Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background   Diffuse interstitial or myocardial replacement fibrosis are common features of a large variety of cardiomyopathies. These alterations contribute to functional changes, particularly to an increased myocardial stiffness (MS). Histological examination is the gold standard for myocardial fibrosis quantification, however, it requires endomyocardial biopsy which is invasive and not without risks. Cardiac magnetic resonance (CMR) can characterize the extent of both diffuse and replacement fibrosis and may have prognostic value in various cardiomyopathies. Echocardiographic shear wave (SW) elastography is an emerging approach for measuring MS in vivo. SWs occur after mechanical excitation of the myocardium, e.g. after mitral valve closure (MVC), and their propagation velocity is directly related to MS, thus providing an opportunity to assess stiffness at end-diastole. Purpose The aim was to investigate if velocities of natural SW can distinguish between interstitial and replacement fibrosis.  Methods We prospectively enrolled 47 patients (22 patients after heart transplant [54.2?±?15.8 years, 82.6% male] and 25 patients with established hypertrophic cardiomyopathy [54.0?±?13.5 years, 80.0% male]) undergoing CMR during their check-up. We performed SW elastography in parasternal long axis views of the LV using a fully programmable experimental scanner (HD-PULSE) equipped with a clinical phased array transducer (Samsung Medison P2-5AC) at 1100?±?250 frames per second. Tissue acceleration maps were extracted from an anatomical M-mode line along the midline of the LV septum. The SW propagation velocity at MVC was measured as the slope in the M-mode image. All patients underwent T1 mapping as well as late gadolinium enhancement (LGE) cardiac magnetic resonance at 1.5 T to assess the presence of diffuse or replacement fibrosis (Figure A). Therefore, patients were divided in three groups: no fibrosis, diffuse fibrosis and replacement fibrosis. Results Mechanical SW’s were observed in 46 subjects starting immediately after MVC and propagating from the LV base to the apex. SW propagation velocity at MVC correlated well with native myocardial T1 values (r?=?0.65, p?<?0.0001) and differed significantly among groups (p?<?0.0001), with a significant post-test between any pair of groups (Figure B). SW velocities below a cut-off of 6.01 m/s showed the highest accuracy to identify patients without any type of fibrosis (sensitivity 88 %, specificity 89%, area under the curve?=?0.93) (Figure C). A cut-off of 8.11 m/s could distinguish replacement fibrosis from diffuse fibrosis with a sensitivity and specificity of 59% and 92 %, respectively (area under the curve?=?0.80) (Figure D). Conclusions   Shear wave velocities after mitral valve closure can distinguish between normal and pathological myocardium and can detect differences between diffuse and replacement fibrosis. Abstract Figure.

Abstract
The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications.

Abstract
The present work introduces a hybrid integration of the self-organizing map and the hidden Markov model (HMM) for anomaly detection in 802.11 wireless networks. The self-organizing hidden Markov model map (SOHMMM) deals with the spatial connections of HMMs, along with the inherent temporal dependencies of data sequences. In essence, an HMM is associated with each neuron of the SOHMMM lattice. In this paper, the SOHMMM algorithm is employed for anomaly detection in 802.11 wireless access point usage data. Furthermore, we extend the SOHMMM online gradient descent unsupervised learning algorithm for multivariate Gaussian emissions. The experimental analysis uses two types of data: synthetic data to investigate the accuracy and convergence of the SOHMMM algorithm and wireless simulation data to verify the significance and efficiency of the algorithm in anomaly detection. The sensitivity and specificity of the SOHMMM algorithm in anomaly detection are compared to two other approaches, namely HMM initialized with universal background model (HMM-UBM) and SOHMMM with zero neighborhood (Z-SOHMMM). The results from the wireless simulation experiments show that SOHMMM outperformed the aforementioned approaches in all the presented anomalous scenarios.

Abstract
Nowadays, recent advances in information technology and communication facilitates using networked controlled systems in different industrial plants. Whereas data is transferred among different components of the networked systems, they are vulnerable to various types of attacks. This important issue in nowadays industrial plants should be treated logically and reasonable protection mechanisms to mitigate such attacks should be provided. This paper considers delay attack impacts on frequency regulation of an electric vehicle aggregator (EVA) system. The command control action is received by the EVA through an imperfect channel containing uncertainties subject to the time-delay attack. A systematic approach based on a direct search algorithm (DSA) is developed to design a resilient proportional-integral (PI) controller for mitigating such attacks. The proposed DSA provides low-conservative results, explores the design space to find a feasible solution, and computes the PI controller gains to assure the stability of the EVA system in the presence of the delay attack. Stability analysis and numerical simulations for a typical attacked EVA frequency regulation are given to show the effectiveness of the developed controller.

Abstract
The factors required to achieve sustainable economic growth in a country are debated for decades, and empirical research in this regard continues to grow. Given the relevance of the topic and the absence of a comprehensive, systematic literature review, we used bibliometric techniques to examine and document several aspects in the empirical literature related to growth, from 1991 to 2020. Five main results are worth highlighting: (a) the share of empirical articles on economic growth show a clear upward trend; (b) among all the groups of countries considered, the emerging economies (EEs) have received the most scientific attention; (c) the economic growth processes of the Latin American and Caribbean EEs have observed negligible scientific attention; (d) the very long-run studies comprise a residual share among the empirical literature on growth; (e) the extant empirical studies on economic growth have addressed mainly the impact of “macroeconomic conditions.” Our findings suggest there is a need to redirect the empirical growth agenda, so as to encourage more scientific attention devoted to the analysis of key determinants of economic growth in the very long run. There should also be increased scrutiny of the processes of economic growth in Latin American and Caribbean EEs. © 2021 Emerging Markets Forum, Washington DC.

Abstract
Data stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example arriving in a data stream. It is an evolution of MetaStream, which recommends learning algorithms for batches of examples. By using a unitary granularity, micro-MetaStream is able to respond more efficiently to changes in data distribution than its predecessor. The meta-data combines meta-features, characteristics describing recent data, with base-level features, the original variables of the new example. In experiments on real-world regression data streams, micro-metaStream outperformed MetaStream and a baseline method at the meta-level and frequently improved the predictive performance at the base-level.