Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Additively manufactured device for dynamic culture of large arrays of 3D tissue engineered constructs

The ability to test large arrays of cell and biomaterial combinations in 3D environments is still rather limited in the context of tissue engineering and regenerative medicine. This limitation can be generally addressed by employing highly automated and reproducible methodologies. This study reports on the development of a highly versatile and upscalable method based on additive manufacturing for the fabrication of arrays of scaffolds, which are enclosed into individualized perfusion chambers. Devices containing eight scaffolds and their corresponding bioreactor chambers are simultaneously fabricated utilizing a dual extrusion additive manufacturing system. To demonstrate the versatility of the concept, the scaffolds, while enclosed into the device, are subsequently surface-coated with a biomimetic calcium phosphate layer by perfusion with simulated body fluid solution. 96 scaffolds are simultaneously seeded and cultured with human osteoblasts under highly controlled bidirectional perfusion dynamic conditions over 4 weeks. Both coated and noncoated resulting scaffolds show homogeneous cell distribution and high cell viability throughout the 4 weeks culture period and CaP-coated scaffolds result in a significantly increased cell number. The methodology developed in this work exemplifies the applicability of additive manufacturing as a tool for further automation of studies in the field of tissue engineering and regenerative medicine.

Boarding control system for improved accessibility to offshore wind turbines: Full-scale testing

This paper considers the dynamic modelling and motion control of a Surface Effect Ship (SES) for safer transfer of personnel and equipment from vessel to-and-from an offshore wind-turbine. The control system designed is referred to as Boarding Control System (BCS). The performance of this system is investigated for a specific wind-farm service vessel—The Wave Craft. On a SES, the pressurized air cushion supports the majority of the weight of the vessel. The control problem considered relates to the actuation of the pressure such that wave-induced vessel motions are minimized. Results are given through simulation, model- and full-scale experimental testing.