Non-linear minimum variance estimation for fault detection systems

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully

A combined NMR and DFT study of Narrow Gap Semiconductors: The case of PbTe

In this study we present an alternative approach to separating contributions to the NMR shift originating from the Knight shift and chemical shielding by a combination of experimental solid-state NMR results and ab initio calculations. The chemical and Knight shifts are normally distinguished through detailed studies of the resonance frequency as function of temperature and carrier concentration, followed by extrapolation of the shift to zero carrier concentration. This approach is time-consuming and requires studies of multiple samples. Here, we analyzed $^{207}$Pb and $^{125}$Te NMR spin-lattice relaxation rates and NMR shifts for bulk and nanoscale PbTe. The shifts are compared with calculations of the $^{207}$Pb and $^{125}$Te chemical shift resonances to determine the chemical shift at zero charge carrier concentration. The results are in good agreement with literature values from carrier concentration-dependent studies. The measurements are also compared to literature reports of the $^{207}$Pb and $^{125}$Te Knight shifts of $n$- and $p$-type PbTe semiconductors. The literature data have been converted to the currently accepted shift scale. We also provide possible evidence for the "self-cleaning effect" property of PbTe nanocrystals whereby defects are removed from the core of the particles, while preserving the crystal structure.Comment: 34 pages, 9 figure

Effect of sedimentary heterogeneities in the sealing formation on predictive analysis of geological CO₂ storage

Numerical models of geologic carbon sequestration (GCS) in saline aquifers use multiphase fluid flow-characteristic curves (relative permeability and capillary pressure) to represent the interactions of the non-wetting CO2 and the wetting brine. Relative permeability data for many sedimentary formations is very scarce, resulting in the utilisation of mathematical correlations to generate the fluid flow characteristics in these formations. The flow models are essential for the prediction of CO2 storage capacity and trapping mechanisms in the geological media. The observation of pressure dissipation across the storage and sealing formations is relevant for storage capacity and geomechanical analysis during CO2 injection. This paper evaluates the relevance of representing relative permeability variations in the sealing formation when modelling geological CO2 sequestration processes. Here we concentrate on gradational changes in the lower part of the caprock, particularly how they affect pressure evolution within the entire sealing formation when duly represented by relative permeability functions. The results demonstrate the importance of accounting for pore size variations in the mathematical model adopted to generate the characteristic curves for GCS analysis. Gradational changes at the base of the caprock influence the magnitude of pressure that propagates vertically into the caprock from the aquifer, especially at the critical zone (i.e. the region overlying the CO2 plume accumulating at the reservoir-seal interface). A higher degree of overpressure and CO2 storage capacity was observed at the base of caprocks that showed gradation. These results illustrate the need to obtain reliable relative permeability functions for GCS, beyond just permeability and porosity data. The study provides a formative principle for geomechanical simulations that study the possibility of pressure-induced caprock failure during CO2 sequestration

Machine learning in and out of equilibrium

The algorithms used to train neural networks, like stochastic gradient descent (SGD), have close parallels to natural processes that navigate a high-dimensional parameter space -- for example protein folding or evolution. Our study uses a Fokker-Planck approach, adapted from statistical physics, to explore these parallels in a single, unified framework. We focus in particular on the stationary state of the system in the long-time limit, which in conventional SGD is out of equilibrium, exhibiting persistent currents in the space of network parameters. As in its physical analogues, the current is associated with an entropy production rate for any given training trajectory. The stationary distribution of these rates obeys the integral and detailed fluctuation theorems -- nonequilibrium generalizations of the second law of thermodynamics. We validate these relations in two numerical examples, a nonlinear regression network and MNIST digit classification. While the fluctuation theorems are universal, there are other aspects of the stationary state that are highly sensitive to the training details. Surprisingly, the effective loss landscape and diffusion matrix that determine the shape of the stationary distribution vary depending on the simple choice of minibatching done with or without replacement. We can take advantage of this nonequilibrium sensitivity to engineer an equilibrium stationary state for a particular application: sampling from a posterior distribution of network weights in Bayesian machine learning. We propose a new variation of stochastic gradient Langevin dynamics (SGLD) that harnesses without replacement minibatching. In an example system where the posterior is exactly known, this SGWORLD algorithm outperforms SGLD, converging to the posterior orders of magnitude faster as a function of the learning rate.Comment: 24 pages, 6 figure

Metabolic synergies in the biotransformation of organic and metallic toxic compounds by a saprotrophic soil fungus

The saprotrophic fungus Penicillium griseofulvum was chosen as model organism to study responses to a mixture of hexachlorocyclohexane (HCH) isomers (α-HCH, β-HCH, γ-HCH, δ-HCH) and of potentially toxic metals (vanadium, lead) in solid and liquid media. The P. griseofulvum FBL 500 strain was isolated from polluted soil containing high concentrations of HCH isomers and potentially toxic elements (Pb, V). Experiments were performed in order to analyse the tolerance/resistance of this fungus to xenobiotics, and to shed further light on fungal potential in inorganic and organic biotransformations. The aim was to examine the ecological and bioremedial potential of this fungus verifying the presence of mechanisms that allow it to transform HCH isomers and metals under different, extreme, test conditions. To our knowledge, this work is the first to provide evidence on the biotransformation of HCH mixtures, in combination with toxic metals, by a saprotrophic non-white-rot fungus and on the metabolic synergies involved

Missed, not missing: Phylogenomic evidence for the existence of Avian FoxP3

The Forkhead box transcription factor FoxP3 is pivotal to the development and function of regulatory T cells (Tregs), which make a major contribution to peripheral tolerance. FoxP3 is believed to perform a regulatory role in all the vertebrate species in which it has been detected. The prevailing view is that FoxP3 is absent in birds and that avian Tregs rely on alternative developmental and suppressive pathways. Prompted by the automated annotation of foxp3 in the ground tit (Parus humilis) genome, we have questioned this assumption. Our analysis of all available avian genomes has revealed that the foxp3 locus is missing, incomplete or of poor quality in the relevant genomic assemblies for nearly all avian species. Nevertheless, in two species, the peregrine falcon (Falco peregrinus) and the saker falcon (F. cherrug), there is compelling evidence for the existence of exons showing synteny with foxp3 in the ground tit. A broader phylogenomic analysis has shown that FoxP3 sequences from these three species are similar to crocodilian sequences, the closest living relatives of birds. In both birds and crocodilians, we have also identified a highly proline-enriched region at the N terminus of FoxP3, a region previously identified only in mammals

Gene family expansions and contractions are associated with host range in plant pathogens of the genus Colletotrichum

Background: Many species belonging to the genus Colletotrichum cause anthracnose disease on a wide range of plant species. In addition to their economic impact, the genus Colletotrichum is a useful model for the study of the evolution of host specificity, speciation and reproductive behaviors. Genome projects of Colletotrichum species have already opened a new era for studying the evolution of pathogenesis in fungi. Results: We sequenced and annotated the genomes of four strains in the Colletotrichum acutatum species complex (CAsc), a clade of broad host range pathogens within the genus. The four CAsc proteomes and secretomes along with those representing an additional 13 species (six Colletotrichum spp. and seven other Sordariomycetes) were classified into protein families using a variety of tools. Hierarchical clustering of gene family and functional domain assignments, and phylogenetic analyses revealed lineage specific losses of carbohydrate-active enzymes (CAZymes) and proteases encoding genes in Colletotrichum species that have narrow host range as well as duplications of these families in the CAsc. We also found a lineage specific expansion of necrosis and ethylene-inducing peptide 1 (Nep1)-like protein (NLPs) families within the CAsc. Conclusions: This study illustrates the plasticity of Colletotrichum genomes, and shows that major changes in host range are associated with relatively recent changes in gene content

Improving the Monitoring, Verification, and Accounting of CO{sub 2} Sequestered in Geologic Systems with Multicomponent Seismic Technology and Rock Physics Modeling

Research done in this study showed that P-SV seismic data provide better spatial resolution of geologic targets at our Appalachian Basin study area than do P-P data. This finding is important because the latter data (P-P) are the principal seismic data used to evaluate rock systems considered for CO{sub 2} sequestration. The increase in P-SV{sub 1} resolution over P-P resolution was particularly significant, with P-SV{sub 1} wavelengths being approximately 40-percent shorter than P-P wavelengths. CO{sub 2} sequestration projects across the Appalachian Basin should take advantage of the increased resolution provided by converted-shear seismic modes relative to P-wave seismic data. In addition to S-wave data providing better resolution of geologic targets, we found S-wave images described reservoir heterogeneities that P-P data could not see. Specifically, a channel-like anomaly was imaged in a key porous sandstone interval by P-SV{sub 1} data, and no indication of the feature existed in P-P data. If any stratigraphic unit is considered for CO{sub 2} storage purposes, it is important to know all heterogeneities internal to the unit to understand reservoir compartmentalization. We conclude it is essential that multicomponent seismic data be used to evaluate all potential reservoir targets whenever a CO{sub 2} storage effort is considered, particularly when sequestration efforts are initiated in the Appalachian Basin. Significant differences were observed between P-wave sequences and S- wave sequences in data windows corresponding to the Oriskany Sandstone, a popular unit considered for CO{sub 2} sequestration. This example demonstrates that S-wave sequences and facies often differ from P-wave sequences and facies and is a principle we have observed in every multicomponent seismic interpretation our research laboratory has done. As a result, we now emphasis elastic wavefield seismic stratigraphy in our reservoir characterization studies, which is a science based on the concept that the same weight must be given to S-wave sequences and facies as is given to P-wave sequences and facies. This philosophy differs from the standard practice of depending on only conventional P-wave seismic stratigraphy to characterize reservoir units. The fundamental physics of elastic wavefield seismic stratigraphy is that S- wave modes sense different sequences and facies across some intervals than does a P-wave mode because S-wave displacement vectors are orthogonal to P- wave displacement vectors and thus react to a different rock fabric than do P waves. Although P and S images are different, both images can still be correct in terms of the rock fabric information they reveal

Vision on metal additive manufacturing:Developments, challenges and future trends

Additive Manufacturing (AM) is one of the innovative technologies to fabricate components, parts, assemblies or tools in various fields of application due to its main characteristics such as direct digital manufacturing, ability to offer both internal and external complex geometries without additional cost, and the potential of varying materials at the voxel level. However, despite high anticipations, AM as a real revolution for serial production of metal components has yet to be seen, mostly due to lacks of fundamental understanding, design engineering tools, and the global robustness of the value chains. This paper aims to provide a vision about the future of metal AM based on the collective knowledge of all ten scientific and technical committees of the International Academy of Production Engineering (CIRP).</p