A combined statistical approach and ground movement model for improving taxi time estimations at airports

With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. Improved on-stand time predictions (for improved resource allocation at the stands) and take-off time predictions (for improved airport-airspace coordination) both require more accurate taxi time predictions, as do the increasingly sophisticated ground movement models which are being developed. Calibrating such models requires historic data showing how long aircraft will actually take to move around the airport, but recorded data usually includes significant delays due to contention between aircraft. This research was motivated by the need to both predict taxi times and to quantify and eliminate the effects of airport load from historic taxi time data, since delays and re-routing are usually explicitly considered in ground movement models. A prediction model is presented here that combines both airport layout and historic taxi time information within a multiple linear regression analysis, identifying the most relevant factors affecting the variability of taxi times for both arrivals and departures. The promising results for two different European hub airports are compared against previous results for US airports.     

Solving Examination Timetabling Problems through Adaption of Heuristic Orderings

Heuristic ordering based methods, very similar to those used for graph colouring problems, have long been applied successfully to the examination timetabling problem. Despite the success of these methods on real life problems, even with limited computing resources, the approach has the fundamental flaw that it is only as effective as the heuristic that is used. We present a method that adapts to suit a particular problem instance “on the fly.” This method provides an alternative to existing forms of ‘backtracking,’ which are often required to cope with the possible unsuitability of a heuristic. We present a range of experiments on benchmark problems to test and evaluate the approach. In comparison to other published approaches to solving this problem, the adaptive method is more general, significantly quicker and easier to implement and produces results that are at least comparable (if not better) than the current state of the art. We also demonstrate the level of generality of this approach by starting it with the inverse of a known good heuristic, a null ordering and random orderings, showing that the adaptive method can transform a bad heuristic ordering into a good one.     

Familial aggregation of early-onset myocardial infarction

Although ischemic heart disease tends to cluster in families, previous studies have reported a modest (2-fold increased risk) to strong (10-fold increased risk) contribution of family history to the explanation of disease occurrence. The authors assessed the familial aggregation of early-onset myocardial infarction in 11,307 adults aged <65 years who participated in the Third National Health and Nutrition Examination Survey. Logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). A parental history was more common in those with (n = 237) than in those without (n = 11,070) a myocardial infarction (19.8 vs. 7.9%, p < or = 0.01). Adults with a parental history were also more likely to have multiple risk factors for cardiovascular disease (OR for four or five risk factors compared with none: 2.9, 95% CI: 1.4, 6.3). After multivariate adjustment, the likelihood of myocardial infarction was more than three times greater among adults with a parental history than among those without (95% CI: 1.7, 6.7). A maternal history of myocardial infarction was strongly associated (OR = 6.1, 95% CI: 2.1, 17.4) with an increased likelihood of myocardial infarction, and a paternal history was associated with a 3-fold (95% CI: 1.5, 6.3) increased likelihood of myocardial infarction after adjustment for cardiovascular disease risk factors. These results suggest a familial aggregation of early-onset myocardial infarction and show that family history is strongly associated with cardiovascular disease risk factors.     

Genetics in primary care: a USA faculty development initiative

The Genetics in Primary Care (GPC) project is a USA national faculty development initiative with the goal of enhancing the training of medical students and primary care residents by developing primary care faculty expertise in genetics. Educational strategies were developed for the project by an executive committee with input from an advisory committee, comprising individuals with primary care, medical education and genetics expertise. These committees identified the key issues in genetics education for primary care as (1) considering inherited disease in the differential diagnosis of common disorders; (2) using appropriate counseling strategies for genetic testing and diagnosis, and (3) understanding the implications of a genetic diagnosis for family members. The group emphasized the importance of a primary care perspective, which suggests that the clinical utility of genetic information is greatest when it has the potential to improve health outcomes. The group also noted that clinical practice already incorporates the use of family history information, providing a basis for discussing the application of genetic concepts in primary care. Genetics and primary care experts agreed that educational efforts will be most successful if they are integrated into existing primary care teaching programs, and use a case-based teaching format that incorporates both clinical and social dimensions of genetic disorders. Three core clinical skills were identified: (1) interpreting family history; (2) recognizing the variable clinical utility of genetic information, and (3) acquiring cultural competency. Three areas of potential controversy were identified as well: (1) the role of nondirective counseling versus shared decision-making in discussions of genetic testing; (2) the intrinsic value of genetic information when it does not influence health outcomes, and (3) indications for a genetics referral. The project provides an opportunity for ongoing discussion about these important issues.     

Epi-convergence Properties of Smoothing by Infimal Convolution

This paper concerns smoothing by infimal convolution for two large classes of functions: convex, proper and lower semicontinous as well as for (the nonconvex class of) convex-composite functions. The smooth approximations are constructed so that they epi-converge (to the underlying nonsmooth function) and fulfill a desirable property with respect to graph convergence of the gradient mappings to the subdifferential of the original function under reasonable assumptions. The close connection between epi-convergence of the smoothing functions and coercivity properties of the smoothing kernel is established.     

Convergence of the Gradient Sampling Algorithm on Directionally Lipschitz Functions

Set-Valued and Variational Analysis - The convergence theory for the gradient sampling algorithm is extended to directionally Lipschitz functions. Although directionally Lipschitz functions are not...     

Effective learning hyper-heuristics for the course timetabling problem

Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selection are contrasted: a static (offline) approach, with a clear distinction between training and execution phases; and a dynamic approach that learns on the fly. The resulting algorithms are tested over the set of real-world instances collected by the first and second International Timetabling competitions. The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms.     

Unified encoding for hyper-heuristics with application to bioinformatics

This paper introduces a new approach to applying hyper-heuristic algorithms to solve combinatorial problems with less effort, taking into account the modelling and algorithm construction process. We propose a unified encoding of a solution and a set of low level heuristics which are domain-independent and which change the solution itself. This approach enables us to address NP-hard problems and generate good approximate solutions in a reasonable time without a large amount of additional work required to tailor search methodologies for the problem in hand. In particular, we focused on solving DNA sequencing by hybrydization with errors, which is known to be strongly NP-hard. The approach was extensively tested by solving multiple instances of well-known combinatorial problems and compared with results generated by meta heuristics that have been tailored for specific problem domains.     

Spectral conditioning and pseudospectral growth

Using the language of pseudospectra, we study the behavior of matrix eigenvalues under two scales of matrix perturbation. First, we relate Lidskii’s analysis of small perturbations to a recent result of Karow on the growth rate of pseudospectra. Then, considering larger perturbations, we follow recent work of Alam and Bora in characterizing the distance from a given matrix to the set of matrices with multiple eigenvalues in terms of the number of connected components of pseudospectra. J. V. Burke’s research was supported in part by National Science Foundation Grant DMS-0505712. A. S. Lewis’s research was supported in part by National Science Foundation Grant DMS-0504032. M. L. Overton’s research was supported in part by National Science Foundation Grant DMS-0412049.     

Investigation into the rotational bands of ~(185)Pt with the particle-rotor model

Theoretical calculations have been performed for the ν9/2+[624](i13/2) and ν7/2-[503](f7/2) bands of 185Pt in the framework of particle-rotor model. The band properties of signature splitting and configuration mixing have been analyzed. The level energy and signature splitting before the band crossing can be well interpreted by introducing triaxiality. The positive-parity yrast band is pro posed to be dominated by the ν9/2+[624](i13/2) component, while the negative-parity band shows strong mixing of ν7/2-[5...