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Freestanding VO 2 membranes on epidermal nanomesh for ultra-sensitive correlated breathable sensors
The interest in highly sensitive sensors is rapidly increasing for detecting very tiny signals for Internet of Things devices. Here, we achieve ultra-sensitive correlated breathable sensors based on freestanding VO 2 membranes. We fabricate the membranes by growing VO 2 films onto sacrificial Sr 3 Al 2 O 6 layer grown on SrTiO 3 , selectively dissolving the Sr 3 Al 2 O 6 in water, and then rendering freestanding VO 2 membrane on nanomesh. The nanomeshes are extremely flexible, sweat permeable, and readily skin-adhesive. The resistance of the VO 2 membranes is reversibly tuned by human’s tiny mechanical stimuli and breath stimuli. The stimuli modulate the Peierls dimerization of one-dimensional V-V chains in the VO 2 lattice which concomitantly controls the electron correlation and hence resistivity. Since our breathable sensors operate based on quantum-mechanical correlation effects, their sensitivity is 1-2 orders of magnitude higher than conventional tactile and respiratory sensors based on other materials. Thus, the freestanding membranes of correlated oxides on epidermal nanomeshes are multifunctional platforms for developing ultra-sensitive correlated breathable sensors.
Integrated photonics on thin-film lithium niobate
Lithium niobate (LN), an outstanding and versatile material, has influenced our daily life for decades—from enabling high-speed optical communications that form the backbone of the Internet to realizing radio-frequency filtering used in our cell phones. This half-century-old material is currently embracing a revolution in thin-film LN integrated photonics. The successes of manufacturing wafer-scale, high-quality thin films of LN-on-insulator (LNOI) and breakthroughs in nanofabrication techniques have made high-performance integrated nanophotonic components possible. With rapid development in the past few years, some of these thin-film LN devices, such as optical modulators and nonlinear wavelength converters, have already outperformed their legacy counterparts realized in bulk LN crystals. Furthermore, the nanophotonic integration has enabled ultra-low-loss resonators in LN, which has unlocked many novel applications such as optical frequency combs and quantum transducers. In this review, we cover—from basic principles to the state of the art—the diverse aspects of integrated thin-film LN photonics, including the materials, basic passive components, and various active devices based on electro-optics, all-optical nonlinearities, and acousto-optics. We also identify challenges that this platform is currently facing and point out future opportunities. The field of integrated LNOI photonics is advancing rapidly and poised to make critical impacts on a broad range of applications in communication, signal processing, and quantum information.
Microwave-optical quantum frequency conversion
Photons at microwave and optical frequencies are principal carriers for quantum information. While microwave photons can be effectively controlled at the local circuit level, optical photons can propagate over long distances. High-fidelity conversion between microwave and optical photons will allow the distribution of quantum states across different quantum technology nodes and enhance the scalability of hybrid quantum systems toward a future “Quantum Internet.” Despite a frequency difference of five orders of magnitude, there has been significant progress recently toward the transfer between microwave and optical photons with steadily improved efficiency in a coherent and bidirectional manner. In this review, we summarize this progress, emphasizing integrated device approaches, and provide a perspective for device implementation that enables quantum state transfer and entanglement distribution across microwave and optical domains.
Addressing delayed case reporting in infectious disease forecast modeling
Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.
National variability in Americans’ COVID-19 protective behaviors: Implications for vaccine roll-out
Protective behaviors such as mask wearing and physical distancing are critical to slow the spread of COVID-19, even in the context of vaccine scale-up. Understanding the variation in self-reported COVID-19 protective behaviors is critical to developing public health messaging. The purpose of the study is to provide nationally representative estimates of five self-reported COVID-19 protective behaviors and correlates of such behaviors. In this cross-sectional survey study of US adults, surveys were administered via internet and telephone. Adults were surveyed from April 30-May 4, 2020, a time of peaking COVID-19 incidence within the US. Participants were recruited from the probability-based AmeriSpeak® national panel. Brief surveys were completed by 994 adults, with 73.0% of respondents reported mask wearing, 82.7% reported physical distancing, 75.1% reported crowd avoidance, 89.8% reported increased hand-washing, and 7.7% reported having prior COVID-19 testing. Multivariate analysis (p critical value .05) indicates that women were more likely to report protective behaviors than men, as were those over age 60. Respondents who self-identified as having low incomes, histories of criminal justice involvement, and Republican Party affiliation, were less likely to report four protective behaviors, though Republicans and individuals with criminal justice histories were more likely to report having received COVID-19 testing. The majority of Americans engaged in COVID-19 protective behaviors, with low-income Americans, those with histories of criminal justice involvement, and self-identified Republicans less likely to engage in these preventive behaviors. Culturally competent public health messaging and interventions might focus on these latter groups to prevent future infections. These findings will remain highly relevant even with vaccines widely available, given the complementarities between vaccines and protective behaviors, as well as the many challenges in delivering vaccines.
Hashes are not suitable to verify fixity of the public archived web
Web archives, such as the Internet Archive, preserve the web and allow access to prior states of web pages. We implicitly trust their versions of archived pages, but as their role moves from preserving curios of the past to facilitating present day adjudication, we are concerned with verifying the fixity of archived web pages, or mementos, to ensure they have always remained unaltered. A widely used technique in digital preservation to verify the fixity of an archived resource is to periodically compute a cryptographic hash value on a resource and then compare it with a previous hash value. If the hash values generated on the same resource are identical, then the fixity of the resource is verified. We tested this process by conducting a study on 16,627 mementos from 17 public web archives. We replayed and downloaded the mementos 39 times using a headless browser over a period of 442 days and generated a hash for each memento after each download, resulting in 39 hashes per memento. The hash is calculated by including not only the content of the base HTML of a memento but also all embedded resources, such as images and style sheets. We expected to always observe the same hash for a memento regardless of the number of downloads. However, our results indicate that 88.45% of mementos produce more than one unique hash value, and about 16% (or one in six) of those mementos always produce different hash values. We identify and quantify the types of changes that cause the same memento to produce different hashes. These results point to the need for defining an archive-aware hashing function, as conventional hashing functions are not suitable for replayed archived web pages.
Smart Manufacturing Pathways for Industrial Decarbonization and Thermal Process Intensification
Rapid decarbonization is fast becoming the primary environmental and sustainability initiative for many economic sectors. Industry consumes more than 30 % of all primary energy in the United States and accounts for nearly 25 % of all greenhouse gas (GHG) emissions. More than 70 % of energy consumed by the industrial sector is related to thermal processes, which are also the largest contributors of carbon emissions, overwhelmingly due to the combustion of fossil fuels. Thermal process intensification (TPI) seeks to dramatically improve the energy performance of thermal systems through technology pillars focusing on alternative energy sources and processes, supplemental technologies, and waste heat management. The impacts of TPI have significant overlap with the goals of industrial decarbonization (ID) that seeks to phase out all GHG emissions from industrial activities. Emerging supplemental technologies such as smart manufacturing (SM) and the industrial internet of things (IoT) enable significant opportunities for the optimization of manufacturing processes. Combining strategies for TPI and ID with SM and IoT can open and enhance existing opportunities for saving time and energy via approaches such as tighter control of temperature zones, better adjustment of thermal systems for variations in production levels and feedstock properties, and increased process throughput. Data collected by smart processes will also enable new advanced solutions such as digital twins and machine learning algorithms to further improve thermal system savings. Herein, this paper examines the individual pathways of TPI, ID, and SM and how the combination of all three can accelerate energy and GHG reductions.
Self-assembly for electronics
Self-assembly, a process in which molecules, polymers, and particles are driven by local interactions to organize into patterns and functional structures, is being exploited in advancing silicon electronics and in emerging, unconventional electronics. Additionally, silicon electronics has relied on lithographic patterning of polymer resists at progressively smaller lengths to scale down device dimensions. Yet, this has become increasingly difficult and costly. Assembly of block copolymers and colloidal nanoparticles allows resolution enhancement and the definition of essential shapes to pattern circuits and memory devices. As we look to a future in which electronics are integrated at large numbers and in new forms for the Internet of Things and wearable and implantable technologies, we also explore a broader material set. Semiconductor nanoparticles and biomolecules are prized for their size-, shape-, and composition-dependent properties and for their solution-based assembly and integration into devices that are enabling unconventional manufacturing and new device functions.
Material aspects of giga-Hertz ZnO TFTs for wireless systems
Abstract Having enabled high-value application capabilities through mass production of flat-panel displays, X-ray imagers, and solar panels, Large-Area Electronics (LAE) holds potential to open new frontiers in wireless applications for the Internet of Things and 5G/6G, by enabling unprecedented spatial control and power efficiency through large size and flexible form factor of radiative apertures. However, this requires boosting operation frequencies from the traditional limits in the range of 10–100’s of mega-Hertz to multi giga-Hertz. In this paper, we discuss critical device metrics, to characterize zinc-oxide (ZnO) thin-film transistor (TFT) operation frequency for both active (for signal amplification) and passive components in LAE-based circuits and systems. We then describe the key structural and material approaches towards recently demonstrated LAE-based giga-Hertz wireless systems employing ZnO TFTs. Bringing LAE to the giga-Hertz regime provides a path towards flexible and meter-scale monolithic integrated wireless systems. Graphical abstract
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation
Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such large architectures lead to increased accuracy, this is usually accompanied by a larger increase in computation and memory requirements during inference. While this is a non-issue in traditional machine learning (ML) pipelines, the recent confluence of machine learning and fields like the Internet of Things (IoT) has rendered such large architectures infeasible for execution in low-resource settings. For some datasets, large monolithic pipelines may be overkill for simpler inputs. To address this problem, previous efforts have proposed decision cascades where inputs are passed through models of increasing complexity until the desired performance is achieved. However, we argue that cascaded prediction leads to sub-optimal throughput and increased computational cost due to wasteful intermediate computations. To address this, we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a non-cascading, cost-aware learning pipeline as an efficient alternative to cascaded decision architectures. Through experimental evaluation on both real-world and standard datasets, we demonstrate that PaSeR achieves better accuracy while minimizing computational cost relative to cascaded models. Further, we introduce a new metric IoU/GigaFlop to evaluate the balance between cost and performance. On the real-world task of battery material phase segmentation, PaSeR yields 179% improvement over SOTA MatPhase model and a 196% improvement over IDK Cascades under the IoU/GigaFlop metric. We also demonstrate PaSeR’s adaptability to complementary models trained on a noisy MNIST dataset, where it outperforms all baselines on IoU/GigaFlop by an average of 44%.
Digital Twin Network (DTN): Concepts, Architecture, and Key Technologies
With the commercial deployment of 5G and the migration of internet from IPv4 to IPv6, the new development of the network technology has highly attracted the attention of the industry. The intelligentization of network is believed as the trend of the new generation of network development. The network provides the foundation for the information transmission in the digital society, and the digitalization of the network itself is the prerequisite for the intelligent development. Facing the goal of the new generation of digital and intelligent network, this paper introduces the new concept of "digital twin network (DTN)", designs the system architecture, and analyzes the key technologies of DTN. By investigating the challenges of DTN, this paper points out the future development direction of digital twin network.
AmeriFlux US-MEF Manitou Experimental Forest
This is the AmeriFlux version of the carbon flux data for the site US-MEF Manitou Experimental Forest. Site Description - This site was established on an existing 30-m tower structure at the Forest Service's Manitou Experimental Forest, Colorado. Manitou Experimental Forest is located approximately 38 km northwest of Colorado Springs, within the Pike National Forest. The tower was constructed and used between 2008 and 2013 by NCAR for the BEACHON project and has line power and internet connections. The tower is located in approximately 12 km^2 of ponderosa pine forest and savannah with a land use history of thinning and prescribed burning, though these disturbances have not occurred for many years. The tower is situated in a broad, flat valley that drains to the north. Early research at the site was focused mainly on grazing, while more recently it has turned broadly to the ecology and management of ponderosa pine ecosystems. In 2002, the Hayman Fire burned approximately 56,000 ha to the west and northwest of the tower location.
AmeriFlux US-SB1 Sweet Briar Land-Atmosphere Research Station
This is the AmeriFlux version of the carbon flux data for the site US-SB1 Sweet Briar Land-Atmosphere Research Station. Site Description - The facility features a 36.5m (120') triangular tower and a climate-controlled shed laboratory with line power and high speed internet. The site is located in a 27 ha loblolly pine (Pinus taeda) plantation on the campus of Sweet Briar College. The average canopy height is approximately 20 meters in the immediate footprint of the tower, where the loblolly trees are approximately 25 years old. Beyond the loblolly plantation, the site is surrounded by regionally representative, middle-aged, mixed deciduous forest that is dominated by red maple (Acer rubrum), tulip poplar (Liriodendron tulipifera), white oak (Quercus alba), and chestnut oak (Quercus prinus)
AmeriFlux FLUXNET-1F US-MEF Manitou Experimental Forest
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-MEF Manitou Experimental Forest. This is the FLUXNET version of the carbon flux data for the site US-MEF Manitou Experimental Forest produced by applying the standard ONEFlux (1F) software. Site Description - This site was established on an existing 30-m tower structure at the Forest Service's Manitou Experimental Forest, Colorado. Manitou Experimental Forest is located approximately 38 km northwest of Colorado Springs, within the Pike National Forest. The tower was constructed and used between 2008 and 2013 by NCAR for the BEACHON project and has line power and internet connections. The tower is located in approximately 12 km^2 of ponderosa pine forest and savannah with a land use history of thinning and prescribed burning, though these disturbances have not occurred for many years. The tower is situated in a broad, flat valley that drains to the north. Early research at the site was focused mainly on grazing, while more recently it has turned broadly to the ecology and management of ponderosa pine ecosystems. In 2002, the Hayman Fire burned approximately 56,000 ha to the west and northwest of the tower location.
Impact of simultaneous activities on frequency fluctuations — comprehensive analyses based on the real measurement data from FNET/GridEye
Simultaneous human activities such as the Super Bowl game would cause certain impacts on frequency fluctuations in power systems. With the help of FNET/GridEye measurements, this work aims to give comprehensive analyses on the frequency fluctuations during Super Bowl LIV held on Feb. 2, 2020, so as to better understand several phenomena caused by simultaneous activities and help system operation and control. First, recent developments of FNET/GridEye are introduced briefly. Second, the frequency fluctuations of Eastern Interconnection (EI), western electricity coordinating council (WECC), and electric reliability council of Texas (ERCOT) power systems during Super Bowl LIV are analyzed. Third, frequency fluctuations of Super Bowl Sunday and ordinary Sundays in 2020 are compared. Finally, the differences of frequency fluctuations among different years during the Super Bowl and their change trend are also given. Furthermore, several possible explanations including the simultaneity of electricity consumption at the beginning of commercial breaks and the halftime show, the increasing usage of the Internet, and the increasing size of TV screens are illustrated in detail in this work.
Seismology in the Cloud: A New Streaming Workflow
Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.
Evolution and Trends of Industrial Control System Cyber Incidents since 2017
The industrial control systems (ICSs) that manage our critical infrastructure are increasingly converging with corporate networks and the Internet as technology and businesses prioritize digital connectivity. These connections make them more vulnerable and available to malicious cyber actors who traditionally targeted the companies’ more public-facing information technology (IT) networks. This paper will review select publicly reported cyber incidents to highlight the continued and growing threat to ICS devices and operational technology (OT) environments. It will summarize the incident and when available, will provide information on the cyber actors, the vulnerabilities they exploited, and any publications the U.S. Government (USG) provided in response. Data belonging to the Department of Homeland Security (DHS) will be used to highlight quantitative trends concerning ICS incidents. This paper builds on “History of Industrial Control System Cyber Incidents” (Hemsley & Fisher 2018), a paper that highlighted select noteworthy threats and incidents to ICS systems up to 2017. This paper will similarly review select incidents occurring after the last previously reviewed incident, Triton/HatMan, December 2017, and will note ICS incident trends including IT/OT convergence and advances in cyber-threat actors’ capabilities in observed in the examined incidents.