Policymaking requires an in-depth understanding of the cause-and-effect relationships between the sustainable development goals. However, due to the complex nature of socio-economic and environmental systems, this is still a challenging task. In the present article, the interconnectedness of the United Nations (UN) sustainability goals is measured using the Granger causality analysis of their indicators. The applicability of the causality analysis is validated through the predictions of the World3 model. The causal relationships are represented as a network of sustainability indicators providing the opportunity for the application of network analysis techniques. Based on the analysis of 801 UN indicator types in 283 geographical regions, approximately 4000 causal relationships were identified and the most important global connections were represented in a causal loop network. The results highlight the drastic deficiency of the analysed datasets, the strong interconnectedness of the sustainability targets and the applicability of the extracted causal loop network. The analysis of the causal loop networks emphasised the problems of poverty, proper sanitation and economic support in sustainable development.

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The identification of process faults is a complex and challenging task due to the high amount of alarms and warnings of control systems. To extract information about the relationships between these discrete events, we utilise multitemporal sequences of alarm and warning signals as inputs of a recurrent neural network-based classifier and visualise the network by principal component analysis. The similarity of the events and their applicability in fault isolation can be evaluated based on the linear embedding layer of the network, which maps the input signals into a continuous-valued vector space. The method is demonstrated in a simulated vinyl acetate production technology. The results illustrate that with the application of recurrent neural network-based sequence learning not only accurate fault classification solutions can be developed, but the visualisation of the model can give useful hints for hazard analysis. KEYWORDS deep learning, fault classification, visualisation of discrete eventsNomenclature: RNN, -recurrent neural network; LSTM, -long short-term memory; PCA, -principal component analysis; VAc, -vinyl acetate; y, -type of fault, = { c 1 , … , c n c } , related to the kth sequence of events.;̂k, -predicted class label of the faults; c j , -jth type fault; n c , -number of fault types; s, -state of the technology; pv, -index of the process variable; a, -the related state signals; e, -event; st, -starting time of an event; et, -ending time of an event; k , -kth sequence of states; S, -the set of states; R, -arbitrary temporal predicate between events; E, -equal temporal predicates between events; B, -before temporal predicates between events; D, -during temporal predicates between events; O, -overlap temporal predicates between events; x k , -the numerical representation of k sequence; P(y k = c j |x k ), -the conditional probability of the given fault; h k ,vector of the activities of the LSTM units; n U , -the number of LSTM units in the RNN; T, -length of the input sequence; W s , -weight matrix of the output layer of the network (referring to the applied softmax function); C t k , -cell state of the tth LSTM unit; t k , -forget gate of the tth LSTM unit; W f , -weight matrix of the forget gate; b, -the bias vector of the corresponding neurons; i t k , -input gate of the tth LSTM unit; o t k , -output gate of the tth LSTM unit; W o , -weight matrix of the output gate; oh t k , -one-hot binary vector representation of the tth symbol of the kth sequence; n s , -the number of states in the D temporal database; n R , -number of types of temporal predicates between events; n o , -number of bits in the one-hot binary vector; n e , -dimension of the embedding layer; W, -weight matrix of embedding layer; sim(s i , s j ), -similarity of the alarms; d max , -the maximum Euclidean distance among the rows of the W matrix;