuine multivalent metal-ion-based energy storage materials with competitive performance. Batteries based on multivalent metal anodes hold great promise for large-scale energy storage but their development is still at an early stage. This Review surveys the main complexity arising from anodes, electrolytes and cathodes, and offers views on the progression path of these technologies.

Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

Abstract: Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.

Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts

Abstract: Reconfigurable intelligent surfaces (RISs) have drawn considerable attention from the research community recently. RISs create favorable propagation conditions by controlling the phase shifts of reflected waves at the surface, thereby enhancing wireless transmissions. In this paper, we study a downlink multi-user system where the transmission from a multi-antenna base station (BS) to various users is achieved by an RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only a limited number of discrete phase shifts can be realized by a finite-sized RIS. A hybrid beamforming scheme is proposed and the sum-rate maximization problem is formulated. Specifically, continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively, and an iterative algorithm is designed to solve this problem. Both theoretical analysis and numerical validations show that the RIS-based system can achieve good sum-rate performance by setting a reasonable size of the RIS and a small number of discrete phase shifts.

Opportunities and Challenges for Organic Electrodes in Electrochemical Energy Storage.

Abstract: As the world moves toward electromobility and a concomitant decarbonization of its electrical supply, modern society is also entering a so-called fourth industrial revolution marked by a boom of electronic devices and digital technologies. Consequently, battery demand has exploded along with the need for ores and metals to fabricate them. Starting from such a critical analysis and integrating robust structural data, this review aims at pointing out there is room to promote organic-based electrochemical energy storage. Combined with recycling solutions, redox-active organic species could decrease the pressure on inorganic compounds and offer valid options in terms of environmental footprint and possible disruptive chemistries to meet the energy storage needs of both today and tomorrow. We review state-of-the-art developments in organic batteries, current challenges, and prospects, and we discuss the fundamental principles that govern the reversible chemistry of organic structures. We provide a comprehensive overview of all reported cell configurations that involve electroactive organic compounds working either in the solid state or in solution for aqueous or nonaqueous electrolytes. These configurations include alkali (Li/Na/K) and multivalent (Mg, Zn)-based electrolytes for conventional "sealed" batteries and redox-flow systems. We also highlight the most promising systems based on such various chemistries relying on appropriate metrics such as operation voltage, specific capacity, specific energy, or cycle life to assess the performances of electrodes.

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Abstract: Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

The 2020 update of the CEAP classification system and reporting standards

Abstract: The CEAP (Clinical-Etiology-Anatomy-Pathophysiology) classification is an internationally accepted standard for describing patients with chronic venous disorders and it has been used for reporting clinical research findings in scientific journals. Developed in 1993, updated in 1996, and revised in 2004, CEAP is a classification system based on clinical manifestations of chronic venous disorders, on current understanding of the etiology, the involved anatomy, and the underlying venous pathology. As the evidence related to these aspects of venous disorders, and specifically of chronic venous diseases (CVD, C2-C6) continue to develop, the CEAP classification needs periodic analysis and revisions. In May of 2017, the American Venous Forum created a CEAP Task Force and charged it to critically analyze the current classification system and recommend revisions, where needed. Guided by four basic principles (preservation of the reproducibility of CEAP, compatibility with prior versions, evidence-based, and practical for clinical use), the Task Force has adopted the revised Delphi process and made several changes. These changes include adding Corona phlebectatica as the C4c clinical subclass, introducing the modifier "r" for recurrent varicose veins and recurrent venous ulcers, and replacing numeric descriptions of the venous segments by their common abbreviations. This report describes all these revisions and the rationale for making these changes.

Reconfigurable Intelligent Surfaces Assisted Communications With Limited Phase Shifts: How Many Phase Shifts Are Enough?

Abstract: Reconfigurable intelligent surface (RIS) has drawn a great attention worldwide as it can create favorable propagation conditions by controlling the phase shifts of the reflected signals at the surface to enhance the communication quality. However, the practical RIS only has limited phase shifts, which will lead to the performance degradation. In this paper, we evaluate the performance of an uplink RIS assisted communication system by giving an approximation of the achievable data rate, and investigate the effect of limited phase shifts on the data rate. In particular, we derive the required number of phase shifts under a data rate degradation constraint. Numerical results verify our analysis.

Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach

Abstract: Federated learning is a newly emerged distributed machine learning paradigm, where the clients are allowed to individually train local deep neural network (DNN) models with local data and then jointly aggregate a global DNN model at the central server. Vehicular edge computing (VEC) aims at exploiting the computation and communication resources at the edge of vehicular networks. Federated learning in VEC is promising to meet the ever-increasing demands of artificial intelligence (AI) applications in intelligent connected vehicles (ICV). Considering image classification as a typical AI application in VEC, the diversity of image quality and computation capability in vehicular clients potentially affects the accuracy and efficiency of federated learning. Accordingly, we propose a selective model aggregation approach, where “fine” local DNN models are selected and sent to the central server by evaluating the local image quality and computation capability. Regarding the implementation of model selection, the central server is not aware of the image quality and computation capability in the vehicular clients, whose privacy is protected under such a federated learning framework. To overcome this information asymmetry, we employ two-dimension contract theory as a distributed framework to facilitate the interactions between the central server and vehicular clients. The formulated problem is then transformed into a tractable problem through successively relaxing and simplifying the constraints, and eventually solved by a greedy algorithm. Using two datasets, i.e., MNIST and BelgiumTSC, our selective model aggregation approach is demonstrated to outperform the original federated averaging (FedAvg) approach in terms of accuracy and efficiency. Meanwhile, our approach also achieves higher utility at the central server compared with the baseline approaches.

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

Abstract: Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this article, we present the primary design aspects for enabling federated learning at the network edge. We model the incentive- based interaction between a global server and participating devices for federated learning via a Stackelberg game to motivate the participation of the devices in the federated learning process. We present several open research challenges with their possible solutions. Finally, we provide an outlook on future research.

Blockchain in global supply chains and cross border trade: a critical synthesis of the state-of-the-art, challenges and opportunities

Abstract: Blockchain possesses the potential of transforming global supply chain management. Gartner predicts that blockchain could be able to track $2 T of goods and services in their movement across the gl...

Joint Communication, Computation, Caching, and Control in Big Data Multi-Access Edge Computing

Abstract: The concept of Multi-access Edge Computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to the resourceful cloud, MEC server has limited resources. When each MEC server operates independently, it cannot handle all computational and big data demands stemming from users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction of data exchange between users devices and remote cloud. Therefore, joint Computing, Caching, Communication, and Control (4C) at the edge with MEC server collaboration is needed. To address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency. However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed. To solve the proximal upper bound problem, the block successive upper bound minimization method is applied. Simulation results show that the proposed approach satisfies computation deadlines and minimizes bandwidth consumption and network latency.

The shape of and solutions to the MTurk quality crisis

Abstract: Amazon's Mechanical Turk is widely used for data collection; however, data quality may be declining due to the use of virtual private servers to fraudulently gain access to studies. Unfortunately, we know little about the scale and consequence of this fraud, and tools for social scientists to detect and prevent this fraud are underdeveloped. We first analyze 38 studies and show that this fraud is not new, but has increased recently. We then show that these fraudulent respondents provide particularly low-quality data and can weaken treatment effects. Finally, we provide two solutions: an easy-to-use application for identifying fraud in the existing datasets and a method for blocking fraudulent respondents in Qualtrics surveys.

New β-Lactam-β-Lactamase Inhibitor Combinations.

Abstract: The limited armamentarium against drug-resistant Gram-negative bacilli has led to the development of several novel β-lactam-β-lactamase inhibitor combinations (BLBLIs). In this review, we summarize their spectrum of in vitro activities, mechanisms of resistance, and pharmacokinetic-pharmacodynamic (PK-PD) characteristics. A summary of available clinical data is provided per drug. Four approved BLBLIs are discussed in detail. All are options for treating multidrug-resistant (MDR) Enterobacterales and Pseudomonas aeruginosa Ceftazidime-avibactam is a potential drug for treating Enterobacterales producing extended-spectrum β-lactamase (ESBL), Klebsiella pneumoniae carbapenemase (KPC), AmpC, and some class D β-lactamases (OXA-48) in addition to carbapenem-resistant Pseudomonas aeruginosa Ceftolozane-tazobactam is a treatment option mainly for carbapenem-resistant P. aeruginosa (non-carbapenemase producing), with some activity against ESBL-producing Enterobacterales Meropenem-vaborbactam has emerged as treatment option for Enterobacterales producing ESBL, KPC, or AmpC, with similar activity as meropenem against P. aeruginosa Imipenem-relebactam has documented activity against Enterobacterales producing ESBL, KPC, and AmpC, with the combination having some additional activity against P. aeruginosa relative to imipenem. None of these drugs present in vitro activity against Enterobacterales or P. aeruginosa producing metallo-β-lactamase (MBL) or against carbapenemase-producing Acinetobacter baumannii Clinical data regarding the use of these drugs to treat MDR bacteria are limited and rely mostly on nonrandomized studies. An overview on eight BLBLIs in development is also provided. These drugs provide various levels of in vitro coverage of carbapenem-resistant Enterobacterales, with several drugs presenting in vitro activity against MBLs (cefepime-zidebactam, aztreonam-avibactam, meropenem-nacubactam, and cefepime-taniborbactam). Among these drugs, some also present in vitro activity against carbapenem-resistant P. aeruginosa (cefepime-zidebactam and cefepime-taniborbactam) and A. baumannii (cefepime-zidebactam and sulbactam-durlobactam).

Volume I. Introduction to DUNE

Abstract: The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology.

6G Wireless Systems: A Vision, Architectural Elements, and Future Directions

Abstract: Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions.

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

Abstract: This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining...

QCD Crossover at Finite Chemical Potential from Lattice Simulations

Abstract: We provide the most accurate results for the QCD transition line so far. We optimize the definition of the crossover temperature ${T}_{c}$, allowing for its very precise determination, and extrapolate from imaginary chemical potential up to real ${\ensuremath{\mu}}_{B}\ensuremath{\approx}300\text{ }\text{ }\mathrm{MeV}$. The definition of ${T}_{c}$ adopted in this work is based on the observation that the chiral susceptibility as a function of the condensate is an almost universal curve at zero and imaginary ${\ensuremath{\mu}}_{B}$. We obtain the parameters ${\ensuremath{\kappa}}_{2}=0.0153(18)$ and ${\ensuremath{\kappa}}_{4}=0.00032(67)$ as a continuum extrapolation based on ${N}_{t}=10$, 12, 16 lattices with physical quark masses. We also extrapolate the peak value of the chiral susceptibility and the width of the chiral transition along the crossover line. In fact, both of these are consistent with a constant function of ${\ensuremath{\mu}}_{B}$. We see no sign of criticality in the explored range.

A Review on the Synthesis and Functionalization of Gold Nanoparticles as a Drug Delivery Vehicle.

Abstract: Metal nanoparticles are being extensively used in biomedical fields due to their small size-to-volume ratio and extensive thermal stability. Gold nanoparticles (AuNPs) are an obvious choice for biomedical applications due to their amenability of synthesis, stabilization, and functionalization, low toxicity, and ease of detection. In the past few decades, various chemical methods have been used for the synthesis of AuNPs, but recently, newer environment friendly green approaches for the synthesis of AuNPs have gained attention. AuNPs can be conjugated with a number of functionalizing moieties including ligands, therapeutic agents, DNA, amino acids, proteins, peptides, and oligonucleotides. Recently, studies have shown that gold nanoparticles not only infiltrate the blood vessels to reach the site of tumor but also enter inside the organelles, suggesting that they can be employed as effective drug carriers. Moreover, after reaching their target site, gold nanoparticles can release their payload upon an external or internal stimulus. This review focuses on recent advances in various methods of synthesis of AuNPs. In addition, strategies of functionalization and mechanisms of application of AuNPs in drug and bio-macromolecule delivery and release of payloads at target site are comprehensively discussed.

The Impact of Covid-19 Experiences and Associated Stress on Anxiety, Depression, and Functional Impairment in American Adults.

Abstract: The COVID-19 pandemic has had a profound impact on health and well-being worldwide and there is increasing recognition of the need to understand the psychological impact of COVID-19 experiences and stress in addition to the physical health consequences. The present study examined how experiences related to COVID-19 and associated stress impact, anxiety, depression, and functional impairment in a convenience sample of 565 American adults (57.9% male) recruited through MTURK. COVID-19 experiences were consistently associated with higher odds of probable anxiety and depression diagnoses (ORs ≥ 3.0). COVID-19 associated stress also predicted large proportions of variance (R2 ≥ 30) in anxiety, depression, health anxiety, and functional impairment in latent variable analyses. These findings highlight that personal experiences related to the diagnosis of COVID-19, mortality in acquaintances, and COVID-19 associated stress is associated with a greatly elevated risk of emotional disorder symptomatology and that the COVID-19 pandemic may result in increased demand for mental health services.

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

Abstract: The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a ...

High-power Mg batteries enabled by heterogeneous enolization redox chemistry and weakly coordinating electrolytes

Abstract: Magnesium batteries have long been pursued as potentially low-cost, high-energy and safe alternatives to Li-ion batteries. However, Mg2+ interacts strongly with electrolyte solutions and cathode materials, leading to sluggish ion dissociation and diffusion, and consequently low power output. Here we report a heterogeneous enolization chemistry involving carbonyl reduction (C=O↔C–O−), which bypasses the dissociation and diffusion difficulties, enabling fast and reversible redox processes. This kinetically favoured cathode is coupled with a tailored, weakly coordinating boron cluster-based electrolyte that allows for dendrite-free Mg plating/stripping at a current density of 20 mA cm−2. The combination affords a Mg battery that delivers a specific power of up to 30.4 kW kg−1, nearly two orders of magnitude higher than that of state-of-the-art Mg batteries. The cathode and electrolyte chemistries elucidated here propel the development of magnesium batteries and would accelerate the adoption of this low-cost and safe battery technology. Owing to sluggish Mg-ion dissociation and diffusion, Mg-based batteries have low power densities. Here the authors carry out rational designs for both the cathode and the electrolyte to enable ultrafast kinetics of a Mg metal battery.

Ultra-conformal drawn-on-skin electronics for multifunctional motion artifact-free sensing and point-of-care treatment.

Abstract: An accurate extraction of physiological and physical signals from human skin is crucial for health monitoring, disease prevention, and treatment. Recent advances in wearable bioelectronics directly embedded to the epidermal surface are a promising solution for future epidermal sensing. However, the existing wearable bioelectronics are susceptible to motion artifacts as they lack proper adhesion and conformal interfacing with the skin during motion. Here, we present ultra-conformal, customizable, and deformable drawn-on-skin electronics, which is robust to motion due to strong adhesion and ultra-conformality of the electronic inks drawn directly on skin. Electronic inks, including conductors, semiconductors, and dielectrics, are drawn on-demand in a freeform manner to develop devices, such as transistors, strain sensors, temperature sensors, heaters, skin hydration sensors, and electrophysiological sensors. Electrophysiological signal monitoring during motion shows drawn-on-skin electronics’ immunity to motion artifacts. Additionally, electrical stimulation based on drawn-on-skin electronics demonstrates accelerated healing of skin wounds. Designing efficient wearable bioelectronics for health monitoring, disease prevention, and treatment, remains a challenge. Here, the authors demonstrate an ultra-conformal, customizable and deformable drawn-on-skin electronics which is robust to motion artifacts and resistant to physical damage.

Flexible and stretchable metal oxide nanofiber networks for multimodal and monolithically integrated wearable electronics.

Abstract: Fiber-based electronics enabling lightweight and mechanically flexible/stretchable functions are desirable for numerous e-textile/e-skin optoelectronic applications. These wearable devices require low-cost manufacturing, high reliability, multifunctionality and long-term stability. Here, we report the preparation of representative classes of 3D-inorganic nanofiber network (FN) films by a blow-spinning technique, including semiconducting indium-gallium-zinc oxide (IGZO) and copper oxide, as well as conducting indium-tin oxide and copper metal. Specifically, thin-film transistors based on IGZO FN exhibit negligible performance degradation after one thousand bending cycles and exceptional room-temperature gas sensing performance. Owing to their great stretchability, these metal oxide FNs can be laminated/embedded on/into elastomers, yielding multifunctional single-sensing resistors as well as fully monolithically integrated e-skin devices. These can detect and differentiate multiple stimuli including analytes, light, strain, pressure, temperature, humidity, body movement, and respiratory functions. All of these FN-based devices exhibit excellent sensitivity, response time, and detection limits, making them promising candidates for versatile wearable electronics.

Extracorporeal Membrane Oxygenation in the Treatment of Severe Pulmonary and Cardiac Compromise in Coronavirus Disease 2019: Experience with 32 Patients.

Abstract: As coronavirus disease 2019 (COVID-19) cases surge worldwide, an urgent need exists to enhance our understanding of the role of extracorporeal membrane oxygenation (ECMO) in the management of severely ill patients with COVID-19 who develop acute respiratory and cardiac compromise refractory to conventional therapy. The purpose of this manuscript is to review our initial clinical experience in 32 patients with confirmed COVID-19 treated with ECMO. A multi-institutional registry and database was created and utilized to assess all patients who were supported with ECMO provided by SpecialtyCare. Data captured included patient characteristics, pre-COVID-19 risk factors and comorbidities, confirmation of COVID-19 diagnosis, features of ECMO support, specific medications utilized to treat COVID-19, and short-term outcomes through hospital discharge. This analysis includes all of our patients with COVID-19 supported with ECMO, with an analytic window starting March 17, 2020, when our first COVID-19 patient was placed on ECMO, and ending April 9, 2020. During the 24 days of this study, 32 consecutive patients with COVID-19 were placed on ECMO at nine different hospitals. As of the time of analysis, 17 remain on ECMO, 10 died before or shortly after decannulation, and five are alive and extubated after removal from ECMO, with one of these five discharged from the hospital. Adjunctive medication in the surviving patients while on ECMO was as follows: four of five survivors received intravenous steroids, three of five survivors received antiviral medications (Remdesivir), two of five survivors were treated with anti-interleukin-6-receptor monoclonal antibodies (Tocilizumab or Sarilumab), and one of five survivors received hydroxychloroquine. Analysis of these 32 COVID-19 patients with severe pulmonary compromise supported with ECMO suggests that ECMO may play a useful role in salvaging select critically ill patients with COVID-19. Additional patient experience and associated clinical and laboratory data must be obtained to further define the optimal role of ECMO in patients with COVID-19 and acute respiratory distress syndrome (ARDS). These initial data may provide useful information to help define the best strategies to care for these challenging patients and may also provide a framework for much-needed future research about the use of ECMO to treat patients with COVID-19.

Effect of High Add Power, Medium Add Power, or Single-Vision Contact Lenses on Myopia Progression in Children: The BLINK Randomized Clinical Trial.

Abstract: Importance Slowing myopia progression could decrease the risk of sight-threatening complications. Objective To determine whether soft multifocal contact lenses slow myopia progression in children, and whether high add power (+2.50 D) slows myopia progression more than medium (+1.50 D) add power lenses. Design, Setting, and Participants A double-masked randomized clinical trial that took place at 2 optometry schools located in Columbus, Ohio, and Houston, Texas. A total of 294 consecutive eligible children aged 7 to 11 years with −0.75 D to −5.00 D of spherical component myopia and less than 1.00 D astigmatism were enrolled between September 22, 2014, and June 20, 2016. Follow-up was completed June 24, 2019. Interventions Participants were randomly assigned to wear high add power (n = 98), medium add power (n = 98), or single-vision (n = 98) contact lenses. Main Outcomes and Measures The primary outcome was the 3-year change in cycloplegic spherical equivalent autorefraction, as measured by the mean of 10 autorefraction readings. There were 11 secondary end points, 4 of which were analyzed for this study, including 3-year eye growth. Results Among 294 randomized participants, 292 (99%) were included in the analyses (mean [SD] age, 10.3 [1.2] years; 177 [60.2%] were female; mean [SD] spherical equivalent refractive error, −2.39 [1.00] D). Adjusted 3-year myopia progression was −0.60 D for high add power, −0.89 D for medium add power, and −1.05 D for single-vision contact lenses. The difference in progression was 0.46 D (95% CI, 0.29-0.63) for high add power vs single vision, 0.30 D (95% CI, 0.13-0.47) for high add vs medium add power, and 0.16 D (95% CI, −0.01 to 0.33) for medium add power vs single vision. Of the 4 secondary end points, there were no statistically significant differences between the groups for 3 of the end points. Adjusted mean eye growth was 0.42 mm for high add power, 0.58 mm for medium add power, and 0.66 mm for single vision. The difference in eye growth was −0.23 mm (95% CI, −0.30 to −0.17) for high add power vs single vision, −0.16 mm (95% CI, −0.23 to −0.09) for high add vs medium add power, and −0.07 mm (95% CI, −0.14 to −0.01) for medium add power vs single vision. Conclusions and Relevance Among children with myopia, treatment with high add power multifocal contact lenses significantly reduced the rate of myopia progression over 3 years compared with medium add power multifocal and single-vision contact lenses. However, further research is needed to understand the clinical importance of the observed differences. Trial Registration ClinicalTrials.gov Identifier:NCT02255474

Hydrogen Generation from Seawater Electrolysis over a Sandwich-like NiCoN|NixP|NiCoN Microsheet Array Catalyst

Abstract: Seawater electrolysis presents a transformative technology for sustainable hydrogen production and environmental remediation. However, the lack of active and robust hydrogen evolution reaction (HER...

Practical Hybrid Beamforming With Finite-Resolution Phase Shifters for Reconfigurable Intelligent Surface Based Multi-User Communications

Abstract: In this paper, we study the reconfigurable intelligent surface (RIS) based downlink multi-user system where a multi-antenna base station (BS) sends signals to various users assisted by the RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only the large-scale fading gain is required at the BS and a limited number of phase shifts can be realized by the finite-sized RIS. To maximize the sum rate, we propose a hybrid beamforming scheme where the continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively. An iterative algorithm is designed for beamforming and theoretical analysis is provided to evaluate how the size of RIS influences the achievable rate. Simulation results show that the RIS-based system can achieve a good sum-rate performance by setting a reasonable size of RIS and a small number of discrete phase shifts.

Reconfigurable Intelligent Surface Based RF Sensing: Design, Optimization, and Implementation

Abstract: Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems, and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.