Estimation of the Impact of Basement Heterogeneity on Thermal History Reconstruction: The Western Siberian Basin

Some of the simplifying assumptions frequently used in basin modelling may adversely impact the quality of the constructed models. One such common assumption consists of using a laterally homogeneous crustal basement, despite the fact that lateral variations in its properties may significantly affect the thermal evolution of the model. We propose a new method for the express evaluation of the impact of the basement’s heterogeneity on thermal history reconstruction and on the assessment of maturity of the source rock. The proposed method is based on reduced-rank inversion, aimed at a simultaneous reconstruction of the petrophysical properties of the heterogeneous basement and of its geometry. The method uses structural information taken from geological maps of the basement and gravity anomaly data. We applied our method to a data collection from Western Siberia and carried out a two-dimensional reconstruction of the evolution of the basin and of the lithosphere. We performed a sensitivity analysis of the reconstructed basin model to assess the effect of uncertainties in the basement’s density and its thermal conductivity for the model’s predictions. The proposed method can be used as an express evaluation tool to assess the necessity and relevance of laterally heterogeneous parametrisations prior to a costly three-dimensional full-rank basin modelling. The method is generally applicable to extensional basins except for salt tectonic provinces.

Inhomogeneous pion condensed phase hosting topologically stable baryons

We discuss the inhomogeneous pion condensed phase within the framework of chiral perturbation theory. We show how the general expression of the condensate can be obtained solving three coupled differential equations, expressing how the pion fields are modulated in space. Upon using some simplifying assumptions, we determine an analytic solution in (3+1)-dimensions. The obtained inhomogeneous condensate is characterized by a non-vanishing topological charge, which can be identified with the baryonic number. In this way, we obtain an inhomogeneous system of pions hosting an arbitrary number of baryons at fixed position in space.

Watt-Level Ka-Band Integrated Doherty Power Amplifiers: Technologies and Power Combination Strategies Invited Paper

This paper discusses some of the design choices underlying the development of watt-level integrated Doherty power amplifiers in the K and Ka band, focusing on compound semiconductor technologies. The key aspect of on-chip power combination is discussed, presenting and comparing some of the possible alternatives. Then, the impact on the achievable bandwidth and performance of different parameters is quantified, adopting an approximate analysis, which focuses on the Doherty output combiner and allows estimating the non-linear performance of the amplifier thanks to some simplifying assumptions, without requiring a full, non-linear model of the active devices. Two sample GaAs and GaN technologies are compared first, considering parameters that are representative of the currently available commercial processes, and then several power combination strategies are analyzed, adopting the GaN technology, which is currently the only one that allows achieving the power levels required by the applications directly on chip. Finally, some hints as to the impact of the output parasitic effects of the transistors on the presented analysis are given.

Density- and elongation speed-dependent error correction in RNA polymerization

Backtracking of RNA polymerase (RNAP) is an important pausing mechanism during DNA transcription that is part of the error correction process that enhances transcription fidelity. We model the backtracking mechanism of RNA polymerase, which usually happens when the polymerase tries to incorporate a noncognate or "mismatched" nucleotide triphosphate. Previous models have made simplifying assumptions such as neglecting the trailing polymerase behind the backtracking polymerase or assuming that the trailing polymerase is stationary. We derive exact analytic solutions of a stochastic model that includes locally interacting RNAPs by explicitly showing how a trailing RNAP influences the probability that an error is corrected or incorporated by the leading backtracking RNAP. We also provide two related methods for computing the mean times for error correction and incorporation given an initial local RNAP configuration. Using these results, we propose an effective interacting-RNAP lattice that can be readily simulated.

Methodology for optimal design and control of wind energy system based on simplifying assumptions

The study presented in this paper concerns the development of a new methodology for design and controlling a wind energy generation chain. This methodology is based on combined Analytical-Finite Element-Experimental method. This type of converter chosen is an AC-DC inverter with IGBTs to improve the robustness of the power chain structure. It offers a reduction of the cost of the power chain and the improvement of the performances of the global studied system, as the control at power factor equal to unity and providing an electromagnetic torque which is added to the useful torque in order to extract the maximal energy. The control algorithms permit to regulate Le charging voltage and current in their rated values considered as optimal battery charging voltage and current. The global model of the power chain is implemented under the Matlab-Sumilink simulation environment for performance and efficiency analysis.

Genetic architecture of dispersal and local adaptation drives accelerating range expansions

Contemporary evolution has the potential to significantly alter biotic responses to global change, including range expansion dynamics and biological invasions. However, predictive models often make highly simplifying assumptions about the genetic architecture underlying relevant traits. This can be problematic since genetic architecture defines evolvability, that is, evolutionary rates, and higher order evolutionary processes, which determine whether evolution will be able to keep up with environmental change or not. Therefore, we here study the impact of the genetic architecture of dispersal and local adaptation, two central traits of high relevance for range expansion dynamics, on the speed and variability of range expansions into an environmental gradient, such as temperature. In our theoretical model we assume that dispersal and local adaptation traits result from the products of two non-interacting gene-regulatory networks (GRNs). We compare our model to simpler quantitative genetics models and show that in the GRN model, range expansions are accelerated, faster and more variable. Increased variability implies that these evolutionary changes reduce predictability. We further find that acceleration in the GRN model is primarily driven by an increase in the rate of local adaptation to novel habitats which results from greater sensitivity to mutation (decreased robustness) and increased gene expression. Our results highlight how processes at microscopic scales, here, within genomes, can impact the predictions of large scale, macroscopic phenomena, such as range expansions, by modulating the rate of evolution.

Modeling of thermochemical conversion of waste biomass – a comprehensive review

Thermochemical processes, which include pyrolysis, torrefaction, gasification, combustion, and hydrothermal conversions, are perceived to be more efficient in converting waste biomass to energy and value-added products than biochemical processes. From the chemical point of view, thermochemical processes are highly complex and sensitive to numerous physicochemical properties, thus making reactor and process modeling more challenging. Nevertheless, the successful commercialization of these processes is contingent upon optimized reactor and process designs, which can be effectively achieved via modeling and simulation. Models of various scales with numerous simplifying assumptions have been developed for specific applications of thermochemical conversion of waste biomass. However, there is a research gap that needs to be explored to elaborate the scale of applicability, limitations, accuracy, validity, and special features of each model. This review study investigates all above mentioned important aspects and features of the existing models for all established industrial thermochemical conversion processes with emphasis on waste biomass, thus addressing the research gap mentioned above and presenting commercial-scale applicability in terms of reactor designing, process control and optimization, and potential ways to upgrade existing models for higher accuracy.

Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS

Stable isotope probing (SIP) combined with nano-scale secondary ion mass spectrometry (nanoSIMS) is a powerful approach to quantify assimilation rates of elements such as C and N into individual microbial cells. Here, we use mathematical modeling to investigate how the derived rate estimates depend on the model used to describe substrate assimilation by a cell during a SIP incubation. We show that the most commonly used model, which is based on the simplifying assumptions of linearly increasing biomass of individual cells over time and no cell division, can yield underestimated assimilation rates when compared to rates derived from a model that accounts for cell division. This difference occurs because the isotopic labeling of a dividing cell increases more rapidly over time compared to a non-dividing cell and becomes more pronounced as the labeling increases above a threshold value that depends on the cell cycle stage of the measured cell. Based on the modeling results, we present formulae for estimating assimilation rates in cells and discuss their underlying assumptions, conditions of applicability, and implications for the interpretation of intercellular variability in assimilation rates derived from nanoSIMS data, including the impacts of storage inclusion metabolism. We offer the formulae as a Matlab script to facilitate rapid data evaluation by nanoSIMS users.

Physical Modeling of Snow Gliding: A Case Study in the NW Italian Alps

Snow gliding, a slow movement downhill of snow cover, is complex to forecast and model and yet is extremely important, because it drives snowpack dynamics in the pre-avalanching phase. Despite recent interest in this process and the development of some studies therein, this phenomenon is poorly understood and represents a major point of uncertainty for avalanche forecasting. This study presents a data-driven, physically based, time-dependent 1D model, Poli-Glide, able to predict the slow movement of snowpacks along a flow line at the daily scale. The objective of the work was to create a useful snow gliding model, requiring few, relatively easily available input data, by (i) modeling snowpack evolution from measured precipitation and air temperature, (ii) evaluating the rate and extent of movement of the snowpack in the gliding phase, and (iii) assessing fracture (i.e., avalanching) timing. Such a model could be then used to provide hazard assessment in areas subject to gliding, thereby, and subsequent avalanching. To do so, some simplifying assumptions were introduced, namely that (i) negligible traction stress occurs within soil, (ii) water percolation into snow occurs at a fixed rate, and (iii) the micro topography of soil is schematized according to a sinusoidal function in the absence of soil erosion. The proposed model was then applied to the “Torrent des Marais-Mont de La Saxe” site in Aosta Valley, monitored during the winters of 2010 and 2011, featuring different weather conditions. The results showed an acceptable capacity of the model to reproduce snowpack deformation patterns and the final snowpack’s displacement. Correlation analysis based upon observed glide rates further confirmed dependence against the chosen variables, thus witnessing the goodness of the model. The results could be a valuable starting point for future research aimed at including more complex parameterizations of the different processes that affect gliding.

Asymptotic formulae for estimating statistical significance in particle physics analyses

Within the framework of likelihood-based statistical tests for particle physics measurements, we derive expressions for estimating the statistical significance of discovery using the asymptotic approximations of Wilks and Wald for four measurement models. These models include arbitrary numbers of signal regions, control regions, and Gaussian constraints. We extend our expressions to use the representative or "Asimov" dataset proposed by Cowan et al. such that they are made data-free. While many of the expressions are complicated and often involve solving systems of coupled, multivariate equations, we show these expressions reduce to closed-form results under simplifying assumptions. We also validate the predicted significances using toy-based data in select cases and show the asymptotic formulae to be more computationally efficient than the toy-based approach. Additionally, different parameters within each measurement model are varied in order to understand their effect on the predicted significance.