Construction Of Reliable Models Research Articles

Knowledge-based performance-driven modeling of antenna structures

The importance of surrogate modeling techniques in the design of modern antenna systems has been continuously growing over the recent years. This phenomenon is a matter of practical necessity rather than simply a fashion. On the one hand, antenna design procedures rely on full-wave electromagnetic (EM) simulation tools. On the other hand, the computational costs incurred by repetitive EM analyses involved in solving common tasks (parameter tuning, uncertainty quantification, multi-criterial design, etc.), are often prohibitive. The replacement of full-wave simulations by fast surrogates may mitigate these issues; as a matter of fact, it is the only viable option for carrying out EM-driven design in many cases. Among available modeling approaches, data-driven surrogates are by far the most popular due to their accessibility and versatility. At the same time, a construction of reliable models is hindered by the curse of dimensionality, high nonlinearity of antenna characteristics, as well as broad ranges of parameters and operating conditions that the model has to cover to ensure its design utility. Recently proposed performance-driven modeling frameworks offer a workaround these issues by restricting the model domain to the parameter space regions that contain high-quality designs (w.r.t. the assumed performance metrics). However, the domain determination requires acquisition of a set of pre-optimized reference designs, which adds to the overall computational cost of the surrogate model setup in a significant manner. This work proposes a novel two-stage knowledge-based approach, where the confined domain is defined without using any reference designs. Instead, a preselected set of random observables is employed to establish an inverse regression model being a basis for domain determination of the final surrogate. Comprehensive numerical validation involving three antenna structures indicates that our methodology offers the computational benefits similar to those of the previous performance-driven methods while considerably reducing the initial setup cost, by a factor of sixty percent on the average, which has been achieved by exploiting the problem-specific knowledge.

Incorporating biological knowledge for construction of fuzzy networks of gene associations

Gene association networks have become one of the most important approaches to modelling of biological processes by means of gene expression data. According to the literature, co-expression-based methods are the main approaches to identification of gene association networks because such methods can identify gene expression patterns in a dataset and can determine relations among genes. These methods usually have two fundamental drawbacks. Firstly, they are dependent on quality of the input dataset for construction of reliable models because of the sensitivity to data noise. Secondly, these methods require that the user select a threshold to determine whether a relation is biologically relevant. Due to these shortcomings, such methods may ignore some relevant information.We present a novel fuzzy approach named FyNE (Fuzzy NEtworks) for modelling of gene association networks. FyNE has two fundamental features. Firstly, it can deal with data noise using a fuzzy-set-based protocol. Secondly, the proposed approach can incorporate prior biological knowledge into the modelling phase, through a fuzzy aggregation function. These features help to gain some insights into doubtful gene relations.The performance of FyNE was tested in four different experiments. Firstly, the improvement offered by FyNE over the results of a co-expression-based method in terms of identification of gene networks was demonstrated on different datasets from different organisms. Secondly, the results produced by FyNE showed its low sensitivity to noise data in a randomness experiment. Additionally, FyNE could infer gene networks with a biological structure in a topological analysis. Finally, the validity of our proposed method was confirmed by comparing its performance with that of some representative methods for identification of gene networks

Structural Variations and Solvent Structure of r(UGGGGU) Quadruplexes Stabilized by Sr2 + Ions

Guanine-rich sequences can, under appropriate conditions, adopt a distinctive, four-stranded, helical fold known as a G-quadruplex. Interest in quadruplex folds has grown in recent years as evidence of their biological relevance has accumulated from both sequence analysis and function-specific assays. The folds are unusually stable and their formation appears to require close management to maintain cell health; regulatory failure correlates with genomic instability and a number of cancer phenotypes. Biologically relevant quadruplex folds are anticipated to form transiently in mRNA and in single-stranded, unwound DNA. To elucidate factors, including bound solvent, that contribute to the stability of RNA quadruplexes, we examine, by X-ray crystallography and small-angle X-ray scattering, the structure of a previously reported tetramolecular quadruplex, UGGGGU stabilized by Sr2+ ions. Crystal forms of the octameric assembly formed by this sequence exhibit unusually strong diffraction and anomalous signal enabling the construction of reliable models to a resolution of 0.88Å. The solvent structure confirms hydration patterns reported for other nucleic acid helical conformations and provides support for the greater stability of RNA quadruplexes relative to DNA. Novel features detected in the octameric RNA assembly include a new crystal form, evidence of multiple conformations and structural variations in the 3′ U tetrad, including one that leads to the formation of a hydrated internal cavity.

A data- and ontology-driven text mining-based construction of reliability model to analyze and predict component failures

A real-life reliability system is proposed by fusing the field warranty failure data with the failure modes extracted from unstructured repair verbatim data by using the ontology-based natural lang...

Image-based vs. mesh-based statistical appearance models of the human femur: Implications for finite element simulations

Statistical appearance models have recently been introduced in bone mechanics to investigate bone geometry and mechanical properties in population studies. The establishment of accurate anatomical correspondences is a critical aspect for the construction of reliable models. Depending on the representation of a bone as an image or a mesh, correspondences are detected using image registration or mesh morphing. The objective of this study was to compare image-based and mesh-based statistical appearance models of the femur for finite element (FE) simulations. To this aim, (i) we compared correspondence detection methods on bone surface and in bone volume; (ii) we created an image-based and a mesh-based statistical appearance models from 130 images, which we validated using compactness, representation and generalization, and we analyzed the FE results on 50 recreated bones vs. original bones; (iii) we created 1000 new instances, and we compared the quality of the FE meshes. Results showed that the image-based approach was more accurate in volume correspondence detection and quality of FE meshes, whereas the mesh-based approach was more accurate for surface correspondence detection and model compactness. Based on our results, we recommend the use of image-based statistical appearance models for FE simulations of the femur.

Radiocarbon dating versus volcanic event stratigraphy: Age modelling of Quaternary marine sequences in the coastal region of the Eastern Tyrrhenian Sea

We present the results of an integrated stratigraphic study conducted on eight marine sediment cores collected in the Naples and Salerno Bays, Eastern Tyrrhenian Sea. The aim of the study is the understanding of the timing and the impacts on the sedimentary environment of explosive volcanic events that occurred over the coastal zone of the Campania region during the latest Quaternary. Accurate dating and correlation have been essential for the construction of reliable models of the sediment architecture and influx rates in this area, as well as for establishing the links between changes in sedimentation and palaeoenvironmental events. While in case of deep marine sequences distal ash layers usually offer a significant potential for accurate geochronology, in the case of shallow marine sequences (continental shelf to upper slope) radiocarbon dating revealed to be essential in order to refine correlation among clusters of marine tephra layers with the equivalent proximal pyroclastic deposit onland, particularly where depositional rates are high and the chemistry and age of tephra layers display little variability. The integrated use of AMS 14 C dating on marine materials and the tephrostratigraphic approach based on reconstructions of historical volcanic events is fundamental in order to minimize the uncertainties that affect chronologic constraints derived from radiocarbon-based age models.