We studied the structure of the 15-minute city by measuring the distances from the services on spatial graphs. While the concept of the 15-minute city is rapidly spreading, its operative definition can be of help for planning and understanding the possibilities of the general idea. For three European cities (Rome, Paris, and London), we developed a method to calculate pedestrian travel time to reach services for all the possible paths on urban graphs, finding that the 15-minute city generally has multiple connected components and that the services have not necessarily been part of it. This algorithm was used to to make a quantitative comparison between the cities, showing that Paris and London have a stronger 15-minute characterization than Rome. By generalizing the method, it was possible to define a 15-minute urban index, which quantitatively characterizes this city feature. The results seem to be promising because, at the cost of the massive use of computational time, a digital model for the city can be generated: a planning tool to simulate urban solutions and a rigorous criterion for evaluating how much a city can be considered a 15-minute city.

Motivation: Several mathematical models have been investigated for the description of viral dynamics in the human body: HIV-1 infection is a particular and interesting scenario, because the virus attacks cells of the immune system that have a role in the antibody production and its high mutation rate permits to escape both the immune response and, in some cases, the drug pressure. The viral genetic evolution is intrinsically a stochastic process, eventually driven by the drug pressure, dependent on the drug combinations and concentration: in this article the viral genotypic drug resistance onset is the main focus addressed. The theoretical basis is the modelling of HIV-1 population dynamics as a predator–prey system of differential equations with a time-dependent therapy efficacy term, while the viral genome mutation evolution follows a Poisson distribution. The instant probabilities of drug resistance are estimated by means of functions trained from in vitro phenotypes, with a roulette-wheel-based mechanisms of resistant selection. Simulations have been designed for treatments made of one and two drugs as well as for combination antiretroviral therapies. The effect of limited adherence to therapy was also analyzed. Sequential treatment change episodes were also exploited with the aim to evaluate optimal synoptic treatment scenarios. Results: The stochastic predator–prey modelling usefully predicted long-term virologic outcomes of evolved HIV-1 strains for selected antiretroviral therapy combinations. For a set of widely used combination therapies, results were consistent with findings reported in literature and with estimates coming from analysis on a large retrospective data base (EuResist). Contact:prosperi@dia.uniroma3.it Supplementary information: Supplementary data are available at Bioinformatics online.

The emergence of Airbnb along with an increase in urban tourism has intensified the pressure on urban areas while adding a new dimension to the dynamics of housing distribution, especially in historic cities. These dynamics affect local economies and significantly alter the characteristics of urban spaces, hence the necessity to not only create policies that foster sustainable tourism development but also to advance urban models that explore the relation between Airbnb and the traditional rental and accommodation sector. Through the case of Venice, the present study sheds light on the potential evolution of Airbnb housing in comparison to the traditional rental and homeowner market. In particular, we sought to understand whether a potential equilibrium between these uses exists and if so, at which point in regard to this equilibrium the historic center of Venice is. To tackle this question, methods derived from the field of game theory and specifically evolutionary game theory were used. With the agents (players) being the housing units, the designed theoretical model explored the population dynamics of the housing units in Venice given the three options of homeownership or long-term renting (residential); short term renting or Airbnb (airbnb); and no use (vacant). The findings of our theoretical population game model were validated and discussed with a dataset describing the usage patterns in the city of Venice during the past 20 years. A verification of the outcome through further case studies could eventually provide insights into the future behavior of tourism’s pressure in historic urban areas.

We studied generalization of a method for extending configurational studies to variables that are not exclusively geographical, in order to allow investigation of generic relationships in the built environment. We observed a number of limitations of the classical approach of configurational studies and we considered how a complex analytical method could be implemented in the study of non-topological variables, such as land use, noise pollution and financial or property rents. In order to do this, we established a system of relationships based on a labeled primary graph. Categorization of the labels of the links was performed on a generic variable, specifically, the predominant use of roads. The graph was then contracted using an optimization algorithm, which we describe. The algorithm allows the network to be reduced to a more streamlined system that can better analyze the relationships between the different categories. Application of the method showed that it is faster to identify weaknesses in urban networks, and then take measures to resolve them. The case study concerns the pedestrianization of the Colosseum area in Rome.

The use of mobile computing technologies can change the experience of visiting cultural sites by making vast digital heritage collections accessible on site. The spread of machine learning technologies on mobile devices is encouraging the interaction of artificial intelligence with the shape of the built environment. However, while some research already applies deep learning image recognition in an urban context, the literature on how to develop effective neural networks to detect architectural features is still limited, as well as the availability of architecture-related datasets. This work presents the steps and results of the prototype development of a mobile app to perform monument recognition using convolutional neural networks. The tool allows users to interact with the physical space and access a digital archive of texts, models, images and other data.

The ideal Renaissance city is designed as a star-shaped fortress, where the streets and squares are organized to speed the movement of people and soldiers. Symmetry and accessibility represent the key features for the organization of the urban space. The resulting city is hierarchized and does not always guarantee an optimal degree of connectivity. Starting from the space syntax definition, we suggest a method to compute urban graphs from the Euclidean representation, the corresponding line graph and the contraction of nodes with the same urban function. We analyze the urban graphs of five historic cities and compare the analysis with the corresponding results from space syntax. Analysis of the spectral gap and the relative asymmetry distribution show a similar structure for these cities. The irregular or reticular housing structure seems to ensure connectivity and accessibility more than the regular grids. However, connectivity is ensured by the most peripheral streets, which in the space syntax representation play a marginal role.