Dung Beetles Research Articles

Control strategy design and performance analysis of a low carbon and high ratio green energy CCHP (LCHRG-CCHP) system

In order to solve the compatibility of combined cooling, heating and power (CCHP) system with green energy and the coupling problem with energy storage equipment, the system structure (LCHRG-CCHP system) proposed in this paper for the low-carbon and high-green energy compatible optimization research of the integrated energy system integrating green energy and flexible hybrid energy storage. Aiming at the high compatibility and low carbon emission of CCHP system to green energy, the traditional CCHP system, the combination of CCHP system and green energy, the combination of CCHP system and green energy and short-term energy storage, and the combination of CCHP system and green energy and long-term and short-term energy storage are compared. The intelligent optimization algorithm combining improved sine dung beetle optimizer and linear support vector machine algorithm (MDBO-LSVM) is used to solve the problem. The results show that in the CCHP system, the coupling of short-term energy storage and long-term energy storage can effectively improve the compatibility of the system with green energy. The proportion of green energy in the system proposed in this paper is increased by 13.32 %, which can reach an average of 23.91 %, while the carbon emissions are reduced by 37.34 %, which effectively improves the energy utilization rate.

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Abstract Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized Variational Mode Decomposition (VMD) and improved Deep Belief Network-Extreme Learning Machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian-Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of Generalized Gaussian Distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract generalized dispersion entropy. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.

Histone deacetylases regulate organ-specific growth in a horned beetle

BackgroundNutrient availability is among the most widespread means by which environmental variability affects developmental outcomes. Because almost all cells within an individual organism share the same genome, structure-specific growth responses must result from changes in gene regulation. Earlier work suggested that histone deacetylases (HDACs) may serve as epigenetic regulators linking nutritional conditions to trait-specific development. Here we expand on this work by assessing the function of diverse HDACs in the structure-specific growth of both sex-shared and sex-specific traits including evolutionarily novel structures in the horned dung beetle Onthophagus taurus.ResultsWe identified five HDAC members whose downregulation yielded highly variable mortality depending on which HDAC member was targeted. We then show that HDAC1, 3, and 4 operate in both a gene- and trait-specific manner in the regulation of nutrition-responsiveness of appendage size and shape. Specifically, HDAC 1, 3, or 4 knockdown diminished wing size similarly while leg development was differentially affected by RNAi targeting HDAC3 and HDAC4. In addition, depletion of HDAC3 transcript resulted in a more rounded shape of genitalia at the pupal stage and decreased the length of adult aedeagus across all body sizes. Most importantly, we find that HDAC3 and HDAC4 pattern the morphology and regulate the scaling of evolutionarily novel head and thoracic horns as a function of nutritional variation.ConclusionCollectively, our results suggest that both functional overlap and division of labor among HDAC members contribute to morphological diversification of both conventional and recently evolved appendages. More generally, our work raises the possibility that HDAC-mediated scaling relationships and their evolution may underpin morphological diversification within and across insect species broadly.

Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

An enhanced dung beetle optimization algorithm (EDBO) is proposed for nonlinear optimization problems with multiple constraints in manufacturing. Firstly, the dung beetle rolling phase is improved by removing the worst value interference and coupling the current solution with the optimal solution to each other, while retaining the advantages of the original formulation. Subsequently, to address the problem that the dung beetle dancing phase focuses only on the information of the current solution, which leads to the overly stochastic and inefficient exploration of the problem space, the globally optimal solution is introduced to steer the dung beetle, and a stochastic factor is added to the optimal solution. Finally, the dung beetle foraging phase introduces the Jacobi curve to further enhance the algorithm’s ability to jump out of the local optimum and avoid the phenomenon of premature convergence. The performance of EDBO in optimization is tested using the CEC2017 function set, and the significance of the algorithm is verified by the Wilcoxon rank-sum test and the Friedman test. The experimental results show that EDBO has strong optimization-seeking accuracy and optimization-seeking stability. By solving four engineering optimization problems of varying degrees, EDBO has proven to have good adaptability and robustness.

Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Motivated by the need for a green and low-carbon economy, we explore the co-scheduling optimization of berths and cranes. Our aim is to balance the carbon tax and operating costs of ports under uncertain conditions, proposing an innovative nonlinear mixed-integer programming formulation. To address this optimization challenge, we have developed an enhanced version of the adaptive spiral flying dung beetle algorithm (ASFDBO). In order to evaluate the performance of the ASFDBO algorithm, we performed a benchmark function test and a convergence analysis with other recognized metaheuristics. In addition, we verified the practical applicability of the ASFDBO algorithm in different test scenarios. Through numerical experiments, we analyze the feasibility and effectiveness of the algorithm’s scheduling solutions and improvement strategies. Results indicate that our collaborative scheduling optimization, which considers both carbon and production costs, achieves feasible solutions and reduces carbon expenses. Finally, we investigate the impact of different carbon tax rates on the joint scheduling optimization of berths and quay cranes, and the results show that a reasonable carbon tax policy can effectively reduce the carbon emissions of ports.

A Novel Inversion Method for Permeability Coefficients of Concrete Face Rockfill Dam Based on Sobol-IDBO-SVR Fusion Surrogate Model

The accurate and efficient inversion of permeability coefficients is significant for the scientific assessment of seepage safety in concrete face rockfill dams. In addressing the optimization challenge of permeability coefficients with few samples, multiple parameters, and strong nonlinearity, this paper proposes a novel intelligent inversion method based on the Sobol-IDBO-SVR fusion surrogate model. Firstly, the Sobol sequence sampling method is introduced to extract high-quality combined samples of permeability coefficients, and the equivalent continuum seepage model is utilized for the forward simulation to obtain the theoretical hydraulic heads at the seepage monitoring points. Subsequently, the support vector regression surrogate model is used to establish the complex mapping relationship between the permeability coefficients and hydraulic heads, and the convergence performance of the dung beetle optimization algorithm is effectively enhanced by fusing multiple strategies. On this basis, we successfully achieve the precise inversion of permeability coefficients driven by multi-intelligence technologies. The engineering application results show that the permeability coefficients determined based on the inversion of the Sobol-IDBO-SVR model can reasonably reflect the seepage characteristics of the concrete face rockfill dam. The maximum relative error between the measured and the inversion values of the hydraulic heads at each monitoring point is only 0.63%, indicating that the inversion accuracy meets the engineering requirements. The method proposed in this study may also provide a beneficial reference for similar parameter inversion problems in engineering projects such as bridges, embankments, and pumping stations.

Evidence of drastic loss of dung beetle diversity after 25 years in an introduced Brazilian pasture

AbstractIn tropical forest ecosystems, pastures are considered a limiting environment for dung beetles, with long‐term negative effects on the diversity of this group. To understand how dung beetle diversity changes over time in introduced Brazilian pastures, studies comparing different periods are needed. Here, we conducted a study sampling dung beetles during 48 consecutive weeks in 1995 and 2020–2021. We used pitfall traps baited with fresh cattle dung in the same introduced pasture located in the Cerrado‐Pantanal ecotone of Brazil. Our study found that the total species richness of dung beetles decreased by 40% in 2020–2021 compared to 1995, which could negatively impact the ecological functions of the pastures. Given the importance of dung beetles for maintaining and functioning of pastures, the decline in dung beetle diversity could lead to the degradation of pasture landscapes. We advocate for management plans for livestock production in introduced pastures that include the long‐term conservation of dung beetle biodiversity. These plans will ensure the continued provision of dung beetle ecological services by dung beetles over the years.

New Data on the Scarabaeinae (Coleoptera: Scarabaeidae) Fauna of Bozdağ at Eskişehir in Central Anatolia Region

Insects are generally dominant in terrestrial ecosystems and play a role in ecologically important functions and ecological processes. Dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) are essential for the first decomposition, especially in forest/pasture ecosystems. This study aimed to determine the faunistic data of the subfamily Scarabaeinae (Coleoptera: Scarabaeidae) depending on three different altitudes in the Bozdağlar region of Eskişehir. In this study, the pitfall trap method collected totally of 40 species (34 species and 6 subspecies) belonging to 10 genera belonging to the Scarabaeinae subfamily of the Scarabaeidae family. Cheironitis haroldi Ballion, 1871, Caccobius. mundus Ménétriés, 1839, Onthophagus. (A.) falzonii Goidanich, 1926, Onthophagus (P.) carpanetoi Pittino, 1982, Onthophagus (P.) similis Scriba, 1790, Onthophagus (P). t. truchmenus Kolenati, 1846 and Ateuchetus armeniacus Ménétriés, 1832, are the new data for Eskişehir province.

Integrated Modular Neural Control for Versatile Locomotion and Object Transportation of a Dung Beetle-Like Robot.

Dung beetles can effectively transport dung pallets of various sizes in any direction across uneven terrain. While this impressive ability can inspire new locomotion and object transportation solutions in multilegged (insect-like) robots, to date, most existing robots use their legs primarily to perform locomotion. Only a few robots can use their legs to achieve both locomotion and object transportation, although they are limited to specific object types/sizes (10%-65% of leg length) on flat terrain. Accordingly, we proposed a novel integrated neural control approach that, like dung beetles, pushes state-of-the-art insect-like robots beyond their current limits toward versatile locomotion and object transportation with different object types/sizes and terrains (flat and uneven). The control method is synthesized based on modular neural mechanisms, integrating central pattern generator (CPG)-based control, adaptive local leg control, descending modulation control, and object manipulation control. We also introduced an object transportation strategy combining walking and periodic hind leg lifting for soft object transportation. We validated our method on a dung beetle-like robot. Our results show that the robot can perform versatile locomotion and use its legs to transport hard and soft objects of various sizes (60%-70% of leg length) and weights (approximately 3%-115% of robot weight) on flat and uneven terrains. The study also suggests possible neural control mechanisms underlying the dung beetle Scarabaeus galenus' versatile locomotion and small dung pallet transportation.

Sparse reconstruction of ultrasonic guided wave signals of fluid-filled pipes by multistrategy hybrid DBO-OMP using dispersive Hanning-windowed chirplet model

Ultrasonic guided waves (UGWs) in fluid-filled pipes are subject to more severe dispersion and coherent noise than those of vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called multistrategy hybrid dung beetle optimization based orthogonal matching pursuit (MHDBO-OMP) using dispersive Hanning-windowed chirplet model was proposed, of which the model not only represents the nonlinear frequency and asymmetric envelope of the dispersive UGW, but also contains the mode and propagation distance information, and this model exhibits better matching and interpretability than the other UGW models. For dispersive perturbed UGW signals with multiple modes and features, MHDBO-OMP achieves high reconstruction accuracy in both signal reconstruction (RES < 0.3) and propagation distance estimation (RES < 3 cm). When applied to experimental UGW signals obtained from water/oil-filled pipes containing cracks and corrosion pits, MHDBO-OMP successfully reconstructs dispersion-compensated signals that solely contain feature signals, and the minimum location error is 0.72 cm while the average location error is 2.65 cm. © 2001 Elsevier Science. All rights reserved.

Assembly of Dung Beetles (Coleoptera, Scarabaeoidea) Inhabiting Cow Dung in the Northern Spurs of the Manchurian-Korean Mountains (Russian Far East)

Assembly of Dung Beetles (Coleoptera, Scarabaeoidea) Inhabiting Cow Dung in the Northern Spurs of the Manchurian-Korean Mountains (Russian Far East)

A novel near infrared spectroscopy analytical strategy for soil nutrients detection based on the DBO-SVR method

Soil is the basis of agricultural production and accessing accurate information on soil nutrients is essential. Traditional methods of soil composition detection, which are based on chemical analysis, are characterized by being costly and polluting. Spectroscopic analysis has proven to be a rapid, non-destructive and effective technique for predicting soil properties in general and potassium, phosphorus and organic matter in particular. However, previous research on soils has rarely combined optimization algorithms with machine learning techniques, which has led to suboptimal model accuracy and convergence speed. In this study, a total of 184 soil samples were collected from three cities of Linhai, Yueqing and Longyou County, Zhejiang Province, China. After measuring pH values, alkali-hydrolyzable nitrogen (SAN), available phosphorus (SAP), available potassium (SAK) and soil organic matter (SOM) contents, along with their corresponding spectroscopic measurements, nine pretreatment methods and their combinations are adopted. A novel assessment model, integrating support vector machine and dung beetle optimization algorithm (DBO-SVR), is proposed to predict pH values and SAN, SAP, SAK, SOM content. Meanwhile, the DBO algorithm is compared with three mainstream optimization algorithms (particle swarm optimization (PSO), whale optimization algorithm (WOA) and grey wolf optimizer (GWO)). Results showed that the DBO-SVR model was shown best performance with Rp, RMSEP and RPD of 0.9842, 0.1306, 5.6485 respectively for prediction of pH value, with Rp, RMSEP and RPD of 0.8802, 15.0574 mg/kg and 2.0508, respectively for assessment of SAN content, with Rp, RMSEP and RPD of 0.9790, 12.8298 mg/kg, and 4.5132, respectively for assessment of SAP content. with Rp, RMSEP and RPD of 0.8677, 22.5107 mg/kg, and 1.9546, respectively for assessment of SAK content, and with Rp, RMSEP and RPD of 0.9273, 2.6427g/kg , and 2.1821, respectively for assessment of SOM content. This study demonstrates that the combination of near-infrared (NIR) spectroscopy and the DBO-SVR algorithm is capable of predicting soil nutrient composition with greater accuracy and efficiency.

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

Dung beetles increase plant growth: a meta-analysis.

The ecosystem services provided by dung beetles are well known and valued. Dung beetles bury dung for feeding and breeding, and it is generally thought that the process of burying dung increases nutrient uptake by plant roots, which promotes plant growth. Many studies have tested the effects of dung beetles on plant growth, but there has been no quantitative synthesis of these studies. Here we use a multi-level meta-analysis to estimate the average effect of dung beetles on plant growth and investigate factors that moderate this effect. We identified 28 publications that investigated dung beetle effects on plant growth. Of these, 24 contained the minimum quantitative data necessary to include in a meta-analysis. Overall, we found that dung beetles increased plant growth by 17%; the 95% CI for possible values for the true increase in plant growth that were most compatible with our data, given our statistical model, ranged from 1% to 35%. We found evidence that the dung beetle-plant growth relationship is influenced by the plant measurement type and the number of beetles accessing the dung. However, beetles did not increase plant growth in all quantitative trials, as individual effect sizes ranged from -72% to 806%, suggesting important context-dependence in the provision of ecosystem services.

Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm

AbstractThis article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm.

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index guiding the improved dung beetle optimizer. An adaptive selection method, using a weight factor, chooses the intrinsic mode function containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.

Ecosystem functions of ants and dung beetles in agriculture‐dominated Amazonian riparian forests

Abstract Land‐use changes and habitat fragmentation can alter biodiversity and associated ecosystem functions. We investigated whether cropland expansion in south‐east Amazonia decreases ecosystem functions performed by ants and dung beetles by altering their communities. We sampled both groups in two forest types, in south‐eastern Amazonia: undisturbed riparian forests and riparian forests surrounded by croplands. We sampled ants and dung beetles with epigaeic pitfall traps, and experimentally assessed seed removal and predation by ants, and faeces and seed removal by dung beetles. Although ant and dung beetle abundance and richness did not differ across riparian forests, species composition of both groups did. Ants found seeds faster in undisturbed riparian forests, but the proportion of seed removal did not differ between forests. The abundance of predatory ants and the predation of termites by ants did not differ between forests. Seed and dung removal by dung beetles did not differ across riparian forests. Despite the observed differences in dung beetle composition, we did not find the effects on ecosystem functions probably due a functional redundancy of species. The quality of ecosystem functions provided by ants is lower in riparian forests surrounded by croplands, while changes in dung beetle diversity did not impact their ecosystem functions, with potential effects on the natural recovery of these disturbed riparian forests.

Physiology, ecology, and evolution of a successful colonizer: the horned dung beetle, Euoniticellus intermedius

AbstractInsects are intentionally introduced to various regions out of their native ranges to perform fundamental functions, such as pest control, and some keep dispersing from introduction sites to become cosmopolitan and even invasive. The African horned dung beetle, Euoniticellus intermedius (Reiche) (Coleoptera: Scarabaeidae), has been intentionally introduced on multiple continents to bury cattle dung and control livestock pests, but has naturally dispersed and became very abundant at various latitudes and elevations out of its native and original introduction ranges. This beetle has been considered invasive, but there is no direct evidence of its effects on displacing native species. As it is highly fecund, E. intermedius has been an important model in experimental studies performed in nature and in the laboratory in multiple fields. In evolutionary biology, it serves as a model for sexual selection, given the sexual dimorphism characterized by the presence of a horn in males which is correlated with individual condition and strength, and which is absent in females. In ecotoxicology, it has been studied regarding physiological mechanisms of responses to contaminants, population declines, and evolutionary responses to challenging toxic conditions. Given its importance in burying dung in cattle pastures, experiments have also determined environmental conditions that limit this ecological function. Despite being unique in its tolerance to a wide variety of stressors and environments, this species is sensitive to current conditions of global change, including warming and pollution. We identify the most promising questions to be solved in physiology, ecology, and evolution, for which E. intermedius would be an ideal study system.

Development of Wind Wake-effect Minimizing Model Based on Combination of Dung Beetle Optimization, Extreme Learning Machine, and Genetic Algorithms

Development of Wind Wake-effect Minimizing Model Based on Combination of Dung Beetle Optimization, Extreme Learning Machine, and Genetic Algorithms

Cephalodesmius carminya, a new flightless dung beetle species from the Central Mackay Coast, Queensland.

A new species from the genus Cephalodesmius Westwood (Coleoptera: Scarabaeidae: Scarabaeinae) is described from the Cental Mackay Coast bioregion, Queensland extending the northern range of the genus. Cephalodesmius carminya Ebert &amp; Gunter new species is known from two localities in the Pine Mountain, Carminya Forest Reserve and 2.5 km west of Eungella dam. A key to the four species of Cephalodesmius is provided and we discuss morphological similarities between Cephalodesmius and its closest relative Canthonosoma Macleay.