Participation of Calamagrostis epigejos (L.) Roth in plant communities of the River Bytomka valley in terms of its biomass use in the power industry

Abandonment of human management is often followed by a decrease in species richness in semi-natural grasslands, mainly due to the increased dominance of clonal grasses such as Calamagrostis epigejos which were formerly repressed by management. The biomass resource of this, and its accompanying, species, i.e. species of the Solidago genus and others e.g. Cirsium rivulare, Deschampsia caespitosa, Molinia coerulea and Filipendula ulmaria, was evaluated in the green wastelands of the River Bytomka valley (Upper Silesia, Poland). It was found that approx. 1.2 t.ha-1 of dry matter can be obtained from approx. 30% of the average share of Calamagrostis epigejos in plant communities of unmown meadows. This is 10 times less than in the case of Miscanthus giganteus, a non-native cultivated grass. An increase in the biomass component of Calamagrostis epigejos reduced that of Solidago sp. (-0.522176, p< 0.05) and other species (-0.465806, p< 0.05). The calorific value of Calamagrostis epigejos biomass is approx. 15.91 MJ.kg-1, which is comparable to the calorific value of coal and close to, inter alia, that of Miscanthus sacchariflorus (19 MJ.kg-1) as an energy crop. The presented research is in its preliminary stages and therefore, it is necessary to investigate the reaction of Calamagrostis epigejos to regular mowing and to removal of the biomass from the studied areas

Requirements Engineering for Well-Being, Aging, and Health:An Overview for Practitioners

Well-Being, Aging, and Health (WBAH) are important aspects of life that affect us all. The requirements for WBAH systems have also become a topic of common interest for researchers from different disciplines. This is unsurprising, given that health-related expenses often represent about 10% of a country's gross domestic product, according to the World Health Organizatio

Participation of Calamagrostis epigejos (L.) Roth in plant communities of the River Bytomka valley in terms of its biomass use in the power industry

This paper presents an attempt to assess the potential use of Calamagrostis epigejos (L.) Roth. as a renewable energy source. Abandonment of human management is often followed by a decrease in species richness in semi-natural grasslands, mainly due to the increased dominance of clonal grasses such as Calamagrostis epigejos which were formerly repressed by management. The biomass resource of this, and its accompanying, species, i.e. species of the Solidago genus and others e.g. Cirsium rivulare, Deschampsia caespitosa, Molinia coerulea and Filipendula ulmaria, was evaluated in the green wastelands of the River Bytomka valley (Upper Silesia, Poland). It was found that approx. 1.2 t·ha−1 of dry matter can be obtained from approx. 30% of the average share of Calamagrostis epigejos in plant communities of unmown meadows. This is 10 times less than in the case of Miscanthus giganteus, a non-native cultivated grass. An increase in the biomass component of Calamagrostis epigejos reduced that of Solidago sp. (−0.522176, p< 0.05) and other species (−0.465806, p< 0.05). The calorific value of Calamagrostis epigejos biomass is approx. 15.91 MJ·kg−1, which is comparable to the calorific value of coal and close to, inter alia, that of Miscanthus sacchariflorus (19 MJ·kg−1) as an energy crop. The presented research is in its preliminary stages and therefore, it is necessary to investigate the reaction of Calamagrostis epigejos to regular mowing and to removal of the biomass from the studied areas

Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names

Context: COSMIC is a widely used functional size measurement (FSM) method that supports software development effort estimation. The FSM methods measure functional product size based on functional requirements. Unfortunately, when the description of the product\u27s functionality is often abstract or incomplete, the size of the product can only be approximated since the object to be measured is not yet fully described. Also, the measurement performed by human-experts can be time-consuming, therefore, it is worth considering automating it. Objective: Our objective is to design a new prediction model capable of approximating COSMIC-size of use cases based only on their names that is easier to train and more accurate than existing techniques. Method: Several neural-network architectures are investigated to build a COSMIC size approximation model. The accuracy of models is evaluated in a simulation study on the dataset of 437 use cases from 27 software development projects in the Management Information Systems (MIS) domain. The accuracy of the models is compared with the Average Use-Case approximation (AUC), and two recently proposed two-step models—Average Use-Case Goal-aware Approximation (AUCG) and Bayesian Network Use-Case Goal AproxImatioN (BN-UCGAIN). Results: The best prediction accuracy was obtained for a convolutional neural network using a word-embedding model trained on Wikipedia+Gigaworld. The accuracy of the model outperformed the baseline AUC model by ca. 20%, and the two-step models by ca. 5–7%. In the worst case, the improvement in the prediction accuracy is visible after estimating 10 use cases. Conclusions: The proposed deep learning model can be used to automatically approximate COSMIC size of software applications for which the requirements are documented in the form of use cases (or at least in the form of use-case names). The advantage of the model is that it does not require collecting historical data other than COSMIC size and names of use cases