130,541 research outputs found
Digital HRM and Electronic HRM: which relation? A conceptual clarification with insights into sustainability through the PRISMA protocol
No full textThe phenomenon of digitalization started a long time ago and in a short time it has entered
companies by evolving their areas, including the HRM. Nowadays, there is more and more
talk of the twin transition, i.e. how to become more digital and sustainable. Nevertheless,
literature on the topic is in its infancy and suffers from some gaps. This article therefore aims
to fill them by providing a systematization of the literature on HRM digitalization, identifying
the relationship between d-HRM and e-HRM, and highlighting how this literature addresses
the issue of corporate sustainability. Following the PRISMA protocol, a systematic literature
review supported by some bibliometric analyses was developed to answer the research
questions fully and exhaustively. This article aims to offer an interpretative framework to
organizations and practitioners to better manage the digital transition and its links with
sustainability. Some suggestions for future research are also provided
Vanillin production using metabolically engineered <it>Escherichia coli </it>under non-growing conditions
No full textAbstract Background Vanillin is one of the most important aromatic flavour compounds used in the food and cosmetic industries. Natural vanillin is extracted from vanilla beans and is relatively expensive. Moreover, the consumer demand for natural vanillin highly exceeds the amount of vanillin extracted by plant sources. This has led to the investigation of other routes to obtain this flavour such as the biotechnological production from ferulic acid. Studies concerning the use of engineered recombinant Escherichia coli cells as biocatalysts for vanillin production are described in the literature, but yield optimization and biotransformation conditions have not been investigated in details. Results Effect of plasmid copy number in metabolic engineering of E. coli for the synthesis of vanillin has been evaluated by the use of genes encoding feruloyl-CoA synthetase and feruloyl hydratase/aldolase from Pseudomonas fluorescens BF13. The higher vanillin production yield was obtained using resting cells of E. coli strain JM109 harbouring a low-copy number vector and a promoter exhibiting a low activity to drive the expression of the catabolic genes. Optimization of the bioconversion of ferulic acid to vanillin was accomplished by a response surface methodology. The experimental conditions that allowed us to obtain high values for response functions were 3.3 mM ferulic acid and 4.5 g/L of biomass, with a yield of 70.6% and specific productivity of 5.9 μmoles/g × min after 3 hours of incubation. The final concentration of vanillin in the medium was increased up to 3.5 mM after a 6-hour incubation by sequential spiking of 1.1 mM ferulic acid. The resting cells could be reused up to four times maintaining the production yield levels over 50%, thus increasing three times the vanillin obtained per gram of biomass. Conclusion Ferulic acid can be efficiently converted to vanillin, without accumulation of undesirable vanillin reduction/oxidation products, using E. coli JM109 cells expressing genes from the ferulic acid-degrader Pseudomonas fluorescens BF13. Optimization of culture conditions and bioconversion parameters, together with the reuse of the biomass, leaded to a final production of 2.52 g of vanillin per liter of culture, which is the highest found in the literature for recombinant strains and the highest achieved so far applying such strains under resting cells conditions.</p
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Forecasting the solar cycle 25 using a multistep Bayesian neural network
No full textABSTRACT
Predicting the solar activity of upcoming cycles is crucial nowadays to anticipate potentially adverse space weather effects on the Earth’s environment produced by coronal transients and traveling interplanetary disturbances. The latest advances in deep learning techniques provide new paradigms to obtain effective prediction models that allow to forecast in detail the evolution of cosmogeophysical time series. Because of the underlying complexity of the dynamo mechanism in the solar interior that is at the origin of the solar cycle phenomenon, the predictions offered by state-of-the-art machine learning algorithms represent valuable tools for our understanding of the cycle progression. As a plus, Bayesian deep learning is particularly compelling thanks to recent advances in the field that provide improvements in both accuracy and uncertainty quantification compared to classical techniques. In this work, a deep learning long short-term memory model is employed to predict the complete profile of Solar Cycle 25, thus forecasting also the advent of the next solar minimum. A rigorous uncertainty estimation of the predicted sunspot number is obtained by applying a Bayesian approach. Two different model validation techniques, namely the Train-Test split and the time series k-fold cross-validation, have been implemented and compared, giving compatible results. The forecasted peak amplitude is lower than that of the preceding cycle. Solar Cycle 25 will last 10.6 ± 0.7 yr, reaching its maximum in the middle of the year 2024. The next solar minimum is predicted in 2030 and will be as deep as the previous one.</jats:p
Bioconversion of ferulate into vanillin by Escherichia coli strain JM109/pBB1 in an immobilized-cell reactor
No full textThe present work deals with a novel bioconversion of ferulate into vanillin using resting cells of Escherichia coli strain JM/109pBB1 as a biocatalyst. Biomass recycling from four successive bioconversion steps demonstrated the possibility of employing the proposed resting cell system for the continuos production of vanillin. Among the tested immobilization supports (polyurethane, synthetic sponge and porous glass)the synthetic sponge proved to be the best material in terms of both vanillin formation (Cv=0.080 g/l) and productivity (Qv=0.019 g/lh) at the end of entrapment tests. Thus, it was used in preliminary continuos tests using a fixed-bed column with immobilized E. coli JM/109pBB1 cells. The highest vanillin yield (YP/S=0.851 mol/mol) was obtained at a dilution rate of 0.022 1/h
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