Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

Context-Aware Adaptive Opportunistic Routing in Mobile Ad-hoc and Sensor Networks

Mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) have been attracting increasing attention for decades due to their broad civilian and military applications. Basically, a MANET or WSN is a network of nodes connected by wireless communication links. Due to the limited transmission range of the radio, many pairs of nodes in MANETs or WSNs may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such types of networks is an important issue and it poses great challenges due to the dynamic nature of MANETs or WSNs. On the one hand, the open-air nature of wireless environments brings many difficulties when an efficient routing solution is required. The wireless channel is unreliable due to fading and interferences, which makes it impossible to maintain a quality path from a source node to a destination node. Additionally, node mobility aggravates network dynamics, which causes frequent topology changes and brings significant overheads for maintaining and recalculating paths. Furthermore, mobile devices and sensors are usually constrained by battery capacity, computing and communication resources, which impose limitations on the functionalities of routing protocols. On the other hand, the wireless medium possesses inherent unique characteristics, which can be exploited to enhance transmission reliability and routing performance. Opportunistic routing (OR) is one promising technique that takes advantage of the spatial diversity and broadcast nature of the wireless medium to improve packet forwarding reliability in multihop wireless communication. OR combats the unreliable wireless links by involving multiple neighboring nodes (forwarding candidates) to choose packet forwarders. In opportunistic routing, a source node does not require an end-to-end path to transmit packets. The packet forwarding decision is made hop-by-hop in a fully distributed fashion. Motivated by the deficiencies of existing opportunistic routing protocols in dynamic environments such as mobile ad-hoc networks or wireless sensor networks, this thesis proposes a novel context-aware adaptive opportunistic routing scheme. Our proposal selects packet forwarders by simultaneously exploiting multiple types of cross-layer context information of nodes and environments. Our approach significantly outperforms other routing protocols that rely solely on a single metric. The adaptivity feature of our proposal enables network nodes to adjust their behaviors at run-time according to network conditions. To accommodate the strict energy constraints in WSNs, this thesis integrates adaptive duty-cycling mechanism to opportunistic routing for wireless sensor nodes. Our approach dynamically adjusts the sleeping intervals of sensor nodes according to the monitored traffic load and the estimated energy consumption rate. Through the integration of duty cycling of sensor nodes and opportunistic routing, our protocol is able to provide a satisfactory balance between good routing performance and energy efficiency for WSNs.

An indicator framework for assessing livelihood resilience in the context of social–ecological dynamics

Livelihood resilience draws attention to the factors and processes that keep livelihoods functioning despite change and thus enriches the livelihood approach which puts people, their differential capabilities to cope with shocks and how to reduce poverty and improve adaptive capacity at the centre of analysis. However, the few studies addressing resilience from a livelihood perspective take different approaches and focus only on some dimensions of livelihoods. This paper presents a framework that can be used for a comprehensive empirical analysis of livelihood resilience. We use a concept of resilience that considers agency as well as structure. A review of both theoretical and empirical literature related to livelihoods and resilience served as the basis to integrate the perspectives. The paper identifies the attributes and indicators of the three dimensions of resilience, namely, buffer capacity, self-organisation and capacity for learning. The framework has not yet been systematically tested; however, potentials and limitations of the components of the framework are explored and discussed by drawing on empirical examples from literature on farming systems. Besides providing a basis for applying the resilience concept in livelihood-oriented research, the framework offers a way to communicate with practitioners on identifying and improving the factors that build resilience. It can thus serve as a tool for monitoring the effectiveness of policies and practices aimed at building livelihood resilience.

A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy

Due to their outstanding resolution and well-constrained chronologies, Greenland ice-core records provide a master record of past climatic changes throughout the Last Interglacial–Glacial cycle in the North Atlantic region. As part of the INTIMATE (INTegration of Ice-core, MArine and TErrestrial records) project, protocols have been proposed to ensure consistent and robust correlation between different records of past climate. A key element of these protocols has been the formal definition and ordinal numbering of the sequence of Greenland Stadials (GS) and Greenland Interstadials (GI) within the most recent glacial period. The GS and GI periods are the Greenland expressions of the characteristic Dansgaard–Oeschger events that represent cold and warm phases of the North Atlantic region, respectively. We present here a more detailed and extended GS/GI template for the whole of the Last Glacial period. It is based on a synchronization of the NGRIP, GRIP, and GISP2 ice-core records that allows the parallel analysis of all three records on a common time scale. The boundaries of the GS and GI periods are defined based on a combination of stable-oxygen isotope ratios of the ice (δ18O, reflecting mainly local temperature) and calcium ion concentrations (reflecting mainly atmospheric dust loading) measured in the ice. The data not only resolve the well-known sequence of Dansgaard–Oeschger events that were first defined and numbered in the ice-core records more than two decades ago, but also better resolve a number of short-lived climatic oscillations, some defined here for the first time. Using this revised scheme, we propose a consistent approach for discriminating and naming all the significant abrupt climatic events of the Last Glacial period that are represented in the Greenland ice records. The final product constitutes an extended and better resolved Greenland stratotype sequence, against which other proxy records can be compared and correlated. It also provides a more secure basis for investigating the dynamics and fundamental causes of these climatic perturbations.

Image Space Adaptive Rendering

In this thesis, we develop an adaptive framework for Monte Carlo rendering, and more specifically for Monte Carlo Path Tracing (MCPT) and its derivatives. MCPT is attractive because it can handle a wide variety of light transport effects, such as depth of field, motion blur, indirect illumination, participating media, and others, in an elegant and unified framework. However, MCPT is a sampling-based approach, and is only guaranteed to converge in the limit, as the sampling rate grows to infinity. At finite sampling rates, MCPT renderings are often plagued by noise artifacts that can be visually distracting. The adaptive framework developed in this thesis leverages two core strategies to address noise artifacts in renderings: adaptive sampling and adaptive reconstruction. Adaptive sampling consists in increasing the sampling rate on a per pixel basis, to ensure that each pixel value is below a predefined error threshold. Adaptive reconstruction leverages the available samples on a per pixel basis, in an attempt to have an optimal trade-off between minimizing the residual noise artifacts and preserving the edges in the image. In our framework, we greedily minimize the relative Mean Squared Error (rMSE) of the rendering by iterating over sampling and reconstruction steps. Given an initial set of samples, the reconstruction step aims at producing the rendering with the lowest rMSE on a per pixel basis, and the next sampling step then further reduces the rMSE by distributing additional samples according to the magnitude of the residual rMSE of the reconstruction. This iterative approach tightly couples the adaptive sampling and adaptive reconstruction strategies, by ensuring that we only sample densely regions of the image where adaptive reconstruction cannot properly resolve the noise. In a first implementation of our framework, we demonstrate the usefulness of our greedy error minimization using a simple reconstruction scheme leveraging a filterbank of isotropic Gaussian filters. In a second implementation, we integrate a powerful edge aware filter that can adapt to the anisotropy of the image. Finally, in a third implementation, we leverage auxiliary feature buffers that encode scene information (such as surface normals, position, or texture), to improve the robustness of the reconstruction in the presence of strong noise.

Application of a computational model for complex fluvial ecosystems: The population dynamics of zebra mussel Dreissena polymorpha as a case study

The potential and adaptive flexibility of population dynamic P-systems (PDP) to study population dynamics suggests that they may be suitable for modelling complex fluvial ecosystems, characterized by a composition of dynamic habitats with many variables that interact simultaneously. Using as a model a reservoir occupied by the zebra mussel Dreissena polymorpha, we designed a computational model based on P systems to study the population dynamics of larvae, in order to evaluate management actions to control or eradicate this invasive species. The population dynamics of this species was simulated under different scenarios ranging from the absence of water flow change to a weekly variation with different flow rates, to the actual hydrodynamic situation of an intermediate flow rate. Our results show that PDP models can be very useful tools to model complex, partially desynchronized, processes that work in parallel. This allows the study of complex hydroecological processes such as the one presented, where reproductive cycles, temperature and water dynamics are involved in the desynchronization of the population dynamics both, within areas and among them. The results obtained may be useful in the management of other reservoirs with similar hydrodynamic situations in which the presence of this invasive species has been documented.

Conservation of the Cinereous Vulture Aegypius monachus in Spain (1966–2011): a bibliometric review of threats, research and adaptive management

Detecting and quantifying threats and researching and implementing management actions are key to improving the conservation status of endangered species. Bibliometric analysis can constitute a useful tool for the evaluation of such questions from a long-term perspective. Taking as a case study the Cinereous Vulture Aegypius monachus in Spain, we tested relationships between population dynamics, research efforts, existing threats and conservation milestones. The population growth of the species (from 206 pairs in 1976 to 2,068 in 2011) was parallelled by the increase in the total number of publications, the number of articles in SCI journals and the number of published works dealing with aspects of conservation, threats and management. These results are discussed in terms of cause-effect relationships taking into account that the influence of other non-mutually exclusive factors could also probably explain such associations. Similarly, we analysed the trend of the Cinereous Vulture breeding population with respect to different threats and indices of food availability, obtaining a positive correlation with the increase in big-game hunting bags in Spain. With respect to conservation milestones, we concluded that the current situation is positive in terms of the protection of the species and its habitat, with the situation in relation to food availability being unclear. Finally, we reviewed the main conservation actions that have been taken for the species in Spain and how these have been progressively modified based on new scientific and technical evidence, as an example of adaptive management applied to conservation.

Effects of climate, fire, and humans on forest dynamics: forest simulations compared to the palaeological record

In order to find out which factors influenced the forest dynamics in northern Italy during the Holocene, a palaeoecological approach involving pollen analysis was combined with ecosystem modelling. The dynamic and distribution based forest model DisCForm was run with different input scenarios for climate, species immigration, fire, and human impact and the similarity of the simulations with the original pollen record was assessed. From the comparisons of the model output and the pollen core, it appears that immigration was most important in the first part of the Holocene, and that fire and human activity had a major influence in the second half. Species not well represented in the simulation outputs are species with a higher abundance in the past than today (Corylus), with their habitat in riparian forests (Alnus) or with a strong response to human impact (Castanea).

Experimental Evidence of an Eco-evolutionary Feedback during Adaptive Divergence

Differences in how organisms modify their environment can evolve rapidly and might influence adaptive population divergence [1, 2]. In a common garden experiment in aquatic mesocosms, we found that adult stickleback from a recently diverged pair of lake and stream populations had contrasting effects on ecosystem metrics. These modifications were caused by both genetic and plastic differences between populations and were sometimes comparable in magnitude to those caused by the presence/ absence of stickleback. Lake and streamfish differentially affected the biomass of zooplankton and phytoplankton, the concentration of phosphorus, and the abundance of several prey (e.g., copepods) and non-prey (e.g., cyanobacteria) species. The adult mediated effects on mesocosm ecosystems influenced the survival and growth of a subsequent generation of juvenile stickleback reared in the same mesocosms. The prior presence of adults decreased the overall growth rate of juveniles, and the prior presence of stream adults lowered overall juvenile survival. Among the survivors, lake juveniles grew faster than co-occurring stream juveniles, except in mesocosm ecosystems previously modified by adult lake fish that were reared on plankton. Overall, our results provide evidence for reciprocal interactions between ecosystem dynamics and evolutionary change (i.e., eco-evolutionary feedbacks) in the early stages of adaptive population divergence.

Beyond deforestation monitoring in conservation hotspots: Analysing landscape mosaic dynamics in north-eastern Madagascar

Due to its extraordinary biodiversity and rapid deforestation, north-eastern Madagascar is a conservation hotspot of global importance. Reducing shifting cultivation is a high priority for policy-makers and conservationists; however, spatially explicit evidence of shifting cultivation is lacking due to the difficulty of mapping it with common remote sensing methods. To overcome this challenge, we adopted a landscape mosaic approach to assess the changes between natural forests, shifting cultivation and permanent cultivation systems at the regional level from 1995 to 2011. Our study confirmed that shifting cultivation is still being used to produce subsistence rice throughout the region, but there is a trend of intensification away from shifting cultivation towards permanent rice production, especially near protected areas. While large continuous forest exists today only in the core zones of protected areas, the agricultural matrix is still dominated by a dense cover of tree crops and smaller forest fragments. We believe that this evidence makes a crucial contribution to the development of interventions to prevent further conversion of forest to agricultural land while improving local land users' well-being.

Dynamics of dissipative Bose-Einstein condensation

We resolve the real-time dynamics of a purely dissipative s=1/2 quantum spin or, equivalently, hard-core boson model on a hypercubic d-dimensional lattice. The considered quantum dissipative process drives the system to a totally symmetric macroscopic superposition in each of the S3 sectors. Different characteristic time scales are identified for the dynamics and we determine their finite-size scaling. We introduce the concept of cumulative entanglement distribution to quantify multiparticle entanglement and show that the considered protocol serves as an efficient method to prepare a macroscopically entangled Bose-Einstein condensate.

Deep water provenance and dynamics of the (de)glacial Atlantic meridional overturning circulation

Reconstructing past modes of ocean circulation is an essential task in paleoclimatology and paleoceanography. To this end, we combine two sedimentary proxies, Nd isotopes (εNd) and the 231Pa/230Th ratio, both of which are not directly involved in the global carbon cycle, but allow the reconstruction of water mass provenance and provide information about the past strength of overturning circulation, respectively. In this study, combined 231Pa/230Th and εNd down-core profiles from six Atlantic Ocean sediment cores are presented. The data set is complemented by the two available combined data sets from the literature. From this we derive a comprehensive picture of spatial and temporal patterns and the dynamic changes of the Atlantic Meridional Overturning Circulation over the past ∼25 ka. Our results provide evidence for a consistent pattern of glacial/stadial advances of Southern Sourced Water along with a northward circulation mode for all cores in the deeper (>3000 m) Atlantic. Results from shallower core sites support an active overturning cell of shoaled Northern Sourced Water during the LGM and the subsequent deglaciation. Furthermore, we report evidence for a short-lived period of intensified AMOC in the early Holocene.