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Objective: Epstein-Barr Virus-Associated Smooth Muscle Tumor (EBV-SMT) is a rare tumor with a higher rate of occurrence in unusual locations in the setting of immunodeficiency. In this study, we evaluated a cohort of ordinary leiomyosarcomas (LMS) for the presence of EBV and described the clinicopathological features deviating from routinely diagnosed cases of EBV-SMT. Material and Methods: The sections of tissue microarrays including 93 classical LMS occurring in various locations were hybridized with EBER and stained for LMP1 antibody using the Leica Bond Autostainer. EBV real-time PCR assay was performed in 2 EBER-positive cases. Results: Among the 93 LMS cases, 2 non-uterine cases (2.2%) were positive for EBER and negative for LMP1, and were referred to as "EBV-positive LMS". Both were females in their 6th decade without immunosuppression. EBV real-time PCR assay revealed the presence of EBV in one of the cases. Tumors were located in the pancreas and chest wall. Morphologically, tumors were rather myxoid, multinodular, and composed of long fascicles of spindle cells with intermediate-to high-grade features. High mitotic activity and focal necrosis were present, whereas no accompanying lymphocytes were detected. One of the patients developed metastatic disease after 3 years. Conclusion: EBV-positive LMS occurring in immunocompetent patients has features distinct from classical EBV-SMT seen in immunosuppressed patients. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos' digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications. [ABSTRACT FROM AUTHOR]

Sentiment analysis has gained attention in computational linguistics, where algorithms are dedicated to recognizing sentiments to take better decisions in each context. However, this is difficult to achieve in social networks due to linguistic features. A text classification model of combined lexical-based and machine-learning techniques is presented in this paper; it uses discourse structure including its relations to improve sentiment analysis. The model aims at extracting information about opinion polarity posted by users; then modelling users' usual polarity to detect significant polarity. The model extracts posted texts on Facebook, then it classifies them according to their polarity, which shows the detection of polarity. It provides interesting results using Ukraine crisis argued by Jordanian news followers. Obtained results show a high accuracy (94.68%). It is useful to have information about users' sentiments. It may serve feedback for politicians and jurists, where direct contact with war victims is extremely difficult. [ABSTRACT FROM AUTHOR]

Using fractional difference equations to describe fractional and variable-order maps, this manuscript discusses the dynamics of the discrete 4D sinusoidal feedback sine iterative chaotic map with infinite collapse (ICMIC) modulation map (SF-SIMM) with fractional-order. Also, it presents a novel variable-order version of SF-SIMM and discusses their chaotic dynamic behavior by employing a distinct function for the variable fractional-order. To establish the existence of chaos in the suggested discrete SF-SIMM, some numerical methods such as phase plots, bifurcation and largest Lyapunov exponent diagrams,  C 0  complexity and 0–1 test are utilized. After that, two different control schemes are used for the conceived discrete system. The states are stabilized and asymptotically forced towards zero by the first controller. The second controller is used to synchronize a pair of maps with non–identical parameters. Finally, MATLAB simulations will be executed to confirm the results provided. [ABSTRACT FROM AUTHOR]

The class imbalance problem occurs when the number of samples in a given dataset is not balanced according to the class labels. In a binary classification problem of two classes, one class contains a minority of samples while the other has a majority. The commonly utilized SMOTE technique generates synthetic samples in the middle of a pair of original samples in the feature space to oversample the minority class. The midlines and randomness characteristics of this technique and its extensions capture the local characteristics of the minority class. Besides, these techniques do not significantly improve the classification problem's performance. Accordingly, this paper proposes a new technique for oversampling using a single-layer perceptron that captures the general characteristics of the data with low resource consumption. The proposed technique uses the weights trained for the network as input to other networks to generate new samples within the min-max range. The results showed that the proposed technique overperformed the SMOTE techniques, as reported using several machine learning techniques, including decision tree, random forest, support vector machine, and K-nearest neighbors. The experiments are conducted using the highly imbalanced credit card transaction dataset, which contains 492 fraudulent transactions out of 284,807 total transactions. The random forest reported the best results, in which the proposed technique improves the recall and the f-measure while maintaining full precision, similar to SMOTE. [ABSTRACT FROM AUTHOR]