New constraints on the gas age-ice age difference along the EPICA ice cores, 0-50 kyr

Gas is trapped in polar ice sheets at ~50–120 m below the surface and is therefore younger than the surrounding ice. Firn densification models are used to evaluate this ice age-gas age difference (Δage) in the past. However, such models need to be validated by data, in particular for periods colder than present day on the East Antarctic plateau. Here we bring new constraints to test a firn densification model applied to the EPICA Dome C (EDC) site for the last 50 kyr, by linking the EDC ice core to the EPICA Dronning Maud Land (EDML) ice core, both in the ice phase (using volcanic horizons) and in the gas phase (using rapid methane variations). We also use the structured 10Be peak, occurring 41 kyr before present (BP) and due to the low geomagnetic field associated with the Laschamp event, to experimentally estimate the Δage during this event. Our results seem to reveal an overestimate of the Δage by the firn densification model during the last glacial period at EDC. Tests with different accumulation rates and temperature scenarios do not entirely resolve this discrepancy. Although the exact reasons for the Δage overestimate at the two EPICA sites remain unknown at this stage, we conclude that current densification model simulations have deficits under glacial climatic conditions. Whatever the cause of the Δage overestimate, our finding suggests that the phase relationship between CO2 and EDC temperature previously inferred for the start of the last deglaciation (lag of CO2 by 800±600 yr) seems to be overestimated.

Provenance discrimination of Lower Cretaceous synrift sandstones (eastern Iberian Chain Spain) Constraints from detrital modes heavy minerals and geochemistry

Petrography, geochemical whole-rock composition, and chemical analyses of tourmaline were performed in order to determine the source areas of Lower Cretaceous Mora, El Castellar, and uppermost Camarillas Formation sandstones from the Iberian Chain, Spain. Sandstones were deposited in intraplate subbasins, which are bound by plutonic and volcanic rocks of Permian, Triassic, and Jurassic age, Paleozoic metamorphic rocks, and Triassic sedimentary rocks. Modal analyses together with petrographic and cathodoluminescence observations allowed us to define three quartz-feldspathic petrofacies and recognize diagenetic processes that modified the original framework composition. Results from average restored petrofacies are: Mora petrofacies = P/F >1 and Q(r)70 F(r)22 R(r)9; El Castellar petrofacies = P/F >1 and Q(r)57 F(r)25 R(r)18; and Camarillas petrofacies = P/F ∼ zero and Q(r)64 F(r)28 R(r)7 (P—plagioclase; F—feldspar; Q—quartz; R—rock fragments; r—restored composition). Trace-element and rare earth element abundances of whole-rock analyses discriminate well between the three petrofacies based on: (1) the Rb concentration, which is indicative of the K content and reflects the amount of K-feldspar modal abundance, and (2) the relative modal abundance of heavy minerals (tourmaline, zircon, titanite, and apatite), which is reproduced by the elements hosted in the observed heavy mineral assemblage (i.e., B and Li for tourmaline; Zr, Hf, and Ta for zircon; Ti, Ta, Nb, and their rare earth elements for titanite; and P, Y, and their rare earth elements for apatite). Tourmaline chemical composition for the three petrofacies ranges from Fe-tourmaline of granitic to Mg-tourmaline of metamorphic origin. The three defined petrofacies suggest a mixed provenance from plutonic and metamorphic source rocks. However, a progressively major influence of granitic source rocks was detected from the lowermost Mora petrofacies toward the uppermost Camarillas petrofacies. This provenance trend is consistent with the uplift and erosion of the Iberian Massif, which coincided with the development of the latest Berriasian synrift regional unconformity and affected all of the Iberian intraplate basins. The uplifting stage of Iberian Massif pluton caused a significant dilution of Paleozoic metamorphic source areas, which were dominant during the sedimentation of the lowermost Mora and El Castellar petrofacies. The association of petrographic data with whole-rock geochemical compositions and tourmaline chemical analysis has proved to be useful for determining source area characteristics, their predominance, and the evolution of source rock types during the deposition of quartz-feldspathic sandstones in intraplate basins. This approach ensures that provenance interpretation is consistent with the geological context.

Probabilistic-based hurricane risk assessment and mitigation considering the potential impacts of climate change

Studies are suggesting that hurricane hazard patterns (e.g. intensity and frequency) may change as a consequence of the changing global climate. As hurricane patterns change, it can be expected that hurricane damage risks and costs may change as a result. This indicates the necessity to develop hurricane risk assessment models that are capable of accounting for changing hurricane hazard patterns, and develop hurricane mitigation and climatic adaptation strategies. This thesis proposes a comprehensive hurricane risk assessment and mitigation strategies that account for a changing global climate and that has the ability of being adapted to various types of infrastructure including residential buildings and power distribution poles. The framework includes hurricane wind field models, hurricane surge height models and hurricane vulnerability models to estimate damage risks due to hurricane wind speed, hurricane frequency, and hurricane-induced storm surge and accounts for the timedependant properties of these parameters as a result of climate change. The research then implements median insured house values, discount rates, housing inventory, etc. to estimate hurricane damage costs to residential construction. The framework was also adapted to timber distribution poles to assess the impacts climate change may have on timber distribution pole failure. This research finds that climate change may have a significant impact on the hurricane damage risks and damage costs of residential construction and timber distribution poles. In an effort to reduce damage costs, this research develops mitigation/adaptation strategies for residential construction and timber distribution poles. The costeffectiveness of these adaptation/mitigation strategies are evaluated through the use of a Life-Cycle Cost (LCC) analysis. In addition, a scenario-based analysis of mitigation strategies for timber distribution poles is included. For both residential construction and timber distribution poles, adaptation/mitigation measures were found to reduce damage costs. Finally, the research develops the Coastal Community Social Vulnerability Index (CCSVI) to include the social vulnerability of a region to hurricane hazards within this hurricane risk assessment. This index quantifies the social vulnerability of a region, by combining various social characteristics of a region with time-dependant parameters of hurricanes (i.e. hurricane wind and hurricane-induced storm surge). Climate change was found to have an impact on the CCSVI (i.e. climate change may have an impact on the social vulnerability of hurricane-prone regions).

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

BETASCAN: probable beta-amyloids identified by pairwise probabilistic analysis

Amyloids and prion proteins are clinically and biologically important beta-structures, whose supersecondary structures are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Recent work has indicated the utility of pairwise probabilistic statistics in beta-structure prediction. We develop here a new strategy for beta-structure prediction, emphasizing the determination of beta-strands and pairs of beta-strands as fundamental units of beta-structure. Our program, BETASCAN, calculates likelihood scores for potential beta-strands and strand-pairs based on correlations observed in parallel beta-sheets. The program then determines the strands and pairs with the greatest local likelihood for all of the sequence's potential beta-structures. BETASCAN suggests multiple alternate folding patterns and assigns relative a priori probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. The algorithm compares favorably with the results of previous algorithms (BETAPRO, PASTA, SALSA, TANGO, and Zyggregator) in beta-structure prediction and amyloid propensity prediction. Accurate prediction is demonstrated for experimentally determined amyloid beta-structures, for a set of known beta-aggregates, and for the parallel beta-strands of beta-helices, amyloid-like globular proteins. BETASCAN is able both to detect beta-strands with higher sensitivity and to detect the edges of beta-strands in a richly beta-like sequence. For two proteins (Abeta and Het-s), there exist multiple sets of experimental data implying contradictory structures; BETASCAN is able to detect each competing structure as a potential structure variant. The ability to correlate multiple alternate beta-structures to experiment opens the possibility of computational investigation of prion strains and structural heterogeneity of amyloid. BETASCAN is publicly accessible on the Web at http://betascan.csail.mit.edu.