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This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the "surface image removal" technique and the method of "high dynamic range" photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images. [ABSTRACT FROM AUTHOR]

The aggregates are essential materials in civil engineering, they are used for railway and road constructions, for hydraulic engineering but they are also the base material of concrete. The crushed stones are exposed to several effects during their lifespan. Therefore, several tests were developed to evaluate their performance. One of the most important aspects is the resistance to degradation. However, degradation tests require special types of equipment and usually take longer than common strength tests which are more likely to be available for rock materials. Therefore, the empirical connections between strength and degradation values can be extremely useful in practice. The paper aimed to collect all available relationships and datasets from the literature that presents the relations between these different parameters - such as Aggregate Impact Value (AIV), Aggregate Crushing Value (ACV), Ten Percent Fines Value (TFV), Los Angeles Abrasion Value (LAAV), and micro-Deval Coefficient (MDE) - and rock strength parameters - such as Uniaxial Compressive Strength (UCS) and Point Load Strength Index (IS(50)) - and to provide the best-fit formula for different rock types. The paper also highlights the difficulties and limitations of the compared relationships. [ABSTRACT FROM AUTHOR]

Self-melting ice asphalt pavement materials inhibit pavement freezing and improve driving safety. This paper aims to study the long-term salt release characteristics of self-melting ice asphalt mixtures and the impact on pavement after complete salt release. Firstly, a method to accelerate the rapid release of salt based on the Los Angeles abrasion tester. Then, long-term salt release patterns were elucidated under the influence of deicing agent dosage, type of asphalt, and type of gradation. Finally, a quantitative analysis of the pavement performance after complete salt release is conducted. The results indicate that the release efficiency of the Los Angeles abrasion tester method has increased by 91 times compared to the magnetic stirrer immersion flushing method and by 114 times compared to the natural soaking method. The SBS-modified self-melting ice asphalt mixture possesses a longer duration of salt release, but the uniformity of salt release is inferior. Salt release duration is directly proportional to the dosage of deicing agents. SMA-13 self-melting ice asphalt mixture exhibits poorer uniformity in salt release. After complete salt release, high-temperature stability of self-melting ice asphalt mixtures decreased by 31.6%, low-temperature performance decreased by 15.4%, water stability decreased by 26.7%, and fatigue life decreased by 35.9%. [ABSTRACT FROM AUTHOR]

This paper presents an extensive numerical study on the seismic performance of steel eccentrically braced frames (EBFs) designed according to the ASCE 7-16 standard. A series of EBF building structures ranging from 2 to 12 stories located in downtown Los Angeles are designed with different yielding links, including conventional and replaceable links. The performance of the structures is investigated through nonlinear time-history analyses (NLTHAs) under a suite of 40 ground motions, selected and scaled to match the target uniform hazard spectrum. The peak deformations are first examined to assess the performance of the buildings as well as the design procedure. Residual deformations are also evaluated and critically compared with acceptable limits, to provide insight into the downtime and recovery time of EBFs after major earthquakes. Previous studies have demonstrated that the response of stable yielding systems to major earthquakes is often accompanied by significant residual deformations, and that residual drifts exceeding 0.5% can cause hindrance to the buildings occupants. In EBFs, in addition to residual drifts, residual link rotations are also relevant and expected to be more severe due to the localization of inelastic deformations in the yielding link. For replaceable modular yielding links, significant residual link rotations after major earthquakes will hinder repairs and the link replacement process, which is one of the important design objectives. The performance of EBFs is also compared with what was observed in previous studies for buckling restrained braced frames and special moment resisting frames. In the end, the results from the extensive NLTHAs are used to establish a relationship between peak drifts and peak link rotations in EBFs. [ABSTRACT FROM AUTHOR]

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers. [ABSTRACT FROM AUTHOR]

Although earlier research has shown that individual differences on the spectrum of attention deficit hyperactivity disorder (ADHD) are highly heritable, emerging evidence suggests that symptoms are associated with complex interactions between genes and environmental influences. This study investigated whether a genetic predisposition [Note that the term 'genetic predisposition' was used in this manuscript to refer to an estimate based on twin modeling (an individual's score on the latent trait that resembles additive genetic influences) in the particular population being examined.] for the symptom dimensions hyperactivity and inattention determines the extent to which unique-environmental influences explain variability in these symptoms. To this purpose, we analysed a sample drawn from the Twins Early Development Study (TEDS) that consisted of item-level scores of 2168 16-year-old twin pairs who completed both the Strengths and Difficulties Questionnaire (SDQ; Goodman, in J Child Psychol Psychiatry 38:581–586, 1997) and the Strength and Weaknesses of ADHD Symptoms and Normal Behavior (SWAN; Swanson, in Paper presented at the meeting of the American Psychological Association, Los Angeles, 1981) questionnaire. To maximize the psychometric information to measure ADHD symptoms, psychometric analyses were performed to investigate whether the items from the two questionnaires could be combined to form two longer subscales. In the estimation of genotype-environment interaction, we corrected for error variance heterogeneity in the measurement of ADHD symptoms through the application of item response theory (IRT) measurement models. A positive interaction was found for both hyperactivity (e.g., β 1 = 2.20 with 95% highest posterior density interval equal to [1.79;2.65] and effect size equal to 3.00) and inattention (e.g., β 1 = 2.16 with 95% highest posterior density interval equal to [1.56;2.79] and effect size equal to 3.07). These results indicate that unique-environmental influences were more important in creating individual differences in both hyperactivity and inattention for twins with a genetic predisposition for these symptoms than for twins without such a predisposition. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic disproportionately affected older adults, particularly those with pre-existing chronic health conditions. To address the health disparity and challenges faced by under-resourced African American older adults in South Los Angeles during this period, we implemented a hybrid (virtual/in-person), pre-post, community-based participatory intervention research project utilizing a faith-based lay health advisor model (COVID-19 Health Ambassador Program (CHAP)). We recruited COVID-19 Health Ambassadors (CHAs) and African American older adults (participants) from faith-based organizations who partook in CHA-led meetings and follow-ups that educated and supported the participants. This paper seeks to evaluate this intervention's implementation using the Consolidated Framework for Implementation Research (CFIR) as a reporting tool with an emphasis on fidelity, challenges, and adaptations based on data collected via stakeholder interviews and surveys. Results: CHAP was delivered to 152 participants by 19 CHAs from 17 faith-based organizations. CHAs assisted with chronic disease management, resolved medication-related challenges, encouraged COVID-19 vaccination, reduced psychological stress and addressed healthcare avoidance behaviors such as COVID-19 vaccine hesitancy among the participants. Challenges encountered include ensuring participant engagement and retention in the virtual format and addressing technological barriers for CHAs and participants. Adaptations made to better suit the needs of participants included providing communication tools and additional training to CHAs to improve their proficiency in using virtual platforms in addition to adapting scientific/educational materials to suit our participants' diverse cultural and linguistic needs. Conclusion: The community-centered hybrid approach in addition to our partnership with faith-based organizations and their respective COVID-19 health ambassadors proved to be essential in assisting underserved African American older adults manage chronic health conditions and address community-wide health disparities during the COVID-19 pandemic. Adaptability, cultural sensitivity, and teamwork are key to implementing health interventions especially in underserved populations. [ABSTRACT FROM AUTHOR]

Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations. [ABSTRACT FROM AUTHOR]

The winner of the 2020 TPS Best Paper Award has been selected (please refer to our TPS home page for details about the award at https://ieee-npss.org/publications/transactions-on-plasma-science/). This year is the second year that the award is being given, and I am pleased to announce that Prof. Chandrashekhar Joshi, Department of Electrical and Computer Engineering, University of California at Los Angeles, Los Angeles, CA, USA, is the winner for his article “Laser-driven plasma accelerators operating in the self- guided, blowout regime.” This article appeared in IEEE TPS, vol. 45, pp. 3134–3146, in December 2017. The award plaque, certificate, and cheque will be presented to Prof. Joshi at an upcoming International Conference on Plasma Science in South Lake Tahoe, CA, USA, in June 2021. This is an open-access article and is freely available to our readers. Congratulations to Prof. Chan Joshi on this accomplishment. [ABSTRACT FROM AUTHOR]

Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings). [ABSTRACT FROM AUTHOR]

Sedimentary phosphate extraction in open-pit operations generates large volumes of waste rock (WR), which are mainly overburdens and interburdens. Traditionally, the WR is mixed and stored on the surface in waste rock piles (WRPs). This paper presents a case study of the Benguerir mine site in Morocco. It investigates the potential valorization of each WR lithology based on the geological and geomechanical properties to reduce their environmental footprint and create added value to "waste." The WR samples (soils and rocks) were collected from drill cores and mining trenches in the Benguerir mine. The geological characterization results using petrographic descriptions indicate the presence of nine phosphate layers and, in addition to the overburdens, eight interburdens. Four types of WR are identified: carbonate, siliceous, marly clay, and phosphate. The geomechanical characterization of soil-like samples showed an average plasticity index (PI) of 50% according to the methylene blue value (MBV) of 7.1, classifying them in the A3–A4 categories as plastic and clayey marl soils. The hard rock samples have excellent mechanical properties in terms of their uniaxial compressive strength (UCS), Los Angeles abrasion value (LA), and micro-Deval value (MD). The average compressive strength is 104 MPa for the flint, 35 MPa for the phosphate flint, 32 MPa for the silexite, 26 MPa for the limestone, 11 MPa for the indurated phosphate, and 8 MPa for the marly limestone. Based on the obtained results, these WRs can be considered as an excellent alternative secondary raw material for use in civil engineering applications, ceramics, and cement industries. [ABSTRACT FROM AUTHOR]

Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., speed, flow) on road segments using partially observed data, plays an essential role in intelligent transportation systems. Generally, traffic estimation problems can be divided into two categories: model-driven approaches and data-driven approaches. The model-driven method is commonly used to solve TSE efficiently and calibrate the parameters of these models. The data-driven method requires a large amount of historical observed traffic data in order to improve performance accurately. In order to combine the advantages of model-driven and data-driven methods, this paper proposed a hybrid framework incorporating the traffic flow model into deep learning (TFMDL) modeling that contains both model-driven and data-driven components. This paper focuses on highway TSE with observed data from loop detectors. We build a hybrid cost function to adjust the weights of model-driven and data-driven proportions. We then evaluate the proposed framework using the open-access performance measurement system (PMS) dataset on a corridor of US I-405 in Los Angeles, California. The experimental results show the advantages of the proposed TFMDL approach in performing better than several benchmark models in terms of estimation accuracy and data efficiency. [ABSTRACT FROM AUTHOR]

Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way. Plain Language Summary: Earthquake nowcasting refers to the determination of hazard for major earthquakes at the present time, the recent past, and the near future. Nowcasting is an idea borrowed from economics, markets, and meteorology, where it has been frequently used. In this paper, we show that there is order hidden within chaotic earthquake seismicity using a very simple transformation of the data. Small earthquakes appear to transition from unstable stick‐slip events that produce seismic waves, to stable sliding where no seismic waves are produced. Our hypothesis is that this transition is due to a material phenomenon called strain‐hardening, that is frequently observed in laboratory rock mechanics experiments. The result is a state variable time series, computed over the last 51 years in California, that strongly resembles the long‐anticipated cycle of stress accumulation and release. Using supervised machine learning techniques, we can optimize the two‐parameter model. From that optimized model, we can rigorously calculate the probability of current hazard from major earthquakes. Extending these methods, we can also compute spatial hazard as well. The result is a new method for assessing earthquake hazard that may be useful for a variety of applications. Key Points: "Chaotic" seismicity contains hidden structure in the form of state variable time seriesStandard data science methods can be used to convert the time series to probabilitiesBoth temporal and spatial probabilities can be computed [ABSTRACT FROM AUTHOR]

The real estate market has a significant impact on people's daily life. Therefore, it is crucial to understand the real estate market from both spatial and temporal perspectives, while there is still a lack of research in real estate industries. In this paper, a regional house price mining and forecasting (RHPMF) framework is proposed to help people intuitively understand the spatial distribution and temporal evolution of the urban estate market based on real-world housing data and urban contexts such as demographics and criminal records. Specifically, the RHPMF framework introduces a context-aware matrix factorization to extract crucial spatial and temporal price factors for revealing the housing market. Meanwhile, the RHPMF can forecast future regional house prices by manipulating the two price factors. Consequently, this study presents extensive exploratory analysis and experiments in Virginia Beach, Philadelphia, and Los Angeles to verify the proposed RHPMF. These case studies indicate that the RHPMF framework can accurately capture the market's spatial distribution and temporal evolution and forecast future regional house prices compared with recent baselines. The experimental results suggest the great potential of the proposed RHPMF for applications in real estate industries. • Unleash a new perspective to understand the regional market of real estate. • A context-aware matrix factorization is proposed to fuse information. • The decomposed factors explain the spatial and temporal evolution. • The fused information improves the forecasting accuracy of house prices. [ABSTRACT FROM AUTHOR]

Runtime verification (RV) refers to methods for formal reasoning about all aspects of the dynamic execution of systems, including hardware, software, and cyber-physical systems. RV includes techniques to assess and enforce correctness of a system against systemic bugs or extrinsic uncertainties. These methods are typically considered lightweight as they may not involve exhaustive verification or proofs, but they provide a higher level of rigor and versatility compared to conventional testing methods. This article introduces the extended versions of selected papers from the peer-reviewed proceedings of the 20th International Conference on Runtime Verification (RV 2020). RV 2020 was supposed to be held in Los Angeles, California, USA in July 2020, but was instead held virtually due to the global Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

As a reflection of the subculture, amateurs, and fans of comics, punk music, science fiction, film, sports, etc., independently create, publish, and distribute shorter publications. These are zine publications. Photocopiers play a big role in their production, and their authors follow do-it-yourself philosophy. Applications for creating e-zines are available on the Internet. As for the shape, they may be the handmade accordion-shaped leaflets, publications in the form of short newspapers resembling A5 notebooks with collages on the cover, as well as small publications placed in a matchbox, large poster-sized zines, etc., or digital zines that quickly take primacy over paper forms. The goal of making such alternative publications is to emphasize the values of socially marginalized groups (dark-skinned people, women, immigrants, LGBTQ + people), activists, music lovers, fans, etc. Unlike publishing houses, zines are not focused on profit but more on creative expression in limited editions and emphasizing togetherness. The paper presents examples of good practice from several renowned American libraries that preserve this type of material, provide services for their use and work on their popularization. The Library of Congress and the Los Angeles Public Library organize zine festivals, which include workshops for making zine publications, as well as exhibitions and sales. The New York Public Library encourages users to write creatively and create a common zine for all age groups, and the Barnard College Library affirms the use of zine in teaching and learning. The cultural and academic value of zine publications is confirmed in all the above ways. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a realistic application and evaluation of a promising structural health monitoring approach that exploits some topological features of building-like structures to develop a reduced-order, reduced-complexity, not-necessarily-linear, substructure model. The approach not only reliably detects the occurrence of anomalous features that can reflect incipient damage and deterioration but also provides the locations of the structure's regions where a single or multiple changes have been detected. The target structure used in this study is a 52-story building in Los Angeles that is instrumented with a relatively dense sensor array and is being continuously monitored through the efforts of the community seismic network (CSN). Two qualitatively different system identification approaches (global and substructuring) are applied to the large data set of ambient acceleration measurements produced by a strong wind event ("Santa Ana winds") to identify the dominant modal characteristics of the building. The results are shown to match the corresponding results from a high-resolution computational model of the building based on a widely used structural analysis software package (ETABS) developed by Computers and Structures, Inc. The main contribution of this study is to demonstrate the practical feasibility of the proposed substructuring approach with a high-order system using both wind and low-amplitude ambient vibration measurements. The approach also assesses the accuracy and reliability of the estimates of the dominant modal features of the structure to subsequently provide a probabilistic measure of confidence in the extent and location of changes if an anomaly is detected. Due to the minimal computational resources needed to implement the proposed substructuring approach, it is efficient for near-real-time applications where important structures need to be continuously monitored for sustainability as well as resiliency requirements. The method is applicable to linear, nonlinear nonhysteretic, and hysteretic systems, with no restriction on the source of the signal for identification purposes. [ABSTRACT FROM AUTHOR]

The present paper contains a translation of Steven Shapin's article entitled Is There a Crisis of Truth? published in the Los Angeles Review of Books in December 2019. The translation was made in contact with the author and with the editor of the aforementioned journal, Michele Pridmore-Brown. Shapin kindly provided us with an appendix about the article, presenting a brief update on the theme, almost two years after its publication. Seeking the best way to present an article that deals with current issues, we bring a brief biography of the author, as well as a reflective effort on how the "Truth Crisis" and denialism have been conveyed and debated. [ABSTRACT FROM AUTHOR]

A biography of Charles Carleton Hirt, founding member of the non-profit organization American Choral Directors Association, is presented. He was born on November 4, 1911, in Los Angeles, California, to Adolph Hirt and Della Belle Hirt and attended an elementary school in Glendale, California. In 1941, Hirt was offered a teaching assignment at the Glendale Junior College by the Glendale Board of Education.

Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, and adequately capture non-linear relationships between an outcome of interest and multiple independent variables. There are relatively few adjustable hyperparameters in the standard random forest models, among them the minimum size of the terminal nodes on each tree. The usual stopping rule, as proposed by Breiman, stops tree expansion by limiting the size of the parent nodes, so that a node cannot be split if it has less than a specified number of observations. Recently an alternative stopping criterion has been proposed, stopping tree expansion so that all terminal nodes have at least a minimum number of observations. The present paper proposes three generalisations of this idea, limiting the growth in regression random forests, based on the variance, range, or inter-centile range. The new approaches are applied to diabetes data obtained from the National Health and Nutrition Examination Survey and four other datasets (Tasmanian Abalone data, Boston Housing crime rate data, Los Angeles ozone concentration data, MIT servo data). Empirical analysis presented herein demonstrate that the new stopping rules yield competitive mean square prediction error to standard random forest models. In general, use of the intercentile range statistic to control tree expansion yields much less variation in mean square prediction error, and mean square prediction error is also closer to the optimal. The Fortran code developed is provided in the Supplementary Material. [ABSTRACT FROM AUTHOR]

Hyperspectral target detection is one of the most challenging tasks in remote sensing due to limited spectral information. Many algorithms based on matrix decomposition (MD) are proposed to promote the separation of the background and targets, but they suffer from two problems: (1) Targets are detected with the criterion of reconstruction residuals, and the imbalanced number of background and target atoms in union dictionary may lead to misclassification of targets. (2) The detection results are susceptible to the quality of the a p r i o r i target spectra, thus obtaining inferior performance because of the inevitable spectral variability. In this paper, we propose a matrix decomposition-based detector named dictionary learning-cooperated matrix decomposition (DLcMD) for hyperspectral target detection. The procedure of DLcMD is two-fold. First, the low rank and sparse matrix decomposition (LRaSMD) is exploited to separate targets from the background due to its insensitivity to the imbalanced number of background and target atoms, which can reduce the misclassification of targets. Inspired by dictionary learning, the target atoms are updated during LRaSMD to alleviate the impact of spectral variability. After that, a binary hypothesis model specifically designed for LRaSMD is proposed, and a generalized likelihood ratio test (GLRT) is performed to obtain the final detection result. Experimental results on five datasets have shown the reliability of the proposed method. Especially in the Los Angeles-II dataset, the area under the curve (AUC) value is nearly 16% higher than the average value of the other seven detectors, which reveals the superiority of DLcMD in hyperspectral target detection. [ABSTRACT FROM AUTHOR]

Rock music is an integral part of American culture. This paper presents a study of sensing and analysing over 57,000 rock music live performances between 2007 and 2017. Spatial traces of 575 rock music artists performing in concerts nationwide were collected from a major music streaming platform Spotify. Location-based concert data were analysed to explore economic and geographic factors linked to the landscape of rock music live performance and to reveal the importance of population demographics and leisure and hospitality (LH) economics to the culture and music industries from a spatial aspect. Over 90% of rock concerts between 2007 and 2017 were found in 250 counties. The aim of the study is to specify and develop a model that reasonably accounts for spatial heterogeneity present in the concert data. By regressing rock concert data against demographic data and LH establishment data, ordinary least squares (OLS) models were better fitted in metropolitan counties than non-metropolitan counties. Spatial dynamics of concerts were revealed by local R2 values and the obtained structure in the form of spatial heterogeneity was then explained using geographically weighted regression (GWR) models. High population density and LH services in industry-leading cities such as New York City, Los Angeles, Chicago and Houston exhibit advantages in explaining rock concert distributions. Findings from the models reflect the live music industry's interrelationships to the LH industry and suggest LH services being essential considerations in selecting concert destinations for rock musicians. [ABSTRACT FROM AUTHOR]

This paper examines how social movement organizations shift scale through the case of the immigrant rights movement. This was largely a local movement for the first decades of its existence. However, in the late 1990s, repressive federal policies increased the salience of national politics for many organizations. While recognizing the importance of national politics, many organizations remained mostly engaged in local politics for nearly a decade. The aim of this paper is to examine why immigrant rights organizations stayed local for so long after the threat shifted to the federal level and why they actually shifted to the national scale when they did. It does so by focusing on the case of Los Angeles. [ABSTRACT FROM AUTHOR]

A rich volume of research has detected urban growth by quantifying the land use/land cover (LU/LC) changes based on remote sensing technologies. However, the research has limitations in identifying various formats of urban growth, particularly small-scale urban growth, such as infill development or redevelopment in urban areas, prompted by smart growth and sustainable urban development. This paper aims to design a framework for the accurate detection of residential infill development in the City of Los Angeles by employing a deep-learning method that has been increasingly applied to analyze phenomena in cities. In order to do so, this paper develops six models that reflect the variations of image classification methods, deep-learning algorithms, and estimation types. The results from the models emphasize the potential of the deep-learning models for the detection of micro-urban growth at a city scale. However, there is room for the improvement of estimation accuracy in the cases that detect some new developments and replacements as not developed parcels. The findings suggest that the performance of the models depends primarily on the articulations of the training dataset rather than the types of deep-learning algorithms. Findings from the models provide the city with insights into land use and transportation planning decision-making based on a better understanding of the spatial distribution patterns of urban growth and development. • Remote sensing data and geospatial technologies are limited in identifying small-scale urban growth due to the low resolutions of satellite data. • Deep-learning methods can be used to detect infill development by automatically classifying and labeling longitudinal satellite images. • The model performance primarily depends on the articulations of the training dataset rather than the types of deep-learning algorithms. [ABSTRACT FROM AUTHOR]

Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Studying the time interval duration between the first accident and the second accident caused by it can provide decision makers with valuable information on how to effectively deal with high-risk second accidents. This paper is aimed to explore the potential influencing factors of the interval duration between the two accidents and predict it. First, the spatiotemporal definition method is applied to identify the cascaded first accident and the second accident. Then, on the basis of using Kaiser-Meyer-Olkin (KMO) measure and Bartlett's sphere test statistics to ensure the applicability of the data to the factor analysis method, the explanatory variables that can significantly affect the interval duration are obtained through the factor analysis method. Finally, the random forest model (RF), which combines the advantages of machine learning methods, is employed to predict the duration of the interval. Traffic accident data set collected in Los Angeles city from February 2016 to June 2020 is used to validate prediction performance in this study. Bayesian method is applied to optimize the hyperparameters in the RF, while three evaluation indicators, including the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), are used to estimate the prediction effect. The test results and comparative experiments confirm that RF is able to predict the interval well and has better prediction performance. This is of great significance for the prediction of the duration of the interval between one accident and the second accident. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

The Regional Connector Transit Corridor (RCTC) project consists of an 89-m long, 17.7-m wide, and 11-m high span crossover cavern constructed beneath critical infrastructure using the sequential excavation method (SEM) at relatively shallow depth (15 m below ground surface) in downtown Los Angeles. The cavern construction involved a complex three-drift, two-sidewall configuration and was excavated in the Fernando formation, a weak clayey siltstone. This paper presents a comprehensive case study of the cavern excavation and initial support construction. The performance of the urban SEM was assessed and complex ground behavior was examined once per excavation step during the construction process. The overall average surface settlement was 20 mm and the largest single-stage incremental movements occurred in the center bench and invert excavation, accounting for 30%–40% of the total deformation. The subsurface experienced less differential settlement during the side drift excavations due to the ovate excavation profile and the restriction from surrounding infrastructure. The measured center crown vertical displacement varied between 5 and 10 mm and the center invert initial support was recorded to heave 8–12 mm. A systematic procedure was developed to apply a deliberate observational method. The lessons learned support that appropriate real-time analysis improves the quality of regular data review, continuously updating the knowledge of risk and enabling prompt adaptions and optimizations of the design and construction along the SEM tunneling process. [ABSTRACT FROM AUTHOR]

This article focuses on the Electrochemical Society meeting held in Los Angeles, California. The first symposium of the Physical and Analytical Electrochemistry Division focused on Durability and Reliability of Low-Temperature Fuel Cells and Fuel Cell Systems. This symposium presented Outstanding Student Paper Awards to students of various universities of the United States. For example, Vishal Mittal of the University of Connecticut got the award for his paper entitled, "Factors Accelerating Membrane Degradation Rate and the Underlying Degradation Mechanism in PEMFC." In this paper, experiments were performed to elucidate the factors and conditions important for photoelectromagnetic degradation.

Runtime verification (RV) refers to methods for formal reasoning about all aspects of the dynamic execution of systems, including hardware, software, and cyber-physical systems. RV includes techniques to assess and enforce correctness of a system against systemic bugs or extrinsic uncertainties. These methods are typically considered lightweight as they may not involve exhaustive verification or proofs, but they provide a higher level of rigor and versatility compared to conventional testing methods. This article introduces the extended versions of selected papers from the peer-reviewed proceedings of the 21st International Conference on Runtime Verification (RV 2021). RV 2021 was supposed to be held in Los Angeles, California, USA in November 2021, but was instead held virtually due to the global Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper presents the characteristics of unmanned aircraft system (UAS) sightings and discusses these characteristics in the context of airport safety using airport operational data. An unmanned aircraft in the vicinity of a commercial airport may be a potential threat to aircraft operations, and may result in operational and economic impacts if it causes an airport delay or shutdown. To ensure a record of UAS activity, the Federal Aviation Administration collects and publishes a UAS Sightings Report. This study analyzed UAS sightings and found that they vary by time of year and time of day, with more sightings in the summer and during the daytime. While all kinds of aircraft may be affected, pilots of general aviation aircraft report more UAS sightings, although overall only 3.3 percent of sightings required the pilot to take evasive action. Only 9.8 percent of UAS sightings occurred at or below 400 feet and 57 percent occurred within five miles of an airport, and sightings are more likely to be reported close to large and medium hub airports. States with a large population (such as California and Florida) and airports in big cities have more UAS sightings. The airports with the most UAS sightings are LaGuardia (LGA), Los Angeles (LAX), John F. Kennedy (JFK), and O'Hare (ORD). [ABSTRACT FROM AUTHOR]

This paper explores the topographical and socio-cultural developments during the Golden Age in New York, Los Angeles and San Francisco, three Beat Generation epicenters, which determined the deconstruction of traditional norms. Modifications at both city and society levels were represented by the emergence of countercultures, such as the Beat. The visibility received by urban problems, due to the increase in social demonstrations and activism, fostered the formation of a unified front that demanded equality and encouraged social and political movements, such as the Civil Rights and the Second Wave Feminism. The socio-political challenges which the American society was confronted with from the 1950s to the 1970s in these three cities, also reveal a few problems regarding the status of the Beats as well as of minorities in metropolises. [ABSTRACT FROM AUTHOR]

This paper examines the geography of local food through a spatial analysis of farms and farmers' markets. It draws on two themes in the geographical literature on local food, which focus on territorial and proximity definitions on one hand and on relationality on the other. Through GIS analysis, this paper explores spatial patterns of ninety-one farmers' markets in Los Angeles County, California, USA; spatial patterns of 282 farms that supplied a sample of thirty-three markets; and intra-urban patterns of those supply chains. The results show an uneven geography of farms across California that supplied the sampled markets, but also show that farms travel just as far to markets in working-class neighborhoods as to wealthier neighborhoods. Conclusions explain how integrating territorial and relational conceptions of local food provide insights into the complex spatiality of production and consumption, and how local food can be understood as an interdependence between places. [ABSTRACT FROM AUTHOR]

A growing constellation of satellites is providing near‐global coverage of column‐averaged CO2 observations. Launched in 2019, NASA's OCO‐3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO‐3 observations and future space‐based column‐observing missions. However, OCO‐3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X‐STILT model to generate upwind influence footprints with less computational expense. The method uses X‐STILT generated influence footprints from a key subset of OCO‐3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO‐3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO‐3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed. Plain Language Summary: Several satellites are providing near‐global observations of Earth's atmospheric carbon dioxide (CO2). One example is NASA's new OCO‐3 instrument which is set to provide spatially dense CO2 measurements over targeted areas. Measurements may contain signals of emissions from cities and power plants. One method of finding the source(s) of observed CO2 is using a Lagrangian particle dispersion model such as X‐STILT. This model takes OCO‐3 measurements and runs atmospheric transport backwards in time to trace out the sources affecting these measurements. However, OCO‐3 and future satellite missions will yield many measurements, significantly increasing the computational cost for X‐STILT and other similar models. This paper presents an algorithm that will reduce the computational effort for X‐STILT by tracing the sources of only a subset of OCO‐3 measurements and then infers (interpolates) the rest. The following two questions are addressed: (1) How many OCO‐3 measurements does X‐STILT need for the interpolations to be accurate? (2) How do meteorology and topography affect the accuracy of the interpolations? Applying the algorithm on simulated OCO‐3 data at two test cities—Los Angeles and Salt Lake City—the time required to elucidate the CO2 sources was reduced by 62% and 78%, respectively. Key Points: Determining sources of spatially dense XCO2 observations with LPDM techniques can become time intensive and strain computational resourcesPresented in this work is an interpolation scheme that eases the computational burden of spatially dense XCO2 source determination studiesEvaluating the efficiency of this interpolation scheme revealed reductions of >50% in computational time at two testing locations [ABSTRACT FROM AUTHOR]

Gang violence remains an ongoing crisis in many communities in the United States. This paper assesses the potential association of gang-occupied neighborhoods with birth outcomes. Adverse birth outcomes serve as a "barometer" of population health, denoting both poor conditions for human development and portending future public health concerns. We draw upon (1) Los Angeles County Vital Statistics Birth Records (2008–2012), (2) GIS information on gang territory boundaries, (3) LA city geo-coded crime data, and (4) the 2010 U.S. Census and 2006–2010 American Community Survey. We find an association between gang-occupied neighborhoods and adverse birth outcomes; however, this association is largely explained by other neighborhood socio-demographic characteristics, crime notwithstanding. We also find that gangland neighborhoods tend to exacerbate the effects of crime for all birth outcomes, but only significantly so for small for gestational age births. Lastly, gang co-residence, crime, and other neighborhood demographics explain a substantial portion of socioeconomic and racial/ethnic disparities in adverse birth outcomes. Gangland neighborhoods appear to be a novel contributor to both population health and health disparities. Future studies should address these relationships in a broad range of metropolitan settings, paying careful attention to causal linkages and moderating effects of gangs and crime. [ABSTRACT FROM AUTHOR]

The article focuses on the career of Cheng-Ming Chuong, a developmental biologist at the University of Southern California in Los Angeles. Chuong has interest on how feathers are generated in adult birds. He decided to try to track down the location of stem cells in feathers. Chuong and his team discovered that in flight feathers, the ring of stem cells tilts towards the side where the quill arises. He plans to continue his characterization of feather stem cells.

In this paper, the level of air quality in ten highly polluted cities in California US state is investigated by looking at persistence and seasonality properties of the time series based on fractional integration framework. Five pollutants, which are carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3) and fine and coarse particulate matters (PM2.5 and PM10), for ten selected cities, including Bakersfield, Los Angeles, and El Centro, are considered. The levels of CO, O3 and PM2.5 are above the exceedance limits for these ten cities, while the results of fractional integration show the possibility of mean reversion for PM2.5 whereas CO levels are still in non-mean reverting range. Levels of pollutants are found to be seasonal in all pollutant cases and across the cities. Since all pollutants pose serious concern to inhabitants of the cities, the California government should intensify more effort in making appropriate policy to reduce pollutant levels in the state. [ABSTRACT FROM AUTHOR]

The Los Angeles Air Force Base Electric Vehicle Demonstration is a currently ongoing vehicle-to-grid demonstration project with the objective of minimizing the cost of operation of a fleet of approximately 30 electric vehicles (EVs) through participation in the California Independent System Operator (CAISO) frequency regulation market. To accomplish this, a hierarchical control system has been developed to optimize, plan, and control the charging, market bidding, and response to grid system operator control of the EVs. This paper presents an overview of the day-ahead optimization model component of the hierarchy. The model is a mixed integer linear program that optimizes daily EV charging and regulation capacity bids strategies in order to minimize operation costs and maximize ancillary service revenue. A deterministic approach is used due to several practical concerns of the demonstration project, including model complexity and the availability and uncertainty of input data in day-ahead decision making, and the limited size of the fleet. The model includes additional user-defined parameters to tune model behavior to better match real-world conditions and minimize the risks of uncertainty. The paper conducts scenario analysis to explore the impact of these parameters on high level model behavior and resulting bid strategy. The parameters explored include hourly regulation prices, local load conditions leading to retail demand charges, forced symmetry constraints for regulation bids, SOC penalty values to reserve higher states-of-charge in vehicles, and expected regulation resource utilization while providing reserves. These analyses show significant sensitivity in the frequency regulation bidding strategy to the regulation utilization, as well as large differences in the regulation prices between regulation up (discharging capacity) and regulation down (charging capacity). Results also suggest enforcing symmetry in regulation appears to have significant impacts in regulation revenue when there is large relative disparities between prices in the up and down direction. Finally, imposing a small cost on low SOC values significantly impacts the fleet-wide average SOC, making the system more resilient to uncertainty in the mobility demands gathered at the time of making day ahead decisions. [ABSTRACT FROM AUTHOR]

The places that people go and interact with others, along with the characteristics of those places, determine degrees of sexual health risk and concomitant prevention opportunities for gay, bisexual, and other men who have sex with men (MSM). The objective of this paper is to use syndemic theory to guide analyses of 20 in-depth interviews with African American and Hispanic/Latinx MSM living in Los Angeles. We describe the places in which African American and Latinx MSM interviewees live and socialize, and how these places influence sexual behavior, drug use, and access to health care. We find common spatial patterns in mobility, incongruence in residential and sexual places, and differing geographic patterns of sex by men who use geo-social hook-up apps. Significant instability in home life and varying forms of mobility and risk-taking were a response to cumulative disadvantage and intersecting structural forces including poverty, racism, and homophobia. Our results strongly suggest that geographic mobility is a syndemic factor for HIV risk among MSM in Los Angeles, as mobility amplified negative impacts of other syndemic factors. Innovative place-interventions to reduce HIV incidence and disparities in HIV need to acknowledge the synergistic factors that drive higher HIV incidence among AA and Latinx MSM. [ABSTRACT FROM AUTHOR]

In many applications involving regression analysis, explanatory variables (or covariates) may be imprecisely measured or may contain missing values. Although there exists a vast literature on measurement error modeling to account for errors-in-variables, and on missing data methodology to handle missingness, very few methods have been developed to simultaneously address both. In this paper, we consider likelihood-based multiple imputation to handle missing data, and combine this with two well-known functional measurement error methods: simulation-extrapolation and corrected score. This unified approach has several appealing characteristics: the model fitting procedure is easy to understand and off-the-shelf software can be incorporated into the modeling framework; no calibration data or a validation subset is required in the model fitting procedure; and the missing data component of the proposed approach is likelihood-based which allows standard likelihood machinery. We demonstrate our methods on simulated datasets and apply them to daily ozone pollution measurements in Los Angeles where observed covariates consist of missing data and imprecise measurements. We conclude that the proposed methods substantially reduce bias and mean squared errors in regression coefficients, in comparison to methods that ignore either measurement error or missingness in covariates. [ABSTRACT FROM AUTHOR]

The impact of airport operations on air quality is a key public health concern for the population surrounding an airport. Air pollution regulations require the assessment of this impact using dispersion models. Modeling dispersion of aircraft-related sources poses challenges because of the large number and variety of airport sources, which include aircraft, ground operation vehicles, and traffic in and out of the airport, most of which are mobile. Emissions from aircraft sources are transient, buoyant, and occur at different heights from the ground. Quantifying these emissions as well as modeling the governing processes is challenging. An added complexity occurs when the airport is situated near a shoreline where meteorological conditions are far from being spatially uniform. These features that characterize the dispersion of airport emissions are being incorporated into the AERMOD model in this paper. This paper examines the impact of shoreline meteorology and urban effects on dispersion by comparing model estimates of SO 2 with corresponding measurements made during a field study conducted at the Los Angeles International Airport (LAX) during winter and summer of 2012 at all the four core sites (AQ, CN, CE, and CS) as a part of the LAX Air Quality Source Apportionment Study (AQSAS). We modified outputs from AERMOD's meteorological preprocessor AERMET to account for 1) the formation of the internal boundary layer that is formed when stable air from the ocean flows onto the warmer land surface of the airport, and 2) urban roughness effects on winds flowing from Los Angeles, east of the airport. Simulations with unmodified AERMET yielded concentrations that were substantially higher than the concentrations at AQ and CS and much lower than those at CN and CE. Model performance improved when AERMOD used the modified meteorology. The fraction of model estimates within a factor of two of the observations improved from 34 to 84% at the CS site and CE site, by up to 79% in winter season whereas in summer, FAC2 values are almost comparable at all the sites. The ratio of robust highest modeled values to measured values improved from 7.72 to 2.53 and 4.92 to 1.94 in winter and summer seasons respectively. [ABSTRACT FROM AUTHOR]

This paper evaluated alternative test methods to determine the crushing potential of aggregates and documented the influence of aggregate characteristics on the engineering properties of asphalt mixes. Six aggregates sampled from different sources were subjected to various aggregate tests, including the Los Angeles abrasion, micro-Deval, soundness magnesium, and acid insoluble tests. A Superpave mix with a 12.5-mm (1/2-in.) nominal maximum aggregate size gradation was produced with each aggregate source using the same PG 64-22 asphalt binder. The mixtures were produced to investigate the impact of aggregate properties on their volumetric properties as well as the tensile strength from the indirect tension tests and rutting potential from the Hamburg wheel tracking tests. Correlation analyses among the measured parameters identified the aggregate crushing value as a promising alternative test method to quantify the crushing potential of aggregates and to adjust the gradation accordingly to produce mixtures with optimized volumetric properties. [ABSTRACT FROM AUTHOR]

• Proposes a non-parametric stochastic subset optimization algorithm for reliability-based importance ranking of bridges. • Proposes iterative approach with adaptive stochastic sampling to improve efficiency of algorithm. • Number of iterations only grows logarithmically with respect to the total number of bridges in the transportation network. • Algorithm is highly efficient and especially useful for large-scale networks with many bridges. Establishing reliability-based importance ranking of bridges in a transportation network usually entails significant computational efforts, especially for large-scale networks. This paper proposes a non-parametric stochastic subset optimization (NP-SSO) algorithm to efficiently identify the reliability-based importance ranking of bridges. It first generates failure samples from an augmented failure distribution and then directly establishes the importance ranking by comparing the number of failure samples for each bridge. To improve the efficiency, an iterative NP-SSO algorithm is established by restricting the search space to the subset of bridges identified in the previous iteration. For each iteration, the modified accept-reject algorithm with adaptive kernel sampling density (AKSD) is adopted to efficiently generate failure samples. The proposed NP-SSO algorithm is highly efficient for identification of important bridges, and the number of iterations only grows logarithmically with respect to the number of bridges, and NP-SSO is especially useful for large-scale networks with many bridges. The effectiveness and efficiency of NP-SSO are demonstrated through identifying the importance ranking of bridges in the transportation network of Los Angeles and Orange counties. [ABSTRACT FROM AUTHOR]

In this paper, we propose a timetable optimization method based on a Multiobjective Cellular genetic algorithm to tackle the multiple vehicle-type problems. The objective is to determine bus assignment in each time period to optimize a quality of service and transport operating cost. The quality of service, represented by the unsatisfied user demand, guarantees a good experience in terms of comfort, safety, availability, improving effects on how passengers perceive wait times. The operational cost contributes to reducing the traffic jams, the flux of unfilled vehicles and fuel consumption, helping to diminish the negative environmental impact. With the operation data of Los Angeles bus route 217 northbound, at peak and off-peak hours, we obtain a set of non-dominated solutions that represent different assignments of vehicles covering a given set of trips in a defined route. The experimental analysis based on several quality indicators, like Hypervolume, Spread, ε -Indicator, and Set Coverage, indicates that our algorithm is a competitive technique comparing with well-known techniques presented in the literature. • An integration of frequency setting and timetabling for multiple vehicle types. • A multiobjective cellular evolutionary algorithm. • Trade-off between the loss of quality of service and operational costs. • A real-world case study based on the route 217 in Los Angles. [ABSTRACT FROM AUTHOR]

A microalgae-based biofacade is a double-skin facade with integrated photobioreactors (PBRs) for solar cultivation of microalgae. One purpose of cultivating microalgae on building facades is to create an 'active' facade that can reduce the energy consumption required to maintain thermal comfort inside the building, by creating exchange loops between the building and the microalgae cultivation system. This paper describes the experimental validation of a thermal model of a building equipped with a biofacade PBR, and its use in analyzing the energy consumption of international-standard buildings in two different climate conditions (oceanic climate using the weather data from Nantes, France, and semi-arid climate using weather data from Los Angeles, USA). The results of this analysis show that the microalgae biofacade almost entirely removes the building's cooling demand, and the warmer the location the stronger the effect. In addition, the heating demand of the biofacade PBR is also greatly reduced (between 92% and 100% depending on the location). The microalgae biofacade is therefore a more sustainable solution to produce valuable biomass and a promising technology for the development of zero-emission buildings; to this end, suggestions for optimizing the process are put forward. [ABSTRACT FROM AUTHOR]

Los Angeles Harbor, in San Pedro Bay, has long drawn scientific researchers, from its days as a 19th century muddy tide flat to today's industrial complex of man-made channels and wharves. A marine biological laboratory was established on Terminal Island as an outpost of the University of California and operating for the summers of 1901 and 1902. As it was a teaching laboratory, it attracted women students and researchers. Two Los Angeles women associated with the laboratory and who made contributions to the advancement of biology were Sarah P. Monks, an instructor at the Los Angeles Normal School and Martha Burton Williamson, a self-taught conchologist. These women were born in the 1840's and grew up at a time when scientific pursuits were not the norm for the proper Victorian women. Both had done research in Los Angeles Harbor before the laboratory on Terminal Island was opened and both continued their independent research in the harbor after the laboratory was relocated to San Diego. Both women had cottages on Terminal Island from where they collected and conducted their research. Monks named her cottage Phataria after a sea star, whose asexual reproduction and autonomy was the subject of her research. Williamson amassed a significant collection of shells, corresponding extensively with malacologists from around the world. Williamson's most significant publication was her 1892 Smithsonian paper on the shells of San Pedro Bay, possibly the first paper published devoted exclusively to the biota of San Pedro Bay and certainly, the first written by a woman. Both faced setbacks in their careers, Monks by not being recognized as author of her anatomy textbook and Williamson for her inability to join the California Academy of Sciences. They both survived residing, at least part-time, within the inhospitable environment of the Terminal Island district of Los Angeles Harbor. They serve as role models for any women who face the prospect of going where few women go in their quest for scientific knowledge. [ABSTRACT FROM AUTHOR]

Legislation in the State of California mandates reductions in emissions of short‐lived climate pollutants of 40% from 2013 levels by 2030 for CH4. Identification of the sector(s) responsible for these emissions and their temporal and spatial variability is a key step in achieving these goals. Here, we determine the emissions of CH4 in Los Angeles from 2011–2017 using a mountaintop remote sensing mapping spectrometer. We show that the pattern of CH4 emissions contains both seasonal and nonseasonal contributions. We find that the seasonal component peaks in the winter and is correlated (R2 = 0.58) with utility natural gas consumption from the residential and commercial sectors and not from the industrial and gas‐fired power plant sectors. The nonseasonal component is (22.9 ± 1.4) Gg CH4/month. If the seasonal correlation is causal, about (1.4 ± 0.1)% of the commercial and residential natural gas consumption in Los Angeles is released into the atmosphere. Plain Language Summary: CH4 is a desirable target for greenhouse gas emission reductions because emission controls will have a rapid impact on radiative forcing. However, its emission budget is highly uncertain and poorly quantified. This paper reports new results from a novel mountaintop remote sensing spectrometer overlooking the Los Angeles basin. The study shows that the megacity's methane emissions are strongly correlated with the consumption of natural gas by residential and commercial consumers, with a leakage rate of (1.4 ± 0.1)%, while the nonseasonal component is (22.9 ± 1.4) Gg CH4/month. By identifying a clear relationship between CH4 emissions and natural gas consumption, our results provide strong constraints on the pathways for fugitive CH4 emissions from the natural gas distribution system in Los Angeles. Key Points: A mountaintop remote sensing spectrometer is used to derive the time series and spatial pattern of methane emissions in LA basinThe methane emissions in the LA basin are strongly correlated with the consumption of natural gas by residential and commercial consumersAbout (1.4 ± 0.1)% of the residential and commercial natural gas consumption in LA is released into the atmosphere [ABSTRACT FROM AUTHOR]

• 3- and 5- story office and hospital buildings designed to current standards. • RC walls modeled with coupled and uncoupled shear and flexural behavior. • Median losses of uncoupled models range from 70% to 130% of the coupled model. • Damage to hospital equipment generates substantial losses to hospital buildings. • Damage to curtain walls of low-rise buildings generates up to 50% of the total loss. This paper investigates implications of using three conceptually different modeling approaches for reinforced concrete (RC) shear walls on seismic performance of several building designs including low-rise and mid-rise office and hospital buildings. Selected archetype buildings are designed to meet current code provisions for a site in downtown Los Angeles and are analyzed considering hazard levels characterized with 50%, 10%, and 2% probabilities of exceedance in 50 years. The study considers different models for structural walls, with either coupled or uncoupled shear and flexural behavior, and either linear or constant curvature distribution along the element height. FEMA P-58 methodology is used to assess damage and associated building repair cost. Performance assessment study illuminates the importance of utilizing high-fidelity models that consider coupled shear and flexural responses when conducting performance assessments for low- and mid-rise RC shear wall buildings; variations in median loss estimates obtained using uncoupled models range from 70% to 130% of the coupled model. In addition, the study reveals that the repair cost for hospitals are approximately 2 to 4 times larger than for office buildings due to significant damage to hospital equipment, warranting the use of RC structural walls for hospital builidngs in the regions of high seismicity. Finally, the study shows that the curtain walls generate significant loss in the case of low-rise RC wall buildings, reaching up to 50% of the total loss and highlighting the need for improved provisions for curtain walls for this type of construction. [ABSTRACT FROM AUTHOR]

Exchanging money, drugs, and other goods for sex has been associated with sexual risk behaviors and increased STIs/HIV. While female sex work is well described, data on men who exchange sex for money or goods are more limited. This paper examined the prevalence and correlates of transactional sex among young men who have sex with men, especially focusing on substance use and HIV status. We conducted a cohort study of 511 participants recruited between August 2014 and December 2017 in Los Angeles, CA. Eligible participants were: (1) between 18 and 45 years of age; (2) male; and (3) if HIV-negative, reported condomless anal intercourse with a male partner in the past 6 months. By design, half were HIV-positive and half HIV-negative. At baseline and semi-annual follow-up visits, computer-assisted self-interviews were used to collect information on demographics, sexual behaviors including transactional sex which was defined as exchange of money, drugs, or a place to stay for anal intercourse. Laboratory testing was conducted for current STI/HIV status. The average age of participants was 31.4 years with 43% identifying as African American, followed by 36% as Hispanic/Latino. The prevalence of recent transactional sex across 1486 study visits was 17% (n = 255), with 74% of those reporting exchanging sex for drugs. The prevalence of transactional sex was higher among those who reported unstable housing (32 vs. 11%; p value < .01), concurrent sexual partnerships (26 vs. 9%; p value < .01), and transgender sex partners (40 vs. 15%; p value < .01). Those who reported receiving money, drugs, or shelter for sex were also more likely to report giving money, drugs, shelter for sex than men who did not report exchange sex (77 vs. 11%; p value < .01). Based on multivariable analyses after adjusting for age and race/ethnicity, HIV viral load was independently associated with transactional sex [adjusted odds ratio (AOR) = 1.4; 95% confidence interval (CI) 1.1-1.7) per log10 increase]. Additionally, those testing positive for an STI were nearly twice as likely to report transactional sex as compared to those without STIs (AOR = 1.9; 95% CI 1.2-3.5). These findings underscore the relatively high prevalence of transactional sex and its potential role in ongoing HIV transmission among this cohort of high-risk HIV-negative and HIV-positive men who have sex with men. [ABSTRACT FROM AUTHOR]