Digital Supply Chain Management in the Tourism and Hospitality Industry: Trends and Prospects

Abstract— The article discusses the basics of the digital supply chain management, its characteristic features and scope of application are covered. The analysis of a condition of development of digital supply chain management in the world reflecting growth of its share in gross domestic product, and the Russian Federation for which characteristic is the state initiative of advance and development of digital processes, but not business structures is carried out. The role of the Internet of things within digitalization of supply chain management is considered, in this regard the optimistic and conservative forecast of structure of the market of the Internet of things till 2025 is submitted. In work characteristic of information space of the sphere of tourism is given, the digital services and platforms which were widely adopted and succeeded offices of the tourist companies are described. The offered model of the digital platform for the sphere of tourism and hospitality of the Russian Federation «Tourism 4.0» with the description of the principles, characteristic of it, can be provided as a result of a combination of key technological capabilities, namely artificial intelligence, the Internet of things, robotization, voice technologies, a supply chain management. The presented «digital funnel» reflects the place of the tourism and hospitality industry among the participants in the digitalization market. Results of a research allowed to distinguish the factors constraining and keeping development of modern technologies in the industry, to reveal trends and regularities in the short term

Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions

<p>Abstract</p> <p>Background</p> <p>Reliable transcription factor binding site (TFBS) prediction methods are essential for computer annotation of large amount of genome sequence data. However, current methods to predict TFBSs are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.</p> <p>Results</p> <p>To improve this situation, we have quantified the performance of several Position Weight Matrix (PWM) algorithms, using exhaustive approaches to find their optimal length and position. We applied these approaches to bio-medically important TFBSs involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses (NF-κB, ISGF3, IRF1, STAT1), obesity and lipid metabolism (PPAR, SREBP, HNF4), regulation of the steroidogenic (SF-1) and cell cycle (E2F) genes expression. We have also gained extra specificity using a method, entitled SiteGA, which takes into account structural interactions within TFBS core and flanking regions, using a genetic algorithm (GA) with a discriminant function of locally positioned dinucleotide (LPD) frequencies.</p> <p>To ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized PWM and SiteGA have shown similar recognition performances. Then we applied SiteGA and optimized PWMs (both separately and together) to sequences in the Eukaryotic Promoter Database (EPD). The resulting SiteGA recognition models can now be used to search sequences for BSs using the web tool, SiteGA.</p> <p>Analysis of dependencies between close and distant LPDs revealed by SiteGA models has shown that the most significant correlations are between close LPDs, and are generally located in the core (footprint) region. A greater number of less significant correlations are mainly between distant LPDs, which spanned both core and flanking regions. When SiteGA and optimized PWM models were applied together, this substantially reduced false positives at least at higher stringencies.</p> <p>Conclusion</p> <p>Based on this analysis, SiteGA adds substantial specificity even to optimized PWMs and may be considered for large-scale genome analysis. It adds to the range of techniques available for TFBS prediction, and EPD analysis has led to a list of genes which appear to be regulated by the above TFs.</p

EBSD characterization of cryogenically rolled type 321 austenitic stainless steel

Electron backscatter diffraction was applied to investigate microstructure evolution during cryogenic rolling of type 321 metastable austenitic stainless steel. As expected, rolling promoted deformation-induced martensitic transformation which developed preferentially in deformation bands. Because a large fraction of the imposed strain was accommodated by deformation banding, grain refinement in the parent austenite phase was minimal. The martensitic transformation was found to follow a general orientation relationship, {111}γ||{0001}ε||{110}α′ and 〈110〉γ||〈11-20〉ε||〈111〉α′, and was characterized by noticeable variant selection