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The application of integrative taxonomic approaches is useful to species delineation based on a combination of distinct types of characters, here morphological features and ribosomal DNA sequences. In this study, we surveyed ectoparasitic nematodes of the subfamily Merliniinae in cultivated and natural environments in Iran. Results of morphological and morphometrical studies, light and scanning electron microscopic observations, and molecular analyses allowed us the identification of fourteen known and one unknown species including representatives of the genera Amplimerlinius (five species), Geocenamus (one species), Merlinius (three species), Nagelus (two species), Paramerlinius (one species), Scutylenchus (two species), and Telomerlinius (one species). The unknown species, Scutylenchus sp., characterized by having 35-50 incisures at mid-body; lateral field with 6 longitudinal incisures; lip region slightly offset by a constriction, flattened at front end; bearing 5-7 annuli; cephalic framework not refractive; stylet robust, 18.3-27 m long; post anal intestinal sac absent; tail elongate conical, dorsally convex, with 24 (19-28) annuli in ventral side, ending to a smooth terminus and males common; spicules 24.5-31 m long. The phylogenetic analyses were carried out using molecular data from nuclear ribosomal DNA (rDNA) genes viz. D2-D3 expansion segments of the large ribosomal subunit (28S rRNA), partial small ribosomal subunit (18S rRNA), and internal transcribed spacer (ITS). The molecular variability of D2-D3 expansion segments of the 28S rRNA and partial 18S rRNA was low in this family in comparison to the ITS region, which could be a more helpful molecular marker in species and genus identification.

The use of multi-stack fuel cells (FCs) is attracting considerable attention in electrified vehicles due to the added degrees of freedom in terms of efficiency and survivability. In a multi-stack FC hybrid electric vehicle, the power sources (FCs and the battery pack) have different energetic characteristics and their operation is influenced by the performance drifts caused by degradation. Hence, efficient power distribution for such a multi-source system is a critical issue. This paper proposes a three-layer online EMS for a recreational vehicle composed of three FCs and a battery pack. In the first layer, two online estimators are responsible for constantly updating the characteristics of each FC and the battery to be used by the power distribution algorithm. In the second layer, a rule-based method is developed to improve the calculation speed of the power distribution algorithm by deciding when it should be activated. The last layer performs the power distribution between FCs and battery using a model-free reinforcement learning (RL) algorithm called Q-learning. The proposed RL-based EMS attempts to meet the requested power while minimizing the costs of hydrogen consumption and degradation of all power sources. To justify the performance of the proposed strategy, a comprehensive benchmark with an offline EMS and two online strategies is performed under two driving cycles. In comparison with the online strategies, the proposed method based on RL reduces the defined trip cost up to 11.5 % and 13.08 % under the Real driving cycle while having a higher cost than the offline strategy by 4.78 %.

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FCHEV with three FCs and a battery pack. GT is a well-approved method for characterizing the interactions in multiagent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rule-based strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rule-based EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system.

Employing semi-empirical models to estimate some characteristics of a fuel cell (FC) stack, such as power and polarization curves, is demanded for efficient design of a power allocation strategy in a FC hybrid electric vehicle. However, the multivariate nature of a FC system has made the design of an accurate model challenging. Since each semi-empirical model has its own pros and cons, this paper puts forward a data fusion approach for online characteristics estimation of a FC stack utilizing four well-known models, namely Mann, Squadrito, Amphlett, and Srinivasan. Despite the other similar techniques, the suggested one utilizes the strengths of each mentioned FC model while avoiding their drawbacks. Kalman filter is employed to identify the parameters of the models online to embrace the uncertainties caused by the alteration of operating conditions and degradation level. Considering the parameters, the output voltage given by each model as well as their covariance are computed. Then, a covariance intersection algorithm is proposed to fuse the estimated output voltages. The fusion of the models’ outputs leads to the estimation of fused characteristics curves. To underline the effectiveness of the proposed method, it is applied to four different experimental datasets extracted from three 500-W Horizon FCs. The obtained results demonstrate the superior performance of the suggested estimator in the sense of mean square error. On average, the mean square error of the data fusion method is 39.64% and 36.59% lower than other studied methods while estimating the polarization curve and power curve, respectively.