Modeling Airline Frequency Competition for Airport Congestion Mitigation

Demand often exceeds capacity at congested airports. Airline frequency competition is partially responsible for the growing demand for airport resources. We propose a game-theoretic model for airline frequency competition under slot constraints. The model is solved to obtain a Nash equilibrium using a successive optimizations approach, wherein individual optimizations are performed using a dynamic programming-based technique. The model predictions are validated against actual frequency data, with the results indicating a close fit to reality. We use the model to evaluate different strategic slot allocation schemes from the perspectives of the airlines and the passengers. The most significant result of this research shows that a small reduction in the total number of allocated slots translates into a substantial reduction in flight and passenger delays and also a considerable improvement in airlines' profits

4-dimensional functional profiling in the convulsant-treated larval zebrafish brain

This is the final version of the article. Available from Springer Nature via the DOI in this record.Functional neuroimaging, using genetically-encoded Ca(2+) sensors in larval zebrafish, offers a powerful combination of high spatiotemporal resolution and higher vertebrate relevance for quantitative neuropharmacological profiling. Here we use zebrafish larvae with pan-neuronal expression of GCaMP6s, combined with light sheet microscopy and a novel image processing pipeline, for the 4D profiling of chemoconvulsant action in multiple brain regions. In untreated larvae, regions associated with autonomic functionality, sensory processing and stress-responsiveness, consistently exhibited elevated spontaneous activity. The application of drugs targeting different convulsant mechanisms (4-Aminopyridine, Pentylenetetrazole, Pilocarpine and Strychnine) resulted in distinct spatiotemporal patterns of activity. These activity patterns showed some interesting parallels with what is known of the distribution of their respective molecular targets, but crucially also revealed system-wide neural circuit responses to stimulation or suppression. Drug concentration-response curves of neural activity were identified in a number of anatomically-defined zebrafish brain regions, and in vivo larval electrophysiology, also conducted in 4dpf larvae, provided additional measures of neural activity. Our quantification of network-wide chemoconvulsant drug activity in the whole zebrafish brain illustrates the power of this approach for neuropharmacological profiling in applications ranging from accelerating studies of drug safety and efficacy, to identifying pharmacologically-altered networks in zebrafish models of human neurological disorders.This work was funded by the Biological and Biotechnology Research Council (CASE studentship BB/L502510/1, with AstraZeneca Safety Health and Environment), and by the University of Exeter and AstraZeneca

A survey on feature weighting based K-Means algorithms

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.Peer reviewedFinal Accepted Versio

A new method for the determination of low-level actinium-227 in geological samples

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Journal of Radioanalytical and Nuclear Chemistry 296 (2013): 279-283, doi:10.1007/s10967-012-2140-0.We developed a new method for the determination of 227Ac in geological samples. The method uses extraction chromatographic techniques and alpha-spectrometry and is applicable for a range of natural matrices. Here we report on the procedure and results of the analysis of water (fresh and seawater) and rock samples. Water samples were acidified and rock samples underwent total dissolution via acid leaching. A DGA (N,N,N’,N’-tetra-n-octyldiglycolamide) extraction chromatographic column was used for the separation of actinium. The actinium fraction was prepared for alpha spectrometric measurement via cerium fluoride micro-precipitation. Recoveries of actinium in water samples were 80±8 % (number of analyses n=14) and in rock samples 70±12 % (n=30). The minimum detectable activities (MDA) were 0.017-0.5 Bq kg-1 for both matrices. Rock sample 227Ac activities ranged from 0.17 to 8.3 Bq kg-1 and water sample activities ranged from below MDA values to 14 Bq kg-1of 227Ac. From the analysis of several standard rock and water samples with the method we found very good agreement between our results and certified values

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Functional brain imaging in larval zebrafish for characterising the effects of seizurogenic compounds acting via a range of pharmacological mechanisms

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Some data may not be made available because of privacy or ethical restrictions.Background and Purpose Functional brain imaging using genetically encoded Ca2+ sensors in larval zebrafish is being developed for studying seizures and epilepsy as a more ethical alternative to rodent models. Despite this, few data have been generated on pharmacological mechanisms of action other than GABAA antagonism. Assessing larval responsiveness across multiple mechanisms is vital to test the translational power of this approach, as well as assessing its validity for detecting unwanted drug‐induced seizures and testing antiepileptic drug efficacy. Experimental Approach Using light‐sheet imaging, we systematically analysed the responsiveness of 4 days post fertilisation (dpf; which are not considered protected under European animal experiment legislation) transgenic larval zebrafish to treatment with 57 compounds spanning more than 12 drug classes with a link to seizure generation in mammals, alongside eight compounds with no such link. Key Results We show 4dpf zebrafish are responsive to a wide range of mechanisms implicated in seizure generation, with cerebellar circuitry activated regardless of the initiating pharmacology. Analysis of functional connectivity revealed compounds targeting cholinergic and monoaminergic reuptake, in particular, showed phenotypic consistency broadly mapping onto what is known about neurotransmitter‐specific circuitry in the larval zebrafish brain. Many seizure‐associated compounds also exhibited altered whole brain functional connectivity compared with controls. Conclusions and Implications This work represents a significant step forward in understanding the translational power of 4dpf larval zebrafish for use in neuropharmacological studies and for studying the events driving transition from small‐scale pharmacological activation of local circuits, to the large network‐wide abnormal synchronous activity associated with seizures.Biotechnology and Biological Sciences Research Council (BBSRC)National Centre for the Replacement Refinement and Reduction of Animals in ResearchUniversity of ExeterMedical Research Council (MRC)AstraZenecaEuropean Unio

Safety of low-carbohydrate diets

Low-carbohydrate diets have re-emerged into the public spotlight and are enjoying a high degree of popularity as people search for a solution to the population\u27s ever-expanding waistline. The current evidence though indicates that low-carbohydrate diets present no significant advantage over more traditional energy-restricted diets on long-term weight loss and maintenance. Furthermore, a higher rate of adverse side-effects can be attributed to low-carbohydrate dieting approaches. Short-term efficacy of low-carbohydrate diets has been demonstrated for some lipid parameters of cardiovascular risk and measures of glucose control and insulin sensitivity, but no studies have ascertained if these effects represent a change in primary outcome measures. Low-carbohydrate diets are likely effective and not harmful in the short term and may have therapeutic benefits for weight-related chronic diseases although weight loss on such a program should be undertaken under medical supervision. While new commercial incarnations of the low-carbohydrate diet are now addressing overall dietary adequacy by encouraging plenty of high-fibre vegetables, fruit, low-glycaemic-index carbohydrates and healthier fat sources, this is not the message that reaches the entire public nor is it the type of diet adopted by many people outside of the world of a well-designed clinical trial. Health effects of long-term ad hoc restriction of inherently beneficial food groups without a concomitant reduction in body weight remains unanswered.<br /

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty

X-ray emission from the Sombrero galaxy: discrete sources

We present a study of discrete X-ray sources in and around the bulge-dominated, massive Sa galaxy, Sombrero (M104), based on new and archival Chandra observations with a total exposure of ~200 ks. With a detection limit of L_X = 1E37 erg/s and a field of view covering a galactocentric radius of ~30 kpc (11.5 arcminute), 383 sources are detected. Cross-correlation with Spitler et al.'s catalogue of Sombrero globular clusters (GCs) identified from HST/ACS observations reveals 41 X-rays sources in GCs, presumably low-mass X-ray binaries (LMXBs). We quantify the differential luminosity functions (LFs) for both the detected GC and field LMXBs, whose power-low indices (~1.1 for the GC-LF and ~1.6 for field-LF) are consistent with previous studies for elliptical galaxies. With precise sky positions of the GCs without a detected X-ray source, we further quantify, through a fluctuation analysis, the GC LF at fainter luminosities down to 1E35 erg/s. The derived index rules out a faint-end slope flatter than 1.1 at a 2 sigma significance, contrary to recent findings in several elliptical galaxies and the bulge of M31. On the other hand, the 2-6 keV unresolved emission places a tight constraint on the field LF, implying a flattened index of ~1.0 below 1E37 erg/s. We also detect 101 sources in the halo of Sombrero. The presence of these sources cannot be interpreted as galactic LMXBs whose spatial distribution empirically follows the starlight. Their number is also higher than the expected number of cosmic AGNs (52+/-11 [1 sigma]) whose surface density is constrained by deep X-ray surveys. We suggest that either the cosmic X-ray background is unusually high in the direction of Sombrero, or a distinct population of X-ray sources is present in the halo of Sombrero.Comment: 11 figures, 5 tables, ApJ in pres