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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]

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]

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]

Researchers have addressed uncertainty in multicriteria decision making from the perspective of the performance values of the alternatives, weighting of the evaluation criteria, and the evaluation methods. Still, they are yet to address the uncertainty caused by the normalization approach. In this paper, we show that different normalization methods, namely sum normalization, min–max normalization, vector normalization, and maximization normalization, can result in different rankings of the alternatives while the performance values and weights are unchanged. We applied the grey system theory to address the problem of uncertainty in this study from three aspects: alternative performance values measurement, criteria weighting, and decision matrix/table normalization within a period. The grey hybrid normalization method is proposed as the main contribution in this paper. Then, we present the rankings of 48 cities under uncertainty to decide the location of a branch office of a Chinese electric vehicle manufacturer as a practical example based on the grey entropy weighting method and grey relational analysis with positive and negative references (GRA-PNR) within the period from the year 2019 to 2021. The research results using this approach ranked New York City the best, with a stock market capitalization of economy validity as the top contributor in terms of weighting. Finally, we used simple additive weighting with grey value and the technique for order of preference by similarity to ideal solution with grey value methods to validate the study results. [ABSTRACT FROM AUTHOR]

Researchers have addressed uncertainty in multicriteria decision making from the perspective of the performance values of the alternatives, weighting of the evaluation criteria, and the evaluation methods. Still, they are yet to address the uncertainty caused by the normalization approach. In this paper, we show that different normalization methods, namely sum normalization, min–max normalization, vector normalization, and maximization normalization, can result in different rankings of the alternatives while the performance values and weights are unchanged. We applied the grey system theory to address the problem of uncertainty in this study from three aspects: alternative performance values measurement, criteria weighting, and decision matrix/table normalization within a period. The grey hybrid normalization method is proposed as the main contribution in this paper. Then, we present the rankings of 48 cities under uncertainty to decide the location of a branch office of a Chinese electric vehicle manufacturer as a practical example based on the grey entropy weighting method and grey relational analysis with positive and negative references (GRA-PNR) within the period from the year 2019 to 2021. The research results using this approach ranked New York City the best, with a stock market capitalization of economy validity as the top contributor in terms of weighting. Finally, we used simple additive weighting with grey value and the technique for order of preference by similarity to ideal solution with grey value methods to validate the study results. [ABSTRACT FROM AUTHOR]

Assessment caps lead to inequities because the same growth limit is applied to high and low growth properties. High growth properties that fall under the cap have assessments artificially lowered relative to prices resulting in lower effective tax rates. This paper presents a method to transition out of a capped assessment system where instead of stipulating the percentage increase in assessment, this paper explores limiting the rate of value that can be captured, defined as the difference in the market value and the capped assessed value. Unlike the assessment cap approach which benefits high-growth properties, this approach conversely shifts the burden to high growth properties that have benefited from the cap. With the value recapture approach the burden is shifted to properties that have benefitted most from artificially low assessments, are furthest from market values, and therefore have more value to recapture. The inequities resulting from capped assessment are well known and can be of two forms - horizontal and vertical inequity. Using New York City data we use Gini measures of equity to show that the value recapture approach converges to an equitable state faster than when assessment increases are capped. The measure of vertical equity is the Modified Kakwani Index whereas the index for horizontal equity is introduced in this paper for the first time as the ratio of the withingroup Gini to the citywide Gini. The within-group Gini is the weighted average of the subregion Ginis. Horizontal equity exists when all the subregions have the same Gini as the citywide Gini, or that the ratio is one. With NYC data, we show that it is possible for the ratio to be at 1 within five years of transitioning out of capped assessments. [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]

Shared bicycles provide a green, environmentally friendly, and healthy mode of transportation that effectively addresses the "final mile" problem in urban travel. However, the uneven distribution of bicycles and the imbalance of user demand can significantly impact user experience and bicycle usage efficiency, which makes it necessary to predict bicycle demand. In this paper, we propose a novel shared-bicycle demand prediction method based on station clustering. First, to address the challenge of capturing patterns in station-level bicycle demand, which exhibits significant fluctuations, we employ a clustering method that combines graph information from the bicycle transfer graph and potential energy. This method aggregates closely related stations into corresponding prediction regions. Second, we use the GCN-CRU-AM (Graph Convolutional Network-Gated Recurrent Unit-Attention Mechanism) model to predict bicycle demand in each region. This model extracts the spatial information and correlation between regions, integrates time feature data and local weather data, and assigns weights to the input features. Finally, experimental results based on the data from Citi Bike System in New York City demonstrate that the proposed model achieves a more accurate demand prediction. [ABSTRACT FROM AUTHOR]

The physical condition of an occupant's home represents a significant factor in determining the dweller's overall quality of life. This paper provides a statistical framework for measuring housing quality in an urban area through a standardized index. This index is constructed using principal component analysis, incorporating demographic, geographic, and economic factors from the New York City Housing and Vacancy Survey. This metric allows for investigating differences in housing quality based upon ownership status. Analysis of the index demonstrates that renters face more housing quality issues than owners. Several of the index's input variables driving these differences were found to exhibit varying effects on housing quality over time, possibly due to events such as the 2008 financial crisis. Implementing this novel statistical framework, housing quality indices can be constructed for other cities to examine housing disparities and inform policies aimed at improving quality of life for urban residents. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation and compression of ride demand from origin-destination (OD) trip event data. By representing the OD event data as a three-way tensor (origin, destination, and time), we model the data as a Poisson process with an intensity tensor that can be decomposed according to a Tucker decomposition. We establish and justify a specific form of nonnegative Tucker-like tensor decomposition that represents OD demand via K latent origin spatial factors and K latent destination spatial factors. We then provide a computational and memory efficient algorithm for performing this decomposition and demonstrate its use for real-time compression and estimation of OD ride demand. Two case studies based on New York City (NYC) taxi and Washington DC (DC) taxi were implemented. Results from the case studies demonstrate the applicability of the proposed method in data compression and short-term forecast for ride demand. Furthermore, we found that the learned latent spatial factors are interpretable and localized to specific areas for both NYC and DC cases. Hence, this method can be used to understand OD trip data through latent spatial factors and be used to identify spatio-temporal patterns for OD trip and travel demand generation mechanism in general. Funding: This work was supported by the U.S. Department of Transportation [UTC/NCST] and the U.S. National Science Foundation [Grant DMS 1712996]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0101. [ABSTRACT FROM AUTHOR]

Presents abstracts of papers presented at the Association of American Geographers' 1942 meeting in New York City. "Rural-Urban Population Ratios," by Alford Archer; "Land Classification in the Matanuska Valley," by Meredith F. Burrill; "Siberian Resources for Soviet Warfare," by George B. Cressey; "Geographical distribution of human productivity," by Ellsworth Huntington. [ABSTRACT FROM AUTHOR]

Introduction: point prevalence surveys have been used as a standardized tool to monitor antibiotic consumption to inform antimicrobial stewardship interventions in many countries. The 2021 WHO model list of Essential Medicines has classified antibiotics into three groups: access, watch and reserve. The aim of this paper is to describe the antibiotics used within a space of three years between 2018 and 2021 at the University of Uyo Teaching Hospital based on WHO AWaRe classification. Methods: three point-prevalence surveys were conducted in the wards in our 500-bed tertiary hospital in 2018, 2019 and 2021. Each ward was surveyed on a particular day within a four-week period. The wards were grouped into medical and surgical for comparison. Antibiotics were classified as access, watch, and reserve. Validated data were analyzed with IBM SPSS Statistics for Windows, Version 20.0 (Armonk, NY: IBM Corp.). Results: a total of 526 patients were surveyed out of which 344 were on antimicrobial therapy with a total of 687 antibiotic prescriptions. The overall prevalence of patients who received at least one antimicrobial was 65.4% (62.4 -72.8%). The Access group of antibiotics made up 48.2% of prescriptions while the watch group made up 50.5% of prescriptions. More watch Antibiotics were prescribed by surgical wards (49.7%) than by medical wards (43.7%). Conclusion: the use of Access group antibiotics in our hospital falls below the WHO target level in both medical and surgical wards. There is a need for strengthening antibiotic stewardship activities to reduce the use of watch group antibiotics and limit antimicrobial resistance. [ABSTRACT FROM AUTHOR]

With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user's work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user's decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) are expected to operate on mobility-on-demand (MoD) platforms because AV technology enables flexible self-relocation and system-optimal coordination. Unlike the existing studies, which focus on MoD with pure AV fleet or conventional vehicles (CVs) fleet, we aim to optimize the real-time fleet management of an MoD system with a mixed autonomy of CVs and AVs. We consider a realistic case that heterogeneous boundedly rational drivers may determine and learn their relocation strategies to improve their own compensation. In contrast, AVs are fully compliant with the platform's operational decisions. To achieve a high level of service provided by a mixed fleet, we propose that the platform prioritizes human drivers in the matching decisions when on-demand requests arrive and dynamically determines the AV relocation tasks and the optimal commission fee to influence drivers' behavior. However, it is challenging to make efficient real-time fleet management decisions when spatiotemporal uncertainty in demand and complex interactions among human drivers and operators are anticipated and considered in the operator's decision making. To tackle the challenges, we develop a two-sided multiagent deep reinforcement learning (DRL) approach in which the operator acts as a supervisor agent on one side and makes centralized decisions on the mixed fleet, and each CV driver acts as an individual agent on the other side and learns to make decentralized decisions noncooperatively. We establish a two-sided multiagent advantage actor-critic algorithm to simultaneously train different agents on the two sides. For the first time, a scalable algorithm is developed here for mixed fleet management. Furthermore, we formulate a two-head policy network to enable the supervisor agent to efficiently make multitask decisions based on one policy network, which greatly reduces the computational time. The two-sided multiagent DRL approach is demonstrated using a case study in New York City using real taxi trip data. Results show that our algorithm can make high-quality decisions quickly and outperform benchmark policies. The efficiency of the two-head policy network is demonstrated by comparing it with the case using two separate policy networks. Our fleet management strategy makes both the platform and the drivers better off, especially in scenarios with high demand volume. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Singapore Ministry of Education Academic Research [Grant MOE2019-T2-2-165] and the Singapore Ministry of Education [Grant R-266-000-135-114]. [ABSTRACT FROM AUTHOR]

Violence involving firearms in the USA is a very important problem. As a consequence, a large number of crimes of this type are recorded every year. However, the solutions proposed have not managed to reduce the number of this type of crime. One of the cities with a large number of violent crimes is New York City. The number of crimes is not homogeneous and depends on the district where they occur. This paper proposes to study the information about the crimes in which firearms are involved with the aim of characterizing the factors on which the occurrence of this type of crime depends, such as the levels of poverty and culture. Since the districts are not homogeneous, the information has been analyzed at the district level. For this, data from the open data portal of the city of New York have been used and machine-learning techniques have been used. The results have shown that the variables on which they depend are different in each district. [ABSTRACT FROM AUTHOR]

Efficient vertical transportation is vital to a skyscraper's functional operation and the convenience and satisfaction of its tenants. This review complements the author's previously published research by updating the readers on innovative hardware and software-based solutions. It lays out, organizes, and combines extensive and scattered material on numerous aspects of elevator design in a straightforward and non-technical narrative. Rope-less elevators, the MULTI, artificial intelligence (AI), the Internet of Things (IoT), and extended reality technologies are some of the developments and advancements this article examines. The analysis also contextualizes current technical developments by reviewing how they are used in significant projects such as the One World Trade Center in New York City. Lastly, the paper examines innovative technologies, such as holographic elevator buttons and ultraviolet rays that disinfect elevators, in response to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The investigation of linguistic landscapes (LL) among the Arab community in downtown Brooklyn, New York City, is an underserved public space in the literature. This research focused on social and commercial or 'bottom-up signs' in LL to understand their purpose, origin and target audience. Drawing upon discourse analysis, the study was conceptualized according to the principles of border theory and geosemiotics. The latter was used to analyze the data, which consisted of random photographs of shopfronts in Brooklyn taken with a digital camera during the summer of 2016. The three semiotic aggregates used for analysis consisted of interaction order, visual and place semiotics. The data analysis showed the multi-layered nature of LL in this urban community and the subjectiveness of spatial borders through a combination of text and symbolic imagery. The paper highlights the importance of commercial signs in the LL among ethnic minority communities. [ABSTRACT FROM AUTHOR]

Three events occurred in the second half of 1946 in three adjoining US States (NJ, NY, and PA) which marked the birth of Hydrosilylation Technology. They occurred before the landmark 1957 JACS paper and the 1958 issued US patent by Speier et al. and before Chalk and Harrod named the reaction. First, on 27 June 1946, Mackenzie et al., of Montclair Research Corp., applied for a patent to prepare addition compounds of hydridosilanes and unsaturated organic compounds. Then, on 9 October 1946, Wagner and Strother of Union Carbide Corp. applied for a patent on a process to produce organic compounds of silicon with Si–C bonds by reacting a hydridosilane and an alkene or alkyne in the presence of a catalyst metal of the platinum group. Finally, Sommer et al., submitted a paper on peroxide-catalyzed hydrosilylation to JACS on 17 December 1946. It was published in January 1947. The landmark patent interference § and priority § case law associated with the Mackenzie et al. and Wagner et al., applications is well known to patent attorneys. This presentation will retrace the origins of hydrosilylation and report events (1946–1960) in the history of the reaction that are most probably unknown to most authors and presenters of hydrosilylation investigations. George Wagner's contribution to the birth of this technology is also highlighted. [ABSTRACT FROM AUTHOR]

This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people's attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users' demographic information, we conduct regression analysis to analyze what factors affected people's attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic. [ABSTRACT FROM AUTHOR]

Taxi travel time estimation based on real-time traffic flow collection in IoT has been well explored; however, it becomes a challenge to use the limited taxi data to estimate the travel time. Most of the existing methods in this scenario rely on shallow feature engineering. Nevertheless, they have limited performance in learning complex moving patterns. Thus, a Latent Semantic Pulse Sequence-based Deep Neural Network (LSPS-DNN) is proposed in this paper to improve the taxi travel time estimation performance by constructing a latent semantic propagation graph representing the latent path sequence. It first extracts the shallow modal features of trips, such as the time period and spatial location at different granularities. The representation of the pulse propagation graph is then extracted from shallow spatial features using a Pulse Coupled Neural Network (PCNN). Further, the propagation graph is encoded with negative sampling to obtain the embedding of deep propagation features between ODs. Meanwhile, we conduct deep network learning based on the Chengdu and NYC taxi datasets; our experimental evaluation results show it has a better performance compared to traditional feature construction methods. [ABSTRACT FROM AUTHOR]

With progressive technological advancements, the time for electric vehicles (EVs) and unmanned aerial vehicles (UAVs) has finally arrived for the masses. However, intelligent transportation systems need to develop appropriate protocols that enable swift predictive communication among these battery-powered devices. In this paper, we highlight the challenges in message routing in a unified paradigm of electric and flying vehicles (EnFVs). We innovate over the existing routing scheme by considering multi-objective EnFVs message routing using a novel modified genetics algorithm. The proposed scheme identifies all possible solutions, outlines the Pareto-front, and considers the optimal solution for the best route. Moreover, the reliability, data rate, and residual energy of vehicles are considered to achieve high communication gains. An exhaustive evaluation of the proposed and three existing schemes using a New York City real geographical trace shows that the proposed scheme outperforms existing solutions and achieves a 90 % + packet delivery ratio, longer connectivity time, shortest average hop distance, and efficient energy consumption. [ABSTRACT FROM AUTHOR]

Background: Healthy NYC is an innovative survey panel created by the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) that offers a cost-effective mechanism for collecting priority and timely health information. Between November 2020 and June 2021, invitations for six different surveys were sent to Healthy NYC panelists by postal mail, email, and text messages. Panelists had the option to complete surveys online or via paper survey. Methods: We analyzed whether panelists varied by sociodemographic characteristics based on the contact mode they provided and the type of invitation that led to their response using logistic regression models. Poisson regression models were used to determine whether the number of invitations received before participating in a survey was associated with sociodemographic characteristics. Results: Younger age and higher education were positively associated with providing an email or text contact. Furthermore, age, race, and income were significant predictors for invitation modes that led to a survey response. Black panelists had 72% greater odds (OR 1.72 95% CI: 1.11–2.68) of responding to a mail invite and 33% lesser odds (OR 0.67, 95% CI: 0.54–0.83) of responding to an email invite compared with White panelists. Additionally, in five of the six surveys, more than half of the respondents completed surveys after two invites. Email invitations garnered the highest participation rates. Conclusions: We recommend using targeted invitation modes as an additional strategy to improve participation in panels. For lower-income panelists who do not provide an email address, it may be reasonable to offer additional response options that do not require internet access. Our study's findings provide insight into how panels can tailor outreach to panelists, especially among underrepresented groups, in the most economical and efficient ways. [ABSTRACT FROM AUTHOR]

The objective of the Ground to Space CALibration Experiment (G-SCALE) is to demonstrate the use of convex mirrors as a radiometric and spatial calibration and validation technology for Earth Observation assets, operating at multiple altitudes and spatial scales. Specifically, point sources with NIST-traceable absolute radiance signal are evaluated for simultaneous vicarious calibration of multi- and hyperspectral sensors in the VNIR/SWIR range, aboard Unmanned Aerial Vehicles (UAVs), manned aircraft, and satellite platforms. We introduce the experimental process, field site, instrumentation, and preliminary results of the G-SCALE, providing context for forthcoming papers that will detail the results of intercomparison between sensor technologies and remote sensing applications utilizing the mirror-based calibration approach, which is scalable across a wide range of pixel sizes with appropriate facilities. The experiment was carried out at the Rochester Institute of Technology's Tait Preserve in Penfield, NY, USA on 23 July 2021. The G-SCALE represents a unique, international collaboration between commercial, academic, and government entities for the purpose of evaluating a novel method to improve vicarious calibration and validation for Earth Observation. [ABSTRACT FROM AUTHOR]

This paper introduces a new software interface to elicit belief distributions of any shape: Click-and-Drag. The interface was tested against the state of the art in the experimental literature--a text-based interface and multiple sliders--and in the online forecasting industry--a distribution-manipulation interface similar to the one used by the most popular crowd-forecasting website. By means of a pre-registered experiment on Amazon Mechanical Turk, quantitative data on the accuracy of reported beliefs in a series of induced-value scenarios varying by granularity, shape, and time constraints, as well as subjective data on user experience were collected. Clickand-Drag outperformed all other interfaces by accuracy and speed, and was self-reported as being more intuitive and less frustrating, confirming the pre-registered hypothesis. Aside of the pre-registered results, Click-and-Drag generated the least drop-out rate from the task, and scored best in a sentiment analysis of an open-ended general question. Further, the interface was used to collect homegrown predictions on temperature in New York City in 2022 and 2042. Click-and-Drag elicited distributions were smoother with less idiosyncratic spikes. Free and open source, ready to use oTree, Qualtrics and Limesurvey plugins for Click-and-Drag, and all other tested interfaces are available at https://beliefelicitation.github.io/. [ABSTRACT FROM AUTHOR]

Introduction: Symbol and digit substitution tasks are key instruments in the evaluation of attention, speed of processing, executive functioning, and cognitive dysfunction. The Match is an electronic (app-based) substitution task developed as part of the University of California San Francisco Brain Health Assessment that has been used to demonstrate cognitive impairment. We describe the paper-based oral symbol/number substitution task (Paper-Match), which was developed as part of the 5-Cog battery to assess for cognitive impairment including dementia and demonstrate it's use in an urban, multi-ethnic, socioeconomically disadvantaged primary care population in Bronx NY. Method: 109 participants, mean age 72.61 SD 6.42 and education 11.70 SD 3.82 years, 81% female, 48.6% Hispanic/Latino and 47.7% African American seen as part of 5-Cog. Participants (36.7% tested in Spanish) completed the Paper-Match (scored based on the number of correct matches in 90 seconds) and a comprehensive neuropsychological battery. Descriptive and correlational analysis were conducted. Results: Mean Paper-Match score was 38.24 SD 11.83 items correct (range 4–51). Paper-Match performance was highly correlated with performance on the Symbol Digit Modalities Test (r = .819, p < .001) and moderately correlated with performance on the Montreal Cognitive Assessment (MOCA; r = .604, p < .001). Years of education and primary language impacted performances. Conclusion: The results of this study provide the initial description and validation of the Paper-Match an oral symbol digit substitution task and its use in a diverse population of older primary care patients. Further research on the psychometric properties and construct validity of the Paper-Match are needed. [ABSTRACT FROM AUTHOR]

This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID‐19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in‐sample and out‐of‐sample. [ABSTRACT FROM AUTHOR]

Rock music is an integral part of American culture. This paper presents a study of sensing and analysing over 57,000 rock music live performances between 2007 and 2017. Spatial traces of 575 rock music artists performing in concerts nationwide were collected from a major music streaming platform Spotify. Location-based concert data were analysed to explore economic and geographic factors linked to the landscape of rock music live performance and to reveal the importance of population demographics and leisure and hospitality (LH) economics to the culture and music industries from a spatial aspect. Over 90% of rock concerts between 2007 and 2017 were found in 250 counties. The aim of the study is to specify and develop a model that reasonably accounts for spatial heterogeneity present in the concert data. By regressing rock concert data against demographic data and LH establishment data, ordinary least squares (OLS) models were better fitted in metropolitan counties than non-metropolitan counties. Spatial dynamics of concerts were revealed by local R2 values and the obtained structure in the form of spatial heterogeneity was then explained using geographically weighted regression (GWR) models. High population density and LH services in industry-leading cities such as New York City, Los Angeles, Chicago and Houston exhibit advantages in explaining rock concert distributions. Findings from the models reflect the live music industry's interrelationships to the LH industry and suggest LH services being essential considerations in selecting concert destinations for rock musicians. [ABSTRACT FROM AUTHOR]

Objectives: This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network. Methods: Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field. Results: 1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992–2000), the development period (2001–2015), and the rapid growth period (2016–2021). There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. Conclusions: Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking. [ABSTRACT FROM AUTHOR]

The Inner Shell Spectroscopy (ISS) beamline on the 8-ID station at the National Synchrotron Light Source II (NSLS-II), Upton, NY, USA, is a high-throughput X-ray absorption spectroscopy beamline designed for in situ, operando, and time-resolved material characterization using high monochromatic flux and scanning speed. This contribution discusses the technical specifications of the beamline in terms of optics, heat load management, monochromator motion control, and data acquisition and processing. Results of the beamline tests demonstrating the quality of the data obtainable on the instrument, possible energy scanning speeds, as well as long-term beamline stability are shown. The ability to directly control the monochromator trajectory to define the acquisition time for each spectral region is highlighted. Examples of studies performed on the beamline are presented. The paper is concluded with a brief outlook for future developments. [ABSTRACT FROM AUTHOR]

This paper studies a general framework for high-order tensor SVD. We propose a new computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to estimate the low tensor-train rank structure from the noisy high-order tensor observation. The proposed TTOI consists of initialization via TT-SVD [Oseledets (2011)] and new iterative backward/forward updates. We develop the general upper bound on estimation error for TTOI with the support of several new representation lemmas on tensor matricizations. By developing a matching information-theoretic lower bound, we also prove that TTOI achieves the minimax optimality under the spiked tensor model. The merits of the proposed TTOI are illustrated through applications to estimation and dimension reduction of high-order Markov processes, numerical studies, and a real data example on New York City taxi travel records. The software of the proposed algorithm is available online (https://github.com/Lili-Zheng-stat/TTOI). [ABSTRACT FROM AUTHOR]

Accurate travel time prediction is one of the most promising intelligent transportation system (ITS) services, which can greatly support route planning, ride-sharing, navigation applications, and effective traffic management. Several factors, like spatial, temporal, and external, have big effects on traffic patterns, and therefore, it is important to develop a mechanism that can jointly capture correlations of these components. However, spatial sparsity issues make travel time prediction very challenging, especially when dealing with the origin-destination (OD) method, since the trajectory data may not be available. In this paper, we introduce a unified deep learning-based framework named joint spatial-temporal correlation (JSTC) mechanism to improve the accuracy of OD travel time prediction. First, we design a spatiotemporal correlation block that combines two modules: self-convolutional attention integrated with a temporal convolutional network (TCN) to capture the spatial correlations along with the temporal dependencies. Then, we enhance our model performance through adopting a multi-head attention module to learn the attentional weights of the spatial, temporal, and external features based on their contributions to the output and speed up the training process. Extensive experiments on three large-scale real-world traffic datasets (NYC, Chengdu, and Xi'an) show the efficiency of our model and its superiority compared to other methods. [ABSTRACT FROM AUTHOR]

Even if shared mobility services are encouraged by transportation policies, they remain underused and inefficient transportation modes because they struggle to find their customer base. This paper aims to estimate the potential demand for such services by focusing on individual trips and determining the number of passengers who perform similar trips. Contrary to existing papers, this study focuses on the demand without assuming any specific shared mobility system. The experiment performed on data coming from New York City conducts to cluster more than 85% of the trips. Consequently, shared mobility services such as ride-sharing can find their customer base and, at a long time, to a significantly reduce the number of cars flowing in the city. After a detailed analysis, commonalities in the clusters are identified: regular patterns from one day to the next exist in shared mobility demand. This regularity makes it possible to anticipate the potential shared mobility demand to help transportation suppliers to optimize their operations. [ABSTRACT FROM AUTHOR]

Taxi demand forecasting plays an important role in ride-hailing services. Accurate taxi demand forecasting can assist taxi companies in pre-allocating taxis, improving vehicle utilization, reducing waiting time, and alleviating traffic congestion. It is a challenging task due to the highly non-linear and complicated spatial-temporal patterns of the taxi data. Most of the existing taxi demand forecasting methods lack the ability to capture the dynamic spatial-temporal dependencies among regions. They either fail to consider the limitations of Graph Neural Networks or do not efficiently capture the long-term temporal dependencies. In this paper, we propose a Spatial-Temporal Diffusion Convolutional Network (ST-DCN) for taxi demand forecasting. The dynamic spatial dependencies are efficiently captured through a two-phase graph diffusion convolutional network where the attention mechanism is introduced. Moreover, a novel temporal convolution module is designed to learn various ranges of temporal dependencies, including recent, daily, and weekly periods. Inside the module, convolution layers are stacked to handle very long sequences. Experimental results on two large-scale real-world taxi datasets from New York City (NYC) and Chengdu demonstrate that our method significantly outperforms seven state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Background: Proper evaluation of ocular fundi is an integral part of neurological examination. Unfortunately, neurology residents are increasingly uncomfortable performing fundoscopy and interpreting findings because of diminishing skills and lack of experience. This became more prominent during the COVID-19 pandemic as fundoscopy requires proximity to the patient. With the recent dramatic improvement of smartphone cameras, fundus photography using the PanOptic Ophthalmoscope (Welch Allyn, Skaneateles Falls, NY) with a smartphone adapter offered an alternative to direct fundoscopic examination. We present the first experience with our own design of a universal smartphone adapter.Methods: This is a single-center case series, consecutive for a single user and certain presenting neurological symptoms, which is aimed to evaluate the feasibility and practicality of a new, universal PanOptic smartphone adapter. Presenting symptoms included headache, ocular symptoms, seizure, or encephalopathy. We used 3D modeling and printing techniques to create the adapter. We also developed a methodology of capturing stereoscopic images of the optic disc using a single smartphone camera, but the method was not systematically evaluated in this paper.Results: Here we present our initial experience of fundus video/photography in patients, who presented with encephalopathy, headache, seizure, vision loss, and other ocular symptoms. Fundoscopic abnormalities were discovered in 11 out of 100 patients. Some were incidental findings and were unrelated to the presentation. In one case, fundoscopy played a critical role in establishing the correct diagnosis.Conclusions: Our custom-designed smartphone adapter allowed obtaining high-quality video and photo recordings using PanOptic Ophthalmoscope. The acquisition of high-quality photos enables a high-yield diagnostic tool and allows revisiting the image in the patient's chart. Improvement of smartphone cameras opens vast horizons for stereo-fundoscopy and 3D reconstruction of the ocular fundus without using sophisticated and costly equipment. Microscopic eye movements allow taking snapshots of two side-by-side photos for 3D reconstruction and stereoscopic image viewing, which is the next level of optic disc assessment. [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]

The built environment accounts for approximately 38% of global emissions. The evaluation of returns linked to investments in building decarbonization is impeded when confronted with uncertainties surrounding future energy prices, building emission regulations, and real estate market conditions. This paper develops an integrated framework that combines energy modeling with investment analysis to support the adoption of decarbonizing technologies under a context of uncertainty. The integrated framework includes projections of costs and benefits associated with the adoption of technology (i.e., changes in rental cashflows, vacancy rates, maintenance and energy costs) under a wide range of potential pathways of the energy markets and regulatory environment, and local real estate market demand for green buildings using Monte Carlo simulations. Additionally, this paper develops the approach to investigate the potential value of flexibility in designing for building electrification (i.e., the ability to fully electrify gas heating systems at a later year). We illustrate the use of this approach in a case study of a new office building in New York City, faced with design choices between natural gas and electrified heating systems. We consider 10,000 scenarios changing future greenhouse emission penalties, investments in the local electric grid, local real estate market conditions and energy prices. In approximately 60 percent of simulations, the most profitable decision for the building owner is to adopt a natural gas-powered heating system. However, adopting a building design that provides a building the flexibility to fully electrify at a later date is more profitable than a natural gas-heating building in 96 percent of scenarios. A sensitivity analysis shows that the most influential parameter in the valuation of a design option is the size of the green premiums associated with improved energy efficiency. [ABSTRACT FROM AUTHOR]

In our research on U.S. immigration detention (the Detention Economies project) in the greater New York City area, Freedom of Information Act (FOIA) requests have been a key data source. Throughout the research process our efforts to gather information in order to interrogate detention economies were thwarted. In this paper we consider the slow, bureaucratic machinations of the state, government information office omissions, and redactions in response to FOIA requests, with analytic intent. Drawing on cultural geography's engagements with 'absent presence' we examine the politics revealed by managing access to, and containing the flow of, information in this manner. We argue that through absent presence, the state contains migrants in immigration detention, constrains information and occludes knowledge about the infrastructure, operation, and beneficiaries of this bloated system, while, simultaneously, enlarging and consolidating power. Our discussion highlights some of the continuities as well as significant expansion of geographies of containment, with reference to immigration enforcement, from the Obama to the Trump administration. We also consider the importance of persistence, perseverance, and collective effort--or muddling through--as necessary research tactics for critical migration scholars and geographers amidst the present culture of obfuscation and secrecy that expands the geographies of state containment. [ABSTRACT FROM AUTHOR]

• An intelligent taxi dispatch system for big cities validated by real-world dataset. • Taxi demand and destination prediction based on historical data. • Realtime taxi assignment and reallocation strategy to optimize multiple goals. • Performance comparison with several popular prediction and optimization strategies. In this paper we describe an intelligent taxi dispatch system that has the goal of reducing the waiting time of the passengers and the idle driving distance of the taxis. The system relies on two separate models that predict the probability distributions of the taxi demand and destinations respectively. The models are learned from historical data and use a combination of long short term memory cells and mixture density networks. Using these predictors, taxi dispatch is formulated as a mixed integer programming problem. We validate the performance of the predictors and the overall system on a real world dataset of taxi trips in New York City. [ABSTRACT FROM AUTHOR]

There remains a great challenge to minimize the spread of epidemics, especially in high-density communities such as colleges and universities. This is particularly true on densely populated, residential college campuses. To construct class and residential networks data from a four-year, residential liberal arts college with 5539 students were obtained from SUNY College at Geneseo, a rural, residential, undergraduate institution in western NY, USA. Equal-sized random networks also were created for each day. Different levels of compliance with mask use (none to 100%), mask efficacy (50% to 100%), and testing frequency (daily, or every 2, 3, 7, 14, 28, or 105 days) were assessed. Tests were assumed to be only 90% accurate and positive results were used to isolate individuals. The effectiveness of contact tracing, and the effect of quarantining neighbors of infectious individuals, was tested. The structure of the college course enrollment and residence networks greatly influenced the dynamics of the epidemics, as compared to the random networks. In particular, average path lengths were longer in the college networks compared to random networks. Students in larger majors generally had shorter average path lengths than students in smaller majors. Average transitivity (clustering) was lower on days when students most frequently were in class (MWF). Degree distributions were generally large and right skewed, ranging from 0 to 719. Simulations began by inoculating twenty students (10 exposed and 10 infectious) with SARS-CoV-2 on the first day of the fall semester and ended once the disease was cleared. Transmission probability was calculated based on an R0 = 2.4. Without interventions epidemics resulted in most students becoming infected and lasted into the second semester. On average students in the college networks experienced fewer infections, shorter duration, and lower epidemic peaks when compared to the dynamics on equal-sized random networks. The most important factors in reducing case numbers were the proportion masking and the frequency of testing, followed by contact tracing and mask efficacy. The paper discusses further high-order interactions and other implications of non-pharmaceutical interventions for disease transmission on a residential college campus. [ABSTRACT FROM AUTHOR]

American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before–after comparisons typically used to evaluate the success of these countermeasures often suffer from selection bias. This paper motivates the tendency for selection bias to overestimate the benefits of traffic safety policy, using New York City's Vision Zero strategy as an example. The NASS General Estimates System, Fatality Analysis Reporting System and other databases are combined into a Bayesian hierarchical model to calculate a more realistic before–after comparison. The results confirm the before–after analysis of New York City's Vision Zero policy did in fact overestimate the effect of the policy, and a more realistic estimate is roughly two-thirds the size. [ABSTRACT FROM AUTHOR]

In 1947, the Cornell psychiatrist Keeve Brodman and a handful of colleagues began developing what would become one of the most widely used health questionnaires of its time—the Cornell Medical Index (CMI). A rigidly standardized form, the CMI presented 195 yes-no questions designed to capture the health status of "the total patient." Over the following decades, Brodman's project of standardizing medical history taking gradually evolved into a project of mathematizing and computerizing diagnosis: out of the CMI grew the Medical Data Screen (MDS), one of the first computerized methods of deriving diagnoses from patient data. This essay follows the life course of these tools through the second half of the twentieth century. It argues for a genealogy of biomedical computing and computerized diagnosis that takes more seriously the continuities between computer-based digital practices and paper-based analog ones. The computerized MDS evolved from, and rested upon, paper data practices associated with questionnaires and surveys. The interlocking histories of the CMI and the MDS prompt a reconsideration of the material and temporal parameters within which the history of computerized medicine has conventionally been understood. [ABSTRACT FROM AUTHOR]