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Introduction: Although many batoid species, known as final hosts of lecanicephalidean cestodes, exist in the Persian Gulf, no knowledge regarding this cestode order is available for this region. This study is the first one focusing on this order in the Persian Gulf., Methods: A total of 11 cowtail stingrays, Pastinachus sephen (Forsskål), were collected from the north eastern Persian Gulf. Isolated cestodes were fixed in 10% seawater-buffered formalin and stored in 70% ethanol. For morphological examinations, worms were stained using acetic carmine; a subset was prepared for SEM., Results: A new species of Polypocephalus Braun, 1878 was described. Polypocephalus barsami sp. nov. is the only species within its genus reported outside the waters of Japan, India, Sri Lanka, and Egypt in the Indian Ocean. Since the microtriches were not described in the generic diagnosis of Polypocephalus, the description of the microtriches is added based on the standardised terminology. Furthermore, the characteristics of this new species were discussed and compared within Polypocephalus, many species of which have poor descriptions., Conclusions: Polypocephalus barsami sp. nov. is the first report of the genus Polypocephalus in the Persian Gulf and the 15th valid species of its genus.

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH 3 ), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.