Publications

Safflower is a promising oilseed crop for the production of specialty oils in the Mediterranean area. Oil quality traits such as high oleic acid content, high linoleic acid content, high saturated fatty acid content, or high gamma-tocopherol content have been developed in this crop. The traits are controlled by the genotype of the developing embryo and therefore they are influenced by the presence of foreign pollen. The objective of this research was to study the rate of cross-fertilization in safflower using the high oleic acid trait as a biochemical marker. An experiment in which each high oleic plant was surrounded by 24 low oleic acid plants was conducted over three environments in the same location at Córdoba, Spain. The average rate of cross-fertilization in the three environments was 5.7, 12.1, and 13.2%, though higher frequencies up to 35.9% were detected at the single-plant level and up to 58.3% at the single-head level. The low average outcrossing frequencies identified in this research indicate no need for large isolation distances between conventional cultivars and cultivars with special oil characteristics. However, the occurrence of a significant outcrossing rate should be taken into consideration if transgenic safflower is to be cultivated close to conventional safflower or in areas of distribution of wild Carthamus species.

No abstract is provided for this article.

ISHS VI International Symposium on Integrated Canopy, Rootstock, Environmental Physiology in Orchard Systems THE PILLNITZ APPLE ROOTSTOCK BREEDING METHODS AND SELECTION RESULTS

This paper evaluates the use of neural network classifiers for the pattern classification problem in remote sensing. The performance of multi-layer perceptron (MLP), radial basis function, and fuzzy ARTMAP networks is evaluated using a Landsat-5 TM scene of the northern section of the city of Vienna, Austria. Classification accuracies obtained from the neural network classifiers are compared with a benchmark, the maximum likelihood classifier. In addition to the evaluation of classification accuracy, the neural networks are analyzed for their generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are obtained using fuzzy ARTMAP followed by MLP (with weight elimination). Their classification error on the training data set are zero and 7.87% respectively; classification error on the testing data set are 10.24% and less than 2 percent. Simulation results serve to illustrate the properties of the various classifiers in general, as well as the stability of the result with respect to various critical control parameters, initial parameter conditions, training time, and different training and testing data sets.

Christian Körner, 2021: Alpine Plant Life. Functional Plant Ecology of High Mountain Ecosystems. – Cham: Springer Nature Switzerland AG. – (Dennis Larsson und Clemens Pachschwöll) Horst Mehlhorn, 2020: Quick Flora Deutschland. Das kleine Pflanzenbestimmungsbuch für Ihren Ausflug in die Natur. – Berlin & Heidelberg: Springer. – (Christa Staudinger und Clemens Pachschwöll) Toni Nikolić, 2019–2020: Flora Croatica. Vaskularna flora Republike Hrvatske. [Gefäßpflanzen-Flora der Republik Kroatien.] 4 Bände. – Zagreb: ALFA. – (Manfred A. Fischer) Timothy C. G. Rich & David McCosh, 2021: Monograph of British and Irish Hieracium section Foliosa and section Prenanthoidea. – Bristol: Botanical Society of Britain and Ireland. – (Günter Gottschlich) Fritz H. Schweingruber, Andrea Kučerová, Lubomír Adamec & Jiří Doležal, 2020: Anatomic Atlas of Aquatic and Wetland Plant Stems. – Cham: Springer Nature Switzerland. – (Peter Englmaier) Barbara M. Thiers, 2020: Herbarium: The Quest to Preserve & Classify the World’s Plants. – Portland (Oregon): Timber Press. – (Clemens Pachschwöll)

No abstract is provided for this article.

This paper attempts to develop a mathematically rigid framework for minimizing the cross‐entropy function in an error backpropagating framework. In doing so, we derive the backpropagation formulae for evaluating the partial derivatives in a computationally efficient way. Various techniques of optimizing the multiple‐class cross‐entropy error function to train single hidden layer neural network classifiers with softmax output transfer functions are investigated on a real‐world multispectral pixel‐by‐pixel classification problem that is of fundamental importance in remote sensing. These techniques include epoch‐based and batch versions of backpropagation of gradient descent, PR‐conjugate gradient, and BFGS quasi‐Newton errors. The method of choice depends upon the nature of the learning task and whether one wants to optimize learning for speed or classification performance. It was found that, comparatively considered, gradient descent error backpropagation provided the best and most stable out‐of‐sample performance results across batch and epoch‐based modes of operation. If the goal is to maximize learning speed and a sacrifice in classification accuracy is acceptable, then PR‐conjugate gradient error backpropagation tends to be superior. If the training set is very large, stochastic epoch‐based versions of local optimizers should be chosen utilizing a larger rather than a smaller epoch size to avoid unacceptable instabilities in the classification results.

Spatial interaction models of the gravity type are widely used to model origin–destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize an origin region of a flow, variables that characterize a destination region of a flow, and finally variables that measure the separation between origin and destination regions. This paper outlines and compares two approaches, the spatial econometric and the eigenfunction‐based spatial filtering approach, to deal with the issue of spatial autocorrelation among flow residuals. An example using patent citation data that capture knowledge flows across 112 European regions serves to illustrate the application and the comparison of the two approaches.

Safety aspects will become essential for the introduction and acceptance of gaseous and liquid hydrogen as an energy carrier and fuel in energy supply systems. Prevention and control of accidental formation and ignition of large volumes of fuel-air mixtures are of primary importance when safety aspects of released gaseous hydrogen are discussed. Detailed knowledge of the overpressure in an accidental situation is essential for the protection of the public as well as for the corresponding plants and safety installations. Considerable progress has been made in the last few years concerning the understanding of the complex phenomena involved in combustion processes of gaseous mixtures. This holds in particular for flame acceleration and maximum turbulent flame speeds in unconfined and confined geometries. Fast turbulent deflagrations often transit spontaneously to detonations if flame speeds are high enough, depending on the combustible and boundary conditions. This paper discusses the potential hazards of hydrogen in the energy market as compared with other and already familiar energy carriers like natural gas and propane.

In this article, a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data‐generating process. The model comprises a spatially filtered component—including the origin‐, destination‐, and origin‐destination‐specific variables—and a spatial residual variable that captures origin‐ and destination‐based spatial autocorrelation. We derive a two‐stage nonlinear least‐squares (NLS) estimator (2NLS) that is hetero‐scedasticity‐robust and, thus, controls for the problem of over‐ or underdispersion that often is present in the empirical analysis of discrete data or, in the case of overdispersion, if spatial autocorrelation is present. This estimator can be shown to have desirable properties for different distributional assumptions, like the observed flows or (spatially) filtered component being either Poisson or negative binomial. In our spatial autoregressive (SAR) model specification, the resulting parameter estimates can be interpreted as the implied total impact effects defined as the sum of direct and indirect spatial feedback effects. Monte Carlo results indicate marginal finite sample biases in the mean and standard deviation of the parameter estimates and convergence to the true parameter values as the sample size increases. In addition, this article illustrates the model by analyzing patent citation flows data across European regions. En el presente artículo, se introduce un modelo de gravedad Poisson, que incorpora la dependencia espacial de la variable explicada, sin apoyarse en presunciones de distribución restrictivas del proceso subyacente de generación de datos. El modelo comprende de un componente espacialmente filtrado, que incluye las variables de origen, destino y origen‐destino específico; y una variable espacial residual que captura la auto‐correlación espacial basada en el origen y destino. Se deriva del calculador (2NLS) de dos etapas no lineales de mínimos cuadrados (NLS), el cual es robusto en heterocedasticidad, y por ello controla el problema de sobre‐dispersión o baja‐dispersión (over and under dispersion) , que a menudo se presenta en el análisis empírico de datos discretos; o, en el caso de de sobre‐dispersión, cuando se presenta la auto correlación espacial. Este calculador puede demostrar tener propiedades deseables para diferentes supuestos distribucionales, como los flujos observados un componente (espacialmente) filtrado, ya sea Poisson o binomial negativo. En nuestra especificación de modelo espacial auto regresivo (SAR), las estimaciones de los parámetros resultantes se pueden interpretar como los efectos de impacto total implícitos, definidos como la suma de efectos espaciales, directos o indirectos, de retroalimentación ( feedback ). Los resultados Monte Carlo indican sesgos marginales de muestras finitas en la media y la desviación estándar de los parámetros estimados, y la convergencia de los valores de los parámetros reales, a medida que aumenta el tamaño de muestra. Este artículo ilustra el modelo mediante el análisis de flujos de datos de citas de patentes, a través de las regiones europeas. 本文提出了一种蕴含空间依赖的泊松引力模型，该模型中解释变量无需依赖潜在数据生成过程的限制性分布假设。该模型由包含起点、终点、起点‐终点特定变量的空间滤波组分和空间残差变量组成，能捕捉到基于起点和终点的空间自相关。我们推导出一个二阶非线性最小二乘（NLS）估计（2NLS），它对异方差具有鲁棒性，从而可控制对于离散或过离散数据经验性分析中经常出现的过离散和低离散问题。如果空间自相关存在，过离散数据分析就是一个例子。对于不同的分布假设，如或泊松分布或是负二项式分布的观测流或（空间）滤波组分，该估计量显示出令人满意的性能。在本文的空间自回归（SAR）模型设定中，参数估计结果可解释为隐含的全局影响效应，并可被定义为直接和间接的空间反馈效应之和。蒙特卡罗结果给出了参数估计中均值、标准差的临界有限样本偏差，且随样本量增大收敛于真正参数值。此外，本文基于欧洲地区专利引用的流数据进行了模型验证。

Die Bedeutung der Pillnitzer Malusarten-Kollektion für die Apfelzüchtung und als internationaler Genfonds was published in Band 34, Heft 2 1986 on page 137.

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects decision rules with a minimal set of features for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The minimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.

Die Nebennieren besitzen als endokrines Organ eine zentrale Bedeutung für die Aufrechterhaltung der biochemischen Homeostase durch die Regulation des Stoffwechsels, sowie der Salz- und Wasserbilanz. Die Nebennierenrindenhormone beeinflussen die Funktion vieler...

In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction model. We consider Alopex based global search, as opposed to local search based upon backpropagation of gradient descents, each in combination with the bootstrapping pairs approach to solve the maximum likelihood learning problem. Interregional telecommunication traffic flow data from Austria are used as test bed for comparing the performance of the two learning procedures. The study illustrates the superiority of Alopex based global search, measured in terms of Kullback and Leibler’s information criterion.

The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.

A general framework to analyze communication media choice behavior in the university setting is proposed which integrates a stated preference experimental design procedure into a discrete choice modeling framework. The framework is empirically tested using hypothetical choice experiments in which traditional mail, courier mail, telephone, facsimile, and electronic mail services were choice options to carry out information communication tasks. For this purpose face-to-face interviews were conducted in six universities in Austria and Switzerland. The choice modeling approach developed emphasizes the influence of communication context specific characteristics, individual and organizational characteristics of the communication initiator as well as the individual's perceptions and feelings about the communication media on the formation of preferences. Empirical results are presented using stated preference models of communication media choice behavior for a series of communication situations. Specific emphasis is laid on crossnational differences in choice behavior.