Los municipios del bien común en España

El austriaco Christian Felber propone la Economía del Bien Común como una alternativa al actual modelo económico. Parte de la base de que toda economía debe buscar el beneficio de la sociedad en su conjunto en contraposición al beneficio individual que fomenta el capitalismo. El trabajo busca definir la forma en la que la Economía del Bien Común se practica en los llamados Municipios del Bien Común de España por ser la forma más pequeña de organización territorial, así como definir el concepto de Municipio del Bien Común y analizar si existen características comunes entre ellos y cuáles son. La investigación se lleva a cabo con ayuda de la obra del propio Felber y de fuentes de Internet. A pesar de la escasez de información recogida se llegan a conclusiones relacionadas con las características demográficas o las prácticas que llevan a cabo dichos municipios. También se hace un análisis más profundo sobre Miranda de Azán, por ser el primer municipio promotor del Bien Común del mundo dejando clara la ventaja en transparencia que tiene sobre otros municipios que no son del bien común.Universidad de Sevilla. Grado en Marketing e Investigación de Mercado

Two Compensation Strategies for Optimal Estimation in Sensor Networks with Random Matrices, Time-Correlated Noises, Deception Attacks and Packet Losses

Due to its great importance in several applied and theoretical fields, the signal estimation problem in multisensor systems has grown into a significant research area. Networked systems are known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance of the estimators substantially. Thus, the development of estimation algorithms accounting for these random phenomena has received a lot of research attention. In this paper, the centralized fusion linear estimation problem is discussed under the assumption that the sensor measurements are affected by random parameter matrices, perturbed by time-correlated additive noises, exposed to random deception attacks and subject to random packet dropouts during transmission. A covariance-based methodology and two compensation strategies based on measurement prediction are used to design recursive filtering and fixed-point smoothing algorithms. The measurement differencing method— typically used to deal with the measurement noise time-correlation—is unsuccessful for these kinds of systems with packet losses because some sensor measurements are randomly lost and, consequently, cannot be processed. Therefore, we adopt an alternative approach based on the direct estimation of the measurement noises and the innovation technique. The two proposed compensation scenarios are contrasted through a simulation example, in which the effect of the different uncertainties on the estimation accuracy is also evaluated.Ministerio de Ciencia e Innovacion, Agencia Estatal de InvestigacionEuropean Commission PID2021-124486NB-I0

Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks

This research was suported by the ``Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación'' of Spain and the European Regional Development Fund [grant number PID2021-124486NB-I00].Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.Ministerio de Ciencia e Innovación MICINNEuropean Regional Development Fund PID2021-124486NB-I00 ERDFAgencia Estatal de Investigación AE

A SOCIAL RECOMMENDATION MECHANISM FOR SOCIAL FUNDRAISING

In recent years, the world incurs many social issue and environmental disaster, so charity giving is become popular. Nowadays, the crowdfunding also become popular and the charity usually use specific type of crowdfunding called Peer-to-Peer fundraising. Many donor relationship management software and solutions have appeared. But they rarely utilize power of social network and majority of them focus on the aspect of fundraiser not on the aspect of donors. In this research, we will propose a social supported recommendation mechanism for non-profit fundraising. We will examine the donor preference, relationship between donor and fundraiser, and the characteristic fundraising dynamics to enhance the success rate of fundraising project and satisfaction rate of the donor

Endocrine disruption in Crohn’s disease: Bisphenol A enhances systemic inflammatory response in patients with gut barrier translocation of dysbiotic microbiota products

This study has been partially funded by PID2019-107036RB-I00, from Ministerio de Ciencia, Innovacion y Universidades, Madrid, Spain, and 2020-0287, from IIS ISABIAL, Hospital General Universitario, Alicante, Spain. RL is recipient of a grant (FPU 18/00063) by Ministerio de Ciencia, Innovacion y Universidades, Madrid, Spain.The relevance of environmental triggers in Crohn's disease remains poorly explored, despite the well-known association between industrialization and disease onset/progression. We have aimed at evaluating the influence of endocrine disrupting chemicals in CD patients. We performed a prospective observational study on consecutive patients diagnosed of CD. Serum levels of endocrine disruptors, short-chain fatty acids, tryptophan and cytokines were measured. Bacterial-DNA and serum endotoxin levels were also evaluated. Gene expression of ER-α, ER-β and GPER was measured in PBMCs. All patients were genotyped for NOD2 and ATG16L1 polymorphisms. A series of 200 CD patients (140 in remission, 60 with active disease) was included in the study. Bisphenol A was significantly higher in patients with active disease versus remission and in colonic versus ileal disease. GPER was significantly increased in active patients and correlated with BPA levels. BPA was significantly increased in patients with bacterial-DNA and correlated with serum endotoxin levels, (r = 0.417; P = .003). Serum butyrate and tryptophan levels were significantly lower in patients with bacterial-DNA and an inverse relationship was present between them and BPA levels (r = −0.491; P = .001) (r = −0.611; P = .001). Serum BPA levels correlated with IL-23 (r = 0.807; P = .001) and IL-17A (r = 0.743; P = .001). The multivariate analysis revealed an independent significant contribution of BPA and bacterial-DNA to serum levels of IL-23 and IL-17A. In conclusion, bisphenol A significantly affects systemic inflammatory response in CD patients with gut barrier disruption and dysbiotic microbiota secretory products in blood. These results provide evidence of an endocrine disruptor playing an actual pathogenic role on CD.Ministerio de Ciencia, Innovacion y Universidades, Madrid, Spain PID2019-107036RB-I00 FPU 18/00063IIS ISABIAL, Hospital General Universitario, Alicante, Spain 2020-028

Networked fusion estimation with multiple uncertainties and time-correlated channel noise

This paper is concerned with the fusion filtering and fixed-point smoothing problems for a class of networked systems with multiple random uncertainties in both the sensor outputs and the transmission connections. To deal with this kind of systems, random parameter matrices are considered in the mathematical models of both the sensor measurements and the data available after transmission. The additive noise in the transmission channel from each sensor is assumed to be sequentially time-correlated. By using the time-differencing approach, the available measurements are transformed into an equivalent set of observations that do not depend on the timecorrelated noise. The innovation approach is then applied to obtain recursive distributed and centralized fusion estimation algorithms for the filtering and fixed-point smoothing estimators of the signal based on the transformed measurements, which are equal to the estimators based on the original ones. The derivation of the algorithms does not require the knowledge of the signal evolution model, but only the mean and covariance functions of the processes involved (covariance information). A simulation example illustrates the utility and effectiveness of the proposed fusion estimation algorithms, as well as the applicability of the current model to deal with different network-induced random phenomena.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal

This paper focuses on the least-squares linear fusion filter design for discrete-time stochastic signals from multisensor measurements perturbed not only by additive noise, but also by different uncertainties that can be comprehensively modeled by random parameter matrices. The additive noises from the different sensors are assumed to be cross-correlated at the same time step and correlated with the signal at the same and subsequent time steps. A covariancebased approach is used to derive easily implementable recursive filtering algorithms under the centralized, distributed and sequential fusion architectures. Although centralized and sequential estimators both have the same accuracy, the evaluation of their computational complexity reveals that the sequential filter can provide a significant reduction of computational cost over the centralized one. The accuracy of the proposed fusion filters is explored by a simulation example, where observation matrices with random parameters are used to describe different kinds of sensor uncertainties.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER [grant number MTM2017- 84199-P]

Networked distributed fusion estimation under uncertain outputs with random transmission delays, packet losses and multi-packet processing

This paper investigates the distributed fusion estimation problem for networked systems whose mul- tisensor measured outputs involve uncertainties modelled by random parameter matrices. Each sensor transmits its measured outputs to a local processor over different communication channels and random failures –one-step delays and packet dropouts–are assumed to occur during the transmission. White sequences of Bernoulli random variables with different probabilities are introduced to describe the ob- servations that are used to update the estimators at each sampling time. Due to the transmission failures, each local processor may receive either one or two data packets, or even nothing and, when the current measurement does not arrive on time, its predictor is used in the design of the estimators to compensate the lack of updated information. By using an innovation approach, local least-squares linear estimators (filter and fixed-point smoother) are obtained at the individual local processors, without requiring the signal evolution model. From these local estimators, distributed fusion filtering and smoothing estimators weighted by matrices are obtained in a unified way, by applying the least-squares criterion. A simula- tion study is presented to examine the performance of the estimators and the influence that both sensor uncertainties and transmission failures have on the estimation accuracy.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)