Search Results (69)

This study is dedicated to the development of a multifunctional device that integrates air conditioning, humidification, and air purification functions, aimed at meeting the demands for energy efficiency, space-saving, and comfortable indoor environments in modern residential and commercial settings. The research focuses on achieving a balance between performance, energy consumption, and noise levels by combining bionic design principles with advanced optimization algorithms to propose innovative design and optimization methods. Specific methods include the establishment and optimization of mathematical models for air conditioning, air purification, and humidification functions. The air conditioning module employs a nonlinear programming model optimized through the Parrot Optimizer (PO) Algorithm to achieve uniform temperature distribution and minimal energy consumption. The air purification function is based on a bionic model and optimized using the Deep ACO Algorithm to ensure high efficiency and low noise levels. The humidification function utilizes a mist diffusion model optimized through the Slime Mold Algorithm (SMA) to enhance performance. Ultimately, a multi-objective optimization model is constructed using the Beluga Whale Optimization (BWO), successfully integrating the three main functions and designing a compact segmented cylindrical device that achieves a balance of high efficiency and multifunctionality. The optimization results indicate that the device exhibits superior performance, with a Clean Air Delivery Rate (CADR) of 400 m³/h, a humidification rate of 1.2 kg/h, a temperature uniformity index of 0.08, and a total power consumption controlled within 1600 W. This study demonstrates the significant potential of bionic design and optimization technology in the development of multifunctional indoor environment control devices, enhancing not only the overall performance of the device but also the comfort and sustainability of the indoor environment. Future work will focus on system scalability, experimental validation, and further optimization of bionic characteristics to expand the device’s applicability and enhance its environmental adaptability. Full article

True slime molds (Eumycetozoa) represent a monophyletic clade within the phylum Amoebozoa, comprising the lineages Myxogastria, Dictyostelia, and Protosporangiida. Although historically misclassified as fungi, recent molecular and biochemical studies underscore their distinct evolutionary trajectories and rich metabolomic profiles. In this review, we synthesize current knowledge on Eumycetozoa as a reservoir of bioactive compounds, detailing how secondary metabolites—including polysaccharides, amino acids, unsaturated fatty acids, terpenoids, and glycosides—vary across plasmodia, fruiting bodies, and spores. A systematic literature search in major scientific databases accounted for legacy nomenclature and leveraged chemoinformatic tools for compound verification. Our findings reveal 298 distinct metabolites that serve ecological roles in nutrient recycling and interspecies interactions, while also showing promise for controlling agricultural pests and pathogens. Notably, certain glycosides, lectins, and polyketides exhibit antimicrobial or cytotoxic activities, indicating their potential utility in managing these biological challenges. By consolidating current data and emphasizing the wide taxonomic range of Eumycetozoa, this review highlights the critical need for comprehensive biochemical and genomic investigations. Such efforts will not only advance our understanding of slime mold metabolomes and their evolutionary significance but also pave the way for innovative, eco-friendly applications. Full article

Tailings ponds can recycle water resources through the water recirculation system by clarifying and purifying the wastewater discharged from the mining production process. Due to factors such as flooding and heavy rainfall, once a tailings dams burst, the spread of heavy metals in the tailings causes underground and surface water pollution, endangering the lives and properties of people downstream. To effectively assess the potential impact of tailings dams bursting, many problems such as the difficulty of taking values in predicting the volume of silt penetration through empirical formulae, model testing, and numerical simulation need to be solved. In this study, 65 engineering cases were collected to develop a sample dataset containing dam height and storage capacity. The Support Vector Machine (SVM) algorithm was used to develop a nonlinear regression model for tailings discharge volume after tailings dam failure. In addition, the model penalty parameter C and kernel function g were optimized using the powerful global search capability of the Slime Mold Algorithm (SMA) to develop an SMA–SVM prediction model for tailings discharge volume. The results indicate that the volume of tailings discharged increases nonlinearly with increasing dam height and tailings storage capacity. The SMA-SVM model showed higher prediction accuracy compared to the predictions made by the Random Forest (RF), Radial Basis Function (RBF), and Least Squares SVM (LS-SVM) algorithms. The average absolute error in tailings discharge volume compared to actual values was 30,000 m³, with an average relative error of less than 25%. This is very close to practical engineering scenarios. The ability of the SMA-SVM optimization algorithm to produce predictions with minimal error relative to actual values was further confirmed by the combination of numerical simulations. In addition, the numerical simulations revealed the flow characteristics and inundation area of the discharged sediment during tailings dam failure, and the research results can provide reference for water resource protection and downstream safety prevention and control of tailings ponds. Full article

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra’s algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas. Full article

Myxomycetes are protists that predate microbial communities in soil and are heavily affected by changing climate conditions. As seen in a more distinct guild of myxomycete, their fructification diversity depends not only on the heterogeneity of vegetation but also on temperature and precipitation. To determine the reverse pattern of microbial diversity established in temperate ecozones, foliar and lignicolous litters were collected along a tropical montane site in the Philippines. Fifty-seven (57) morphospecies of myxomycetes from 15 genera were determined. Alpha-diversity analysis revealed a significant decline in species richness and diversity with increasing elevation. Beta-diversity analysis, integrating non-metric multidimensional scaling (NMDS), PERMANOVA, and hierarchical clustering, revealed the complex relationships between species turnover and community composition across elevational gradients. These results conform to the hypothesis that species richness decreases as elevation increases, supporting that tropical ecozones follow the general trend of myxomycete diversity that was first observed in the temperate ecozones. The strong role of elevation in shaping myxomycete community structure is further emphasized. This indicates that conservation management efforts should become more stringent in the areas found at the lower elevation of a tropical montane forest, which are more ecologically sensitive to human-induced stressors and climate-related pressures. Full article

During the trophic period of myxomycetes, the plasmodia of myxomycetes can perform crawling feeding and phagocytosis of bacteria, fungi, and organic matter. Culture-based studies have suggested that plasmodia are associated with one or several species of bacteria; however, by amplicon sequencing, it was shown that up to 31–52 bacteria species could be detected in one myxomycete, suggesting that the bacterial diversity associated with myxomycetes was likely to be underestimated. To fill this gap and characterize myxomycetes’ microbiota and functional traits, the diversity and functional characteristics of microbiota associated with the plasmodia of six myxomycetes species were investigated by metagenomic sequencing. The results indicate that the plasmodia harbored diverse microbial communities, including eukaryotes, viruses, archaea, and the dominant bacteria. The associated microbiomes represented more than 22.27% of the plasmodia genome, suggesting that these microbes may not merely be parasitic or present as food but rather may play functional roles within the plasmodium. The six myxomycetes contained similar bacteria, but the bacteria community compositions in each myxomycete were species-specific. Functional analysis revealed a highly conserved microbial functional profile across the six plasmodia, suggesting they may serve a specific function for the myxomycetes. While the host-specific selection may shape the microbial community compositions within plasmodia, functional redundancy ensures functional stability across different myxomycetes. Full article

Dictyostelids represent a crucial element in the protist community, and their abundant presence in Jilin Province underscores their indispensable role in biodiversity conservation. In the present study, a resource survey of dictyostelids used random sampling to collect 28 soil samples from five localities in Changbai Korean Autonomous County, Jilin Province. In addition, a compilation of dictyostelid species reported from Jilin Province was developed, based on a thorough review of the literature. The survey yielded fifteen isolates of dictyostelids, comprising six species from four genera. Notably, two new species (Dictyostelium longigracilis sp. nov. and Dictyostelium macrosoriobrevipes sp. nov.) were described using morphological characteristics and SSU gene-based phylogenetic analyses. One other species (Polysphondylium patagonicum) was recorded as new for China, while another (Cavenderia aureostipes) was recorded as a new record for Jilin Province. The dictyostelid assemblage in Jilin Province is dominated by the genus Dictyostelium (51.4%), with a total of 35 species, which represent 59.3% of the current total known for all of China. These findings provide a scientific basis for the protection of species diversity and resource utilization of dictyostelids in Jilin Province. Full article

Morchella spp. (true morels) are precious edible mushrooms consumed around the world, with a delicious taste, rich nutritional value, and unique healthcare effects. Various fungi and bacteria have been reported to colonize the ascocarps of Morchella, damaging their fruiting bodies and leading to serious economic losses in cultivation. The species identification of these colonizing organisms is crucial for understanding their colonization mechanisms on morels. Slime molds, which have characteristics of both “fungi” and “animals”, can occasionally colonize crops and edible fungi. However, there have been no reports of dictyostelid cellular slime molds (dictyostelids) colonizing plants and fungi to date. In this study, we discovered that dictyostelids colonized the surface of one wild ascoma of Morchella in the forest of Chongqing, China, with the tissues being black and rotten. Macro- and micro-morphological observations, along with molecular phylogenetic analyses, identified the specimens investigated in this study as Dictyostelium implicatum and Morchella sp. Mel-21. The results provide new knowledge of dictyostelid colonization on organisms and contribute to the diversity of species colonizing true morels. Moreover, this is also the first report of dictyostelids distributed in Chongqing, China. This study enhances our insights into the life history and potential ecological significance of dictyostelids and updates their distribution area in China. Further research will be conducted to uncover the mechanisms behind the colonization observed in this study. Full article

This study introduces a novel methodology for enhancing the efficiency of solar-powered unmanned aerial vehicles (UAVs) through azimuthal solar synchronization and aerodynamic neuro-optimization, leveraging the principles of slime mold neural networks. The objective is to broaden the operational capabilities of solar UAVs, enabling them to perform over extended ranges and in varied weather conditions. Our approach integrates a computational model of slime mold networks with a simulation environment to optimize both the solar energy collection and the aerodynamic performance of UAVs. Specifically, we focus on improving the UAVs’ aerodynamic efficiency in flight, aligning it with energy optimization strategies to ensure sustained operation. The findings demonstrated significant improvements in the UAVs’ range and weather resilience, thereby enhancing their utility for a variety of missions, including environmental monitoring and search and rescue operations. These advancements underscore the potential of integrating biomimicry and neural-network-based optimization in expanding the functional scope of solar UAVs. Full article

The production of municipal solid waste (MSW) has led to an unprecedented level of environmental pollution, worsening the global challenges posed by climate change. Researchers and policymakers have recently made significant strides in the field of sustainable and renewable energy sources, which are viable from technological, environmental, and economic perspectives. Consequently, the waste-to-energy programs enhance nations’ socioeconomic status while positively impacting the environment. To predict the higher heating value (HHV) of MSW fuel based on carbon, hydrogen, oxygen, nitrogen, and sulfur content, the current study introduces a Gradient Boosting Regression Tree (GBRT) model optimized with the Slime Mold Algorithm (SMA). This model was evaluated using an additional 50 data points after being trained with 202 MSW biomass data points. The performance of the model was assessed using three metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). The results indicated that our model outperformed previously developed models in terms of accuracy and reliability. Additionally, a graphical user interface (GUI) was developed to facilitate the practical application of the model, allowing users to easily input data and receive predictions on the enthalpy of the combustion of MSW fuel. Full article

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time. Full article

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. Full article

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. Full article

Cellular slime molds are excellent model organisms in the field of cell and developmental biology because of their simple developmental patterns. During our studies on the identification of bioactive molecules from secondary metabolites of cellular slime molds toward the development of novel pharmaceuticals, we revealed the structural diversity of secondary metabolites. Cellular slime molds grow by feeding on bacteria, such as Klebsiella aerogenes and Escherichia coli, without using medium components. Although changing the feeding bacteria is expected to affect dramatically the secondary metabolite production, the effect of the feeding bacteria on the production of secondary metabolites is not known. Herein, we report the isolation and structure elucidation of clavapyrone (1) from Dictyostelium clavatum, intermedipyrone (2) from D. magnum, and magnumiol (3) from D. intermedium. These compounds are not obtained from usual cultural conditions with Klebsiella aerogenes but obtained from coincubated conditions with Pseudomonas spp. The results demonstrate the diversity of the secondary metabolites of cellular slime molds and suggest that widening the range of feeding bacteria for cellular slime molds would increase their application potential in drug discovery. Full article

In the early stage of architectural design, addressing the challenges posed by negative spaces in high-density urban environments is crucial for enhancing spatial efficiency and building sustainability. Multiple studies employed digital methods and tools to address these issues, such as parametric design, simulation, and genetic algorithms, to investigate architectural generation approaches for urban negative spaces. This article proposes an integrated design process that involves finding the location and form of negative spaces, generating solutions using slime mold and wasp algorithms, and optimizing and analyzing solutions using the Wallacei plugin in Grasshopper. This comprehensive approach underscores the potential of parametric design to yield a multitude of solutions while also acknowledging the convergence challenges encountered during simulations, particularly in optimizing for optimal sunlight exposure during the winter solstice and minimal solar radiation in the summer. Analyzing the optimization goals and parameter values of the 15th Pareto optimal solution in the 100th generation reveals: (1) a higher number of units leads to positive correlation growth in both objectives; (2) within a certain number of units, parametrically generated solutions facilitate the convergence of optimization goals, yielding optimal outcomes. Therefore, factors such as the range of unit quantities and proportions need consideration during early-stage parametric design and simulation. This study explores a design methodology for negative spaces in high-density urban cities, validating the feasibility of various mainstream generation methods and offering insights for future research. Full article