Carbon and Nitrogen Stable Isotopic Patterns in South Florida Coastal Ecosystems: Modern and Paleoceanographic Perspectives

Long term management plans for restoration of natural flow conditions through the Everglades increase the importance of understanding potential nutrient impacts of increased freshwater delivery on coastal biogeochemistry. The present study sought to increase understanding of the coastal marine system of South Florida under modern conditions and through the anthropogenic changes in the last century, on scales ranging from individual nutrient cycle processes to seasonal patterns in organic material (OM) under varying hydrodynamic regime, to century scale analysis of sedimentary records. In all applications, carbon and nitrogen stable isotopic compositions of OM were examined as natural recorders of change and nutrient cycling in the coastal system. High spatial and temporal variability in stable isotopic compositions were observed on all time scales. During a transient phytoplankton bloom, ä15N values suggested nitrogen fixation as a nutrient source supporting enhanced productivity. Seasonally, particulate organic material (POM) from ten sites along the Florida Reef Tract and in Florida Bay demonstrated variable fluctuations dependent on hydrodynamic setting. Three separate intra-annual patterns were observed, yet statistical differences were observed between groupings of Florida Bay and Atlantic Ocean sites. The POM ä15N values ranged on a quarterly basis by 7‰, while ä13C varied by 22‰. From a sediment history perspective, four cores collected from Florida Bay further demonstrated the spatial and temporal variability of the system in isotopic composition of bulk OM over time. Source inputs of OM varied with location, with terrestrial inputs dominating proximal to Everglades freshwater discharge, seagrasses dominating in open estuary cores, and a marine mixture of phytoplankton and seagrass in a core from the boundary zone between Florida Bay and the Gulf of Mexico. Significant shifts in OM geochemistry were observed coincident with anthropogenic events of the 20th century, including railroad and road construction in the Florida Keys and Everglades, and also the extensive drainage changes in Everglades hydrology. The sediment record also preserved evidence of the major hurricanes of the last century, with excursions in geochemical composition coincident with Category 4-5 storms.

(Table A1) Diatom isotope measurements from ODP Hole 145-882A

In comparison to other sectors of the marine system, the palaeoceanography of the subarctic North Pacific Ocean is poorly constrained. New diatom isotope records of d13C, d18O, d30Si (d13Cdiatom, d18Odiatom, and d30Sidiatom) are presented alongside existing geochemical and isotope records to document changes in photic zone conditions, including nutrient supply and the efficiency of the soft-tissue biological pump, between Marine Isotope Stage (MIS) 4 and MIS 5e. Peaks in opal productivity in MIS 5b/c and MIS 5e are both associated with the breakdown of the regional halocline stratification and increased nutrient supply to the photic zone. Whereas the MIS 5e peak is associated with low rates of nutrient utilisation, the MIS 5b/c peak is associated with significantly higher rates of nutrient utilisation. Both peaks, together with other smaller increases in productivity in MIS 4 and 5a, culminate with a significant increase in freshwater input which strengthens/re-establishes the halocline and limits further upwelling of sub-surface waters to the photic zone. Whilst d30Sidiatom and previously published records of diatom d15N (d15Ndiatom) (Brunelle et al., 2007, 2010) show similar trends until the latter half of MIS 5a, the records become anti-correlated after this juncture and into MIS 4, suggesting a possible change in photic zone state such as may occur with a shift to iron or silicon limitation.

Diatom oxygen and silicon isotopes data from IODP Hole 323-U1341B in the Bering Sea through the late Pliocene and over the onset of major Northern Hemisphere Glaciation

The Bering Sea is one of the most biologically productive regions in the marine system and plays a key role in regulating the flow of waters to the Arctic Ocean and into the subarctic North Pacific Ocean. Cores from Integrated Ocean Drilling Program (IODP) Expedition 323 to the Bering Sea provide the first opportunity to obtain reconstructions from the region that extend back to the Pliocene. Previous research at Bowers Ridge, south Bering Sea, has revealed stable levels of siliceous productivity over the onset of major Northern Hemisphere Glaciation (NHG) (circa 2.85-2.73 Ma). However, diatom silica isotope records of oxygen (d18Odiatom) and silicon (d30Sidiatom) presented here demonstrate that this interval was associated with a progressive increase in the supply of silicic acid to the region, superimposed on shift to a more dynamic environment characterized by colder temperatures and increased sea ice. This concluded at 2.58 Ma with a sharp increase in diatom productivity, further increases in photic zone nutrient availability and a permanent shift to colder sea surface conditions. These transitions are suggested to reflect a gradually more intense nutrient leakage from the subarctic northwest Pacific Ocean, with increases in productivity further aided by increased sea ice- and wind-driven mixing in the Bering Sea. In suggesting a linkage in biogeochemical cycling between the south Bering Sea and subarctic Northwest Pacific Ocean, mainly via the Kamchatka Strait, this work highlights the need to consider the interconnectivity of these two systems when future reconstructions are carried out in the region.

A Data Driven Tool supported CLD Creation Approach

The creation of Causal Loop Diagrams (CLDs) is a major phase in the System Dynamics (SD) life-cycle, since the created CLDs express dependencies and feedback in the system under study, as well as, guide modellers in building meaningful simulation models. The cre-ation of CLDs is still subject to the modeller's domain expertise (mental model) and her ability to abstract the system, because of the strong de-pendency on semantic knowledge. Since the beginning of SD, available system data sources (written and numerical models) have always been sparsely available, very limited and imperfect and thus of little benefit to the whole modelling process. However, in recent years, we have seen an explosion in generated data, especially in all business related domains that are analysed via Business Dynamics (BD). In this paper, we intro-duce a systematic tool supported CLD creation approach, which analyses and utilises available disparate data sources within the business domain. We demonstrate the application of our methodology on a given business use-case and evaluate the resulting CLD. Finally, we propose directions for future research to further push the automation in the CLD creation and increase confidence in the generated CLDs.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Using modeling as a co-design approach in the planning process of new care environments

Current research shows a relationship between healthcare architecture and patient-related Outcomes. The planning and designing of new healthcare environments is a complex process; the needs of the various end-users of the environment must be considered, including the patients, the patients’ significant others, and the staff. The aim of this study was to explore the experiences of healthcare professionals participating in group modelling utilizing system dynamics in the pre-design phase of new healthcare environments. We engaged healthcare professionals in a series of workshops using system dynamics to discuss the planning of healthcare environments in the beginning of a construction, and then interviewed them about their experience. An explorative and qualitative design was used to describe participants’ experiences of participating in the group modelling projects. Participants (n=20) were recruited from a larger intervention study using group modeling and system dynamics in planning and designing projects. The interviews were analysed by qualitative content analysis. Two themes were formed, representing the experiences in the group modeling process: ‘Partaking in the G-M created knowledge and empowerment’and ‘Partaking in the G-M was different from what was expected and required time and skills’. The method can support participants in design teams to focus more on their healthcare organization, their care activities and their aims rather than focusing on detailed layout solutions. This clarification is important when decisions about the design are discussed and prepared and will most likely lead to greater readiness for future building process.

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

The Control of a Mathematical Analog of a Tentacle

A 2-dimensional dynamic analog of squid tentacles was presented. The tentacle analog consists of a multi-cell structure, which can be easily replicated to a large scale. Each cell of the model is a quadrilateral with unit masses at the corners. Each side of the quadrilateral is a spring-damper system in parallel. The spring constants are the controls for the system. The dynamics are subject to the constraint that the area of each quadrilateral must remain constant. The system dynamics was analyzed, and various equilibrium points were found with different controls. Then these equilibrium points were further determined experimentally, demonstrated to be asymptotically stable. A simulation built in MATLAB was used to find the convergence rates under different controls and damping coefficients. Finally, a control scheme was developed and used to drive the system to several configurations observed in real tentacle.

Balancing Belligerents or Feeding the Beast: Transforming Conflict Traps

Since the end of the Cold War, recurring civil conflicts have been the dominant form of violent armed conflict in the world, accounting for 70% of conflicts active between 2000-2013. Duration and intensity of episodes within recurring conflicts in Africa exhibit four behaviors characteristic of archetypal dynamic system structures. The overarching questions asked in this study are whether these patterns are robustly correlated with fundamental concepts of resiliency in dynamic systems that scale from micro-to macro levels; are they consistent with theoretical risk factors and causal mechanisms; and what are the policy implications. Econometric analysis and dynamic systems modeling of 36 conflicts in Africa between 1989 -2014 are combined with process tracing in a case study of Somalia to evaluate correlations between state characteristics, peace operations and foreign aid on the likelihood of observed conflict patterns, test hypothesized causal mechanisms across scales, and develop policy recommendations for increasing human security while decreasing resiliency of belligerents. Findings are that observed conflict patterns scale from micro to macro levels; are strongly correlated with state characteristics that proxy a mix of cooperative (e.g., gender equality) and coercive (e.g., security forces) conflict-balancing mechanisms; and are weakly correlated with UN and regional peace operations and humanitarian aid. Interactions between peace operations and aid interventions that effect conflict persistence at micro levels are not seen in macro level analysis, due to interdependent, micro-level feedback mechanisms, sequencing, and lagged effects. This study finds that the dynamic system structures associated with observed conflict patterns contain tipping points between balancing mechanisms at the interface of micro-macro level interactions that are determined as much by factors related to how intervention policies are designed and implemented, as what they are. Policy implications are that reducing risk of conflict persistence requires that peace operations and aid interventions (1) simultaneously increase transparency, promote inclusivity (with emphasis on gender equality), and empower local civilian involvement in accountability measures at the local levels; (2) build bridges to horizontally and vertically integrate across levels; and (3) pave pathways towards conflict transformation mechanisms and justice that scale from the individual, to community, regional, and national levels.

Modelo para gestionar el conocimiento en el sector textil de Medellín, empleando dinámica de sistemas

Este artículo evalúa la relación de causalidad entre la gestión del conocimiento y las capacidades de innovación tecnológica, y el efecto de esta relación sobre los resultados operacionales del sector textil en la ciudad de Medellín. Se empleó la metodología de dinámica de sistemas, con simulación de esce­narios para valorar las condiciones actuales de las organizaciones del sector en términos de acumulación de conocimiento y capacidades. La información se obtuvo mediante entrevistas a expertos y acceso a información especializada del sector. Se evidencia que una mejora de la relación entre la gestión del conocimiento e innovación tecnológica genera un incremento aproximado del 15% en los ingresos operacionales del sector. Asimismo, se encontró que a medida que las variables comunes de interés (Es­trategias organizacionales, canales de comunicación, formación, cultura, acciones de fortalecimiento en I+D), se acercan a los valores deseados, la acumulación de conocimiento y de capacidades de innovación tecnológica alcanzan los valores objetivos.

Introduction to Multi-Agent Simulation

When designing systems that are complex, dynamic and stochastic in nature, simulation is generally recognised as one of the best design support technologies, and a valuable aid in the strategic and tactical decision making process. A simulation model consists of a set of rules that define how a system changes over time, given its current state. Unlike analytical models, a simulation model is not solved but is run and the changes of system states can be observed at any point in time. This provides an insight into system dynamics rather than just predicting the output of a system based on specific inputs. Simulation is not a decision making tool but a decision support tool, allowing better informed decisions to be made. Due to the complexity of the real world, a simulation model can only be an approximation of the target system. The essence of the art of simulation modelling is abstraction and simplification. Only those characteristics that are important for the study and analysis of the target system should be included in the simulation model. The purpose of simulation is either to better understand the operation of a target system, or to make predictions about a target system’s performance. It can be viewed as an artificial white-room which allows one to gain insight but also to test new theories and practices without disrupting the daily routine of the focal organisation. What you can expect to gain from a simulation study is very well summarised by FIRMA (2000). His idea is that if the theory that has been framed about the target system holds, and if this theory has been adequately translated into a computer model this would allow you to answer some of the following questions: · Which kind of behaviour can be expected under arbitrarily given parameter combinations and initial conditions? · Which kind of behaviour will a given target system display in the future? · Which state will the target system reach in the future? The required accuracy of the simulation model very much depends on the type of question one is trying to answer. In order to be able to respond to the first question the simulation model needs to be an explanatory model. This requires less data accuracy. In comparison, the simulation model required to answer the latter two questions has to be predictive in nature and therefore needs highly accurate input data to achieve credible outputs. These predictions involve showing trends, rather than giving precise and absolute predictions of the target system performance. The numerical results of a simulation experiment on their own are most often not very useful and need to be rigorously analysed with statistical methods. These results then need to be considered in the context of the real system and interpreted in a qualitative way to make meaningful recommendations or compile best practice guidelines. One needs a good working knowledge about the behaviour of the real system to be able to fully exploit the understanding gained from simulation experiments. The goal of this chapter is to brace the newcomer to the topic of what we think is a valuable asset to the toolset of analysts and decision makers. We will give you a summary of information we have gathered from the literature and of the experiences that we have made first hand during the last five years, whilst obtaining a better understanding of this exciting technology. We hope that this will help you to avoid some pitfalls that we have unwittingly encountered. Section 2 is an introduction to the different types of simulation used in Operational Research and Management Science with a clear focus on agent-based simulation. In Section 3 we outline the theoretical background of multi-agent systems and their elements to prepare you for Section 4 where we discuss how to develop a multi-agent simulation model. Section 5 outlines a simple example of a multi-agent system. Section 6 provides a collection of resources for further studies and finally in Section 7 we will conclude the chapter with a short summary.

Managing Fisheries in the context of an integrated policy: the importance of spatial issues

A variety of conservation policies now frame the management of fishing activity and so do also the spatial planning of different sectorial activities. These framework policies are additional to classical fishery management. There is a risk that the policies applying on the marine system are not coherent from a fisheries point of view. The spatial management of fishing activity at regional scale has the potential to meet multiple management objectives, on a habitat basis. Here we consider how to integrate multiple objectives of different policies into integrated ocean management scenarios. In the EU, European Directives and the CFP are now implementing the ecosystem approach to the management of human activity at sea. In this context, we further identify three research needs: • Develop Management Strategy Evaluation (MSE) for multiple-objective and multiple-sector spatial management schemes • Improve knowledge on and evaluation of functional habitats • Develop spatially-explicit end-to-end models with appropriate complexity for spatial MSE The contribution is based on the results of a workshop of the EraNet COFASP.

Aproximación al desarrollo de un sistema de transporte masivo a través de la dinámica de sistemas

El porvenir social y económico de una ciudad depende, en gran medida, de la eficiencia de su sistema de transporte; esto se ve reflejado en la capacidad de transportar personas y bienes de una forma sostenible, con los recursos disponibles. En la actualidad se encuentran en desarrollo sistemas de transporte masivo tipo Bus Rapid Transit [BRT] en siete ciudades colombianas, situación que genera la necesidad de dar seguimiento a su progreso y al crecimiento de su participación en la demanda de viajes unipersonales. El siguiente trabajo busca, a través de una simulación en dinámica de sistemas, describir el desarrollo de un sistema de transporte masivo, con el fin de otorgar una visión acerca del impacto de los parámetros operativos y la reinversión en el sistema y en el desarrollo e incremento de su demanda. Se plantean tres escenarios para evaluar diferentes políticas operativas y de reinversión en el sistema, analizando el comportamiento en su desarrollo.

Análisis sistémico de los riesgos de servicio y financiero en la cadena de abastecimiento

Partiendo de un modelo desarrollado por medio de la dinámica de sistemas, que representa una cadena de abastecimiento, este trabajo tiene como finalidad establecer una aproximación sistémica a la gestión de los riesgos de servicio y financiero, teniendo en cuenta la toma de decisiones desde el área operativa y su influencia sobre el riesgo en otras áreas de la empresa.