Holocene variations in the Scottish marine radiocarbon reservoir effect

We assessed the evidence for variations in the marine radiocarbon reservoir effect (MRE) at coastal, archaeological Iron Age sites in north and west Scotland by comparing AMS measurements of paired marine and terrestrial materials (4 pairs per context). DeltaR values were calculated from measurements on material from 3 sites using 6 sets of samples, all of which were deposited around 2000 BP. The weighted mean of the DeltaR determinations was -79 +/- 17 C-14 yr, which indicates a consistent, reduced offset between atmospheric and surface ocean C-14 specific activity for these sites during this period, relative to the present day (DeltaR = similar to0 C-14 yr). We discuss the significance of this revised AR correction by using the example of wheelhouse chronologies at Hornish Point and their development in relation to brochs. In addition, we assess the importance of using the concepts of MRE correction and AR variations when constructing chronologies using C-14 measurements made on materials that contain marine- derived carbon

Developing natural resource models using the object modeling system: feasibility and challenges

International audienceCurrent challenges in natural resource management have created demand for integrated, flexible, and easily parameterized hydrologic models. Most of these monolithic models are not modular, thus modifications (e.g., changes in process representation) require considerable time, effort, and expense. In this paper, the feasibility and challenges of using the Object Modeling System (OMS) for natural resource model development will be explored. The OMS is a Java-based modeling framework that facilitates simulation model development, evaluation, and deployment. In general, the OMS consists of a library of science, control, and database modules and a means to assemble the selected modules into an application-specific modeling package. The framework is supported by data dictionary, data retrieval, GIS, graphical visualization, and statistical analysis utility modules. Specific features of the OMS that will be discussed include: 1) how to reduce duplication of effort in natural resource modeling; 2) how to make natural resource models easier to build, apply, and evaluate; 3) how to facilitate long-term maintainability of existing and new natural resource models; and 4) how to improve the quality of natural resource model code and ensure credibility of model implementations. Examples of integrating a simple water balance model and a large monolithic model into the OMS will be presented

Emotional intelligence in organizational behavior and industrial-organizational psychology

This chapter argues that, although there is mixed evidence about the link between emotional intelligence (EI) and workplace applications, steadily maturing research in the field is providing increasing confidence regarding the predictive ability of EI. It provides a broad overview of EI in organizational-behaviour research and industrial and organisational (I/O) psychology, including a review of applications and coverage of some of the contentious issues in the field. The chapter concludes by placing EI research within the context of the wider framework of research on the role of emotions in organizational settings

8.2 ka event North Sea hydrography determined by bivalve shell stable isotope geochemistry

This is the final version. Available on open access from Nature Research via the DOI in this recordThe abrupt 8.2 ka cold event has been widely described from Greenland and North Atlantic records. However, its expression in shelf seas is poorly documented, and the temporal resolution of most marine records is inadequate to precisely determine the chronology of major events. A robust hydrographical reconstruction can provide an insight on climatic reaction times to perturbations to the Atlantic Meridional Overturning Circulation. Here we present an annually-resolved temperature and water column stratification reconstruction based on stable isotope geochemistry of Arctica islandica shells from the Fladen Ground (northern North Sea) temporally coherent with Greenland ice core records. Our age model is based on a growth increment chronology obtained from four radiometrically-dated shells covering the 8290–8100 cal BP interval. Our results indicate that a sudden sea level rise (SSLR) event-driven column stratification occurred between ages 8320–8220 cal BP. Thirty years later, cold conditions inhibited water column stratification but an eventual incursion of sub-Arctic waters into the North Sea re-established density-driven stratification. The water temperatures reached their minimum of ~3.7 °C 55 years after the SSLR. Intermittently-mixed conditions were later established when the sub-Arctic waters receded.Natural Environment Research Council (NERC)European Union FP

Investigation of growth responses in saprophytic fungi to charred biomass

We present the results of a study testing the response of two saprophytic white-rot fungi species, Pleurotus pulmonarius and Coriolus versicolor, to charred biomass (charcoal) as a growth substrate. We used a combination of optical microscopy, scanning electron microscopy, elemental abundance measurements, and isotope ratio mass spectrometry (<sup>13</sup>C and <sup>15</sup>N) to investigate fungal colonisation of control and incubated samples of Scots Pine (Pinus sylvestris) wood, and charcoal from the same species produced at 300 °C and 400 °C. Both species of fungi colonise the surface and interior of wood and charcoals over time periods of less than 70 days; however, distinctly different growth forms are evident between the exterior and interior of the charcoal substrate, with hyphal penetration concentrated along lines of structural weakness. Although the fungi were able to degrade and metabolise the pine wood, charcoal does not form a readily available source of fungal nutrients at least for these species under the conditions used in this study

The Cloud Services Innovation Platform-Enabling Service-Based Environmental Modelling Using Infrastructure-As-A-Service Cloud Computing

Service oriented architectures allow modelling engines to be hosted over the Internet abstracting physical hardware configuration and software deployments from model users. Many existing environmental models are deployed as desktop applications running on user\u27s personal computers (PCs). Migration to service - based modelling centralizes the modelling functions to service hosts on the Internet . Users no longer require high-end PCs to run models and model updates encapsulating science advances can be disseminated more rapidly by hosting the modelling functions centrally via an Internet host instead of requiring software updates to user\u27s PCs . In this paper we present the Cloud Services Innovation Platform (CSIP), an Infrastructure -as -a -Service cloud application architecture , used to prototype development of distributed and scalable environmental modelling services. CSIP aims to provide modelling as a service to support both interactive (synchronous) and batch (asynchronous) modelling. CSIP enables c loud-based computing resources to be harnessed for both new and existing environmental models supporting the disaggregation of work into subtasks which execute in parallel using a scalable number of virtual machines. This paper presents CSIP \u27s implementation using the RUSLE2 model as a prototype model. RUSLE2 model service benchmarks are presented to demonstrate performance gains from using cloud resources. We also provide benchmarks for virtualization overhead observed using popular virtual machine hypervisors and demonstrate how application profile characteristics significantly impact performance when virtualized

The Virtual Machine (VM) Scaler: An Infrastructure Manager Supporting Environmental Modeling on IaaS Clouds

Infrastructure-as-a-service (IaaS) clouds provide a new medium for deployment of environmental modeling applications. Harnessing advancements in virtualization, IaaS clouds can provide dynamic scalable infrastructure to better support scientific modeling computational demands. Providing scientific modeling as-a-service requires dynamic scaling of server infrastructure to adapt to changing user workloads. This paper presents the Virtual Machine (VM) Scaler, an autonomic resource manager for IaaS Clouds. We have developed VM-Scaler, a REST/JSON-based web services application which supports infrastructure provisioning and management to support scientific modeling for the Cloud Services Innovation Platform (CSIP) [Lloyd et al. 2012]. VM-Scaler harnesses the Amazon Elastic Compute Cloud (EC2) application programming interface to support model- service scalability, cloud management, and infrastructure configuration for supporting modeling workloads. VM-Scaler provides cloud control while abstracting the underlying IaaS cloud from the end user. VM-Scaler is extensible to support any EC2 compatible cloud and currently supports the Amazon public cloud and Eucalyptus private clouds versions 3.1 and 3.3. VM-Scaler provides a platform to improve scientific model deployment by supporting experimentation with: hot spot detection schemes, VM management and placement approaches, and model job scheduling/proxy services

The Virtual Machine (VM) Scaler: An Infrastructure Manager Supporting Environmental Modeling on IaaS Clouds

Infrastructure-as-a-service (IaaS) clouds provide a new medium for deployment of environmental modeling applications. Harnessing advancements in virtualization, IaaS clouds can provide dynamic scalable infrastructure to better support scientific modeling computational demands. Providing scientific modeling as-a-service requires dynamic scaling of server infrastructure to adapt to changing user workloads. This paper presents the Virtual Machine (VM) Scaler, an autonomic resource manager for IaaS Clouds. We have developed VM-Scaler, a REST/JSON-based web services application which supports infrastructure provisioning and management to support scientific modeling for the Cloud Services Innovation Platform (CSIP) [Lloyd et al. 2012]. VM-Scaler harnesses the Amazon Elastic Compute Cloud (EC2) application programming interface to support model- service scalability, cloud management, and infrastructure configuration for supporting modeling workloads. VM-Scaler provides cloud control while abstracting the underlying IaaS cloud from the end user. VM-Scaler is extensible to support any EC2 compatible cloud and currently supports the Amazon public cloud and Eucalyptus private clouds versions 3.1 and 3.3. VM-Scaler provides a platform to improve scientific model deployment by supporting experimentation with: hot spot detection schemes, VM management and placement approaches, and model job scheduling/proxy services