Environmental assessment of urban mobility: combining life cycle assessment with land-use and transport interaction modelling – application to Lyon (France)

In France, greenhouse gas (GHG) emissions from transport have grown steadily since 1950 and transport is now the main source of emissions. Despite technological improvements, urban sprawl increases the environmental stress due to car use. This study evaluates urban mobility through assessments of the transport system and travel habits, by applying life cycle assessment methods to the results of mobility simulations that were produced by a Land Use and Transport Interactions (LUTI) model. The environmental impacts of four life cycle phases of urban mobility in the Lyon area (exhausts, fuel processing, infrastructure and vehicle life cycle) were estimated through nine indicators (global warming potential, particulate matter emissions, photochemical oxidant emissions, terrestrial acidification, fossil resource depletion, metal depletion, non-renewable energy use, renewable energy use and land occupancy). GHG emissions were estimated to be 3.02 kg CO2-eq inhabitant−1 day−1 , strongly linked to car use, and indirect impacts represented 21% of GHG emissions, which is consistent with previous studies. Combining life cycle assessment (LCA) with a LUTI model allows changes in the vehicle mix and fuel sources combined with demographic shifts to be assessed, and provides environmental perspectives for transport policy makers and urban planners. It can also provide detailed analysis, by allowing levels of emissions that are generated by different categories of households to be differentiated, according to their revenue and location. Public policies can then focus more accurately on the emitters and be assessed from both an environmental and social point of view

Large Scale Cross-Correlations in Internet Traffic

The Internet is a complex network of interconnected routers and the existence of collective behavior such as congestion suggests that the correlations between different connections play a crucial role. It is thus critical to measure and quantify these correlations. We use methods of random matrix theory (RMT) to analyze the cross-correlation matrix C of information flow changes of 650 connections between 26 routers of the French scientific network `Renater'. We find that C has the universal properties of the Gaussian orthogonal ensemble of random matrices: The distribution of eigenvalues--up to a rescaling which exhibits a typical correlation time of the order 10 minutes--and the spacing distribution follow the predictions of RMT. There are some deviations for large eigenvalues which contain network-specific information and which identify genuine correlations between connections. The study of the most correlated connections reveals the existence of `active centers' which are exchanging information with a large number of routers thereby inducing correlations between the corresponding connections. These strong correlations could be a reason for the observed self-similarity in the WWW traffic.Comment: 7 pages, 6 figures, final versio

Activating Generalized Fuzzy Implications from Galois Connections

This paper deals with the relation between fuzzy implications and Galois connections, trying to raise the awareness that the fuzzy implications are indispensable to generalise Formal Concept Analysis. The concrete goal of the paper is to make evident that Galois connections, which are at the heart of some of the generalizations of Formal Concept Analysis, can be interpreted as fuzzy incidents. Thus knowledge processing, discovery, exploration and visualization as well as data mining are new research areas for fuzzy implications as they are areas where Formal Concept Analysis has a niche.F.J. Valverde-Albacete—was partially supported by EU FP7 project LiMoSINe, (contract 288024). C. Peláez-Moreno—was partially supported by the Spanish Government-CICYT project 2011-268007/TEC.Publicad

A Multi-commodity network flow model for cloud service environments

Next-generation systems, such as the big data cloud, have to cope with several challenges, e.g., move of excessive amount of data at a dictated speed, and thus, require the investigation of concepts additional to security in order to ensure their orderly function. Resilience is such a concept, which when ensured by systems or networks they are able to provide and maintain an acceptable level of service in the face of various faults and challenges. In this paper, we investigate the multi-commodity flows problem, as a task within our D 2 R 2 +DR resilience strategy, and in the context of big data cloud systems. Specifically, proximal gradient optimization is proposed for determining optimal computation flows since such algorithms are highly attractive for solving big data problems. Many such problems can be formulated as the global consensus optimization ones, and can be solved in a distributed manner by the alternating direction method of multipliers (ADMM) algorithm. Numerical evaluation of the proposed model is carried out in the context of specific deployments of a situation-aware information infrastructure

Small-world networks: Evidence for a crossover picture

Watts and Strogatz [Nature 393, 440 (1998)] have recently introduced a model for disordered networks and reported that, even for very small values of the disorder $p$ in the links, the network behaves as a small-world. Here, we test the hypothesis that the appearance of small-world behavior is not a phase-transition but a crossover phenomenon which depends both on the network size $n$ and on the degree of disorder $p$. We propose that the average distance $\ell$ between any two vertices of the network is a scaling function of $n / n^*$. The crossover size $n^*$ above which the network behaves as a small-world is shown to scale as $n^*(p \ll 1) \sim p^{-\tau}$ with $\tau \approx 2/3$.Comment: 5 pages, 5 postscript figures (1 in color), Latex/Revtex/multicols/epsf. Accepted for publication in Physical Review Letter

Benefit and Harm of Active Surveillance for Biopsy-proven Renal Oncocytoma : A Systematic Review and Pooled Analysis

Context: Active surveillance (AS) of biopsy-proven renal oncocytomas may reduce overtreatment. However, on biopsy, the risk of misdiagnosis owing principally to entities with peculiar hybrids and overlap morphology, and phenotypes argues for early intervention. Objective: To assess the benefit and harm of AS in biopsy-proven renal oncocytoma. Evidence acquisition: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). We systematically searched PubMed, Scopus, and Web of Science databases from September 26 up to October 2021, for studies that analyzed the outcomes of AS in patients with biopsy-proven renal oncocytoma. Evidence synthesis: A total of ten studies with 633 patients met our inclusion criteria and were included for analysis. After a median follow-up of 34.5 mo (95% confidence interval [CI] 30.6-38.4), the overall definitive treatment rate from AS to definitive treatment was 17.3% (n = 75/433, six studies). The pooled pathological agreement between the initial renal mass biopsy and the surgical pathology report was 91.1%. The main indications for surgery during follow-up were rapid tumor growth and patient request. The pooled median growth rate was 1.55 mm/yr (95% CI 0.9-2.2). No metastasis or death related to renal oncocytoma was reported. Conclusions: Annual tumor growth of biopsy-proven renal oncocytoma is low. AS is oncologically safe, with favorable compliance of patients. Crossover to definitive treatment revealed a strong concordance between biopsy and final pathology. Further studies on the long-term outcomes of AS are needed. Patient summary: In this study, we examined the benefit and harm of active surveillance (AS) in biopsy-proven oncocytoma. Based on the available data, AS appears oncologically safe and may represent a promising alternative to immediate treatment. Patients should be included in AS decision discussions

Dynamic analysis of repetitive decision-free discreteevent processes: The algebra of timed marked graphs and algorithmic issues

A model to analyze certain classes of discrete event dynamic systems is presented. Previous research on timed marked graphs is reviewed and extended. This model is useful to analyze asynchronous and repetitive production processes. In particular, applications to certain classes of flexible manufacturing systems are provided in a companion paper. Here, an algebraic representation of timed marked graphs in terms of reccurrence equations is provided. These equations are linear in a nonconventional algebra, that is described. Also, an algorithm to properly characterize the periodic behavior of repetitive production processes is descrbed. This model extends the concepts from PERT/CPM analysis to repetitive production processes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44155/1/10479_2005_Article_BF02248590.pd

Prevalence of and Predictive Factors for Burnout Among French Urologists in Training

The burnout rate among young doctors currently seems to be increasing [1]. It is essential to be able to diagnose and prevent this condition to better take care of young caregivers. Burnout is defined as a “feeling of intense exhaustion, loss of control and inability to achieve concrete results at work” according to the World Health Organisation. The assessment questionnaire used most often is the Maslach Burnout Inventory (MBI), which covers (1) emotional exhaustion, (2) depersonalisation, and (3) personal accomplishment [2]

Serum Neurotrophin Profile in Systemic Sclerosis

International audienceBACKGROUND: Neurotrophins (NTs) are able to activate lymphocytes and fibroblasts; they can modulate angiogenesis and sympathic vascular function. Thus, they can be implicated in the three pathogenic processes of systemic sclerosis (SSc). The aims of this study are to determine blood levels of Nerve Growth Factor (NGF), Brain-Derived Neurotrophic Factor (BDNF) and Neurotrophin-3 (NT-3) in SSc and to correlate them with clinical and biological data.METHODS: Serum samples were obtained from 55 SSc patients and 32 control subjects to measure NTs levels by ELISA and to determine their relationships with SSc profiles. FINDINGS: Serum NGF levels were higher in SSc patients (288.26 ± 170.34 pg/mL) than in control subjects (170.34 ± 50.8 pg/mL, p<0.001) and correlated with gammaglobulins levels and the presence of both anti-cardiolipin and anti-Scl-70 antibodies (p<0.05). In contrast, BDNF levels were lower in SSc patients than in controls (1121.9 ± 158.1 vs 1372.9 ± 190.9 pg/mL, p<0.0001), especially in pulmonary arterial hypertension and diffuse SSc as compared to limited forms (all p<0.05). NT-3 levels were similar in SSc and in the control group (2657.2 ± 2296 vs 2959.3 ± 2555 pg/mL, NS). BDNF levels correlated negatively with increased NGF levels in the SSc group (and not in controls). CONCLUSION: Low BDNF serum levels were not previously documented in SSc, particularly in the diffuse SSc subset and in patients with pulmonary hypertension or anti-Scl-70 antibodies. The negative correlation between NGF and BDNF levels observed in SSc and not in healthy controls could be implicated in sympathic vascular dysfunction in SSc