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"Breaks in the Air: The Birth of Rap Radio in New York City" by John Klaess is a significant contribution to the history of New York hip-hop culture. The book explores the early days of rap radio in New York, when stations like WBLS played a crucial role in shaping the sound and culture of hip-hop. Klaess uses interviews and cassette recordings to provide insights into the inseparable relationship between rap, radio, and diverse listening communities. The book also examines the impact of deregulation on the commercial radio industry and the role of radio in advancing Black social progress. Overall, "Breaks in the Air" offers a valuable perspective on the history of rap radio in New York City. [Extracted from the article]

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

Throughout the 1970s, a drive towards the diversification of mediums available to artists led to artists working across numerous mediums not usually associated with visual art. While some came to work with sound as a central focus in their practice, most used sound as a medium to be employed in particular artworks for conceptual purposes. The paper is focused on five audio and sound exhibitions presented in New York City between 1978 and 1984. These large group exhibitions were held in alternative art spaces and not-for-profit art galleries and have received little critical attention beyond reviews published in local newspapers and art magazines. As such, these exhibitions have all but disappeared from the history of sound in art and from art history in general. I will argue that this cluster of group shows signal to an emergent practice that was moving past the post-medium condition and conceptual art towards the postmodernism of the 1980s. These exhibitions point to an understanding of sound as a medium of visual art that is at odds with contemporary scholarship in the sonic arts, which favours a music-based understanding of approaches to sound within the art gallery. [ABSTRACT FROM AUTHOR]

The critical platform studies literature is increasingly considering the role of social difference as a structuring logic in the platform economy, complementing understandings of worker precarity facilitated by worker misclassification and algorithmic management. Contributing to this literature, this paper demonstrates how platforms and police produce carceral geographies that manage and exploit immigrant delivery workers as surplus populations. The carceral geographies of the platform economy account for both how carceral space produces and manages the surplus populations from which platform capital draws its workers, facilitating the disposability and exploitation of workers. Focusing on South Asian delivery workers in New York City, the paper uses the example of bike registrations to show how police and platforms expand carceral spaces in immigrant communities, increasing their vulnerability to premature death and violence. Finally, it suggests how delivery worker organising offers instances of situated resistance that challenge platform capital and carceral logics. [ABSTRACT FROM AUTHOR]

This paper makes the case for the connection between making land-use regulatory changes and the process of destruction and redevelopment. Under the capitalist imperative, buildings that do not fulfil the full potential for profit are likely to be demolished (or refurbished) but demolition and new development are not shaped exclusively by the immutable laws of the market as they are mediated and facilitated by specific institutional contexts. Using the case of East Midtown rezoning in New York City, the paper examines the amendments of two land-use regulatory mechanisms: the enlargement of development rights (rezoning or upzoning) and the relaxation of the spatial limitations on the usage of existing unused rights (transferable development rights). While apparently unconnected, upzoning and transferable development rights are part of the regulatory framework that seeks to secure the ongoing generation of the highest-possible profits for private as well as public interests. By examining the choreography of rezoning and transferable development rights, the paper shows how the mechanics of creative destruction work while substantiating an existing body of knowledge on land use policies and practices in New York City. When used together, rezoning and transferable development rights are instrumental in remaking the built environment. Essentially, the rezoning of a 78-block area in East Midtown Manhattan unlocks captured and latent development rights that otherwise could not come about, and demonstrates the necessity of institutional arrangements to make creative destruction actually work. [ABSTRACT FROM AUTHOR]

Greenery initiatives, such as green infrastructures (GIs), create sustainable and climate-resilient environments. However, they can also have unintended consequences, such as displacement and gentrification in low-income areas. This paper proposes an integrated machine learning (ML) approach that combines both unsupervised and supervised ML algorithms. First, 35 indicators that contribute to green gentrification were identified and categorised into 7 categories: social, economic, demographic, housing, household, amenities, and GIs. Second, data was collected for all census tracts in New York City. Third, the green gentrification susceptibility was modelled into 6 levels using k-means clustering analysis, which is an unsupervised ML model. Fourth, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) was used to map the census tracts to their green gentrification susceptibility level. Finally, different supervised ML algorithms were trained and tested to predict the green gentrification susceptibility. The results showed that the artificial neural network (ANN) model is the most accurate in classifying and predicting the green gentrification susceptibility with an overall accuracy of 96%. Moreover, the outcomes showed that the Normal Difference Vegetation Index (NDVI), the proximity to GIs, the GIs frequency, and the total area of GIs were identified as the most important indicators to predict green gentrification susceptibility. Ultimately, the proposed approach allows practitioners and researchers to perform micro-level (i.e. on the census-tracts level) predictions and inferences about green gentrification susceptibility. This allows more focused and targeted mitigation actions to be designed and implemented in the most affected communities, thus promoting environmental justice. [ABSTRACT FROM AUTHOR]

Drones are becoming indispensable in emergency search and rescue (SAR), particularly in intricate urban areas where rapid and accurate response is crucial. This study addresses the pressing need for enhancing drone navigation in such complex, dynamic urban environments, where obstacles like building layouts and varying wind conditions create unique challenges. Particularly, the need for adapting drone autonomous navigation in correspondence with dynamic wind conditions in urban settings is emphasized because it is important for drones to avoid loss of control or crashes during SAR. This paper introduces a pioneering method integrating multiobjective reinforcement learning (MORL) with a convolutional autoencoder to train autonomous drones in comprehending and reacting to aerodynamic features in urban SAR. MORL enables the drone to optimize multiple goals, whereas the convolutional autoencoder generates synthetic wind simulations with a substantially lower computation cost compared to traditional computational fluid dynamics (CFD) simulations. A unique data transfer structure is also proposed, which fosters a seamless integration of perception and decision-making between machine learning (ML) and reinforcement learning (RL) components. This approach uses imagery data, specific to building layouts, allowing the drone to autonomously formulate policies, prioritize navigation decisions, optimize paths, and mitigate the impact of wind, all while negating the necessity for conventional aerodynamic force sensors. The method was validated with a model of New York City, offering substantial implications for enhancing automation algorithms in urban SAR. This innovation enables the possibility of more efficient, precise, and timely drone SAR operations within intricate urban landscapes. [ABSTRACT FROM AUTHOR]

Puerto Rico is one of the world's oldest colonies, with thousands of its people dislocated to the United States (U.S.) mainland in the wake of Hurricane Maria and the ongoing economic crisis. However, since the 2019 protests ousting then governor Rosello, Puerto Ricans across the Diaspora are imagining new emancipatory realities, including the possibility of independence. This paper draws on data from the Colonial Mentality Study in New York City (CMS‐NYC, N = 19) to explore how Puerto Ricans in the Diaspora narrativize new political possibilities despite the challenges posed by post‐disaster migration and racial and economic inequality. Using a decolonial psychoanalytic approach, we show how two colonial logics—moving "up and out" of Puerto Rico and "up and in" American capitalism—are textured by discourses of racial inferiority and upward mobility, and illustrate how these are experienced by Puerto Ricans who identify as Multiracial (Multiracial‐Identified Puerto Rican, N = 11), and Puerto Ricans who identify as Black (BIPR, N = 8). Reading our findings in the sociogenic context of race, class, and colonialism in Puerto Rico, and race and class among Puerto Ricans in NYC, we explore how racism toward Puerto Ricans and racism among Puerto Ricans intersect with notions of upward mobility, revealing how anti‐Blackness supports economic inequality in the U.S. mainland alongside with Puerto Rico's colonial situation. Complementing decolonial psychoanalytic theory with the Afro‐Puerto Rican radical tradition, we outline the implications of this research for future scholarship, clinical practice, and political action. [ABSTRACT FROM AUTHOR]

Examining the impacts of ranked choice voting (RCV) on voter efficacy is important as more areas consider adoption. The greater number of choices provided by longer RCV ballots may introduce opportunities for voters to misunderstand the ballot, make errant marks, or accidentally mark two or more candidates for one ranking, resulting in voidable ballots due to "overvoting." Using ballot data from the 2013 general election, the 2017, and 2021 New York City democratic mayoral primaries, this paper asks whether voidable overvote ballots are more concentrated in constituencies with lower levels of educational attainment, average household incomes, and differing racial make-ups, and if this relationship is more pronounced under RCV than traditional elections. In the first RCV election in 2021, voters in locations with lower levels of educational attainment and median household incomes had higher shares of overvote voidable ballots than those in locations with higher educational attainment and incomes. [ABSTRACT FROM AUTHOR]

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as "sensors", which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. [ABSTRACT FROM AUTHOR]

Community-clinical linkage models (CCLM) have the potential to reduce health disparities, especially in underserved communities; however, the COVID-19 pandemic drastically impacted their implementation. This paper explores the impact of the pandemic on the implementation of CCLM intervention led by community health workers (CHWs) to address diabetes disparities among South Asian patients in New York City. Guided by the Consolidated Framework for Implementation Research (CFIR), 22 stakeholders were interviewed: 7 primary care providers, 7 CHWs, 5 community-based organization (CBO) representatives, and 3 research staff. Semi-structured interviews were conducted; interviews were audio-recorded and transcribed. CFIR constructs guided the identification of barriers and adaptations made across several dimensions of the study's implementation context. We also explored stakeholder-identified adaptations used to mitigate the challenges in the intervention delivery using the Model for Adaptation Design and Impact (MADI) framework. (1) Communication and engagement refers to how stakeholders communicated with participants during the intervention period, including difficulties experienced staying connected with intervention activities during the lockdown. The study team and CHWs developed simple, plain-language guides designed to enhance digital literacy. (2) Intervention/research process describes intervention characteristics and challenges stakeholders faced in implementing components of the intervention during the lockdown. CHWs modified the health curriculum materials delivered remotely to support engagement in the intervention and health promotion. (3) community and implementation context pertains to the social and economic consequences of the lockdown and their effect on intervention implementation. CHWs and CBOs enhanced efforts to provide emotional/mental health support and connected community members to resources to address social needs. Study findings articulate a repository of recommendations for the adaptation of community-delivered programs in under-served communities during a time of public health crises. [ABSTRACT FROM AUTHOR]

This paper explores the ways in which Mexican immigrants experience, narrate, and describe stress and the extent to which it impacts their efforts at engaging in physical activity using a biocitizenship framework. Data were derived from a mixed-method study among Mexicans living in New York City recruited from a large Catholic church. The qualitative sample of 25 participated in quantitative and qualitative components of the study and as such we include some of these quantitative indicators as descriptors. Our main qualitative findings reveal that study participants experience stress and time constraint as factors that contribute to the waning of their physical and mental well-being. As such, time constraints for many of our participants were among the factors that contributed to high perceived levels of stress. They attributed this to their difficulty maintaining a physically active lifestyle due to factors like the fast-paced lifestyle in New York, working long hours, and not having enough time to exercise, though some important differences in narratives were noted across gender. Findings have implications for interventions aimed at improving the health of immigrants in general and Mexican immigrants in New York City specifically. [ABSTRACT FROM AUTHOR]

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]

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset. [ABSTRACT FROM AUTHOR]

In this paper we consider working-class abolitionist feminists' conceptualizations of safety. Taking seriously the notion that "freedom is a place," we look at how working-class South Asians and Indo Caribbeans in New York City build livable worlds at the intersecting crises of capitalism, white supremacy, and patriarchy. We look at Desis Rising Up and Moving (DRUM)'s organizing efforts following the expansion of immigrant detention and policing that came after the 1996 immigration laws and their contemporary organizing around gender-based violence and oppression as examples of a materialist articulation of abolition feminism. Drawing on DRUM's archive and autoethnographic reflections, we show how DRUM develops and practices an abolition feminism that addresses both systematized racial state violence and patriarchal violence in working-class Asian American communities. We show how DRUM's experiments with community-based intervention (CBI) around domestic violence aims to reduce violence and meet the material needs of gender-oppressed individuals in the community, while expanding the horizon for socialized care and a world without police. [ABSTRACT FROM AUTHOR]

Lateral roots, including adventitious roots, are the main component of rapeseed roots with support, absorb, and synthesis functions and their morphological parameters directly affecting the plant's aboveground growth and yield. Root biomass, as a material base for lateral root growth, can be used as a link between plant phenotypes and their physiological processes, as well as to enhance root 3D growth model mechanisms and accuracy. To quantify the relationships between lateral root morphological indices and the corresponding organ biomass for rapeseed, we used two cultivars, NY 22 (conventional) and NZ 1818 (hybrid), and conducted cultivar and fertilizing cylindrical tube experiments during the 2016–2019, with two fertilizer levels, no fertilizer, and 180 kg N ha−1 fertilizer. The lateral root biomass and morphological parameters were determined during the whole growth period. The biomass‐based lateral root morphological parameter models were developed by analyzing the quantitative relationship between the lateral root morphological indices and their corresponding biomass, and the descriptive models were verified with independent experimental data. The results showed that the correlation (r) of simulated and observed values for the lateral root morphological parameters are all greater than 0.9 with significant levels at p < 0.001. The absolute values of the average absolute difference (da) of simulated and observed values for the lateral root length (LLR), lateral root average diameter (ADLR), lateral root surface area (SALR), and lateral root volume (VLR) are −30.408 cm, −0.003 mm, 12.902 cm2, and 0.039 cm3, respectively. The RMSE values are 175.183 cm, 0.010 mm, 59.710 cm2, and 1.513 cm3, respectively. The ratio of da to the average observed values (dap) for the LLR and VLR are all less than 5%, and the ADLR and SALR are all <6%. The models developed in this paper have good performance and reliability for predicting lateral root morphological parameters of rapeseed. The study provides a mechanistic method for linking the rapeseed growth model with the morphological model using corresponding organic biomass and laying a good foundation for establishing a 3D morphological model for rapeseed root system based on biomass. [ABSTRACT FROM AUTHOR]

Ride-sharing contributes significantly to lowering trip expenses, easing traffic congestion and decreasing air pollution. However, current order pairing approaches in ride-sharing usually focus on minimizing total trip distances or maximizing platform profits, overlooking the drivers' desire for increased earnings. As a result, drivers might provide dishonest information to gain higher profits, leading to inefficient order pairing for the ride-sharing platform and potential losses for both the platform and drivers. In this paper, we address this challenging issue by developing efficient order pairing mechanisms that maximize the social welfare of the platform and drivers. Specifically, we introduce two truthful auction-based order pairing mechanisms, SWMOM-VCG and SWMOM-GM, where drivers bid on platform-published orders to complete them and earn profits. We provide theoretical proof that both mechanisms fulfill the criteria of individual rationality, profitability, truthfulness and so on. Using real taxi order data from New York City, we assess the performance of both mechanisms and show that they achieve greater social welfare compared to existing methods. Additionally, we find that SWMOM-GM requires less computation time than SWMOM-VCG for order pairing, with only a minor reduction in social welfare. [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]

The outbreak of COVID-19 caused unprecedented disruptions to public transport services. As such, this paper proposes a methodology for analyzing COVID-19 impact on public transport mobility. The proposed methodology includes: (1) a new unsupervised machine learning (UML) method, which utilizes a decoder-encoder architecture and a flow property-based learning objective function, to estimate the origin-destination (OD) flows of public transport systems from boarding-alighting data; and (2) a temporal-spatial analysis method to analyze OD flow change before and during COVID-19 to unveil its impact on mobility across time and space. The validation of the UML method showed that it achieved a coefficient of determination of 0.836 when estimating OD flows using boarding-alighting data. Upon the successful validation, the proposed methodology was implemented to analyze the impact of COVID-19 on the mobility of the New York City subway system. The implementation results indicate that (1) the rise in the number of weekly new COVID-19 cases intensified the impact on the public transport mobility, but not as strongly as public health interventions; and (2) the inflows to and outflows from the center of the city were more sensitive to the impact of COVID-19. • A new methodology for analyzing COVID-19 impact on public transport mobility. • It includes a proposed unsupervised ML method to estimate public transport system OD flows from boarding-alighting data. • It includes a temporal-spatial analysis method to analyze OD flow changes before and during COVID-19. • The proposed methodology was implemented in analyzing COVID-19 impact in the New York City area. • The methodology could support analyzing the impact of natural disasters on public transport mobility across time and space. [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]

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]

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]