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Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers. [ABSTRACT FROM AUTHOR]

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods. [ABSTRACT FROM AUTHOR]

We are starting to understand the magnitude of economic damage from the COVID-19 pandemic. Current estimates cover the national or state level, but tell us little about how massive business closures may be affecting urban vitality at the intrametropolitan level. A particular concern is whether urban areas with high poor or minority populations are more deeply affected. This paper combines InfoGroup Historic Business Data and Google Map API to analyze business closures at the neighborhood level in Franklin County, Ohio, encompassing the Columbus Metropolitan Area. As expected, retail and restaurant sectors had the highest number of closures, but closure rates were higher in other sectors. Descriptive and multivariate analyzes reveal that downtown Columbus has been severely affected, but no disadvantaging effect for communities of color or neighborhoods with concentrations of poor residents is found to be statistically significant. [ABSTRACT FROM AUTHOR]

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually. • PySWMM is an open-source Python wrapper for EPA SWMM. • Embedding SWMM into Python's scientific computing ecosystem expands its capabilities. • Two utility CSO management applications leveraging PySWMM for real-time control and decision support are described. • Application results demonstrate how intelligent control can help reduce CSOs by tens of millions of gallons annually. [ABSTRACT FROM AUTHOR]

Environmental surveillance of pathogens underlying infectious disease is critical to ensure public health. Recent efforts to track SARS-CoV-2 have utilized wastewater sampling to infer community trends in viral abundance and variant composition. Indoor dust has also been used for building-level inferences, though to date no sequencing data providing variant-scale resolution have been reported from dust samples, and strategies to monitor circulating variants in dust are needed to help inform public health decisions. In this study, we demonstrate that SARS-CoV-2 lineages can be detected and sequenced from indoor bulk dust samples. We collected 93 vacuum bags from April 2021 to March 2022 from buildings on The Ohio State University's (OSU) Columbus campus, and the dust was used to develop and apply an amplicon-based whole-genome sequencing protocol to identify the variants present and estimate their relative abundances. Three variants of concern were detected in the dust: Alpha, Delta, and Omicron. Alpha was found in our earliest sample in April 2021 with an estimated frequency of 100%. Delta was the primary variant present from October of 2021 to January 2022, with an average estimated frequency of 91% (±1.3%). Omicron became the primary variant in January 2022 and was the dominant strain in circulation through March with an estimated frequency of 87% (±3.2%). The detection of these variants on OSU's campus correlates with the circulation of these variants in the surrounding population (Delta p<0.0001 and Omicron p = 0.02). Overall, these results support the hypothesis that dust can be used to track COVID-19 variants in buildings. [ABSTRACT FROM AUTHOR]

Background: Adoption of electronic health care records (EHRs) has proliferated since 2000. While EHR transitions are widely understood to be disruptive, little attention has been paid to their effect on health professions trainees' (HPTs) ability to learn and conduct work. Veterans Health Administration's (VA) massive transition from its homegrown EHR (CPRS/Vista) to the commercial Oracle Cerner presents an unparalleled-in-scope opportunity to gain insight on trainee work functions and their ability to obtain requisite experience during transitions. Objective: To identify how an organizational EHR transition affected HPT work and learning at the third VA go-live site. Design: A formative mixed-method evaluation of HPT experiences with VHA's EHR transition including interviews with HPTs and supervisors at Chalmers P. Wylie VA Outpatient Clinic in Columbus, OH, before (~60 min), during (15–30 min), and after (~60 min) go-live (December 2021–July 2022). We also conducted pre- (March 2022–April 2022) and post-go live (May 2022–June 2022) HPT and employee surveys. Participants: We conducted 24 interviews with HPTs (n=4), site leaders (n=2), and academic affiliates (n=2) using snowball sampling. We recruited HPTs in pre- (n=13) and post-go-live (n=10) surveys and employees in pre- (n=408) and post-go-live (n=458) surveys. Approach: We conducted interviews using a semi-structured guide and grounded prompts. We coded interviews and survey free text data using a priori and emergent codes, subsequently conducting thematic analysis. We conducted descriptive statistical analysis of survey responses and merged interview and survey data streams. Key Results: Our preliminary findings indicate that the EHR transition comprehensively affected HPT experiences, disrupting processes from onboarding and training to clinical care contributions and training-to-career retention. Conclusions: Understanding HPTs' challenges during EHR transitions is critical to effective training. Mitigating the identified barriers to HPT training and providing patient care may lessen their dissatisfaction and ensure quality patient care during EHR transitions. [ABSTRACT FROM AUTHOR]

Background: Significant heterogeneity exists among people who use drugs (PWUD). We identify distinct profiles of syringe service program (SSP) clients to (a) evaluate differential risk factors across subgroups and (b) inform harm reduction programming. Methods: Latent class analysis (LCA) was applied to identify subgroups of participants (N = 3418) in a SSP in Columbus, Ohio, from 2019 to 2021. Demographics (age, sex, race/ethnicity, sexual orientation, housing status) and drug use characteristics (substance[s] used, syringe gauge, needle length, using alone, mixing drugs, sharing supplies, reducing use, self-reported perceptions on the impact of use, and treatment/support resources) were used as indicators to define latent classes. A five-class LCA model was developed, and logistic regression was then employed to compare risk factors at program initiation and at follow-up visits between latent classes. Results: Five latent classes were identified: (1) heterosexual males using opioids/stimulants with housing instability and limited resources for treatment/support (16.1%), (2) heterosexual individuals using opioids with stable housing and resources for treatment/support (33.1%), (3) individuals using methamphetamine (12.4%), (4) young white individuals using opioids/methamphetamine (20.5%), and (5) females using opioids/cocaine (17.9%). Class 2 served as the reference group for logistic regression models, and at the time of entry, class 1 was more likely to report history of substance use treatment, overdose, HCV, sharing supplies, and mixing drugs, with persistently higher odds of sharing supplies and mixing drugs at follow-up. Class 3 was more likely to report history of overdose, sharing supplies, and mixing drugs, but outcomes at follow-up were comparable. Class 4 was the least likely to report history of overdose, HCV, and mixing drugs, but the most likely to report HIV. Class 5 was more likely to report history of substance use treatment, overdose, HCV, sharing supplies, and mixing drugs at entry, and higher reports of accessing substance use treatment and testing positive for HCV persisted at follow-up. Conclusions: Considerable heterogeneity exists among PWUD, leading to differential risk factors that may persist throughout engagement in harm reduction services. LCA can identify distinct profiles of PWUD accessing services to tailor interventions that address risks, improve outcomes, and mitigate disparities. [ABSTRACT FROM AUTHOR]

LIDAL (Light Ion Detector for ALTEA, Anomalous Long-Term Effects on Astronauts) is a radiation detector designed to measure the flux, the energy spectra and, for the first time, the time-of-flight of ions in a space habitat. It features a combination of striped silicon sensors for the measurement of deposited energy (using the ALTEA device, which operated from 2006 to 2012 in the International Space Station) and fast scintillators for the time-of-flight measurement. LIDAL was tested and calibrated using the proton beam line at TIFPA (Trento Institute for Fundamental Physics Application) and the carbon beam line at CNAO (National Center for Oncology Hadron-therapy) in 2019. The performance of the time-of-flight system featured a time resolution (sigma) less than 100 ps. Here, we describe the detector and the results of these tests, providing ground calibration curves along with the methodology established for processing the detector's data. LIDAL was uploaded in the International Space Station in November 2019 and it has been operative in the Columbus module since January 2020. [ABSTRACT FROM AUTHOR]

Introduction: In the United States, rates of sexually transmitted infections (STIs) have increased year after year for the past six consecutive years. Even so, the majority of research has focused on younger populations with little work examining infections and prevention methods among older adults. Methods: Data come from the Columbus Health Aging Project (N = 794). This study was designed to assess several domains of health among adults aged 50 years and older in Columbus, Ohio with a particular focus on addressing disparities based on sexual and gender identity. Multivariable logistic regression models were used to examine the association between sociodemographic factors and risk of STI acquisition, HIV diagnosis, and several common prevention methods, adjusting for known confounders. Results: Key results suggest that cisgender women, intersex individuals, and transgender women are less likely to use condoms relative to cisgender men. Meanwhile, white individuals were least likely to use condoms while bisexual individuals were most likely. Transgender women and those living with family/roommates were most likely to use PrEP/PEP relative to cisgender men and those living with a spouse or partner. Cisgender women, compared to cisgender men, were most likely to report not using any prevention method. Conclusion: This study highlights the need for better research among older adults in order to ascertain how interventions may be targeted to specific populations. Future research should aim to educate individuals differently based on their specific needs rather than treating older adults as a homogenous population or ignoring their sexually active nature entirely. [ABSTRACT FROM AUTHOR]

As safe agents of last resort, carbapenems are reserved for the treatment of infections caused by multidrug-resistant organisms. The impact of β-lactam antibiotics, cefotaxime, and meropenem on the frequency and diversity of carbapenemase-producing organisms recovered from environmental samples has not been fully established. Therefore, this methodological study aimed at determining β-lactam drugs used in selective enrichment and their impact on the recovery of carbapenemase-producing Enterobacterales (CPE) from untreated wastewater. We used a longitudinal study design where 1L wastewater samples were collected weekly from wastewater treatment plant (WWTP) influent and quarterly from contributing sanitary sewers in Columbus, Ohio USA with 52 total samples collected. Aliquots of 500 mL were passed through membrane filters of decreasing pore sizes to enable all the water to pass through and capture bacteria. For each sample, the resulting filters were placed into two modified MacConkey (MAC) broths, one supplemented with 0.5 μg/mL of meropenem and 70 μg/mL of ZnSO4 and the other supplemented with 2 μg/mL cefotaxime. The inoculated broth was then incubated at 37° C overnight, after which they were streaked onto two types of correspondingly-modified MAC agar plates supplemented with 0.5 μg/mL and 1.0 μg/mL of meropenem and 70 μg/mL of ZnSO4 and incubated at 37°C overnight. The isolates were identified based on morphological and biochemical characteristics. Then, up to four distinct colonies of each isolate's pure culture per sample were tested for carbapenemase production using the Carba-NP test. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (MS) MALDI-TOF MS was used to identify carbapenemase-producing organisms. In total 391 Carba-NP positive isolates were recovered from the 52 wastewater samples: 305 (78%) isolates had blaKPC, 73 (19%) carried blaNDM, and 14 (4%) harbored both blaKPC and blaNDM resistance genes. CPE genes of both blaKPC and blaNDM were recovered in both types of modified MAC broths, with 84 (21%) having a blaKPC gene, 22 (6%) carrying blaNDM and 9 (2%) harbored both a blaKPC and blaNDM of isolates recovered from MAC medium incorporated with 0.5ug/mL meropenem and 70ug/mL ZnSO4. The most prevalent isolates were Klebsiella pneumoniae, Escherichia coli, and Citrobacter spp. [ABSTRACT FROM AUTHOR]

Dockless micromobility services have potential as a fast and flexible solution to short-distance trips and public transit's first-mile/last-mile (FM/LM) access problem; however, these services also have limitations, including uneven spatial distribution, low capacity, and user out of pocket expense. This can impact on the ability of micromobility to enhance public transit accessibility. We introduce accessibility increment measures – the amount by which public transit accessibility improves due to micromobility services. We apply these measures to hypothetical trips using public transit and micromobility data from Columbus, Ohio, USA. We find dockless scooters can increase accessibility by multimodal public transit trips, with increments in the first mile significantly outweighing last mile accessibility increments. Accessibility increments are highly concentrated in the city center due to the distributions of scooters and bus stops. We also find that scooters' accessibility increment contribution is highly unequal: a small number of scooters contribute most of the accessibility increments. Monetary cost simulations show that the first-mile accessibility increment will rapidly decrease and last-mile increment slightly increase with lower willingness to pay. Capacity simulations show a group of users' accessibility increment will rapidly decrease as the group size increases, but this depends on whether they are competing or collaborating for scooters. Our results show that despite showing promising potentials, vendors and policymakers still need to address these issues to make collaboration between public transit and dockless micromobility sustainable and equitable. The paper provides measures and evidence for future transit and micromobility planning for scooter vendors and transit authorities. • Micromobility can incrementally improve public accessibility. • Accessibility increments are greater for first mile public transit access than last mile. • Accessibility improvements are very unequal across space and mobile devices. • Ability to pay and capacity constraints limit accessibility improvements. • Collaboration between public transit and micromobility is promising but faces challenges. [ABSTRACT FROM AUTHOR]

A key issue in the spatial and temporal analysis of residential burglary is the choice of scale: spatial patterns might differ appreciably for different time periods and vary across geographic units of analysis. Based on point pattern analysis of burglary incidents in Columbus, Ohio during a 9-year period, this study develops an empirical framework to identify a useful spatial scale and its dependence on temporal aggregation. Our analysis reveals that residential burglary in Columbus clusters at a characteristic scale of 2.2 km. An ANOVA test shows no significant impact of temporal aggregation on spatial scale of clustering. This study demonstrates the value of point pattern analysis in identifying a scale for the analysis of crime patterns. Furthermore, the characteristic scale of clustering determined using our method has great potential applications: (1) it can reflect the spatial environment of criminogenic processes and thus be used to define the spatial boundary for place-based policing; (2) it can serve as a candidate for the bandwidth (search radius) for hot spot policing; (3) its independence of temporal aggregation implies that police officials need not be concerned about the shifting sizes of risk-areas depending on the time of the year. [ABSTRACT FROM AUTHOR]