E-Scooter Sharing: Leveraging Open Data for System Design

With the shift toward a Mobility-as-a-Service paradigm, electric scooter sharing systems are becoming a popular transportation mean in cities. Given their novelty, we lack of consolidated approaches to study and compare different system design options. In this work, we propose a simulation approach that leverages open data to create a demand model that captures and generalises the usage of this transportation mean in a city. This calls for ingenuity to deal with coarse open data granularity. In particular, we create a flexible, data-driven demand model by using modulated Poisson processes for temporal estimation, and Kernel Density Estimation (KDE) for spatial estimation. We next use this demand model alongside a configurable e-scooter sharing simulator to compare performance of different electric scooter sharing design options, such as the impact of the number of scooters and the cost of managing their charging. We focus on the municipalities of Minneapolis and Louisville which provide large scale open data about e-scooter sharing rides. Our approach let researchers, municipalities and scooter sharing providers to follow a data driven approach to compare and improve the design of e-scooter sharing system in smart cities

Impact of Charging Infrastructure and Policies on Electric Car Sharing Systems

Electric Free Floating Car Sharing systems offer a convenient and environmentally-friendly way to move in cities. However, their design and deployment is not a trivial task. In this work, we focus on fleet charging management, aiming at maximizing the number of trips of users, while minimizing the cost of relocating cars for charging. In particular, we compare two different car charging infrastructures: a centralised charging hub in a highly dynamic zone of the city, and a distributed set of charging poles around the most-used zones, where users can eventually contribute to plug cars. For this scope, we build a data-driven mobility demand model and a simulator that we use to study the performance and costs of fleet charging management. As a case study, we first consider the city of Turin. Then, we extend the results to three other cities (Milan, New York City and Vancouver). Results show that, given enough charging capacity, a distributed infrastructure is superior in terms of both satisfied trips and charging relocation cost. Additionally, with the contribution of users, the relocation cost might decrease even further

Benefits of Relocation on E-scooter Sharing - a Data-Informed Approach

E-scooter sharing lets people rent an e-scooter while the system owner manages the fleet. Relocation is fundamental to increase system utilization and revenues, but it is also an expensive task. In this paper we aim at assessing the benefits of relocation while quantifying its economic costs. For this, we rely on trace driven simulations where we build upon millions of actual rentals from two cities, Austin and Louisville. Firstly, we build prediction models to estimate which areas will present a surplus or a lack of e-scooters. We compare a simple stationary model with a state-of-art deep-learning model. Secondly, we replay the exact same traces to quantify the benefits of a relocation heuristic, comparing different system options. Our results show that relocation is fundamental to maximize the number of trips the system can satisfy. Interestingly, even a light and simple relocation policy with few relocations per hour can improve the percentage of satisfied trips by up to 42%. This can also translate in a fleet size reduction without impacting the performances. However, when projected into the economic benefits, the additional costs of relocation must be carefully considered to avoid wasting its benefits

UMAP: Urban Mobility Analysis Platform to Harvest Car Sharing Data

Car sharing is nowadays a popular transport means in smart cities. In particular, the free-floating paradigm lets the users look for available cars, book one, and then start and stop the rental at their will, within the city area. This is done by using a smartphone app, which in turn contacts a web-based backend to exchange information. In this paper we present UMAP, a platform to harvest data freely made available on the web to extract driving habits in cities. We design UMAP to fetch data from car sharing platforms in real time, and process it to extract more advanced information about driving patterns and user’s habits while augmenting data with mapping and direction information fetched from other web platforms. This information is stored in a data lake where historical series are built, and later analyzed using easy to design and customize analytics modules. We prove the flexibility of UMAP by presenting a case of study for the city of Turin. We collect car sharing usage data over 50 days, and characterize both the temporal and spatial properties of rentals, as well as users’ habits in using the service, which we contrast with public transportation alternatives. Results provide insights about the driving style and needs, that are useful for smart city planners, and prove the feasibility of our approach

New perspectives in the prediction of postoperative complications for high-risk ulcerative colitis patients: machine learning preliminary approach

OBJECTIVE: Patients with acute severe and medical refractory ulcerative colitis have a high risk of postoperative complications after total abdominal colectomy (TAC). The objective of this retrospective study is to use machine learning to analyze and predict short-term outcomes. PATIENTS AND METHODS: 32 patients with ulcerative colitis were treated with total abdominal colectomy between 2011 and 2017. Biographical data, preoperative therapy, blood chemistry, nutritional status, surgical technique, blood transfusion and preoperative length of stay were the features selected for the statistical analyses and were used as input for the machine learning algorithms to predict the rate of complications. RESULTS: Traditional statistical analysis showed an overall postoperative morbidity rate of 34% and a mortality rate of 3%. Preoperative low serum albumin levels (4 days), blood transfusions (≥1 unit) and body temperature (≥37.5°C) demonstrated a major impact on infectious morbidity with statistical significance (p<0.05). Patients treated with steroids and rescue therapy presented a higher risk of minor infectious complications (p<0.05). Evaluating only preoperative features, machine learning algorithms were able to predict minor postoperative complications with a high strike rate (84.3%), high sensitivity (87.5%) and high specificity (83.3%) during the testing phase. CONCLUSIONS: Machine learning is demonstrated to be useful in predicting the rate of minor postoperative complications in high-risk ulcerative colitis patients, despite the small sample size. It represents a major step forward in data analysis by implementing a retrospective study from a prospective point of view

Rare ATG7 genetic variants predispose patients to severe fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver disorders and has a strong heritable component. The aim of this study was to identify new loci that contribute to severe NAFLD by examining rare variants

IL32 downregulation lowers triglycerides and type I collagen in di-lineage human primary liver organoids

Steatotic liver disease (SLD) prevails as the most common chronic liver disease yet lack approved treatments due to incomplete understanding of pathogenesis. Recently, elevated hepatic and circulating interleukin 32 (IL -32) levels were found in individuals with severe SLD. However, the mechanistic link between IL -32 and intracellular triglyceride metabolism remains to be elucidated. We demonstrate in vitro that incubation with IL -32b protein leads to an increase in intracellular triglyceride synthesis, while downregulation of IL32 by small interfering RNA leads to lower triglyceride synthesis and secretion in organoids from human primary hepatocytes. This reduction requires the upregulation of Phospholipase A2 group IIA (PLA2G2A). Furthermore, downregulation of IL32 results in lower intracellular type I collagen levels in di -lineage human primary hepatic organoids. Finally, we identify a genetic variant of IL32 (rs76580947) associated with lower circulating IL -32 and protection against SLD measured by non-invasive tests. These data suggest that IL32 downregulation may be beneficial against SLD

Data Driven Scalability and Profitability Analysis in Free Floating Electric Car Sharing Systems

In this paper, we analyse the impact of system design options with different demand intensities for electric vehicle free-floating car sharing systems (EV-FFCS). We consider three different cities for which we collected rental data from a car sharing system. Using these data, we build demand and supply models of an EV-FFCS. We evaluate the performance of different design options from both the customers’ and the operators’ perspectives, i.e., quality of service and profitability. We study the number of chargers, their placement and the size of the fleet. We observe the impact on the system when demand is constant and then when demand increases. The results show that it is critical to scale the capacity of the charging infrastructure proportionally to the mobility demand. Conversely, the same fleet size can accommodate a 300% increase in demand, not satisfying less than 15% of it. Moreover, the observed demand and supply would likely not generate profits for the EV system. This is due to the high cost of electric vehicles and the need to manage the fleet for charging operations. The figure changes with at least a 5-fold increase in demand, with the current fleet size becoming profitable

Environmental and Economic Comparison of ICEV and EV in Car Sharing

Transport electrification is increasingly seen as a necessary action to curb climate change. Free floating car sharing (FFCS) is a transport mode whose real benefits and disadvantages are still largely discussed. Here we compare possible FFCS which adopt either internal combustion engine vehicles (ICEV) or electric vehicles (EV). We focus on three main aspects: satisfied demand, emission, and system profitability. We use a realistic simulator to thoroughly compare the different dynamics of ICEV and EV based FFCS systems. Our simulator models mobility demand, fleet status, refueling operations, and estimates the satisfied demand, determines the equivalent CO2 emission from the fuel production up to the fuel consumption, and computes operational profit. As case study we consider the city of Turin, where we compare 4 FFCS systems based on a fleet of Fiat 500 with different engines, i.e., gasoline, diesel, LPG, and electric. We run simulations using the demand as derived from trips recorded by the currently operational FFCS, and using the fueling infrastructures today present in the city. Results show that the EV FFCS system can satisfy the same demand as the ICEV based solutions. As expected, the EV fleet reduces emission. However, the current higher cost of EVs makes the FFCS system less profitable than ICEV solutions - questioning its adoption. Interestingly, cheap low-power chargers result the best solution for the EV FFCS, reducing also maintenance costs. We believe our approach and our simulator, which we make available for the community, is a first step to thoroughly compare the implications of different engines in shared mobility

Novel Arginine- and Proline-Rich Candidacidal Peptides Obtained through a Bioinformatic Approach

Antimicrobial resistance is a major public health concern worldwide. Albeit to a lesser extent than bacteria, fungi are also becoming increasingly resistant to antifungal drugs. Moreover, due to the small number of antifungal classes, therapy options are limited, complicating the clinical management of mycoses. In this view, antimicrobial peptides (AMPs) are a potential alternative to conventional drugs. Among these, Proline-rich antimicrobial peptides (PrAMPs), almost exclusively of animal origins, are of particular interest due to their peculiar mode of action. In this study, a search for new arginine- and proline-rich peptides from plants has been carried out with a bioinformatic approach by sequence alignment and antimicrobial prediction tools. Two peptide candidates were tested against planktonic cells and biofilms of Candida albicans and Candida glabrata strains, including resistant isolates. These peptides showed similar potent activity, with half-maximal effective concentration values in the micromolar range. In addition, some structural and functional features, revealing peculiar mechanistic behaviors, were investigated.</jats:p