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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]

This paper explores how the Taiwanese-Chinese religious movement Yiguandao ("Way of Pervading Unity") creates a community of non-Chinese followers by utilizing Daoism-related beliefs and practices. Drawing on ethnographic data from fieldwork conducted in California in early 2018, published Yiguandao materials, and internet resources, the paper argues that Yiguandao activists specifically invest their messages in Daoist symbols, beliefs, and practices to reach out to non-Chinese sympathizers of "Asian philosophies" and eventually to establish a global and cross-cultural community of Dao followers. By discussing a case study from Los Angeles, the paper seeks to understand the patterns of Yiguandao activists' engagement with Daoism and how their efforts blend into establishing a global Daoist field. Finally, it thereby invites us to rethink the often haphazardly drawn boundaries—both by practitioners and scholars—between Daoism and other religious phenomena, including New Age, spirituality, and Chinese popular sects. [ABSTRACT FROM AUTHOR]

Mega-events like the Olympics and the Football World Cup routinely harm host cities and societies, largely due to their linkages with ambitious urban development agendas. Concurrently, resident protest has had only limited success in mitigating mega-event-related damages, notwithstanding the growth of resistance networks at local, national, and transnational scales. Contextualised within the broader processes of the Agenda 2020 and New Norm Olympic reforms, this paper explores the tactics of protest against the Summer Olympics in Paris 2024 and Los Angeles 2028. In so doing, the paper demonstrates how the reforms have moderated some of the more egregious aspects of mega-event harm, while nevertheless preserving some fundamental problems with hosting, albeit in more diffuse or disguised forms. The paper makes sense of these processes through the notion of Potemkinism, conceptualised as a dynamic between the superficial and the substantive, and predicated on obfuscation or concealment. The paper also presents a taxonomy of tactics adopted by host city residents to counter the problems that persist in these processes of Potemkin reform. [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]

An instrument of wealth accumulation and racial segregation in housing markets, the intersections between zoning and labor are often overlooked. Extending theories of space, race, and class, and drawing on historical and archival evidence, I elaborate three ways that American land-use zoning emerged to shape labor markets in the early 20th century: (1) zoning constrained households from engaging in subsistence and direct market activity, acting as a regulatory source of labor commodification; (2) zoning first emerged as a xenophobic tool for regulating labor competition; and (3) zoning introduced racialized boundaries distinguishing formal work from a sphere of economic informality. I illustrate these theoretical-historical propositions with a study of the frontier origins of American land-use zoning laws in late-19th -century Los Angeles. This first and influential approach to residential zoning emerged amid racial dynamics of western settlement and the class politics of a national "free" labor market. I demonstrate how efforts to regulate the settlement and labor practices of poor urban households and Chinese immigrants on in Los Angeles became the direct legal basis for the dichotomous categories of "residential" and "industrial" districts that the city first defended before the U.S. Supreme Court in 1915. By theorizing the intersections of land-use zoning and labor markets, this paper provides a theoretical framework and historical genealogy for future work to consider entanglements of housing and labor politics amid the concurrent flexibilization of work and land use in the present. [ABSTRACT FROM AUTHOR]

Water reuse with Advanced Water Treatment (AWT) is increasingly appealing for urban areas seeking water supply reliability. In cities facing water scarcity, how can large-scale reuse support reliability and how do reuse operations affect water quality, energy use, and water conservation? This paper presents a systems analysis of metropolitan-scale water reuse and its effects on water supply and quality. For the case study of Los Angeles County, California, USA, hydroeconomic modeling is used to evaluate reuse as a contributor to water supply given urban water conservation, drought-induced water scarcity, and costs and benefits for supply and demand. Results indicate that AWT can be a viable source of supply especially when coupled with conservation. Across modeled scenarios, reuse provides as much as 30% of regional supplies. New water reuse with AWT becomes viable when imported water availability is 50% or less of historic values. Existing indirect potable reuse operations in the county remain important. Systemwide energy intensity of operations increases with greater reuse in the absence of water conservation. Modeled influent flow rates to wastewater treatment plants resemble historical values, but extreme flow events could pose risks. The paper offers a holistic framework to evaluate water reuse as a component of urban water management. [ABSTRACT FROM AUTHOR]

Traffic congestion occurs when there is a mismatch between the demand for road use and the available capacity. The volume-delay function (VDF) can quantify the relationship between travel time and the volume of traffic on a particular link, and also provide insight into the state of a traffic system, such as whether it is congested or uncongested. In this paper, we present a VDF model that is based on the fundamental diagram and has two main components: (1) an improved VDF with fewer parameters that can handle both congested and uncongested traffic conditions, based on a fundamental diagram, and (2) a model-based VDF practical calibration framework for practical traffic applications that can determine key parameters for a link in a corridor. Our experiments using corridors in Los Angeles and Beijing demonstrate that our proposed analytical methods effectively calculate road impedance under congested conditions. The results indicate that the proposed model is superior to other existing models in terms of the root mean squared error (RMSE) and mean absolute error (MAE). In addition, our calibrated results indicate that the travel time index (TTI) in Los Angeles is 2.12, in Beijing is 1.74. The model proposed in this paper provides a useful calibration tool for enhancing model performance and improving the accuracy of travel time and speed estimates in traffic assignment. [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]

Objectives: The objective of this paper is to improve understanding of the spatial relationship of the built environment (BE) with urban gun crime and how the influence of environmental features varies across geographic location and socioeconomic context. Specifically, we analyze associations between incidents of reported firearms violence and distance to built environment features, accounting for neighborhood socio-economic status. Methods: This study compares reported crime data with the outputs from a Monte Carlo Simulation using the Network Cross-K Function for Stochastic Spatial Events on street networks. Where data is available, we examine ten features—transit stations, universities/colleges, convenience stores, gas stations, liquor licenses, alcohol outlets, tobacco retailers, lodging, restaurants, and schools—across diverse metropolitan areas to uncover features that exert "attractive" or "repellent" influence on firearms violence. We present results from four U.S. cities and examine how results vary by socio-economic status of census tracts. Results: Attractive features include tobacco retailers in Pittsburgh, hotels/motels, alcohol outlets, and restaurants in New Orleans, and rail transit stations in Los Angeles. We uncover localized attractive and repellent relationships within the lowest and highest socio-economic areas. We also identify several firearms crime repellents, including universities/colleges and public/private schools. Conclusions: We find that attractors vary by city and socio-economic status, indicating that the unique underlying environmental context of each city facilitates firearms crime differently. [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]

As the world becomes increasingly "smaller," global governance too shifts beyond states and formal agreements to include norms and actors at all levels of government and society. Cities, in particular, are emergent actors in global affairs. This paper examines a new type of experiential learning model for this burgeoning local–global governance: academic partnerships with city governments for undergraduate international relations courses. Occidental College and Pomona College have partnered with the Los Angeles Mayor's Office to localize and adopt the United Nations Sustainable Development Goals (SDGs). This study focuses on two Spring 2021 Task Force courses, where students worked in consultation with the Mayor's Office to conduct policy research on local implementation of SDG 5 on gender equality. It analyzes the pedagogical value of the Task Force courses with a focus on student learning outcomes and challenges. Through qualitative content analysis of student reflection papers, we find that the courses generate substantial student learning, particularly in higher forms of learning, including development of research and professional skills and metacognitive reflection on "doing theory," or praxis, within the international relations discipline. Further, these partnerships also benefit the city, as they generate valuable data that inform policymaking. [ABSTRACT FROM AUTHOR]

With the transportation sector being the largest contributor of greenhouse gas emissions in the United States, states are actively planning the deployment of plug-in electric vehicles (PEVs). Mass adoption of PEVs requires attracting potential buyers living in multi-unit dwellings (MUDs). Given the current low adoption rate of PEVs in MUDs and MUD residents' reliance on public charging, this paper studies whether there is a positive correlation between the number of public Level 2 (L2) chargers and MUD density (measured by total square footage of MUD per capita) across census block groups (CBGs) in LA County. The results show that high MUD-density CBGs and low MUD-density CBGs do not differ much in terms of the number of chargers. The charger-to-PEV-ratio range for MUD residents derived in this paper is below the ideal charger-to-PEV-ratio range in the literature. A direct policy implication is that more charging infrastructure should be made available to MUD residents. This includes public L2 chargers near MUDs, which are discussed in this paper, and onsite MUD charging, which is still at an early stage. • Correlation between EV charger quantity and MUD density in LA County is estimated. • Findings suggest insufficient public Level 2 chargers for the use of MUD EV owners. • MUD charger-to-EV-ratios are computed across census block groups (CBGs). • An MUD charger sufficiency map for LA County is created at CBG level. • This paper concludes that more chargers should be made available to MUD residents. [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]

By reducing fossil fuel use, renewable energy improves the economy, quality of life, and environment. These impacts make renewable energy forecasting crucial for lowering fossil fuel utilization. This paper aims to mathematically improve time series forecasting literature by focusing on solar irradiance applications in Los Angeles, Denver, and Hawaii solar irradiance sites. A three-phased time series forecasting hybrid method is devised for this endeavor. The ARFIMA is used to forecast the original solar irradiance time series in phase I. Next, the dataset's residuals, are retrieved by subtracting the phase I results from the observed time series to prepare the scenario for the following phase. A novel enhanced fractional Brownian motion is used for residual forecasting in phase II. The parameter estimation in phase II is implemented adaptively to capture the dynamic statistical characteristics of the time series efficiently. Finally, the phases I and II results are numerically conglomerated to form the final forecasting results in phase III. The residual forecasting part, in phase II, reveals a substantial superiority. Also, when comparing the proposed hybrid algorithm results to other existing cutting-edge algorithms applied to the same solar irradiance applications, the output demonstrates that the suggested algorithm has a significantly improved performance. [ABSTRACT FROM AUTHOR]

Recycled concrete aggregates (RCAs) have a high potential to be used as replacements for natural aggregates in the construction of pavements with similar or even better characteristics. This paper evaluates the influence of RCA as a partial replacement in a granular sub-base of 37.5 mm nominal maximum size (GSB38). Specifically, two replacement percentages of RCA by weight of the coarse aggregate of the GSB38 were evaluated: 10 and 15. To estimate the influence on the mechanical behavior, abrasion tests were carried out in the Los Angeles (LA) abrasion and California bearing ratio (CBR) methods. For GSB38, the average abrasion results were 30.54%. The CBR for the same material at a penetration of 0.254 cm was 26.90, 61.00 and 76.20% and at a penetration of 0.508 cm was 33.00, 65.70 and 82.00% for 10, 25 and 56 blows per layer, respectively. For GSB38 with 10% RCA, similar and even better behavior than natural GSB38 was found in terms of LA abrasion, with an average result of 38.68%. The CBR for the same alternative at a penetration of 0.254 cm was 13.20, 52.80 and 57.70% and at a penetration of 0.508 cm was 18.00, 61.30 and 68.90% for 10, 25 and 56 blows per layer, respectively. But GSB38 with 15% RCA showed an average abrasion of 45.51%, indicating a decrease compared with natural GSB38 and GSB38 with 10% RCA. The CBR for the same alternative also shows a decreasing trend. [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]

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]

Dynamic charging has the potential to overcome the barriers to electric vehicle (EV) adoption by converting road segments into powered lanes (electric roadways or ERs). This paper focuses on examining public acceptance by determining the factors that affect the short-term and long-term intention to purchase an EV and drive on ERs; identifying the clusters of the technology acceptance; and assessing the value of travel time savings (VTTS) for driving on ERs, in general, and for different clusters. A survey was conducted in Los Angeles, California and distributed online, gathering 600 responses. The results showed that respondents' innovativeness, lifestyle and environmental consciousness significantly affect the intention to drive on ERs in the short- and long-run. Public acceptance, in general, seems to be related to charging patterns, safety of commute route, and safety concerns for ERs, among other factors, and depends on the implementation time of the technology. Higher VTTS were found for electrified lanes exclusive for EVs on interstate and arterial roads. People who were classified as positive toward ERs had higher VTTS for ERs followed by those who were neutral and those who were concerned about ERs. This paper can help policymakers and transport operators devise strategies to accelerate EV adoption by appropriately implementing the ER technology. [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]

Neoliberal tenets have colored the past half-century of education reform, marked by top-down accountability, market competition, and regimented learning aims. Federal drift and post-pandemic recovery, along with economic uncertainty, will blur policy priorities going forward. Less noticed, local networks of pro-equity activists have come to challenge urban bureaucracies, while advancing fairness, diverse forms of schooling, respectful and rigorous social relations inside schools. This paper examines the case of Los Angeles, where a coalition of Black and Latina leaders, civil rights attorneys, social-justice nonprofits, and pedagogical reformers succeeded to progressively fund schools, extend college-prep courses, and decriminalize discipline. Student achievement, in turn, climbed steadily upward for nearly two decades, 2002 to 2019. This pluralist network of advocates carved-out a third civic terrain, challenging corporate elites and traditional labor leaders. Rooted in humanist ideals, these colorful activists countered the individualistic and competitive values of neoliberal advocates, while motivating lasting institutional change. [ABSTRACT FROM AUTHOR]

The national demand for social justice, racial equality, and criminal justice reform rightfully and painfully continues to sweep the consciousness of this nation. Community colleges were founded on educational excellence, opportunity, and access and should reflect principles of equity, justice, and community. This mission calls upon us, the most egalitarian institutions, to lead by example by making structural and permanent changes to root out the ill effects of racism and bias from our classrooms, curriculum, and workplace. In this paper, we examined equity‐minded leadership, culture, and climate at the Los Angeles Community College District, the nation's largest, and their efforts to transform their institutions, guided by their Framework for Racial Equity and Social Justice. The Framework speaks to the values of the District and directs specific action steps and commitments that will allow the colleges and District to actively build anti‐racist organizational capacity and resilience and move forward towards a more socially and racially‐just academic community. [ABSTRACT FROM AUTHOR]

Urban mobility has been a priority for many cities worldwide due to the high population volume that moves around to meet personal and professional needs. However, transportation modes show inefficiencies that cannot be faced by adopting an approach to maximizing performance. In other words, it is necessary to recognize and measure the disutilities (drawbacks) of the transportation system to create effective public policies to deal with the problem. Therefore, this paper aims to develop a methodology to measure the Total Disutilities of modes of transportation. Mathematical modeling is developed using transport literature, and an application is conducted using data from the 10 most populated American cities. The results show that Dallas, Houston, Chicago, Los Angeles, New York, San Antonio, and Philadelphia presented the lowest Total Disutilities costs (USD) per kilometer. Moreover, the higher disutilities costs occurred in Automobiles in New York and in Bus, Motorcycle, Bicycles or Scooters, and Walking in San Antonio. On the other hand, the lower disutilities costs were established in Automobile in Dallas and in Bus or Train, Motorcycle, Bicycle or Scooter, and Walking in Los Angeles. [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]

Accurate state of charge (SOC) estimation is considered as the main barrier to adopt Lithium-ion battery-based electric vehicles as alternatives to conventional internal combustion engine vehicles. There are several SOC estimation techniques recommended by many researchers still; they are all not accurate. This paper presents a robust and accurate SOC estimation battery model developed using an independently recurrent neural network (IndRNN). The proposed SOC estimation battery model is developed by training IndRNN constructively under various experimental datasets collected from Lithium Nickel Cobalt Aluminium Oxide battery cell at different ambient temperatures. Without any prior knowledge about battery internals, the proposed battery model successfully characterizes the non-linear behaviour of the battery effectively. Furthermore, its performance is proved by comparing with similar RNN architecture such as gated recurrent unit (GRU) and long short-term memory (LSTM). The attained results demonstrate that an IndRNN outperformed both GRU and LSTM in terms of accuracy under different electric vehicle drive cycle with minimal root mean square error of 0.7633% and mean absolute error of 0.6389% for diverse temperature conditions. Abbreviations: ANN: artificial neural network; BMS: battery management system; BPNN: back-propagation neural network; DFS: deep feature selection; EV: electric vehicle; FL: fuzzy logic; FNN: feedforward neural network; GA: genetic algorithm; GRU: gated recurrent unit; HWFET: highway fuel economy test; ICE: internal combustion engine; IndRNN: independently recurrent neural network; LA92: Los Angeles 92; LiB: lithium-ion battery; LSTM: long short-term memory; MAE: mean absolute error; MAX: maximum error; RNN: recurrent neural network; RMSE: root mean square error; ReLU: rectified linear unit; SOC: state of charge; SOH: state of health; SVM: support vector machine; US06: supplemental federal test procedure; UDDS: urban dynamometer driving schedule. [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]

While infill development is widely accepted by cities as an alternative to urban sprawl, a very dearth of research has attempted to measure infill development and identify contributing factors to infill development. Filling this research gap, this paper models residential infill development in the City of Los Angeles by employing five machine learning (ML) algorithms. This paper attempts to identify the best-performing ML algorithms by comparing the performance of the ML algorithms. Of the five ML algorithms tested, the random forest (RF) and k-nearest neighbor (kNN) algorithms are selected as the best-performing algorithms. The RF algorithm ranks independent variables from most to least important. Overall, the ranks suggested that residential infill development in the City of Los Angeles is significantly influenced by the physical conditions of property and neighborhood rather than socioeconomic characteristics. Diverse land uses, good housing mixes, and rail transit accessibility also, importantly, contributed to the infill development. This finding suggests that the city's planning efforts, such as the promotion of accessory dwelling unit (ADU) development and the expansion of rail transit, can create a virtuous circle for sustainable urban development. [ABSTRACT FROM AUTHOR]

This paper presents an implementation of a Weighted Ordered Weighted Averaging (WOWA) algorithm within a GIS-based multicriteria decision analysis (MCDA). Conventional OWA procedures do not account for varied relative importances of the decision evaluation criteria. This research focuses on the procedure for aggregating criteria-weights into OWA operators by implementing a special Weighted OWA (WOWA) operator that unifies conventional OWA with Weighted Linear Combination (WLC) techniques within a spatial decision context. The paper then demonstrates the application of WOWA within a vector-based spatial problem for identifying Disadvantaged Communities within San Fernando Valley, Los Angeles. [ABSTRACT FROM AUTHOR]

With the continuous enrichment of traffic Internet-of-Things data acquisition methods, more and more spatiotemporal data on road networks is collected in real time by various sensors and multimedia devices. The data-driven deep learning approach can make full use of real-time data from a road network to predict future traffic status. By mining the spatiotemporal relationships between road units, the ability to predict network evolutionary behaviors is improved, which provides a new method of traffic management. There are strong semantic relations between road intersections or road sections in terms of traffic evolution. Modeling the network only from a shallow spatial topological perspective ignores the important intrinsic association of the dynamic network. In this paper, we propose a semantic associative neural network (SANN) for traffic evolution analysis by modeling the propagation effects and similarity patterns between road units. Considering the inadequacy of the fixed adjacent matrix, graph convolution is used to encode the semantic features of a road network and embed them in a bidirectional recurrent neural network for sequence prediction. Finally, the experiments are conducted based on speed data sets to prove the effectiveness of the proposed method. The model achieved a well-predicted accuracy of 95.33% and 84.08% on Pems-Bay and Los Angeles data sets. [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]

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]

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]

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 study exploring how migrants' belonging shapes their remittances among Bangladeshi migrants in Los Angeles. Based on migrants' perception, the paper recognizes the centrality of the family and origin community in migrants' remittances both in the NELM and in the transnational perspectives. It empirically investigates migrants' belonging and recognizes their membership in their parents' family, their own nuclear family, joint family including siblings with their respective families, extended family including multiple generations and a community of origin. It finds migrants' belonging simultaneously to the destination and origin countries, and also confirms the presence of 'friction' in such belonging, indicative of both cooperation and conflict in migrants' remittances allowing for explaining changes in their remittances such as remittances decay and resurgence. This study offers guideline for further empirical research on migrants' transnationalism and remittances with policy implications regarding the developmental consequences of remittances. [ABSTRACT FROM AUTHOR]

• A new MDCEV model with ordered preferences (MDCEV-OP) to analyze multiple discrete-continuous choices at a disaggregate level. • Application for disaggregate episode-level analysis of individuals' activity participation and time allocation. • Proposed framework with IID Gumbel error terms yields closed-from likelihood expression. • Proposed model provides better fit and predictive accuracy than the traditional MDCEV model applied at the episode level. This paper formulates a novel, multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP) to analyze multiple discrete-continuous (MDC) choices at a disaggregate level, including the number of instances different alternatives are chosen and the amount of consumption at each instance of choice. In doing so, the proposed model ensures a logically consistent prediction of multiple choice instances of an alternative. Specifically, the model prevents the prediction of positive allocation to the jth instance (i.e., a frequency of j episodes) of a good while predicting zero allocation to the (j-1)th or lower-numbered instances of that alternative. This is achieved by imposing a non-increasing order on the baseline-preference parameters of different choice instances of an alternative. The model results in a conditional likelihood function, where the likelihood arising from the optimality conditions of the utility maximization problem is conditioned on the ordering of baseline marginal utilities. Combining this strategy with independent and identically distributed (IID) Gumbel stochastic terms in the utility functions results in a closed-form likelihood expression that is not much more difficult than that of the MDCEV model. The proposed framework is applied for an empirical analysis of disaggregate, episode-level activity participation and time allocation behavior of non-working adults in Los Angeles, California. The empirical MDCEV-OP model provided better fit and predictive accuracy (in both estimation and validation datasets) for analyzing episode-level activity participation than a disaggregate MDCEV model that does not recognize the logical ordering of episodes. At the same time, the activity-level predictions aggregated from the episode-level predictions of the MDCEV-OP model do not deviate significantly from the predictions of an aggregate MDCEV model. These results highlight the efficacy of the proposed model for analyzing episode-level activity generation and time allocation. [ABSTRACT FROM AUTHOR]

The article focuses on Paper trash from Los Angeles (LA) City Hall and two LA office buildings that may soon be on its way to China. The paper is being analyzed for its export value, and, sometime this summer, China Paper Partners of San Francisco, an affiliate of the Conservatree Paper Co., plans to begin shipping the paper trash to the People's Republic of China, where it will be sorted and recycled. Instead of going into already overburdened landfills on this side of the Pacific, as much as 1,000 tons of paper trash per day may make the voyage to China, where paper material is in great demand.

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.

The 1972 paper by Professor Jack McKee of the California Institute of Technology, consulting engineer Finley Laverty and Raymond Hertzel of the California Regional Water Quality Control Board‐Los Angeles (McKee et al., 1972) was an early, important contribution to the understanding of groundwater contamination with organic liquids and to the integrated use of field, laboratory, and modeling studies to develop remediation approaches. The work presented in the paper was ahead of the curve and helped provide the foundation for the intense research, development, and education in investigation and remediation of groundwater contamination problems that began around 1980 and lasted for two decades. Reading the paper in the current context of more than 40 years of research and field experience provides perspective on how little was known in 1972 about groundwater contamination and how to address it, and how well‐formulated and forward‐looking was the work of McKee et al. Practitioner points: McKee et al. (1972) was an early, important contribution to the understanding of groundwater contamination with organic liquids and to the integrated use of field, laboratory and modeling studies to develop remediation approaches.McKee et al. present the results of site investigation and remediation planning studies for a large‐scale groundwater contamination site in the Los Angeles‐Glendale, California area.Four specific remediation goals were established, and a comprehensive program of site investigation, research, and modeling was developed to support the achievement of the remediation goals.The comprehensive site investigation and remedial planning involving field, laboratory, and modeling studies described by McKee et al. was a forerunner of what came into standard contaminated site remediation practice in the 1980s and 1990s. [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]

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]

Summary: This paper investigates a Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network (FRCG‐MESN) to identify battery state of charge (SOC). First, an echo state network with multiple reservoirs is established to estimate battery SOC by using its discharge terminal voltage and current as the inputs, and the FR conjugate gradient algorithm is explored to tune the output weights of the MESN to avoid calculating the inverse matrix. Second, an appropriate amount of Gaussian noise with zero mean is added to the training set to prevent overfitting. Finally, a battery test platform is adopted to sample the discharging data under two working conditions: the Los Angeles 92 and the urban dynamometer driving schedule. The simulation results show that the presented FRCG‐MESN method can accurately identify the battery SOC with different initial SOCs. Applying an appropriate amount of noise to the training set can prevent overfitting efficiently. [ABSTRACT FROM AUTHOR]

Cities worldwide have embraced the idea of integrated stormwater management using more decentralised and green infrastructures to enhance urban sustainability. Los Angeles County has recently introduced a new stormwater tax that envisions achieving sustainable stormwater futures through far-reaching infrastructural change. While embracing such visions, however, different actors seek to use tax revenues for diverging infrastructure designs. Our paper therefore explores shifting stormwater politics in Los Angeles by highlighting the social relations underlying technology. Technical disputes that we frame as combinations of material artefacts, discourses, expertise and institutions provoke the specific "material politics" behind the emergence of a hybrid stormwater system in which centralised stormwater practices of incumbent public utilities predominate but increasingly co-exist with more decentralised landscape-centred practices and become interdependent on them. We argue that technical disputes reflect ambiguities about a future stormwater system and engender the renegotiation of responsibility, knowledge orders and the overall rationale of stormwater management. An inherited focus on controlling stormwater volumes thwarts attempts to couple stormwater and urban greening improvements more tightly. We conclude by emphasising that infrastructures are relational systems that carry many potential stormwater futures and by outlining ways to better align stormwater management with wider urban sustainability objectives. [ABSTRACT FROM AUTHOR]

Census blocks are administrative units that serve as statistical areas for the decennial Census in the United States. Visible and nonvisible features bound blocks, including roads, railroads, streams, property lines, and city boundaries. The Census Bureau builds blocks using the Master Address File (MAF), which includes field-verified geographic information about the location of housing unit addresses. Unfortunately, there are substantial errors in the counts of housing units at the block level, even with the purported quality checks by the Census Bureau. This paper aims to detail a method of identifying problematic blocks (i.e., ghost blocks) that report the presence of housing units, but no such units exist. Further, we identify the implications of using ghost blocks in location models using the maximal covering location problem (MCLP) in a case study for sensor locations in Los Angeles, California. We discuss policy implications and strategies to address these errors for developing higher-fidelity location models. • Details a method of identifying problematic U.S. Census blocks that report the presence of housing units, but no such units exist. • Identifies the implications of using ghost blocks in location models. • Discusses policy implications of using ghost blocks. • Provides strategies to address data uncertainty and to develop higher-fidelity location models. [ABSTRACT FROM AUTHOR]