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To the Editor, Erectile dysfunction (ED) is one of the most prevalent conditions affecting men globally, with significant psychological and social consequences. The prevalence varies across different populations, and it is estimated around 50% in men aged between 40 to 70. The etiology of ED is multifactorial, involving a complex crosstalk between psychological, hormonal, neurogenic, vascular, and structural factors. Recently, the role of iron homeostasis (IH) is emerging. [...]

We consider a constrained optimal control problem and an extension of it, in which the set of strict-sense trajectories is enlarged. Extension is a common procedure in optimal control used to derive necessary and sufficient optimality conditions for the original problem from the extended one, which usually admits a minimizer and has a more regular structure. However, this procedure fails if the two problems have different infima. Therefore, it is relevant to identify such situations. Following on from earlier work by Warga but adopting perturbation techniques developed in nonsmooth analysis, we investigate the relation between the occurrence of an infimum gap and the abnormality of necessary conditions. For the notion of a local minimizer based on control distance and an extension, including the impulsive one, we prove that (i) a local extended minimizer that is not a local minimizer of the original problem, and (ii) a local strict-sense minimizer that is not a local minimizer of the extended problem both satisfy the extended maximum principle in abnormal form. The main novelty is result (ii), as until now, it has only been shown that a strict-sense minimizer that is not an extended minimizer is abnormal for an ‘averaged version’ of the maximum principle.

In key moments of urban history, urban design is confronted with the emergence of new paradigmatic design models, such as the garden city and the radiant city. Recently, the Smart City seems to have gained centre stage in the public debate. However, despite its emblazoned technological features, the Smart City remains a hazy concept in the urban design domain to such an extent that almost any form can be built under the Smart City label. While this may sound libertarian and progressist, it is also concerning since different urban forms are associated with different societal outcomes. This paper aims to investigate the forms of Smart City districts through morphometric comparison. More specifically, it proposes a replicable methodology based on 18 metrics of urban form and statistical analysis to compare a Smart City district with other city areas with known design models of reference. Such a methodology is applied to three case studies on the French Riviera: Méridia, a Smart City district, Hôtel-des-Postes, a 19th-century traditional district, and Sophia Antipolis, a sprawling technopark. The results show that Méridia has a hybrid form that partly resembles Hôtel-des-Postes (higher densities, gridiron plan, and functional mix) and partly Sophia Antipolis (bulky buildings with large setbacks). However, the top–down approach used in the production of the physical space ultimately renders Méridia more similar to Sophia Antipolis than Hôtel-des-Postes. This study provides one of the first morphometric characterisations of a Smart City district, but also a replicable methodology that can further the morphological understanding of the Smart City phenomenon worldwide.

This paper is the outcome of a think-tank on interfaces. After expounding the main formalizations, the group has come to, the authors demonstrate in a conceptual approach faced to an empirical reality, the interest of reasoning in multiscale interfaces, based on the example of the Nice coastal interface – a physical interface between land and sea, but also a cultural interface with a strong emphasis on tourism, so strong that it determines not only the town’s economy but also, and above all, its planning. We will analyse how the two main components of this interface (geology and society) interact at multiples scales which should be simultaneously considered to better understand the functioning of this geographic space.

This paper compares Hierarchical Clustering, Bayesian Networks and Self-Organizing Map Neural Networks (SOM and superSOM) approaches used for clustering purposes in geographic space. The same dataset, covering the Republic of India and made of 55 indicators for 640 spatial units (administrative districts), is used in the three analyses. Indicators descry the several aspects of urban, economic and socio-demographic development in India. Bayesian Networks use a likelihood function while SOM/SuperSOM and Hierarchical Clustering minimize variance of Euclidean distance in variable space, the former by preserving the topological properties within the output space and the latter by successively combining similar items. Relatively similar multi-step protocols have been implemented for the three techniques, to take into account variable redundancy. Methods as well as clustering results are compared. From this perspective, the aim of the paper is to highlight the similarities between the protocols and to evaluate the differences between the segmentation approaches (geographical and variable space comparisons). A few key points are also discussed such as the data pre-processing steps, the conception of latent factors and the choice of the number of clusters.

The aim of this work is to show how the integration of gold nanoparticles (AuNPs) into multi-wall-carbon-nanotubes (MWCNTs) based screen-printed electrodes and into graphene-based screen-printed electrodes (GPHs) could represent a potential way to further enhance the electrochemical properties of those electrodes based on nanoparticles. Laccase from Trametes versicolor (TvL) was immobilized over MWCNTs and GPH previously modified with AuNPs (of 5 and 10 nm). The characterization of the modified electrode surface has been carried out by cyclic voltammetry. The results showed that the use of AuNPs for modification of both graphene and MWCNTs screen-printed electrode surfaces would increase the electrochemical performances of the electrodes. MWCNTs showed better results than GPH in terms of higher electroactive area formation after modification with AuNPs. The two modified nanostructured electrodes were successively proven to efficiently immobilize the TvL; the electrochemical sensing properties of the GPH- and MWCNT-based AuNPs-TvL biosensors were investigated by choosing 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic-acid diammonium salt (ABTS), catechol and caffeic acid as laccase mediators; and the kinetic parameters of the laccase biosensor were carefully evaluated.

The Japanese city presents a certain number of peculiarities in the organization of its physical space (weak zoning regulations, fast piecemeal destruction/reconstruction of buildings and blocks, high compacity, incremental reorganization). Compared to countries where urban fabrics are more perennial and easily distinguishable (old centers, modern planned projects, residential areas, etc.), in Japanese metropolitan areas we often observe higher heterogeneity and more complex spatial patterns. Even within such a model, it should be possible to recognize the internal organization of the physical city. The aim of this paper is thus to study the spatial structure of the contemporary Japanese city, generalizing on the case study of Osaka and Kobe. In order to achieve this goal, we will need to identify urban forms at different local scales (building types, urban fabrics) and to analyze them at a wider scale to delineate morphological regions and their structuring of the overall layout of the contemporary Japanese city. Several analytical protocols are used together with field observations and literature. The results, and more particularly the building and urban fabrics types and their location within the Osaka-Kobe metropolitan area, are interpreted in the light of Japanese history and model of urbanization. A synoptic graphical model of an urban morphological structure based upon Osaka is produced and proposed as an interpretative pattern for the Japanese metropolitan city in general.

Understanding what aspects of the urban environment are associated with better socioeconomic/liveability outcomes is a long standing research topic. Several quantitative studies have investigated such relationships. However, most of such works analysed single correlations, thus failing to obtain a more complete picture of how the urban environment can contribute to explain the observed phenomena. More recently, multivariate models have been suggested. However, they use a limited set of metrics, propose a coarse spatial unit of analysis, and assume linearity and independence among regressors. In this paper, we propose a quantitative methodology to study the relationship between a more comprehensive set of metrics of the urban environment and the valorisation of street segments that handles non-linearity and possible interactions among variables, through the use of Machine Learning (ML). The proposed methodology was tested on the French Riviera and outputs show a moderate predictive capacity (i.e., adjusted R 2 = 0.75 ) and insightful explanations on the nuanced relationships between selected features of the urban environment and street values. These findings are clearly location specific; however, the methodology is replicable and can thus inspire future research of this kind in different geographic contexts.

A catalase-based (NAF/MWCNTs) nanocomposite film modified glassy carbon electrode for hydrogen peroxide (H2O2) detection was developed. The developed biosensor was characterized in terms of its bioelectrochemical properties. Cyclic voltammetry (CV) technique was employed to study the redox features of the enzyme in the absence and in the presence of nanomaterials dispersed in Nafion® polymeric solution. The electron transfer coefficient, α, and the electron transfer rate constant, ks, were found to be 0.42 and 1.71 s−1, at pH 7.0, respectively. Subsequently, the same modification steps were applied to mesoporous graphite screen-printed electrodes. Also, these electrodes were characterized in terms of their main electrochemical and kinetic parameters. The biosensor performances improved considerably after modification with nanomaterials. Moreover, the association of Nafion with carbon nanotubes retained the biological activity of the redox protein. The enzyme electrode response was linear in the range 2.5–1150 μmol L−1, with LOD of 0.83 μmol L−1. From the experimental data, we can assess the possibility of using the modified biosensor as a useful tool for H2O2 determination in packaged beverages.

Formal ontologies have proved to be a very useful tool to manage interoperability among data, systems and knowledge. In this paper we will show how formal ontologies can evolve from a crisp, deterministic framework (ontologies of hard knowledge) to new probabilistic, fuzzy or possibilistic frameworks (ontologies of soft knowledge). This can considerably enlarge the application potential of formal ontologies in geographic analysis and planning, where soft knowledge is intrinsically linked to the complexity of the phenomena under study. The paper briefly presents these new uncertainty-based formal ontologies. It then highlights how ontologies are formal tools to define both concepts and relations among concepts. An example from the domain of urban geography finally shows how the cause-to-effect relation between household preferences and urban sprawl can be encoded within a crisp, a probabilistic and a possibilistic ontology, respectively. The ontology formalism will also determine the kind of reasoning that can be developed from available knowledge. Uncertain ontologies can be seen as the preliminary phase of more complex uncertainty-based models. The advantages of moving to uncertainty-based models is evident: whether it is in the analysis of geographic space or in decision support for planning, reasoning on geographic space is almost always reasoning with uncertain knowledge of geographic phenomena.

The spatial structure of mobility flows is a key element in order to understand the functional reorganisation of the Provence-Alpes-Côte d’Azur region (France). Mobility flows can be used to define urban networks and to follow their evolution over time. This paper analyses inter-municipal dominant flows of daily and residential mobility derived from census data over the 1980s and the 1990s. On the whole, regional urban networks grow stronger and become more complex over time. They shape two metropolitan networks showing different network configurations. Other differences can be observed between the spatial extent of commuting dominant flows, which are linked to the daily functioning of metropolitan areas, and that of residential mobility dominant flows, which are more linked to household life cycles.

This paper proposes the development of causal probabilistic models for spatial strategic foresight, using decision graphs (decisional Bayesian Networks). The proposed models integrate uncertainty, spatial interaction among a limited number of key sub-spaces and the evolution of the spatial system over time. This modeling approach is applied to scenario building for future development of the French Riviera metropolitan area. The limits of the proposed methodology are finally discussed, as well as its potential of integration in strategic planning and its perspectives of future development.

There is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an important role in the achievement of the city’s wider sustainability objectives.In this paper we explore the characteristics of sustainable urban mobility systems through the technique of Bayesian networks. At the frontier between multivariate statistics and artificial intelligence, Bayesian networks provide powerful models of causal knowledge in an uncertain context. Using data on urban structure, transportation offer, mobility demand, resource consumption and environmental externalities from seventy-five world cities, we developed a systemic model of the city-transportation-environment interaction in the form of a Bayesian network. The network could then be used to infer the features of the city with sustainable mobility.The Bayesian model indicates that the city with sustainable mobility is most probably a dense city with highly efficient transit and multimodal mobility. It produces high levels of accessibility without relying on a fast road network. The achievement of sustainability objectives for urban mobility is probably compatible with all socioeconomic contexts.By measuring the distance of world cities from the inferred sustainability profile, we finally derive a geography of sustainability for mobility systems. The cities closest to the sustainability profile are in Central Europe as well as in affluent countries of the Far East. Car-dependent American cities are the farthest from the desired sustainability profile.

At the frontier between multivariate statistics and artificial intelligence, Bayesian networks are a powerful tool to model systemic knowledge. A particularly interesting use of Bayesian networks is the search of causal links between variables in a database. In this paper, we analyze a database on urban daily mobility covering more than one hundred big world cities. Through the generating algorithms of Bayesian networks, we produced two networks derived from two different hypotheses on the causal hierarchy among the variables. Car ownership is identified as a crucial variable when considering the network resulting from the hypothesis that daily mobility determines urban structure. This network has a higher likelihood than the one resulting from the opposite hypothesis. On the other hand, the two networks converge on the importance of the offer of urban freeway and of the average speed of the road network. These two factors explain the modal split and the costs and externalities of urban transportation.

There is an internationally shared agreement on the use of indicators for evaluating the sustainability of the observed patterns of urban development. The most commonly used framework to organise and select such indicators is the Pressures-State-Responses scheme. First proposed by the OECD to organise environmental indicators, its inadequacy is increasingly evident. The weaknesses of the conceptual frameworks currently used in urban sustainability analysis induced us to develop a more coherent theoretical framework in the form of a systemic scheme. The new scheme combines the causal approach of the Pressures-State-Responses model and the focus on urban sustainability issues of the extended urban metabolism model by Newman and Kenworthy. In this paper, the new scheme was used to derive a systemic framework for the assessment of the sustainability of urban mobility. First, we adapted the general scheme to the analysis of urban mobility. Successively, using the cognitive mapping software Decision Explorer, we further developed this scheme to obtain a more disaggregated conceptual model. A selection table could be derived for every element of the model to guide the selection of pertinent indicators based on the systemic links of the element. We could thus define a set of some sixty system indicators to be used in the assessment of the sustainability of urban mobility. The selected set of indicators reflects the causal links between phenomena and can monitor the attainment of sustainability objectives for the urban mobility system. All indicators allow geographical comparison and integrate the role played by different urban actors and modes of transportation. This work has been carried out within the first year of a joint PhD research program at the University of Nice-Sophia Antipolis and at the Polytechnic of Milan.

The spatial organization of stores plays a crucial role in the production and development of the urban form while, at the same time, being strongly influenced from the physical constraints of the latter. Yet the connection between retail distribution and the physical form of cities remains underexplored. This paper aims to unify the perspectives of two distinct disciplines—retail geography and urban morphology—rooted in the late 19th and early 20th centuries. Retail geography, tracing its origins to classical works in American and German regional economics, and urban morphology, emerging from the convergence of architectural and geographical schools in England, Italy and France, have evolved independently since the late '60s. This paper reviews their development, highlighting theoretical and empirical studies elucidating the interrelationship between retail distribution and urban form. While both disciplines acknowledge the mutual influence of retail and cities, they have progressed independently, with retail geography focusing on the evolution of the retail sector and urban morphology emphasizing the impact of physical form on socioeconomic functions. Identifying common ground, this paper underscores spatial affinities and organizational aspects often overlooked. The paper concludes by encouraging a unified understanding, recognizing the potential for a more nuanced comprehension of the intricate dynamics between retail distribution and the physical features of cities. This approach paves the way for future interdisciplinary research, fostering collaboration in unravelling the complexities that shape urban environments.