Finite time distributions of stochastically modeled chemical systems with absolute concentration robustness

Recent research in both the experimental and mathematical communities has focused on biochemical interaction systems that satisfy an "absolute concentration robustness" (ACR) property. The ACR property was first discovered experimentally when, in a number of different systems, the concentrations of key system components at equilibrium were observed to be robust to the total concentration levels of the system. Followup mathematical work focused on deterministic models of biochemical systems and demonstrated how chemical reaction network theory can be utilized to explain this robustness. Later mathematical work focused on the behavior of this same class of reaction networks, though under the assumption that the dynamics were stochastic. Under the stochastic assumption, it was proven that the system will undergo an extinction event with a probability of one so long as the system is conservative, showing starkly different long-time behavior than in the deterministic setting. Here we consider a general class of stochastic models that intersects with the class of ACR systems studied previously. We consider a specific system scaling over compact time intervals and prove that in a limit of this scaling the distribution of the abundances of the ACR species converges to a certain product-form Poisson distribution whose mean is the ACR value of the deterministic model. This result is in agreement with recent conjectures pertaining to the behavior of ACR networks endowed with stochastic kinetics, and helps to resolve the conflicting theoretical results pertaining to deterministic and stochastic models in this setting

Stochastic Chemical Reaction Networks for Robustly Approximating Arbitrary Probability Distributions

We show that discrete distributions on the d-dimensional non-negative integer lattice can be approximated arbitrarily well via the marginals of stationary distributions for various classes of stochastic chemical reaction networks. We begin by providing a class of detailed balanced networks and prove that they can approximate any discrete distribution to any desired accuracy. However, these detailed balanced constructions rely on the ability to initialize a system precisely, and are therefore susceptible to perturbations in the initial conditions. We therefore provide another construction based on the ability to approximate point mass distributions and prove that this construction is capable of approximating arbitrary discrete distributions for any choice of initial condition. In particular, the developed models are ergodic, so their limit distributions are robust to a finite number of perturbations over time in the counts of molecules

insar decorrelation to assess and prevent volcanic risk

SAR� can� be� invaluable� describing� pre�eruption� surface� deformation� and� improving� the� understanding� of� volcanic� processes.� This� work� studies� correlation� of� pairs� of� SAR� images� focusing� on� the� inༀ䃻uence� of� surface,� climate� conditions� and� acquisition� band.� Chosen� L�band� and� C�band� images� (ENVISAT,� ERS� and� ALOS)� cover� most� of� the� Yellowstone� caldera� (USA)� over� a� span� of� 4� years,� sampling� all� the� seasons.� Interferograms� and� correlation� maps� are� generated� and� studied� in� relation� to� snow� depth� and� temperature.� To� isolate� temporal� decorrelation� pairs� of� images� with� the� shortest� baseline� are� chosen.� Results� show� good� performance� during� winter,� bad� attitude� towards� wet� snow� and� good� coherence� during� summer� with� L�band� performing� better� over� vegetation

A new path method for exponential ergodicity of Markov processes on Zd\mathbb Z^d, with applications to stochastic reaction networks

This paper provides a new path method that can be used to determine when an ergodic continuous-time Markov chain on $\mathbb Z^d$ converges exponentially fast to its stationary distribution in $L^2$. Specifically, we provide general conditions that guarantee the positivity of the spectral gap. Importantly, our results do not require the assumption of time-reversibility of the Markov model. We then apply our new method to the well-studied class of stochastically modeled reaction networks. Notably, we show that each complex-balanced model that is also ``open'' has a positive spectral gap, and is therefore exponentially ergodic. We further illustrate how our results can be applied for models that are not necessarily complex-balanced. Moreover, we provide an example of a detailed-balanced (in the sense of reaction network theory), and hence complex-balanced, stochastic reaction network that is not exponentially ergodic. We believe this to be the first such example in the literature.Comment: 44 page

Emissions and combustion performance of a micro gas turbine powered with liquefied wood and its blends

The combustion of a viscous biofuel, liquefied wood (LW) produced via solvolysis of lignocellulosic biomass in acidified glycols, has been studied in a small gas turbine rig. The test rig includes a modified injection line which is compatible with acidic, viscous biofuels allowing fuel preheating and two pilot injectors, and a re-designed combustion chamber. The link between fuel properties and combustion performance of liquefied wood is investigated by burning the biofuel at different blending ratios with ethanol. Exhaust emissions have been compared to reference measurements with diesel fuel and ethanol. Combustion analysis is supported by the investigation of the engine operating parameters and the main emission species at different electrical loads. The experimental study reveals that it is possible to establish efficient operation of the micro gas turbine while utilizing liquefied wood-ethanol blends with high share of liquefied wood

Geochemical Proxies and Mineralogical Fingerprints of Sedimentary Processes in a Closed Shallow Lake Basin Since 1850

AbstractLake systems are essential for the environment, the biosphere, and humans but are highly impacted by anthropogenic activities accentuated by climate change. Understanding how lake ecosystems change due to human impacts and natural forces is crucial to managing their current state and possible future restoration. The high sensitivity of shallow closed lakes to natural and anthropogenic forcing makes these lacustrine ecosystems highly prone to variations in precipitation and sedimentation processes. These variation processes, occurring in the water column, produce geochemical markers or proxies recorded in lake sedimentary archives. This study investigated specific proxies on high-resolution sedimentary archives (2–3 years resolution) of the Trasimeno lake (Central Italy). The Trasimeno lake underwent three different hydrological phases during the twentieth century due to several fluctuations induced mainly by human activities and climate change. The Trasimeno lake, a large and shallow basin located in the Mediterranean area, is a good case study to assess the effects of intense anthropogenic activity related to agriculture, tourism, industry, and climate changes during the Anthropocene. The aim is to identify the main characteristics of the main sedimentary events in the lake during the last 150 years, determining the concentrations of major and trace elements, the amount of organic matter, and the mineralogical composition of the sediments. This type of work demonstrates that studying sediment archives at high resolution is a viable method for reconstructing the lake's history through the evolution/trends of the geochemical proxies stored in the sediment records. This effort makes it possible to assess past anthropogenic impact and, under the objectives of the European Green Deal (zero-pollution ambition for a toxic-free environment), to monitor, prevent, and remedy pollution related to soil and water compartments. Graphical abstrac

Iron Speciation of Natural and Anthropogenic Dust by Spectroscopic and Chemical Methods

In this work, we have characterized the iron local structure in samples of two different types of atmospheric dust using X-ray absorption spectroscopy and selective leaching experiments. Specifically, we have investigated samples of long-range transported Saharan dust and freshly emitted steel plant fumes with the aim of individuating possible fingerprints of iron in the two cases. Findings include (1) prevalence of octahedral coordinated Fe 3 + for all samples; (2) presence of 6-fold coordinated Fe 3 + , aluminosilicates and iron oxy(hydr)oxides in Saharan dust and (3) of Fe-bearing spinel-like structures in the industrial fumes; (4) general predominance of the residual insoluble fraction with a notable difference: 69% for Saharan dust and 93% for steel production emissions, associated with aluminosilicates and non-reducible iron oxy(hydr)oxides, and Fe spinels, respectively. The remarkable differences between the X-ray absorption spectroscopy (XAS) spectra and leaching test results for the two sample types suggest the possibility to exploit the present approach in more complex cases. To this aim, two additional case studies of mixed aerosol samples are presented and discussed