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"All the President's Men" is a film analyzed by Christian Keathley and Robert B. Ray in their book. The authors argue that the film focuses on the experience of disorientation and uncertainty rather than the outcome of the investigation. The film, which follows the reporters Bob Woodward and Carl Bernstein as they investigate the Watergate scandal, recreates the feeling of being lost in a maze of clues and information. The book provides a close reading of the film's narrative structure, mise-en-scène, and cinematography, and argues for its status as an American classic. The authors also discuss the blend of art movie, classical studio-era, and New Hollywood elements in the film, as well as the deliberate choice to obscure and omit relevant story information. The book offers valuable insights for students of close reading methods and scholars of Hollywood history and style. [Extracted from the article]

"Black Women and the Changing Television Landscape" by Lisa M. Anderson is a book that examines the portrayal of Black women on television throughout history. Anderson, an associate professor of women and gender studies, builds on her previous work to explore the complex and evolving representations of Black women in media. Using a semiotic approach and drawing on the work of Black feminist scholars, Anderson analyzes specific television shows and personalities from the 1950s to the present. The book goes beyond simplistic judgments and aims to understand the historical and cultural contexts in which these representations exist, as well as the agency of Black women in shaping them. It is a valuable resource for scholars in cultural, media, and television studies. [Extracted from the article]

The paper addresses the problem of imaginative resistance in science, that is, why and under what circumstances imagination sometimes resists certain scenarios. In the first part, the paper presents and discusses two accounts concerning the problem and relevant for the main thesis of this study. The first position is that of Gendler (Journal of Philosophy 97:55–81, 2000), (Gendler, in: Nichols (ed) The Architecture of the Imagination: New essays on pretence, possibility and fiction, Oxford University Press, New York, 2006a), (Gendler & Liao, in: Gibson, Carroll (eds) The routledge companion to philosophy of literature, Routledge, New York, 2016), according to which imaginative resistance mainly concerns evaluative scenarios, presenting deviant moral attitudes. The second account examined is that of Kim et al. (in: Cova, Réhault (eds) Advances in experimental philosophy of aesthetics, Bloomsbury, London, 2018), who insisted on the link between imaginative resistance on the one hand and counterfactual and counterdescriptive scenarios on the other. In the light of both theories, this paper discusses the importance of addressing the problem of imaginative resistance in the scientific enterprise in the light of some mechanisms of embodied simulation, based on the activity of mirror neurons and investigated within the framework of the Embodied Simulation Theory. [ABSTRACT FROM AUTHOR]

The next Point-of-Interest (POI) recommendation, in recent years, has attracted an extensive amount of attention from the academic community. RNN-based methods cannot establish effective long-term dependencies among the input sequences when capturing the user's motion patterns, resulting in inadequate exploitation of user preferences. Besides, the majority of prior studies often neglect high-order neighborhood information in users' check-in trajectory and their social relationships, yielding suboptimal recommendation efficacy. To address these issues, this paper proposes a novel Double-Layer Attention Network model, named DLAN. Firstly, DLAN incorporates a multi-head attention module that can combine first-order and high-order neighborhood information in user check-in trajectories, thereby effectively and parallelly capturing both long- and short-term preferences of users and overcoming the problem that RNN-based methods cannot establish long-term dependencies between sequences. Secondly, this paper designs a user similarity weighting layer to measure the influence of other users on the target users leverage the social relationships among them. Finally, comprehensive experiments are conducted on user check-in data from two cities, New York (NYC) and Tokyo (TKY), and the results demonstrate that DLAN achieves a performance in Accuracy and Mean Reverse Rank enhancement by 8.07% -36.67% compared to the state-of-the-art method. Moreover, to investigate the effect of dimensionality and the number of heads of the multi-head attention mechanism on the performance of the DLAN model, we have done sufficient sensitivity experiments. [ABSTRACT FROM AUTHOR]

In this paper we introduce the Intensive Mobile Treatment (IMT) model, which arose from a 2016 New York City initiative to engage individuals who were "falling through the cracks" of the mental health, housing, and criminal justice systems. People who are referred to IMT often have extensive histories of trauma. They experience structural racism and discrimination within systems and thus can present as distrustful of treatment teams. We detail the structure of the program as we practice it at our non-profit agency and outline the psychodynamic concepts that inform our work with challenging populations. We acknowledge IMT's role in engaging in advocacy and addressing social justice in our work. We also discuss how through this model we are able to both mitigate and tolerate risk in participants with difficult-to-manage behaviors. This is typically a long-term, non-linear process. We address how this impacts the team dynamic as a whole and explain how with long-term, trusting therapeutic relationships, participants can change and grow over time. We also explain the ways in which our non-billing model plays an integral role in the treatment we are able to provide and identify several challenges and areas for program growth. In outlining our model and its methodology, we hope to empower other practitioners to adapt IMT to other settings beyond the New York City area. [ABSTRACT FROM AUTHOR]

Background: More than 20 million children in the United States lack access to primary health care. Practice Learning: Research shows that students with regular access to physical and mental health services have fewer absences, are more social, less likely to participate in risky behaviors, have improved focus and higher test scores. Implication For School Health Policy, Practice, And Equity: School‐based health centers (SBHCs) can be an important, valuable and viable health care delivery option to meet the full‐range of primary health care needs of students where they spend the majority of their wake hours, ie, in school. Children in rural and other underserved communities, as well as those underinsured, non‐insured, economically challenged, underserved, and the most vulnerable among us are especially at risk. Conclusions: This paper discusses the history, value, and importance of SBHCs from myriad perspectives, including physical and emotional wellbeing, academic and social success, and the promotion of a positive transition to adulthood. In addition, the authors' experiences that resulted in building the first SBHC in the Mid‐Hudson Valley Region of New York State are shared. These experiences form the foundation for creating an important roadmap for individuals and school leaders that are interested in bringing a SBHC to their school and district. [ABSTRACT FROM AUTHOR]

This paper suggests looking at veganism as a set of knowledge that produces and reproduces habitual food practices and changes in semiotic habits and the meanings attached to foods. A semiotic analysis of veganism as a system of knowledge suggests its being a translated text that recognizes scientific and medical knowledge and the non-vegan alimentary system. However, it reconfigures them by communicating a series of signs whose meanings reverse those suggested by omnivores. The identification of signs that constitute the vegan knowledge enables me to look at veganism as complex system, in which syntagmatic and associative relationships are not fully determined (Barthes [1984]. Elements of Semiology. New York: Hill and Wang). Although contesting the essentiality of animal-based foods, vegan meals suggest two overlapping approaches to meals. In one, the plant-based protein serves as the centrepiece of the meal, imitating conventional meals. The other offers an alternative meal structure, in which syntagmatic relations are constructed as a sequence of daily meals, each consisting of an aggregation of simultaneously served dishes out of which diners construct their own repast, thus broadening the scope of associative relationships. [ABSTRACT FROM AUTHOR]

This paper investigates a taken-for-granted institutional form, shelters for unhoused New Yorkers, through the neo-institutional lens of institutional inertia and critical case study methodology. It focuses on the external shock of COVID-19, NYC's use of unoccupied hotels for social distancing, and the return to shelters when COVID waned. For guidance, we examine other instances of interrupted institutional inertia following shocks. Using Lewin's force field analysis, we explore why changes to some institutional forms amidst COVID persisted while novel approaches to shelter dissipated. We conclude that directly involving unhoused people in the design and implementation of homeless services may improve outcomes. Human service professionals share a body of knowledge and assumptions – a kind of echo chamber that amplifies and confirms beliefs. It is important to look beyond traditional and familiar models of service delivery to find alternative ideas and approaches that may be effective. Returning to Kurt Lewin's concept of force fields provides opportunities to think effectively and holistically about how to modify or change services, policies, and organizations. People served by human services – those with lived experiences – possess unique expertise that can inform organizational decisions and planning in new and helpful ways. As practitioners, we need to find ways to be more inclusive of the perspectives and ideas of those our programs serve through participatory methods of planning, decision making, and evaluation. [ABSTRACT FROM AUTHOR]

Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST‐Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID‐19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST‐Plus‐assisted RD evaluations. The lag between admission and diagnosis improved with MUST‐Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre‐/post‐implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST‐Plus, a machine learning‐based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning‐based processes to improve malnutrition screening and facilitate timely intervention. Key points/Highlights: Malnutrition is prevalent among hospitalised patients and frequently goes unrecognised, with the potential for severe sequelae. Accurate diagnosis, documentation and treatment of malnutrition have the potential of having a positive impact on morbidity rate, mortality rate, length of inpatient stay, readmission rate and hospital revenue. The tool's successful application highlights its potential to optimise malnutrition screening in healthcare systems, offering potential benefits for patient outcomes and hospital finances. [ABSTRACT FROM AUTHOR]

This paper develops a critical perspective on the use of conversational agents (CAs) with children at home. Drawing on interviews with eleven parents of pre-school children living in Norway, we illustrate the ways in which parents resisted the values epitomised by CAs. We problematise CAs' attributes in light of parents' ontological perceptions of what it means to be human and outline how their attitudes correspond to Bourdieu's [1998a. Acts of Resistance. New York: New Press] concept of acts of resistance. For example, parents saw artificial conversation designed for profit as a potential threat to users' autonomy and the instant gratification of CAs as a threat to children's development. Parents' antecedent beliefs map onto the ontological tensions between human and non-human attributes and challenge the neoliberal discourse by demanding freedom and equality for users rather than productivity and economic gain. Parents' comments reflect the belief that artificial conversation with a machine inappropriately and ineffectively mimics a nuanced and intimate human-to-human experience in service of profit motives. [ABSTRACT FROM AUTHOR]

Death and dying are woven throughout the work of home care aides, and yet the care they provide at the end of life (EOL) remains poorly understood. This is due in part to the multiple circumstances under which aides provide EOL care. In this paper, we elucidate the EOL care experiences of aides working in home care agencies in New York City. We conducted in-depth interviews with 29 home care aides, and we analyzed these data using inductive, team-based methods. Our findings show that aides may not be aware of or accept a client's EOL status, and they may avoid EOL care. These conditions shape EOL care, and we detail the committed forms of care aides provide when they are aware and accepting. We recommend improved training, support systems, and policy change to enhance aides' contributions to EOL care, while protecting aides' health and well-being. [ABSTRACT FROM AUTHOR]

In 2022, a New York civil court concluded that a polyamorous partner should not be automatically excluded from noneviction protection (O'Neill). The decision was hailed as particularly groundbreaking and a 'game changer'. On the other side of the globe, the New Zealand Supreme Court concluded that polyamorous unions could be entitled to the same property-sharing regime as couples. Upon closer examination, the two decisions use function-based modes of recognition to confer similar protections upon the polyamorous union. However, this paper will illustrate some of the limitations inherent in this approach. At present, functional recognition exhibits a continued attachment to the traditional marital family; this aspect, combined with the unique complexity of polyamorous arrangements, renders this route to legal recognition potentially inappropriate. The decisions examined either fail to understand the nature of the arrangement or choose to distort it in order to make polyamory legally intelligible. Both decisions are emblematic of a broader difficulty of functional recognition to provide answers to the legal demands of this type of relationship. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic had an unprecedented impact on transportation worldwide. Significant decreases in transportation across all modes were evident and sustained as governments worldwide implemented various countrywide closures and quarantine restrictions to slow the spread of the virus. This paper quantifies and assesses daily vehicle counts by Federal Highway Administration (FHWA) vehicle classifications during the COVID-19 pandemic in New York and Florida throughout 2020. The study found that duringMarch and April of 2020, traffic among all investigated FHWA categories was significantly reduced in both Florida and New York. However, commodity carriers in both states were able to recover faster and remained more consistent than passenger vehicles. This pattern was also observed in both urban and rural communities in Florida. The findings of this work demonstrate how commodity carrier movements, assessed through FHWA vehicle category counts, were less impacted by the governmental restrictions during the pandemic than passenger transportation. While overall traffic volume dropped by more than half in most places at the height of the pandemic, larger commodity-carrying vehicles remained nearly unchanged from the prior year by June of 2020. This was likely because of the critical need to maintain trucking movements to sustain populations. Understanding how truck traffic and freight movements more broadly were impacted during the COVID-19 pandemic is critical in preserving the continuity of service and preventing supply shortages in the event of future outbreaks. [ABSTRACT FROM AUTHOR]

For a long time, I have been interested in belonging: in how we are forced but also need to belong in communities, in how collective identifications form our subjective identities, in how belonging is the subject-matter of our very sense of self. I have also been interested in un-belonging, in what happens to us when we are refused or choose to refuse a collective affiliation, in the dynamic of alienation, betrayal and freedom that ensues. The paper delves into these themes by journeying through the landscape of my own difficult belonging in the land where I was born: Israel-Palestine, sunk these days in a state of catastrophic war; my experience of belonging as it is being negotiated in my own analysis, carried over the phone between Tel Aviv and New York. It is a personal-theoretical postcard from a place of existential crisis but also hope. [ABSTRACT FROM AUTHOR]

Background: During the past two decades of cannabis legalization, the prevalence of medical cannabis (MC) use has increased and there has also been an upward trend in alcohol consumption. As less restricted cannabis laws generate more adult cannabis users, there is concern that more individuals may be simultaneously using medical cannabis with alcohol. A few studies have examined simultaneous use of medical cannabis with alcohol, but none of those studies also assessed patients' current or previous non-medical cannabis use. This paper explores simultaneous alcohol and medical cannabis use among medical cannabis patients with a specific focus on previous history of cannabis use and current non-medical cannabis use. Methods: A retrospective cohort study of MC patients (N = 319) from four dispensaries located in New York. Bivariate chi-square tests and multivariable logistic regression are used to estimate the extent to which sociodemographic and other factors were associated with simultaneous use. Results: Approximately 29% of the sample engaged in simultaneous use and a large share of these users report previous (44%) or current (66%) use of cannabis for non-medical purposes. MC patients who either previously or currently use cannabis non-medicinally, men, and patients using MC to treat a pain-related condition, were significantly more likely to report simultaneous alcohol/MC use. Conclusions: Findings indicate that there may be differential risks related to alcohol/MC use, which should be considered by cannabis regulatory policies and prevention/treatment programs. If patients are using cannabis and/or alcohol to manage pain, clinicians should screen for both alcohol and cannabis use risk factors. [ABSTRACT FROM AUTHOR]

Experiential learning in Master of Social Work (MSW) programs can be an instrumental pedagogical method of juxtaposing theoretical knowledge with tangible hands-on approaches, enhancing student experience through incorporating curriculum beyond classroom settings. This paper seeks to employ the use of educational theorist David Kolb's Theory of Experiential Learning as the pedagogical framework to explore the impact of a community partnership with an upstate New York organ donation organization on master level social work students. Furthermore, this paper pursues an increased understanding of experiential learning and factors connecting social work theoretical concepts with the learning process in MSW programming. Student discussions voiced and illuminated a deeper understanding of macrocommunity concepts and demonstrated skills reflecting EPAS. Students' responses to participation in the experiential learning activity were emotional, compelling, and profound. Common areas of understanding gained by students were a clear awareness of the need for social work advocacy, measures to address social justice, and increased education for organ donations. [ABSTRACT FROM AUTHOR]

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors. [ABSTRACT FROM AUTHOR]

Of concern is a problem of strain gradient porous elastic theory with nonlinear damping terms, whose constitutive equations contain higher-order derivatives of the displacement in the basic postulates. The paper is based on the theory of 'consistency' due to Aouadi et al. [J. Therm. Stress. 43(2)(2020), 191–209] and Ieşan [American Institute of Physics, Conference Proceedings, 1329 (2011), 130–149], and contains four results. We firstly show that the system is global well posed by using maximal monotone operator. The second main result is the existence of global attractors which is proved by the method developed by Chueshov and Lasiecka [Long-time behavior of second order evolution equations with nonlinear damping. Mem. Amer. Math. Soc. vol. 195, no. 912, Providence, 2008; Von Karman evolution equations: well-posedness and long-time dynamics. Springer Monographs in Mathematics, Springer, New York, 2010]. By showing the system is gradient and asymptotically smooth, we establish the existence of global attractors with finite fractal dimension via a stabilizability inequality. Then we study the continuity of global attractors regarding the parameter in a residual dense set. The above results allow the damping terms with polynomial growth. Finally we discuss the exponential decay and global boundedness to the linear case of damping terms of the system. The assumption of equal-speed wave propagations is not needed for all of results obtained. [ABSTRACT FROM AUTHOR]

In this paper, we extend the optional randomized response technique (ORRT) developed by Chaudhuri and Mukerjee [Optionally randomized response techniques. Bull. Calcutta Statist. Assoc. 1985;34:225–230; Randomized response: theory and techniques. New York: Marcel Dekker, Inc.; 1988] to the situation of estimating the proportion of two sensitive characteristics and their overlap. Lee, Sedory and Singh [Estimating at least seven measures of qualitative variables from a single sample using randomized response technique. Stat Prob Lett. 2013;83(1):399–409; Estimation of odds ratio, attributable risk, relative risk, correlation coefficient and other parameters using randomized response techniques. Behaviormetrika. 2021;48:371–392.] have shown that their crossed model performs better than their simple model from an efficiency point of views. Here we investigated a further improvement in the crossed model along the lines of Chaudhuri and Mukerjee [Optionally randomized response techniques. Bull. Calcutta Statist. Assoc. 1985;34:225–230; Randomized response: theory and techniques. New York: Marcel Dekker, Inc.; 1988]. New unbiased estimators are proposed, their variance expressions are derived and estimators of variances are suggested. Lastly, we carry out a simulation study to investigate the behaviour of the proposed estimators with respect to their competitors. [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]

This paper considers an infinitely repeated three-player zero-sum game with two-sided incomplete information, in which an informed player plays two zero-sum games simultaneously at each stage against two uninformed players. This is a generalization of the model in Aumann et al. (Repeated games with incomplete information. MIT Press, New York, 1995) of two-player zero-sum one-sided incomplete information games. Under a correlated prior, the informed player faces the problem of how to optimally disclose information among two uninformed players in order to maximize his long-term average payoffs (i.e., undiscounted payoffs). Our objective is to understand the adverse effects of "information spillover" from one game to the other in the equilibrium payoff set of the informed player. We provide conditions under which the informed player can fully overcome such adverse effects and characterize equilibrium payoffs. In a second result, we show how the effects of information spillover on the equilibrium payoff set of the informed player might be severe. Finally, we compare our findings on the equilibrium-payoff set of the informed player with those of Bayesian Persuasion models with multiple receivers. [ABSTRACT FROM AUTHOR]

Does our food for thought come virtually from the internet? When we take a long view, the instant stroke of a keyboard pales in comparison to the centuries-old evolution of real food harvesting and the generation and exchange of ideas, which have resulted in creative capital. The vernacular architecture of the agricultural built environment has almost dematerialized in its transition from the ancient pre-industrial era to the post-truth world, to become almost only an imagined concept. The symbology of the common threshing terrace of a Portuguese remote mountain village can now be found in multiple spaces of the urban realm, including in the metaphor of a community arts incubator's modus operandi in Albany, New York (USA). How has the concept of food for thought developed and materialized? How has it evolved? And what are some of the expected ways it might be utilized in the future? The purpose of the paper is to trace the evolution of this concept and its elements via the BLC Framework. The methodology employs a time analysis of approximately three centuries to distinguish three distinct evolutionary phases: (i) the pre-industrial era, (ii) the industrial era, and (iii) the information-driven era. The key finding is an up-to-date discussion of the food for thought concept in two distinct geographical worlds and three-time eras, as well as a set of lessons learned according to a protest poem and a rock song. The results are presented in the form of five lessons learned with implications for public policy: the first two lessons pertain to issues of procedural justice as encapsulated in the anonymous 'The Goose' poem, while the last three result from a discussion of selected verses in Nick Cave's 'Fable of the Brown Ape' rock song. [ABSTRACT FROM AUTHOR]

Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and High-Resolution Rapid Refresh (HRRR) 2-m temperature, 10-m wind speed, and precipitation accumulation forecasts initialized at 1200 UTC are verified against New York State Mesonet (NYSM) observations from 1 January 2018 through 31 December 2021. NYSM observations at 126 site locations are used to calculate standard error statistics (e.g., forecast error, root-mean-square error) for temperature and wind speed and contingency table statistics for precipitation across forecast hours, meteorological seasons, and regions. The majority of the focus is placed on the first 18 forecast hours to allow for comparison among all three models. A daily NYSM station-mean temperature error analysis identified a slight cold bias at temperatures below 25°C in the GFS, a cool-to-warm bias as forecast temperatures warm in the HRRR, and a warm bias at temperatures above 30°C in each model. Differences arise when considering temperature biases with respect to lead times and seasons. Wind speeds are overforecast at all ranges in each season, and forecast wind speeds ≥ 18 m s−1 are rarely observed. Performance diagrams indicate overall good forecast performance at precipitation thresholds of 0.1–1.5 mm, but with a high frequency bias in the GFS and NAM. This paper provides an overview of deterministic forecast performance across New York State, with the aim of sharing common biases associated with temperature, wind speed, and precipitation with operational forecasters and is the first step in developing a real-time model forecast uncertainty prediction tool. [ABSTRACT FROM AUTHOR]

A major application of the inverse scattering and tomography methods is imaging all types of structural, physical, chemical and biological features of matter. The term vortex beam refers to a beam of electromagnetic radiation, electrons, photons or others—whose phase changes in corkscrew-like manner along the direction of propagation. The paper is devoted to the use of scalar Bessel beams of integer and fractional mode for the reconstruction of scattering potential. In practical applications, one naturally deals with Bessel beams truncated in the radial direction. The inversion formula for truncated Bessel beams is also obtained. Instead of the conventional Fourier diffraction theorem (Kak and Slaney in Principles of computerized tomographic imaging, SIAM, New York, 2001), the relations connecting the scattered field and the scattering potential in the Fourier space are obtained in the explicit form. [ABSTRACT FROM AUTHOR]

In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits users, drivers, and the provider. We propose an intuitive mean to improve idle ride-sharing vehicles' repositioning: repositioning heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, current and expected future fleet distribution, and the location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution in the future, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on the previously learned policy in every iteration. We then update the policy based on the simulation's outcome and use it in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations and therefore, revenue loss for the platform and drivers significantly while leading to a better service level for the users and to a fairer treatment of drivers. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) [Grant 494812908]. M. W. Ulmer's work is funded by the German Research Foundation Emmy Noether Programme [Grant 444657906]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1202. [ABSTRACT FROM AUTHOR]

This paper contributes to the growing consensus of voices who articulate how the newfound North American consciousness around "anti-Asian hate" must connect individual acts of violence to structural forms of state and white supremacist violence. Focusing on the policing of Asian massage work, the essay argues that this unique human-itarian commitment reflects the slow economic violence of rescue. It contextualizes the 2021 Atlanta massage murders alongside the work of Red Canary Song (RCS), a grassroots coalition of migrant massage workers, sex workers, and allies organizing against the policing of massage work in New York. The essay echoes an abolitionist call to not only dismantle policing and punishment mechanisms—as these forms of policing have penetrated most aspects of civilian life—but demands the urgency to build a vast network of community resources coupled with imaginative ways of organizing. Abolitionist feminist frameworks must necessarily encompass economic justice, migrant justice, housing justice, and language justice to name a few. One strategy espoused by RCS builds worker power and the capacity for organizing towards the decriminalization of poverty, migration, and low-wage informal and sex work alike. For Asian migrant workers, the fight for justice is not only about the resources needed to live, sleep, and work safely, it is also about changing the deep-seated mix of paternalism and resentment that refuses to acknowledge Asian working-class communities to ability to make choices within constraints. [ABSTRACT FROM AUTHOR]

• Propose a duopoly evolutionary game model for the dynamic ride-hailing market. • Capture bounded rationality, strategic learning, and dynamic competition between platforms. • Identify the existence of stationary points and derive simplified stability conditions. • Establish two extended models to capture the service aggregator and mixed electric/fuel fleets. • Provide a fresh perspective for exploring dynamic ride-hailing competition and market evolution. The platform competition in a real-world ride-hailing market is complex and dynamic. Existing research mainly relies on static competitive equilibrium, lacking an understanding of the dynamic evolution process of platform competition. This paper develops a generic duopoly evolutionary game model to capture dynamic decision adjustments and the evolution of market conditions. Bounded rationality and strategic learning are integrated into the modeling to dissect the interaction and competition between two platforms. The existence of stationary points where the system states remain unchanged over time is identified, and simplified stability conditions are derived. A pure strategy pair is found stable when constituting a strict Nash equilibrium and corresponding to positive payoffs. We establish two extended models to capture the nascent service aggregator and mixed electric/fuel fleets and compare them with the basic model. The model is calibrated employing the ride-hailing data of New York and is applicable for analyzing the market evolution under different states. The impacts of potential demand drop, aggregated services, and mixed fleets on platform decision and driver-passenger choice evolution are unveiled. It is found that the platforms possess self-recovery capability when facing a deteriorated external environment. The decision-making of the small platform exhibits a more substantial inertia and volatility since the large platform's decision can easily influence the market supply and demand. In the initial stage, the service aggregator slightly harms the large platform, but in the long run, it benefits both platforms. Besides, both platforms reduce prices while inflating commission ratios under mixed fleets. This study provides a fresh perspective for exploring the real-world dynamic ride-hailing competition and market evolution. [ABSTRACT FROM AUTHOR]

The U.S. opioid overdose crisis persists. Outpatient behavioral health services (BHS) are essential components of a comprehensive response to opioid use disorder and overdose fatalities. The Helping to End Addiction Long-Term® (HEALing) Communities Study developed the Communities That HEAL (CTH) intervention to reduce opioid overdose deaths in 67 communities in Kentucky, Ohio, New York, and Massachusetts through the implementation of evidence-based practices (EBPs), including BHS. This paper compares the rate of individuals receiving outpatient BHS in Wave 1 intervention communities (n = 34) to waitlisted Wave 2 communities (n = 33). Medicaid data included individuals ≥18 years of age receiving any of five BHS categories: intensive outpatient, outpatient, case management, peer support, and case management or peer support. Negative binomial regression models estimated the rate of receiving each BHS for Wave 1 and Wave 2. Effect modification analyses evaluated changes in the effect of the CTH intervention between Wave 1 and Wave 2 by research site, rurality, age, sex, and race/ethnicity. No significant differences were detected between intervention and waitlisted communities in the rate of individuals receiving any of the five BHS categories. None of the interaction effects used to test the effect modification were significant. Several factors should be considered when interpreting results—no significant intervention effects were observed through Medicaid claims data, the best available data source but limited in terms of capturing individuals reached by the intervention. Also, the 12-month evaluation window may have been too brief to see improved outcomes considering the time required to stand-up BHS. Clinical Trials.gov http://www.clinicaltrials.gov : Identifier: NCT04111939 • Evidence-based practices to reduce opioid overdose deaths implemented in 67 communities. • Outpatient behavioral health services are key element of opioid addiction treatment. • Similar receipt of behavioral services in control and intervention communities. • More research needed to test the impact of comprehensive, community-driven interventions. [ABSTRACT FROM AUTHOR]

• NYSolarCast is an open-source solar forecasting system configurable for any region. • NYSolarCast predicts gridded GHI and both utility-scale and distributed PV power. • NYSolarCast integrates NWP models, machine learning, and real-time weather data. • NYSolarCast was validated for 1 year of 0–6-h GHI and power forecasts in New York. • NYSolarCast GHI forecasts performed comparably to a commercial reference forecast. Solar energy generation capacity will need to be greatly increased to meet aggressive clean energy targets by New York State (NYS), which are 70% renewable energy (RE) generation by 2030, and 100% by 2040. Because solar energy is a variable, weather-driven resource, accurate forecasts of solar energy generation both in nowcast (intra-day) and day-ahead time horizons are necessary for electric utilities and independent system operators, as they maintain grid stability and maximize RE use. Meeting this need for NYS, a gridded, open-source solar power forecasting system called NYSolarCast was developed (https://github.com/ncar/NYSolarCast%5fdelivery). NYSolarCast makes 15-min resolution predictions of global horizontal irradiance (GHI) on a 3-km grid covering all of NYS, which are then used to estimate solar power generation both for select utility-scale PV plants (15-min resolution) and for zone-aggregated distributed PV (1-h resolution). The statewide GHI forecasts are made by applying the StatCast statistical forecasting model, which is trained on over two years of GHI observations from pyranometers at all 126 NYS Mesonet stations and gridded numerical weather prediction forecasts from both the Weather Research and Forecasting model tuned for solar applications (WRF-Solar®) and NOAA's operational High-Resolution Rapid Refresh (HRRR) model. Forecasts are made at each NYS Mesonet site and then blended outward into the rest of the grid. This paper gives an overview of NYSolarCast performance for intra-day (0–6-h) GHI and power forecasts during a one-year period. [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]

Neighborhood socioeconomic status (SES) and greenspace can affect respiratory health. However, it is unclear whether effects of neighborhood SES and greenspace on respiratory health still exist regardless of temperature variations. This paper conducted a two-stage, age-stratified case time-series study. The first goal is to examine the associations between two temperature metrics (daily mean temperature [DMT] and diurnal temperature range [DTR]) and respiratory emergency department (ED) visits among four age groups in New York City. The second goal is to evaluate whether neighborhood SES and greenspace would be determinants of respiratory ED visits independent from temperature varying factors. A distributed lag nonlinear model was applied on ED data from 135 zip codes (October 2016 - February 2020). Our first-stage analysis indicated that older adults aged 65+ had higher risk of ED visits (RR=2.78, 95% eCI: 2.41, 3.22; with 7 days of lag) on days with low DMT (-10 °C), followed by adults aged 18-64 (RR=2.48, 95% eCI: 2.32, 2.65), children and youth aged 5-17 (RR=1.38, 95% eCI: 1.24, 1.53), and young children aged 0-4 (RR=1.04, 95% eCI: 0.96, 1.13). However, no excess respiratory ED visits were observed on days with high DMT (30 °C). Higher DTR was associated with higher risk, with children and youth more susceptible when DTR was high (DTR 20 °C; RR=5.70, 95% eCI: 3.42, 9.49; with 7 days of lag). The second-stage analysis indicated neighborhood SES and greenspace had significant associations with respiratory ED visits regardless of temperature variations. Specifically, Higher income and greenspace exposure were negatively associated with ED visits among all age groups. Neighborhood SES and greenspace could affect respiratory morbidity regardless of weather conditions. Daily temperature variations accelerated the short-term risk among population subgroups under different weather conditions (e.g., higher risk of days with low DMT among older adults, higher risk of days with high DTR among children and youth aged 5-17), which could create co-effects with neighborhood SES and greenspace on respiratory health. • Respiratory ED visit was significantly associated with both DMT and DTR. • Older adults more vulnerable to low DMT (RR=2.78, 95% eCI: 2.41, 3.22). • Youth aged 5-17 more vulnerable to high DTR (RR=5.70, 95% eCI: 3.42, 9.49). • SES and greenspace associated with respiratory ED visit regardless of temperature. • Intracity examination of impacts of DMT and DTR on health allows local actions. [ABSTRACT FROM AUTHOR]

Air pollution has a significant impact on human health and influences housing choices. Existing studies on determinant factors affecting property prices, including air pollution, mainly employed hedonic and spatial regression models that have limitations in capturing non-linear relationships and local interactions. Neglecting the non-linear relationships can lead to incomplete estimation impacts between explainable features and prices. To fill these gaps, this study used machine learning algorithms and SHAP (SHapley Additive exPlanations) to identify the relative importance of air pollutant variables on land values and their non-linear mechanism. The results showed that the Extreme Gradient Boosting (XGBoost) outperformed Ordinary Least Squares (OLS), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), K-Nearest Neighbors (K-NN), and Extra Tree Regression (ETR) models in price prediction and capturing the non-linear relationship between air pollution and land values. Notably, Ozone (O 3) and Nitric Oxide (NO) concentrations contribute 1.64% and 1.47% to land value variation, respectively. The results confirmed the non-linear relationship and identified that the threshold effect between air pollution and land values is at mean concentration values. Furthermore, we observed that the negative influence of O 3 and NO on land values appears as their concentration level is higher than 29.3 and 13 ppb, respectively. This paper contributes valuable insights by advancing our understanding of the non-linear mechanism impact of air pollution on house and land values, informing environmental policymaking, and shedding light on housing decisions. [ABSTRACT FROM AUTHOR]