An identification procedure of multi-input Wiener models for the distortion analysis of nonlinear circuits

In this contribution, a system identification procedure of a two-input Wiener model suitable for the analysis of the disturbance behavior of integrated nonlinear circuits is presented. The identified block model is comprised of two linear dynamic and one static nonlinear block, which are determined using an parameterized approach. In order to characterize the linear blocks, an correlation analysis using a white noise input in combination with a model reduction scheme is adopted. After having characterized the linear blocks, from the output spectrum under single tone excitation at each input a linear set of equations will be set up, whose solution gives the coefficients of the nonlinear block. By this data based black box approach, the distortion behavior of a nonlinear circuit under the influence of an interfering signal at an arbitrary input port can be determined. Such an interfering signal can be, for example, an electromagnetic interference signal which conductively couples into the port of consideration. © 2011 Author(s).

Modular continuous crystallizer: operation, experimental arrangement and identification of process parameters

Crystallization is employed in different industrial processes. The method and operation can differ depending on the nature of the substances involved. The aim of this study is to examine the effect of various operating conditions on the crystal properties in a chemical engineering design window with a focus on ultrasound assisted cooling crystallization. Batch to batch variations, minimal manufacturing steps and faster production times are factors which continuous crystallization seeks to resolve. Continuous processes scale-up is considered straightforward compared to batch processes owing to increase of processing time in the specific reactor. In cooling crystallization process, ultrasound can be used to control the crystal properties. Different model compounds were used to define the suitable process parameters for the modular crystallizer using equal operating conditions in each module. A final temperature of 20oC was employed in all experiments while the operating conditions differed. The studied process parameters and configuration of the crystallizer were manipulated to achieve a continuous operation without crystal clogging along the crystallization path. The results from the continuous experiment were compared with the batch crystallization results and analysed using the Malvern Morphologi G3 instrument to determine the crystal morphology and CSD. The modular crystallizer was operated successfully with three different residence times. At optimal process conditions, a longer residence time gives smaller crystals and narrower CSD. Based on the findings, at a constant initial solution concentration, the residence time had clear influence on crystal properties. The equal supersaturation criterion in each module offered better results compared to other cooling profiles. The combination of continuous crystallization and ultrasound has large potential to overcome clogging, obtain reproducible and narrow CSD, specific crystal morphologies and uniform particle sizes, and exclusion of milling stages in comparison to batch processes.

A Fuzzy Decision Strategy for Topic Identification and Dynamic Selection of Language Models

International audience

Inverse identification of the bending stiffness of a braided polyethylene twine subject to large deformation: application to the identification of the mesh opening rigidity of fishing nets.

The evaluation of the mesh opening stiffness of fishing nets is an important issue in assessing the selectivity of trawls. It appeared that a larger bending rigidity of twines decreases the mesh opening and could reduce the escapement of fish. Nevertheless, netting structure is complex. A netting is made up of braided twines made of polyethylene or polyamide. These twines are tied with non-symmetrical knots. Thus, these assemblies develop contact-friction interactions. Moreover, the netting can be subject to large deformation. In this study, we investigate the responses of netting samples to different types of solicitations. Samples are loaded and unloaded with creep and relaxation stages, with different boundary conditions. Then, two models have been developed: an analytical model and a finite element model. The last one was used to assess, with an inverse identification algorithm, the bending stiffness of twines. In this paper, experimental results and a model for netting structures made up of braided twines are presented. During dry forming of a composite, for example, the matrix is not present or not active, and relative sliding can occur between constitutive fibres. So an accurate modelling of the mechanical behaviour of fibrous material is necessary. This study offers experimental data which could permit to improve current models of contact-friction interactions [4], to validate models for large deformation analysis of fibrous materials [1] on a new experimental case, then to improve the evaluation of the mesh opening stiffness of a fishing net

DEVELOPMENT OF A NUCLEIC ACID-BASED SPECIFIC GROWTH MODEL FOR JUVENILE BLUE CRAB, CALLINECTES SAPIDUS

The evaluation and identification of habitats that function as nurseries for marine species has the potential to improve conservation and management. A key assessment of nursery habitat is estimating individual growth. However, the discrete growth of crustaceans presents a challenge for many traditional in situ techniques to accurately estimate growth over a short temporal scale. To evaluate the use of nucleic acid ratios (R:D) for juvenile blue crab (Callinectes sapidus), I developed and validated an R:D-based index of growth in the laboratory. R:D based growth estimates of crabs collected in the Patuxent River, MD indicated growth ranged from 0.8-25.9 (mg·g-1·d-1). Overall, there was no effect of size on growth, whereas there was a weak, but significant effect of date. These data provide insight into patterns of habitat-specific growth. These results highlight the complexity of the biological and physical factors which regulate growth of juvenile blue crabs in the field.

Identification of telomere dysfunction in Friedreich ataxia.

Background: Friedreich ataxia (FRDA) is a progressive inherited neurodegenerative disorder caused by mutation of the FXN gene, resulting in decreased frataxin expression, mitochondrial dysfunction and oxidative stress. A recent study has identified shorter telomeres in FRDA patient leukocytes as a possible disease biomarker. Results: Here we aimed to investigate both telomere structure and function in FRDA cells. Our results confirmed telomere shortening in FRDA patient leukocytes and identified similar telomere shortening in FRDA patient autopsy cerebellar tissues. However, FRDA fibroblasts showed significantly longer telomeres at early passage, occurring in the absence of telomerase activity, but with activation of an alternative lengthening of telomeres (ALT)-like mechanism. These cells also showed accelerated telomere shortening as population doubling increases. Furthermore, telomere dysfunction-induced foci (TIF) analysis revealed that FRDA fibroblasts have dysfunctional telomeres. Conclusions: Our finding of dysfunctional telomeres in FRDA cells provides further insight into FRDA molecular disease mechanisms, which may have implications for future FRDA therapy.

Application of dental morphological characteristics for medical-legal identification: sexual diagnosis in a portuguese population

Licenced under a Creative Commons Attribution 3.0.

Pathway analysis for identification of potential biomarkers in severe cutaneous drug hypersensitivity reactions

Purpose: To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using pathways analysis in order to identify some potential biomarkers that may be used for early detection of SJS and epidermal necrolysis (TEN) manifestations. Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which are online software and databases. Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for topological analysis. It was found by geneMANIA database search that majority of these genes were coexpressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction with regards to hypersensitivity. Conclusion: Analysis of differential gene expressions by topological and database approaches in the current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed.

Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2

The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.

On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

A multiscale multisurface constitutive model for the thermo-plastic behavior of polyethylene

We present a multiscale model bridging length and time scales from molecular to continuum levels with the objective of predicting the yield behavior of amorphous glassy polyethylene (PE). Constitutive pa- rameters are obtained from molecular dynamics (MD) simulations, decreasing the requirement for ad- hoc experiments. Consequently, we achieve: (1) the identification of multisurface yield functions; (2) the high strain rate involved in MD simulations is upscaled to continuum via quasi-static simulations. Validation demonstrates that the entire multisurface yield functions can be scaled to quasi-static rates where the yield stresses are possibly predicted by a proposed scaling law; (3) a hierarchical multiscale model is constructed to predict temperature and strain rate dependent yield strength of the PE.

Damage Identification of Structures through Vibration-Based Structural Monitoring Systems

The thesis has been carried out within the “SHAPE Project - Predicting Strength Changes in Bridges from Frequency Data Safety, Hazard, and Poly-harmonic Evaluation” (ERA-NET Plus Infravation Call 2014) which dealt with the structural assessment of existing bridges and laboratory structural reproductions through the use of vibration-based monitoring systems, for detecting changes in their natural frequencies and correlating them with the occurrence of damage. The main purpose of this PhD dissertation has been the detection of the variation of the main natural frequencies as a consequence of a previous-established damage configuration provided on a structure. Firstly, the effect of local damage on the modal feature has been discussed mainly concerning a steel frame and a composite steel-concrete bridge. Concerning the variation of the fundamental frequency of the small bridge, the increasing severity of two local damages has been investigated. Moreover, the comparison with a 3D FE model is even presented establishing a link between the dynamic properties and the damage features. Then, moving towards a diffused damage pattern, four concrete beams and a small concrete deck were loaded achieving the yielding of the steel reinforcement. The stiffness deterioration in terms of frequency shifts has been reconsidered by collecting a large set of dynamic experiments on simply supported R.C. beams discussed in the literature. The comparison of the load-frequency curves suggested a significant agreement among all the experiments. Thus, in the framework of damage mechanics, the “breathing cracks” phenomenon has been discussed leading to an analytical formula able to explain the frequency decay observed experimentally. Lastly, some dynamic investigations of two existing bridges and the corresponding FE Models are presented in Chapter 4. Moreover, concerning the bridge in Bologna, two prototypes of a network of accelerometers were installed and the data of a few months of monitoring have been discussed.

Longitudinal Micro CT-driven biomarkers as a tool to evaluate lung fibrosis progression and antifibrotic efficacy in a refined Bleomycin mouse model

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease with no curative pharmacological treatment. Animal models play an essential role in revealing molecular mechanisms involved in the pathogenesis of the disease. Bleomycin (BLM)-induced lung fibrosis is the most widely used and characterized model for anti-fibrotic drugs screening. However, several issues have been reported, such as the identification of an optimal BLM dose and administration scheme as well as gender-specificity. Moreover, the balance between disease resolution, an appropriate time window for therapeutic intervention and animal welfare remains critical aspects yet to be fully elucidated. In this thesis, Micro CT imaging has been used as a tool to identify the ideal BLM dose regimen to induce sustained lung fibrosis in mice as well as to assess the anti-fibrotic effect of Nintedanib (NINT) treatment upon this BLM administration regimen. In order to select the optimal BLM dose scheme, C57bl/6 male mice were treated with BLM via oropharyngeal aspiration (OA), following either double or triple BLM administration. The triple BLM administration resulted in the most promising scheme, able to balance disease resolution, appropriate time-window for therapeutic intervention and animal welfare. The fibrosis progression was longitudinally assessed by micro-CT every 7 days for 5 weeks after BLM administration and 5 animals were sacrificed at each timepoint for the BALF and histological evaluation. The antifibrotic effect of NINT was assessed following different treatment regimens in this model. Herein, we have developed an optimized mouse model of pulmonary fibrosis, enabling three weeks of the therapeutic window to screen putative anti-fibrotic drugs. micro-CT scanning, allowed us to monitor the progression of lung fibrosis and the therapeutical response longitudinally in the same subject, drastically reducing the number of animals involved in the experiment.

Study of genes involved in her2-positive breast cancer progression for identification of new therapeutic targets

HER2 overexpression is observed in 20-30% of invasive breast carcinomas and it is correlated with poor prognosis. Although targeted therapies have revolutionized the treatment of HER2-positive breast cancer, a high number of patients presented primary or acquired resistance to monoclonal antibodies and tyrosine kinase inhibitors. Tumor heterogenicity, epithelial to mesenchymal transition (EMT) and cancer stem cells are key factors in target therapy resistance and tumor progression. The aim of this project was to discover alternative therapeutic strategies to over-come tumor resistance by harnessing immune system and looking for new targetable molecules. The results reported introduce a virus-like particles-based vaccine against HER2 as promising therapeutic approach to treat HER2-positive tumors. The high and persistent anti-HER2 antibody titers elicited by the vaccine significantly inhibited tumor growth and metastases onset. Furthermore, the polyclonal response induced by the vaccine also inhibited human HER2-positive breast cancer cells resistant to trastuzumab in vitro, suggesting its efficacy also on trastuzumab resistant tumors. To identify new therapeutic targets to treat progressed breast cancer, we took advantage from a dynamic model of HER2 expression obtained in our laboratory, in which HER2 loss and cancer progression were associated with the acquisition of EMT and stemness features. Targeting EMT-involved molecules, such as PDGFR-β, or the induction of epithelial markers, like E-cadherin, proved to be successful strategy to impair HER2-negative tumor growth. Density alterations, which might be induced by anti-HER2 target therapies, in cell culture condition of a cell line with a labile HER2 expression, caused HER2 loss probably as consequence of more aggressive subpopulations which prevail over the others. These subpopulations showed an increased EMT and stemness profile, confirming that targeting EMT-involved molecules or antigen expressed by cancer stem cells together with anti-HER2 target therapies is a valid strategy to inhibit HER2-positive cells and simultaneously prevent selection of more aggressive clone.

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.