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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 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]

The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas (GHG) emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing and Charging (JRC) scheduling for electric trucks. The objective of the associated optimization problem is to minimize the cost of transportation, charging, and tardiness. A large number of possible combinations of road segments as well as a large number of combinations of charging decisions and charging durations leads to a combinatorial explosion in the possible decisions electric trucks can make. The resulting mixed-integer linear programming (MILP) problem is thus extremely challenging because of the combinatorial complexity even in the deterministic case. Therefore, a Surrogate Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the overall problem into significantly less complex truck subproblems. In the coordination aspect, each truck subproblem is solved independently of other subproblems based on the values of Lagrangian multipliers. In addition to serving as a means of guiding and coordinating trucks, multipliers can also serve as a basis for transparent and explanatory decision-making by trucks. Testing results demonstrate that even small instances cannot be solved using the off-the-shelf solver CPLEX after several days of solving. The SLBLR method, on the other hand, can obtain near-optimal solutions within a few minutes for small cases, and within 30 min for large ones. Furthermore, it has been demonstrated that as battery capacity increases, the total cost decreases significantly; moreover, as the charging power increases, the number of trucks required decreases as well. • Present a formulation of a joint heavy-duty vehicle fleet routing & charging problem. • Use surrogate level-based Lagrangian relaxation approach to solve the problem. • The proposed method obtains near-optimal solutions for realistic testing cases. [ABSTRACT FROM AUTHOR]

Documentary films and the law make a well-suited pair, due to four key similarities. Firstly, the accessibility of criminal law and its reliance on familiar concepts of good and evil. Secondly, the narrative structure: the arc of accusation, evidence and judgement within a criminal trial mirrors the arc of set up, confrontation and resolution found in cinema. Thirdly, the use of evidence, particularly the use of 'evidence verité' and the assumption of truth associated with photographs. Finally, the law and documentaries have a similar understanding of the concept of truth, as a product of persuasion and argument. As true-crime documentaries garner more attention , the stylistic choices of filmmakers carry more weight on the opinions of their audiences and subsequently the criminal justice system itself. Discussion in this paper focuses on the stylistic choices in documentaries such as Making a Murderer (Demos, Moira, and Laura Ricciardi. 2015. Making a Murderer. Los Angeles, CA: Netflix), The Staircase (de Lestrade, 2014), Southwest of Salem: the story of the San Antonio four (Esquenazi, Deborah. 2016. Southwest of Salem: The Story of the San Antonio Four. New York: Investigation Discovery), and The Night in Question (Theroux, Louis. 2019. The Night in Question. BBC), and their use of emotion, dialogue and footage, and the necessary transparency concerning these choices as well as production choices, to encourage audiences to understand true-crime documentaries as a subjective performance. [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]

• Examine the concentration in gun violence shootings before and during the pandemic in three cities. • Examine how the increase in shooting hot spots impacted racial disparities in victimization rates. • Examine how the surge in gun violence during the pandemic increased disparities by place and race. • Between 36% and 55% of the increase in shootings occurred in the top decile of census block groups. • The spatial concentration of gun violence victimization by race-ethnicity was compounded during the pandemic. Gun violence rates increased in U.S. cities in 2020 and into 2021. Gun violence rates in U.S. cities is typically concentrated in racially segregated neighborhoods with higher poverty levels. However, poverty levels and demographics alone do not explain the high concentration of violence or its relative change over time. In this paper, we examine the extent to which the increase in shooting victimization in Philadelphia, New York, and Los Angeles during the 2020–2021 pandemic was concentrated in gun violence hot spots, and how the increase impacted race and ethnic disparities in shooting victimization rates. We find that 36% (Philadelphia), 47% (New York), and 55% (Los Angeles) of the increase in shootings observed during the period 2020–2021 occurred in the top decile of census block groups, by aggregate number of shootings, and that the race/ethnicity of victims in these gun violence hot spots were disproportionately Black and Hispanic. We discuss the implications of these findings as they relate to racial disparities in victimization and place-based efforts to reduce gun violence. [ABSTRACT FROM AUTHOR]

This paper develops an analytical framework to estimate commute accessibility and adoption of various ridehailing service concepts across the US by synthesizing individual commute trips using national Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data. Focusing on potential improvements in cost and time that could be enabled by vehicle automation, we use this modeling framework to simulate a lower-price autonomous service (e.g., 50% or 75% lower) with variable wait times and implementation levels (solo, pooled, and first/last mile transit connections services, alone or in combination) to determine how they might affect adoption rates. These results are compared across metrics of accessibility and trip density, as well as socioeconomic factors such as household income. We find – unsurprisingly – that major cities (e.g. New York, Los Angeles, and Chicago) support the highest adoption rates for ridehailing services. Decreases in price tend to increase market share and accessibility. The effect of a decrease in price is more drastic for lower income groups. The proposed method for synthesizing trips using the LODES contributes to current travel demand forecasting methods and the proposed analytic framework can be flexibly implemented with any other mode choice model, extended to non-commute trips, or applied to different levels of geographic aggregation. [ABSTRACT FROM AUTHOR]