Model of Active Structural Monitoring and Decision-making for Dynamic Identification of Buildings, Monuments and Engineering Facilities

Structural monitoring and dynamic identification of the manmade and natural hazard objects is under consideration. Math model of testing object by set of weak stationary dynamic actions is offered. The response of structures to the set of signals is under processing for getting important information about object condition in high frequency band. Making decision procedure into active monitoring system is discussed as well. As an example the monitoring outcome of pillar-type monument is given.

A Conceptual Foresight Model to Investigate the Adoption of Radio Frequency Identification Technology in the English National Health Service

Radio Frequency Identification Technology (RFID) adoption in healthcare settings has the potential to reduce errors, improve patient safety, streamline operational processes and enable the sharing of information throughout supply chains. RFID adoption in the English NHS is limited to isolated pilot studies. Firstly, this study investigates the drivers and inhibitors to RFID adoption in the English NHS from the perspective of the GS1 Healthcare User Group (HUG) tasked with coordinating adoption across private and public sectors. Secondly a conceptual model has been developed and deployed, combining two of foresight’s most popular methods; scenario planning and technology roadmapping. The model addresses the weaknesses of each foresight technique as well as capitalizing on their individual, inherent strengths. Semi structured interviews, scenario planning workshops and a technology roadmapping exercise were conducted with the members of the HUG over an 18-month period. An action research mode of enquiry was utilized with a thematic analysis approach for the identification and discussion of the drivers and inhibitors of RFID adoption. The results of the conceptual model are analysed in comparison to other similar models. There are implications for managers responsible for RFID adoption in both the NHS and its commercial partners, and for foresight practitioners. Managers can leverage the insights gained from identifying the drivers and inhibitors to RFID adoption by making efforts to influence the removal of inhibitors and supporting the continuation of the drivers. The academic contribution of this aspect of the thesis is in the field of RFID adoption in healthcare settings. Drivers and inhibitors to RFID adoption in the English NHS are compared to those found in other settings. The implication for technology foresight practitioners is a proof of concept of a model combining scenario planning and technology roadmapping using a novel process. The academic contribution to the field of technology foresight is the conceptual development of foresight model that combines two popular techniques and then a deployment of the conceptual foresight model in a healthcare setting exploring the future of RFID technology.

Use of Homeostatic Model Assessment Indexes for the Identification of Metabolic Syndrome and Insulin Resistance among Cuban-Americans: A Cross Sectional Study

Aim: to determine cut off points for The Homeostatic Model Assessment Index 1 and 2 (HOMA-1 and HOMA-2) for identifying insulin resistance and metabolic syndrome among a Cuban-American population. Study Design: Cross sectional. Place and Duration of Study: Florida International University, Robert Stempel School of Public Health and Social Work, Department of Dietetics and Nutrition, Miami, FL from July 2010 to December 2011. Methodology: Subjects without diabetes residing in South Florida were enrolled (N=146, aged 37 to 83 years). The HOMA1-IR and HOMA2-IR 90th percentile in the healthy group (n=75) was used as the cut-off point for insulin resistance. A ROC curve was constructed to determine the cut-off point for metabolic syndrome. Results: HOMA1-IR was associated with BMI, central obesity, and triglycerides (P3.95 and >2.20 and for metabolic syndrome were >2.98 (63.4% sensitivity and 73.3% specificity) and >1.55 (60.6% sensitivity and 66.7% specificity), respectively. Conclusion: HOMA cut-off points may be used as a screening tool to identify insulin resistance and metabolic syndrome among Cuban-Americans living in South Florida.

Identification of blood pressure genes in the Dahl salt-sensitive hypertension model

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Identification of blood pressure genes in the Dahl salt-sensitive hypertension model

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Parameter Identification in a Tuberculosis Model for Cameroon

A deterministic model of tuberculosis in Cameroon is designed and analyzed with respect to its transmission dynamics. The model includes lack of access to treatment and weak diagnosis capacity as well as both frequency-and density-dependent transmissions. It is shown that the model is mathematically well-posed and epidemiologically reasonable. Solutions are non-negative and bounded whenever the initial values are non-negative. A sensitivity analysis of model parameters is performed and the most sensitive ones are identified by means of a state-of-the-art Gauss-Newton method. In particular, parameters representing the proportion of individuals having access to medical facilities are seen to have a large impact on the dynamics of the disease. The model predicts that a gradual increase of these parameters could significantly reduce the disease burden on the population within the next 15 years.

Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.

Modeling Alzheimer disease with iPSCs and mouse model for the identification of risk factors, new targets, and potential therapeutical strategies

Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly. Donepezil is the first-line drug used for AD. In section one, the experimental activity was oriented to evaluate and characterize molecular and cellular mechanisms that contribute to neurodegeneration induced by the Aβ1-42 oligomers (Aβ1-42O) and potential neuroprotective effects of the hybrids feruloyl-donepezil compound called PQM130. The effects of PQM130 were compared to donepezil in a murine AD model, obtained by intracerebroventricular (i.c.v.) injection of Aβ1-42O. The intraperitoneal administration of PQM130 (0.5-1 mg/kg) after i.c.v. Aβ1-42O injection improved learning and memory, protecting mice against spatial cognition decline. Moreover, it reduced oxidative stress, neuroinflammation and neuronal apoptosis, induced cell survival and protein synthesis in mice hippocampus. PQM130 modulated different pathways than donepezil, and it is more effective in counteracting Aβ1-42O damage. The section two of the experimental activity was focused on studying a loss of function variants of ABCA7. GWA studies identified mutations in the ABCA7 gene as a risk factor for AD. The mechanism through which ABCA7 contributes to AD is not clear. ABCA7 regulates lipid metabolism and critically controls phagocytic function. To investigate ABCA7 functions, CRISPR/Cas9 technology was used to engineer human iPSCs and to carry the genetic variant Y622*, which results in a premature stop codon, causing ABCA7 loss-of-function. From iPSCs, astrocytes were generated. This study revealed the effects of ABCA7 loss in astrocytes. ABCA7 Y622* mutation induced dysfunctional endocytic trafficking, impairing Aβ clearance, lipid dysregulation and cell homeostasis disruption, alterations that could contribute to AD. Though further studies are needed to confirm the PQM130 neuroprotective role and ABCA7 function in AD, the provided results showed a better understanding of AD pathophysiology, a new therapeutic approach to treat AD, and illustrated an innovative methodology for studying the disease.

The Effect of Spatial Scale on Predicting Time Series: A Study on Epidemiological System Identification

A susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice ) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented. Copyright (C) 2009 L. H. A. Monteiro et al.

System Identification and Prediction of Dengue Fever Incidence in Rio de Janeiro

Identification, prediction, and control of a system are engineering subjects, regardless of the nature of the system. Here, the temporal evolution of the number of individuals with dengue fever weekly recorded in the city of Rio de Janeiro, Brazil, during 2007, is used to identify SIS (susceptible-infective-susceptible) and SIR (susceptible-infective-removed) models formulated in terms of cellular automaton (CA). In the identification process, a genetic algorithm (GA) is utilized to find the probabilities of the state transition S -> I able of reproducing in the CA lattice the historical series of 2007. These probabilities depend on the number of infective neighbors. Time-varying and non-time-varying probabilities, three different sizes of lattices, and two kinds of coupling topology among the cells are taken into consideration. Then, these epidemiological models built by combining CA and GA are employed for predicting the cases of sick persons in 2008. Such models can be useful for forecasting and controlling the spreading of this infectious disease.

Identification of three distinguishable phenotypes in golden retriever muscular dystrophy

Duchenne muscular dystrophy (DMD) is a human disease characterized by progressive and irreversible skeletal muscle degeneration caused by mutations in genes coding for important muscle proteins. Unfortunately, there is no efficient treatment for this disease; it causes progressive loss of motor and muscular ability until death. The canine model (golden retriever muscular dystrophy) is similar to DMD, showing similar clinical signs. Fifteen dogs were followed from birth and closely observed for clinical signs. Dogs had their disease status confirmed by polymerase chain reaction analysis and genotyping. Clinical observations of musculoskeletal, morphological, gastrointestinal, respiratory, cardiovascular, and renal features allowed us to identify three distinguishable phenotypes in dystrophic dogs: mild (grade I), moderate (grade II) and severe (grade III). These three groups showed no difference in dystrophic alterations of muscle morphology and creatine kinase levels. This information will be useful for therapeutic trials, because DMD also shows significant, inter- and intra-familiar clinical variability. Additionally, being aware of phenotypic differences in this animal model is essential for correct interpretation and understanding of results obtained in pre-clinical trials.

A Lagrangian Identification of the Main Sources of Moisture Affecting Northeastern Brazil during Its Pre-Rainy and Rainy Seasons

This work examines the sources of moisture affecting the semi-arid Brazilian Northeast (NEB) during its pre-rainy and rainy season (JFMAM) through a Lagrangian diagnosis method. The FLEXPART model identifies the humidity contributions to the moisture budget over a region through the continuous computation of changes in the specific humidity along back or forward trajectories up to 10 days period. The numerical experiments were done for the period that spans between 2000 and 2004 and results were aggregated on a monthly basis. Results show that besides a minor local recycling component, the vast majority of moisture reaching NEB area is originated in the south Atlantic basin and that the nearby wet Amazon basin bears almost no impact. Moreover, although the maximum precipitation in the ""Poligono das Secas'' region (PS) occurs in March and the maximum precipitation associated with air parcels emanating from the South Atlantic towards PS is observed along January to March, the highest moisture contribution from this oceanic region occurs slightly later (April). A dynamical analysis suggests that the maximum precipitation observed in the PS sector does not coincide with the maximum moisture supply probably due to the combined effect of the Walker and Hadley cells in inhibiting the rising motions over the region in the months following April.

Respiratory allergy to Blomia tropicalis: Immune response in four syngeneic mouse strains and assessment of a low allergen-dose, short-term experimental model

Background: The dust mite Blomia tropicalis is an important source of aeroallergens in tropical areas. Although a mouse model for B. tropicalis extract (BtE)-induced asthma has been described, no study comparing different mouse strains in this asthma model has been reported. The relevance and reproducibility of experimental animal models of allergy depends on the genetic background of the animal, the molecular composition of the allergen and the experimental protocol. Objectives: This work had two objectives. The first was to study the anti-B. tropicalis allergic responses in different mouse strains using a short-term model of respiratory allergy to BtE. This study included the comparison of the allergic responses elicited by BtE with those elicited by ovalbumin in mice of the strain that responded better to BtE sensitization. The second objective was to investigate whether the best responder mouse strain could be used in an experimental model of allergy employing relatively low BtE doses. Methods: Groups of mice of four different syngeneic strains were sensitized subcutaneously with 100 mu g of BtE on days 0 and 7 and challenged four times intranasally, at days 8, 10, 12, and 14, with 10 mu g of BtE. A/J mice, that were the best responders to BtE sensitization, were used to compare the B. tropicalis-specific asthma experimental model with the conventional experimental model of ovalbumin (OVA)-specific asthma. A/J mice were also sensitized with a lower dose of BtE. Results: Mice of all strains had lung inflammatory-cell infiltration and increased levels of anti-BtE IgE antibodies, but these responses were significantly more intense in A/J mice than in CBA/J, BALB/c or C57BL/6J mice. Immunization of A/J mice with BtE induced a more intense airway eosinophil influx, higher levels of total IgE, similar airway hyperreactivity to methacholine but less intense mucous production, and lower levels of specific IgE, IgG1 and IgG2 antibodies than sensitization with OVA. Finally, immunization with a relatively low BtE dose (10 mu g per subcutaneous injection per mouse) was able to sensitize A/J mice, which were the best responders to high-dose BtE immunization, for the development of allergy-associated immune and lung inflammatory responses. Conclusions: The described short-term model of BtE-induced allergic lung disease is reproducible in different syngeneic mouse strains, and mice of the A/J strain was the most responsive to it. In addition, it was shown that OVA and BtE induce quantitatively different immune responses in A/J mice and that the experimental model can be set up with low amounts of BtE.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.