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Background: For the past 25 years, the ITS rDNA (ITS1-5.8S-ITS2) of Gyrodactylidae has been crucial for species identification, description, and phylogeny. This family includes 25 genera parasitizing marine and freshwater fish, initially distinguished by morphological differences in attachment and/or male copulatory organs. Gyrodactylus Nordmann, 1832, the most species-rich genus, has approximately 500 described species and an additional 25,000 species suspected. The genus is not monophyletic, and the functionally adaptive nature of morphological diagnostic characters complicates the delimitation of new genera. Methods: A phylogeny based on ITS rDNA was proposed to address these challenges, using only complete sequences of primitive taxa. Fifty-four sequences were aligned with the MUSCLE v5.1 algorithm, creating a 1590 ps long matrix. Maximum Likelihood (ML) and Bayesian Inference (BI) methods with the models TVM+F+G4 and SYM+G4 for ITS1–ITS2 and 5.8S, respectively, were inferred using IQ-TREE v2.3.5 and BEAST v2.7.6.0. Results: The findings revealed eleven main lineages. Four of them are proposed for classification into new genera: Cichlidarus gen. nov., Iraqemembranatus gen. nov., Macracanthus gen. nov., and Rysavyius gen. nov. Elevating the subgenus G. (Gyrodactylus) to genus rank was supported. Conclusions: The presented phylogeny provides a foundation for developing a classification system within Gyrodactylidae that is both reasonable and comprehensive. Full article

During an emergency rescue operation, rescuers have to deal with many different health complications like cardiovascular, respiratory, neurological, psychiatric, etc. The identification process of the common health complications in rescue events is not very difficult or time-consuming because the health vital symptoms or primary observations are enough to identify, but it is quite difficult with some complications related to neurology e.g., schizophrenia, epilepsy with non-motor seizures, or retrograde amnesia because they cannot be identified with the trend of health vital data. The symptoms have a wide spectrum and are often non-distinguishable from other types of complications. Further, waiting for results from medical tests like MRI and ECG is time-consuming and not suitable for emergency cases where a quick treatment path is an obvious necessity after the diagnosis. In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. The novelty lies in the procedure of generating input features from raw rescue data used in AI models, as the data are not like traditional clinical data collected from hospital repositories. Rather, the data were gathered directly from more than 200,000 rescue cases and required natural language processing techniques to extract meaningful information. A step-by-step analysis of developing multiple AI models that can facilitate the fast identification of neurological complications, in general, is presented in this paper. Advanced data analytics are used to analyze the complete record of 273,183 rescue events in a duration of almost 10 years, including rescuers’ analysis of the complications and their diagnostic methods. To develop the detection model, seven different machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were used. Observing the model’s performance, we conclude that the neural network and extreme gradient boosting show the best performance in terms of selected evaluation criteria. To utilize this result in practical scenarios, the paper also depicts the possibility of embedding such machine learning models in hardware like FPGA. The goal is to achieve fast detection results, which is a primary requirement in any rescue mission. An inference time analysis of the selected ML models and VTA AI accelerator of Apache-TVM machine learning compiler used for the FPGA is also presented in this research. Full article

In the present work, we establish fixed-point results for a pair of mappings satisfying some contractive conditions on rational expressions with coefficients as point-dependent control functions in the setting of tricomplex-valued metric spaces. The proven results are extension and generalisation of some of the literature’s well-known results. We also explore some of the applications to our key results. Full article