Completing Queries: Rewriting of IncompleteWeb Queries under Schema Constraints

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

Analyse zur Netto-Lebensmittelproduktion und zum Ackerflächenbedarf von Rindermastsystemen

Die zukünftigen Rahmenbedingungen lassen eine steigende Ressourcenkonkurrenz zwischen Nahrungs- und Futtermittelproduktion erwarten. Gerade die Rindermast wird in diesem Zusammenhang sehr oft kritisch betrachtet. Das Ziel der vorliegenden Arbeit war es daher, unterschiedlich intensive Rindermastsysteme hinsichtlich ihres Ackerflächenbedarfs und ihres Beitrags zur Netto- Lebensmittelproduktion zu untersuchen. Die Analysen beruhen auf Daten eines Mastversuchs mit Kalbinnen, Ochsen und Stieren der Rasse Fleckvieh auf Grassilage- bzw. Maissilagebasis. Die Tiere der Grassilagegruppen wurden auf drei unterschiedlichen Kraftfutterniveaus (extensiv, niedrig, hoch) und die Maissilagegruppen auf hohem Kraftfutterniveau gemästet. Die Lebensmittelkonversionseffizienz (LKE, humanernährungstauglicher Output/ humanernährungstauglicher Input) verringert sich bei den Grassilage- Fütterungsgruppen mit steigender Kraftfuttergabe und war für die Maissilage- Fütterungsgruppen sowohl auf Basis Bruttoenergie als auch Rohprotein an niedrigsten. Im Mittel über alle Versuchsgruppen lag die LKE-Energie mit 0,29 (0,6-0,56) und LKE- Protein mit 0,44 (0,21- 0,87) unter den derzeitigen Bedingungen deutlich unter 1, was einer negativen Netto- Lebensmittelproduktion entspricht. Allerdings muss berücksichtigt werden, dass die Proteinqualität auf der Output- Seite um den Faktor 1,5 bis 1,9 höher war als auf der Input- Seite. Kombiniert man diese qualitativen Unterschiede mit den quantitativen Änderungen im humanernährungstauglichen Protein, so steigern extensiv gefütterte Kalbinnen und Ochsen die Wertigkeit des Proteins Für die menschliche Ernährung. Mit steigender Fütterungsintensität stieg die Nährstoffversorgung über Futtermittel von Ackerflächen in den Grassilagegruppen deutlich an und der höchste Ackerflächenbedarf ergab sich in den Maissilagegruppen

Lateral Ordering of InAs Quantum Dots on Cross-hatch Patterned GaInP

We report the use of partially relaxed tensile as well as compressively strained GaInP layers for lateral ordering of InAs quantum dots with the aid of misfit dislocation networks. The strained layers and the InAs QDs were characterized by means of atomic force microscopy, scanning electron microscopy, and X-ray reciprocal space mapping. The QD-ordering properties of compressive GaInP are found to be very similar with respect to the use of compressive GaInAs, while a significantly stronger ordering of QDs was observed on tensile GaInP. Furthermore, we observed a change of the major type of dislocation in GaInP layers as the growth temperature was modified

Implementation of safe infant sleep recommendations during night-time sleep in the first year of life in a German birth cohort

The aim of our study was to assess the extent to which families followed recommendations, issued by the German society for sleep medicine, for the prevention of sudden infant death syndrome (SIDS) during night-time sleep. Analyzing longitudinal data from a birth cohort located at the University Children’s Hospital Regensburg in Bavaria (Germany), we determined data regarding the infant's sleep location, sleep settings and body position, and exposure to environmental factors. Data were collected in a structured interview after birth and by standardized questionnaires at 4 weeks, 6 months, and 1 year of life, respectively. The majority of 1,400 surveyed infants (94% at 4 weeks) were reported to sleep in the parents’ sleeping room during the first months of life. While the most common furniture was a bedside sleeper (used by 48%), we also observed a considerable proportion of families who regularly practiced bed-sharing and, for 16% of infants, the parents’ bed was the default sleeping place. 12% of infants were still put regularly in the prone position. The vast majority (87%) of the infants were breastfed at some timepoint and 17% lived in a household with one or more smokers. Although most parents implemented many SIDS recommendations, our analysis illustrates a considerable gap between recommendations and intentions after birth on the one hand and actual implementation in real life on the other. The number-one deviation from the current SIDS guidelines during night-time sleep was bed-sharing with an adult

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Energy-optimal motion planning for multiple robotic vehicles with collision avoidance

\u3cp\u3eWe propose a numerical algorithm for multiple-vehicle motion planning that explicitly takes into account the vehicle dynamics, temporal and spatial specifications, and energy-related requirements. As a motivating example, we consider the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) without colliding among themselves and with obstacles, subject to the requirement that the overall energy required for vehicle motion be minimized. With the theoretical setup adopted, the vehicle dynamics are explicitly taken into account at the planning level. This paper formulates the problem of multiple-vehicle motion planning in a rigorous mathematical setting, describes the optimization algorithm used to solve it, and discusses the key implementation details. The efficacy of the method is illustrated through numerical examples for the simultaneous arrival problem. The initial guess to start the optimization procedure is obtained from simple geometrical considerations, e.g., by joining the desired initial and final positions of the vehicles via straight lines. Even though the initial trajectories thus obtained may result in intervehicle and vehicle/obstacle collisions, we show that the optimization procedure that we employ in this paper will generate collision-free trajectories that also minimize the overall energy spent by each vehicle and meet the required temporal and spatial constraints. The method developed applies to a very general class of vehicles; however, for clarity of exposition, we adopt as an illustrative example the case of wheeled robots.\u3c/p\u3

Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO

This chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO, a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references to get further details on the method. The applications of the method to the constrained optimal motion planning of single and multiple vehicles is presented. Interesting applications that have been tackled with this method include, e.g., computing minimum-time trajectories for a race car, exploiting the energy from the surrounding environment for long endurance missions of unmanned aerial vehicles (UAVs), and cooperative motion planning of autonomous underwater vehicles (AUVs) for environmental surveying

Constrained optimal motion planning for autonomous vehicles using PRONTO

\u3cp\u3eThis chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO, a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references to get further details on the method. The applications of the method to the constrained optimal motion planning of single and multiple vehicles is presented. Interesting applications that have been tackled with this method include, e.g., computing minimum-time trajectories for a race car, exploiting the energy from the surrounding environment for long endurance missions of unmanned aerial vehicles (UAVs), and cooperative motion planning of autonomous underwater vehicles (AUVs) for environmental surveying.\u3c/p\u3

Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO

This chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO , a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references to get further details on the method. The applications of the method to the constrained optimal motion planning of single and multiple vehicles is presented. Interesting applications that have been tackled with this method include, e.g., computing minimum-time trajectories for a race car, exploiting the energy from the surrounding environment for long endurance missions of unmanned aerial vehicles (UAVs) , and cooperative motion planning of autonomous underwater vehicles (AUVs) for environmental surveying