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This paper analyses how Vision Zero (VZ) efforts in New York City (NYC) account for equity and social justice implications of road safety work. VZ policy documents, research literature, popular science and opinion articles on road safety work in the city were studied with a prime focus on equity and social justice. Twelve semi-structured interviews with stakeholders involved in road safety and transport planning in the city and at national level were conducted to gain an in-depth understanding of policy design, the adoption process, and the role of equity considerations in the city's road safety work. The results show that major equity and social justice issues arise in the adoption and implementation of VZ. These issues are primarily related to equity and fairness in the distribution of life saving interventions, the socio-economic impacts of road safety strategies, and the nature of community engagement in policy design and implementation. The findings point to a need for VZ practitioners to give due considerations to equity and social justice implications of VZ policies and strategies. Among others, it supports the need for understanding the nature of past equity and social justice problems in road safety and transport planning in the VZ policy design process. Moreover, the findings suggest the need for empirical studies on the socio-economic implications of VZ strategies and interventions. • There were major road safety related inequities and injustices prior to the adoption of Vision Zero in New York City. • The promotion of Vision Zero also gives rise novel equity and social justice concerns related to its adoption process and the choice of road safety strategies and interventions. • A successful and morally acceptable implementation of Vision Zero demands recognizing and proactively accounting for ethically problematic aspects of road safety work, including those related to equity and social justice. • Vision Zero should integrate equity and social justice into road safety work, for example, by adopting a broader conceptualization of a data driven approach that includes socioeconomic and community data in addition to quantitative crash data. • The promotion of Vision Zero should allow meaningful inclusion and participation of different road user and socioeconomic groups in the goal setting and implementation of Vision Zero policies. [ABSTRACT FROM AUTHOR]

As resilience planning gains prominence in urban sustainable development, green infrastructures (GIs) are widely recognized as effective solutions to address climate risks and environmental challenges. While urban greening initiatives have multiple benefits, they can also lead to unintended consequences such as environmental injustice, increasing property values, and the displacement and gentrification of vulnerable residents in the affected areas. While gentrification remains a significant concern for urban sustainability initiatives that aim to promote environmental justice, a consensus upon approaches and methods for precisely identifying communities undergoing gentrification is still lacking. While most of the existing research studies focused on examining the relationship between GIs and gentrification, little-to-no research was conducted to quantify the green gentrification vulnerability of different communities. To this end, and to bridge this knowledge gap, this paper proposes and develops a novel index called the green gentrification vulnerability index (GGVI). First, an extensive list of 35 indicators that contribute to green gentrification was identified and grouped into 7 categories: economic, social, housing, household, demographic, amenities, and GIs. Second, data were gathered on the 35 indicators for all the 2325 census tracts across New York City (NYC) in the US. Third, the data were preprocessed to ensure their suitability for implementing Principal Component Analysis (PCA). Fourth, PCA was implemented for each of the 7 categories separately to weight their respective green gentrification indicators and calculate vulnerability scores. Finally, these vulnerability scores were used to develop the GGVI. The research findings provided insights on the importance of the 35 indicators as well as the 7 green gentrification-related vulnerability categories in relation to their contribution to the GGVI. The 7 categories were ranked as follows: amenities, GIs, economic, demographic, social, housing, and households. In addition, proximity to parks, frequency of parks, proximity to GIs, and total area of parks were found to be the most important GIs-related indicators that could lead to green gentrification. Overall, this paper contributes to the growing body of research that addresses green gentrification by being the first study that proposes and develops a GGVI that can be used to identify at-risk communities from green gentrification and ultimately help practitioners, urban planners, decision-makers, and researchers in developing new policies and strategies aimed to mitigate the unintended consequences of green gentrification (i.e., displacement of vulnerable communities) and promote environmental justice and urban sustainability. • Green gentrification vulnerability was studied based on 35 indicators. • The importance of the different indicators was determined. • Principal Component Analysis was used to develop a GGVI. • The proposed index allows census-track green gentrification vulnerability analysis. [ABSTRACT FROM AUTHOR]

Recent research on police misconduct has adopted a network perspective, while recent work on correctional officer (CO) use of force has similarly framed it as an organizational behavior, learned through socialization and deployed with considerable discretion. This paper leverages the network paradigm in police misconduct research to study the use of force by COs. Using data on lawsuits involving the New York City Department of Correction from 2013 to 2022, this paper forms a co-offending network of those COs who have been sued for excessive force. This paper then uses descriptive social network analysis and key player selection and community detection methods. After describing the basic network structure of COs who use force together, this paper identifies brokers who connect disparate parts of the CO network; it also uses a community detection algorithm to identify clusters of COs involved in force incidents. Finally, the paper compares this network to an analogous network of police use of force, finding structural similarities. This analysis bridges disparate work on correctional officer use of force and police misconduct; for policymakers who seek to curtail COs' excessive use of force, this paper might provide a blueprint for identifying key players and clusters. • Correction officer use of force is an organizational, socialized behavior. • Network methods can be used to study both police and CO use of force. • CO use of force has a networked structure; I identify network brokers and clusters. • These results can guide the targeted disruption of excessive force in jails. [ABSTRACT FROM AUTHOR]

This paper critically examines the concept of "green resilience" – the mobilization of gardens and urban green spaces for enhancing the resilience of communities and cities in the face of increasing uncertainty and risk. Drawing inspiration from seminal work in human-plant geographies, it explores the governmental functions and political implications of green resilience, arguing that these spaces serve as key forms of state rule and biopolitical power. The study employs the notion of green resilience as a "composition," emphasizing the assembling of diverse human and nonhuman actors in resilience projects. The developed compositional ontology highlights the importance of aesthetic and visual practices in green resilience projects and reveals how these are continuously decomposed and recomposed by the involved actors. The paper develops its arguments through a "paradigmatic case study" of various green resilience projects in New York City – including survivor trees, memorial groves, and community gardens. The study illuminates the multiplicity of green resilience initiatives and the underlying governmental logic by comparing these projects. Through its analysis, the paper contributes to the literature on resilience as an emerging governance paradigm. It reveals how green resilience projects embody a multiplicity of practices, uncovers the first comprehensive comparative analysis of various forms of green resilience, and offers a critique of resilience from within. The study concludes by identifying three tensions or contradictions of (green) resilience as a form of biopolitical government: visibility and invisibility, knowledge and ignorance, and control and emergence. [ABSTRACT FROM AUTHOR]

In this paper I bring together scholarship from the 'reparative turn' in the social sciences with the long tradition of critiquing schooling in the sociology of education. Most recently in this tradition, Ball and Collet-Sabé (2021) argue convincingly that the modern school is an 'intolerable institution' bound in its very epistemology to reproduce conditions of future uncertainty and inequity. I draw on Hong's (2014) framing of neoliberalism as a process of erasure and disavowal in order to link Ball and Collet-Sabé 's contention with the concept of reparative futures (Sriprakash et al. 2020). I do so through a consideration of my 2014–15 ethnography of Bronx High, a public high school in New York City. Through the story of seniors at Bronx High, I demonstrate how schooling is profoundly oriented towards particular anticipatory practices - namely, a modernist, neoliberal reckoning of the future that is both illusory in its promises and unsustainable in its ambitions. Bronx High exemplifies the deleterious impacts of living in a future-oriented present that appears wilfully detached from injustices of the past. However, this is not the whole story. The day-to-day actions of teachers and students reveal complex, multiple engagements with future-reckoning, including moments, often at the ambivalent margins of practice, where reparative futures-thinking offers a different way of engaging with the past, the present, and life after high school. • This paper explores reparative futures thinking for reimagining education beyond the modern school. • I explore this through my 2014–15 ethnography of a US public high school. • Through the story of seniors at Bronx High, I demonstrate how schooling is profoundly oriented towards particular anticipatory practices - namely, a modernist reckoning of the future. • However, the day-to-day actions reveal complexengagements with future-reckoning, including examples of reparative futures-thinking. [ABSTRACT FROM AUTHOR]

Spine surgery accounts for a large proportion of neurosurgical procedures, with approximately 313 million spine surgeries conducted annually worldwide. Considering delayed recovery and postoperative complications that are commonly reported, there has been a recent shift toward minimally invasive spine procedures conducted under local anesthesia. Despite proven success, there exists a limited body of literature on the use of awake surgery in spinal procedures. A bibliometric analysis was conducted to map the current landscape of work in this field. 190 articles were identified from the Web of Science (Clarivate, NY) database. A comprehensive bibliometric analysis was performed on a narrowed list of the most relevant articles using Bibliometrix, an R-based programming tool. There has been a rise in academic papers published on the topic of awake spine surgery since 2016, with an increase in publication count by approximately 18% annually and each article cited approximately ten times on average to date. The year 2022 saw an uptick in publications, with 9 throughout the entire year. The most impactful article, with a total of 95 citations, was published by Sairyo et al.1 Thematic analysis revealed that the terms "lumbar spine" and "stenosis" are well-developed topics in the literature, whereas the topics of "complications," "fusion," and "cost-analysis" are less well-developed topics. This study provides a comprehensive overview of the most-cited articles in the field of awake spine surgery. Specifically, it identifies areas that are well represented in the literature and those which are underrepresented and should be areas of continued future research. [ABSTRACT FROM AUTHOR]

Drones can be used for tasks such as data collection and logistics in civil engineering. Current research on autonomous drones mainly focuses on planning a safe path and avoiding obstacles in a static environment. However, navigating a drone in complex environments like urban areas involves many dynamic constraints, such as building layout, winds, and signal coverage, which are interdependent. The wind factor is the most important among these environmental factors, which may cause a drone to lose control or even crash. This paper presents a multi-objective navigation reinforcement learning algorithm (MONRL) for the drone to navigate and avoid obstacles in an unknown environment when dynamic wind zones present, with only imagery data about the building layouts. Based on a deep reinforcement learning and memory architecture, the drone develops policies to prioritize navigation decisions, optimizing the path while minimizing negative impact of winds with only sparse sensor data, in our case, camera inputs. By leveraging the advantages of the proposed method in estimating the environmental factors from previous trials, no aerodynamic force sensors are needed for the drone to develop effective strategies to navigate to target while counteracting to milder winds and dodging aways from stronger winds. The proposed method was tested in a virtual environment, and a real model of New York City. The method is expected to contribute to new automation algorithms for urban aerial logistics and future automated civil infrastructure inspection. • A multi-objective reinforcement learning method was proposed for autonomous drones to avoid dynamic winds while navigating. • By leveraging a memory RL architecture, no aerodynamic force sensors are needed to sense and react to winds. • A framework for representing complex aerodynamic forces with mathematical formulation was proposed to supportRL trials. • Experiments performed in a VR simulator with data from New York City to confirm the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

• Deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. • The performance of the proposed approach is benchmarked against Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). • Using New York City cycle as a validation drive cycle, the approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. • The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. • The proposed method has 38% less training time when compared to the DDQN approach. Fuel cell hybrid electric vehicles offer a promising solution for sustainable and environment friendly transportation, but they necessitate efficient energy management strategies (EMSs) to optimize their fuel economy. However, designing an optimal leaning-based EMS becomes challenging in the presence of limited training data. This paper presents a deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. The approach introduces a deep REINFORCE framework with a deep neural network baseline and entropy regularization to develop a stochastic policy for EMS. The performance of the proposed approach is benchmarked against three EMSs: i) a state-of- art deep deterministic reinforcement learning technique called Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). Using New York City cycle as a validation drive cycle, the deep REINFORCE approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. Moreover, the training results show that the proposed algorithm reduces training time by 38% compared to the DDQN approach. The proposed deep REINFORCE-based EMS shows superiority not only in terms of fuel economy, but also in terms of dealing with epistemic uncertainty. [ABSTRACT FROM AUTHOR]

[Display omitted] • NY could selectively detect Cu2+ in aqueous solutions. • NY had a detection limit for Cu2+ as low as 13 nM. • NY had mechanochrotic properties by exhibiting orange-red fluorescence when subjected to mechanical grinding. • NY was successfully applied to the imaging of human HeLa cells and targeting mitochondria. In this paper, a novel coumarin-derived fluorescent probe NY was designed and synthesized. NY displayed a significant ratiometric fluorescence response towards Cu2+ in PBS buffer (10 mM, pH = 7.4), with the emission wavelength blue-shifted from 580 to 495 nm, and a fluorescence change from orange to green was evident under a 365 nm UV light. Meanwhile, NY had the advantages of high selectivity, short response time (5 min), low detection limit (1.3 × 10-8 M) and large binding constant (1.45 × 105 M-1) towards Cu2+. The binding mechanism between NY and Cu2+ was elucidated by FT-IR, 1H NMR titration, TOF-MS and Job's plot analysis. In addition, NY was successfully employed in the detection of Cu2+ within environmental water and vegetable samples with satisfactory results. Laser confocal microscopy imaging results showed that NY could easily penetrate HeLa cells membrane to target mitochondria and image Cu2+ in living cells. Furthermore, NY demonstrated mechanochromic properties by exhibiting orange-red fluorescence when subjected to mechanical grinding. [ABSTRACT FROM AUTHOR]

Freight transportation is critical to metropolitan regions' economic competitiveness and long-term viability. However, it is one of the activities that produce the most negative externalities, including greenhouse gas emissions. The study of the relationship between freight transportation, land use, and demand management is critical for promoting enlightened policies that increase the social benefits of supply chains while avoiding their undesirable effects, such as climate change. Nonetheless, the tools available to public planners for incorporating freight transportation into their analyses are limited. In this context, this paper aims to highlight the impact that freight demand management strategies and freight-efficient land uses can have on increasing urban freight accessibility. In this study, the authors suggest a freight accessibility framework based on two measures of accessibility (an isochrone-based model and a gravity-based model) to analyze the spatiotemporal variation of freight accessibility in the New York City metropolitan statistical area for four sectors—retail trade, accommodation and food services, transportation and warehousing, and e-commerce. The results indicate that accessibility measures provide valuable insights towards policymaking, particularly to policies that target land use (e.g., location of warehouses), and policies that aim to shift the traffic of freight vehicles to periods with less traffic congestion. [ABSTRACT FROM AUTHOR]