Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area

Bushfires pose a severe risk, among others, to humans, wildlife, and infrastructures. Rapid detection of fires is crucial for fire-extinguishing activities and rescue missions. Besides, mapping burned areas also supports evacuation and accessibility to emergency facilities. In this study, we propose a generic approach for detecting fires and burned areas based on machine learning (ML) approaches and remote sensing data. While most studies investigated either the detection of fires or mapping burned areas, we addressed and evaluated, in particular, the combined detection on three selected case study regions. Multispectral Sentinel-2 images represent the input data for the supervised ML models. First, we generated the reference data for the three target classes, burned, unburned, and fire, since no reference data were available. Second, the three regional fire datasets were preprocessed and divided into training, validation, and test subsets according to a defined schema. Furthermore, an undersampling approach ensured the balancing of the datasets. Third, seven selected supervised classification approaches were used and evaluated, including tree-based models, a self-organizing map, an artificial neural network, and a one-dimensional convolutional neural network (1D-CNN). All selected ML approaches achieved satisfying classification results. Moreover, they performed a highly accurate fire detection, while separating burned and unburned areas was slightly more challenging. The 1D-CNN and extremely randomized tree were the best-performing models with an overall accuracy score of 98 % on the test subsets. Even on an unknown test dataset, the 1D-CNN achieved high classification accuracies. This generalization is even more valuable for any use-case scenario, including the organization of fire-fighting activities or civil protection. The proposed combined detection could be extended and enhanced with crowdsourced data in further studies

Periodic outgassing as a result of unsteady convection in Ray Lava Lake, Mount Erebus, Antarctica

Persistently active lava lakes show continuous outgassing and open convection over years to decades. Ray Lake, the lava lake at Mount Erebus, Ross Island, Antarctica, maintains long-term, near steady-state behavior in temperature, heat flux, gas flux, lake level, and composition. This activity is superposed by periodic small pulses of gas and hot magma every 5-18 minutes and disrupted by sporadic Strombolian eruptions. The periodic pulses have been attributed to a variety of potential processes including unstable bidirectional flow in the conduit feeding the lake. In contrast to hypotheses invoking a conduit source for the observed periodicity, we test the hypothesis that the behavior could be the result of dynamics within the lake itself, independent of periodic influx from the conduit. We perform numerical simulations of convection in Ray Lake driven by both constant and periodic inflow of gas-rich magma from the conduit to identify whether the two cases have different observational signatures at the surface. Our simulations show dripping diapirs or pulsing plumes leading to observable surface behavior with periodicities in the range of 5-20 minutes. We conclude that a convective speed faster than the inflow speed can result in periodic behavior without requiring periodicity in conduit dynamics. This finding suggests that the surface behavior of lava lakes might be less indicative of volcanic conduit processes in persistently outgassing volcanoes than previously thought, and that dynamics within the lava lake itself may modify or overprint patterns emerging from the conduit

Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature

Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people's fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (a) algorithmic predictors, (b) human predictors, (c) comparative effects (human decision-making vs. algorithmic decision-making), and (d) consequences of ADM. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible ADM

Effect of L-carnitine on the hepatic transcript profile in piglets as animal model

<p>Abstract</p> <p>Background</p> <p>Carnitine has attracted scientific interest due to several health-related effects, like protection against neurodegeneration, mitochondrial decay, and oxidative stress as well as improvement of glucose tolerance and insulin sensitivity. The mechanisms underlying most of the health-related effects of carnitine are largely unknown.</p> <p>Methods</p> <p>To gain insight into mechanisms through which carnitine exerts its beneficial metabolic effects, we fed piglets either a control or a carnitine supplemented diet, and analysed the transcriptome in the liver.</p> <p>Results</p> <p>Transcript profiling revealed 563 genes to be differentially expressed in liver by carnitine supplementation. Clustering analysis of the identified genes revealed that most of the top-ranked annotation term clusters were dealing with metabolic processes. Representative genes of these clusters which were significantly up-regulated by carnitine were involved in cellular fatty acid uptake, fatty acid activation, fatty acid β-oxidation, glucose uptake, and glycolysis. In contrast, genes involved in gluconeogenesis were down-regulated by carnitine. Moreover, clustering analysis identified genes involved in the insulin signaling cascade to be significantly associated with carnitine supplementation. Furthermore, clustering analysis revealed that biological processes dealing with posttranscriptional RNA processing were significantly associated with carnitine supplementation.</p> <p>Conclusion</p> <p>The data suggest that carnitine supplementation has beneficial effects on lipid and glucose homeostasis by inducing genes involved in fatty acid catabolism and glycolysis and repressing genes involved in gluconeogenesis.</p

Empirical Insights in the Current Development of Smart Contracts

Blockchain technology enables a lot of knowledge possibilities in an even more digital business environment. Besides the well-known bitcoin application, another implementation so called smart contracts recently arised. Smart contracts profit from the blockchain mechanism benefits (e.g., transaction security). A huge advantage of smart contracts is, that they provide trust between transaction partners without integrating a third party. Practice has already noticed their advantages and implementing smart contracts more and more into their business. However, research is still under development and a common scientifical foundation is still missing. Particularly, research lacks in empirical and practical findings. This paper responds to that gap. Conducting an expert study relevant data was collected and analyzed regarding empirical standards. The results we found and present in this on-going research paper give insights about basic aspects, challenges in the implementation as well as the use cases of smart contracts

Influence of the magnetic field on the plasmonic properties of transparent Ni anti-dot arrays

Extraordinary optical transmission is observed due to the excitation of surface plasmon polaritons (SPPs) in 2-Dimensional hexagonal anti-dot patterns of pure Ni thin films, grown on sapphire substrates. A strong enhancement of the polar Kerr rotation is recorded at the surface plasmon related transmission maximum. Angular resolved reflectivity measurements under an applied field, reveal an enhancement and a shift of the normalized reflectivity difference upon reversal of the magnetic saturation (transverse magneto-optical Kerr effect-TMOKE). The change of the TMOKE signal clearly shows the magnetic field modulation of the dispersion relation of SPPs launched in a 2D patterned ferromagnetic Ni film

Les études surveillées au secondaire: dans quelle mesure répondent-elles aux attentes et besoins des élèves ? Enquête dans un établissement secondaire lausannois.

<p>The GO terms were sorted by the number of genes in an ascending order from top to bottom (P-value <0.005).</p

Glacier Monitoring Based on Multi-Spectral and Multi-Temporal Satellite Data: A Case Study for Classification with Respect to Different Snow and Ice Types

Remote sensing techniques are frequently applied for the surveying of remote areas, where the use of conventional surveying techniques remains difficult and impracticable. In this paper, we focus on one of the remote glacier areas, namely the Tyndall Glacier area in the Southern Patagonian Icefield in Chile. Based on optical remote sensing data in the form of multi-spectral Sentinel-2 imagery, we analyze the extent of different snow and ice classes on the surface of the glacier by means of pixel-wise classification. Our study comprises three main steps: (1) Labeled Sentinel-2 compliant data are obtained from theoretical spectral reflectance curves, as there are no training data available for the investigated area; (2) Four different classification approaches are used and compared in their ability to identify the defined five snow and ice types, thereof two unsupervised approaches (k-means clustering and rule-based classification via snow and ice indices) and two supervised approaches (Linear Discriminant Analysis and Random Forest classifier); (3) We first focus on the pixel-wise classification of Sentinel-2 imagery, and we then use the best-performing approach for a multi-temporal analysis of the Tyndall Glacier area. While the achieved classification results reveal that all of the used classification approaches are suitable for detecting different snow and ice classes on the glacier surface, the multi-temporal analysis clearly reveals the seasonal development of the glacier. The change of snow and ice types on the glacier surface is evident, especially between the end of ablation season (April) and the end of accumulation season (September) in Southern Chile