Climate and sea-level variations along the northwestern Tethyan margin during the Valanginian C-isotope excursion: Mineralogical evidence from the Vocontian Basin (SE France)

A high resolution mineralogical study (bulk-rock and clay-fraction) was carried out upon the hemipelagic strata of the Angles section (Vocontian Basin, SE France) in which the Valanginian positive C-isotope excursion occurs. To investigate sea-level fluctuations and climate change respectively, a Detrital Index (DI: (phyllosilicates and quartz)/calcite) and a Weathering Index (WI: kaolinite/(illite + chlorite)) were established and compared to second-order sea-level fluctuations. In addition, the mineralogical data were compared with the High Nutrient Index (HNI, based on calcareous nannofossil taxa) data obtained by Duchamp-Alphonse et al. (2007), in order to assess the link between the hydrolysis conditions recorded on the surrounding continents and the trophic conditions inferred for the Vocontian Basin. It appears that the mineralogical distribution along the northwestern Tethyan margin is mainly influenced by sea-level changes during the Early Valanginian (Pertransiens to Stephanophorus ammonite Zones) and by climate variations from the late Early Valanginian to the base of the Hauterivian (top of the Stephanophorus to the Radiatus ammonite Zones). The sea-level fall observed in the Pertransiens ammonite Zone (Early Valanginian) is well expressed by an increase in detrital inputs (an increase in the DI) associated with a more proximal source and a shallower marine environment, whereas the sea-level rise recorded in the Stephanophorus ammonite Zone corresponds to a decrease in detrital influx (a decrease in the DI) as the source becomes more distal and the environment deeper. Interpretation of both DI and WI, indicates that the positive C-isotope excursion (top of the Stephanophorus to the Verrucosum ammonite Zones) is associated with an increase of detrital inputs under a stable, warm and humid climate, probably related to greenhouse conditions, the strongest hydrolysis conditions being reached at the maximum of the positive C-isotope excursion. From the Verrucosum ammonite Zone to the base of the Hauterivian (Radiatus ammonite Zone) climatic conditions evolved from weak hydrolysis conditions and, most likely, a cooler climate (resulting in a decrease in detrital inputs) to a seasonal climate in which more humid seasons alternated with more arid ones. The comparison of the WI to the HNI shows that the nutrification recorded al: the Angles section from the top of the Stephanophorus to the Radiatus ammonite Zones (including the positive C-isotope shift), is associated with climatic changes in the source areas. At that time, increased nutrient inputs were generally triggered by increased weathering processes in the source areas due to acceleration in the hydrological cycle under greenhouse conditions This scenario accords with the widely questioned palaeoenvironmental model proposed by Lini et al., (1992) and suggests that increasing greenhouse conditions are the main factor that drove the palaeoenvironmental changes observed in the hemipelagic realm of the Vocontian Basin, during the Valanginian positive C-isotope shift. This high-resolution mineralogical study highlights short-term climatic changes during the Valanginian, probably associated to rapid changes in the C-cycle. Coeval Massive Parana-Etendeka flood basalt eruptions may explain such rapid perturbations. (C) 2011 Elsevier B.V. All rights reserved.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Timing of Capacity Change: Models for Capital Intensive Industry

The suitable timing of capacity investments is a remarkable issue especially in capital intensive industries. Despite its importance, fairly few studies have been published on the topic. In the present study models for the timing of capacity change in capital intensive industry are developed. The study considers mainly the optimal timing of single capacity changes. The review of earlier research describes connections between cost, capacity and timing literature, and empirical examples are used to describe the starting point of the study and to test the developed models. The study includes four models, which describe the timing question from different perspectives. The first model, which minimizes unit costs, has been built for capacity expansion and replacement situations. It is shown that the optimal timing of an investment can be presented with the capacity and cost advantage ratios. After the unit cost minimization model the view is extended to the direction of profit maximization. The second model states that early investments are preferable if the change of fixed costs is small compared to the change of the contribution margin. The third model is a numerical discounted cash flow model, which emphasizes the roles of start-up time, capacity utilization rate and value of waiting as drivers of the profitable timing of a project. The last model expands the view from project level to company level and connects the flexibility of assets and cost structures to the timing problem. The main results of the research are the solutions of the models and analysis or simulations done with the models. The relevance and applicability of the results are verified by evaluating the logic of the models and by numerical cases.

A non-extensive maximum entropy based regulations method for bad conditioned inverse problems

A regularization method based on the non-extensive maximum entropy principle is devised. Special emphasis is given to the q=1/2 case. We show that, when the residual principle is considered as constraint, the q=1/2 generalized distribution of Tsallis yields a regularized solution for bad-conditioned problems. The so devised regularized distribution is endowed with a component which corresponds to the well known regularized solution of Tikhonov (1977).

Frames: a maximum entropy statistical estimate of the inverse problem

A maximum entropy statistical treatment of an inverse problem concerning frame theory is presented. The problem arises from the fact that a frame is an overcomplete set of vectors that defines a mapping with no unique inverse. Although any vector in the concomitant space can be expressed as a linear combination of frame elements, the coefficients of the expansion are not unique. Frame theory guarantees the existence of a set of coefficients which is “optimal” in a minimum norm sense. We show here that these coefficients are also “optimal” from a maximum entropy viewpoint.

Field data and prediction models of pesticide spray drift on coffee crop

The objective of this work was to generate drift curves from pesticide applications on coffee plants and to compare them with two European drift-prediction models. The used methodology is based on the ISO 22866 standard. The experimental design was a randomized complete block with ten replicates in a 2x20 split-plot arrangement. The evaluated factors were: two types of nozzles (hollow cone with and without air induction) and 20 parallel distances to the crop line outside of the target area, spaced at 2.5 m. Blotting papers were used as a target and placed in each of the evaluated distances. The spray solution was composed of water+rhodamine B fluorescent tracer at a concentration of 100 mg L-1, for detection by fluorimetry. A spray volume of 400 L ha-1 was applied using a hydropneumatic sprayer. The air-induction nozzle reduces the drift up to 20 m from the treated area. The application with the hollow cone nozzle results in 6.68% maximum drift in the nearest collector of the treated area. The German and Dutch models overestimate the drift at distances closest to the crop, although the Dutch model more closely approximates the drift curves generated by both spray nozzles.

The maximum voltage drop in an on-chip power distribution network: analysis of square, triangular and hexagonal power pad arrangements

A mathematical model of the voltage drop which arises in on-chip power distribution networks is used to compare the maximum voltage drop in the case of different geometric arrangements of the pads supplying power to the chip. These include the square or Manhattan power pad arrangement, which currently predominates, as well as equilateral triangular and hexagonal arrangements. In agreement with the findings in the literature and with physical and SPICE models, the equilateral triangular power pad arrangement is found to minimize the maximum voltage drop. This headline finding is a consequence of relatively simple formulas for the voltage drop, with explicit error bounds, which are established using complex analysis techniques, and elliptic functions in particular.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data

Maximum entropy modeling (Maxent) is a widely used algorithm for predicting species distributions across space and time. Properly assessing the uncertainty in such predictions is non-trivial and requires validation with independent datasets. Notably, model complexity (number of model parameters) remains a major concern in relation to overfitting and, hence, transferability of Maxent models. An emerging approach is to validate the cross-temporal transferability of model predictions using paleoecological data. In this study, we assess the effect of model complexity on the performance of Maxent projections across time using two European plant species (Alnus giutinosa (L.) Gaertn. and Corylus avellana L) with an extensive late Quaternary fossil record in Spain as a study case. We fit 110 models with different levels of complexity under present time and tested model performance using AUC (area under the receiver operating characteristic curve) and AlCc (corrected Akaike Information Criterion) through the standard procedure of randomly partitioning current occurrence data. We then compared these results to an independent validation by projecting the models to mid-Holocene (6000 years before present) climatic conditions in Spain to assess their ability to predict fossil pollen presence-absence and abundance. We find that calibrating Maxent models with default settings result in the generation of overly complex models. While model performance increased with model complexity when predicting current distributions, it was higher with intermediate complexity when predicting mid-Holocene distributions. Hence, models of intermediate complexity resulted in the best trade-off to predict species distributions across time. Reliable temporal model transferability is especially relevant for forecasting species distributions under future climate change. Consequently, species-specific model tuning should be used to find the best modeling settings to control for complexity, notably with paleoecological data to independently validate model projections. For cross-temporal projections of species distributions for which paleoecological data is not available, models of intermediate complexity should be selected.

The maximum voltage drop in an on-chip power distribution network: analysis of square, triangular and hexagonal power pad arrangements

A mathematical model of the voltage drop which arises in on-chip power distribution networks is used to compare the maximum voltage drop in the case of different geometric arrangements of the pads supplying power to the chip. These include the square or Manhattan power pad arrangement, which currently predominates, as well as equilateral triangular and hexagonal arrangements. In agreement with the findings in the literature and with physical and SPICE models, the equilateral triangular power pad arrangement is found to minimize the maximum voltage drop. This headline finding is a consequence of relatively simple formulas for the voltage drop, with explicit error bounds, which are established using complex analysis techniques, and elliptic functions in particular.

Analysis of electricity distribution network operation business models and capitalization of control room functions with DMS

Electricity distribution network operation (NO) models are challenged as they are expected to continue to undergo changes during the coming decades in the fairly developed and regulated Nordic electricity market. Network asset managers are to adapt to competitive technoeconomical business models regarding the operation of increasingly intelligent distribution networks. Factors driving the changes for new business models within network operation include: increased investments in distributed automation (DA), regulative frameworks for annual profit limits and quality through outage cost, increasing end-customer demands, climatic changes and increasing use of data system tools, such as Distribution Management System (DMS). The doctoral thesis addresses the questions a) whether there exist conditions and qualifications for competitive markets within electricity distribution network operation and b) if so, identification of limitations and required business mechanisms. This doctoral thesis aims to provide an analytical business framework, primarily for electric utilities, for evaluation and development purposes of dedicated network operation models to meet future market dynamics within network operation. In the thesis, the generic build-up of a business model has been addressed through the use of the strategicbusiness hierarchy levels of mission, vision and strategy for definition of the strategic direction of the business followed by the planning, management and process execution levels of enterprisestrategy execution. Research questions within electricity distribution network operation are addressed at the specified hierarchy levels. The results of the research represent interdisciplinary findings in the areas of electrical engineering and production economics. The main scientific contributions include further development of the extended transaction cost economics (TCE) for government decisions within electricity networks and validation of the usability of the methodology for the electricity distribution industry. Moreover, DMS benefit evaluations in the thesis based on the outage cost calculations propose theoretical maximum benefits of DMS applications equalling roughly 25% of the annual outage costs and 10% of the respective operative costs in the case electric utility. Hence, the annual measurable theoretical benefits from the use of DMS applications are considerable. The theoretical results in the thesis are generally validated by surveys and questionnaires.

Plasma cytokine response, lipid peroxidation and NF-κB activation in skeletal muscle following maximum progressive swimming

Our objective was to determine lipid peroxidation and nuclear factor-κB (NF-κB) activation in skeletal muscle and the plasma cytokine profile following maximum progressive swimming. Adult male Swiss mice (N = 15) adapted to the aquatic environment were randomly divided into three groups: immediately after exercise (EX1), 3 h after exercise (EX2) and control. Animals from the exercising groups swam until exhaustion, with an initial workload of 2% of body mass attached to the tail. Control mice did not perform any exercise but were kept immersed in water for 20 min. Maximum swimming led to reactive oxygen species (ROS) generation in skeletal muscle, as indicated by increased thiobarbituric acid reactive species (TBARS) levels (4062.67 ±1487.10 vs 19,072.48 ± 8738.16 nmol malondialdehyde (MDA)/mg protein, control vs EX1). Exercise also promoted NF-κB activation in soleus muscle. Cytokine secretion following exercise was marked by increased plasma interleukin-6 (IL-6) levels 3 h post-exercise (P < 0.05). Interleukin-10 (IL-10) levels were reduced following exercise and remained reduced 3 h post-exercise (P < 0.05). Plasma levels of other cytokines investigated, monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ) and interleukin-12 (IL-12), were not altered by exercise. The present findings showed that maximum swimming, as well as other exercise models, led to lipid peroxidation and NF-κB activation in skeletal muscle and increased plasma IL-6 levels. The plasma cytokine response was also marked by reduced IL-10 levels. These results were attributed to exercise type and intensity.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Continental margin architecture : the palynological signature of glacioeustasy /

Palynomorphs from two siliciclastic margins were examined to gain insights into continental margin architecture. Sea level change is thought to be one of the primary controls on continental margin architecture. Because Late Neogene glacioeustasy has been well studied marine sediments deposited during the Late Neogene were examined to test this concept. Cores from the outer shelf and upper slope were taken from the New Jersey margin in the western North Atlantic Ocean and from the Sunda Shelf margin in the South China Sea. Continental margin architecture is often described in a sequence stratigraphic context. One of the main goals of both coring projects was to test the theoretical sequence stratigraphic models developed by a research group at Exxon (e.g. Wilgus et al., 1988). Palynomorphs provide one of the few methods of inferring continental margin architecture in monotonous, siliciclastic marine sediments where calcareous sediments are rare (e.g. New Jersey margin). In this study theoretical models of the palynological signature expected in sediment packages deposited during the various increments of a glacioeustatic cycle were designed. These models were based on the modem palynomorph trends and taphonomic factors thought to control palynomorph distribution. Both terrestrial (pollen and spores) and marine (dinocysts) palynomorphs were examined. The palynological model was then compared with New Jersey margin and Sunda Shelf margin sediments. The predicted palynological trends provided a means of identifying a complete cycle of glacioeustatic change (Oxygen Isotope Stage 5e to present) in the uppermost 80 meters of sediment on the slope at the New Jersey margin. Sediment availability, not sea meters of sediment on the slope at the New Jersey margin. Sediment availability, not sea level change, is thought to be the major factor controlling margin architecture during the late Pleistocene here at the upper slope. This is likely a function of the glacial scouring of the continents which significantly increases sediment availability during glacial stages. The subaerially exposed continental shelf during the lowstand periods would have been subject to significant amounts of erosion fi:om the proglacial rivers flowing fi-om the southern regions of the ice-sheet. The slope site is non-depositional today and was also non-depositional during the last full interglacial period. The palynomorph data obtained fi-om the South China Sea indicate that the major difference between the New Jersey Margin sites and the Sunda Shelf margin sites is the variation in sediment supply and the rate of sediment accumulation. There was significantly less variation in sediment supply between glacial and interglacial periods and less overall sediment accumulation at the Sunda Shelf margin. The data presented here indicate that under certain conditions the theoretical palynological models allow the identification of individual sequence stratigraphic units and therefore, allow inferences regarding continental margin architecture. The major condition required in this approach is that a complete and reliable database of the contemporaneous palynomorphs be available.

Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models

Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a limited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. My master thesis research focuses on designing accelerated life testing experiments for reliability estimation. We consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing the stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are D-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is correct. Due to the nature of prediction made from ALT experimental data, attained under the stress levels higher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be tested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust designs is also explored.