Search Results (53)

The high penetration of distributed photovoltaics (PV) into distribution networks alters the system’s short-circuit current characteristics, posing risks of maloperation and failure-to-operate to conventional inverse-time overcurrent protection. Based on an equivalent model of distributed PV during faults, this paper analyzes its impact on the protection characteristics of traditional distribution networks. With protection selectivity and the physical constraints of protection devices as conditions, an optimization model for inverse-time overcurrent protection is established, aiming to minimize the total operation time. To enhance the solution capability for this complex optimization problem, the standard Osprey Optimization Algorithm (OOA) is improved through the incorporation of three strategies: arccosine chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic spiral search strategy combining mechanisms from the Whale and Marine Predators algorithms. Based on this improved algorithm, an optimized protection scheme for distribution networks with distributed PV is proposed. Simulations conducted in PSCAD/EMTDC (V4.6.2) and MATLAB (R2023b) verify that the proposed method effectively prevents protection maloperation and failure-to-operate under both fault current contribution and extraction scenarios of PV, while also reducing the overall relay operation time. Full article

The European catfish (Silurus glanis, Linnaeus 1758), commonly known as the wels catfish, is one of the largest freshwater fish in Europe and an ecologically and economically important species in both natural ecosystems and aquaculture. Its broad native distribution, together with the rapid growth of farming practices, increases concerns about pathogen dissemination and their potential impact on biodiversity, animal health, and potential risks to human healthcare. This review is based on a structured literature search following PRISMA recommendations for narrative reviews and summarizes current knowledge on the main pathogen groups affecting S. glanis—viruses (ranaviruses, alloherpesviruses), bacteria (Aeromonas spp., Edwardsiella spp.), protozoan and metazoan parasites (Ichthyophthirius multifiliis, Thaparocleidus spp., Eustrongylides spp., Contracaecum larvae), and oomycetes (Saprolegnia spp., Branchiomyces spp.). Within the One Health approach, particular attention is given to zoonotic pathogens such as Aeromonas spp., Edwardsiella tarda, and helminths like Eustrongylides and Contracaecum, which may cause risks to human health through contaminated water or consumption of raw or undercooked fish. The review integrates findings from field surveys, regional case studies such as those from the Danube basin, and data from the authors’ doctoral research. Because the wels catfish is increasingly cultivated and serves as an apex predator in natural habitats, its effective disease management is critical for both aquaculture and wild populations, and also for the food chains at all. Strengthened surveillance, health monitoring, and biosecurity measures are essential preventing the introduction and spread of pathogens into new hosts and habitats. Through the underlining of major catfish pathogen groups, this review highlights key challenges within the One Health approach and underscores the need for integrated health monitoring, biosecurity, and environmental management strategies. Full article

Background: Picky eating (PE), also termed food selectivity, is one of the most common feeding concerns in childhood. Although often a transient developmental stage, persistent or severe selectivity may lead to nutritional deficiencies, growth impairment, and psychosocial consequences. Methods: This narrative review is based on literature searches conducted in April 2025 across PubMed, Web of Science, Embase, Medline, and Google Scholar. Articles published between 2015 and 2025 were included if they addressed the epidemiology, etiology, assessment, or management of PE in children aged 0–18 years. Additional seminal references predating this period were also considered. Results: Prevalence estimates of PE vary widely (13–50%), with peak incidence between ages two and six. Contributing factors include genetic predisposition, sensory sensitivities, temperament, family feeding practices, environmental influences, and adverse feeding experiences. Distinction from avoidant/restrictive food intake disorder (ARFID) and pediatric feeding disorder (PFD) is essential, as these conditions carry greater risk of nutritional and psychosocial impairment. Assessment relies on caregiver-report instruments, clinical observation, growth monitoring, and targeted nutritional evaluation. Effective management integrates parental education, responsive feeding strategies, repeated exposure to novel foods, and, when indicated, nutritional supplementation or referral to multidisciplinary teams. Sensory-based therapies, behavioral interventions, and psychoeducational programs show particular benefit in persistent cases. Conclusions: While most children outgrow PE without adverse outcomes, a subset remains at risk of long-term nutritional compromise and psychosocial difficulties. Early recognition, family-centered guidance, and evidence-based interventions are essential. Future research should refine diagnostic criteria, develop culturally sensitive assessment tools, and evaluate innovative therapies to improve outcomes. Full article

Cascade reservoir optimization faces significant challenges due to multi-dimensional, non-convex, and nonlinear characteristics with coupled constraints. As reservoir numbers increase, computational complexity escalates dramatically, limiting conventional optimization methods’ effectiveness. This paper proposes HHONMPA, a hybrid algorithm combining Harris Hawks Optimization (HHO) with Marine Predators Algorithm (MPA). The method uses SPM chaotic mapping for population initialization to enhance diversity and integrates both algorithms’ predatory behaviors. During exploration, it employs Brownian motion and improved Lévy flight strategies for global search, while exploitation uses enhanced HHO for local optimization. A novel Dual-Period Oscillation Attenuation Strategy dynamically adjusts parameters to balance exploration-exploitation. Performance validation using CEC2017 benchmark functions shows HHONMPA significantly outperforms the original HHO and MPA in solution accuracy and convergence speed, confirmed through statistical testing. Engineering validation applies the algorithm to the Lower Jinsha River—Three Gorges four-reservoir system, conducting experiments across various hydrological scenarios. Results demonstrate substantial improvements in search accuracy and convergence efficiency compared to existing methods. HHONMPA effectively addresses large-scale cascade reservoir optimization challenges, offering promising prospects for water resource management and hydropower scheduling applications. Full article

This paper tackles a core challenge in reverse engineering: high-fidelity reconstruction of continuous B-spline surfaces from discrete point clouds, where optimal knot placement remains pivotal yet not fully resolved. We propose a new fitting method based on the Hungry Predation Algorithm (HPA) to improve efficiency, accuracy, and robustness. This method introduces a hybrid knot-guidance strategy that combines geometry-aware preselection with a complexity-driven probabilistic distribution to address knot placement. On the optimization side, HPA simulates starvation-driven predator–prey dynamics to enhance global search capability, maintain population diversity, and accelerate convergence. We also develop an adaptive parameter adjustment framework that automatically tunes key settings according to surface complexity and accuracy thresholds. Comparative experiments against classical approaches, six state-of-the-art optimizers, and the commercial CAD system CATIA demonstrate HPA’s superiority in control-point reduction, fitting accuracy, and computational efficiency. This method shows high applicability to engineering-scale tasks (e.g., ship hull design), where the point-to-surface RMSE (e.g., <10⁻³ L_max) achieved satisfies stringent requirements for downstream hydrodynamic performance analysis and manufacturing. Full article

Paracoccus marginatus (Hemiptera: Pseudococcidae) poses a significant threat to over 200 plant species, severely impacting agricultural productivity. Diomus guilavoguii (Coleoptera: Coccinellidae) is a natural predator of P. marginatus. To develop an effective and environmentally friendly management strategy against P. marginatus, this study investigates the predation relationship between D. guilavoguii and P. marginatus by focusing on functional response and mutual interference under controlled laboratory conditions (Petri dishes). The results indicated that D. guilavoguii exhibits a type II functional response toward P. marginatus, with adults of D. guilavoguii demonstrating superior efficiency in preying upon P. marginatus (the theoretical daily maximum predation rate for female adults of D. guilavoguii on young mealybugs is 416.667). However, a decrease in the predators’ search effect was observed with increasing prey density. Additionally, interspecific interference competition intensified as the number of predators increased, resulting in reduced predation efficiency. Consequently, D. guilavoguii shows promise biological control agent for the management of P. marginatus under laboratory conditions, although further studies in greenhouse and field environments are required to validate its potential in practical pest management. Full article

Lygus pratensis is a major pest of cotton, causing serious damage to cotton production. This study designed species-specific PCR detection primers for L. pratensis, established a detection system to identify L. pratensis DNA in the intestinal contents of predatory natural enemies, and investigated the control potential of four species’ predatory natural enemies against L. pratensis. The results indicated that 826 predatory natural enemies were collected from cotton fields belonging to two classes, five orders, and twelve families. Among these, 9 species of insecta natural enemies accounted for 54.12% of the total number of predatory natural enemies collected, while 14 species of arachnida predatory natural enemies comprised 45.88%. Of the 806 natural enemies tested, 5.58% were found to be positive for L. pratensis, all of which were arachnid predators, specifically Ebrechtella tricuspidata, Xysticus ephippiatus, Hylyphantes graminicola, and Oxyopes sertatus. The predation response of these four spider species to the fourth to fifth instar nymphs and adults of L. pratensis adhered to the Holling II model. The theoretical predation (a′/Th), daily maximum predation rate (T/Th), and searching effect for the fourth to fifth instar nymphs and adults of L. pratensis of the four spider species were assessed. According to the results, the species can be ranked in terms of their predatory and searching efficiency as follows: O. sertatus > E. tricuspidata > X. ephippiatus > H. graminicola. Four species of spiders had the highest theoretical predation against L. pratensis nymphs, ranging from 23.71 to 60.86, and adults, ranging from 22.14 to 50.25. Therefore, these four spider species could be utilized for L. pratensis management. This study identified the main predatory natural enemies of L. pratensis and their pest control capabilities, providing a scientific basis for selecting and utilizing natural enemies in integrated pest management (IPM) strategies. This will help promote ecological and green pest control of L. pratensis in cotton-growing areas. Full article

To enhance the convergence efficiency and solution precision of the Red-billed Blue Magpie Optimizer (RBMO), this study proposes a Multi-Strategy Enhanced Red-billed Blue Magpie Optimizer (MRBMO). The principal methodological innovations encompass three aspects: (1) Development of a novel dynamic boundary constraint handling mechanism that strengthens algorithmic exploration capabilities through adaptive regression strategy adjustment for boundary-transgressing particles; (2) Incorporation of an elite guidance strategy during the predation phase, establishing a guided search framework that integrates historical individual optimal information while employing a Lévy Flight strategy to modulate search step sizes, thereby achieving effective balance between global exploration and local exploitation capabilities; (3) Comprehensive experimental evaluations conducted on the CEC2017 and CEC2022 benchmark test suites demonstrate that MRBMO significantly outperforms classical enhanced algorithms and exhibits competitive performance against state-of-the-art optimizers across 41 standardized test functions. The practical efficacy of the algorithm is further validated through successful applications to four classical engineering design problems, confirming its robust problem-solving capabilities. Full article

The escalating global contamination of aquatic ecosystems by pharmaceuticals and endocrine-disrupting chemicals (EDCs) stemming from diverse anthropogenic sources represents a critical and pervasive threat to planetary Earth. These contaminants exhibit bioaccumulative properties in long-lived organisms and undergo trophic biomagnification, leading to elevated concentrations in apex predators. This review synthesizes current knowledge regarding the far-reaching impacts of pharmaceutical and EDC pollution on the reproductive biology of aquatic fauna, focusing on the heightened vulnerability of the endangered African penguin. A rigorous literature review across key scientific databases—PubMed, Scopus, Web of Science, and Google Scholar—using targeted search terms (e.g., penguins, contaminants of emerging concern, penguin species, seabird species, Antarctica, pharmaceuticals, personal care products, EDCs) underpins this analysis. This review explores the anthropogenic sources of pharmaceuticals and EDCs in aquatic ecosystems. It discusses the mechanisms by which these chemicals disrupt the reproductive physiology of aquatic fauna. Recent studies on the ecological and population-level consequences of these contaminants are also reviewed. Furthermore, the review elaborates on the urgent need for comprehensive mitigating strategies to address their effects on vulnerable penguin populations. These approaches hold the potential to unlock innovative pathways for conservation initiatives and the formulation of robust environmental management policies aimed at safeguarding aquatic ecosystems and the diverse life they support. Full article

This paper investigates the formation joint resource scheduling problem from the perspective of cooperative jamming against radar systems. First, the formation survivability is redefined based on the task requirements. Then, a hierarchical adaptive scheduling strategy solution framework is constructed for state prediction and detection fusion of the networked radar system. Considering the scene constraints, an Improved Adaptive Parameter Evolution Marine Predators Algorithm is designed as an optimizer and embedded in the proposed framework to jointly optimize the platform beam allocation and jamming mode selection. Based on the original algorithm, real number random coding is used to perform dimensional conversion of decision variables, an adaptive parameter evolution mechanism is designed to reduce the dependence on algorithm parameters, and an adaptive selection mechanism for dominant strategies and a search intensity control strategy are proposed to help decision-makers explore the optimal resource scheduling strategy quickly and accurately. Finally, considering the formation maneuvering behavior and incomplete information, the proposed method is compared with existing base strategies in different typical scenarios. It is proved that the proposed strategy can fully exploit the limited jamming resources and maximize the survivability of the formation in radar system cooperative jamming scenarios, demonstrating superior jamming performance and shorter decision time. Full article

To solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping is integrated into the initialization step to improve the searching ability of the early stage. Secondly, a hybrid search strategy is used to enhance the ability to search and jump out of local optimum. Thirdly, the golden sine guiding mechanism is applied to accelerate the convergence of the algorithm. Finally, a stage-adjustment strategy is proposed to make the transition of stages more smoothly. Six specific test functions chosen from the CEC2017 function and the benchmark function are used to evaluate the performance of MMPA. It shows that this modified algorithm has good optimization capability and stability compared to MPA, grey wolf optimizer, sine cosine algorithm, and sea horse optimizer. The results of coverage tests show that MMPA has a better uniformity of node distribution compared to MPA. The average coverage rates of MMPA are the highest compared to the commonly used metaheuristic-based algorithms, which are 91.8% in scenario 1, 95.98% in scenario 2, and 93.88% in scenario 3, respectively. This demonstrates the superiority of this proposed algorithm in coverage optimization of the wireless sensor network. Full article

Adaptive movement in response to individual interactions represents a fundamental evolutionary solution found by both unicellular organisms and metazoans to avoid predators, search for resources or conspecifics for mating, and engage in other collaborative endeavors. Displacement processes are known to affect interspecific relationships, especially when linked to foraging strategies. Various displacement phenomena occur in marine plankton, ranging from the large-scale diel vertical migration of zooplankton to microscale interactions around microalgal cells. Among these symbiotic interactions, collaboration between the centric diatom Chaetoceros coarctatus and the peritrich ciliate Vorticella oceanica is widely known and has been recorded in several studies. Here, using 2D and 3D tracking records, we describe the movement patterns of the non-motile, chain-forming diatoms (C. coarctatus) carried by epibiotic ciliates (V. oceanica). The reported data on the Chaetoceros–Vorticella association illustrated the consortium’s ability to generate distinct motility patterns. We established that the currents generated by the attached ciliates, along with the variability in the contraction and relaxation of ciliate stalks in response to food concentration, resulted in three types of trajectories for the consortium. The characteristics of these distinct paths were determined using robust statistical methods, indicating that the different displacement behaviors allowed the consortium to adequately explore distributed resources and remain within the food-rich layers provided in the experimental containers. A simple mechanical–stochastic model was successfully applied to simulate the observed displacement patterns, further supporting the proposed mechanisms of collective response to the environment. Full article

The Electric Fish Optimization (EFO) algorithm is inspired by the predation behavior and communication of weak electric fish. It is a novel meta-heuristic algorithm that attracts researchers because it has few tunable parameters, high robustness, and strong global search capabilities. Nevertheless, when operating in complex environments, the EFO algorithm encounters several challenges including premature convergence, susceptibility to local optima, and issues related to passive electric field localization stagnation. To address these challenges, this study introduces Adaptive Electric Fish Optimization Algorithm Based on Standstill Label and Level Flight (SLLF-EFO). This hybrid approach incorporates the Golden Sine Algorithm and good point set theory to augment the EFO algorithm’s capabilities, employs a variable-step-size Levy flight strategy to efficiently address passive electric field localization stagnation problems, and utilizes a standstill label strategy to mitigate the algorithm’s tendency to fall into local optima during the iterative process. By leveraging multiple solutions to optimize the EFO algorithm, this framework enhances its adaptability in complex environments. Experimental results from benchmark functions reveal that the proposed SLLF-EFO algorithm exhibits improved performance in complex settings, demonstrating enhanced search speed and optimization accuracy. This comprehensive optimization not only enhances the robustness and reliability of the EFO algorithm but also provides valuable insights for its future applications. Full article

This paper addresses the green permutation flow shop scheduling problem (GPFSP) with energy consumption consideration, aiming to minimize the maximum completion time and total energy consumption as optimization objectives, and proposes a new method that integrates end-to-end deep reinforcement learning (DRL) with the multi-objective evolutionary algorithm based on decomposition (MOEA/D), termed GDRL-MOEA/D. To improve the quality of solutions, the study first employs DRL to model the PFSP as a sequence-to-sequence model (DRL-PFSP) to obtain relatively better solutions. Subsequently, the solutions generated by the DRL-PFSP model are used as the initial population for the MOEA/D, and the proposed job postponement energy-saving strategy is incorporated to enhance the solution effectiveness of the MOEA/D. Finally, by comparing the GDRL-MOEA/D with the MOEA/D, NSGA-II, the marine predators algorithm (MPA), the sparrow search algorithm (SSA), the artificial hummingbird algorithm (AHA), and the seagull optimization algorithm (SOA) through experimental tests, the results demonstrate that the GDRL-MOEA/D has a significant advantage in terms of solution quality. Full article

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang’s calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms. Full article