High Arctic coasts at risk-the case study of coastal zone development and degradation associated with climate changes and multidirectional human impacts in Longyearbyen (Adventfjorden, Svalbard)

Volcanology is an extremely important scientific discipline. Shedding light on how and why volcanoes erupt, how eruptions can be predicted and their impact on humans and the environment is crucial to public safety, economies and businesses. Understanding volcanoes means eruptions can be anticipated and at-risk communities can be forewarned, enabling them to implement mitigation measures. Professor Tsukasa Ohba is a scientist based at the Graduate School of International Resource Studies, Akita University, Japan, and specialises in volcanology and petrology. Ohba and his team are focusing on volcanic phenomena including: phreatic eruptions (a steam-driven eruption driven by the heat from magma interacting with water); lahar (volcanic mudflow); and monogenetic basalt eruptions (which consist of a group of small monogenetic volcanoes, each of which erupts only once). The researchers are working to understand the mechanisms of these phenomena using Petrology. Petrology is one of the traditional methods in volcanology but has not been applied to disastrous eruptions before. The teams research will contribute to volcanic hazard mitigation.

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A Comparative Analysis of Two Major Approaches for Mapping the Wildland-Urban Interface: A Case Study in California

Land ◽

10.3390/land10070679 ◽

2021 ◽

Vol 10 (7) ◽

pp. 679

Author(s):

Avi Bar-Massada

Keyword(s):

At Risk ◽

Comparative Analysis ◽

Spatial Patterns ◽

Practical Purpose ◽

Spatial Arrangement ◽

Spatial Accuracy ◽

Wildland Urban Interface ◽

Spatial Correspondence ◽

Data Requirements

The Wildland Urban Interface (WUI) is where human settlements border or intermingle with undeveloped land, often with multiple detrimental consequences. Therefore, mapping the WUI is required in order to identify areas-at-risk. There are two main WUI mapping methods, the point-based approach and the zonal approach. Both differ in data requirements and may produce considerably different maps, yet they were never compared before. My objective was to systematically compare the point-based and the zonal-based WUI maps of California, and to test the efficacy of a new database of building locations in the context of WUI mapping. I assessed the spatial accuracy of the building database, and then compared the spatial patterns of WUI maps by estimating the effect of multiple ancillary variables on the amount of agreement between maps. I found that the building database is highly accurate and is suitable for WUI mapping. The point-based approach estimated a consistently larger WUI area across California compared to the zonal approach. The spatial correspondence between maps was low-to-moderate, and was significantly affected by building numbers and by their spatial arrangement. The discrepancy between WUI maps suggests that they are not directly comparable within and across landscapes, and that each WUI map should serve a distinct practical purpose.

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A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor

Applied Sciences ◽

10.3390/app11135826 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5826

Author(s):

Evangelos Axiotis ◽

Andreas Kontogiannis ◽

Eleftherios Kalpoutzakis ◽

George Giannakopoulos

Keyword(s):

Machine Learning ◽

Coastal Zone ◽

Extraction Process ◽

Asia Minor ◽

Efficiency And Effectiveness ◽

Effectiveness And Efficiency ◽

Intelligent Tools ◽

Southern Balkans

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, and the different quality of language use across sources present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research aimed at the southern Balkans and the coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “expert–apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a machine learning (ML) algorithm, utilizing a combination of active learning (AL) and reinforcement learning (RL), and the expert is the human researcher. ML-powered research improved the effectiveness and efficiency of the domain expert by 3.1 and 5.14 times, respectively, fetching a total number of 420 relevant ethnopharmacological documents in only 7 h versus an estimated 36 h of human-expert effort. Therefore, utilizing artificial intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

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