Fiber-reinforced polymer (FRP) composites are increasingly popular due to their superior strength to weight ratio. In contrast to significant recent advances in automating the FRP manufacturing process via 3D printing, quality inspection and defect detection remain largely manual and inefficient. In this paper, we propose a new approach to automatically detect, from microscope images, one of the major defects in 3D printed FRP parts: fiber-deficient areas (or equivalently, resin-rich areas). From cross-sectional microscope images, we detect the locations and sizes of fibers, construct their Voronoi diagram, and employ α-shape theory to determine fiber-deficient areas. Our Voronoi diagram and α-shape construction algorithms are specialized to exploit typical characteristics of 3D printed FRP parts, giving significant efficiency gains. Our algorithms robustly handle real-world inputs containing hundreds of thousands of fiber cross-sections, whether in general or non-general position.
Intra-individual variability of steady-state evoked potentials (ssEPs) is correlated with attention fluctuations and reveals the participant’s ability to sustain attention. We previously presented an analytical method to measure the variation of discrete Fourier measurements at the frequency of interest extracted from ssEP data and to model the Fourier estimates on the two-dimensional complex plane with an ellipse. In this paper, we will introduce the ratio of the major to minor axes of the ellipse, which we call the Length-to-Width Ratio (LWR), as an index of individual ability to control attention and show how to calculate the confidence interval of the LWR to be able to compare the LWR between conditions within a participant as well as between participants within or between studies. The method will enable us to find out the most sensitive electroencephalography electrode to attention fluctuation, to explore the neural correlates of attention, to differentiate cases with inconsistent control of attention from normal participants, and to objectively monitor the effects of therapeutic interventions on attention.
Manipulation of the host cell plasma membrane is critical during infection by intracellular bacterial pathogens, particularly during bacterial entry into and exit from host cells. To manipulate host cells, bacteria deploy secreted proteins that modulate or modify host cell components. Here, we review recent advances that suggest common themes by which bacteria manipulate the host cell plasma membrane. One theme is that bacteria use diverse strategies to target or influence host cell plasma membrane composition and shape. A second theme is that bacteria take advantage of host cell plasma membrane-associated pathways such as signal transduction, endocytosis, and exocytosis. Future investigation into how bacterial and host factors contribute to plasma membrane manipulation by bacterial pathogens will reveal new insights into pathogenesis and fundamental principles of plasma membrane biology.
With the development of artificial intelligence (AI), it gains in popularity to use AI to solve problems in civil engineering. However, the research on AI is mainly focused on the field of structural health monitoring, and less on the field of structural design. As one new direction in the AI domain, the generative adversarial network (GAN) method has developed rapidly, which is able to synthesize high-quality images based on demand. Therefore, it opens a new window for AI-aided automatic structure design. In this paper, a novel GAN-based method, namely FrameGAN, is proposed to realize automated component layout design of steel frame-brace structures. By collecting and processing drawings designed by senior structural engineers, FrameGAN and two mainstream GAN models (pix2pix and pix2pixHD) are tested and compared, which demonstrates the superiority of the proposed FrameGAN. In addition, the design results of FrameGAN are compared and analyzed with those of senior structural engineers based on two unique evaluation metrics, i.e., expert grading and objective comparison. The results show that the design of FrameGAN is close to that of structural engineers, which indicates the availability of FrameGAN in the component layout design of steel frame-brace structures.
Lipids are structurally diverse biomolecules that serve multiple roles in cells. As such, they are used as biomarkers in the modern ocean and as paleoproxies to explore the geological past. Here, I review lipid geochemistry, biosynthesis, and compartmentalization; the varied uses of lipids as biomarkers; and the evolution of analytical techniques used to measure and characterize lipids. Advancements in high-resolution accurate-mass mass spectrometry have revolutionized the lipidomic and metabolomic fields, both of which are quickly being integrated into marine meta-omic studies. Lipidomics allows us to analyze tens of thousands of features, providing an open analytical window and the ability to quantify unknown compounds that can be structurally elucidated later. However, lipidome annotation is not a trivial matter and represents one of the biggest challenges for oceanographers, owing in part to the lack of marine lipids in current in silico databases and data repositories. A case study reveals the gaps in our knowledge and open opportunities to answer fundamental questions about molecular-level control of chemical reactions and global-scale patterns in the lipidscape. Expected final online publication date for the Annual Review of Marine Science, Volume 15 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Elastomeric bridge bearings are widely used in bridges to accommodate the deformations produced by mechanical and environmental loads. As their acceptable performance is critical for the bridge performance, finite element analysis (FEA) can be applied to supplement test results on the performance of elastomeric bearings. However, uncertainties are present in both the material properties of their components and the boundary conditions. Therefore, this study provides an initial exploration on how these uncertainties will affect the performance of the bearings under compression. The elastomeric bridge bearing is first modeled using the finite element (FE) method, and then probabilistic analysis is applied using the Monte Carlo simulation (MCS). Material properties of the elastomer and steel components of the bearing and the friction coefficient at the bearing–support interfaces are treated as random variables, and a probabilistic analysis is performed that shows how specific parameters will influence the output response, including the vertical stiffness, and induced stresses and strains. In addition, the study also provides an initial exploration into the sensitivity of the bearing’s response to epistemic uncertainties in these input parameters. The probabilistic FEA results can ease the development of numerical models of elastomeric bridge bearings, and they can be used to improve the code provisions associated with the design of these bearings.
Masonry arch bridges are numerous across European transportation networks. Many are ageing structures, with service lives of 100–150 years to date, and exhibit historic damage and repairs, leading to uncertainty regarding structural behaviour. For skewed bridges particularly, this can be complicated and three-dimensional, and detailed experimental data describing behaviour are rare. In 2018–2019, the authors deployed Fibre Bragg Grating (FBG) strain monitoring at a recently repaired, skewed masonry rail bridge in the UK. Following an on-site trial, the FBG monitoring system was substantially upgraded in 2020 to enable long-term, autonomous, remote sensing. This new system is introduced, including processes to automate data classification based on the date and time of measurements, and train class/operator, direction, and speed. This system has recorded the bridge responses to thousands of trains. Data analysis is presented, focusing particularly on seasonal and long-term variation of behaviour. Findings include the impact of ambient temperature; an inverse relationship is observed. Decreasing temperature causes thermal contraction of the masonry, allowing cracks to open and increasing the potential for bridge movements. After decoupling such effects, residual long-term changes may correspond to damage. Therefore, this system can provide valuable asset management information on the early onset of bridge deterioration.
Historically, Southern California suffers from the worst traffic congestion and air quality levels in the country. During the COVID-19 pandemic in 2020, we observed a major reduction in economic and social activities within the region, leading to changes in roadway traffic and air pollution levels in a variety of ways. Within six weeks of the pandemic-induced lockdowns, freeway traffic volume dropped as low as 50%; however, it has since gradually increased back to pre-pandemic levels. The changes in freeway traffic volume have not been uniform across the Southern California region, and neighborhoods with different socio-economic profiles were affected differently. These disparities have brought up environmental justice concerns, particularly for disadvantaged communities that live adjacent to major roadways and warehouse centers. We monitored the changes in vehicle and human activities across communities in Southern California and explored correlations that are useful for developing various mitigation measures at both the local and regional levels. In this study, we go beyond regional analysis and examine the effects of the pandemic on traffic at a transportation corridor and local levels to examine possible equity issues. Results show that, in general, the level of traffic dropped less in disadvantaged neighborhoods during the pandemic. Further, traffic flow rebounded in these neighborhoods faster than in other communities.
During the COVID-19 outbreak, the risk of infection is not neglectable in a public transportation system. To satisfy the demands while controlling the spread of COVID-19, public transportation agencies have proposed various rules, such as increasing train frequency and requiring face coverings. In this chapter, we summarize newly developed evaluation methodologies, and evaluate the impacts of COVID-19 policies. We also present key findings regarding the impacts of different policies using these new methods. We find that the goal of stopping the pandemic coincided with minimizing the total delay when the service area was homogenous in infection rate. For heterogenous cities, minimizing the risk is equivalent to minimizing weighted travel time, where the weight is the infection rate. We also find that the results obtained from different models could be different due to their assumptions on the lost demand. If the demand is elastic, closing part of the system can prevent the spread of the pandemic, otherwise, closing will lead to longer waiting time, higher passenger density, and infection risk.
The COVID-19 pandemic dramatically affected the ability of localities to pay for their transportation systems. We explore the effects of the pandemic on local option sales taxes (LOSTs), an increasingly common revenue source for transportation in California and across the U.S. LOSTs have many advantages over alternative finance instruments, including that they can raise prodigious amounts of revenue. However, LOSTs rely on consumer spending, which lags during times of economic weakness. This is precisely what we observed in California counties during the initial months of the pandemic. LOST revenues did recover after the initial economic shock of COVID-19, albeit to a lower level than they would likely have otherwise. LOST revenue trends during the pandemic were affected by national and regional economic conditions and government policy as well. This public health crisis illustrates both the pitfalls and resilience of LOSTs during economic downturns and recoveries. The lessons from the pandemic’s effects on LOSTs will be useful for policymakers and analysts in preparing for inevitable future crises and associated economic turbulence.
The global tragedy of the COVID-19 pandemic devastated communities and societies. The pandemic also upended public transit and shared mobility, causing declines in ridership, losses in revenue sources, and challenges in ensuring social equity. Despite ongoing uncertainty, guidance can instruct recovery and build a more resilient, socially equitable, and environmentally friendly transportation future. This chapter summarizes a recent scenario planning exercise conducted by the University of California Institute of Transportation Studies in collaboration with the Transportation Research Board (TRB) Executive Committee in Spring to Fall 2020. The exercise convened 36 transportation experts in the United States who developed policy actions and research options crafted to guide near- and long-term public transit and shared mobility. Clear themes emerged from the study regarding key actions for public transit operators in the areas of: (1) innovation and technology, (2) planning and operations, (3) customer focus, and (4) workforce development. A second grouping of broader policy strategies for both public transit and shared mobility included: (1) immediate policy and actions across actors, (2) alignment of societal objectives, (3) federal transportation spending authorization, and (4) finance and subsidies. While the exercise reiterated the need for rapid actions, thoughtful planning and decision-making can prepare both sectors for a more cooperative, multimodal ecosystem.
During the pandemic, from March 2020 through March 2021, we monitored three San Francisco Bay Area transit agencies: two large—AC Transit and VTA; and one small—Tri Delta Transit. As the lockdown was imposed, white-collar commuters, students and older adults stopped using public transit. Initially, the ridership fell by 90%, and then for a year slowly climbed up to less than 50% for AC Transit and VTA, and to around 60% of the pre-pandemic numbers for Tri Delta Transit. This ridership recovery was not consistent. Local drops occurred during protests in June 2020, during fare reinstatements, and during the second COVID wave in Winter 2021. We found that the agencies’ response to the pandemic consisted of three parts: (1) maintaining health and safety of their employees; (2) minimizing transmission risk for riders by keeping buses clean and enabling social distancing through capping the number of bus passengers; and (3) changing their service. During the pandemic, we also observed a direct relationship between the socioeconomic level of population and transit ridership. More specifically, we observed higher ridership in low-income areas with a high percentage of Latino, Black and Asian population. These communities are populated by people, who generally rent their homes, do not have a car, but need to go to work, either because they belong to an essential workforce and/ or are undocumented immigrants who cannot afford staying jobless. On the other hand, in the wealthy neighborhoods of the Bay Area, transit activity all but disappeared.
In the processes for designing and assessing structural systems, it is essential to evaluate their reliability against stochastic loads caused by natural or human-made hazards, e.g., wind loads, earthquakes, and collisions. The first-passage probability is a crucial measure of a system's reliability under such conditions. However, the first-passage probability estimation generally requires repetitive dynamic simulations and thus may result in high computational cost. This paper proposes a new active-learning-based surrogate method that efficiently estimates the first-passage probability under stochastic wind excitations to address the computational issue. The proposed method introduces an alternative formulation using the conditional distribution of the maximum response given time-invariant uncertain parameters to handle the high dimensionality of stochastic excitation sequences. The method employs the Gaussian-process-based surrogate model with heteroscedastic noises to fit the distribution parameter functions considering uncertainties arising from the structural system and the environmental loads. In addition, an adaptive training process of surrogates is introduced to identify the best experimental designs achieving efficient convergence. The numerical examples of an eight-story building and a transmission tower demonstrate that the proposed method can produce accurate estimation results with fewer structural simulations than existing methods.
In this chapter, we discuss how the solid Earth deforms in response to tidal forcings, resulting in crustal displacements of ~ 10 cm. These displacements are collectively known as body tides. We summarize technologies employed to measure such displacements and outline the theoretical framework—Love number theory—that underpins many predictions of body tides from a historical and current perspective. We detail the development of Love number theory, driven by the need to consider more complexities in solid Earth physics (e.g., nonrotation to rotation, elasticity to anelasticity, spherical symmetry to asymmetry) given that observations of their associated crustal displacement have reached unprecedented levels of accuracy and precision. We also briefly discuss non-Love number-based methods able to account for higher-level complexity. We review several ways in which body tides have been used to investigate Earth structure and conclude with promising directions of body tide studies within and beyond Earth.
In this paper, we propose a Ferroelectric Tunnel Junction (FTJ)-based true random number generator (TRNG) that utilizes the stochastic domain switching phenomenon in ferroelectric materials. Ferroelectrics are promising for extracting randomness owing to their innate switching entropy in the multi-domain state. The random numbers generated by the proposed TRNG are shown to pass all the NIST SP 800-22 tests. The robustness of the proposed TRNG is also validated at various temperature and process corners. Important metrics such as power, bit rate, and energy/bit are calculated. This is the first comprehensive work demonstrating a ferroelectric-based TRNG with all these metrics. Compared to state-of-the-art TRNGs using other emerging technologies, we can achieve a higher bit rate with lower power consumption. We also perform material-level optimization with different ferroelectric materials, and showcase the trade-off between the bit rate and the power consumption. The proposed TRNG shows high robustness and reliability, and thus has the potential for implementing a low power on-chip solution.
Hotel room cleaners are a vulnerable population at risk for cardiovascular disease. To evaluate their workload heart rate (HR), % heart rate reserve (%HRR), blood pressure (BP), metabolic equivalent (MET), and energy expenditure (EE) were measured over two workdays and two off-workdays. The mean age was 45.5 (SD 8.2) years with a mean 10.4 (SD 7.8) years of work experience. Mean average and peak HR, %HRR, MET, and EE were significantly higher during a workday than an off-workday for the entire work shift, first and last hour of work. Mean average HR and %HRR saw the largest increase between the lunch and post-lunch interim. One-fourth of subjects exceeded the recommended 30% HRR threshold for 8-hour shifts. Some workers experienced a substantial increase in HR and DBP over a workday indicating physiologic fatigue and thus may be at increased risk for cardiovascular disease and premature death due to excessive physical work demands.
Population divergence leading to speciation is often explained by physical barriers causing allopatric distributions of historically connected populations. Environmental barriers have increasingly been shown to cause population divergence through local adaptation to distinct ecological characteristics. In this study, we evaluate population structuring and phylogeographic history within the Yucatán banded gecko Coleonyx elegans Gray 1845 to assess the role of both physical and environmental barriers in shaping the spatio-genetic distribution a Mesoamerican tropical forest taxon. We generated RADseq and multi-locus Sanger datasets that included sampling across the entire species’ range. Results find support for two distinct evolutionary lineages that diverged during the late Pliocene and show recent population expansions. Furthermore, these genetic lineages largely align with subspecies boundaries defined by morphology. Several mountain ranges identified as phylogeographic barriers in other taxa act as physical barriers to gene flow between the two clades. Despite the absence of a physical barrier between lineages across the lowland Isthmus of Tehuantepec, no introgression was observed. Here, a steep environmental cline associated with seasonality of precipitation corresponds exactly with the distributional limits of the lineages, whose closest samples are only 30 kilometers apart. The combination of molecular and environmental evidence, and in conjunction with previous morphological evidence, allow us to reassess the current taxonomy in an integrative framework. Based on our findings, we elevate the previously recognized subspecies from the Pacific versant, the Colima Banded Gecko C. nemoralis Klauber 1945, to full species status and comment on conservation implications.
Understanding the spatial and temporal habitat use of a population is a necessary step for recovery planning. For Chinook salmon (Oncorhynchus tshawytscha), variation in their migration and habitat use complicate predicting how restoring habitats could impact total recruitment. To evaluate how juvenile life history variation affects a population’s response to potential restoration, we developed a stage-structured model for a Chinook salmon population in a northern California river with a seasonally closed estuary. We modeled the timing of juvenile migration and estuarine use as a function of freshwater conditions and fish abundance. We used the model to evaluate the sensitivity of the population to different estuary and freshwater restoration scenarios that could affect population parameters at different life stages. The population’s run size increased most in response to freshwater restoration that enhanced spawning productivity (egg and fry survival), followed by spawner capacity. In contrast, estuary restoration scenarios affected only a subset of Chinook salmon (average 15%), and as a result, did not have a large impact on the total recruitment of a cohort. Under current condition, estuary rearing fish were over six times less likely to survive than fish that migrate to the ocean in the spring or early summer before estuary closure. Because estuary residents experienced low survival in the estuary and in the ocean, improvements to both estuary survival and growth would be needed to increase their total survival. When life cycle monitoring data is available, life cycle models such as ours generate predictions at scales relevant to conservation and are an advantageous approach to managing and conserving anadromous salmon that use multiple habitats throughout their life cycle.
Car use creates significant externalities for urban residents worldwide. City characteristics such as the configuration of the urban landscape and street network likely influence the use and attractiveness of automobiles, especially in rapidly urbanizing areas such as Latin America. The understanding of factors associated with motorization can inform planning measures to reduce car usage, and to promote healthier, safer, and more sustainable urban lifestyles. We harmonized official passenger vehicle data from 300 cities with >100,000 inhabitants in Brazil, Chile, Colombia, and Mexico, and we calculated urban landscape metrics from satellite imagery and street network metrics from OpenStreetMaps. Analyzed cities had an average of 273.3 cars per 1,000 residents in 2015 and showed an average car rate increase of 30 % between 2010 and 2015. We used negative binomial regression to examine the association between car rates and urban landscape and street network characteristics, and linear regression to examine the association between the same characteristics and car rate increases. Car rates in the 300 cities analyzed showed a partial positive association with development fragmentation, and a consistent positive association with urban form complexity and circuity of the street network. In addition, the increase in car rates between 2010 and 2015 showed a negative association with population density. Implementing regional policies to reduce development fragmentation, to promote compact urban forms and less circuitous street networks may help reducing motorization in Latin American cities. Special attention needs to be paid to low density areas, where the increase in vehicle rates has been more pronounced.
While technology transfer at universities has received considerable attention in the innovation and entrepreneurship literature, we know much less about technology transfer at national/federal labs and (non-university) public research institutes. In this article and the related special section, we aim to fill this void. We provide a rationale for our special section on technology transfer from national/federal labs and public research institutes, summarize the papers in the special section, highlight research questions, theories, data and methods, key findings and conclusions. We conclude by outlining a research agenda for multi-level research on agents, institutions, and regions to improve our understanding of the managerial and public policy implications of technology transfer from these institutions.
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