Slime Mold Research Articles

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

Slime mould algorithm with mechanism of leadership and self-phagocytosis for multilevel thresholding of color image

Threshold based segmentation method is widely used because of its simplicity, high efficiency and easy implementation. However, as the number of thresholds increases, the algorithm's search space also increases, increasing computational complexity, which reduces computational efficiency and segmentation accuracy. Additionally, the existing multi-threshold (MT) segmentation methods still struggle with slow convergence speed and low solution accuracy. The optimum threshold for the image is determined in this study using the Slime Mould Algorithm with the Mechanism of Leadership and Self-phagocytosis (SMA-MLS). Firstly, the initial population production method based on Logistic-Tent mapping is introduced to enhance the diversity of algorithm solutions. Secondly, the position update formula with leadership mechanism is proposed to improve the convergence speed and accuracy. In addition, the adaptive combined mutation mechanism balances the exploration and exploitation ability. Finally, the self-phagocytosis mechanism maintains population diversity and heritability. A series of benchmark test suites are employed to evaluate the performance of the algorithm. Experimental results demonstrate that SMA-MLS does show good performance. It is superior in terms of convergence accuracy, convergence speed and stability. In the MT segmentation experiments, by using Kapur as the objective function and working with color images, SMA-MLS proved to be effective in the MT segmentation problem of images. Moreover, SMA-MLS will be applied to mechanical parameter optimization, neural network and other fields in future research.

The use of slime molds as a design guide in urban design, A case of Turkey/İzmir/Karabağlar

Urban design is a complex and challenging process that encompasses many disciplines, such as urban planning, architecture, and landscape. On the urban design scale, it’s important to consider short paths compatible with pedestrian behaviors, especially in public spaces, in terms of designing healthier and more accessible urban areas. But conventional design processes, which are mostly based on the designer’s observations or intuitions, fail to satisfy user behaviors in usage. Studies reveal that P. polycephalum slime molds have the potential to guide good urban planning. However, it is noteworthy that there are not enough studies yet in the disciplines of architecture and urban design on the design of pedestrian ways in small-scale areas, and there is a need for research. In this direction, this study is the first step of a method that is aimed at being developed for the disciplines of architecture and urban design in the long process. The main hypothesis is that a design template can be created as a result of graphical analysis of living slime mold experiments on the map, especially in undesigned empty urban areas, for architects and planners, and it can provide creative suggestions to the designer. In this direction, in this study, a graphic design template proposal was created based on live cell experiments at a site in Izmir, Turkey, which the local government aims to design as a pedestrianized zone and open for competition. Slime molds are used directly as a computer rather than as inspiration for a computer, and a pedestrian-oriented design template proposal develops with the behavioral models of slime molds. Compared to the award-winning designs developed for the same area, it has been shown that even within the framework of simple graphical measurements, it gives better results and creates more optimal routes on main and side roads. It is envisaged that this study can play a pioneering role for studies involving more detailed mathematical models that can begin in the field of architecture and urban design.

Enhancing slime mould algorithm for engineering optimization: leveraging covariance matrix adaptation and best position management

Abstract The slime mould algorithm (SMA), as an emerging and promising swarm intelligence algorithm, has been studied in various fields. However, SMA suffers from issues such as easily getting trapped in local optima and slow convergence, which pose challenges when applied to practical problems. Therefore, this study proposes an improved SMA, named HESMA, by incorporating the covariance matrix adaptation evolution strategy (CMA-ES) and storing the best position of each individual (SBP). On one hand, CMA-ES enhances the algorithm’s local exploration capability, addressing the issue of the algorithm being unable to explore the vicinity of the optimal solution. On the other hand, SBP enhances the convergence speed of the algorithm and prevents it from diverging to other inferior solutions. Finally, to validate the effectiveness of our proposed algorithm, this study conducted experiments on 30 IEEE CEC 2017 benchmark functions and compared HESMA with 12 conventional metaheuristic algorithms. The results demonstrated that HESMA indeed achieved improvements over SMA. Furthermore, to highlight the performance of HESMA further, this study compared it with 13 advanced algorithms, and the results showed that HESMA outperformed these advanced algorithms significantly. Next, this study applied HESMA to five engineering optimization problems, and the experimental results revealed that HESMA exhibited significant advantages in solving real-world engineering optimization problems. These findings further support the effectiveness and practicality of our algorithm in addressing complex engineering design challenges.

An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_2$$\\end{document}-norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.

Slime molds (Myxomycetes) causing a "disease" in crop plants and cultivated mushrooms.

Myxomycetes (plasmodial slime molds) are eukaryotic protist predators that are associated with wood, leaf litter, and soil in forests, where they feed on bacteria, protozoans, and (to a more limited extent) fungi. The health of crop plants is essential because they represent a primary food source for humans. However, when myxomycetes produce numerous fruiting bodies on the stems and leaves of crop plants, which is herein referred to as a myxomycete colonization, this has the potential of interfering with plant photosynthesis, transpiration and respiration by blocking out light and covering stomata. Myxomycetes are not pathogens, but their occurrence on plants can be mistakenly interpreted as some type of infection. However, this phenomenon has been largely ignored. This paper provides a comprehensive overview of the taxonomic and economic diversity of the organisms involved in myxomycete colonization. In addition, the various types of myxomycete colonization reported in the literature are described and discussed, a number of images provided, and cultural and chemical prevention and control measures are summarized. The latter should be of significant relevance for local production of crops and plant protective stations. While myxomycetes are not pathogens of crop plants, some species can seriously impact commercially grown mushrooms. Reports of myxomycetes affecting mushrooms are also described in this paper.

An improved log-cosine variation slime mold - simplified gated recurrent neural network for the high-precision state of charge estimation of lithium-ion batteries

Accurate estimation of SOC is crucial for the safe and efficient utilization of electric vehicles, portable devices and renewable energy systems. Neural networks have become a powerful tool in the field of SOC estimation, but they have a complex structure, difficult parameter tuning, and high performance requirements. For this reason, this paper proposes a simplified gated recurrent neural network (SGRN) that greatly simplifies the unitary network structure and parameters. A Nadam gradient optimizer is introduced to update the network weight parameters, and the network hyperparameters are optimized using the log-cosine variant slime mold algorithm (LVSM). The algorithm is validated under HPPC, BBDST, and DST operating conditions under 25 °C, 10 °C, and − 5 °C. The results show that the LVSM-SGRN algorithm has higher accuracy, lower performance consumption, and better temperature adaptability than the conventional LSTM algorithm as well as the EKF algorithm. This contributes to the integration and large-scale industrialization of the algorithm, and comprehensively improves the safety of lithium-ion batteries and accuracy of SOC estimation.

A metaheuristic-optimization-based neural network for icing prediction on transmission lines

Ice accretion on overhead transmission line systems is a leading cause of power outages and can lead to substantial economic losses in northern regions. Therefore, accurately and rapidly predicting ice accretion on power lines is crucial for ensuring the safe operation of the power grid. This study introduces a machine learning method for predicting the ice-to-liquid ratio (ILR), an important parameter for assessing ice accretion efficiency. While estimating ILR is vital for operational forecasting, many existing ice accretion models do not include this capability. A feedforward neural network (FFNN) trained with stochastic gradient descent and various metaheuristic optimizers - specifically particle swarm optimization, grey wolf optimizer, whale optimizer, and slime mold optimizer - is employed to forecast hourly ILR. Environmental data required for training and testing the FFNN model were obtained from the Automated Surface Observing System (ASOS). A global sensitivity analysis using the Sobol index, evaluated via the coefficients of a polynomial chaos expansion, was conducted to identify the most influential input parameters. The results indicate that only four input parameters significantly contribute to the variance in the response: precipitation, temperature, dew point temperature, and wind speed. Furthermore, the FFNN model trained with metaheuristic optimizers outperformed the stochastic gradient descent approach. With the predicted ILR, ice accumulation can be easily calculated as the product of ILR and the amount of liquid precipitation depth.

Adaptive Channel Equalization for Digital Communication with Tunicate Swarm Algorithm

In wireless communication systems, the information is transferred as digital symbols these symbols are affected by noise interference while transmitting through the wireless channel. In today’s world, the increased demand for rapid data transmission rates leads to more inter-symbol interference in the received signal. To diminish the effects of inter-symbol interference adaptive channel equalization is utilized in digital communication. In this paper, the inter-symbol interference effect in the finite impulse response channel is reduced in terms of an adaptive equalizer method. The weights/coefficients of the equalizer are optimized through a tunicate swarm algorithm. Channel equalization involves optimizing the channel coefficients to mitigate the impact of inter-symbol interference. This equalization process can be viewed as an iterative optimization task, aimed at lessening the mean square error among the transmitted signal and the equalizer’s output. The implementation of the proposed method is executed using MATLAB software. We assess its effectiveness through a comparative analysis, considering metrics such as mean square error, learning rate, bit error rate, convergence rate, and computational time. Furthermore, we conduct a comparative evaluation with other optimization approaches, including the Bat Algorithm, Slime Mould Algorithm, and Harris Hawks Optimization Algorithm. This comparison demonstrates the superiority of the proposed algorithm over traditional optimization techniques.

SExpSMA-based T5: Serial exponential-slime mould algorithm based T5 model for question answer and distractor generation

Generally, multiple choice questions are an effective and extensive form used in standard tests in order to evaluate the learner’s skills and knowledge. Nonetheless, the multiple-choice question composition particularly the distractor construction is quite difficult. The distracters are needed to be both plausible and inappropriate and adequate to mystify the learners who did not master the information. Thus, the distractor generation emergence is important that can help several standard tests in an extensive range of domain. In this research, question-answer generation system is developed with a distractor model by developing an optimized T5 model. At first, BERT tokenization is used to pre-process the passage/context and question, which are given as the input to train the approach. Then, the question and answer generation is performed by utilizing the T5 approach that is trained by proposed Serial Exponential-Slime Mould approach (SExpSMA). Exponential weighted moving average is extended to Serial Exponential weighted moving average and incorporated in Slime Mould Algorithm (SMA) to propose SExpSMA. In addition, the proposed SExpSMA-based T5 model is employed to generate distractors for the questions. Eventually, experimentation analysis exhibits that proposed SExpSMA-based T5 model achieves better outcomes regarding the metrics, like ROUGE, BLEU, and METEOR with the values of 0.919, 0.918, 0.488, respectively.

Identifying and estimating solar cell parameters using an enhanced slime mould algorithm

This study proposed an enhanced slime mould algorithm (ESMA) for identifying the solar cells’ parameters for five photovoltaic (PV) models, making two modifications to the original slime mould algorithm (SMA). The first modification was an arbitrary average position among the new individual position of slime and the best individual position of the slime mound currently found to resolve the issue of local optimum. Second, the tangent hyperbolical function of the formula p in the original SMA was replaced with an exponential function to create more variations for selecting the updated equation. The proposed ESMA was used to resolve the problem of estimating PV parameters based on the empirical current-voltage (I-V) data. Specifically, ESMA was evaluated on the parameter estimation in several photovoltaic models, i.e., the one-diode model (ODM), dual-diode model (DDM), and PV-module model (PMM). In general, ESMA outperformed the original SMA and other recent algorithms. Also, in order to provide a close approximation of the empirical I-V data of the real PV modules and cells, ESMA was able to determine the optimal parameter values for photovoltaic models.

First Report of White Mucus Disease Caused by Fuligo gyrosa on Agrocybe chaxingu in China.

Agrocybe chaxingu is an edible and medicinal mushroom widely cultivated in China (Liu et al. 2021). Agrocybe chaxingu is extremely well-liked for the unique flavor and nutritional value. In May 2021, a serious white mucus disease was observed in the farms of A. chaxingu in the Ganxian district of Ganzhou City, Jiangxi Province, China, with an approximate disease incidence of 20%. In the years of 2022 and 2023, the same white mucus disease on A. chaxingu was observed in the farms in Nanchang City, Jiujiang City and Guangchang County, Jiangxi Province, China. The disease generally occurs on the media, stipe or pileus of A. chaxingu under condition of high humidity. The plasmodial slime molds migrated from the surface of culture media (78% hardwood sawdust, 15% wheat bran, 5% tea seed shell, 1% lime, and 1% gypsum) to the base of fruiting bodies, stipes and finally to pilei, showing as moist, sticky, and white reticulated structures. The infected fruiting bodies of A. chaxingu were completely covered by reticulated plasmodia, displaying a white or pale-yellow color. This resulted in the growth cessation, wilting and eventual death of fruiting body. Microscopic observation found that the plasmodia of slime mold enveloped the hyphae of A. chaxingu, resulting in the fragmentation of the hyphae. The disease can spread quickly, resulting in a 30% reduction in production. Slime mold cultures were isolated by transferring diseased fruiting bodies of A. chaxingu onto oat-agar medium (2% agar and 1% oatmeal) at 25 ℃. The isolates can be obtained after being subcultured for two to three generations. Purified plasmodia were placed on the semi-defined medium (1% tryptone, 1% glucose, 0.15% yeast extract, chick embryo extract and a balanced salt solution) to confirm the absence of bacteria (Daniel et al. 1964) and thus obtained the pure culture. Specimen of the voucher has been deposited in the Institute of Agricultural Applied Microbiology, Jiangxi Academy of Agricultural Sciences as number IAAM-W0002. The vegetative plasmodia have a large and well-developed scalloped structure that were white or milky white in colour. The white plasmodium became opaque pale yellow when exposed to light before fruiting. The veins merged and thickened. Fruiting bodies can be formed on the lid or side of the Petri dish under light condition. The fruiting bodies formed papillae with irregular shape, and then the color changed from translucent yellow to greyish black. Spores were usually spherical or subglobose, free, greyish brown in mass, purplish brown, 7-12 μm in diameter under light microscopy. These morphological characteristics were found to be consistent with those of Fuligo gyrosa (Synonym: Physarum gyrosum) (Kim et al. 2009; Shi et al. 2005; Jahn 1902). The identity of the isolates was further confirmed by sequence analysis of the 18S ribosomal RNA gene with primer SMNUR101/NS4 (Rusk et al. 1995; White et al. 1990). Using BLASTn searches, the sequence of 18S rRNA gene (GenBank accession number OR186216) matched the sequence of F. gyrosa (GenBank accession number LC744593) with the identity of 99.91% and coverage of 97%. A phylogenetic tree based on the 18S rRNA gene also demonstrated that the slime mold clustered with F. gyrosa. Over ten isolates have been obtained from the diseased A. chaxingu samples in different factories and identified as F. gyrosa. To test the pathogenicity of F. gyrosa, five healthy young fruiting bodies (three to five days of primordium) of A. chaxingu cultivated in mushroom-growing room were gently inoculated by a 12 mm diameter oat-agar medium with plasmodia at 24 ± 2 ℃ and then were kept with relative humidity of 90%-95%. Five fruiting bodies inoculated with a 12 mm oat-agar medium served as controls. After 5 days, white mucus characteristics and three fifths of death symptoms were observed on the fruiting bodies inoculated with the plasmodia, while the controls remained asymptomatic. The slime mold on the inoculated fruiting bodies was morphologically identical to F. gyrosa that was observed on the initial diseased fruiting bodies. It was also observed the envelopment A. chaxingu hyphae by the plasmodia of slime mold and fragmentation of the hyphae, and the fragmentation was not observed in the controls. Reisolations were prepared from the inoculated fruiting bodies and confirmed to be F. gyrosa based on morphological characteristics and 18S rRNA sequence, thus fulfilling Koch's postulates. Fuligo gyrosa has been reported to cause severe disease in oriental melon in Korea (Kim et al. 2009). This is the first report of F. gyrosa causing white mucus disease in cultivated A. chaxingu. The findings will provide important information on prevention and control of the disease, and be helpful for the development of A. chaxingu industry.

Multi-objective optimization of solar resource allocation in radial distribution systems using a refined slime mold algorithm

The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.

Bearing Fault Diagnosis Based on SMA-VMD and CNN-LSTM

To address the challenges of manual parameter setting dependency and low accuracy in bearing fault diagnosis using Variational Mode Decomposition (VMD), a method integrating Slime Mould Algorithm (SMA), VMD, and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network is proposed. Firstly, the SMA is employed to optimize the critical parameters of VMD using minimum information entropy as the fitness function, which resolves the issues related to manual parameter settings in VMD. Subsequently, the optimal parameter combination is used to process the bearing signals and extract the relevant Intrinsic Mode Functions (IMF). Finally, these IMFs are input into the CNN-LSTM network for fault diagnosis. The results demonstrate that this method significantly enhances the accuracy of motor bearing fault diagnosis, further improving the fault diagnosis accuracy.

Nannostelium ampullaceum gen. et sp. nov., a tiny new member of the protosteloid amoeba of the Cavosteliida (Variosea, Amoebozoa)

Protosteloid amoebae are slime molds characterized by simple sporocarp comprised only of a stalk and typically single spore. Recent molecular phylogenetic studies showed that protosteloid amoebae are scattered among the Amoebozoa, but most of them belongs to the Variosea. The Cavosteliida is one of the largest protosteloid group of the Variosea. We have established novel protosteloid amoeba strain YIP-63 from the fruiting body of jelly fugus Auricularia sp. The strain YIP-63 is unique to have a tiny sporocarp and amoeba comparing to the other protosteloid species, and trophic amoebae forming both round and branched shape. The molecular phylogenetic analyses based on 18S rRNA gene suggest that YIP-63 represents a novel lineage in the Cavosteliida. Therefore, we propose the new genus and new species for the strain YIP-63 as Nannostelium ampullaceum gen. et sp. nov. We provide morphological and molecular data on this novel protosteloid amoeba.

Diversity of Cellular Slime Molds (Dictyostelids) in the Fanjing Mountain Nature Reserve and Geographical Distribution Comparisons with Other Representative Nature Reserves in Different Climate Zones of China.

Protected areas are widely considered an essential strategy for biodiversity conservation. Dictyostelids are unique protists known to have important ecological functions in promoting soil and plant health through their top-down regulation of ecosystem processes, such as decomposition, that involve bacterial populations. But the relationship between dictyostelid diversity within protected areas remains poorly understood, especially on a large scale. Herein, we report data on the distribution of dictyostelids, identified with ITS + SSU rRNA molecular and morphology-based taxonomy, from soil samples collected in the Fanjing Mountain protected area of Guizhou Province, Southwest China. We compared the biodiversity data of dictyostelids in Fanjing Mountain with similar data from previously sampled sites in four other protected areas, including Changbai Mountain (CB), Gushan Mountain (GS), Baiyun Mountain (BY), and Qinghai-Tibet Plateau (QT) in China. We identified four species of dictyostelids belonging to three genera (Dictyostelium, Heterostelium, and Polysphondylium) and herein provide information on the taxonomy of these species. Two species (Heterostelium pallidum and Dictyostelium purpureum) are common and widely distributed throughout the world, but one species (Polysphondylium fuscans) was new to China. Our data indicate that there is no distinguishable significant correlation between the dictyostelid species studied and environmental factors. Overall, the similarity index between Baiyun Mountain in Henan Province and Fanjing Mountain in Guizhou Province, located at approximately the same longitude, is the highest, and the Jaccard similarity coefficients (Jaccard index) of family, genus, and species are 100%, 100%, and 12.5%, respectively. From a species perspective, species in the same climate zone are not closely related, but obvious geographical distributions are evident in different climate zones. This preliminary study provided evidence of the ecological adaptation of dictyostelids to different biological niches.

The type material in the David W. Mitchell myxomycete collection at the Royal Botanic Garden, Kew

A study of the myxomycete (slime mould) type material in the David W. Mitchell collection, deposited at the K Fungarium of the Royal Botanic Garden Kew, is presented. His collection includes almost 8000 boxes with specimens of myxomycetes, slides, notebooks, and other materials. The search and organisation of the material preserved in Mitchell’s collection, reveal a total of 58 samples of type material (2 holotypes, 29 isotypes, 24 paratypes, 1 isolectotype, 1 syntype, and 1 cultotype). These include type material of 11 taxa authored or co-authored by Mitchell and 42 taxa received as exchange from colleagues. Information and images of the boxes, labels and/or slides of this type material, origin and conservation of the holotypes, and complementary data on these collections, are included. In addition, a list of the publications on myxomycetes of this author and a list of the nomenclatural proposals (new taxa or combinations) published by D.W. Mitchell, are provided.

Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices.