University of Oviedo

About: University of Oviedo is a education organization based out in Oviedo, Spain. It is known for research contribution in the topics: Population & Catalysis. The organization has 13423 authors who have published 31649 publications receiving 844799 citations. The organization is also known as: Universidá d'Uviéu & Universidad de Oviedo.

Environmental management system certification and its influence on corporate practices

Abstract: Purpose – The purpose of this paper is to analyze the existence of differences in the implementation of environmental practices between companies that possess some form of certified environmental management system (ISO 14001 or EMS) and those that do not have any such system. This study also investigates whether companies with a certified EMS are also making additional environmental demands on their suppliers.Design/methodology/approach – An empirical study utilizing survey data from automotive supplier organizations was completed. A total of 157 in‐person interviews were conducted with managers of Spanish companies which are automotive supplier organizations. Logistic regressions and non‐parametric tests are used to evaluate hypotheses.Findings – A positive relation was found to exist between the possession of certified EMS, specifically ISO 14001 and eco‐management and audit scheme, and the environmental demands that these organizations impose on their suppliers. This finding implies that environmental ...

Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes

Abstract: The poor correlation of mutational landscapes with phenotypes limits our understanding of the pathogenesis and metastasis of pancreatic ductal adenocarcinoma (PDAC). Here we show that oncogenic dosage-variation has a critical role in PDAC biology and phenotypic diversification. We find an increase in gene dosage of mutant KRAS in human PDAC precursors, which drives both early tumorigenesis and metastasis and thus rationalizes early PDAC dissemination. To overcome the limitations posed to gene dosage studies by the stromal richness of PDAC, we have developed large cell culture resources of metastatic mouse PDAC. Integration of cell culture genomes, transcriptomes and tumour phenotypes with functional studies and human data reveals additional widespread effects of oncogenic dosage variation on cell morphology and plasticity, histopathology and clinical outcome, with the highest KrasMUT levels underlying aggressive undifferentiated phenotypes. We also identify alternative oncogenic gains (Myc, Yap1 or Nfkb2), which collaborate with heterozygous KrasMUT in driving tumorigenesis, but have lower metastatic potential. Mechanistically, different oncogenic gains and dosages evolve along distinct evolutionary routes, licensed by defined allelic states and/or combinations of hallmark tumour suppressor alterations (Cdkn2a, Trp53, Tgfβ-pathway). Thus, evolutionary constraints and contingencies direct oncogenic dosage gain and variation along defined routes to drive the early progression of PDAC and shape its downstream biology. Our study uncovers universal principles of Ras-driven oncogenesis that have potential relevance beyond pancreatic cancer.

Support Vector Machines Used to Estimate the Battery State of Charge

Abstract: The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO4) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.