Search Results (114)

The Argentine ant (Linepithema humile), listed among the world’s 100 worst invasive alien species, is notoriously difficult to control due mainly to its formation of large, expansive supercolonies. Despite the drawbacks of chemical control, biological alternatives have not been previously explored for this species. In this study, we evaluated six native entomopathogenic fungal strains against Argentine ants from four behaviorally distinct supercolonies, identified through aggression assays and collected from both urban and natural sites within the species’ native range. Ants were inoculated with 1 × 10⁸ conidia/mL using three methods: topical application, spray, and immersion. Mortality was recorded over 14 days, and the cause of death was confirmed by fungal outgrowth from cadavers. Among all strains, Beauveria bassiana Li053 consistently induced high mortality across all supercolonies and inoculation methods, with LT₅₀ values between 2 and 5 days and final mortality rates exceeding 80%. Fungal infection was confirmed in 87–92% of cadavers. Dose–response assays revealed that higher conidial concentrations accelerated and increased mortality, with an LC50 estimated at 1 × 10⁶ conidia/mL. These results demonstrate that B. bassiana Li053 is a promising candidate for the biological control of L. humile and merits further evaluation under field conditions. Full article

Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. Focusing on complex smart city scenarios—including intelligent transportation coordination and UAV swarm scheduling—we identify technical breakthroughs in MARL applications for policy space modeling and distributed decision optimization. By incorporating bio-inspired optimization approaches, the investigation particularly highlights evolutionary computation mechanisms for dynamic strategy generation in search planning, alongside population-based learning paradigms for enhancing exploration efficiency in policy refinement. The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems. Full article

Inspired by call centers, this paper models them as a constant retrial queue, with feedback and delayed vacations to balance high efficiency and low cost for service agents. After completing the service, the server randomly waits for an idle period. If customers arrive during this period, the service is provided immediately, otherwise, the server will take a vacation. We first derive steady-state probabilities and key performance measures. Then, the system cost is modeled. Particle Swarm Optimization (PSO), Ant Colony Algorithm (ACA) and Sparrow Search Algorithm (SSA) are applied to obtain the minimum system cost, respectively. To verify the correctness of the theoretical results of the system model, we simulate the model using Monte Carlo simulation to obtain the probabilities of different server states and the expected number of customers in the system, and then compare them with the theoretical values. At the same time, the sensitivity of the performance measures obtained by Monte Carlo simulation to the system parameters is also analyzed. Finally, customer behavior is analyzed, and equilibrium and socially optimal arrival rates are derived. In addition, the efficiency of the system is evaluated by examining efficiency indicators such as throughput and price of anarchy. Full article

The emergence of antimicrobial resistance is a significant challenge in global healthcare, necessitating innovative techniques to address multidrug-resistant pathogens. Multidrug-resistant pathogens like Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa pose significant public health threats, as they are increasingly resistant to common antibiotics, leading to more severe and difficult-to-treat infections. These pathogens are part of the ESKAPE group, which includes Enterococcus faecium, Staphylococcus aureus, and Enterobacter species. Animal venoms, derived from a wide range of species such as snakes, scorpions, spiders, bees, wasps, and ants, represent a rich source of bioactive peptides. Venoms have been a valuable source for drug discovery, providing unique compounds with therapeutic potential. Venom-derived drugs are known for their increased bioactivity, specificity, and stability compared to synthetic alternatives. These compounds are being investigated for various conditions, including treatments for diabetes, pain relief, cancer, and infections, showcasing their remarkable antimicrobial efficacy. In this review, we provide a comprehensive investigation into the potential of venom-derived compounds for developing new antimicrobial agents, including antibacterial, antifungal, antiviral, and antiparasitic therapeutics. Key venom components, including melittin from bee venom, phospholipase A₂ from snake venom, and chlorotoxin from scorpion venom, exhibit potent antimicrobial effects through mechanisms such as membrane disruption, enzymatic inhibition, and immune modulation. We also explore the challenges related to the development and clinical use of venom-derived antimicrobials, including toxicity, stability, and delivery mechanisms. These compounds hold immense promise as transformative tools against resistant pathogens, offering a unique avenue for groundbreaking advancements in antimicrobial research and therapeutic development. Full article

In intralogistics, autonomous mobile robots (AMRs) operate without predefined paths, leading to complex traffic patterns and potential conflicts that impact system efficiency. This paper proposes a bio-inspired optimization method for autonomously generating spatial movement constraints for autonomous mobile robots (AMRs). Unlike traditional multi-agent pathfinding (MAPF) approaches, which focus on temporal coordination, our approach proactively reduces conflicts by adapting a weighted directed grid graph to improve traffic flow. This is achieved through four mechanisms inspired by ant colony systems: (1) a movement reward that decreases the weight of traversed edges, similar to pheromone deposition, (2) a delay penalty that increases edge weights along delayed paths, (3) a collision penalty that increases weights at conflict locations, and (4) an evaporation mechanism that prevents premature convergence to suboptimal solutions. Compared to the existing approaches, the proposed approach addresses the entire intralogistic problem, including plant layout, task distribution, release and dispatching algorithms, and fleet size. Its autonomous movement rule generation and low computational complexity make it well suited for dynamic intralogistic environments. Validated through physics-based simulations in Gazebo across three scenarios, a standard MAPF benchmark, and two industrial environments, the movement constraints generated using the proposed method improved the system throughput by up to 10% compared to unconstrained navigation and up to 4% compared to expert-designed solutions while reducing the need for conflict-resolution interventions. Full article

Recently, significant research efforts have been directed toward leveraging Artificial Intelligence for sensor data processing and system control. In particular, it is essential to determine the optimal path and trajectory by calculating sensor data for effective control systems. For instance, model-predictive control based on Proportional-Integral-Derivative models is intuitive, efficient, and provides outstanding control performance. However, challenges in tracking persist, which requires active research and development to integrate and optimize the control system in terms of Machine Learning. Specifically, Reinforcement Learning, a branch of Machine Learning, has been used in several research fields to solve optimal control problems. In this paper, we propose an Extended Maximum Actor–Critic using a Reinforcement Learning-based method to combine the advantages of both value and policy to enhance the learning stability of actor–critic for optimization of system control. The proposed method integrates the actor and the maximized actor in the learning process to evaluate and identify actions with the highest value, facilitating effective learning exploration. Additionally, to enhance the efficiency and robustness of the agent learning process, we propose Prioritized Hindsight Experience Replay, combining the advantages of Prioritized Experience Replay and Hindsight Experience Replay. To verify this, we performed evaluations and experiments to examine the improved training stability in the MuJoCo environment, which is a simulator based on Reinforcement Learning. The proposed Prioritized Hindsight Experience Replay method significantly enhances the experience to be compared with the standard replay buffer and PER in experimental simulators, such as the simple HalfCheetah-v4 and the complex Ant-v4. Thus, Prioritized Hindsight Experience Replay achieves a higher success rate than PER in FetchReach-v2, demonstrating the significant effectiveness of our proposed method in more complex reward environments. Full article

Background: SARS-CoV-2 is classified as a class 3 biological agent; therefore, autopsies on positive subjects must be performed in BSL 3 sectorial rooms. However, many centers lacking such facilities perform molecular nasopharyngeal swabs for SARS-CoV-2 on corpses before autopsy. This approach, though, is marked by prolonged reporting times and extremely high costs. This study aims to compare the results of molecular swabs (RT-PCR) with rapid antigen swabs (RAT) in order to assess if RAT can serve as the sole test for determining corpse positivity or negativity. Methods: Sixty corpses with positive molecular nasopharyngeal swabs for SARS-CoV-2, performed either ante-mortem or post-mortem, were selected. Afterward, they underwent rapid antigen swabs within 0 to 11 days after the last molecular exam. Results: Out of 60 corpses with positive molecular swabs, 52 antigen swabs were positive (86.67%), and 8 were negative (13.33%), indicating a sensitivity of 86.66% and specificity of 100%. Conclusions: Considering the sensitivity and specificity values observed in this study, RAT could be used as the primary investigation on corpses, especially in centers that lack BSL 3 sectorial rooms. Molecular swabs could then serve as a secondary test for subjects negative on RAT. Full article

In 2019, a disease outbreak struck a hybrid sturgeon farm (Huso dauricus ♀ × A. schrenckii ♂) in Tiantai, Zhejiang province, leading to the deaths of 8000 sturgeons. The sturgeons exhibited reduced appetite, lethargic and uncoordinated swimming, and physical signs such as reddish petechiae and ulcers on the body and fins. Hemorrhagic spots were observed on the kidneys, spleen, and gonads, alongside reddish intestines with hemorrhagic ascites in the abdominal cavity. ST-1902 was isolated and identified as Aeromonas hydrophila through physiological and biochemical characterization and 16S rDNA sequence analysis. The pathogenicity of ST-1902 was confirmed through a challenge test, with a median lethal dosage (LD50) of 7.9 × 10⁶ CFU/IND. Histopathological examination showed hyperplasia and neoplasm-like changes in the epicedial mesothelial tissues, enlarged and necrosis renal tissue, and serious hemosiderosis in spleen and gills. Virulent genes (Aer, Epa, Alt, Hly, and Act) were detected in ST-1902, corresponding to typical β-hemolysis, extracellular protease, and enterotoxin. Moreover, antimicrobial experiment detection indicated ST-1902 is sensitive to quinolones and phenicols but resistant to sulfamethoxazole, aminoglycoside antibiotics with Sul1, and Intl and Ant (3”)-I. These results suggest that A. hydrophila was the causative agent of the sturgeon disease and highlight the emerging threat it poses to the sturgeon industry. Full article

Nanobodies have gained attention as potential therapeutic and diagnostic agents for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to their ability to bind and neutralize the virus. However, rapid, scalable, and robust production of nanobodies for SARS-CoV-2 remains a crucial challenge. In this study, we developed a visual and high-efficiency biomanufacturing method for nanobodies with Escherichia coli by fusing the super-folder green fluorescent protein (sfGFP) to the N-terminus or C-terminus of the nanobody. Several receptor-binding domain (RBD)-specific nanobodies of the SARS-CoV-2 spike protein (S) were secreted onto the surface of E. coli cells and even into the culture medium, including Fu2, ANTE, mNb6, MR3-MR3, and n3113.1. The nanobodies secreted by E. coli retained equal activity as prior research, regardless of whether sfGFP was removed. Since some of the nanobodies bound to different regions of the RBD, we combined two nanobodies to improve the affinity. Fu2-sfGFP-ANTE was constructed to be bispecific for the RBD, and the bispecific nanobody exhibited significantly higher affinity than Fu2 (35.0-fold), ANTE (7.3-fold), and the combination of the two nanobodies (3.3-fold). Notably, Fu2-sfGFP-ANTE can be normally secreted into the culture medium and outer membrane. The novel nanobody production system enhances the efficiency of nanobody expression and streamlines the downstream purification process, enabling large-scale, cost-effective nanobody production. In addition, E. coli cells secreting the nanobodies on their surface facilitates screening and characterization of antigen-binding clones. Full article

Self-organization in complex systems is a process associated with reduced internal entropy and the emergence of structures that may enable the system to function more effectively and robustly in its environment and in a more competitive way with other states of the system or with other systems. This phenomenon typically occurs in the presence of energy gradients, facilitating energy transfer and entropy production. As a dynamic process, self-organization is best studied using dynamic measures and principles. The principles of minimizing unit action, entropy, and information while maximizing their total values are proposed as some of the dynamic variational principles guiding self-organization. The least action principle (LAP) is the proposed driver for self-organization; however, it cannot operate in isolation; it requires the mechanism of feedback loops with the rest of the system’s characteristics to drive the process. Average action efficiency (AAE) is introduced as a potential quantitative measure of self-organization, reflecting the system’s efficiency as the ratio of events to total action per unit of time. Positive feedback loops link AAE to other system characteristics, potentially explaining power–law relationships, quantity–AAE transitions, and exponential growth patterns observed in complex systems. To explore this framework, we apply it to agent-based simulations of ants navigating between two locations on a 2D grid. The principles align with observed self-organization dynamics, and the results and comparisons with real-world data appear to support the model. By analyzing AAE, this study seeks to address fundamental questions about the nature of self-organization and system organization, such as “Why and how do complex systems self-organize? What is organization and how organized is a system?”. We present AAE for the discussed simulation and whenever no external forces act on the system. Given so many specific cases in nature, the method will need to be adapted to reflect their specific interactions. These findings suggest that the proposed models offer a useful perspective for understanding and potentially improving the design of complex systems. Full article

The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge in achieving stable and high-performance cooperative behavior. Addressing the need for the balanced exploration and exploitation of multi-agent ant robots within a partially observable continuous action space, this study introduces a multi-agent centralized strategy gradient algorithm grounded in a local state transition mechanism. In order to solve this challenge, the algorithm learns local state and local state-action representation from local observations and action values, thereby establishing a “local state transition” mechanism autonomously. As the input of the actor network, the automatically extracted local observation representation reduces the input state dimension, enhances the local state features closely related to the local state transition, and promotes the agent to use the local state features that affect the next observation state. To mitigate non-stationarity and reliability assignment issues in multi-agent environments, a centralized critic network evaluates the current joint strategy. The proposed algorithm, NST-FACMAC, is evaluated alongside other multi-agent deterministic strategy algorithms in a continuous control simulation environment using a multi-agent ant robot. The experimental results indicate accelerated convergence and higher average reward values in cooperative multi-agent ant simulation environments. Notably, in four simulated environments named Ant-v2 (2 $\times$ 4), Ant-v2 (2 $\times$ 4d), Ant-v2 (4 $\times$ 2), and Manyant (2 $\times$ 3), the algorithm demonstrates performance improvements of approximately 1.9%, 4.8%, 11.9%, and 36.1%, respectively, compared to the best baseline algorithm. These findings underscore the algorithm’s effectiveness in enhancing the stability of multi-agent ant robot control within dynamic environments. Full article

Effective berth allocation in container terminals is crucial for optimizing port operations, given the limited space and the increasing volume of container traffic. This study addresses the discrete dynamic berth allocation problem (DDBAP) under uncertain ship arrival times and varying load capacities. A novel deep Q-network (DQN)-based model is proposed, leveraging a custom state space, rule-based actions, and an optimized reward function to dynamically allocate berths and schedule vessel arrivals. Comparative experiments were conducted with traditional algorithms, including ant colony optimization (ACO), parallel ant colony optimization (PACO), and ant colony optimization combined with genetic algorithm (ACOGA). The results show that DQN outperforms these methods significantly, achieving superior efficiency and effectiveness, particularly under high variability in ship arrivals and load conditions. Specifically, the DQN model reduced the total waiting time of vessels by 58.3% compared to ACO (262.85 h), by 57.9% compared to PACO (259.5 h), and by 57.4% compared to ACOGA (257.4 h), with a total waiting time of 109.45 h. Despite its impressive performance, DQN requires substantial computational power during the training phase and is sensitive to data quality. These findings underscore the potential of reinforcement learning to optimize berth allocation under dynamic conditions. Future work will explore multi-agent reinforcement learning (MARL) and real-time adaptive mechanisms to further enhance the robustness and scalability of the model. Full article

The Swarm Guiding and Communication System (SGCS) is a decision-making and information-sharing framework for robot swarms that only needs close-range peer-to-peer communication and no centralized control. Each robot makes decisions based on an incomplete virtual pheromone map that is updated on each interaction with another robot, imitating ant colonial behavior. Similar systems rely on continuous communication with no range limitations, environment modification, or centralized control. A computer simulation is developed to assess the effectiveness and robustness of the framework in covering an area. Consistency and the time needed for 99% coverage are compared with an unbiased random walk. The pheromone approach is shown to outperfom the unbiased one regardless of number of agents. Innate resilience to individual failures is also demonstrated. Full article

Water pricing is an economic instrument traditionally used to reduce water demand. However, its effective implementation requires knowledge of the extent to which users reduce water consumption with increasing water prices. The price elasticity of water demand has been estimated using econometric regression, which relies on cross-sectional and time-series water data. As an alternative, we propose the use of agent-based modelling, which does not require reliable historical data on water prices and consumption and enables the simulation of multiple scenarios with different consumer profiles, behaviour profiles and water price changes, thereby allowing comprehensive understanding of price elasticity estimates. To illustrate the potential use of agent-based modelling for the estimation of water demand price elasticity, we performed an empirical application to a residential area in Chile. Price elasticity estimates ranged from −0.0159 to −0.1036 (mean −0.0250), indicating that residential water consumption is inelastic to price changes. This result is consistent with previous findings. Agent-based modelling is an alternative for the ex-ante assessment of the potential effectiveness of water pricing policies intended to reduce residential water demand. Full article

Global milk consumption exceeds 800 million tons a year and is still growing. Milk quality and its products are critical to human health. A teat cup makes direct contact with the cow’s teats during milking and its cleanliness is very important for the quality of raw milk. In this study, the microorganism from post-milking teat cup liners were collected from six dairy farms in Shandong Province of China, the bacterial species were identified using microbial mass spectrometry, the minimum inhibitory concentrations of the isolated strains against ten antimicrobial agents were determined using the broth microdilution method, and the antimicrobial resistance genes were detected by PCR. The results indicated that the most frequently isolated bacteria in this study were Bacillus licheniformis (39/276, 14.13%), followed by Bacillus pumilus (20/276, 7.25%), Bacillus cereus (17/276, 6.16%), and Bacillus subtili (16/276, 5.80%). The isolates exhibited the highest average resistance to lincomycin (87.37%), followed by sulfadiazine (61.05%) and streptomycin (42.63%); the highest detection rate of resistance genes was Sul1 (55.43%), followed by ant(4’) (51.09%), tet(M) (25.36%), bla_KPC (3.62%) and qnrS (3.62%). These findings imply the necessity for enhanced measures in disinfecting cow udders and milking equipment, highlighting the persistently challenging issue of antimicrobial resistance in Shandong Province. Full article