On automorphisms of algebraic curves

An irreducible, algebraic curve $\mathcal X_g$ of genus $g\geq 2$ defined over an algebraically closed field $k$ of characteristic \mbox{char } \, k = p \geq 0, has finite automorphism group \mbox{Aut} (\mathcal X_g). In this paper we describe methods of determining the list of groups \mbox{Aut} (\mathcal X_g) for a fixed $g\geq 2$. Moreover, equations of the corresponding families of curves are given when possible

Structural health monitoring of a footbridge using Echo State Networks and NARMAX

Echo State Networks (ESNs) and a Nonlinear Auto-Regressive Moving Average model with eXogenous inputs (NARMAX) have been applied to multi-sensor time-series data arising from a test footbridge which has been subjected to multiple potentially damaging interventions. The aim of the work was to automatically classify known potentially damaging events, while also allowing engineers to observe and localise any long term damage trends. The techniques reported here used data from ten temperature sensors as inputs and were tasked with predicting the output signal from eight tilt sensors embedded at various points over the bridge. Initially, interventions were identified by both ESNs and NARMAX. In addition, training ESNs using data up to the first event, and determining the ESNs’ subsequent predictions, allowed inferences to be made not only about when and where the interventions occurred, but also the level of damage caused, without requiring any prior data pre-processing or extrapolation. Finally, ESNs were successfully used as classifiers to characterise various different types of intervention that had taken place

Ticarcillin hypersusceptibility in pseudomonas aeruginosa in cystic fibrosis

Background: A subpopulation of Pseudomonas aeruginosa (PsA) exists in cysticfibrosis (CF) patients that&nbsp; is&nbsp; hypersusceptible&nbsp; to&nbsp; ticarcillin,&nbsp; a&nbsp; carboxypenicillin,&nbsp; in&nbsp; vitro (Tichs strain)&nbsp; defined&nbsp; as&nbsp; a minimum inhibitory concentration (MIC) ≤4μg/ml. Methods: In a retrospective cohort study, isolates of PsA from CF (23),&nbsp; non-cystic fibrosis bronchiectasis (NCFB) (17) and control (18) patients were analysed. MICs for each isolate were determined using agar dilution against six antibiotics and interpreted using EUCAST breakpoints. Prevalence of Tichs in&nbsp; each&nbsp; cohort&nbsp; was&nbsp; calculated.&nbsp; A&nbsp; point&nbsp; prevalence&nbsp; survey&nbsp; was&nbsp; conducted&nbsp; in&nbsp; CF&nbsp; to&nbsp; review&nbsp; the&nbsp; patients’ clinical progress following PsA isolation. Results: Prevalence of the Tichs strain in PsA was 48%, 76% and 0% in the CF, NCFB and control cohorts respectively. A statistically significant difference in geometric mean MIC was seen between the Tichs and non-Tichs&nbsp; cohorts in CF for ticarcillin (as expected) and temocillin (p=0.041and p=0.036 respectively). A similar trend was observed in NCFB for ticarcillin (p=0.038) and temocillin (p=0.067), although statistical significance was not reached for the latter.In&nbsp; CF,&nbsp; the&nbsp; Tichs&nbsp; strain&nbsp; demonstrated&nbsp; lower&nbsp; MICs&nbsp; to&nbsp; all antibiotics&nbsp; tested&nbsp; apart&nbsp; from&nbsp; gentamicin compared&nbsp; to&nbsp; their&nbsp; non-Tichs counterparts. Those&nbsp; who&nbsp; had the Tichs strain&nbsp; in&nbsp; CF&nbsp; had&nbsp; fewer&nbsp; antibiotics (13.9&nbsp; days&nbsp; versus&nbsp; 23.5&nbsp; days,&nbsp; Tichs&nbsp; and&nbsp; non-Tichs respectively)&nbsp; although&nbsp; this&nbsp; result&nbsp; was&nbsp; not&nbsp; statistically significant p=0.202. Conclusion: Our&nbsp; data&nbsp; supports&nbsp; the&nbsp; existence&nbsp; of&nbsp; a&nbsp; Tichs strain&nbsp; of&nbsp; PsA&nbsp; in&nbsp; our&nbsp; CF&nbsp; and&nbsp; NCFB&nbsp; patient populations. This strain correlated with reduced MICs to temocillin in CF, to which PsA would normally be resistant, which may be of clinical relevance.</p

Modelling long-distance route choice using mobile phone call detail record data: A case study of Senegal

The growing mobile phone penetration rates have led to the emergence of large-scale call detail records (CDRs) that could serve as a low-cost data source for travel behaviour modelling. However, to the best of our knowledge, there is no previous study evaluating the potential of CDR data in the context of route choice behaviour modelling. Being event-driven, the data are discontinuous and only able to yield partial trajectories, thus presenting serious challenges for route identification. This paper proposes techniques for inferring the users' chosen routes or subsets of their likely routes from partial CDR trajectories, as well as data fusion with external sources of information such as route costs, and then adapts the broad choice framework to the current modelling scenario. The model results show that CDR data can capture the expected travel behaviour and the derived values of travel time are found to be realistic for the study area