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Flettner rotors are wind propulsion systems using the Magnus effect to generate thrust, thereby reduce fuel consumption and carbon emissions in the ships. However, rotor unbalance can cause excessive vibrations and energy loss, affecting the performance and stability of the system. There is a need to have a system onboard, which can predict the vibrations. The paper proposes a deep learning approach to predict the vibrations and unbalanced forces of a Flettner rotor based on the data of ECO Flettner rotor onboard the vessel MV Fehn pollux. The paper develops two methods to estimate the direction and magnitude of the unbalanced forces using the reading values of the strain gauges. The work also compares two recurrent neural network models, namely Long-short term memory and Gated Recurrent Unit, for vibration prediction and evaluates their performance using Mean Absolute Error and Root Mean Squared Error metrics. The results show that Longshort term memory model outperforms Gated Recurrent Unit model in prediction accuracy and can be implemented on the system onboard to monitor and prevent rotor unbalance. The paper also suggests some possible solutions for automatic self-balancing of the rotor and identifies some areas for future work. [ABSTRACT FROM AUTHOR]

To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 × 10−2, 89.21, 1.63, 4.90 × 10−2, 0.97, 2.92 × 10−1, 0.88, and 4.84, respectively. The model's predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Deep Belief Network (DBN) architecture, the 'Interval Type-2 Fuzzy DBN (IT2FDBN)', which models the weights and biases with IT2 FSs. Thus, it introduces a novel algorithm for augmented deep leaning, which has the capability to address all the limitations of the classical DBN (CDBN) and T1 fuzzy DBN (T1FDBN). We comparatively evaluate the performance of the IT2FDBN by conducting experiments using the popular MNIST handwritten digit recognition datasets. Additionally, to demonstrate its robustness and generalization capabilities, we also conduct experiments taking two noisy variants of MNIST dataset, viz. the MNIST with AWGN (additive white Gaussian noise) and the MNIST with motion blur. We conduct extensive simulations by considering different combinations of nodes in the hidden layers of the DBN for better model selection. We thoroughly compare the results using well-known performance measures such as root mean square error (RMSE) and Error rate. We show that, in terms of RMSE values and error rates, the proposed IT2FDBN outperforms both T1FDBN and CDBN across all the three datasets. Further, we also provide the results of convergence, runtime-based comparison, and statistical analysis in support of our proposal. [ABSTRACT FROM AUTHOR]

• Present a novel POI recommendation method using BERT. • Consider a personalized travel route for each tourist based on Topic. • Present an improved measurement method of users' preferences. Route schema is challenging for tourists because they must choose Points of Interest (POIs) in unknown areas that meet their preferences and limitations. Historically, sequential methods were utilized to generate recommendations based on previous user interactions. Despite their efficacy, however, such left-to-right unidirectional models are suboptimal due to the following factors: a) user behavior sequences are restricted in their ability to utilize hidden representations in unidirectional architectures; b) a rigidly ordered sequence is frequently assumed but is not always possible. This paper proposes a novel personalized sequential recommendation model, termed BERTSeqHybrid, which utilizes Bidirectional Encoder Representations from Transformers (BERT) to circumvent these limitations. In addition to contextual data from POIs, asymmetric schemas, and topic modeling are employed to improve the user-user similarity model. Furthermore, a novel method for evaluating user preferences is proposed utilizing explicit demographic data to mitigate the cold start problem. In the experimental evaluation, the developed methodology, which was applied to two different datasets (Yelp and Flickr), produced superior root mean square error RMSE, F-Score, mean average precision (MAP), and normalized discounted cumulative gain (NDCG) indexes. [ABSTRACT FROM AUTHOR]