The current state of the population of the golden potato cyst nematode Globodera rostochiensis (Nematoda, Heteroderidae) in the northwest of Ukraine

The golden potato cyst nematode (GPCN) Globodera rostochiensis (Wollenweber, 1923) Skarbilovich, 1959 is a highly specific parasite of the roots of the nightshade plants (Solanaceae). Thus, the state of the pest population demands constant monitoring and control of distribution and numbers. The distribution of G. rostochiensis in Volyn region of Ukraine was studied in 2008–2018 using the data of the state institution the Volyn Regional Phytosanitary Laboratory. The present article gives the analysis of the study results. The disease foci were detected by visual above-ground inspection of potato plantings, also by manually collecting soil samples before planting the potatoes and after harvesting, and consequently analyzing theme. The initial (pre-planting) and conclusive (after harvest) population density of GPCN in the soil was determined by the number of cysts and the mean number of larvae and eggs in cysts obtained from 100 cm3 of soil. Cysts were isolated from soil samples by the standard funnel flotation method. The dead and living larvae were identified visually by the shape of the body and the state of internal organs. According to the results of assessing pre-planting and post-harvest nematode numbers in soil, the reproduction coefficient Рf/Рі was calculated. In Volyn region, G. rostochiensis was first observed in 1968 on farmland and since then, the parasite has spread. Pest foci were recorded in 15 districts of the region in 303 settlements on the area of 946.123 hectares. It was however found that during the latest decade, the area of soils affected by G. rostochiensis in Volyn region decreased by 147.647 hectares. The largest infected areas (over 100 ha) were located in Kovel, Rozhyshche and Manevistky districts, the least infected area was observed in Ivanychi district of the region. The pest was not found in Lutsk district. The highest infection rate was recorded in Rozhyshche district. The highest ratio of viable cysts was observed in the soils of Volodymyr-Volynsky and Rozhyshche districts. The soils of the southern districts (located in the natural zone of forests and steppe) of the region demonstrated 1.5 times higher infection rates compared to soils of the northern districts (in the natural zone of mixed forests). The soils of the southern districts also harboured stable and strong pest populations. The pre-planting soil infection rates proved to directly depend on the reproduction coefficient of GPCN. If the values of Рі, initial infection rate, were lower than 1,000 larvae and eggs per 100 cm3, the reproduction coefficient was 1.18. Increase in the pre-planting infection rate to 2,000 eggs and larvae per 100 cm3 did not affect the reproduction coefficient. At approximately 5,000 eggs and larvae per 100 cm3 the reproduction coefficient exceeded 2, which should be considered in developing the pest control measures

A Novel Keyword Ontology Generator Method Tested on “Digital Transformation in Higher Education” Topic

Ponencia de la conferencia "1st International Workshop on Higher Education Learning Methodologies and Technologies Online, HELMeTO 2019; Novedrate; Italy; 6 June 2019 through 7 June 2019"The practice of using ontology to understand a field of study through the analysis of keywords was not found to have been documented, and a five-step method is therefore presented to generate ontologies of keywords: data selection and extraction, data unification strategy, keyword processing, weight and connection standardisation and final representation. Using the proposed method, an experimental evaluation was undertaken using as the field of study the digital transformation in universities and university institutions, generating a knowledge graph that enables the clear visualisation of the various connections among different topics in a chosen field of study. Finally, the effectiveness and the observed limitations are discussed while stressing that each researcher should perform a thorough analysis of those relationships, enabling to obtain useful information for teaching and learning processes, especially in higher education environments

Towards Evidence-Based Academic Advising Using Learning Analytics

Academic advising is a process between the advisee, adviser and the academic institution which provides the degree requirements and courses contained in it. Content-wise planning and management of the student’ study path, guidance on studies and academic career support is the main joint activity of advising. The purpose of this article is to propose the use of learning analytics methods, more precisely robust clustering, for creation of groups of actual study profiles of students. This allows academic advisers to provide evidence-based information on the study paths that have actually happened similarly to individual students. Moreover, academic institutions can focus on management and updates of course schedule having an effect of clearly characterized and recognized group of students. Using this approach a model of automated academic advising process, which can determine the study profiles, is presented. The presented model shows the whole automated process, where the learners will be profiled regularly, and where the proper study path will be suggested.peerReviewe