Search Results (2,361)

In complex agricultural production environments, small-target pests—characterized by tiny scales, strong background confusion, and close dependence on environmental conditions—pose major challenges to precise monitoring and green pest control. To facilitate the transition from experience-driven to data-driven pest management, a multi-scale vision–sensor collaborative recognition method is proposed for field and protected agriculture scenarios to improve the accuracy and stability of small-target pest recognition under complex conditions. The method jointly models multi-scale visual representations and pest ecological mechanisms: a multi-scale visual feature module enhances fine-grained texture and morphological cues of small targets in deep networks, alleviating feature sparsity and scale mismatch, while environmental sensor data, including temperature, humidity, and illumination, are introduced as priors to modulate visual features and explicitly incorporate ecological constraints into the discrimination process. Stable multimodal fusion and pest category prediction are then achieved through a vision–sensor collaborative discrimination module. Experiments on a multimodal dataset collected from real farmland and greenhouse environments in Linhe District, Bayannur City, Inner Mongolia, demonstrate that the proposed method achieves approximately 93.1% accuracy, 92.0% precision, 91.2% recall, and a 91.6% $F_1$-score on the test set, significantly outperforming traditional machine learning approaches, single-scale deep learning models, and multi-scale vision baselines without environmental priors. Category-level evaluations show balanced performance across multiple small-target pests, including aphids, thrips, whiteflies, leafhoppers, spider mites, and leaf beetles, while ablation studies confirm the critical contributions of multi-scale visual modeling, environmental prior modulation, and vision–sensor collaborative discrimination. Full article

In the complex interplay among parasitic wasps, their insect hosts, and pathogenic microbes, the system involving Sclerodermus guani (Hymenoptera: Bethylidae) (a parasitoid wasp), Monochamus alternatus (Coleoptera: Cerambycidae) (the pine sawyer beetle, its host), and Beauveria bassiana (Hypocreales: Cordycipitaceae) (a fungus) presents a unique scenario where wasp offspring develop within a nearly sealed host gallery. This nursery is vulnerable to fungal invasion, often introduced by the foraging female wasps or M. alternatus itself, creating a three-way interaction where the fungus can infect both M. alternatus and S. guani. To assess how the route and timing of fungal exposure impact the S. guani population, we simulated this system by introducing different concentrations of B. bassiana either directly to the female wasps or to M. alternatus prior to parasitism. We further examined the effect of exposure timing by applying the fungus at different developmental stages of the S. guani offspring. Key population parameters, including the reproductive capacity of female wasps, the survival and developmental fitness of S. guani offspring and the germination period of hyphae, were measured. The results indicated that the most severe damage to populations of S. guani occurs when its host, M. alternatus, is infected by B. bassiana. Among the various developmental stages, S. guani offspring exhibited the greatest vulnerability during mid-to-late larval stages, whereas the egg and pupa within cocoon stages demonstrated a higher tolerance. We conclude that both the pathway and the timing of fungal exposure are critical factors influencing its impact. These findings provide valuable insights for optimizing the integrated use of biological agents in pest management, informing strategies that mitigate adverse effects on beneficial parasitoid wasps. Full article

In the modern fauna, click beetle larvae are important ecosystem components, fulfilling different ecological functions. The fossil record of click beetle larvae is still scarce. Even in the very diverse fauna of the Kachin amber forest (Myanmar, Cretaceous, ca. 100 million years old), only three morphotypes of click beetle larvae have been reported so far. Here, we add a fourth morphotype, characterised by very long setae. The mouthparts indicate a predatory lifestyle. The long and quite stiff-appearing setae might have protected the larvae, for example, when hunting in termite nests, which is a strategy that some extant click beetle larvae apply. This would also imply a closer association with wood and thus a greater likelihood of preservation in amber. Here, we present twelve larvae of this new morphotype, representing two or three possible species, including an ontogenetic series for one of these. Full article

High-voltage circuit breakers (HVCBs) are critical switching devices whose mechanical reliability directly affects power system safety and operational continuity. Accurate fault diagnosis remains challenging due to nonlinear vibration characteristics and the sensitivity of support vector machines (SVMs) to hyperparameter selection. To address this issue, a multi-strategy improved dung beetle optimization–support vector machine (MIDBO–SVM) framework is proposed for vibration-based mechanical fault diagnosis. Frequency-domain features are extracted from vibration signals using the fast Fourier transform to characterize fault-related spectral variations. A multi-strategy improved dung beetle optimization (MIDBO) algorithm incorporating chaotic initialization, adaptive search regulation, and mutation enhancement is developed to improve population diversity, global exploration, and convergence stability. The optimized MIDBO is used to determine the penalty and kernel parameters of the SVM, constructing a robust and well-generalized diagnostic model. Experimental results show that MIDBO–SVM achieves a diagnostic accuracy of 96.67%, outperforming conventional SVM (86.25%) and random forest (89.17%). The proposed method also demonstrates faster convergence and maintains accuracy above 86% under imbalanced sample conditions, confirming its robustness and generalization capability. These advantages contribute to more reliable mechanical condition assessment and improved maintenance decision support for HVCBs. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article

Metaheuristic methods for three-dimensional (3D) unmanned aerial vehicle (UAV) path planning often suffer from premature convergence and reduced accuracy in complex high-dimensional spaces, in which waypoint-based decision variables exhibit structured dependencies and segment-level regularities. In a symmetry-inspired operational sense, these regularities can be interpreted as exploitable dependency patterns across path segments and permutation invariance among homogeneous UAVs, which are often overlooked by standard algorithms. The paper proposes an enhanced dung beetle optimizer (LEDBO) that integrates interaction-aware variable handling, adaptive role regulation, and a fitness-state-driven hybrid search mechanism. Correlation-based variable grouping clusters dependent waypoints into segments to exploit statistical dependency patterns among waypoint-coordinate variables and enhance local refinement. A three-level adaptive role-regulation scheme adjusts search behaviors according to convergence status and population diversity, thereby mitigating stagnation. Meanwhile, a fitness-state-driven hybrid engine combines Nelder–Mead local refinement with Lévy-flight global exploration to balance exploitation and exploration across stages. Experiments on the CEC2017 benchmark suite and complex 3D UAV path-planning simulations demonstrate that LEDBO achieves better solution quality, convergence behavior, and robustness than representative metaheuristics, producing smoother, shorter, and safer trajectories. The results suggest that incorporating interaction-aware variable grouping and adaptive search regulation can improve UAV path planning and related high-dimensional continuous optimization tasks. Full article

A new species of Proctolaelaps (Acari: Mesostigmata: Melicharidae), Proctolaelaps ambrosiae sp. nov., is described from south Florida, USA, based on adult females found in phoretic association with ambrosia beetles infesting avocado (Persea americana) trees. Mites were removed from adults of Xyleborinus saxesenii and Xyleborus affinis (Coleoptera: Curculionidae: Scolytinae: Xyleborini) captured in flight and were also collected from beetle galleries in infested avocado wood. The new species is diagnosed based on a combination of morphological characters and molecular markers (nuclear 28S rRNA and ITS, and mitochondrial COI), supporting its distinctiveness from related taxa. This study represents the first formal description of a Proctolaelaps species documented in phoretic association with xyleborine ambrosia beetles and their galleries, contributing to the knowledge of melicharid diversity in woody microhabitats and providing baseline data for future ecological and applied studies of ambrosia beetle systems in avocado. Full article

Background: Dendroctonus armandi, an oligophagous beetle primarily infesting Pinus armandii, is geographically restricted and persistent in central China, causing significant ecological and economic losses. However, the intrinsic factors driving its continuous occurrence remain unclear. We examined the genetic variation patterns across the species’ range to explore its phylogeographic structure. Methods: We analyzed mitochondrial DNA sequence (mtDNA) data to assess population genetic structure and estimate the divergence times of distinct lineages. Results: Phylogenetic analysis identified four haplogroups corresponding to the Minshan (MSM), Qinling (QLM), Micang (MCM), and Ta-pa (TPM) Mountains. Demographic analyses revealed that QLM and TPM haplogroups have undergone population expansion events. Divergence time estimates indicated four lineages diverged during the Late Pleistocene. Notably, D. armandi may have followed two horizontal and one vertical independent colonization routes. The first route extended from MSM into QLM and then spread eastward along the QLM; the second route progressed from MSM into MCM and continued eastward into TPM; and the third route migrated southward from QLM into TPM. Conclusions: Climate oscillations, geographical isolation, and the patchy distribution of host trees collectively shaped the phylogeographic patterns of D. armandi. These findings elucidate the evolution and adaptability of D. armandi in mountainous environments. Full article

Outbreaks of Ips hauseri, a major bark beetle pest in Central Asian Picea schrenkiana forests, have intensified under climate warming and prolonged droughts. However, the reproductive behavior and gallery construction strategies of this species remain poorly understood, limiting our ability to predict its population dynamics. Here, we dissected 219 galleries from infested spruce trees in Hami, Xinjiang, during an outbreak period (2024–2025). We identified 11 distinct gallery morphologies, with harem size (number of females per male) ranging from one to seven. Gallery length was positively correlated with egg production. Reproductive output peaked at a harem size of five, beyond which both gallery dimensions and fecundity declined. Host tree diameter at breast height (DBH) significantly influenced gallery complexity, with larger trees supporting more maternal galleries. Upward-oriented galleries were longer and contained more eggs than downward ones. Intraspecific competition, mediated by gallery adjacency and spatial orientation, strongly affected offspring development. Our results demonstrate that I. hauseri exhibits flexible gallery architecture and reproductive adjustments in response to resource availability and competition—a behavioral plasticity that likely contributes to its outbreak potential. Monitoring gallery morphology and harem size could enhance early detection and population forecasting for this increasingly damaging forest pest. Full article

Recent advances in molecular genetics, nucleic acid synthesis, and bioinformatics have provided novel opportunities for plants’ protection against insect pests. Currently, both DNA and RNA serve as active insecticidal ingredients, transcending their traditional role as carriers of genetic information. This novel activity is achieved through two fundamentally distinct mechanisms. The first one is DNA containment (DNAc), employing oligonucleotide insecticides based on contact unmodified antisense DNA biotechnology (CUADb), also known as ’genetic zipper’ technology. The second one is RNA interference (RNAi), employing RNA biocontrols based on double-stranded RNA (dsRNA) technology. The investigation of the molecular mechanism underlying the antisense activity of nucleic acids emerged in the early 1960s. While the antisense effects of RNA in gene silencing through interference (RNAi) was documented in the late 1990s as antiviral immune responses in nematodes, the CUADb antisense approach initially emerged as a powerful strategy for pest control against lepidopterans in 2008. The CUADb approach relies on disrupting rRNA biogenesis and ribosome production, while RNAi shows the best results in mRNA degradation and no efficient result is known for rRNA. The efficacy of these approaches appears to be species dependent. For example, CUADb demonstrates optimal activity against Sternorrhyncha (e.g., aphids, mealybugs, psyllids, and scale insects), thrips, and mites. In turn, the RNAi strategy shows a strong insecticidal potential against beetles from the Tenebrionidae and Chrysomelidae families. Here, we will review the differences between the two technologies, their mechanisms of action and the current challenges facing their adoption. Full article

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article

Unmanned aerial vehicles (UAVs), a key enabler for the Internet of Things’ (IoT) evolution to 3D spatial dimensions, play a critical role in data collection across fields. However, path planning in obstacle-rich and threat-prone environments remains a core bottleneck for their safe and efficient operation. Traditional meta-heuristic algorithms suffer from insufficient exploration, slow convergence, and local optima issues. To address this, we propose an enhanced multi-mechanism DBO algorithm (MMDBO), integrating SPM chaotic mapping, dynamic global exploration, adaptive T-distribution, and dynamic weight mechanisms. Comparative experiments against five classical algorithms on 12 benchmarks test functions and three complex terrains show MMDBO achieves superior performance across the majority of key path-planning metrics—including flight trajectory length, altitude profile fidelity, and path smoothness—while incurring only a modest increase in computational time. The results of the statistical test further indicate that the MMDBO algorithm significantly outperforms the comparison algorithms in both convergence speed and accuracy. These advances deliver actionable, highly reliable guidance for UAV flight path optimization. Full article

Propelled by the “dual-carbon” strategy, green and intelligent ports are rapidly advancing toward low-carbon and intelligent development. However, the large-scale incorporation of renewable energy and the extensive electrification of transport equipment have substantially heightened system volatility and scheduling complexity. To address the challenges associated with multi-energy coupling and economic operation in medium and large ports, a hierarchical collaborative optimization scheduling strategy is proposed. The upper layer employs an improved Bio-enhanced Dung Beetle Optimization (BDBO) algorithm for parameter optimization and carbon-cost minimization. Meanwhile, the lower layer establishes a rolling time-series control mechanism grounded in Adaptive Dynamic Hierarchical Decoupling Planning (ADHDP), thereby constituting an integrated BDBO-ADHDP dual-agent system. Simulation results across four seasonal scenarios demonstrate that the proposed methodology outperforms DQN, PSO, GA, ACO, and DBO algorithms in reducing grid power purchases, enhancing renewable energy utilization, mitigating curtailment, and lowering operational costs. Moreover, it achieves faster convergence, superior robustness, and effective carbon-emission control. This study substantiates the efficacy of the proposed strategy within green port integrated energy systems and highlights its potential for broader application in other multi-energy coupled systems. Full article

In view of the highly coupled, time-varying, and susceptible to differences in aircraft configuration of the Boeing 787 Electric Environmental Control System (E-ECS), a simplified mechanism model based on effectiveness-number of transfer units is proposed. Firstly, considering the influence of differences in aircraft configuration, part number, and optional components, a heat conduction correction coefficient is introduced to adjust the calculation process of heat exchange efficiency. Secondly, the steady-state characteristic equation of the electric compressor/turbine is established by utilizing the principle of isentropic work. Then, the outlet temperature value of the water removal component is calculated by using secondary heat recovery technology. Finally, to solve the problem of easily getting stuck in local optima during high-dimensional parameter identification, an adaptive hybrid optimization algorithm combining Dung Beetle Optimization (DBO) with mutation operator and Particle Swarm Optimization (PSO) is proposed. The experimental results show that the proposed mechanism model can achieve dynamic representation of the outlet temperature of each component of E-ECS under different aircraft stages. The DBO-PSO algorithm has a fast convergence speed and a low probability of falling into local optima. The temperature values calculated by the model have high computational accuracy, which can provide reliable data support for component level E-ECS health monitoring and early fault warning. Full article

Bacillus thuringiensis is the most extensively studied entomopathogenic bacterium worldwide; however, its sublethal effects on beetles remain poorly characterized. The aim of this study was to evaluate the toxicity of a previously selected Argentine strain of B. thuringiensis on second-instar Alphitobius diaperinus larvae during an initial 14 days of exposure, and to assess its effects at day 14 and throughout the remainder of the life cycle until death. Three treatments were applied: control, LC₃₀, and LC₅₀. Larval, pupal, and adult weight and body surface area were recorded, and nutritional composition was quantified using colorimetric methods. Insect status was monitored every 48–72 h over a total period of 540 days, until the death of the last individual. Among the evaluated variables, statistically significant differences between control and treatment groups were detected in larval area and weight, in the survival analysis and in two nutritional components: total protein and lipid content per larva. Overall, the results demonstrate that initial sublethal exposure to B. thuringiensis induces chronic lethal effects with delayed mortality in A. diaperinus, indicating irreversible physiological damage. This provides valuable information not only for understanding the biology of this insect but also for stakeholders involved in the productive scaling of beetle-targeted bioinputs. Full article