New Optimization Algorithms for Structural Reliability Analysis

Solution of structural reliability problems by the First Order method require optimization algorithms to find the smallest distance between a limit state function and the origin of standard Gaussian space. The Hassofer-Lind-Rackwitz-Fiessler (HLRF) algorithm, developed specifically for this purpose, has been shown to be efficient but not robust, as it fails to converge for a significant number of problems. On the other hand, recent developments in general (augmented Lagrangian) optimization techniques have not been tested in aplication to structural reliability problems. In the present article, three new optimization algorithms for structural reliability analysis are presented. One algorithm is based on the HLRF, but uses a new differentiable merit function with Wolfe conditions to select step length in linear search. It is shown in the article that, under certain assumptions, the proposed algorithm generates a sequence that converges to the local minimizer of the problem. Two new augmented Lagrangian methods are also presented, which use quadratic penalties to solve nonlinear problems with equality constraints. Performance and robustness of the new algorithms is compared to the classic augmented Lagrangian method, to HLRF and to the improved HLRF (iHLRF) algorithms, in the solution of 25 benchmark problems from the literature. The new proposed HLRF algorithm is shown to be more robust than HLRF or iHLRF, and as efficient as the iHLRF algorithm. The two augmented Lagrangian methods proposed herein are shown to be more robust and more efficient than the classical augmented Lagrangian method.

Space and Motion Perception and Discomfort in Air Travel

RAMOS RT, MATTOS DA, REBOUCAS ITS, RANVAUD RD. Space and motion perception and discomfort in air travel. Aviat Space Environ Med 2012; 83:1162-6. Introduction: The perception of comfort during air trips is determined by several factors. External factors like cabin design and environmental parameters (temperature, humidity, air pressure, noise, and vibration) interact with individual characteristics (anxiety traits, fear of flying, and personality) from arrival at the airport to landing at the destination. In this study, we investigated the influence of space and motion discomfort (SMD), fear of heights, and anxiety on comfort perception during all phases of air travel. Methods: We evaluated 51 frequent air travelers through a modified version of the Flight Anxiety Situations Questionnaire (FAS), in which new items were added and where the subjects were asked to report their level of discomfort or anxiety (not fear) for each phase of air travel (Chronbach's alpha = 0.974). Correlations were investigated among these scales: State-Trait Anxiety Inventory (STAB, Cohen's Acrophobia Questionnaire, and the Situational Characteristics Questionnaire (SitQ, designed to estimate SMD levels). Results: Scores of SitQ correlated with discomfort in situations involving space and movement perception (Pearson's rho = 0.311), while discomfort was associated with cognitive mechanisms related to scores in the anxiety scales (Pearson's rho = 0.375). Anxiety traits were important determinants of comfort perception before and after flight, while the influence of SMD was more significant during the time spent in the aircraft cabin. Discussion: SMD seems to be an important modulator of comfort perception in air travel. Its influence on physical well being and probably on cognitive performance, with possible effects on flight safety, deserves further investigation.

Transiting exoplanets from the CoRoT space mission XX. CoRoT-20b: A very high density, high eccentricity transiting giant planet

We report the discovery by the CoRoT space mission of a new giant planet, CoRoT-20b. The planet has a mass of 4.24 +/- 0.23 M-Jup and a radius of 0.84 +/- 0.04 R-Jup. With a mean density of 8.87 +/- 1.10 g cm(-3), it is among the most compact planets known so far. Evolutionary models for the planet suggest a mass of heavy elements of the order of 800 M-circle plus if embedded in a central core, requiring a revision either of the planet formation models or both planet evolution and structure models. We note however that smaller amounts of heavy elements are expected by more realistic models in which they are mixed throughout the envelope. The planet orbits a G-type star with an orbital period of 9.24 days and an eccentricity of 0.56. The star's projected rotational velocity is v sin i = 4.5 +/- 1.0 km s(-1), corresponding to a spin period of 11.5 +/- 3.1 days if its axis of rotation is perpendicular to the orbital plane. In the framework of Darwinian theories and neglecting stellar magnetic breaking, we calculate the tidal evolution of the system and show that CoRoT-20b is presently one of the very few Darwin-stable planets that is evolving toward a triple synchronous state with equality of the orbital, planetary and stellar spin periods.

Morphometric analysis of the placenta in the New World mouse Necromys lasiurus (Rodentia, Cricetidae): a comparison of placental development in cricetids and murids

Background: Stereology is an established method to extrapolate three-dimensional quantities from two-dimensional images. It was applied to placentation in the mouse, but not yet for other rodents. Herein, we provide the first study on quantitative placental development in a sigmodontine rodent species with relatively similar gestational time. Placental structure was also compared to the mouse, in order to evaluate similarities and differences in developmental patterns at the end of gestation. Methods: Fetal and placental tissues of Necromys lasiurus were collected and weighed at 3 different stages of gestation (early, mid and late gestation) for placental stereology. The total and relative volumes of placenta and of its main layers were investigated. Volume fractions of labyrinth components were quantified by the One Stop method in 31 placentae collected from different individuals, using the Mercator® software. Data generated at the end of gestation from N. lasiurus placentae were compared to those of Mus musculus domesticus obtained at the same stage. Results: A significant increase in the total absolute volumes of the placenta and its main layers occurred from early to mid-gestation, followed by a reduction near term, with the labyrinth layer becoming the most prominent area. Moreover, at the end of gestation, the total volume of the mouse placenta was significantly increased compared to that of N. lasiurus although the proportions of the labyrinth layer and junctional zones were similar. Analysis of the volume fractions of the components in the labyrinth indicated a significant increase in fetal vessels and sinusoidal giant cells, a decrease in labyrinthine trophoblast whereas the proportion of maternal blood space remained stable in the course of gestation. On the other hand, in the mouse, volume fractions of fetal vessels and sinusoidal giant cells decreased whereas the volume fraction of labyrinthine trophoblast increased compared to N. lasiurus placenta. Conclusions: Placental development differed between N. lasiurus and M. musculus domesticus. In particular, the low placental efficiency in N. lasiurus seemed to induce morphological optimization of fetomaternal exchanges. In conclusion, despite similar structural aspects of placentation in these species, the quantitative dynamics showed important differences.

Spontaneous combined rupture of a pelvicalyceal cyst into the collector system and retroperitoneal space during the acquisition of computed tomography scan images: a case report

Abstract Introduction Pelvicalyceal cysts are common findings in autopsies and can manifest with a variety of patterns. These cystic lesions are usually a benign entity with no clinical significance unless they enlarge enough to cause compression of the adjacent collecting system and consequently obstructive uropathy. Few cases of the spontaneous rupture of pelvicalyceal renal cysts have been published and to the best of our knowledge there is no report of a combined rupture to collector system and retroperitoneal space documented during a multiphase computed tomography. Case presentation We report a case of a ‘real-time’ spontaneous rupture of a pelvicalyceal cyst into the collecting system with fistulization into the retroperitoneum. The patient was a 78-year-old Caucasian man with a previous history of renal stones and a large pelvicalyceal renal cyst who was admitted to our Emergency department with acute right flank pain. A multiphase computed tomography was performed and the pre-contrast images demonstrated a right pelvicalyceal renal cyst measuring 12.0 × 6.1cm in the lower pole causing moderate dilation of the upper right renal collection system. In addition, a partially obstructive stone on the left distal ureter with mild left hydronephrosis was noted. The nephrographic phase did not add any new information. The excretory phase (10-minute delay) demonstrated a spontaneous rupture of the cyst into the pelvicalyceal system with posterior fistulization into the retroperitoneal space. Conclusion In this case study we present time-related changes of a rare pelvicalyceal cyst complication, which to the best of our knowledge has fortunately not been previously documented. Analysis of the sequential images and comparison with an earlier scan allowed us to better understand the physiopathological process of the rupture, the clinical presentation and to elaborate hypotheses for its etiopathogenesis.

A new approach for modeling and control of nonlinear systems via norm-bounded linear differential inclusions

A systematic approach to model nonlinear systems using norm-bounded linear differential inclusions (NLDIs) is proposed in this paper. The resulting NLDI model is suitable for the application of linear control design techniques and, therefore, it is possible to fulfill certain specifications for the underlying nonlinear system, within an operating region of interest in the state-space, using a linear controller designed for this NLDI model. Hence, a procedure to design a dynamic output feedback controller for the NLDI model is also proposed in this paper. One of the main contributions of the proposed modeling and control approach is the use of the mean-value theorem to represent the nonlinear system by a linear parameter-varying model, which is then mapped into a polytopic linear differential inclusion (PLDI) within the region of interest. To avoid the combinatorial problem that is inherent of polytopic models for medium- and large-sized systems, the PLDI is transformed into an NLDI, and the whole process is carried out ensuring that all trajectories of the underlying nonlinear system are also trajectories of the resulting NLDI within the operating region of interest. Furthermore, it is also possible to choose a particular structure for the NLDI parameters to reduce the conservatism in the representation of the nonlinear system by the NLDI model, and this feature is also one important contribution of this paper. Once the NLDI representation of the nonlinear system is obtained, the paper proposes the application of a linear control design method to this representation. The design is based on quadratic Lyapunov functions and formulated as search problem over a set of bilinear matrix inequalities (BMIs), which is solved using a two-step separation procedure that maps the BMIs into a set of corresponding linear matrix inequalities. Two numerical examples are given to demonstrate the effectiveness of the proposed approach.

Modulation of host cell signaling pathways as a therapeutic approach in periodontal disease

Recently, new treatment approaches have been developed to target the host component of periodontal disease. This review aims at providing updated information on host-modulating therapies, focusing on treatment strategies for inhibiting signal transduction pathways involved in inflammation. Pharmacological inhibitors of MAPK, NFκB and JAK/STAT pathways are being developed to manage rheumatoid arthritis, periodontal disease and other inflammatory diseases. Through these agents, inflammatory mediators can be inhibited at cell signaling level, interfering on transcription factors activation and inflammatory gene expression. Although these drugs offer great potential to modulate host response, their main limitations are lack of specificity and developments of side effects. After overcoming these limitations, adjunctive host modulating drugs will provide new therapeutic strategies for periodontal treatment.

Conceptual problem for noncommutative inflation and the new approach for nonrelativistic inflationary equation of state

In a previous paper, we connected the phenomenological noncommutative inflation of Alexander, Brandenberger and Magueijo [ Phys. Rev. D 67 081301 (2003)] and Koh and Brandenberger [ J. Cosmol. Astropart Phys. 2007 21 ()] with the formal representation theory of groups and algebras and analyzed minimal conditions that the deformed dispersion relation should satisfy in order to lead to a successful inflation. In that paper, we showed that elementary tools of algebra allow a group-like procedure in which even Hopf algebras (roughly the symmetries of noncommutative spaces) could lead to the equation of state of inflationary radiation. Nevertheless, in this paper, we show that there exists a conceptual problem with the kind of representation that leads to the fundamental equations of the model. The problem comes from an incompatibility between one of the minimal conditions for successful inflation (the momentum of individual photons being bounded from above) and the Fock-space structure of the representation which leads to the fundamental inflationary equations of state. We show that the Fock structure, although mathematically allowed, would lead to problems with the overall consistency of physics, like leading to a problematic scattering theory, for example. We suggest replacing the Fock space by one of two possible structures that we propose. One of them relates to the general theory of Hopf algebras (here explained at an elementary level) while the other is based on a representation theorem of von Neumann algebras (a generalization of the Clebsch-Gordan coefficients), a proposal already suggested by us to take into account interactions in the inflationary equation of state.

Two new species and a review of the inseminating freshwater fish genus Monotocheirodon (Characiformes: Characidae) from Peru and Bolivia

Two new species of inseminating freshwater fishes of the genus Monotocheirodon, family Characidae, are described from Peru. Males and females of both new species have an external, visually obvious urogenital papilla that was not detected in the females in previous studies, with this longer in males, which use it as an inseminating organ. A third inseminating species from Bolivia, Monotocheirodon pearsoni, unstudied in any detail since its original description in 1924, is redescribed. This latter species lacks an inseminating organ. Monotocheirodon is redescribed, its phylogenetic relationships are briefly discussed and it is suggested that it is possibly related to the stevardiin genera Ceratobranchia, Othonocheirodus, and Odontostoechus.

Updated status and performance for the cosmic origins spectrograph onboard the Hubble Space Telescope

The Cosmic Origins Spectrograph (COS) was installed on the Hubble Space Telescope in May 2009. Although COS was initially designed to perform high-sensitivity medium- and low-resolution spectroscopy of astronomical objects in the 1150-3200 Å wavelength range, new wavelength settings have recently become available that allow medium-resolution spectroscopy down to 900 Å, at effective areas comparable to those of FUSE. Here we provide an update on the implementation of the new short wavelength settings G130M/1222, 1096, and 1055. We discuss changes to the Far-Ultraviolet (FUV) and Near-Ultraviolet (NUV) dark rates, FUV pulse height filtering, new and improved flux calibrations for FUV Lifetime Positions 1 and 2, changes in sensitivity for both the NUV and FUV channels, and give a general overview of the calibration projects undertaken in Cycles 19 and 20.

Stochastic lattice model describing a vector-borne disease

We employ the approach of stochastic dynamics to describe the dissemination of vector-borne diseases such as dengue, and we focus our attention on the characterization of the threshold of the epidemic. The coexistence space comprises two representative spatial structures for both human and mosquito populations. The human population has its evolution described by a process that is similar to the Susceptible-Infected-Recovered (SIR) dynamics. The population of mosquitoes follows a dynamic of the type of the Susceptible Infected-Susceptible (SIS) model. The coexistence space is a bipartite lattice constituted by two structures representing the human and mosquito populations. We develop a truncation scheme to solve the evolution equations for the densities and the two-site correlations from which we get the threshold of the disease and the reproductive ratio. We present a precise deØnition of the reproductive ratio which reveals the importance of the correlations developed in the early stage of the disease. According to our deØnition, the reproductive rate is directed related to the conditional probability of the occurrence of a susceptible human (mosquito) given the presence in the neighborhood of an infected mosquito (human). The threshold of the epidemic as well as the phase transition between the epidemic and the non-epidemic states are also obtained by performing Monte Carlo simulations. References: [1] David R. de Souza, T^ania Tom∂e, , Suani R. T. Pinho, Florisneide R. Barreto and M∂ario J. de Oliveira, Phys. Rev. E 87, 012709 (2013). [2] D. R. de Souza, T. Tom∂e and R. M. ZiÆ, J. Stat. Mech. P03006 (2011).

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.

New Synthetic Polyamines as Multi-Target-Directed Ligands for the Treatment of Alzheimer's Disease and Cancer: Design, Synthesis and Biological Evaluation

Alzheimer's disease (AD) and cancer represent two of the main causes of death worldwide. They are complex multifactorial diseases and several biochemical targets have been recognized to play a fundamental role in their development. Basing on their complex nature, a promising therapeutical approach could be represented by the so-called "Multi-Target-Directed Ligand" approach. This new strategy is based on the assumption that a single molecule could hit several targets responsible for the onset and/or progression of the pathology. In particular in AD, most currently prescribed drugs aim to increase the level of acetylcholine in the brain by inhibiting the enzyme acetylcholinesterase (AChE). However, clinical experience shows that AChE inhibition is a palliative treatment, and the simple modulation of a single target does not address AD aetiology. Research into newer and more potent anti-AD agents is thus focused on compounds whose properties go beyond AChE inhibition (such as inhibition of the enzyme β-secretase and inhibition of the aggregation of beta-amyloid). Therefore, the MTDL strategy seems a more appropriate approach for addressing the complexity of AD and may provide new drugs for tackling its multifactorial nature. In this thesis, it is described the design of new MTDLs able to tackle the multifactorial nature of AD. Such new MTDLs designed are less flexible analogues of Caproctamine, one of the first MTDL owing biological properties useful for the AD treatment. These new compounds are able to inhibit the enzymes AChE, beta-secretase and to inhibit both AChE-induced and self-induced beta-amyloid aggregation. In particular, the most potent compound of the series is able to inhibit AChE in subnanomolar range, to inhibit β-secretase in micromolar concentration and to inhibit both AChE-induced and self-induced beta-amyloid aggregation in micromolar concentration. Cancer, as AD, is a very complex pathology and many different therapeutical approaches are currently use for the treatment of such pathology. However, due to its multifactorial nature the MTDL approach could be, in principle, apply also to this pathology. Aim of this thesis has been the development of new molecules owing different structural motifs able to simultaneously interact with some of the multitude of targets responsible for the pathology. The designed compounds displayed cytotoxic activity in different cancer cell lines. In particular, the most potent compounds of the series have been further evaluated and they were able to bind DNA resulting 100-fold more potent than the reference compound Mitonafide. Furthermore, these compounds were able to trigger apoptosis through caspases activation and to inhibit PIN1 (preliminary result). This last protein is a very promising target because it is overexpressed in many human cancers, it functions as critical catalyst for multiple oncogenic pathways and in several cancer cell lines depletion of PIN1 determines arrest of mitosis followed by apoptosis induction. In conclusion, this study may represent a promising starting pint for the development of new MTDLs hopefully useful for cancer and AD treatment.

Substructuring approache in state space models for dynamic system parameters identification

In the recent decade, the request for structural health monitoring expertise increased exponentially in the United States. The aging issues that most of the transportation structures are experiencing can put in serious jeopardy the economic system of a region as well as of a country. At the same time, the monitoring of structures is a central topic of discussion in Europe, where the preservation of historical buildings has been addressed over the last four centuries. More recently, various concerns arose about security performance of civil structures after tragic events such the 9/11 or the 2011 Japan earthquake: engineers looks for a design able to resist exceptional loadings due to earthquakes, hurricanes and terrorist attacks. After events of such a kind, the assessment of the remaining life of the structure is at least as important as the initial performance design. Consequently, it appears very clear that the introduction of reliable and accessible damage assessment techniques is crucial for the localization of issues and for a correct and immediate rehabilitation. The System Identification is a branch of the more general Control Theory. In Civil Engineering, this field addresses the techniques needed to find mechanical characteristics as the stiffness or the mass starting from the signals captured by sensors. The objective of the Dynamic Structural Identification (DSI) is to define, starting from experimental measurements, the modal fundamental parameters of a generic structure in order to characterize, via a mathematical model, the dynamic behavior. The knowledge of these parameters is helpful in the Model Updating procedure, that permits to define corrected theoretical models through experimental validation. The main aim of this technique is to minimize the differences between the theoretical model results and in situ measurements of dynamic data. Therefore, the new model becomes a very effective control practice when it comes to rehabilitation of structures or damage assessment. The instrumentation of a whole structure is an unfeasible procedure sometimes because of the high cost involved or, sometimes, because it’s not possible to physically reach each point of the structure. Therefore, numerous scholars have been trying to address this problem. In general two are the main involved methods. Since the limited number of sensors, in a first case, it’s possible to gather time histories only for some locations, then to move the instruments to another location and replay the procedure. Otherwise, if the number of sensors is enough and the structure does not present a complicate geometry, it’s usually sufficient to detect only the principal first modes. This two problems are well presented in the works of Balsamo [1] for the application to a simple system and Jun [2] for the analysis of system with a limited number of sensors. Once the system identification has been carried, it is possible to access the actual system characteristics. A frequent practice is to create an updated FEM model and assess whether the structure fulfills or not the requested functions. Once again the objective of this work is to present a general methodology to analyze big structure using a limited number of instrumentation and at the same time, obtaining the most information about an identified structure without recalling methodologies of difficult interpretation. A general framework of the state space identification procedure via OKID/ERA algorithm is developed and implemented in Matlab. Then, some simple examples are proposed to highlight the principal characteristics and advantage of this methodology. A new algebraic manipulation for a prolific use of substructuring results is developed and implemented.