Predicting stream baseflow using genetic programing

Developing reliable methods to estimate stream baseflow has been a subject of research over the past decades due to its importance in catchment response and sustainable watershed management (e.g. ground water recharge vs. extraction). Limitations and complexities of existing methods have been addressed by a number of researchers. For instance, physically based numerical models are complex, requiring substantial computational time and data which may not be always available. Artificial Intelligence (AI) tools such as Genetic Programming (GP) have been used widely to reduce the challenges associated with complex hydrological systems without losing the physical meanings. However, up to date, in the absence of complex numerical models, baseflow is frequently estimated using statistically derived empirical equations without significant physical insights. This study investigates the capability of GP in estimating baseflow for a small monitored semi-urban catchment (0.021 km2) located in Singapore. A Recursive Digital Filter (RDF) is first adopted to separate the baseflow from observed streamflow. GP is then used to derive an empirical equation to relate the filtered baseflow time series particularly with groundwater table fluctuations which are relatively easy to be measured and are physically related to baseflow generation. The equation is then validated with a longer time series of baseflow data from a groundwater numerical model. These results indicate that GP is an effective tool in determining baseflow.postprin

Dialogue Design for a Robot-Based Face-Mirroring Game to Engage Autistic Children with Emotional Expressions

We present design strategies for Human Robot Interaction for school-aged autistic children with limited receptive language. Applying these strategies to the DE-ENIGMA project (large EU project addressing emotion recognition in autistic children) supported development of a new activity for in facial expression imitation whereby the robot imitates the child’s face to encourage the child to notice facial expressions in a play-based game. A usability case study with 15 typically-developing children aged 4–6 at an English-language school in the Netherlands was performed to observe the feasibility of the setup and make design revisions before exposing the robot to autistic children

Development and assessment of adaptive urban flood risk infrastructure under conditions of deep uncertainty

Globally, the risk associated with urban pluvial flooding is growing due to the confluence of climate change and urbanisation. The inherently complex nature of these processes makes it unclear what the impacts will be— including when and where they will happen, or to what degree. It is therefore said that adaptation to these risks occurs under conditions of deep uncer- tainty. Deep Uncertainty implies that methods which were historically effective at planning are no longer appropriate. Regardless new infrastructure must be built and existing infrastructure adapted. This thesis explores Decision Making under Deep Uncertainty and its implications for the sustainable long-term planning of drainage infrastructure. To address this issue, the effects of potential changes were explored through use of an Adaptation Tipping Points methodology. This methodology was highly modified to extend its applicability far beyond previous work. This method was applied to a case study which was modelled within Infoworks ICM, the drainage system was then stress tested against changes to the 30-year return period design storm. The effectiveness of 53 potential strategies to alleviate flooding by the potential changes were evaluated. Adaptation Pathways were generated, providing decision makers information regarding incre- mental adaptations for changes in the depth and intensity of the design storm. It is shown that the most effective Adaptation Pathway can vary significantly depending on which changes to the design storm take place. This is due to the non-linear interaction of multiple drainage processes and the importance of path dependence. This thesis developed a novel methodology to calculate the costs and benefits of a set of Adap- tation Pathways. This method builds upon real options to provide a cost-benefit analysis which encompassed both flood benefits and ecosystem services. Furthermore, the effect of uncertainty on financial performance was assessed. It is shown that the financial performance of pathways is highly influenced by the order in which solutions are implemented. However, very rarely does the uncertainty surrounding a project’s financial viability obfuscate whether it will be a success or not. The final result was a set of fully costed Adaptation Pathways that decision makers could use to identify the best adaptation strategy.Open Acces

Hematopoietic Stem Cells Are the Major Source of Multilineage Hematopoiesis in Adult Animals

Hematopoietic stem cells (HSCs) sustain long-term reconstitution of hematopoiesis in transplantation recipients, yet their role in the endogenous steady-state hematopoiesis remains unclear. In particular, recent studies suggested that HSCs provide a relatively minor contribution to immune cell development in adults. We directed transgene expression in a fraction of HSCs that maintained reconstituting activity during serial transplantations. Inducible genetic labeling showed that transgene-expressing HSCs gave rise to other phenotypic HSCs, confirming their top position in the differentiation hierarchy. The labeled HSCs rapidly contributed to committed progenitors of all lineages and to mature myeloid cells and lymphocytes, but not to B-1a cells or tissue macrophages. Importantly, labeled HSCs gave rise to more than two-thirds of all myeloid cells and platelets in adult mice, and this contribution could be accelerated by an induced interferon response. Thus, classically defined HSCs maintain immune cell development in the steady state and during systemic cytokine responses

Uncertainty, Flexibility And Design: Real-Options-Based Assessment Of Urban Blue Green Infrastructure

Climate change and rapid urbanization requires a new long-term forward assessment of sustainable urban water management projects. This challenge is further complicated by difficulties of assessing sustainable designs and various design scenarios from an economic standpoint. A conventional approach for economic assessment of urban water management projects, such as Discounted Cash Flow (DCF) analysis, fails to account for uncertainties associated with rainfall intensities, thermal stresses, as well as other uncertainties associated with future changes in technological domains. Such an approach also fails to include the value of flexibility, which enables managers to adapt and reconfigure systems over time as uncertainty unfolds. This work describes an integrated framework to valuation of investments in urban water management systems under uncertainty. It extends the conventional DCF analysis through explicit considerations of flexibility in design and management or urban blue green infrastructure. The approach considers and incorporates flexibility as an intelligent decision-making mechanisms resulting in avoidance of future downside risks and increase in opportunities for upside gains over a range of possible futures. Kent Ridge catchment in Singapore was chosen to assess and demonstrate the value of extension of a standard drainage canal system with flexible deployment of urban blue green infrastructure. Results show that integrating uncertainty and flexibility explicitly into the decision-making process can reduce initial capital expenditure, improve value for investment, and enable decision-makers to learn more about system requirements during the lifetime of the project