Evaluating Impacts of Shared E-scooters from the Lens of Sustainable Transportation

As the popularity of shared micromobility is increasing worldwide, city governments are struggling to regulate and manage these innovative travel technologies that have several benefits, including increasing accessibility, reducing emissions, and providing affordable travel options. This dissertation evaluates the impacts of shared micromobility from the perspective of sustainable transportation to provide recommendations to decision-makers, planners, and engineers for improving these emerging travel technologies. The dissertation focuses on four core aspects of shared micromobility as follows: 1) Safety: I evaluated police crash reports of motor vehicle involving e-scooter and bicycle crashes using the most recent PBCAT crash typology to provide a comprehensive picture of demographics of riders crashing and crash characteristics, as well as mechanism of crash and crash risk, 2) Economics: I estimated the demand elasticity of e-scooters deployed, segmented by weekday type, land use, category of service providers based on fleet size using negative binomial fixed effect regression model and K-means clustering, 3) Expanding micromobility to emerging economies: Using dynamic stated preference pivoting survey and panel data mixed logit model, I assessed the intentions to adopt shared micromobility in mid-sized cities of developing countries, where these innovative technology could be the first wave of decarbonizing transportation sector, and 4) Micromobility data application: I identified five usage-clusters of shared e-scooter trips using combination of Principal Component Analysis (PCA) and K-means clustering to propose a novel framework for using micromobility data to inform data-driven decision on broader policy goals. Based on the key findings of the research, I provide five recommendations as follows: 1) decision-makers should be proactive in incorporating new travel technologies like shared micromobility, 2) city governments should leverage shared micromobility usage and operation data to empower the decision-making process, 3) each shared micromobility vehicles should be approached uniquely for improving road safety, 4) city governments should consider regulating the number of service providers and their fleet sizes, and 5) decision-makers should prioritize expanding shared micromobility in emerging economies as one of the first efforts to the decarbonizing transportation sector

Riding an e-scooter at nighttime is more dangerous than at daytime

With rapidly increasing e-scooter usage in the United States [1], a growing number of studies aim to understand the safety aspect of these emerging modes. The existing literature has a limited understanding of time-of-day and seasonal patterns of e-scooter crashes. While many e-scooter safety policies are based on the number of crashes [2, 3], accounting for exposure provides a measure of risk to inform effective preventive strategies [4]. This study focuses on motor-vehicle involved crashes since they constitute the most severe and fatal injuries. We compared daytime and nighttime motor-vehicle involved e-scooter crashes and combined them with micromobility trip data to generate exposure variables and estimate crash risk. The key research question of this paper is as follows: 1. Are crashes or crash rates disproportionately higher at night than in the day? [From: Introduction

Estimation of thyroid profile in patients with diabetes mellitus in New Civil Hospital, Surat

Background: Diabetes mellitus (DM) and thyroid diseases are the two common endocrinopathies seen commonly in the population. There is inter-dependence between insulin and thyroid hormones for normal cellular metabolism so that DM and thyroid diseases can mutually influence the other disease process. The excess or deficit of one hormone may result in functional derangement of other. Diabetes being a most common endocrine metabolic disorder, the variety of thyroid abnormalities may co-exist and interact with DM. Early detection of thyroid dysfunction and its treatment can delay the long-term complications of DM. The present study was planned to determine prevalence of thyroid dysfunction in DM patients and therefore to provide the appropriate guidelines.Methods: The study was cross-sectional. 100 patients were enrolled for the study. Among them 50 were control (non-diabetic) and 50 were cases (diabetic). They were enrolled in the study from medicine outpatient department’s and inpatient department’s according to inclusion and exclusion criteria. Their thyroid profile (free T3, T4 and thyroid stimulating hormone) was done by chemiluminescence assay method.Results: Results were analyzed by unpaired-t-test. Prevalence of thyroid dysfunction was found significantly high in DM patients. p<0.05 value considered as statistically significant.Conclusions: Screening for thyroid disease among patients with diabetes mellitus should be routinely performed for early detection and treatment of thyroid dysfunction to delay the complications of diabetes

Effect of plant growth regulators on flowering behavior of cashew cv. Vengurla-4 grown in the hilly tracts of South Gujarat

A trial was conducted at Subhir and Chikhalda locations in Dang district of South Gujarat, India to assess the effect of Ethrel, NAA and GA3 on the flowering behavior of cashew cultivar Vengurla-4 during 2013-14. Three concentrations each of GA3 (50, 75, 100 ppm), Ethrel (10, 30, 50 ppm) and NAA (50, 75, 100ppm) were applied as foliar sprays 20 days before blossoming and 20 days after full bloom in twenty year old trees of cashew cultivar Vengurla-4. Trees sprayed with 50 ppm Ethrel had significantly the highest number of flowering panicles per squaremeter (13.09), number of perfect flowers per panicle (87.11) and sex ratio (0.24) across locations and in pooled data. However, this was at par with 10 ppm Ethrel which emerged as the second best treatment of the trial. This study demonstrated the potential of Ethrel in improving various flowering parameters of cashew which are important determinations in increasing nut production

Linkless Link Prediction via Relational Distillation

Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood data dependency limits GNNs in practical deployments. Conversely, the known efficient MLPs are much less effective than GNNs due to the lack of relational knowledge. In this work, to combine the advantages of GNNs and MLPs, we start with exploring direct knowledge distillation (KD) methods for link prediction, i.e., predicted logit-based matching and node representation-based matching. Upon observing direct KD analogs do not perform well for link prediction, we propose a relational KD framework, Linkless Link Prediction (LLP), to distill knowledge for link prediction with MLPs. Unlike simple KD methods that match independent link logits or node representations, LLP distills relational knowledge that is centered around each (anchor) node to the student MLP. Specifically, we propose rank-based matching and distribution-based matching strategies that complement each other. Extensive experiments demonstrate that LLP boosts the link prediction performance of MLPs with significant margins, and even outperforms the teacher GNNs on 7 out of 8 benchmarks. LLP also achieves a 70.68x speedup in link prediction inference compared to GNNs on the large-scale OGB dataset

Node Duplication Improves Cold-start Link Prediction

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show that GNNs struggle to produce good results on low-degree nodes despite their overall strong performance. In practical applications of LP, like recommendation systems, improving performance on low-degree nodes is critical, as it amounts to tackling the cold-start problem of improving the experiences of users with few observed interactions. In this paper, we investigate improving GNNs' LP performance on low-degree nodes while preserving their performance on high-degree nodes and propose a simple yet surprisingly effective augmentation technique called NodeDup. Specifically, NodeDup duplicates low-degree nodes and creates links between nodes and their own duplicates before following the standard supervised LP training scheme. By leveraging a ''multi-view'' perspective for low-degree nodes, NodeDup shows significant LP performance improvements on low-degree nodes without compromising any performance on high-degree nodes. Additionally, as a plug-and-play augmentation module, NodeDup can be easily applied to existing GNNs with very light computational cost. Extensive experiments show that NodeDup achieves 38.49%, 13.34%, and 6.76% improvements on isolated, low-degree, and warm nodes, respectively, on average across all datasets compared to GNNs and state-of-the-art cold-start methods

Quantifying the Impact of New Mobility on Transit Ridership

USDOT Grant 69A3552047141This Final Report presents the outcomes of Community Analysis Research Project C3 that analyzed the impacts of new mobility modes \u2013 particularly micromobility \u2013 on transit ridership. Micromobility includes modes such as bicycles, electric bicycles (e-bikes) and electric scooters (e-scooters). This research focused specifically on shared electric scooters (e-scooters) in Nashville, Tennessee because of the availability of detailed e-scooter trip and device location data that were obtained through a data request to Nashville\u2019s Metropolitan Planning Organization. T-SCORE Project C3 was divided into two primary parts. The first part of the research performed an empirical analysis to quantify the impacts of the shared e-scooters on bus ridership in Nashville, Tennessee. Fixed effects regression models were estimated to explore six hypotheses about the relationship between bus ridership and shared e-scooters using both infrastructure-based and trip-based measures. The findings suggest that utilitarian shared e-scooter trips are associated with a decrease of 0.94% in bus ridership in Nashville on a typical weekday, whereas shared e-scooter social trips are associated with an increase of 0.86% in bus ridership in Nashville on a typical weekday. These findings suggest that shared e-scooters were associated with a net decrease of about 0.08% of total bus ridership on a typical weekday in Nashville, which is a minimal impact. The second part of T-SCORE Project C3 proposed a mixed methods approach to select locations to place shared e-scooter corrals near transit stops to encourage the use of shared e-scooters connecting to transit using Nashville, Tennessee as a case study. The method first used machine learning techniques to identify shared e-scooters trips that complement transit. Then, a multi-criteria scoring system was applied to rank bus stops based on shared e-scooter activity and bus service characteristics. Based on this scoring system, bus stops with the 50 highest scores were selected as potential locations for shared e-scooter corrals. Then, the capacity for the potential parking locations was estimated based on the hourly shared e-scooter usage. The results suggest that the 50 proposed corral locations could capture about 44% of shared e-scooter demand. The findings of this part of the research project could guide the implementation of shared e-scooter corrals in Nashville and inform other cities about how to select locations for shared e-scooter corrals near transit

Tier 1 University Transportation Center Match Funds for the Strategic Implications of Changing Public Transportation Travel Trends

69A3552047141Even before the onset of the COVID-19 pandemic, public transit ridership was declining in many metropolitan areas in the United States. To regain riders, transit agencies and their partners must make decisions about which strategies and policies to pursue within the constraints of their operating environments. To help address this, the Transit-Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) Tier 1 University Transportation Center was set up as a research consortium from 2020 to 2023 led by Georgia Tech with research partners at the University of Kentucky, Brigham Young University and University of Tennessee, Knoxville (UTK). The T-SCORE Center had two primary research tracks: (1) Community Analysis (led by the University of Tennessee; included in this report) and (2) Multi-Modal Optimization and Simulation (led by the University of Kentucky; not included). The Community Analysis research track employed a combination of quantitative and qualitative research methods to assess three main drivers of change that have affected transit ridership: price and socioeconomic factors, the competitive landscape, and system disruptions, including COVID-19. The research approach for the Community Analysis track was divided into separate projects, and the UTK team led three projects that aimed to: (1) quantify the impact of different factors affecting transit ridership - including the COVID-19 pandemic - at a nationwide scale; (2) assess the impacts of shared micromobility, particularly electric scooters, on transit ridership; and (3) evaluate new fare payment technologies and emerging pricing strategies, with the vision of taking a step toward Mobility-as-a-Service (MaaS). The findings of these three Community Analysis projects can help inform transit agencies and city officials making decisions about how to increase transit ridership and plan for a sustainable future

Engineering hydrophobically modified chitosan for enhancing the dispersion of respirable microparticles of levofloxacin

The potential of amphiphilic chitosan formed by grafting octanoyl chains on the chitosan backbone for pulmonary delivery of levofloxacin has been studied. The success of polymer synthesis was confirmed using FT-IR and NMR, whilst antimicrobial activity was assessed against Pseudomonas aeruginosa. Highly dispersible dry powders for delivery as aerosols were prepared with different amounts of chitosan and octanoyl chitosan to study the effect of hydrophobic modification and varying concentration of polymer on aerosolization of drug. Powders were prepared by spray-drying from an aqueous solution containing levofloxacin and chitosan/amphiphilic octanoyl chitosan. L-leucine was also used to assess its effect on aerosolization. Following spray-drying, the resultant powders were characterized using scanning electron microscopy, laser diffraction, dynamic light scattering, HPLC, differential scanning calorimetry, thermogravimetric analysis and X-ray powder diffraction. The in vitro aerosolization profile was determined using a Next Generation Impactor, whilst in vitro antimicrobial assessment was performed using MIC assay. Microparticles of chitosan have the property of mucoadhesion leading to potential increased residence time in the pulmonary mucus, making it important to test the toxicity of these formulations. In-vitro cytotoxicity evaluation using MTT assay was performed on A549 cell line to determine the toxicity of formulations and hence feasibility of use. The MTT assay confirmed that the polymers and the formulations were non-cytotoxic. Hydrophobically modifying chitosan showed significantly lower MIC (4-fold) than the commercial chitosan against P. aeruginosa. The powders generated were of suitable aerodynamic size for inhalation having a mass median aerodynamic diameter less than 4.5 lm for formulations containing octanoyl chitosan. These highly dispersible powders have minimal moisture adsorption and hence an emitted dose of more than 90% and a fine particle fraction (FPF) of 52%. Powders with non-modified chitosan showed lower dispersibility, with an emitted dose of 72% and FPF of 20%, as a result of high moisture adsorption onto the chitosan matrix leading to cohesiveness and subsequently decreased dispersibility

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries