Optimal stochastic power flow using enhanced multi-objective mayfly algorithm.

For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2-4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results.

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards.

Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar scenarios. This performance was observed to be consistent even under large-scale attacks.

Unravelling the diversity of the genus Afronurus Lestage, 1924 (Ephemeroptera, Heptageniidae) in Thailand.

The genus Afronurus in Thailand is investigated using an integrative approach (morphology based, ootaxonomy and molecular data) for species delimitation. A total of four species of Afronurus was identified; A.cervina (Braasch & Soldán, 1984), A.gilliesiana (Braasch, 1990), A.rainulfiana (Braasch, 1990), and A.rubromaculata (You et al., 1981). The subimago of A.gilliesiana is described for the first time based on reared specimens. The egg structure of all four species is also described for the first time. Morphological and molecular data strongly support their species delimitation. The egg chorionic structure of the genus Afronurus, together with other morphological characters, is useful for species identification. A key to mature nymphs of the known species is provided.

Toxicity of 6PPD-quinone to four freshwater invertebrate species.

The antioxidant N-(1,3-Dimethylbutyl)-N'-phenyl-p- phenylenediamine (6PPD) is used to protect the rubber in tires from oxidation, which extends the life of the tire. When oxidized, 6PPD is transformed into 6PPD-quinone (6PPDQ). 6PPDQ, along with other tire ingredients, can enter aquatic ecosystems through the transport of tire wear particles in runoff during a precipitation event. The mass mortality of coho salmon following precipitation events in urban areas lead to the discovery that 6PPDQ is the likely cause due to coho salmon's relatively high sensitivity to 6PPDQ. The assessment of 6PPDQ toxicity to other aquatic species has expanded, but it has focused on fish. This study investigated the toxicity of 6PPDQ to four freshwater invertebrate species, larval burrowing mayfly (Hexagenia spp.), juvenile cladoceran (Daphnia magna), file ramshorn snail embryo (Planorbella pilsbryi), and adult washboard mussel (Megalonaias nervosa). For all four species, the highest concentration of 6PPDQ tested did not result in significant mortality. This translated into the determination of the highest concentration that did not cause significant mortality (NOEC) for Hexagenia spp., D. magna, P. pilsbryi, and M. nervosa of 232.0, 42.0, 11.7, and 17.9 µg/L, respectively. The data from this study indicate that freshwater invertebrates are not as sensitive to 6PPDQ as some salmonid species (e.g., coho salmon Oncorhynchus kisutch). This study also analyzed 6PPDQ in road runoff from around the city of Guelph in Ontario, Canada. 6PPQ was detected in all samples but the concentration was two orders of magnitude lower than the NOECs for the four tested species of freshwater invertebrate.

Taxonomic review of the Oriental genus Polyplocia Lestage, 1921 (Ephemeroptera, Euthyplociidae), with two new records for Thailand.

Previously, the euthyplociid mayfly from Thailand was reported as Polyplocia spp. without designation to any nominal species. In this study, the genus Polyplocia Lestage, 1921 in Thailand was reviewed. Two species are recognized: Polyplociaorientalis Nguyen & Bae, 2003 and P.nebulosa Gonçalves & Peters, 2016. This is the first report of P.nebulosa in Continental Asia. Cytochrome c oxidase subunit I (COI) data and illustrations based on nymphal characters were used to confirm two different species in Thailand. Additional morphological characteristics of the nymph and eggs of the two species from Thailand are also shown in detail. The taxonomic status of Polyplocia in the Oriental region is reviewed. Keys to known nymphal and imaginal stages are provided and the distribution of Polyplocia in the Oriental region is discussed.

New Stage and Species Description on Brazilian Thraulodes Ulmer, 1920 (Ephemeroptera, Leptophlebiidae).

We examined material from two poorly documented areas in Brazil concerning the mayfly genus Thraulodes: Minas Gerais, the largest state in Southeastern Brazil, but also the least studied regarding mayflies; and Tocantins, located in the North-Central area of the country. As a consequence of these efforts, we describe a new species from Minas Gerais based on imagos and nymphs, as well as the nymphs of Thraulodes solstitium Orlando, Salles, Boldrini & Krolow, 2021. Thraulodes viola sp. nov. is characterized by the femora translucid with brown maculae and blackish stripe in distal half, and a light brown maculae in proximal half; abdominal terga II-VI translucent white with transversal reddish mark on posterior margins, one antero-lateral reddish dot, one antero-sublateral blackish spot, and one postero-submedian brown spot; penis lobes sinuous sideways, with lateral pouch present and very developed, and apico-lateral area with an ear-like projection; nymph with galea-lacinia dark brown, 14-16 medium-sized pectinate setae in subapical-ventral row. The nymph of T. solstitium nymph is characterized by the galea-lacinia dark brown, 11 medium-sized pectinate setae in a subapical-ventral row.

AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems.

The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes a metaheuristic algorithm for continuous and constrained global optimization problems, which combines the MOA, the Aquila Optimizer (AO), and the opposition-based learning (OBL) strategy, called AOBLMOA, to overcome the shortcomings of the MOA. The proposed algorithm first fuses the high soar with vertical stoop method and the low flight with slow descent attack method in the AO into the position movement process of the male mayfly population in the MOA. Then, it incorporates the contour flight with short glide attack and the walk and grab prey methods in the AO into the positional movement of female mayfly populations in the MOA. Finally, it replaces the gene mutation behavior of offspring mayfly populations in the MOA with the OBL strategy. To verify the optimization ability of the new algorithm, we conduct three sets of experiments. In the first experiment, we apply AOBLMOA to 19 benchmark functions to test whether it is the optimal strategy among multiple combined strategies. In the second experiment, we test AOBLMOA by using 30 CEC2017 numerical optimization problems and compare it with state-of-the-art metaheuristic algorithms. In the third experiment, 10 CEC2020 real-world constrained optimization problems are used to demonstrate the applicability of AOBLMOA to engineering design problems. The experimental results show that the proposed AOBLMOA is effective and superior and is feasible in numerical optimization problems and engineering design problems.

SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19.

This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method..

Feature level fine grained sentiment analysis using boosted long short-term memory with improvised local search whale optimization.

Background: In the modern era, Internet-based e-commerce world, consumers express their thoughts on the product or service through ranking and reviews. Sentiment analysis uncovers contextual inferences in user sentiment, assisting the commercial industry and end users in understanding the perception of the product or service. Variations in textual arrangement, complex logic, and sequence length are some of the challenges to accurately forecast the sentiment score of user reviews. Therefore, a novel improvised local search whale optimization improved long short-term memory (LSTM) for feature-level sentiment analysis of online product reviews is proposed in this study. Methods: The proposed feature-level sentiment analysis method includes 'data collection', 'pre-processing', 'feature extraction', 'feature selection', and finally 'sentiment classification'. First, the product reviews given from different customers are acquired, and then the retrieved data is pre-processed. These pre-processed data go through a feature extraction procedure using a modified inverse class frequency algorithm (LFMI) based on log term frequency. Then the feature is selected via levy flight-based mayfly optimization algorithm (LFMO). At last, the selected data is transformed to the improvised local search whale optimization boosted long short-term memory (ILW-LSTM) model, which categorizes the sentiment of the customer reviews as 'positive', 'negative', 'very positive', 'very negative', and 'neutral'. The 'Prompt Cloud dataset' is used for the performance study of the suggested classifiers. Our suggested ILW-LSTM model is put to the test using standard performance evaluation. The primary metrics used to assess our suggested model are 'accuracy', 'recall', 'precision', and 'F1-score'. Results and Conclusion: The proposed ILW-LSTM method provides an accuracy of 97%. In comparison to other leading algorithms, the outcome reveals that the ILW-LSTM model outperformed well in feature-level sentiment classification.

A new species of Siphlonurus Eaton, 1868 (Ephemeroptera, Siphlonuridae) from Yunnan, China.

Siphlonurusdongxi Li & Tong, sp. nov. from Shangri-La City, Yunnan Province, China, is described based on egg, nymph, and winged stages. The new species is closely related to S.davidi (Navás, 1932), and can be distinguished by the colour of the imago, the forking point of MP, the penis, posterolateral spines of tergum IX of imagoes, and first abdominal terga nymph, as well as the structure of the egg. The new species and S.davidi have the same morphological and structural characteristics, such as the long cubital area with many intercalaries, cross veins between C, Sc, RA, and RSa1 surrounded with distinct pigments, the strong curvature of vein CuP in the forewing, the broad expansion of the hindwing, the membranous penis lobes fused without teeth, supporting the proposition of a new species complex, the Siphlonurusdavidi group. The structures of the penis and the egg of the new species could help understand the origin and evolution of the genus Siphlonurus.

Hybrid optimized feature selection and deep learning based COVID-19 disease prediction.

The COVID-19 virus has affected many people around the globe with several issues. Moreover, it causes a worldwide pandemic, and it makes more than one million deaths. Countries around the globe had to announce a complete lockdown when the corona virus causes the community to spread. In real-time, Polymerase Chain Reaction (RT-PCR) test is conducted to detect COVID-19, which is not effective and sensitive. Hence, this research presents the proposed Caviar-MFFO-assisted Deep LSTM scheme for COVID-19 detection. In this research, the COVID-19 cases data is utilized to process the COVID-19 detection. This method extracts the various technical indicators that improve the efficiency of COVID-19 detection. Moreover, the significant features fit for COVID-19 detection are selected using proposed mayfly with fruit fly optimization (MFFO). In addition, COVID-19 is detected by Deep Long Short Term Memory (Deep LSTM), and the Conditional Autoregressive Value at Risk MFFO (Caviar-MFFO) is modeled to train the weight of Deep LSTM. The experimental analysis reveals that the proposed Caviar-MFFO assisted Deep LSTM method provided efficient performance based on the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), and achieved the recovered cases with the minimal values of 1.438 and 1.199, whereas the developed model achieved the death cases with the values of 4.582 and 2.140 for MSE and RMSE. In addition, 6.127 and 2.475 are achieved by the developed model based on infected cases.

Future forecasting prediction of Covid-19 using hybrid deep learning algorithm.

Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

Salinity-induced ionoregulatory changes in the gill proteome of the mayfly, Neocloeon triangulifer.

Ecologists have observed declines in the biodiversity of sensitive freshwater organisms in response to increasing concentrations of major ions (salinization). Yet, how changing salinities physiologically challenge aquatic organisms, such as mayflies, remains remarkably understudied. Moreover, it is not well understood the degree to which species respond and acclimate to salinity changes. Our lab is developing the Baetid mayfly, N. triangulifer, as a model organism for physiological research. We have previously described acclimatory changes in both ion flux rates and altered mRNA transcript levels in response to chronic exposures to elevated major ion concentrations at the whole animal level. In the present study, we use shotgun proteomics to identify the specific proteins associated with apical ion transport and how their abundance changes in response to chronic salinity exposures in gills. Gills were isolated from the penultimate nymphal stage of N. triangulifer reared under control culture conditions, elevated NaCl (157 mg L-1 Na), elevated CaCl2 (121 mg L-1 Ca), elevated Ca/MgSO4 (735 mg L-1 SO4). These conditions mirrored those from previously published physiological work. We also acutely exposed nymphs to dilute (50% dilution of culture water with deionized water) to explore proteomic changes in the gills in response to dilute conditions. We report 710 unique peptide sequences among treatment groups, including important apical ion transporters such as Ca-ATPase, Na/K ATPase, and V-ATPase. Treatment with elevated NaCl and Ca/MgSO4 appeared to cause more significant differential protein expression (452 and 345, respectively) compared to CaCl2 and dilute groups (134 and 17, respectively). Finally, we demonstrated the breadth of physiological functions in gills by exploring non-transport related pathways found in our dataset, including ATP synthesis, calcium signaling, and oxidative stress response. We discuss our results in the context of freshwater salinization and the challenges of working with non-model species without fully sequenced and annotated genomes.

A preliminary review of Isonychia Eaton, 1871 from Chinese mainland with a re-description of I.kiangsinensis Hsu, 1936 (Insecta, Ephemeroptera, Isonychiidae).

Previously, seven species of the genus Isonychia Eaton, 1871 were reported in China, but they have never been systematically reviewed. After examining our collections from the Chinese mainland, six species and one additional subspecies have been recognized, compared, and photographed. Among them, I.kiangsinensis is redescribed in all stages and a neotype is designated. Its males have triangular penes and nymphs have three dark pigments on each gill. A synonym of I.guixiensisWu et al., 1992 (I.sinensisWu et al., 1992) is confirmed. The males of this species have nearly cylindrical penes and clear abdominal markings. Finally, two species and one subspecies are recorded for the first time in China: I.ussuricasibiricaTiunova et al., 2004, I.ussuricaussurica Bajkova, 1970 and I.vshivkovaevshivkovaeTiunova et al., 2004. Together with the I.ignota (Walker, 1853), I.sexpetalaTiunova et al., 2004, I.formosana (Ulmer, 1912) and possible I.japonica (Ulmer, 1920), they show the rich diversity of the genus Isonychia in China.

A Review of the Genus Serratella Edmunds, 1959 in China with Description of a New Species (Ephemeroptera: Ephemerellidae).

Species in the genus Serratella Edmunds, 1959 from China have never been compared and photographed systematically. Six valid Chinese Serratella species are recognized and revised in this paper. Among them, the imagos of S. brevicauda Jacobus et al., 2009 are unknown; the nymph of this species has a stout, strong body, with remarkably short caudal filaments and maxillary palpi. In contrast, only the imago stage of Serratella fusongensis (Su and You, 1988) (=Serratella longipennis Zhou et al., 1997, syn. nov.) is known; it has relatively long penes with small dorsal projections. The nymphs of S. setigera Bajkova, 1967 have small abdominal tergal spines but distinct, stout, blunt bristles on the spines, and the apexes of the male penes are round and shallowly divided. The fourth species, S. acutiformis sp. nov., which was collected from Western China, has sharp penial apexes (imagos) and large abdominal spines (nymphs). Unlike the former four species, S. ignita (Poda, 1761) and S. zapekinae Bajkova, 1967 has sub-quadrate penes without prominent dorsal projections. The nymph of S. ignita has lateral hair-like setae on the caudal filaments, while the nymph of S. zapekinae lacks such setae but has pairs of tubercles on the head and pronotum. Some characters used in the generic delineation of the genera Ephemerella Walsh, 1862 and Serratella, such as nymphal maxillary palpi and hair-like setae on caudal filaments as well as features of the imaginal penes and forelegs, are varied in Chinese species. However, all species in this paper have bifurcate ventral lamellae of gill VI. Our work highlights a need for further comparative systematic study of the genera Serratella, Ephemerella, and another related genus Torleya Lestage, 1917.

A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks.

In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network's lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.

Comparative Mitogenome Analyses of Subgenera and Species Groups in Epeorus (Ephemeroptera: Heptageniidae).

Epeorus Eaton, 1881 is a diverse mayfly genus in Heptageniidae comprising more than 100 species which are further divided into nine subgenera and several species groups. However, the classification and the phylogenetic relationships among them are still uncertain. Here, 15 complete mitochondrial genomes of Epeorus were sequenced and compared together with six available ones of same genus in the NCBI database. Based on morphological classification, the 21 mitogenomes were classified into six subgenera (Proepeorus, Epeorus s.str., Belovius, Iron, Caucasiron and Siniron) and four species groups (G1, G2, montanus and longimanus). Among all analyzed mitogenomes, the gene rearrangement of trnI-trnM-trnQ-NCR-ND2 was first found occurring in three species of group G1, whereas the gene block trnI-trnM-trnQ-trnM-ND2 was observed in all other mitogenomes of Epeorus. Furthermore, the genetic composition and codon usage of species in group G1 were also significantly different from all other Epeorus species, except group longimanus. The intergenic spacer between trnA and trnR, which has the stem-loop secondary structure, occurred in all 21 mitogenomes, and the sequences of stems and loops were conserved within species groups. Furthermore, the phylogenetic analyses strongly support the monophyly of all species groups, although three of six recognized subgenera Proepeorus, Belovius, and Iron, were shown as the non-monophyletic groups.

Characterizing Hox genes in mayflies (Ephemeroptera), with Hexagenia limbata as a new mayfly model.

BACKGROUND: Hox genes are key regulators of appendage development in the insect body plan. The body plan of mayfly (Ephemeroptera) nymphs differs due to the presence of abdominal appendages called gills. Despite mayflies' phylogenetic position in Paleoptera and novel morphology amongst insects, little is known of their developmental genetics, such as the appendage-regulating Hox genes. To address this issue we present an annotated, early instar transcriptome and embryonic expression profiles for Antennapedia, Ultrabithorax, and Abdominal A proteins in the mayfly Hexagenia limbata, identify putative Hox protein sequences in the mayflies H. limbata, Cloeon dipterum, and Ephemera danica, and describe the genomic organization of the Hox gene cluster in E. danica. RESULTS: Transcriptomic sequencing of early instar H. limbata nymphs yielded a high-quality assembly of 83,795 contigs, of which 22,975 were annotated against Folsomia candida, Nilaparvata lugens, Zootermopsis nevadensis and UniRef90 protein databases. Homeodomain protein phylogeny and peptide annotations identified coding sequences for eight of the ten canonical Hox genes (excluding zerknüllt/Hox3 and fushi tarazu) in H. limbata and C. dipterum, and all ten in E. danica. Mayfly Hox protein sequences and embryonic expression patterns of Antp, Ubx, and Abd-A appear highly conserved with those seen in other non-holometabolan insects. Similarly, the genomic organization of the Hox cluster in E. danica resembles that seen in most insects. CONCLUSIONS: We present evidence that mayfly Hox peptide sequences and the embryonic expression patterns for Antp, Ubx, and Abd-A are extensively conserved with other insects, as is organization of the mayfly Hox gene cluster. The protein data suggest mayfly Antp, Ubx, and Abd-A play appendage promoting and repressing roles during embryogenesis in the thorax and abdomen, respectively, as in other insects. The identified expression of eight Hox genes, including Ubx and abd-A, in early instar nymphs further indicates a post-embryonic role, possibly in gill development. These data provide a basis for H. limbata as a complementary Ephemeridae model to the growing repertoire of mayfly model species and molecular techniques.

The acclimatory response of the mayfly Neocloeon triangulifer to dilute conditions is linked to the plasticity of sodium transport.

Relative to a growing body of knowledge about the negative consequences of freshwater salinization, little is known about how aquatic insects respond to progressively ion-poor conditions. Here, we examined life-history and physiological acclimation in Neocloeon triangulifer by rearing nymphs from 1-day post-egg hatch to adulthood across a gradient of decreasing Na concentrations (15, 8, 4, 2 and 1 mg l-1 Na). We found no significant changes in survival, growth, development time and whole-body Na content across these treatments. Radiotracer data revealed that nymphs acclimated to their dilute exposures by increasing their rates of Na uptake and were able to maintain a relatively narrow range of uptake rates (±s.e.m.) of 38.5 ± 4.2 µg Na g-1 h-1 across all treatments. By contrast, the Na uptake rates observed in naive nymphs were much more concentration dependent. This acclimatory response is partially explained by differences in ionocyte counts on the gills of nymphs reared under different salinities. Acclimated nymphs were surprisingly less retentive of their sodium composition when subjected to deionized water challenge. By contrasting our findings with a previous N. triangulifer salinity acclimation study, we show a physiological affinity for dilute conditions in this emerging mayfly model.