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Several concepts that model processes of spreading (of information, disease, objects, etc.) in graphs or networks have been studied. In many contexts, we assume that some vertices of a graph G are contaminated initially, before the process starts. By the q -forcing rule, a contaminated vertex having at most q uncontaminated neighbors enforces all the neighbors to become contaminated, while by the p -percolation rule, an uncontaminated vertex becomes contaminated if at least p of its neighbors are contaminated. If given a set S of initially contaminated vertices all vertices eventually become contaminated when continuously applying the q -forcing rule (respectively the p -percolation rule), S is called a q -forcing set (respectively, a p -percolating set) in G. In this paper, we consider sets S that are at the same time q -forcing sets and p -percolating sets, and call them (p , q) -spreading sets. Given positive integers p and q , the minimum cardinality of a (p , q) -spreading set in G is a (p , q) -spreading number, σ (p , q) (G) , of G. While q -forcing sets have been studied in a dozen of papers, the decision version of the corresponding graph invariant has not been considered earlier, and we fill the gap by proving its NP-completeness. This, in turn, enables us to prove the NP-completeness of the decision version of the (p , q) -spreading number in graphs for an arbitrary choice of p and q. Again, for every p ∈ N and q ∈ N ∪ { ∞ } , we find a linear-time algorithm for determining the (p , q) -spreading number of a tree, where in the case p ≥ 2 we apply Riedl's algorithm from [Largest and smallest minimal percolating sets in trees, Electron. J. Combin. 19 (2012) Paper 64] on p -percolation in trees. In addition, we present a lower and an upper bound on the (p , q) -spreading number of a tree and characterize extremal families of trees. In the case of square grids, we combine some results of Bollobás from [The Art of Mathematics: Coffee Time in Memphis. Cambridge Univ. Press, New York, 2006], and the AIM Minimum Rank-Special Graphs Work Group from [Zero forcing sets and the minimum rank of graphs, Linear algebra Appl. 428 2008 1628–1648], and new results on (2 , 1) -spreading and (4 , q) -spreading to obtain σ (p , q) (P m □ P n) for all (p , q) ∈ (N ∖ { 3 }) × (N ∪ { ∞ }) and all m , n ∈ N. [ABSTRACT FROM AUTHOR]

• A pipeline is formulated to extract window to wall ratios from building façades. • A grammar based and learning based method are compared for performance efficacy. • The learning-based method shows higher accuracy than the grammar-based method. • A hybrid method combining the strengths of the methods achieves higher accuracy. • An algorithm utilizing the learning-based method extracts detailed building heights. Urban feature extraction methods have presented multiple opportunities in the field of environmental building performance studies, and urban assessments. With reference to façade features, window size and position are key parameters that influence occupant perception and environmental performance of indoor spaces. A myriad of methods have been proposed to automatically detect façade window layouts from street view images yet it is still unclear how to assess the strengths and limitations of these methods or how they can be combined for higher detection performance. This paper aims to add clarity for those aiming to use computer vision techniques for façade layout extraction for urban level sustainability assessments. An automated pipeline to enable the extraction and detection of WWRs (Window to Wall ratios) is introduced that is based on two fundamentally different computational approaches; a grammar-based edge detection framework (Method 1) and a learning-based method (Method 2) that utilizes CNNs (Convolutional Neural Networks). The paper then compares the detection efficacy of both methods, focusing on WWR accuracy, in New York and Lisbon, including their ability to extract more detailed façade properties such as floor-to-floor heights. The study finds that the learning-based method shows lower error scores across the two cities. In Lisbon, 69 % of conditions were detected within the 10 % error range and 91 % were within the 20 % range under Method 1. Under Method 2, 82.5 % of conditions were within the 10 % error range and 95 % were within the 20 % range. In New York, 66 % of conditions were within the 10 % error range and 90 % were within the 20 % range under Method 1 while 77.5 % of conditions were within the 10 % error range and 93 % were within the 20 % range under Method 2. Finally, a hybrid method is proposed to leverage the strengths of the two models, and higher accuracies are obtained in both the New York and Lisbon dataset. In New York, 96.5 % are now detected within the 20 % error range and 81.5 % within the 10 % error range. In Lisbon, 96 % are now detected within the 20 % error range and 83.5 % within the 10 % error range. With reference to the total building height extraction formulated under Method 2, the results show a relative error of 3.5 % in height estimation in the sample set. [ABSTRACT FROM AUTHOR]

Climate change is altering the context of human mobility due to disasters such as sea-level rise, forest fires, and other extreme events. It is also changing how places plan and adapt to these chaotic events. In this paper we examine how climate destinations, or host communities, are preparing for potential climate related in-migration in their climate adaptation planning. While no place will be left untouched by climate change, a number of cities and communities across Upstate New York consider themselves "climate havens" due to their lower risk to many climate impacts, such as sea level rise, access to freshwater resources, and infrastructure to accommodate growth. Within this context, what does it mean to plan a just climate haven? What types of policy and planning might be needed? How are planners and practitioners framing adaptation approaches? Using Q-methodology, we reveal two dominant perspectives among planners and activists in Upstate New York on how to plan just climate havens. The first is a transformational view that looks to address the social, political, and economic drivers of vulnerability. The second is a recognition view that looks to address legal and legislative shortcomings in how to support climate migrants. Points of consensus on how to build just climate havens between the two groups include co-production and co-design, workforce development, food security, and safe and affordable housing. We suggest that these two perspectives frame justice in two distinct—but overlapping—ways and provide a shared basis on how to consider planning a just climate haven. • Two perspectives frame how to plan just climate havens: Transformation and Recognition. • Perspectives unite around workforce development, food security, housing, and co-produced solutions, and material gains. • Climate destinations in Central New York need to build capacity to plan and adapt to future climate scenarios. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic and consequential shutdown measures, many mental health professionals started providing therapy to patients exclusively via telehealth. Our research center, which specializes in studying and treating obsessive-compulsive disorder (OCD), historically has provided in-person exposure and response prevention (ERP) to adults with OCD, but shifted to telehealth during the pandemic. Unlike in other modes of talk therapy, ERP's emphasis on therapist-supervised exposures presented unique opportunities and challenges to delivering treatment entirely via a virtual platform. This paper provides case examples to illustrate lessons we learned delivering ERP exclusively via telehealth in New York from March 2020 through June 2021 and offers recommendations for future study and practice. Though we observed a number of drawbacks to fully remote ERP, we also discovered advantages to delivering ERP this way, meriting additional research attention. [ABSTRACT FROM AUTHOR]

• The lives of Black girls, femmes, transgender, and nonbinary (TGNB) young people who identify as LGBTQ + are punctuated by experiences of oppression-based, interpersonal, and systemic trauma. • Healing and creative resistance are strong driving forces facilitated by pride in one's identities, artistic expression, and mindfulness facilitated by time spent in nature and parks. • Individual and collective dreaming and futuring are central to the living of LGBTQ + identified Black girls, femmes, and TGNB young people. Black LGBTQ + girls, femmes, transgender and nonbinary youth and young adults experience high rates of sexual violence, and other forms of trauma, including oppression-based trauma related to racism, misogynoir, homophobia and transphobia. Black LGBTQ + girls, femmes, transgender and nonbinary young people, whose experiences are underexplored within social science research, also experience disparately harsh discipline and pushout within school settings while being disproportionately represented in the foster care and juvenile justice systems. However, they are also a community with a tremendous history of creative resistance, and legacies of activism and powerful healing practices. Within this paper we have disaggregated the interview data of 13 LGBTQ + identified participants from a larger qualitative study with 24 Black girls, femmes, transgender and nonbinary youth, aged 16–25, who reside in New York State. Data were analyzed using critical thematic analysis and three main themes emerged: 1) the significance of participants' experiences of childhood trauma, 2) participants' conceptualizations and experiences of healing and resistance, and 3) participants aspirations and dreams for their futures and the futures of their communities. [ABSTRACT FROM AUTHOR]

• Frontline communities are often excluded and tokenized in coastal resiliency planning. • Interviews with New York-area community leaders reveal pathways to better planning. • Centering and investing in community-level expertise can improve planning outcomes. • Equitable partnerships require ongoing dialogue, accountability and self-evaluation. • Iterative co-production of resiliency research yields valuable, actionable insight. Community-based organizations (CBOs) in frontline coastal communities grapple with social and environmental injustices compounded by climate change risks. In response, CBOs have developed deep expertise in climate adaptation tailored to their local communities. Yet these groups are often effectively excluded from resilience planning processes that are top-down and involve perfunctory and often performative consultations. This paper asks: What do community leaders seek from adaptation planning, and how do they recommend such processes be improved? Drawing on the experiences of ten CBOs in coastal New York and New Jersey, the majority representing BIPOC environmental justice communities, this article advances community-driven priorities for coastal resilience planning outcomes and processes. We conducted structured 60–90-minute interviews with ten CBO leaders between February-March 2022, collaboratively completed an iterative content analysis of the interview data and community plans, and workshopped core findings in multiple sessions and conversations with participating CBOs through early 2024. CBO leaders had consensus on resilience planning priorities: they oppose top-down approaches where planners bring a predetermined agenda, and seek true partnership through a relational approach that values grassroots perspectives to co-produce equitable and just strategies to address climate risk. Recommendations for decision-makers center on the need to build on existing community-led plans, invest in community leadership within planning processes, act with transparency to foster trust, partnership and co-planning with communities, and self-evaluate their practice. Lessons for researchers seeking to support community leadership within resilience planning include the need to establish lasting and mutually supportive relationships with community partners to enable knowledge co-production. [ABSTRACT FROM AUTHOR]

Urban mobility services face the challenge of planning their operations efficiently while complying with user preferences. In this paper, we introduce a new mathematical model called a choice-driven dial-a-ride problem (CD-DARP) which is a generalization of the dynamic DARP where passenger behavior is integrated in the operational planning using choice models and assortment optimization. We look at two types of mobility services, private and shared. Our problem extends the dynamic DARP by (i) changing its objective function to profit maximization, where both cost and revenue are variables, and (ii) incorporating assortment optimization with routing decisions in a dynamic setting. We propose a pricing scheme based on a choice model designed to offer service alternatives at the time a customer makes a request. We introduce a tailored algorithm to efficiently solve the dynamic CD-DARP. Computational results indicate that our proposed approach outperforms dynamic DARP in terms of reducing routing costs and improving the number of customers served. • A new framework is introduced for DDARP incorporating customer choice. • The routing cost is reduced while satisfying customers' requests. • CD-DARP provides more flexibility for the operator to efficiently manage the fleet. • We evaluate the proposed model on synthetic as well as New York taxi data. • Flexible time windows helps improving the service level. [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]

We evaluated caretaker knowledge of and attitudes toward the levonorgestrel-containing intrauterine device (L-IUD) as a treatment option for adolescents and young adults with heavy menstrual bleeding. A survey assessed demographic characteristics, menstrual history, perception of the L-IUD, and knowledge of the indications, risks, and benefits of the L-IUD. Participant recruitment occurred in the offices of a pediatric gynecologist, pediatric hematologist, and adolescent medicine physician in a hospital-based practice in New York. English-speaking caretakers of patients with a chief concern of heavy menstrual bleeding (<21 years old) were invited to participate. Electronic and paper surveys were administered prior to seeing the physician. The primary outcome was participant willingness to allow their daughter to use the L-IUD for heavy menstrual bleeding. Additional outcomes were explored by calculating a mean knowledge score and conducting a descriptive analysis of the perception questions. Forty surveys were included. Seventy percent of participants said they would allow their daughter to use birth control pills for heavy menstrual bleeding, whereas only 10% said the same of the L-IUD. The mean knowledge score was 73.3% (±15.7). Sixty-five percent of participants expressed the need for additional information on the L-IUD. Caretaker acceptance of the L-IUD as treatment for their daughter with heavy menstrual bleeding is limited. Although baseline knowledge was high, many indicated needing additional information. Because heavy menstrual bleeding can negatively impact quality of life, potential caretaker bias must be addressed to ensure full access to this highly effective treatment option. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had significant effects on urban and regional food systems. Local administrations worldwide have been challenged to design and implement policies to mitigate immediate food system disruptions while planning for longer-term equity and resilience. The fast pace and high degree of uncertainty of the pandemic have made systematic tracking and assessment of food system change and related policy responses arduous. To address this gap, this paper applies the multilevel perspective on sociotechnical transitions and the multiple streams framework on policy change to 16 months of food policy (March 2020 through June 2021) during the New York State-issued COVID-19 state of emergency, comprising more than 300 food policies advanced by New York City and State legislators and administrators. Content analysis of these policies revealed the most salient policy areas during this period, the status of legislation, and key programs and budget allocations, as well as local food governance and the organizational spaces within which food policy operates. The paper shows that food policy domains that gained prominence focused on support for food businesses and food workers and on ensuring and expanding food access through food security and nutrition policies. Most COVID-19 food policies were incremental and were limited to the duration of the emergency, yet the crisis allowed for enactment of novel policies that deviated from the common policy issues or the typical scale of changes proposed pre-pandemic. Taken together, and viewed through a multilevel and policy streams framework, the findings provide insight into the trajectory of food policymaking in New York during the pandemic and the areas that food justice activists, researchers, and policy makers should focus on as the COVID-19 pandemic is abated. • NY City and State advanced more than 300 food policies during the COVID emergency. • Policies focusing on food businesses, food workers, and food access were prominent. • Most policies were incremental and limited to the duration of the emergency. • The crisis propelled the enactment of several breakthrough food policies. • Pandemic food policymaking continues beyond the emergency. [ABSTRACT FROM AUTHOR]

• First study to report role of "bag bans" on retail return polyethylene contamination. • 1.4 – 2.8x increase in post-"bag ban" contamination from concentration effect. • 43-category contamination speciation increases understanding of the material stream. Plastic films are abundant, but seldom recycled in the United States (US). It is both economically and operationally challenging to recycle plastic films because their light weight makes accumulating material difficult. Additionally, films are not allowed in most curbside recycling programs in the US because they tangle around equipment used at material recovery facilities (MRFs) and effect a MRFs' ability to effectively sort other materials. As a result, the only recycling pathway for post-consumer plastic films in the US is retail return collection programs. Currently, only the most abundant type of post-consumer plastic film, polyethylene (PE), is accepted by these programs in the US. Although PE films come in many forms, grocery bags are the most well-known, and therefore most abundant, film in the retail return recycling stream. The New York State (NYS) Plastic Bag Waste Reduction Law prohibits the distribution of single use plastic bags. This work explored how ending the distribution of grocery bags impacted retail return recycling programs for PE films in Western New York. We show that a loss of the most recognizable and abundant PE film results in a 1.4 – 2.8x increase in contamination rates, devaluing films that are still collected. As a result, increased contamination rates may threaten the viability of this recycling pathway. The study also provides a complete speciation of this recycling stream with a novel level of detail, as existing research on this recycling pathway is limited. [ABSTRACT FROM AUTHOR]

• Marginalized families are disproportionately affected by COVID-19 pandemic. • Clinical vignettes describe integration of evidence-based practice and advocacy. • Child-focused clinicians can support marginalized communities as clinician-advocates. The novel coronavirus (COVID-19) has disproportionately impacted the health and socioeconomic outcomes for low-income populations, people of color, and immigrant children and families in the United States. As inequities in resources (i.e., food, internet, housing), health care, and education increased for marginalized families as a result of COVID-19, child-focused clinicians had to broaden their professional scope and implement new advocacy efforts. The current paper uses clinical vignettes taken from a New York State Office of Mental Health–licensed child and adolescent outpatient clinic in the Bronx, New York. The vignettes highlight the social inequities that impacted marginalized children and families during the pandemic, as well as the clinical team's response through the integration of evidence-base practice and advocacy. Implications for practice with vulnerable populations as the COVID-19 pandemic persists are discussed. [ABSTRACT FROM AUTHOR]

The Helping End Addictions Long Term (HEALing) Communities Study (HCS) seeks to significantly reduce overdose deaths in 67 highly impacted communities in Kentucky (KY), Massachusetts (MA), New York (NY), and Ohio (OH) by implementing evidence-based practices (EBPs) to reduce overdose deaths. The Opioid-overdose Reduction Continuum of Care Approach (ORCCA) organizes EBP strategies under three menus: Overdose Education and Naloxone Distribution (OEND), Medication Treatment for Opioid Use Disorder (MOUD), and Safer Prescribing and Dispensing Practices (SPDP). The ORCCA sets requirements for strategy selection but allows flexibility to address community needs. This paper describes and compiles strategy selection and examines two hypotheses: 1) OEND selections will differ significantly between communities with higher versus lower opioid-involved overdose deaths; 2) MOUD selections will differ significantly between urban versus rural settings. Wave 1 communities (n = 33) provided data on EBP strategy selections. Selections were recorded as a combination of EBP menu, sector (behavioral health, criminal justice, and healthcare), and venue (e.g., jail, drug court, etc.); target medication(s) were recorded for MOUD strategies. Strategy counts and proportions were calculated overall and by site (KY, MA, NY, OH), setting (rural/urban), and opioid-involved overdose deaths (high/low). Strategy selection exceeded ORCCA requirements across all 33 communities, with OEND strategies accounting for more (40.8%) than MOUD (35.1%), or SPDP (24.1%) strategies. Site-adjusted differences were not significant for either hypothesis related to OEND or MOUD strategy selection. HCS communities selected strategies from the ORCCA menu well beyond minimum requirements using a flexible approach to address unique needs. • The HEALing Communities Study (HCS) seeks to reduce overdose deaths in 67 highly impacted communities in Kentucky, Massachustees, New York, and Ohio. • Overdose deaths are reduced by the implementation of evidence-based practices. • The Opioid-overdose Reduction Continuum of Care Approach organizes EBP strategies under three menus: Overdose Education and Naloxone Distribution, Medication Treatment for Opioid Use Disorder, and Safer Prescribing and Dispensing Practices. • This paper describes strategies selected by communities and whether selection of overdose education and naloxone distribution differes by opioid overdose deaths and whether medicaiton for opioid use disorder differs between urban and rural settings. [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]

• Galleries in New York had always played important roles in NY art world. • The boundaries between NY museums and other organizations gradually collapsed. • Boundaries had been formed between the university and other organizations. • NY galleries and museums in other American cities had strong connections in the 1940s. • The connection between NY galleries and NY museums became more apparent in the 1960s. This paper examines the composition and transition of symbolic boundaries in the New York art world of painting and sculpture from 1940 to 1969 through social network analysis of inter-organizational ties between art venues. As culture-led redevelopments become more controversial in the age of neoliberal urban management, recent studies make it clear that symbolic boundaries and boundary works of urban cultures become more important topics for urban studies. So far, existing research on symbolic boundaries in urban culture has focused on social class and urban organizations, such as amenities and business establishments, and have pointed to the loss of the symbolic boundary between the museum and the gallery, this paper will show that more diverse changes were occurring in addition to such changes while following the critical methods of the previous studies. Utilizing the exhibition histories of 43 prominent American avant-garde artists based in New York, I obtain affiliation network data of artists-by-art venues. Based on the affiliation data, this paper examines the social networks among art venues, such as museums, galleries and universities, using block modeling and other methods of social network analysis. [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]

• COVID-19 travel-restriction policies can affect road traffic accident patterns. • NYC data shows a decrease in number of accidents but an increase in severity. • There has been a sharp increase in severe accidents involving cyclists. • Bike sharing trips can help predict safety risks for cyclists across space and time. • Hot zones for severe cyclist accidents are spatially and temporally concentrated. Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists' safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists' severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of spatio-temporal factors on human comfort was quantified. • Seasonal change is the main factor affecting physical comfort. • The artificial impermeable matrix increases the surface thermal field temperature. • Natural factors can always effectively reduce the surface temperature. • Human comfort can be improved to a certain extent through human intervention. In recent decades, urbanization and the dramatic increase in urban populations have exacerbated the urban heat island effect. At present, much attention has been paid to the causes and patterns of the urban heat island effect. However, there are few quantitative studies on the impact of the urban heat island effect. By extracting post-world climate data, DEM data, and land use data for 20 years between 2001 and 2020, this paper first studies the variation pattern and spatial distribution characteristics of urban heat islands in New York State, summarizes the seasonal distribution characteristics of temperature, and then uses Giles formula to calculate Tom's discomfort index and evaluate human thermal comfort, and evaluates the effect of heat island effect on human sensory thermal comfort. The results show that on the time scale, the surface temperature in the study area generally showed a slow upward trend over the past 20 years. For example, in July, the maximum and minimum temperatures increased by 3.2 °C and 4.1 °C, respectively. At the spatial scale, most of the heat island areas in the study area were distributed in the New York City agglomeration, especially from May to October, when the heat island effect was particularly obvious. The temperature map showed obvious high temperatures. Compared with 2001, the human discomfort index (DI) increased between June and August 2020. The land use map shows that as the city expands, people's DI index also increases, and the proportion of people who feel uncomfortable with heat increases to 50%. Except for the New York City cluster, other areas were mostly athermic-free. This result shows that excessive urban development concentration seriously affects residents' quality of life. We should pay attention to the superimposed impact of climate change and urban heat islands on the human discomfort index, and adjust the local high temperature and thermal field area through reasonable planning, strengthening greening, and using building technology to make cities more livable. [ABSTRACT FROM AUTHOR]

Social determinants of health may impact stage and aggressiveness of cancer presentation. As neighborhoods may influence adjacent neighborhoods across often porous and arbitrary borders, an analysis that measures the spatial impact of these social determinants provides a more holistic evaluation than an analysis that treats each neighborhood as independent and unrelated units. In this paper, we study the impact of sociodemographic and environmental characteristics of neighborhoods on cancer presentation in New York City, using both patient-level analysis and spatial autoregression on the New York State Public Access Cancer Epidemiology Data (NYSPACED) and the NYC Open neighborhood-level dataset. Two outcomes of interest were chosen: Presentation with high grade prostate cancer and presentation with distant disease. We performed logistic regression on individual patient level analysis. We created generalized cross sectional autoregressive spatial regression models to assess direct and indirect (via adjacent neighborhoods) impact of neighborhood level independent variables. We analyzed the following neighborhood level variables: 1) Percent population that was Black, 2) Percent population living in poverty, 3) Percent population obese based on self-reported height and weight, 4) Percent who report being current smokers, 5) Percent who report eating one or more servings of fruits and/or vegetables in the last day, and 6) annual average micrograms of fine particulate matter per cubic meter of air. We identified 95,749 patients diagnosed with prostate adenocarcinoma in New York City from 2001-2018. With reported Black race as the referent value, White patients were less likely to present with high grade disease (OR 0.92 [95% CI 0.89-0.95]). Similarly, self-identified White patients were also less likely to present with distant disease (vs. Black, OR 0.76 [95% CI 0.72-0.81]). Spatial autoregression models revealed no neighborhood level variables associated with direct or indirect impacts on presentation with high grade disease. However, there were several variables initially associated with distant disease presentation, including race, poverty, obesity, fruit and vegetable consumption, and air pollution. However, only neighborhood poverty and obesity levels continued to have significant direct impact on presentation with distant disease when included in the regression model with percent Black residents. In conclusion, we discovered a strong association between Race and poverty with the likelihood of presenting with advanced prostate cancer, at both the individual and neighborhood levels. Geographically targeted outreach to improve prostate cancer screening and access to care should be focused on Black men and neighborhoods with high levels of poverty. [ABSTRACT FROM AUTHOR]

In location-based social networks (LBSNs), point-of-interest (POI) recommendation systems help users identify unvisited POIs by filtering large amounts of information. Accurate POI recommendations can effectively improve user satisfaction and save time in finding POIs. In recent years, the graph convolution network (GCN) technique, which enhances the representational ability of neural networks by learning the embeddings of users and items, has been widely adopted in recommendation systems to improve accuracy. Combining GCN with various information, such as time and geographical information, can further improve recommendation performance. However, existing GCN-based techniques simply adopt time information by modeling users' check-in sequences, which is insufficient and ignores users' time-based high-order connectivity. Note that time-based high-order connectivity refers to the relationship between indirect neighbors with similar preferences in the same time slot. In this paper, we propose a new time-aware GCN model to extract rich collaborative signals contained in time information. Our work is the first to divide user check-ins into multiple subgraphs, i.e., time slots, based on time information. We further propose an edge propagation module to adjust edge affiliation, where edges represent check-ins, to propagate user's time-based preference to multiple time slots. The propagation module is based on an unsupervised learning algorithm and does not require additional ground-truth labels. Experimental results confirm that our method outperforms state-of-the-art GCN models in all baselines, improving Recall @ 5 from 0.0803 to 0.0874 (8.84%) on the Gowalla dataset and from 0.0360 to 0.0388 (7.78%) on the New York dataset. The proposed subgraph mining technique and novel edge-based propagation module have high scalability and can be applied to other subgraph construction models. [ABSTRACT FROM AUTHOR]

• Freight carriers can take multiple reactions to toll increases. • The willingness to take the following four reactions were investigated: pass on the costs to receivers; reduce delivery frequency; reduce travel distance; and change the time of day. • The four reactions were positively correlated so that they are complementary to each other. • The type of commodity and market power matter when determining which reactions to take. Freight carriers' behavior greatly varies given the number of parties involved in the process from the origin end to the destination end and the heterogeneity of commodities. Accordingly, their reaction to transportation policies including road pricing is composed of multidimensional choices, e.g., they can take a series of actions in multiple stages in response to policies, which have been overlooked in the literature. To better understand carriers' behavior, this paper investigated freight carriers' behavior in reaction to toll increases in New York State using a stated preference survey. The following four reactions in response to hypothetical toll increase scenarios were jointly investigated: (a) passing on the costs to receivers; (b) reducing delivery frequency; (c) reducing travel distance on toll roads; and (d) changing time of day of travel. The descriptive analysis showed that reducing mileage on toll roads and passing on the costs are the two most preferred reactions while changing delivery time to avoid high toll is the least favorable reaction. A multivariate Probit model was also developed to quantitatively explore factors affecting the willingness to take the reactions. As a result, all the four reactions were positively correlated, suggesting that the reactions selected by carriers are complementary to each other. Factors (including commodity type and company size) affecting each reaction were investigated. Implications for planning and policymaking based on the results were also discussed. [ABSTRACT FROM AUTHOR]

• Thirteen of eighteen dentists obtained positions as university professors in the USA. • New York and Illinois offered the most favorable university environments. • No other destination country offered comparable opportunities for dentist émigrés to thrive academically. • Specialization in sought-after sub-disciplines within dentistry facilitated success. • Austrian dentists benefited from the internationally renowned Vienna School. This study is the first to examine the collective of dental lecturers and scientists who emigrated from Nazi Germany to the United States of America. We pay special attention to the socio-demographic characteristics, emigration journeys, and further professional development of these individuals in the country of immigration. The paper is based on primary sources from various German, Austrian, and United States archives and a systematic evaluation of the secondary literature on the persons concerned. We identified a total of eighteen male emigrants. The majority of these dentists left the "Greater" German Reich between 1938 and 1941. Thirteen of the eighteen lecturers were able to find a position in American academia, mainly as full professors. Two-thirds of them settled in New York and Illinois. The study concludes that most of the emigrated dentists studied here succeeded in continuing or even expanding their academic careers in the USA, although they usually had to retake their final dental examinations. No other destination country for immigration offered similarly favorable conditions. Not a single dentist decided to remigrate after 1945. [ABSTRACT FROM AUTHOR]

This paper seeks to address the profound power resilience inequity in New York City by means of strategic allocation of electric vehicle (EV) charging infrastructure to support the power grid operation in challenging scenarios, such as when facing high demand or during natural disasters. First, we uncover the most disproportionately affected communities in New York by developing a metric of power resilience inequity to measure the combined impact of power failure-related factors on these areas. We employ data-driven approaches to infer the statistical relationships between communities' power resilience index, their available EV charging infrastructure, and several other prominent socio-demographical variables. This inference yields the development of a machine learning model that can predict the reduction of power resilience inequity after deployment of the proposed resource allocation strategy. We further develop an optimization framework that jointly considers equity and efficiency to guide the optimized distribution of EV charging infrastructure across the city. A number of case studies are leveraged to demonstrate the capability of the devised approach in enhancing urban power resilience equity, yielding favorable results in marginalized communities. [ABSTRACT FROM AUTHOR]

Bike-sharing data have been a valuable source for urban transport research. While most studies focus on Origin-Destination (OD) data of users' bike trips in bike-sharing researches, little has been investigated on mobility patterns from the perspective of bikes. Bike mobility can not only reflect human mobility patterns but also provide practical insights for understanding the bike-sharing system and assisting bike management. In particular, continuous bike movement information can provide abundant spatiotemporal connection information for spatial context analysis in data without user ID information. This paper studies bike mobility by shifting the perspective from OD analysis to the new analysis primitive of bike mobility chain (BMC). First, bike-sharing OD trip data are reconstructed to obtain BMCs. Second, the reconstructed BMCs are feature-augmented, based on which the spatiotemporal characteristics of bike mobility are analyzed. Finally, the word embedding model and the clustering algorithm are applied to the BMCs, which embody rich spatiotemporal inter-station connectivity, to facilitate mining stations with similar bike mobility. The methodology is applied for the comparative study of bike-sharing activities on workdays and holidays using data collected from New York Citi-Bike. The experimental results manifest locality patterns of bike mobility in both mobility chain indicators and multi-scale station embedding analysis results, and reveal disparities in BMC statistic indicators and spatial distribution mode of stations with similar bike mobility. These results can provide a new empirical reference for urban bike management in different time periods. As a more general implication, this study broadens the perspective for OD-based data analysis and paves the path to leverage the thriving trajectory-based research for further investigations. [ABSTRACT FROM AUTHOR]

Transactive energy systems use dynamic electricity markets to present an improved way to control large numbers of distributed energy resources while maintaining user privacy, autonomy and scalability. However, this access to dynamic electricity prices can introduce negative emergent behavior like high peak loads if not adequately regulated by grid operators. To solve this, this paper proposes a regulated peer-to-peer market structure for residential prosumers. Prosumers bid energy demand or supply curves into a peer-to-peer market, which can then be directly used by the utility to quantify and dispatch demand flexibility. A utility surcharge mechanism can then be employed to dispatch this flexibility, and achieve significant improvements to reliability. This framework is tested in a simulation environment of electrified residential buildings with significantly enhanced fidelity representing the real-world housing stock. New York State and the month of January was chosen for the demonstration purpose, considering that January is one of the most challenging months for fully electrified systems in cold climates as solar production occurs at opposite times of the highest heating load. Results show that the surcharge can reducing peak load and ramp rates by nearly 50%, while also encouraging consumption of locally generated electricity. • Unregulated peer-to-peer electricity markets neglect grid network constraints. • These markets can be regulated by a surcharge imposed by the grid operator. • This surcharge dispatches demand flexibility in a predictable, quantifiable manner. • Decentralized demand flexibility is then quantified by aggregating demand bid curves. • This regulation method is decentralized, scalable, and privacy-preserving. [ABSTRACT FROM AUTHOR]

In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio's value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, which is solved using a multi-objective genetic algorithm. To show the performance of this procedure, we use a data set of asset prices from the New York Stock Market. • The portfolio's value time series implicitly includes correlations among the assets. • Expected return and risk of a portfolio are derived by predicting its future value. • Efficient portfolios are provided by a multi-objective genetic algorithm. [ABSTRACT FROM AUTHOR]

Traffic prediction (forecasting) is a key problem in intelligent transportation. It helps engineers to obtain traffic trends in advance so that they can make favorable decisions quickly and accurately, or even improve the working mode of existing systems. However, it is very challenging to design a model for such problem that fully utilize the factors related to traffic. This paper investigates machine learning in traffic prediction and proposes Multiple Information Spatial–Temporal Attention based Graph Convolution Networks (MISTAGCN). The model consists of two parts. The first part utilizes combinations of different inputs and graph structures to compute the corresponding latent variables. In the second part of the model, the latent variables are comprehensively integrated and deeply mined. In particular, as the basic component of the model, a STBlock integrates mechanisms such as temporal attention, spatial attention, graph convolution, ordinary convolution and residual connections to fully explore the potential information contained in the data. Experiments on Haikou online car-hailing dataset and New York yellow taxi trip dataset illustrate that the proposed model outperforms state-of-art baseline models. • A novel deep learning model for multi–steps traffic prediction problems is proposed. • A unified STBlock structure is devised to process multi channel information. • The same STBlock structure is utilized for information fusion. • Novel formulas are devised to calculate spatial and temporal attentions. • Extensive experiments are conducted to verify the competitiveness of this model. [ABSTRACT FROM AUTHOR]

Over the past several years, police reformers in the United States have tried to develop alternatives to arrest and encourage police to use them more widely, but this work has moved forward with little awareness of similar efforts in the past. This paper provides an overview of one important strand of that history. Drawing on original archival research, it reconstructs the origins of the summons in lieu of arrest in early-20th century New York, and it chronicles the subsequent spread and transformation of this new form of police authority throughout the country. I argue that this story challenges the dominant understanding of the character of 20th century police reform. Where most historians have argued that early 20th century reforms aimed to focus police attention more narrowly on crime control, the police summons arose as an important tool to accommodate the broad array of tasks that American society increasingly expected the police to handle. Where most historians have argued that Progressive-era police reform was driven by an emerging corps of national policing experts who aimed to brush away the dysfunctional practices of the past, the development of the summons emerged from an intensely local process of experimentation that tried to understand and build on the foundation of existing practices. In these respects, the development and spread of the summons provides a distinctive image of the forms that police reform has taken in the past, as well as the forms it might potentially take in the future. [ABSTRACT FROM AUTHOR]

Creating predictive models to process large amounts of information is a major challenge for which the latest big data techniques provide solutions. In this paper, we present a methodology based on time series algorithms and multiplex networks focused on Big Data solutions. This methodology allows to process a large amount of information and obtain a way to group the most effective and useful information, allowing to solve problems with a large number of time-varying variables in an efficient way. This approach offers the possibility of predicting the evolution of housing prices using cab rides between different areas of the city. The methodology presented to combine all this information is based both on the original use of some unsupervised machine learning techniques and on the use of certain attributes of the time series and their representation as a complex multiplex network, achieving a very significant reduction in the dimensionality of the resulting data representation. The result is a forecast that reduces the representation of cab rides to a small dataset able to forecast real estate prices. [ABSTRACT FROM AUTHOR]

• Typical inspection intervals may be dangerously long for unhealthy bridges. • WPHM associates bridge health states with their failure probabilities. • SMDP determines optimal bridge inspection and repair policies. • Inspection/repair policies depend on pre-specified inspection intervals. • Proposed policy found to save almost 30% in total costs for example bridge. Routine bridge inspections enable timely maintenance plans based on potential risks. These inspections are performed at fixed time intervals, which may be unnecessarily short for bridges in excellent conditions and precariously long for those in poor conditions. Also, deterioration models used in current bridge management approaches fail to include the effect of age, causing inaccurate predictions. This paper develops a hazard-based bridge management approach where inspection frequencies and repair suggestions are optimized based on hazard levels. A two-dimensional Markov model describes the age-dependent bridge deterioration, and a semi-Markov decision process optimizes inspection intervals and repair decisions. The objective is to minimize the expected long-term total annual costs, including expenses associated with maintenance and traffic detours. The modeling process and maintenance management framework was demonstrated on an example bridge superstructure in New York. The results showed lower annual and total costs than the conventional management method. As expected, sensitivity analyses indicated that the pre-specified choices of inspection intervals for low and high hazard levels have a significant effect on the calculated hazard threshold levels. The proposed maintenance management method is adaptable and practical; bridge authorities can identify appropriate repair actions and make inspection decisions based on simple threshold comparisons. [ABSTRACT FROM AUTHOR]

Background: Several studies have pointed to a sub-sample of people who use drugs (PWUD) who prefer the use of non-pharmaceutical fentanyl (NPF) and, as such, pose a unique challenge for public health initiatives amidst the continued rise in overdose mortality. However, matters of drug preference and autonomy of choice remain under-studied and often misunderstood. This paper examined the experiences of PWUD reporting a preference for NPF or an NPF-heroin mixture, specifically how they navigate the perceived benefits of NPF and its established risks.Methods: 22 in-depth interviews were conducted in New York City between March 2018 and August 2019 with PWUD who self-reported a preference for NPF or an NPF-heroin mix. Interviews were audio-recorded, and the resulting transcripts analyzed using a thematic approach.Results: Participants highlighted various factors that contributed to expressed preference for NPF or an NPF-heroin mix, including a desire to feel good, financial resources, drug availability, decreased consumer autonomy, and physiological demand. Participants reported practicing several risk reduction strategies; however, they highlighted that many, particularly carrying naloxone and always using with someone else, were difficult to implement in the context of illicit drug use.Conclusions: Our results demonstrate participants' decreased consumer agency and greater exposure to systemic factors in the illicit markets, highlighting the need for expansion of various services, including drug checking resources and systems of outreach for PWUD who do not use intravenously. To promote tailored interventions, continued efforts in overdose prevention ought to more thoughtfully consider the context, perceptions, preferences, and behaviors of PWUD. [ABSTRACT FROM AUTHOR]

To explore the presence of predatory food and beverage marketing in different neighborhoods in New York City (NYC), this study describes the methodology of an outdoor environmental scan of the physical environment. The study was conducted in four NYC neighborhoods over a three-week period, in which pairs of trained researchers canvassed designated neighborhoods to document the presence of food and beverage marketing using photographs taken on digital smart phone devices. Commercial areas in the vicinity of NYC Public Schools and NYC Housing Authority campuses located in four neighborhoods with the highest and lowest nutrition related health indicators were studied: South Bronx, Pelham Throggs Neck, Upper West Side, Chelsea/Greenwich Village. Advertisements were coded against 50+ indicators to quantify pertinent variables including the frequency and content of food and beverages advertised and all forms of predatory marketing observed. Comparisons of prevalence and content of food and beverage advertisements and predatory marketing were made across neighborhoods with the highest and lowest health indicators, using chi-squared analysis, and a significance level of p < 0.05. This article demonstrates a disproportionate presence of predatory marketing in low income NYC neighborhoods with negative health outcomes compared to wealthier neighborhoods. Further, this paper demonstrates the benefits and limitations of using an environmental scan methodology to assess predatory food and beverage marketing in a large urban area such as NYC. [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]

• Two-dimensional Markov chains describe bridge health conditions. • WPHM associates the Markovian states with bridge failure probabilities. • Q-learning optimizes the repair scheme of single bridge. • Integer programming helps allocate the maintenance budget and adjust repair schemes. • Deficient budgets notably affect networks' maintenance plans and carbon emissions. Current bridge management systems provide maintenance strategies that balance costs and structural safety of bridge networks. However, they are cost-oriented rather than sustainability-oriented. Moreover, the existing literature lacks research on reducing the environmental impacts of a bridge network during operation and maintenance periods. This paper develops a condition-based maintenance approach for highway bridges, aiming to minimize the total carbon emissions of bridge networks subject to maintenance budget constraints. A two-dimensional Markov chain model is applied to predict the deterioration processes of bridges, and a Q-learning algorithm is proposed to determine the maintenance strategy for a single bridge. Then, integer programming optimizes the budget allocation process for a bridge network. The proposed method is demonstrated using the concrete bridges in New York state. Sensitivity analyses indicate the impacts of budget levels on maintenance plans and total carbon emissions. 10% drop in budget level results in 30 additional bridge repairs delayed as well as 39 tonne extra annual carbon emissions. The proposed optimization framework would contribute to intelligent infrastructure asset management and sustainable society. [ABSTRACT FROM AUTHOR]

Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility. The asymmetric nature of bike demand causes the need for rebalancing bike stations, which is typically done during nighttime. To determine the optimal starting inventory level of a station for a given day, a User Dissatisfaction Function (UDF) models user pickups and returns as non-homogeneous Poisson processes with piece-wise linear rates. In this paper, we devise a deep generative model directly applicable in the UDF by introducing a variational Poisson recurrent neural network model (VP-RNN) to forecast future pickup and return rates. We empirically evaluate our approach against both traditional and learning-based forecasting methods on real trip travel data from the city of New York, USA, and show how our model outperforms benchmarks in terms of system efficiency and demand satisfaction. By explicitly focusing on the combination of decision-making algorithms with learning-based forecasting methods, we highlight a number of shortcomings in literature. Crucially, we show how more accurate predictions do not necessarily translate into better inventory decisions. By providing insights into the interplay between forecasts, model assumptions, and decisions, we point out that forecasts and decision models should be carefully evaluated and harmonized to optimally control shared mobility systems. • Predictive and prescriptive performance does not always align. • Predictive models should be evaluated on inventory decision quality. • Variational Poisson RNN formulated for pickup and return rate prediction. • Experiments with synthetic and real-world data demonstrate solution approach. • The misalignment between prediction and decision objectives is quantified. [ABSTRACT FROM AUTHOR]

• Redlined areas have lower percent tree cover and forest ecosystem services than non-redlined areas. • Redlined areas in cities have higher percent impervious cover than non-redlined areas. • Differences among redline classes are dominated by differences in impervious cover. • As impervious cover increases, tree cover and associated ecosystem services tend to decrease. In the 1930's the Federal Home Loan Bank Board established a program to appraise real estate risk levels in several cities. Four classes indicating level of security for real estate investments were developed: A (green) – best, B (blue) – still desirable, C (yellow) – declining, and D (red) – hazardous. Recent studies have shown that heat island effects are greater, imperviousness is higher, and tree cover lower in areas that were formerly redlined (class D). This paper analyzed all redlined areas in U.S. cities and confirms that redlined areas (class D) have lower tree cover, greater impervious cover and lower forest ecosystem service values than other classes, with tree cover declining and impervious cover increasing as security risk class increased. Nationally, tree cover averaged 40.1 percent in class A and 20.8 percent in Class D; impervious cover averaged 30.6 in Class A and 53.0 percent in Class D. Loss of annual ecosystem services in riskier redlined areas (classes B-D) compared to the highest rated zone (class A) conservatively equates to $308 million nationally if classes B-D had the percent tree cover exhibited in Class A. At the city scale, these losses in foregone services can reach up to $100 million per year (New York, NY). As percent tree cover and percent tree cover stocking declines as percent impervious cover increases, differences in the physical impervious structure of the redlined areas at the time of designation influence tree cover differences. As redlined areas were often delimited in areas with higher population density and impervious cover, these areas tend have lower tree cover today. Even if stocking levels were increased to levels exhibited in Class A (57.8%) among all classes, tree cover would still decrease as class risk increases due to less available greenspace in lower graded classes. These patterns illustrate the importance of impervious cover on the distribution of ecosystem services and understanding the impacts of redlining practices. City policies could be directed to help offset these disparities by enhancing tree cover and reducing impervious cover in these under-resourced areas. [ABSTRACT FROM AUTHOR]

Access to and affordability of electric vehicle (EV) charging infrastructure are the two prominent barriers for EV adopttion. While major efforts are underway in the United States to roll-out public EV charging infrastructure, persistent social disparities in EV adoption call for interventions. In this paper, we analyze the existing EV charging infrastructure across New York City (NYC) to identify such socio-demographic and transportation features that correlate with the current distribution of EV charging stations. Our results demonstrate that population density is not correlated with the density of EV chargers, hindering New York's EV adoption and decarbonization goals. On the contrary, the distribution of EV charging stations is heavily skewed against low–income, Black–identifying, and disinvested neighborhoods in NYC, however, positively correlated to presence of highways in a zip code. The results underscore the need for policy frameworks that incorporate equity and justice in the roll-out of EV charging infrastructure. [ABSTRACT FROM AUTHOR]

Sookhan et al.'s paper consists of two parts: an analysis of glacial bedforms in the Seneca-Cayuga Finger Lake area and a paleoglacial history following the Valley Heads readvance. This comment concerns only the paleoglacial history. The biggest problem with their history is that a radiocarbon age was used for the Valley Heads readvance whereas a calendar age was used for the Bølling Allerød interstadial, leading to their erroneous conclusion that the entire Quaternary fill of the Cayuga trough was deposited in a single pulse lasting only a few hundred years. The duration between the Valley Heads readvance and the end of proglacial deposition there was actually about 4000 years. There are also serious questions concerning their descriptions of the nature of the deposits during and after the Valley Heads readvance. Sookhan et al.'s claim of subaqueous and "chaotically-bedded outwash" is without any observational support. Because the prerequisites for their model of southward ice acceleration do not exist, that model is suspect and an alternative is suggested. [ABSTRACT FROM AUTHOR]

• Introduces the concept of the Near-on-Demand Mobility service. • Proposes algorithms for large-scale request assignment and vehicle rebalancing. • Assesses the impact of design aspects on its performance using data from China and United States. • Develops a simulation platform facilitating the evaluation of a wide range of TNC designs. Mobility-On-Demand (MoD) services have been transforming the urban mobility ecosystem. However, they raise a lot of concerns for their impact on congestion, Vehicle Miles Traveled (VMT), and competition with transit. There are also questions about their long-term survival because of inherent inefficiencies in their operations. Considering the popularity of the MoD services, increasing ride-pooling is an important means to address these concerns. Shareability depends not only on riders' attitudes and preferences but also on operating models deployed by providers. The paper introduces an advance requests operating model for ride pooling, users may request rides in advance of their desired departure times. A platform with efficient algorithms for request matching, vehicle routing, rebalancing, and flexible user preferences is developed. Data from Chengdu, China, and New York, United States are used to evaluate the performance of the advance requests system. The impact of various design aspects of the system (e.g. advance requests horizon, vehicle capacity) on its performance is investigated. The sensitivity of the results to user preferences in terms of the level of service (time to be served and excess trip time), willingness to share and place requests in advance, and traffic conditions are explored. The results suggest that significant benefits in terms of sustainability, level of service, and fleet utilization can be realized when advance requests are along with an increased willingness to share. Furthermore, even "near-on-demand" (relative short planning horizons) operations can offer benefits for all stakeholders (users, operators, cities). [ABSTRACT FROM AUTHOR]

• Optimal risk-informed participation strategies of producers depend on T&D coordination schemes. • Sequence of Gate Closure Times for wholesale and distribution markets introduces uncertainty in the profit of the strategic producer. • Regulated distribution environment is the most lucrative setting for strategic producers to maximize profits without risk exposure. • Sequential market-clearing model reduces the profit of strategic producer as compared to joint market-clearing model. • Incorporation of CVaR for quantifying risk renders the strategic producer more risk-averse at the expense of reduced profits. Power producers can exhibit strategic behavior in electricity markets to maximize their profits. This behavior is more pronounced with the deregulation of distribution markets, which offers an opportunity for profit arbitrage between transmission and distribution (T&D) markets. However, the temporally distinct nature of these two markets introduces a significant risk in profit for such producers. This paper finds its motivation from the perspective of a strategic producer and develops a Single Leader Multi-Follower (SLMF) game for deriving its participation strategies in T&D markets, while accounting for different T&D coordination schemes based on the individual market Gate Closure Times (GCT). We compare and contrast joint and sequential market-clearing models with regulated and deregulated distribution environments and evaluate the risk of producer by leveraging consistent and coherent risk measures. SLMF game is reformulated as a Mathematical Program with Equilibrium Constraints (MPEC) and is solved using the seminal Scholtes's relaxation scheme. We validate the efficacy of our model and solution approach via the case study carried out on the 11-zone New York ISO, and 7-bus Manhattan power networks, used as T&D markets, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs , including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016–2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors. • Permutation-based analysis is utilized to analyze influenza spread. • Surprise analysis enables simultaneous testing of multiple hypotheses. • Static and temporal networks can be analyzed through surprise analysis. • Influenza spread data is from an observational study conducted in Western New York. • Reveals patterns in influenza spread, accounting for socio-economic factors. [ABSTRACT FROM AUTHOR]

In the present work, a comprehensive numerical model was developed to predict the levelised cost of energy (LCOE) for offshore wind farms. A case study is further performed based on the potential developments at the offshore area of the New York State. In the present work, some specific local limitations in the United States are considered by following in line with the present European development experience. A ten-year historical wind data set is used to evaluate the wind farm energy production. The effects of distance to shore, rated power, life span, operation height, farm capacity and seasonal operation plan on LCOE are evaluated. An optimal site giving an LCOE of 123.4 $2018/MWh is found in this paper. In addition, a novel factor named as wind farm energy density (WFED) is suggested in the present study. It shows that when considering the limited coastal area as an issue, a large capacity wind farm may not have good performance compared with a lower capacity wind farm in terms of energy production. The 508 MW wind farm has a better WFED compared with either a 330 MW wind farm or an 800 MW wind farm under the current investigation. • LCOE analysis for a potential offshore wind development at the New York State is provide. • Ten years historical wind data is used for estimating the power output. • LCOE of three different rated power wind turbines is calculated. • An optimal site giving an LCOE of 123.4 $2018/MWh is found. • A novel factor – wind farm energy density factor is raised. [ABSTRACT FROM AUTHOR]