Sequenzierung mit Ant-Colony-Systemen am Beispiel Querverteil-Wagen

Dieser Beitrag zeigt die Anwendung des Ant-Colony-System (ACS) Algorithmus auf die Sequenzierung von Querverteil-Wagen in einem Lager. Wir erweitern den Basisalgorithmus der Ant-Colony-Optimierung (ACO) für die Minimierung der Bearbeitungszeit einer Menge von Fahraufträgen für die Querverteil-Wagen. Im Vergleich zu dem Greedy-Algorithmus ist der ACO-Algorithmus wettbewerbsfähig und schnell. In vielen Lagerverwaltungssystemen werden die Fahraufträge nach dem FIFO-Prinzip (First-in-First-out) ausgeführt. In diesem Beitrag wird der ACO-Algorithmus genutzt, um eine optimale Sequenz der Fahraufträge zu bilden.

A note on quasi-stationary distributions of birth-death processes and the SIS logistic epidemic

For Markov processes on the positive integers with the origin as an absorbing state, Ferrari, Kesten, Martinez and Picco studied the existence of quasi-stationary and limiting conditional distributions by characterizing quasi-stationary distributions as fixed points of a transformation Phi on the space of probability distributions on {1, 2,.. }. In the case of a birth-death process, the components of Phi(nu) can be written down explicitly for any given distribution nu. Using this explicit representation, we will show that Phi preserves likelihood ratio ordering between distributions. A conjecture of Kryscio and Lefevre concerning the quasi-stationary distribution of the SIS logistic epidemic follows as a corollary.

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Disfunções orgânicas de pacientes internados em Unidades de Terapia Intensiva segundo o Logistic Organ Dysfunction System

Foi objetivo do estudo verificar as associações entre a probabilidade de morte, número e tipo de insuficiências orgânicas na admissão de pacientes na Unidade de Terapia Intensiva (UTI), segundo o Logistic Organ Dysfunction System (LODS), e as seguintes variáveis: tempo de internação, condição de saída e readmissão na unidade. Estudo prospectivo longitudinal de 600 pacientes adultos internados em UTI gerais de quatro hospitais do Município de São Paulo. Como resultados, a probabilidade de morte apresentou associação com as condições de saída da UTI (p<0,001). Também houve associação do número de insuficiências orgânicas com as condição de saída (p<0,001) e tempo de internação na UTI (p<0,001). Quanto ao tipo de insuficiências e tempo de internação na Unidade houve diferença apenas entre os pacientes com insuficiência neurológica (p<0,001), pulmonar (p<0,001) e renal (p=0,020). A readmissão dos pacientes na UTI não teve associação com nenhuma das variáveis estudadas.

Evaluation of ammonia volatilization losses by adjusted parameters of a logistic function

The dynamics of N losses in fertilizer by ammonia volatilization is affected by several factors, making investigation of these dynamics more complex. Moreover, some features of the behavior of the variable can lead to deviation from normal distribution, making the main commonly adopted statistical strategies inadequate for data analysis. Thus, the purpose of this study was to evaluate the patterns of cumulative N losses from urea through ammonia volatilization in order to find a more adequate and detailed way of assessing the behavior of the variable. For that reason, changes in patterns of ammonia volatilization losses as a result of applying different combinations of two soil classes [Planossolo and Chernossolo (Typic Albaqualf and Vertic Argiaquolls)] and different rates of urea (50, 100 and 150 kg ha-1 N), in the presence or absence of a urease inhibitor, were evaluated, adopting a 2 × 3 × 2 factorial design with four replications. Univariate and multivariate analysis of variance were performed using the adjusted parameter values of a logistic function as a response variable. The results obtained from multivariate analysis indicated a prominent effect of the soil class factor on the set of parameters, indicating greater relevance of soil adsorption potential on ammonia volatilization losses. Univariate analysis showed that the parameters related to total N losses and rate of volatilization were more affected by soil class and the rate of urea applied. The urease inhibitor affected only the rate and inflection point parameters, decreasing the rate of losses and delaying the beginning of the process, but had no effect on total ammonia losses. Patterns of ammonia volatilization losses provide details on behavior of the variable, details which can be used to develop and adopt more accurate techniques for more efficient use of urea.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.

Existence of multiple attractors and the nature of bifurcations in a discontinuous logistic map

We present the analytical investigations on a logistic map with a discontinuity at the centre. An explanation for the bifurcation phenomenon in discontinuous systems is presented. We establish that whenever the elements of an n-cycle (n > 1) approach the discontinuities of the nth iterate of the map, a bifurcation other than the usual period-doubling one takes place. The periods of the cycles decrease in an arithmetic progression, as the control parameter is varied. The system also shows the presence of multiple attractors. Our results are verified by numerical experiments as well.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Choosing between Cox proportional hazards and logistic models for interval-censored data via bootstrap

This work develops a new methodology in order to discriminate models for interval-censored data based on bootstrap residual simulation by observing the deviance difference from one model in relation to another, according to Hinde (1992). Generally, this sort of data can generate a large number of tied observations and, in this case, survival time can be regarded as discrete. Therefore, the Cox proportional hazards model for grouped data (Prentice & Gloeckler, 1978) and the logistic model (Lawless, 1982) can befitted by means of generalized linear models. Whitehead (1989) considered censoring to be an indicative variable with a binomial distribution and fitted the Cox proportional hazards model using complementary log-log as a link function. In addition, a logistic model can be fitted using logit as a link function. The proposed methodology arises as an alternative to the score tests developed by Colosimo et al. (2000), where such models can be obtained for discrete binary data as particular cases from the Aranda-Ordaz distribution asymmetric family. These tests are thus developed with a basis on link functions to generate such a fit. The example that motivates this study was the dataset from an experiment carried out on a flax cultivar planted on four substrata susceptible to the pathogen Fusarium oxysoprum. The response variable, which is the time until blighting, was observed in intervals during 52 days. The results were compared with the model fit and the AIC values.

Solving the Levins' paradox in the logistic model to the population growth

We introduce a new method to improve Markov maps by means of a Bayesian approach. The method starts from an initial map model, wherefrom a likelihood function is defined which is regulated by a temperature-like parameter. Then, the new constraints are added by the use of Bayes rule in the prior distribution. We applied the method to the logistic map of population growth of a single species. We show that the population size is limited for all ranges of parameters, allowing thus to overcome difficulties in interpretation of the concept of carrying capacity known as the Levins paradox. © Published under licence by IOP Publishing Ltd.

Relationship between the logistic EuroSCORE and the Society of Thoracic Surgeons Predicted Risk of Mortality score in patients implanted with the CoreValve ReValving system--a Bern-Rotterdam Study

Background Surgical risk scores, such as the logistic EuroSCORE (LES) and Society of Thoracic Surgeons Predicted Risk of Mortality (STS) score, are commonly used to identify high-risk or “inoperable” patients for transcatheter aortic valve implantation (TAVI). In Europe, the LES plays an important role in selecting patients for implantation with the Medtronic CoreValve System. What is less clear, however, is the role of the STS score of these patients and the relationship between the LES and STS. Objective The purpose of this study is to examine the correlation between LES and STS scores and their performance characteristics in high-risk surgical patients implanted with the Medtronic CoreValve System. Methods All consecutive patients (n = 168) in whom a CoreValve bioprosthesis was implanted between November 2005 and June 2009 at 2 centers (Bern University Hospital, Bern, Switzerland, and Erasmus Medical Center, Rotterdam, The Netherlands) were included for analysis. Patient demographics were recorded in a prospective database. Logistic EuroSCORE and STS scores were calculated on a prospective and retrospective basis, respectively. Results Observed mortality was 11.1%. The mean LES was 3 times higher than the mean STS score (LES 20.2% ± 13.9% vs STS 6.7% ± 5.8%). Based on the various LES and STS cutoff values used in previous and ongoing TAVI trials, 53% of patients had an LES ≥15%, 16% had an STS ≥10%, and 40% had an LES ≥20% or STS ≥10%. Pearson correlation coefficient revealed a reasonable (moderate) linear relationship between the LES and STS scores, r = 0.58, P < .001. Although the STS score outperformed the LES, both models had suboptimal discriminatory power (c-statistic, 0.49 for LES and 0.69 for STS) and calibration. Conclusions Clinical judgment and the Heart Team concept should play a key role in selecting patients for TAVI, whereas currently available surgical risk score algorithms should be used to guide clinical decision making.

Combined anatomical and clinical factors for the long-term risk stratification of patients undergoing percutaneous coronary intervention: the Logistic Clinical SYNTAX score

The SYNTAX score (SXscore), an anatomical-based scoring tool reflecting the complexity of coronary anatomy, has established itself as an important long-term prognostic factor in patients undergoing percutaneous coronary intervention (PCI). The incorporation of clinical factors may further augment the utility of the SXscore to longer-term risk stratify the individual patient for clinical outcomes.

Predicting childhood sexual or physical abuse: a logistic regression geo-mapping approach to prevention.

This study investigates the degree to which gender, ethnicity, relationship to perpetrator, and geomapped socio-economic factors significantly predict the incidence of childhood sexual abuse, physical abuse and non- abuse. These variables are then linked to geographic identifiers using geographic information system (GIS) technology to develop a geo-mapping framework for child sexual and physical abuse prevention.