Driven by digital solutions, the bioeconomy is taking major steps forward in recent years toward achievement of the long-lasting goal of transition from a traditional fossil economy to a bioeconomy-based circular economy.The coupling of digitalization and bioeconomy is leading towards a digitalized bioeconomy that can satisfy the shift in consumers' preferences for eco-consciousness, which in turn induces coupling of up-down stream operation in the value chain.Thus, the co-evolution of the coupling of digitalization and bioeconomy and of upstream and downstream operations is transforming the forest-based bioeconomy into a digital platform industry.Aiming at addressing this transformation, a model was developed that explains above mentioned dynamism and demonstrated its reliability through an empirical analysis focusing on the development trajectory of UPM (forest-based ecosystem leader in Europe and a world pioneer in the circular economy) over the last quarter century, highlighting its efforts towards planned obsolescence-driven circular economy.It was comprehended that with the advancement of digital innovations, UPM has incorporated a self-propagating function that accelerates digital solution. Furthermore, this self-propagating function was triggered by coupling with a downstream leader, Amazon, in the United States.The dynamism in transforming a forest-based bioeconomy into a digital platform industry is thus clarified, and new insights common to all industries in the digital economy are provided.

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On the microblogging site Twitter, users can forward any message they receive to all of their followers. This is called a retweet and is usually done when users find a message particularly interesting and worth sharing with others. Thus, retweets reflect what the Twitter community considers interesting on a global scale, and can be used as a function of interestingness to generate a model to describe the content-based characteristics of retweets. In this paper, we analyze a set of high-and low-level content-based features on several large collections of Twitter messages. We train a prediction model to forecast for a given tweet its likelihood of being retweeted based on its contents. From the parameters learned by the model we deduce what are the influential content features that contribute to the likelihood of a retweet. As a result we obtain insights into what makes a message on Twitter worth retweeting and, thus, interesting.

A comparison of 10 most popular Multiple Sequence Alignment (MSA) tools, namely, MUSCLE, MAFFT(L-INS-i), MAFFT (FFT-NS-2), T-Coffee, ProbCons, SATe, Clustal Omega, Kalign, Multalin, and Dialign-TX is presented. We also focused on the significance of some implementations embedded in algorithm of each tool. Based on 10 simulated trees of different number of taxa generated by R, 400 known alignments and sequence files were constructed using indel-Seq-Gen. A total of 4000 test alignments were generated to study the effect of sequence length, indel size, deletion rate, and insertion rate. Results showed that alignment quality was highly dependent on the number of deletions and insertions in the sequences and that the sequence length and indel size had a weaker effect. Overall, ProbCons was consistently on the top of list of the evaluated MSA tools. SATe, being little less accurate, was 529.10% faster than ProbCons and 236.72% faster than MAFFT(L-INS-i). Among other tools, Kalign and MUSCLE achieved the highest sum of pairs. We also considered BALiBASE benchmark datasets and the results relative to BAliBASE- and indel-Seq-Gen-generated alignments were consistent in the most cases.

Digital solutions transform the forest-based bioeconomy into a digital platform industry-A suggestion for a disruptive business model in the digital economy,