Human ethics issues for Tertiary Learning Advisors: A workshop kit for emerging researchers

In today’s changing environment it is important that Tertiary Learning Advisors(TLAs) research aspects of their practice. However, when TLAs embark on teaching and learning research with their own students as participants, they can encounter a range of thorny ethical issues that need to be considered and addressed when designing their research and completing their Human Ethics applications. At the 2013 Association of Tertiary Learning Advisors Aotearoa New Zealand (ATLAANZ) conference, we facilitated a workshop for emerging TLA researchers and others with an interest in fostering ethical research in their institution. The purpose of the workshop was to highlight the key ethical issues facing TLAs engaged in research into teaching and learning, and to explore how research projects could be designed to better accommodate ethical principles. This workshop kit, a revised version of the conference workshop, is intended to provide a resource for colleagues to use and adapt in their own institutions

Assessment of hydro-meteorological drought in the Danube Plain, Bulgaria

Svrha ovoga rada procjena je pojava hidrometeorološke suše na području Dunavske ravnice u sjevernoj Bugarskoj. Sušu kao elementarnu nepogodu najbolje karakteriziraju višestruki klimatski i hidrološki parametri. U ovom se radu meteorološka suša analizira standardiziranim oborinskim indeksom (engl. Standardized Precipitation Index – SPI), a hidrološka suša definirana je indeksom protoka (engl. Streamflow Drought Index – SDI). Oba su indeksa izračunata za vremensko razdoblje od 6 i 12 mjeseci u razdoblju od 1993. do 2009. godine. Buduća vjerojatnost pojave suše analizirana je za dva razdoblja: 2021. – 2050. i 2051. – 2080. prilagodbom podataka iz regionalnoga modela KNMI. Rezultati temeljeni na standardiziranom oborinskom indeksu (SPI) i indeksu protoka (SDI) pokazali su da je tijekom razdoblja istraživanja sva promatrana područja pogodila blaga do umjerena suša. Tijekom druge polovine 21. stoljeća očekuje se povećanje učestalosti pojave umjerenih suša.The purpose of this study is to evaluate occurrences of hydro-meteorological drought in the Danube Plain territory, located in northern Bulgaria. As a natural hazard, drought is best characterized by multiple climatological and hydrological parameters. In this study, meteorological drought is analyzed with the Standardized Precipitation Index (SPI), and hydrologic drought is defined by the Streamflow Drought Index (SDI). Both indices are calculated on a time scale of 6 to 12 months over the 1993–2009 period. Future drought occurrence probability is analyzed for two periods: 2021–2050 and 2051–2080 by downscaling the data from regional model KNMI. The results based on the SPI and SDI showed that almost all the investigated areas suffered from mild to moderate droughts during the study period. It is expected that there will be an increasing frequency of occurrences of moderate drought during the second half of 21st century

Data Mining for Fog Prediction and Low Clouds Detection

his paper describes our contribution to the research of parametrized models and methods for detection and prediction of significant meteorological phenomena, especially fog and low cloud cover. The project covered methods for integration of distributed meteorological data necessary for running the prediction models, training models and then mining the data in order to be able to efficiently and quickly predict even sparsely occurring phenomena. The detection and prediction methods are based on knowledge discovery -- data mining of meteorological data using neural networks and decision trees. The mined data were mainly METAR aerodrome messages, meteorological data from specialized stations and cloud data from special airport sensors -- laser ceilometers

Corpus Wide Argument Mining -- a Working Solution

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates

Assessing the Contribution of Data Mining Methods to Avoid Aircraft Run-Off from the Runway to Increase the Safety and Reduce the Negative Environmental Impacts

The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution &ldquo Safety Support Tools for Avoiding Runway Excursions&rdquo . This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection. Document type: Articl

Testing the paradox of enrichment along a land use gradient in a multitrophic aboveground and belowground community

In the light of ongoing land use changes, it is important to understand how multitrophic communities perform at different land use intensities. The paradox of enrichment predicts that fertilization leads to destabilization and extinction of predator-prey systems. We tested this prediction for a land use intensity gradient from natural to highly fertilized agricultural ecosystems. We included multiple aboveground and belowground trophic levels and land use-dependent searching efficiencies of insects. To overcome logistic constraints of field experiments, we used a successfully validated simulation model to investigate plant responses to removal of herbivores and their enemies. Consistent with our predictions, instability measured by herbivore-induced plant mortality increased with increasing land use intensity. Simultaneously, the balance between herbivores and natural enemies turned increasingly towards herbivore dominance and natural enemy failure. Under natural conditions, there were more frequently significant effects of belowground herbivores and their natural enemies on plant performance, whereas there were more aboveground effects in agroecosystems. This result was partly due to the “boom-bust” behavior of the shoot herbivore population. Plant responses to herbivore or natural enemy removal were much more abrupt than the imposed smooth land use intensity gradient. This may be due to the presence of multiple trophic levels aboveground and belowground. Our model suggests that destabilization and extinction are more likely to occur in agroecosystems than in natural communities, but the shape of the relationship is nonlinear under the influence of multiple trophic interactions.

Fuzzy Model for Quantitative Assessment of Environmental Start-up Projects in Air Transport

The purpose of this paper is to develop an applied fuzzy model of information technology to obtain quantitative estimates of environmental start-up projects in air transport. The developed model will become a useful tool for venture funds, business angels, or crowdfunding platforms for the development of innovative air transport businesses. Obtaining a quantitative estimate of the environmental start-up projects will increase the sustainability of the decision making on the security of financing of such projects by investors. This article develops a fuzzy evaluation model of project start-ups in air transport as an application of our neuro-fuzzy model in a specific air transport environment. The applied model provides output ranking of start-up project teams in air transport based on a four-layer neuro-fuzzy network. The presented model declares the possibilities of the application to solve these economic problems and offers the space for subsequent research focused on its usability in several areas of start-up development, in sectors and processes differentiated. The benefits are also visible for several types of policies, with an emphasis on decision-making processes in regulatory mechanisms to support the state funding in Slovakia, the EU etc. Document type: Articl

Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours

Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML knowledge, which poses a significant barrier for many potential users. To lift this barrier, we introduce Label Sleuth, a free open source system for labeling and creating text classifiers. This system is unique for (a) being a no-code system, making NLP accessible to non-experts, (b) guiding users through the entire labeling process until they obtain a custom classifier, making the process efficient -- from cold start to classifier in a few hours, and (c) being open for configuration and extension by developers. By open sourcing Label Sleuth we hope to build a community of users and developers that will broaden the utilization of NLP models.Comment: 7 pages, 2 figure