Search

In this paper, the notion of sign-consensus for the Lipschitz nonlinear multi-agents over a signed graph is introduced and controller design conditions are provided for realizing consensus among signs of states of agents. The previous works on sign-consensus are limited to the linear systems only. The attainment of sign-consensus for the nonlinear multi-agent systems is a non-trivial control problem, for which Lipschitz nonlinear dynamics are reformulated using the linear parameter varying (LPV) transformation. To guarantee the sign-consensus in a network of multiple agents, fully-distributed adaptive protocol is applied using reformulated Lipschitz nonlinear dynamics, and two design conditions are provided based on the Lipschitz constant oriented and LPV-based approaches. The first approach is comparatively less computationally complex while LPV approach holds significance for developing less conservative sign-consensus control scheme. To the best of our knowledge, distributed sign consensus methods for Lipschitz systems, based on LPV formulation, have been analyzed for the first time. The proposed approach is adaptive and can be regarded as fully-distributed, as it does not require the central knowledge of graph properties. Further, results are extended for nonlinear systems undergoing disturbances and ultimate boundedness of sign-consensus error is guaranteed. Numerical simulations on groups of Chua’s circuits and mechanical oscillators are presented to validate the results.

PurposeAmidst the plethora of mass communication methods that technology bestowed business with, print advertisements still remain an effective and widely utilized advertising tool, and retain a diachronically venerable position in international marketing practice. Bar and transcending mere academic fascination or curiosity, this research provides insights into the past, an understanding of the present and an outlook into the future. In this vein, through a methodical and comprehensive critical review of extant literature on print advertisements since 1965, this research aims to identify gaps in extant knowledge, to map its trends and divergences, to trace its paradigm shifts and to ultimately develop agendas for truly significant future research.Design/methodology/approachThis spatial-temporal study reviews 256 methodically selected articles, using VantagePoint software, and adopts a novel methodology through natural language processing (NLP), text mining, auto-correlation maps, and bubble maps to conduct and present a robust analysis and explicit findings.FindingsUsing also the VOSviewer for density and network visualization, the results identify the predominant literature themes and, conversely, the relatively under-researched areas, and provide a more insightful collective interpretation of extant works, while laying the foundation for future research of greater value and significance to academia and industry.Originality/valueThis study transcends the partial and/or limited analyses and perspectives of extant literature to present scholars with the first comprehensive and long term meta-analysis or systematic study of print advertising, with explicit findings of both scholarly and executive worth.

Here the idea of data driven is introduced in model predictive control to establish our proposed data-driven model predictive control. Considering one first-order discrete time nonlinear dynamical system, the main essence of data driven means the actual output value in cost function for model predictive control is identified through input–output observed data in case of unknown but bounded noise and martingale difference sequence. After substituting the identified actual output in cost function, the total cost function in model predictive control is reformulated as its standard form, i.e. one quadratic program problem with input and output constraints. Then semidefinite relaxation scheme is used to derive a lower bound for its optimal value, and the robust counterpart of an uncertain quadratic program is reduced to one conic quadratic problem. The above semidefinite relaxation scheme and conic quadratic problem correspond to the similar robust analysis based on convex optimization theory. Finally, one simulation example is used to prove the efficiency of our proposed theory.

Exploiting the link between land identification and farm accountancy data, we use a uniquely detailed database to conduct a robust analysis of land fragmentation and its effect on technical inefficiency on dairy farms in Ireland. Using a stochastic production frontier model, our results show that the number of parcels, the average distance between parcels and the main farm, and the portion of land separate from the main farm all increase technical inefficiency. Such inefficiency can be reduced through increased parcel area, reduced travel distances, advisory services contact, intensive practices and hired labour. Our findings support the need for policy to improve land and labour mobility, providing evidence to support incentives to promote the transfer and long‐term leasing of agricultural land.

Size is a fundamental cellular trait that is important in determining phytoplankton physiological and ecological processes. Fossil coccospheres, the external calcite structure produced by the excretion of interlocking plates by the phytoplankton coccolithophores, can provide a rare window into cell size in the past. Coccospheres are delicate however and are therefore poorly preserved in sediment. We demonstrate a novel technique combining imaging flow cytometry and cross-polarised light (ISX+PL) to rapidly and reliably visually isolate and quantify the morphological characteristics of coccospheres from marine sediment by exploiting their unique optical and morphological properties. Imaging flow cytometry combines the morphological information provided by microscopy with high sample numbers associated with flow cytometry. High throughput imaging overcomes the constraints of labour-intensive manual microscopy and allows statistically robust analysis of morphological features and coccosphere concentration despite low coccosphere concentrations in sediments. Applying this technique to the fine-fraction of sediments, hundreds of coccospheres can be visually isolated quickly with minimal sample preparation. This approach has the potential to enable rapid processing of down-core sediment records and/or high spatial coverage from surface sediments and may prove valuable in investigating the interplay between climate change and coccolithophore physiological/ecological response.

The concepts of technology fusion describe the phenomenon of technology overlap. Despite its importance, the evolutionary trajectories of technology fusion are not explored well. The main contributions of this article have two aspects: One is the mathematical modeling of a two-dimensional technology fusion system with technical efficiency and convergence parameters that can determine the evolution trajectories of technology fusion; the other is to reveal the dynamics of the evolution process through robust analysis and improving the stability of technology fusion under external disturbances. A novel technology fusion analysis method is proposed for technology fusion by applying dynamic development scenarios of manufacturing technology and information technology to be robust under external disturbances. In this article, firstly, we introduce technical efficiency and convergence parameters to establish a two-dimensional technology fusion system. Secondly, according to the characteristics of different fusion stages, we select the order parameters at each stage and establish order parameters function to determine the evolution trajectories of technology fusion. Finally, the fixed-point theory and the bifurcation theory are used to analyze the robustness of each stage, and the robust theorem at different technology fusion stages is proposed. The research results show: (1) We find that the characteristics analysis of technology fusion can divide the technology fusion process into three different stages, and the main characteristics of each stage as the order parameter can better reveal the evolution trajectory of technology fusion. (2) We find that the dynamics of technology fusion are caused by competition between the driving force and the dissipating force in the process of technology fusion. (3) We find that the dynamics of the technology fusion process may cause technology fusion to fall into chaos. (4) We find that by reasonably changing the relationship between the driving force and the dissipating force, the technology fusion process can be kept stable.

Optimal policies in Markov decision problems may be quite sensitive with regard to transition probabilities. In practice, some transition probabilities may be uncertain. The goals of the present study are to find the robust range for a certain optimal policy and to obtain value intervals of exact transition probabilities. Our research yields powerful contributions for Markov decision processes (MDPs) with uncertain transition probabilities. We first propose a method for estimating unknown transition probabilities based on maximum likelihood. Since the estimation may be far from accurate, and the highest expected total reward of the MDP may be sensitive to these transition probabilities, we analyze the robustness of an optimal policy and propose an approach for robust analysis. After giving the definition of a robust optimal policy with uncertain transition probabilities represented as sets of numbers, we formulate a model to obtain the optimal policy. Finally, we define the value intervals of the exact transition probabilities and construct models to determine the lower and upper bounds. Numerical examples are given to show the practicability of our methods.

This study is a literature review that aims to identify the reported relationship between energy poverty and the physical and mental health of older adults. To accomplish this, a qualitative review was done by examining papers from WOS and SCOPUS, looking for their definition of energy poverty and the main health issues reported. The papers reviewed define energy poverty mainly from an economic dimension and identify a high prevalence of respiratory, circulatory, cardiovascular and rheumatic diseases, along with feelings of loneliness and isolation that facilitate the development of mental disorders. It is necessary to expand the study of this topic in Latin America and the Caribbean, where there is little information to perform a robust analysis of this phenomenon and its effects on the well-being of the elderly.

Introduction: Total joint arthroplasty is projected to expand rapidly by 2030. With large numbers of patients undergoing TJA, the choice of incisional closure has come into question. We compared the 2-Ocyl cyanoacrylate closure system of Dermabond ® Prineo ® with Exofin Fusion ® to compare rates of adverse wound outcomes after total joint arthroplasty. Secondary outcome measures were age, sex, and medical comorbidities between groups.Methods: We retrospectively reviewed adverse wound outcomes with skin closure in TJA in 281 patients (160 Dermabond Prineo and 121 Exofin Fusion). Clinical charts were analyzed out to the 6-week post-op visit.Results: The rate of overall adverse superficial wound outcomes was similar between the two groups with Dermabond Prineo (N=20) and Exofin Fusion (N=19). The rate of cellulitis was significantly higher for Dermabond Prineo when compared to Exofin Fusion (P=0.033). No other significant differences were found for rate of superficial or deep wound complications or for secondary outcomes.Conclusions: The two 2-octyl wound closure systems had similar adverse superficial wound complications. Except for Dermabond Prineo having a higher rate of post-operative cellulitis, there were no statistically significant differences for other superficial or deep adverse wound outcomes or secondary outcomes. A future randomized control trial or prospective cohort study is needed for a more robust analysis.

Purpose To develop a fully automated vessel wall (VW) analysis workflow (fully automated and robust analysis technique for popliteal artery evaluation, FRAPPE) on the popliteal artery in standardized knee MR images. Methods Popliteal artery locations were detected from each MR slice by a deep neural network model and connected into a 3D artery centerline. Vessel wall regions around the centerline were then segmented using another neural network model for segmentation in polar coordinate system. Contours from vessel wall segmentations were used for vascular feature calculation, such as mean wall thickness and wall area. A transfer learning and active learning framework was applied in training the localization and segmentation neural network models to maintain accuracy while reducing manual annotations. This new popliteal artery analysis technique (FRAPPE) was validated against manual segmentation qualitatively and quantitatively in a series of 225 cases from the Osteoarthritis Initiative (OAI) dataset. Results FRAPPE demonstrated high accuracy and robustness in locating popliteal arteries, segmenting artery walls, and quantifying arterial features. Qualitative evaluations showed 1.2% of slices had noticeable major errors, including segmenting the wrong target and irregular vessel wall contours. The mean Dice similarity coefficient with manual segmentation was 0.79, which is comparable to inter-rater variations. Repeatability evaluations show most of the vascular features have good to excellent repeatability from repeated scans of same subjects, with intra-class coefficient ranging from 0.80 to 0.98. Conclusion This technique can be used in large population-based studies, such as OAI, to efficiently assess the burden of atherosclerosis from routine MR knee scans.

Purpose The purpose of this study is to propose an integrated model based on expectation-confirmation model (ECM) and technology acceptance model (TAM) to examine whether environmental, human, organizational and technology factors as the antecedents to physicians’ beliefs can affect their satisfaction and continuance intention of the cloud-based hospital information system (HIS). Design/methodology/approach Sample data for this study were collected from physicians at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 471 (78.5 per cent) usable questionnaires were analyzed using structural equation modeling in this study. Findings This study proposes a solid research model that is based on ECM and TAM, four types of factors, environmental factor, human factor, organizational factors and technology factors, as antecedents to physicians’ continuance intention of the cloud-based HIS have been examined. This study’s results strongly support the research model with all hypothesized links being significant, and control variables have no confounding effects on physicians’ continuance intention of the cloud-based HIS. Originality/value This study contributes to physicians’ continuance intention toward the cloud-based HIS based on ECM, TAM, technology-organization-environment framework and human-organization-technology fit framework, and reveals deep insights into the evaluation of determinants in the field of physicians’ continuance intention of the cloud-based HIS. Accordingly, it is particularly worth mentioning that IS and non-IS determinants are simultaneously evaluated and taken into consideration in this study’s theoretical development of physicians’ continuance intention of the cloud-based HIS to acquire a more comprehensive and robust analysis.

In this paper, a visual-detection (VD)-based fruit fly optimization algorithm (FOA) is proposed for solving a robust analysis problem of integrated energy systems (IES) with energy storage based on the information gap decision theory (IGDT). In the searching phase, the VD-based decision delay and visual feature detection are incorporated within the FOA. VD-FOA changes the search radius of fruit fly according to the variance of smell concentration, which can solve the problem that fruit fly optimization algorithm is easy to fall into local optimization, where standard test functions are also adopted to test the proposed algorithm. The proposed VD-FOA is superior to the basic FOA and is applied to solve the IGDT-based robust analysis problem of IES with energy storage. The simulation results show the applicability and effectiveness of the proposed algorithm.

In this paper, a robust analysis of SO 2 concentration measurements taken at the Belisario air quality monitoring station, Quito, Ecuador is carried out. The analyzed data contain 12 years of measurements, from January 2008 to December 2019. In addition, this set of measurements was decomposed into variables that represent each year, month, day of the week, and hour of the day in groups of two hours. For the analysis, classic, nonparametric and robust statistical methods were used, and the data were classified based on criteria established by the Quiteno Air Quality Index, taking confidence intervals into account. The results showed that the level of air pollution at the Belisario station due to the SO 2 concentration is acceptable. In addition, the trend in the level of SO 2 concentration decreased over the years studied, with a sharp drop from 2008 to 2012, then a small rise in 2013 and another fall until 2019, presenting decreasing oscillations that tend toward a desirable level of pollution. In this paper, it was shown that the air pollution at the Belisario station due to the concentration of SO 2 in the last 12 years is not harmful to humans, with the measurement precision provided by robust statistical methods. Therefore, it can be concluded that the measures that have been taken by the Quito city council over the last few years are yielding good results.

Eccrine sweat is a rich and largely unexplored biofluid that contains a range of important biomarkers, from electrolytes, metabolites, micronutrients and hormones to exogenous agents, each of which can change in concentration with diet, stress level, hydration status and physiologic or metabolic state. Traditionally, clinicians and researchers have used absorbent pads and benchtop analyzers to collect and analyze the biochemical constituents of sweat in controlled, laboratory settings. Recently reported wearable microfluidic and electrochemical sensing devices represent significant advances in this context, with capabilities for rapid, in situ evaluations, in many cases with improved repeatability and accuracy. A limitation is that assays performed in these platforms offer limited control of reaction kinetics and mixing of different reagents and samples. Here, we present a multi-layered microfluidic device platform with designs that eliminate these constraints, to enable integrated enzymatic assays with demonstrations of in situ analysis of the concentrations of ammonia and ethanol in microliter volumes of sweat. Careful characterization of the reaction kinetics and their optimization using statistical techniques yield robust analysis protocols. Human subject studies with sweat initiated by warm-water bathing highlight the operational features of these systems.

Electric vehicle presence is continually increasing following various incentives for environmental impact reduction. This increase will directly impact the distribution grids. The additional loading will result in a change in operating conditions. Both the voltage profile and the line loading will be affected requiring investments in grid reinforcement. Since charging stations can be activated in numerous combinations, it is important to detect the worst case in terms of grid impact. This paper presents a model for the worst-case analysis of charging station impact on the distribution grid. The presented model is validated on a representative case study.

In Performing Endurance, Lara Shalson builds a robust analysis of ways endurance manifests itself in artistic and political arenas. Examining protest sit-ins from the civil rights movement and influential performance art works by Yoko Ono, Marina Abramović, and Tehching Hsieh, this book establishes endurance as a significant form, worthy of a more nuanced scholarly engagement.

Background Patient-derived xenograft and cell line models are popular models for clinical cancer research. However, the inevitable inclusion of a mouse genome in a patient-derived model is a remaining concern in the analysis. Although multiple tools and filtering strategies have been developed to account for this, research has yet to demonstrate the exact impact of the mouse genome and the optimal use of these tools and filtering strategies in an analysis pipeline. Results We construct a benchmark dataset of 5 liver tissues from 3 mouse strains using human whole-exome sequencing kit. Next-generation sequencing reads from mouse tissues are mappable to 49% of the human genome and 409 cancer genes. In total, 1,207,556 mouse-specific alleles are aligned to the human genome reference, including 467,232 (38.7%) alleles with high sensitivity to contamination, which are pervasive causes of false cancer mutations in public databases and are signatures for predicting global contamination. Next, we assess the performance of 8 filtering methods in terms of mouse read filtration and reduction of mouse-specific alleles. All filtering tools generally perform well, although differences in algorithm strictness and efficiency of mouse allele removal are observed. Therefore, we develop a best practice pipeline that contains the estimation of contamination level, mouse read filtration, and variant filtration. Conclusions The inclusion of mouse cells in patient-derived models hinders genomic analysis and should be addressed carefully. Our suggested guidelines improve the robustness and maximize the utility of genomic analysis of these models.

Quantitative analysis of large-scale data is often complicated by the presence of diverse subgroups, which reduce the accuracy of inferences they make on held-out data. To address the challenge of heterogeneous data analysis, we propose a method that discovers latent confounders by simultaneously partitioning the data into overlapping clusters (disaggregation) and modeling the behavior within them (regression). When applied to real-world data, our method discovers meaningful clusters and their characteristic behaviors, thus giving insight into group differences and their impact on the outcome of interest. By accounting for latent confounders, our framework facilitates exploratory analysis of noisy, heterogeneous data and can be used to learn predictive models that better generalize to new data.

Metabolomics has been shown to be promising for diverse applications in basic, applied, and clinical research. These applications often require large-scale data, and while the technology to perform such experiments exists, downstream analysis remains challenging. Different tools exist in a variety of ecosystems, but they often do not scale to large data and are not integrated into a single coherent workflow. Moreover, the outcome of processing is very sensitive to a multitude of algorithmic parameters. Hence, parameter optimization is not only critical but also challenging. We present SLAW, a scalable and yet easy-to-use workflow for processing untargeted LC-MS data in metabolomics and lipidomics. The capabilities of SLAW include (1) state-of-the-art peak-picking algorithms, (2) a new automated parameter optimization routine, (3) an efficient sample alignment procedure, (4) gap filling by data recursion, and (5) the extraction of consolidated MS2 and an isotopic pattern across all samples. Importantly, both the workflow and the parameter optimization were designed for robust analysis of untargeted studies with thousands of individual LC-MSn runs. We compared SLAW to two state-of-the-art workflows based on openMS and XCMS. SLAW was able to detect and align more reproducible features in all data sets considered. SLAW scaled well, and its analysis of a data set with 2500 LC-MS files consumed 40% less memory and was 6 times faster than that using the XCMS-based workflow. SLAW also extracted 2-fold more isotopic patterns and MS2 spectra, which in 60% of the cases led to positive matches against a spectral library., Analytical Chemistry, 93 (45), ISSN:1520-6882, ISSN:0003-2700

“An excess of everything is bad”. This famous old proverb fits well with the current condition of Australian household debt that is continuously rising. Research in Australia’s household indebtedness is scarce and strategies to control the rising household debt remain contentious. The government of Australia has introduced financial literacy and financial capability measures to help control the rising household debt. Given that the literature highlights the importance of improving financial wellbeing, we analyse if financial wellbeing is a factor, which could be relevant to the reduced household debt. We use the Household, Income and Labour Dynamics in Australia panel survey in our analysis and find that improved financial wellbeing is associated with the reduced debt-taking behaviour of Australians. Our robust analysis confirms our findings. Finally, our empirical results suggest that improving households’ perception of their personal financial situation can bring improvement in their financial decisions, including the decision to take on debt.

Ideally, pharmacological analysis is based on quantitative measurements applied toward unveiling biological mechanisms and guiding medicinal chemistry efforts in drug discovery and basic research. The most robust analysis frameworks generate findings which can be dissociated from the specific experimental setting to provide insights of a broader scope. With insufficient efficacy being the leading cause of drug failures in late-stage clinical trials, more rigorous preclinical definitions might assist in better translation. This chapter details a new method for accessing the Black and Leff operational model of agonism using a stable Flp-In™ T-REx™ HEK293 cell line under tetracycline-dependent control of OTR expression. We cover steps for performing the Gq-coupled HTRF® IP-One assay, curve-fitting data, calculating and statistically representing system-independent drug activity, predicting responses in different sensitivities, and plotting of multivariate analyses.

Made available in DSpace on 2021-06-25T10:24:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Biotechnology and Biological Sciences Research Council Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Although reference genes have previously been used in the expression analysis of genes involved in sugarcane flowering they had not been experimentally validated for stability and consistency of expression between different samples over a wide range of experimental conditions. Here we report the analysis of candidate reference genes in different tissue types, at different temporal time-points, in both short and long day photoperiodic treatments. The stability of the candidate reference genes in all conditions was evaluated with NormFinder, BestKeeper, and RefFinder algorithms that complement each other for a more robust analysis. As the Normfinder algorithm was more appropriate for our experimental conditions, greater emphasis was placed on Normfinder when choosing the most stable genes. UBQ1 and TUB were shown to be the most stable reference genes to use for normalizing RT-qPCR gene expression data during floral induction, whilst 25SrRNA1 and GAPDH were the least stable. Their use as a reference gene pair was validated by analyzing the expression of two differentially expressed target genes (PIL5 and LHP1). The UBQ1/TUB reference genes combination was able to reveal small significant differences in gene expression of the two target genes that were not detectable when using the least stable reference gene combination. These results can be used to inform the choice of reference genes to use in the study of the sugarcane floral induction pathway. Our work also demonstrates that both PIL5 and LHP1 are significantly up-regulated in the initial stages of photoperiodic induction of flowering in sugarcane. Faculdade de Ciências Agrárias e Veterinárias UNESP, Campus Jaboticabal Universidade Estadual de Campinas UNICAMP Instituto de Ciência e Tecnologia da Universidade Federal de São Paulo Departamento de Biotecnologia Escola de Engenharia de Lorena/Universidade de São Paulo EEL/USP Instituto Agronômico de Campinas Centro de Cana School of Life Sciences University of Warwick, Gibbet Hill Faculdade de Ciências Agrárias e Veterinárias UNESP, Campus Jaboticabal FAPESP: 2016/02679-9 Biotechnology and Biological Sciences Research Council: 2018/08380-0 CAPES: Finance Code 001

Background An artificial intelligence vessel segmentation tool, Fully Automated and Robust Analysis Technique for Popliteal Artery Evaluation (FRAPPE), was used to analyze a large databank of popliteal arteries imaged through the OAI (Osteoarthritis Initiative) to study the impact of atherosclerosis risk factors on vessel dimensions and characterize remodeling patterns. Methods and Results Magnetic resonance images from 4668 subjects contributing 9189 popliteal arteries were analyzed using FRAPPE. Age ranged from 45 to 79 years (median, 61), and 58% were women. Mean lumen diameter, mean outer wall diameter, and mean wall thickness (MWT) were measured per artery. Their median values were 5.8 mm (interquartile range, 5.2–6.5 mm), 7.3 mm (interquartile range, 6.7–8.1 mm), and 0.78 mm (interquartile range, 0.73–0.84 mm) respectively. MWT was associated with multiple cardiovascular risk factors, with age (4.2% increase in MWT per 10‐year increase in age; 95% CI, 3.9%–4.5%) and sex (8.6% higher MWT in men than women; 95% CI, 7.7%–9.3%) being predominant. On average, lumen and outer wall diameters increased with increasing MWT until the thickness was 0.92 mm for men and 0.84 mm for women. After this point, lumen diameter decreased steadily, more rapidly in men than women (−7.9% versus −6.1% per 25% increase in MWT; P Conclusions FRAPPE has enabled the analysis of the large OAI knee magnetic resonance imaging data set, successfully showing that popliteal atherosclerosis is predominantly associated with age and sex. The average vessel remodeling pattern consisted of an early phase of compensatory enlargement, followed by a negative remodeling, which is more pronounced in men.

Orientador: Pedro Luis Dias Peres Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação Resumo: A principal contribuição desta tese é a proposta de uma metodologia para solução de desigualdades matriciais lineares dependentes de parâmetros que freqüentemente aparecem em problemas de análise e controle robusto de sistema lineares com incertezas na forma politópica. O método consiste na parametrização das soluções em termos de polinômios homogêneos com coeficientes matriciais de grau arbitrário. Para a construção dessas soluções, um procedimento baseado em resoluções de problemas de otimiza¸c¿ao na forma de um número finito de desigualdades matriciais lineares 'e proposto, resultando em seqüências de relaxações que convergem para uma solução polinomial homogênea sempre que uma solução existe. Problemas de análise robusta e custo garantido s¿ao analisados em detalhes tanto para sistemas a tempo contínuo quanto para sistemas discretos no tempo. Vários exemplos numéricos são apresentados ilustrando a eficiência dos métodos propostos em termos da acurácia dos resultados e do esforço computacional quando comparados com outros métodos da literatura Abstract: This thesis proposes, as main contribution, a new methodology to solve parameterdependent linear matrix inequalities which frequently appear in robust analysis and control problems of linear system with polytopic uncertainties. The proposed method relies on the parametrization of the solutions in terms of homogeneous polynomials of arbitrary degree with matrix valued coefficients. For constructing such solutions, a procedure based on optimization problems formulated in terms of a finite number of linear matrix inequalities is proposed, yielding sequences of relaxations which converge to a homogeneous polynomial solution whenever a solution exists. Problems of robust analysis and guaranteed costs are analyzed in details for continuous and discrete-time uncertain systems. Several numerical examples are presented illustrating the efficiency of the proposed methods in terms of accuracy and computational burden when compared to other methods from the literature Doutorado Automação Doutor em Engenharia Elétrica

In this article, we have deigned a new mechanism for the construction of confusion component which is one of the most important and integral part of any confidential scheme in secure communication. The privacy of digital information is one of the most vital issues of the digitally advanced world. The proposed nonlinear component which is usually termed as substitution box (S-box) is constructed by utilizing quantum map. Moreover, we have performed the robust analysis for our anticipated nonlinear component and compared it with already existing standards.

With plate-buckling problem still being an active area of research in the field of mechanics of materials, researchers need to validate the proposed solutions and theories, using robust analysis tools such like ANSYS, which is a robust finite element analysis tool. This article investigates modeling and buckling analysis of rectangular plates in ANSYS mechanical APDL through a series of comparative studies conducted on various models and options. Shell and solid models of homogeneous and functionally graded plate (FGP) using various elements are investigated. Expressing boundary conditions on both shell and solid models of plate for buckling analysis is discussed. Procedures of buckling analysis in ANSYS are presented and effects of meshing, boundary conditions and element options are examined though comparative studies. Results obtained from ANSYS are compared to various analytical solutions.

The significance of distributed data warehouses is to initiate the proliferation of various analytical applications. However, with the increase of ubiquitous devices, it is likely that massive volumes of data will be generated, which poses further problems based on the degradation of data quality. The practical reasons for the degradation of data quality in distributed warehouses are identified as heterogeneous data, uncertain inferior data which further affect predictions. The proposed system presents an integrated optimization model to address all the quality degradation problems and to provide a better computational model which effectively incorporates a higher degree of quality assurance. An analytical methodology is adopted in order to develop the proposed quality assurance model for distributed data warehouses.