Fixed Points of Closed and Compact Composite Sequences of Operators and Projectors in a Class of Banach Spaces

Some results on fixed points related to the contractive compositions of bounded operators in a class of complete metric spaces which can be also considered as Banach's spaces are discussed through the paper. The class of composite operators under study can include, in particular, sequences of projection operators under, in general, oblique projective operators. In this paper we are concerned with composite operators which include sequences of pairs of contractive operators involving, in general, oblique projection operators. The results are generalized to sequences of, in general, nonconstant bounded closed operators which can have bounded, closed, and compact limit operators, such that the relevant composite sequences are also compact operators. It is proven that in both cases, Banach contraction principle guarantees the existence of unique fixed points under contractive conditions.

On a Class of Self-Adjoint Compact Operators in Hilbert Spaces and Their Relations with Their Finite-Range Truncations

This paper investigates a class of self-adjoint compact operators in Hilbert spaces related to their truncated versions with finite-dimensional ranges. The comparisons are established in terms of worst-case norm errors of the composite operators generated from iterated computations. Some boundedness properties of the worst-case norms of the errors in their respective fixed points in which they exist are also given. The iterated sequences are expanded in separable Hilbert spaces through the use of numerable orthonormal bases.

Gizarte erantzukizun eta garapen jasangarriaren inguruko ekimenak: Global Compact

[EU]Gradu Amaierako lan honetan, enpresen gizarte erantzukizunak azkenaldian hartu duen garrantzia kontuan hartuta, kontzeptu hau jorratuko da eta eremu honetan burututako ekimen ezberdinen artean bat aukeratuko da, honen garapena eta inplementazioa aztertzeko. Hain zuzen ere, lan honetan erabiliko den ekimena, Munduko Hitzarmena edo Global Compact-a izango da. Hau, nazioarte mailako ekimen bat izanik, 10 printzipio proposatzen ditu, lau eremu ezberdinetan banatuz: Giza eskubideak, lan-arauak, ingurumena eta ustelkeriaren kontrako borroka. Bukatzeko, Global Compact-a aplikatzen duten sektore ezberdinetako hiru enpresa aukeratuko dira eta bakoitza dimentsio bat aztertzeko erabilia izango da, soziala, ekonomikoa eta ingurumenekoa hurrenez hurren. Azkenik, lanean zehar aztertutakoa kontuan hartuta zein ondoriotara iritsi garen adieraziko da.

The Environmental, Social, Governance, and Financial Performance Effects on Companies that Adopt the United Nations Global Compact

This paper aims to investigate companies' environmental, social, governance (ESG), and financial implications of their commitment to the United Nations Global Compact (UNGC). The focus is placed on companies operating in the three countries with the highest number of UNGC participants: Spain, France, and Japan. The results clearly reveal that adoption of the UNGC often requires an organizational change that fosters stakeholder engagement, ultimately resulting in improvements in companies' ESG performance. Additionally, the results reveal that ESG performance has a significant impact on financial performance for companies that adopted the principles of the UNGC. These findings provide both non-financial and financial incentives to companies to commit to this voluntary corporate social responsibility (CSR) initiative, which will have important implications on companies' strategic management policies that aim to foster sustainable businesses and community development. Finally, the linkages between the UNGC-committed companies' ESG and financial performance may be influenced by geographical spread, mainly due to the appearance of differences in the institutional, societal, and cultural settings.

Structural Basis for Feed-Forward Transcriptional Regulation of Membrane Lipid Homeostasis in Staphylococcus aureus

11 p.

Generating cluster submodels from a multistage stochastic mixed integer optimization model using break stage

We present a scheme to generate clusters submodels with stage ordering from a (symmetric or a nonsymmetric one) multistage stochastic mixed integer optimization model using break stage. We consider a stochastic model in compact representation and MPS format with a known scenario tree. The cluster submodels are built by storing first the 0-1 the variables, stage by stage, and then the continuous ones, also stage by stage. A C++ experimental code has been implemented for reordering the stochastic model as well as the cluster decomposition after the relaxation of the non-anticipativiy constraints until the so-called breakstage. The computational experience shows better performance of the stage ordering in terms of elapsed time in a randomly generated testbed of multistage stochastic mixed integer problems.

Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance

[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and learning (estimating) such distributions when the metric on permutations is the Cayley distance. We propose new methods for both operations, whose performance is shown through several experiments. We also introduce novel procedures to count and randomly generate permutations at a given Cayley distance both with and without certain structural restrictions. An application in the field of biology is given to motivate the interest of this model.

Interface electronics for an RF resonance-based displacement sensor

6 p. Paper of the 17th Conference on Sensors and Their Applications held in Dubrovnik, Croatia. Sep 16-18, 2013

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification