Search Results (522)

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

Small stalls and regulated feedings restrict horses’ natural foraging and locomotion, increasing risks to welfare. Environmental enrichment may promote more naturalistic behavioral time budgets, yet little is known about how enrichment type or timing affects physiology and behavior. This study examined nine stabled Quarter Horses provided with hay feeders, activity balls, or mirrors across randomized trials. Each trial included 30 min observations, four times per day, with enrichment removed between sessions and 5-day washouts between trials. Nightwatch^® Smart Halters™ recorded heart and respiration rates, while behaviors were video-scored using instantaneous scan sampling. Observers were not blind to the treatments. Enrichment effects, item type, time of day, and possible interactions for each variable were tested using a GLMM; Tukey’s HSD multiple comparison procedure was used for post hoc comparisons (at p ≤ 0.05). Enrichment significantly increased heart rate compared with the control (p = 0.03), indicating heightened arousal, with hay feeders producing the strongest effects. Respiration rate was unaffected. Mirrors reduced evening heart rates compared with other times (p = 0.02). Across treatments, enrichment increased foraging (p = 0.01) and locomotion (p = 0.03), while reducing frustration behaviors (p = 0.03). Hay feeders produced time budgets most similar to wild horses, suggesting greater effectiveness at meeting behavioral needs. Effects were most pronounced at 12:00 h and 16:00 h, outside routine meals. Overall, enrichment may improve physiological and behavioral outcomes, supporting its role in promoting welfare for stabled horses. Larger studies are needed to assess item-specific and long-term impacts. Full article

Honey robbing, as an extreme adaptive response of honey bee colonies to resource scarcity, poses devastating threats to apiaries, yet the underlying molecular mechanisms remain poorly understood. We compared morphological traits and survival rates between robber bees and normal foragers and conducted proteomic sequencing of bee head samples. The results demonstrated that robber bees exhibited darker tergite coloration and significantly shortened lifespan. Proteomic analysis revealed that the darker coloration was primarily attributed to enhanced cuticular melanin deposition mediated by upregulated laccase-5, while the shortened lifespan mainly resulted from oxidative stress and immune suppression: the downregulation of heat shock protein 75 kDa and glutathione transferase weakened antioxidant capacity, and despite compensatory upregulation of the cytochrome P450 enzyme system, flavin-containing monooxygenases and other enzymes, oxidative damage continued to accumulate. Concurrently, downregulation of Defense protein 3 and C-type lectin 5 caused immune deficiency in robber bees. The results also showed metabolic and protein synthesis reprogramming in robber bees, specifically manifested by upregulated key enzymes in nicotinate and nicotinamide metabolism, the pentose phosphate pathway, and nucleotide metabolism, along with activation of protein synthesis-transport-export systems. We found that robber bees employ a “metabolic-synthetic co-enhancement” physiological strategy to boost short-term foraging efficiency, but this strategy simultaneously induces oxidative damage and immune suppression, ultimately shortening their lifespan. This study provides the first proteomic evidence revealing the physiological trade-offs underlying this behavior at the molecular level, offering novel insights into the physiological costs of behavioral adaptation in animals. Full article

Demographic constraints can have a profound effect on behavioral ecology. Yet examinations of intraspecific variation considering both sex and age are rare. We assess age and sex-specific habitat use, movement, and behavior in variegated toad-headed agamas (Phrynocephalus versicolor) in the Gobi Desert, Mongolia. We predicted that juveniles would move and forage more than either adult sex and would engage in more random movement paths (i.e., higher entropy) than adults. We conducted 15 min focal observations, marking locations every 30 s to delineate the movement path of individuals. We recorded foraging and tail displays throughout the observation and habitat data at each marker. We found no sex-specific variation in behavior, number of moves, or entropy, but did record sex-specific variation in habitat use and movement paths. Age-specific variation in behavior, movement, entropy, and habitat use was prevalent and nuanced. Juveniles ate, dug, moved, and tail displayed more than adults, and they had movement paths with higher entropy than either adult sex. Sex and age-based variation in behavior, movement, and habitat use could arise from differential body size, experience, or reproductive status. Future work is needed to understand the function of tail displays and the relationship of entropy in movement paths to behavioral ecology. Full article

The greater cane rat algorithm (GCRA) is a swarm intelligence algorithm inspired by the discerning and intelligent foraging behavior of the greater cane rats, which facilitates mating during the rainy season and non-mating during the dry season. However, the basic GCRA exhibits serious drawbacks of high parameter sensitivity, insufficient solution accuracy, high computational complexity, susceptibility to local optima and overfitting, poor dynamic adaptability, and a severe curse of dimensionality. In this paper, a hybrid nonlinear greater cane rat algorithm with sine–cosine algorithm named (SCGCRA) is proposed for resolving the benchmark functions and constrained engineering designs; the objective is to balance exploration and exploitation to identify the globally optimal precise solution. The SCGCRA utilizes the periodic oscillatory fluctuation characteristics of the sine–cosine algorithm and the dynamic regulation and decision-making of nonlinear control strategy to improve search efficiency and flexibility, enhance convergence speed and solution accuracy, increase population diversity and quality, avoid premature convergence and search stagnation, remedy the disequilibrium between exploration and exploitation, achieve synergistic complementarity and reduce sensitivity, and realize repeated expansion and contraction. Twenty-three benchmark functions and six real-world engineering designs are utilized to verify the reliability and practicality of the SCGCRA. The experimental results demonstrate that the SCGCRA exhibits certain superiority and adaptability in achieving a faster convergence speed, higher solution accuracy, and stronger stability and robustness. Full article

Apis cerana cerana is a key social insect, and its ability to recognize chemical signals is crucial for maintaining colony homeostasis and coordinating collective behaviors, such as foraging, nursing, and defense. The legs of insects play a significant role in gustatory perception and proximity olfactory perception. In this study, the leg sensilla of A. c. cerana were observed by scanning electron microscopy (SEM). Two types of sensilla were observed, including sensilla trichodea (Str I, Str II, Str III, Str IV, Str V, and Str VI) and sensilla chaetica (Sch I, Sch II, and Sch III). The two unique structures of the tibial spur (Tsp I, Tsp II) and antennal brush (Abr) are carefully observed. The electrophysiological responses of workers at different ages to diverse chemical compounds were measured via electrolegogram (ELG) recordings on their legs. The results showed that 1-day-old A. c. cerana was more sensitive to nonanal; 10-day-old and 25-day-old A. c. cerana were more sensitive to ocimene. The results of behavioral responses showed that nonanal and ocimene can significantly attract 10-day-old workers of A. c. cerana. This study establishes a foundation for further exploration of the mechanisms by which the legs of A. c. cerana facilitate colony-level communication through chemical signals. It also provides an important theoretical basis for understanding their social organization and information transmission. Full article

The aims of the study were to evaluate the effectiveness of audio detection for identifying feeding sounds in free grazing sheep and to assess whether the recognition of these sounds could be influenced by pasture characteristics. Twelve Valle del Belice dry ewes were grazed on two mixed swards: on 10 May, grass-rich sward (G); on 13 May, legume-rich sward (L). Each ewe was fitted with a collar equipped with a point of view (POV) camera. All audio files (without viewing the videos) were listened to and sounds recognized as herbage prehension and rumination activity were highlighted. Time spent eating and ruminating was then calculated. To validate the audio file analysis, all video files were subjected to observation of the same behavioral aspects detected with audio. The regression between the prehensions number estimated using sound alone and the actual values recorded through video was significant (r² 0.743; p < 0.001). No differences were found in recognizing grazing behavior between data obtained by listening or watching the videos and between the two swards. The acoustic analysis of the single bites on grass and legume forages reveals significant differences between the two forage classes (p ≤ 0.001) particularly in terms of energy, temporal structure, and spectral features. Since sheep showed a strong selective activity towards legumes even in the grass-rich sward (selectivity index 3.1), this may have reduced acoustic differences between swards. Full article

This study presents an improved variant of the greater cane rat algorithm (GCRA), called adaptive and global-guided greater cane rat algorithm (AGG-GCRA), which aims to alleviate some key limitations of the original GCRA regarding convergence speed, solution precision, and stability. GCRA simulates the foraging behavior of the greater cane rat during both mating and non-mating seasons, demonstrating intelligent exploration capabilities. However, the original algorithm still faces challenges such as premature convergence and inadequate local exploitation when applied to complex optimization problems. To address these issues, this paper introduces four key improvements to the GCRA: (1) a global optimum guidance term to enhance the convergence directionality; (2) a flexible parameter adjustment system designed to maintain a dynamic balance between exploration and exploitation; (3) a mechanism for retaining top-quality solutions to ensure the preservation of optimal results.; and (4) a local perturbation mechanism to help escape local optima. To comprehensively evaluate the optimization performance of AGG-GCRA, 20 separate experiments were carried out across 26 standard benchmark functions and six real-world engineering optimization problems, with comparisons made against 11 advanced metaheuristic optimization methods. The findings indicate that AGG-GCRA surpasses the competing algorithms in aspects of convergence rate, solution precision, and robustness. In the stability analysis, AGG-GCRA consistently obtained the global optimal solution in multiple runs for five engineering cases, achieving an average rank of first place and a standard deviation close to zero, highlighting its exceptional global search capabilities and excellent repeatability. Statistical tests, including the Friedman ranking and Wilcoxon signed-rank tests, provide additional validation for the effectiveness and importance of the proposed algorithm. In conclusion, AGG-GCRA provides an efficient and stable intelligent optimization tool for solving various optimization problems. Full article

The climbing perch, Anabas testudineus, is one of the most widely distributed freshwater amphibious fishes in South and Southeast Asia, inhabiting both natural and artificial water bodies polluted by plastic waste. Current mesocosm experimental study aimed to investigate behavioral responses of wild fish to floating expanded polystyrene (EPS) pellets, with a focus on the biofilm developing on their surface. For biofilm formation, the pellets (diameter 3–4 mm) were exposed for two, six, and fourteen days in an irrigation canal inhabited by climbing perch. Development of an intensive biofilm was observed on days 6 and 14 of exposure, characterized by a high diversity of organisms, including protozoa, cyanobacteria, algae, amoebae, and fungi. Fish feeding behavior was observed in the presence of feed pellets, clean EPS pellets, and three variants of EPS pellets with biofilm developed on their surfaces in the freshwater environment. The fish rapidly grasped and ingested feed pellets compared to all variants of plastic pellets. Climbing perch grasped all types of EPS pellets but always rejected them after oral cavity testing. The time to the first grasp was significantly longer for both clean EPS and EPS exposed for two days compared to feed pellets. Biofilm appeared to function as a taste deterrent for the fish: the duration of oral cavity testing was negatively correlated with the EPS pellet exposure timings in natural conditions. We suggest that floating plastic stimulates foraging behavior in the fish, and the duration of this behavior was significantly longer than that observed with feed pellets. The similarity of positive buoyant EPS pellets to natural food objects may stimulate the fish movements towards the water surface, which likely results in greater energy expenditure and increased risk of predation, without any apparent benefit to the individual. Full article

Artificial protozoa optimizer (APO), as a newly proposed meta-heuristic algorithm, is inspired by the foraging, dormancy, and reproduction behaviors of protozoa in nature. Compared with traditional optimization algorithms, APO demonstrates strong competitive advantages; nevertheless, it is not without inherent limitations, such as slow convergence and a proclivity towards local optimization. In order to enhance the efficacy of the algorithm, this paper puts forth a multi-strategy fusion artificial protozoa optimizer, referred to as MSAPO. In the initialization stage, MSAPO employs the piecewise chaotic opposition-based learning strategy, which results in a uniform population distribution, circumvents initialization bias, and enhances the global exploration capability of the algorithm. Subsequently, cyclone foraging strategy is implemented during the heterotrophic foraging phase. enabling the algorithm to identify the optimal search direction with greater precision, guided by the globally optimal individuals. This reduces random wandering, significantly accelerating the optimization search and enhancing the ability to jump out of the local optimal solutions. Furthermore, the incorporation of hybrid mutation strategy in the reproduction stage enables the algorithm to adaptively transform the mutation patterns during the iteration process, facilitating a strategic balance between rapid escape from local optima in the initial stages and precise convergence in the subsequent stages. Ultimately, crisscross strategy is incorporated at the conclusion of the algorithm’s iteration. This not only enhances the algorithm’s global search capacity but also augments its capability to circumvent local optima through the integrated application of horizontal and vertical crossover techniques. This paper presents a comparative analysis of MSAPO with other prominent optimization algorithms on the three-dimensional CEC2017 and the highest-dimensional CEC2022 test sets, and the results of numerical experiments show that MSAPO outperforms the compared algorithms, and ranks first in the performance evaluation in a comprehensive way. In addition, in eight real-world engineering design problem experiments, MSAPO almost always achieves the theoretical optimal value, which fully confirms its high efficiency and applicability, thus verifying the great potential of MSAPO in solving complex optimization problems. Full article

Honeybee populations are increasingly threatened by various environmental stressors, including pesticides, pathogens, and climate change. Emerging research highlights the vital role of pollen polyphenols in supporting honeybee health through a network of antioxidants, immune responses, and detoxification mechanisms. This review synthesizes current findings on the chemical diversity, bioactivity, and functional relevance of polyphenolic compounds in honeybee nutrition. Pollen polyphenols, which include flavonoids and phenolic acids, possess remarkably high antioxidant potential, up to 235 times greater than that of nectar. They also significantly increase the expression of antioxidant enzymes, immune system genes, and detoxification pathways such as cytochrome P450s and glutathione-S-transferases. These compounds also demonstrate antimicrobial effects against key pathogens and mitigate the toxic effects of pesticides. The content and composition of polyphenols vary seasonally and geographically, impacting the resilience of honeybee colonies. Field and laboratory studies confirm that polyphenol-rich diets improve survival, gland development, and stress resistance. Advanced analytical techniques, including metabolomics, have expanded our understanding of polyphenol profiles and their effects on honeybee physiology. However, knowledge gaps remain in pharmacokinetics and structure–function relationships. Integrating this evidence into conservation strategies and good beekeeping practices, such as habitat diversification and targeted feed supplementation, is crucial for maintaining honeybee health and ecosystem services in a rapidly changing environment. Full article

The objective of this research was to determine the in-pen behavior and carcass yield of MCPs fed two different diets and slaughtered at two different live weights. The MCP biotype has a slow growth rate because they are fed with forage resources and locally available unconventional feeds. Sixteen castrated MCPs were used: eight pigs under 40 kg live weight and eight pigs over 40 kg. The diets were prepared with corn, soy, vitamins, and minerals, different protein levels, and the same energy content. Pigs fed the corn–soybean diet had a higher daily feed intake (500 g/d) and a significantly increased daily weight gain (160 g/d) compared to pigs fed on the corn-only diet, achieving slaughter weight in less time (4.33 times faster in pigs slaughtered at 40 kg live weight and 2.44 times faster for pigs slaughtered at 80 kg live weight). Regarding carcass yield, fat was 10% higher in pigs slaughtered at 80 kg compared to those at 40 kg. Soybean meal feeding improved the in-pen performance of MCPs but did not improve carcass yield. At higher slaughter weights, fat yield increased, but meat yield did not change. Full article

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and exploitation within the search space. Despite its innovative approach, the Honey Badger Algorithm (HBA) faces challenges such as slow convergence rates, an imbalanced trade-off between exploration and exploitation, and a tendency to become trapped in local optima. To address these issues, we propose an enhanced version of the Honey Badger Algorithm (HBA), namely the Multi-Strategy Honey Badger Algorithm (MSHBA), which incorporates a Cubic Chaotic Mapping mechanism for population initialization. This integration aims to enhance the uniformity and diversity of the initial population distribution. In the mining and honey-seeking stages, the position of the honey badger is updated based on the best fitness value within the population. This strategy may lead to premature convergence due to population aggregation around the fittest individual. To counteract this tendency and enhance the algorithm’s global optimization capability, we introduce a random search strategy. Furthermore, an elite tangential search and a differential mutation strategy are employed after three iterations without detecting a new best value in the population, thereby enhancing the algorithm’s efficacy. A comprehensive performance evaluation, conducted across a suite of established benchmark functions, reveals that the MSHBA excels in 26 out of 29 IEEE CEC 2017 benchmarks. Subsequent statistical analysis corroborates the superior performance of the MSHBA. Moreover, the MSHBA has been successfully applied to four engineering design problems, highlighting its capability for addressing constrained engineering design challenges and outperforming other optimization algorithms in this domain. Full article

Understanding long-term movement ecology is critical for conserving endangered species; however, comprehensive spatiotemporal analyses remain limited. In this study, we leveraged a decade-long GPS tracking dataset (2014–2024) of 31 endangered Crested Ibis (Nipponia nippon) individuals to elucidate their spatiotemporal behavioral patterns. The study focused on three key aspects: (1) fidelity to nesting, foraging, and roosting sites; (2) movement patterns and their ecological drivers; and (3) foraging habitat preferences across regions and activity periods. The results revealed exceptional fidelity to nesting, foraging (mean value = 0.253), and roosting sites (mean value = 0.261), underscoring the species’ pronounced spatial memory. Temporal factors emerged as the primary drivers of movement patterns, demonstrated by a significant annual reduction in home range size (p < 0.01) and a decline in daily flight distance in 2019 (β = −1890 ± 772 m, p < 0.05) and 2022 (p = 0.052). Behavioral factors also significantly influenced daily flight distance, with notable variations across different activity periods. Foraging habitat selection exhibited considerable spatial heterogeneity (14.2% constrained variance, p < 0.01). Cultivated lands, particularly paddy fields (Yangxian population) and drylands (Tongchuan population), served as core foraging zones. In contrast, spatiotemporal variables such as age had limited effects (<5% variance). This study provides the first empirical evidence of long-term site fidelity and habitat partitioning in the Crested Ibis, emphasizing the importance of landscape-level conservation planning. To this end, we propose two targeted strategies: establishing habitat corridors to enhance connectivity and safeguarding stable foraging areas within agricultural landscapes. These findings contribute to movement ecology theory while offering actionable frameworks for endangered species management. Full article

Bee pollination is an indispensable part of agricultural production and a crucial factor in maintaining ecosystem balance and biodiversity. Understanding foraging behavior and pollination effects is essential for the management of bee pollination. Over a two-year experiment, we evaluated the foraging behavior and pollination effects of bumblebee Bombus terrestris and Chinese honeybee Apis cerana cerana on cherries in orchards. Results showed that all bees exhibited enhanced foraging activity as daytime temperatures rose in early spring. However, the daytime foraging activity of bumblebees differs from that of Chinese honeybees. The number of bumblebees leaving the hive exhibited two peak periods, whereas Chinese honeybees showed only one peak period. Bumblebees had longer working hours and greater pollen-carrying capacity than Chinese honeybees. Undoubtedly, cherries pollinated by bees had higher yields, as indicated by a greater fruit setting rate and yield. Thus, as effective pollinators, their pollination significantly boosts production and presents a viable option for widespread use in cherry cultivation. However, the risk of biological invasion by exotic bumblebees cannot be overlooked before extensive use. Full article