Simulations- und VR-basierte Steuerungsverifikation zellularer Intralogistiksysteme

Die hohe Komplexität zellularer intralogistischer Systeme und deren Steuerungsarchitektur legt die Verwendung moderner Simulations- und Visualisierungstechniken nahe, um schon im Vorfeld Aussagen über die Leistungsfähigkeit und Zukunftssicherheit eines geplanten Systems treffen zu können. In dieser Arbeit wird ein Konzept für ein Simulationssystem zur VR-basierten Steuerungsverifikation zellularer Intralogistiksysteme vorgestellt. Beschrieben wird die Erstellung eines Simulationsmodells für eine real existierende Anlage und es wird ein Überblick über die Bestandteile der Simulation, insbesondere die Anbindung der Steuerung des realen agentenbasierten Systems, gegeben.

Adaptive Materialbereitstellung in flexiblen Produktionssystemen auf Grundlage einer agentenbasierten Transportsteuerung

Zur Sicherstellung einer schnellen und flexiblen Anpassung an sich ändernde Anforderungen sind innerbetriebliche Materialbereitstellungskonzepte in immer stärkerem Maße zu flexibilisieren. Hierdurch kann die Erreichung logistischer Ziele in einem dynamischen Produktionsumfeld gesteigert werden. Der Beitrag stellt ein Konzept für eine adaptive Materialbereitstellung in flexiblen Produktionssystemen auf Grundlage einer agentenbasierten Transportplanung und -steuerung vor. Der Fokus liegt hierbei auf der Planung und Steuerung der auf Basis von Materialbedarfsmeldungen ausgelösten innerbetrieblichen Transporte. Neben Pendeltouren zur Versorgung des Produktionssystems findet auch das dynamische Pickup-and-Delivery-Problem Berücksichtigung. Das vorgestellte Konzept ist an den Anforderungen selbstorganisierender Produktionsprozesse ausgerichtet.

Retention in care under universal antiretroviral therapy for HIV-infected pregnant and breastfeeding women ('Option B+') in Malawi

OBJECTIVE To explore the levels and determinants of loss to follow-up (LTF) under universal lifelong antiretroviral therapy (ART) for pregnant and breastfeeding women ('Option B+') in Malawi. DESIGN, SETTING, AND PARTICIPANTS We examined retention in care, from the date of ART initiation up to 6 months, for women in the Option B+ program. We analysed nationwide facility-level data on women who started ART at 540 facilities (n = 21 939), as well as individual-level data on patients who started ART at 19 large facilities (n = 11 534). RESULTS Of the women who started ART under Option B+ (n = 21 939), 17% appeared to be lost to follow-up 6 months after ART initiation. Most losses occurred in the first 3 months of therapy. Option B+ patients who started therapy during pregnancy were five times more likely than women who started ART in WHO stage 3/4 or with a CD4 cell count 350 cells/μl or less, to never return after their initial clinic visit [odds ratio (OR) 5.0, 95% confidence interval (CI) 4.2-6.1]. Option B+ patients who started therapy while breastfeeding were twice as likely to miss their first follow-up visit (OR 2.2, 95% CI 1.8-2.8). LTF was highest in pregnant Option B+ patients who began ART at large clinics on the day they were diagnosed with HIV. LTF varied considerably between facilities, ranging from 0 to 58%. CONCLUSION Decreasing LTF will improve the effectiveness of the Option B+ approach. Tailored interventions, like community or family-based models of care could improve its effectiveness.

Spike timing dependent plasticity: a consequence of more fundamental learning rules.

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse - the "first law" of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

Modeled Interaction of Comet 67P/Churyumov-Gerasimenko with the Solar Wind Inside 2 AU

Periodic comets move around the Sun on elliptical orbits. As such comet 67P/Churyumov-Gerasimenko (hereafter 67P) spends a portion of time in the inner solar system where it is exposed to increased solar insolation. Therefore given the change in heliocentric distance, in case of 67P from aphelion at 5.68 AU to perihelion at ~1.24 AU, the comet’s activity—the production of neutral gas and dust—undergoes significant variations. As a consequence, during the inbound portion, the mass loading of the solar wind increases and extends to larger spatial scales. This paper investigates how this interaction changes the character of the plasma environment of the comet by means of multifluid MHD simulations. The multifluid MHD model is capable of separating the dynamics of the solar wind ions and the pick-up ions created through photoionization and electron impact ionization in the coma of the comet. We show how two of the major boundaries, the bow shock and the diamagnetic cavity, form and develop as the comet moves through the inner solar system. Likewise for 67P, although most likely shifted back in time with respect to perihelion passage, this process is reversed on the outbound portion of the orbit. The presented model herein is able to reproduce some of the key features previously only accessible to particle-based models that take full account of the ions’ gyration. The results shown herein are in decent agreement to these hybrid-type kinetic simulations.

Information-Centric Content Retrieval for Delay-Tolerant Networks

The shift from host-centric to information-centric networking (ICN) promises seamless communication in mobile networks. However, most existing works either consider well-connected networks with high node density or introduce modifications to {ICN} message processing for delay-tolerant Networking (DTN). In this work, we present agent-based content retrieval, which provides information-centric {DTN} support as an application module without modifications to {ICN} message processing. This enables flexible interoperability in changing environments. If no content source can be found via wireless multi-hop routing, requesters may exploit the mobility of neighbor nodes (called agents) by delegating content retrieval to them. Agents that receive a delegation and move closer to content sources can retrieve data and return it back to requesters. We show that agent-based content retrieval may be even more efficient in scenarios where multi-hop communication is possible. Furthermore, we show that broadcast communication may not be necessarily the best option since dynamic unicast requests have little overhead and can better exploit short contact times between nodes (no broadcast delays required for duplicate suppression).

Information-centric communication in mobile and wireless networks

Information-centric networking (ICN) is a new communication paradigm that has been proposed to cope with drawbacks of host-based communication protocols, namely scalability and security. In this thesis, we base our work on Named Data Networking (NDN), which is a popular ICN architecture, and investigate NDN in the context of wireless and mobile ad hoc networks. In a first part, we focus on NDN efficiency (and potential improvements) in wireless environments by investigating NDN in wireless one-hop communication, i.e., without any routing protocols. A basic requirement to initiate informationcentric communication is the knowledge of existing and available content names. Therefore, we develop three opportunistic content discovery algorithms and evaluate them in diverse scenarios for different node densities and content distributions. After content names are known, requesters can retrieve content opportunistically from any neighbor node that provides the content. However, in case of short contact times to content sources, content retrieval may be disrupted. Therefore, we develop a requester application that keeps meta information of disrupted content retrievals and enables resume operations when a new content source has been found. Besides message efficiency, we also evaluate power consumption of information-centric broadcast and unicast communication. Based on our findings, we develop two mechanisms to increase efficiency of information-centric wireless one-hop communication. The first approach called Dynamic Unicast (DU) avoids broadcast communication whenever possible since broadcast transmissions result in more duplicate Data transmissions, lower data rates and higher energy consumption on mobile nodes, which are not interested in overheard Data, compared to unicast communication. Hence, DU uses broadcast communication only until a content source has been found and then retrieves content directly via unicast from the same source. The second approach called RC-NDN targets efficiency of wireless broadcast communication by reducing the number of duplicate Data transmissions. In particular, RC-NDN is a Data encoding scheme for content sources that increases diversity in wireless broadcast transmissions such that multiple concurrent requesters can profit from each others’ (overheard) message transmissions. If requesters and content sources are not in one-hop distance to each other, requests need to be forwarded via multi-hop routing. Therefore, in a second part of this thesis, we investigate information-centric wireless multi-hop communication. First, we consider multi-hop broadcast communication in the context of rather static community networks. We introduce the concept of preferred forwarders, which relay Interest messages slightly faster than non-preferred forwarders to reduce redundant duplicate message transmissions. While this approach works well in static networks, the performance may degrade in mobile networks if preferred forwarders may regularly move away. Thus, to enable routing in mobile ad hoc networks, we extend DU for multi-hop communication. Compared to one-hop communication, multi-hop DU requires efficient path update mechanisms (since multi-hop paths may expire quickly) and new forwarding strategies to maintain NDN benefits (request aggregation and caching) such that only a few messages need to be transmitted over the entire end-to-end path even in case of multiple concurrent requesters. To perform quick retransmission in case of collisions or other transmission errors, we implement and evaluate retransmission timers from related work and compare them to CCNTimer, which is a new algorithm that enables shorter content retrieval times in information-centric wireless multi-hop communication. Yet, in case of intermittent connectivity between requesters and content sources, multi-hop routing protocols may not work because they require continuous end-to-end paths. Therefore, we present agent-based content retrieval (ACR) for delay-tolerant networks. In ACR, requester nodes can delegate content retrieval to mobile agent nodes, which move closer to content sources, can retrieve content and return it to requesters. Thus, ACR exploits the mobility of agent nodes to retrieve content from remote locations. To enable delay-tolerant communication via agents, retrieved content needs to be stored persistently such that requesters can verify its authenticity via original publisher signatures. To achieve this, we develop a persistent caching concept that maintains received popular content in repositories and deletes unpopular content if free space is required. Since our persistent caching concept can complement regular short-term caching in the content store, it can also be used for network caching to store popular delay-tolerant content at edge routers (to reduce network traffic and improve network performance) while real-time traffic can still be maintained and served from the content store.

A Mathematical Model for Simplifying Representations of Objects in a Geographic Information System

The study of operations on representations of objects is well documented in the realm of spatial engineering. However, the mathematical structure and formal proof of these operational phenomena are not thoroughly explored. Other works have often focused on query-based models that seek to order classes and instances of objects in the form of semantic hierarchies or graphs. In some models, nodes of graphs represent objects and are connected by edges that represent different types of coarsening operators. This work, however, studies how the coarsening operator "simplification" can manipulate partitions of finite sets, independent from objects and their attributes. Partitions that are "simplified first have a collection of elements filtered (removed), and then the remaining partition is amalgamated (some sub-collections are unified). Simplification has many interesting mathematical properties. A finite composition of simplifications can also be accomplished with some single simplification. Also, if one partition is a simplification of the other, the simplified partition is defined to be less than the other partition according to the simp relation. This relation is shown to be a partial-order relation based on simplification. Collections of partitions can not only be proven to have a partial- order structure, but also have a lattice structure and are complete. In regard to a geographic information system (GIs), partitions related to subsets of attribute domains for objects are called views. Objects belong to different views based whether or not their attribute values lie in the underlying view domain. Given a particular view, objects with their attribute n-tuple codings contained in the view are part of the actualization set on views, and objects are labeled according to the particular subset of the view in which their coding lies. Though the scope of the work does not mainly focus on queries related directly to geographic objects, it provides verification for the existence of particular views in a system with this underlying structure. Given a finite attribute domain, one can say with mathematical certainty that different views of objects are partially ordered by simplification, and every collection of views has a greatest lower bound and least upper bound, which provides the validity for exploring queries in this regard.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Analysis of the Fracture of Reinforced Concrete Flat Elements Subjected to Explosions. Experimental Procedure and Numerical Validation

Many of the material models most frequently used for the numerical simulation of the behavior of concrete when subjected to high strain rates have been originally developed for the simulation of ballistic impact. Therefore, they are plasticity-based models in which the compressive behavior is modeled in a complex way, while their tensile failure criterion is of a rather simpler nature. As concrete elements usually fail in tensión when subjected to blast loading, available concrete material models for high strain rates may not represent accurately their real behavior. In this research work an experimental program of reinforced concrete fíat elements subjected to blast load is presented. Altogether four detonation tests are conducted, in which 12 slabs of two different concrete types are subjected to the same blast load. The results of the experimental program are then used for the development and adjustment of numerical tools needed in the modeling of concrete elements subjected to blast.

Assessment of Narrow-Body Transport Airplane Evacuation by Numerical Simulation

This paper presents the results obtained with a new agent-based computer model that can simulate the evacuation of narrow-body transport airplanes in the conditions prescribed by the airworthiness regulations for certification. The model, described in detail in a former paper, has been verified with real data of narrow-body certification demonstrations. Numerical simulations of around 20 narrow-body aircraft, representative of current designs in various market segments, show the capabilities of the model and provide relevant information on the relationship between cabin features and emergency evacuation. The longitudinal location of emergency exits seems to be even more important than their size or the overall margin with respect to the prescribed number and type of exits indicated by the airworthiness requirements

A core ontological model for semantic sensor web infrastructures

Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects (i.e., vocabulary, syntax, structure, semantics, representation, and context).

Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications

Learning analytics is the analysis of static and dynamic data extracted from virtual learning environments, in order to understand and optimize the learning process. Generally, this dynamic data is generated by the interactions which take place in the virtual learning environment. At the present time, many implementations for grouping of data have been proposed, but there is no consensus yet on which interactions and groups must be measured and analyzed. There is also no agreement on what is the influence of these interactions, if any, on learning outcomes, academic performance or student success. This study presents three different extant interaction typologies in e-learning and analyzes the relation of their components with students? academic performance. The three different classifications are based on the agents involved in the learning process, the frequency of use and the participation mode, respectively. The main findings from the research are: a) that agent-based classifications offer a better explanation of student academic performance; b) that at least one component in each typology predicts academic performance; and c) that student-teacher and student-student, evaluating students, and active interactions, respectively, have a significant impact on academic performance, while the other interaction types are not significantly related to academic performance.

What if criminals optimize their choice? Optimal strategies to defend public goods

We investigate optimal strategies to defend valuable goods against the attacks of a thief. Given the value of the goods and the probability of success for the thief, we look for the strategy that assures the largest benefit to each player irrespective of the strategy of his opponent. Two complementary approaches are used: agent-based modeling and game theory. It is shown that the compromise between the value of the goods and the probability of success defines the mixed Nash equilibrium of the game, that is compared with the results of the agent-based simulations and discussed in terms of the system parameters.

Statistical mechanics approaches to granular media: between micromechanics and macromechanics

The mechanical behavior of granular materials has been traditionally approached through two theoretical and computational frameworks: macromechanics and micromechanics. Macromechanics focuses on continuum based models. In consequence it is assumed that the matter in the granular material is homogeneous and continuously distributed over its volume so that the smallest element cut from the body possesses the same physical properties as the body. In particular, it has some equivalent mechanical properties, represented by complex and non-linear constitutive relationships. Engineering problems are usually solved using computational methods such as FEM or FDM. On the other hand, micromechanics is the analysis of heterogeneous materials on the level of their individual constituents. In granular materials, if the properties of particles are known, a micromechanical approach can lead to a predictive response of the whole heterogeneous material. Two classes of numerical techniques can be differentiated: computational micromechanics, which consists on applying continuum mechanics on each of the phases of a representative volume element and then solving numerically the equations, and atomistic methods (DEM), which consist on applying rigid body dynamics together with interaction potentials to the particles. Statistical mechanics approaches arise between micro and macromechanics. It tries to state which the expected macroscopic properties of a granular system are, by starting from a micromechanical analysis of the features of the particles and the interactions. The main objective of this paper is to introduce this approach.