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In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19.

This paper introduces a DSS to support coordination in disaster relief distribution.An MIP and an agent-based simulation with optimization procedures are presented.Road closures and off-road as well as unmanned aerial vehicles are considered.Computational experiments are based on flood scenarios of two Austrian regions.Results highlight potentials of coordinated disaster relief distribution. This paper presents a simulation and optimization based decision-support system (DSS) to facilitate disaster relief coordination between private and relief organizations. The DSS simulates disasters and plans shipments of relief goods via transfer points to demand points in the affected area. This enables decision-makers to analyze the last-mile distribution of goods by scheduling and routing trucks, off-road as well as unmanned aerial vehicles. Roads which are closed or opened during a disaster are considered allowing a dynamic adaptation to real world conditions and a comprehensive analysis over a rolling time-horizon. A mixed-integer problem formulation, an agent-based simulation, a heuristic-based scheduling and routing procedure as well as a Tabu Search metaheuristic are applied to analyze the given decision problem. Coordination between private and relief organization shows to be especially beneficial if time-losses resulting from closed roads are high and substantial unexpected demand occurs. Furthermore, results highlight the importance of selecting suitable transfer points and the potential of simulation and optimization based DSSs to improve disaster relief distribution.

The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods., Highlights • An enhanced text representation model for content-based early fake news detection. • Global semantic relations between sentences modeled with graph convolution network. • Local sentence representation extracted by applying 1D convolution on local context. • Document representation obtained by incorporating the sentence order through LSTM. • Generalization ability verified on cross-source and cross-domain datasets.

In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of- the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations.

An important paradigm in decision-making models is utility-maximization where most models do not include actors' motives. Fuzzy set theory on the other hand offers a method to simulate human decision-making. However, the literature describing expert-driven fuzzy logic models, rarely gives precise details on the methodology (to be) used. To fill the gap, this paper describes a methodology of 10 steps to model individual actor's drivers, motives, hereby taking into account the ecological, social and economic context. Testing the methodology on the composition of mixed farming systems in the Mekong Delta, Vietnam, showed that manual model development is not a waterfall approach but requires feedback loops, except for model implementation. Using feed-back loops, the proposed 10 step method allowed to include human drivers and motives other than utility-maximization and to maintain a degree of transparency hard to achieve when using automated procedures.

Research highlights? We employ Machine Learning techniques in species' potential distribution modelling. ? The distribution of 35 species from Latin America was modelled by classifiers. ? The best performance in potential distribution modeling was achieved by random trees. Species' potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species' potential distribution.