Using urban climate modelling and improved land use classifications to support climate change adaptation in urban environments: A case study for the city of Klagenfurt, Austria

This study outlines the results of current and future climate scenarios, and potentially realizable climate adaptation measures, for the city of Klagenfurt, Austria. For this purpose, we used the microscale urban climate model (MUKLIMO_3), in conjunction with the cuboid method, to calculate climate indices such as the average number of summer and hot days per year. For the baseline simulation, we used meteorological measurements from 1981 to 2010 from the weather station located at Klagenfurt Airport. Individual building structures and canopy cover from several land monitoring services were used to derive accurate properties for land use classes in the study domain. To characterize the effectiveness of climate adaptation strategies, we compared changes in the climate indices for several (future) climate adaptation scenarios to the reference simulation. Specifically, we considered two major adaptation pathways: (i) an increase in the albedo values of sealed areas (i.e., roofs, walls and streets) and (ii) an increase in green surfaces (i.e., lawns on streets and at roof level) and high vegetated areas (i.e., trees). The results indicate that some climate adaptation measures show higher potential in mitigating hot days than others, varying between reductions of 2.3 to 11.0%. An overall combination of adaptation measures leads to a maximum reduction of up to 44.0%, indicating a clear potential for reduction/mitigation of urban heat loads. Furthermore, the results for the future scenarios reveal the possibility to remain at the current level of urban heat load during the daytime over the next three decades for the overall combination of measures

Urban Heat Island Hazard and Risk Indices for Austria

This collection contains two geotiffs: the UHI Hazard Index and the UHI Risk Index, both for Austria at a 100 m resolution. The methodology for their development is described in the attached factsheets (in English and German)

Active learning for sound event classification using Bayesian neural networks with Gaussian variational posterior

Manual annotation of audio material is cumbersome. Active learning aims at minimizing the annotation effort by iteratively selecting an acquisition batch of unlabeled data, asking a human to annotate the selected data and re-training a classifier until an annotation budget is depleted. In this paper we propose the Gaussian-dense active learning (GDAL) algorithm to train a sound event classifier. The classifier is a Bayesian neural network where the weights are normally distributed. This is in contrast to conventional neural networks where weights are not distributed, but have assigned values. The Bayesian nature of the classifier empowers GDAL to select acquisition batches from a set of unlabeled audio clips based on their estimated informativeness. Evaluation results on the UrbanSound8k dataset show that GDAL outperforms a state-of-the-art algorithm based on medoid active learning for all considered annotation budgets and an algorithm based on dropout active learning for sufficiently large annotation budgets

Active learning for sound event classification using Monte-Carlo dropout and PANN embeddings

Labeling audio material to train classifiers comes with a large amount of human labor. In this paper, we propose an active learning method for sound event classification, where a human annotator is asked to manually label sound segments up to a certain labeling budget. The sound event classifier is incrementally re-trained on pseudo-labeled sound segments and manually labeled segments. The segments to be labeled during the active learning process are selected based on the model uncertainty of the classifier, which we propose to estimate using Monte Carlo dropout, a technique for Bayesian inference in neural networks. Evaluation results on the UrbanSound8K dataset show that the proposed active learning method, which uses pre-trained audio neural network (PANN) embeddings as input features, outperforms two baseline methods based on medoid clustering, especially for low labeling budgets

The effect of ethanol on lactate and base deficit as predictors of morbidity and mortality in trauma

Objective—The objective of this study was to assess the predictive value of lactate and base deficit in determining outcomes in trauma patients who are positive for ethanol. Methods—Retrospective cohort study of patients admitted to a level 1 trauma center between 2005 and 2014. Adult patients who had a serum ethanol, lactate, base deficit, and negative urine drug screen obtained upon presentation were included. Results—Data for 2482 patients were analyzed with 1127 having an elevated lactate and 1092 an elevated base deficit. In these subgroups, patients with a positive serum ethanol had significantly lower 72-hour mortality, overall mortality, and hospital length of stay compared with the negative ethanol group. Abnormal lactate (odds ratio [OR], 2.607; 95% confidence interval [CI], 1.629– 4.173; P = .000) and base deficit (OR, 1.917; 95% CI, 1.183–3.105; P = .008) were determined to be the strongest predictors of mortality in the ethanol-negative patients. Injury Severity Score was found to be the lone predictor of mortality in patients positive for ethanol (OR, 1.104; 95% CI, 1.070–1.138; P=.000). Area under the curve and Youden index analyses supported a relationship between abnormal lactate, base deficit, and mortality in ethanol-positive patients when the serum lactate was greater than 4.45 mmol/L and base deficit was greater than −6.95 mmol/L. Conclusions—Previously established relationships between elevated lactate, base deficit, and outcome do not remain consistent in patients presenting with positive serum ethanol concentrations. Ethanol skews the relationship between lactate, base deficit, and mortality thus resetting the threshold in which lactate and base deficit are associated with increased mortality