Search Results (1,842)

This paper investigates finite-time resource allocation problems (RAPs) for uncertain nonlinear fractional-order multi-agent systems (FOMASs), considering global equality and local inequality constraints. Each agent is described by high-order dynamics with multiple-input multiple-output and only knows its local objective function. Due to the characteristics of dynamic systems, the outputs of agents are inconsistent with their inputs, making it challenging to satisfy the inequality constraints when solving RAPs. To address this complex optimization control problem, a novel hierarchical algorithm is proposed, consisting of a distributed estimator and a local controller. Specifically, the distributed estimator is established by adopting the $ϵ$-exact penalty function and the gradient descent method. This estimator enables the system states to reach the optimal solution of RAPs within a finite time. Furthermore, the local controller is presented based on the fractional-order tracking differentiator and adaptive neural control approach. Under this controller, the system states are slaved to track the optimal signals generated by the estimator within a finite time. In both the estimator and controller algorithms, the finite-time stability is uniformly guaranteed with the help of Lyapunov functions. Finally, the effectiveness of our algorithm is demonstrated through three simulation examples. Full article

In this study, the cyclic pursuit formation stabilization problem in target-enclosing operations by multiple homogeneous dynamic agents is investigated. To this end, a Lyapunov $\mathcal{D}$-stability problem is first formulated to cover the transient performance requirements for multi-agent systems. Then, a simple diagrammatic Lyapunov $\mathcal{D}$-stability criterion for cyclic pursuit formation is derived. The formation control scheme combined with a cyclic-pursuit-based distributed online path generator satisfying this condition guarantees both the required transient performance and global convergence properties with theoretical rigor. It is shown that the maximization of the connectivity gain in a cyclic-pursuit-based online path generator can be reduced to an optimization problem subject to linear matrix inequality constraints derived using the generalized Kalman-Yakubovich–Popov lemma. This approach provides a permissible range of connectivity gain, which not only guarantees global formation stability/convergence properties but also satisfies the required performance specification. Several numerical examples are provided to confirm the effectiveness of the proposed method. Full article

The development of affordable and reliable methods for quantitative determination of stable atomic nuclei in aqueous solutions and adjuvant agents used in tumor chemotherapy is an important task in modern pharmaceutical chemistry. This work quantified the deuterium/prothium isotope ratio in aqueous solutions through an original two-dimensional diffuse laser scattering (2D-DLS) software and hardware system based on chemometric processing of discrete interference patterns (dynamic speckle patterns). For this purpose, 10 mathematical descriptors (d_i), similar to QSAR descriptors, were used. Correlation analysis of bivariate “log d_i—D/H” plots shows an individual set of multi-descriptors for a given sample with a given D/H ratio (ppm). A diagnostic sign (DS) of differentiation was established: the samples were considered homeomorphic if 6 out of 10 descriptors differed by less than 15% (n ≥ 180). The analytical range (r = 0.987) between the upper (D/H ≤ 2 ppm) and lower (D/H = 180 ppm) limits for the quantification of stable hydrogen nuclei in water and aqueous solutions were established. Using the Spirotox method, a «safe zone» for protozoan survival was determined between 50 and 130 ppm D/H. Here, we discuss the dispersive (DLS, LALLS) and optical properties (refractive index, optical rotation angle) of the solutions with different D/H ratios that define the diffuse laser radiation due to surface density inhomogeneities. The obtained findings may pave the way for the future use of a portable, in situ diffuse laser light scattering instrument to determine deuterium in water and aqueous adjuvants. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, are being seen as the most promising type of autonomous vehicles in the context of intelligent transportation system (ITS) technology. A key enabling factor for the current development of ITS technology based on autonomous vehicles is the task allocation architecture. This approach allows tasks to be efficiently assigned to robots of a multi-agent system, taking into account both the robots’ capabilities and service requirements. Consequently, this study provides an overview of the application of drones in ITSs, focusing on the applications of task allocation algorithms for UAV networks. Currently, there are different types of algorithms that are employed for task allocation in drone-based intelligent transportation systems, including market-based approaches, game-theory-based algorithms, optimization-based algorithms, machine learning techniques, and other hybrid methodologies. This paper offers a comprehensive literature review of how such approaches are being utilized to optimize the allocation of tasks in UAV-based ITSs. The main characteristics, constraints, and limitations are detailed to highlight their advantages, current achievements, and applicability to different types of UAV-based ITSs. Current research trends in this field as well as gaps in the literature are also thoughtfully discussed. Full article

Niclosamide, an FDA-approved anti-parasitic drug, has demonstrated significant potential as a repurposed anti-cancer agent due to its ability to interfere with multiple oncogenic pathways. However, its clinical application has been hindered by poor solubility and bioavailability. Lipid-based nanocarrier systems such as liposomes, solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), and lipid nanoemulsions (LNE), along with lipid prodrugs, have successfully been employed by researchers to overcome these limitations and improve niclosamide’s pharmacokinetic profile. Lipids are the core organic compounds which serve as the foundation of these advanced drug delivery methods and in turn play a critical role in enhancing niclosamide’s therapeutic efficacy through improving drug solubility and bioavailability. Lipid-based nanoparticles encapsulate niclosamide, protect it from degradation, facilitate drug delivery and release, and may facilitate targeted delivery in the future. While niclosamide holds significant potential as an anticancer agent due to its multi-pathway inhibitory effects, the challenges associated with its poor bioavailability and rapid clearance underscore the need for innovative delivery methods and chemical modifications to unlock its full therapeutic potential. This review aims to present the latest instances of lipid-based delivery of niclosamide and to compile successful strategies which may be employed when aiming to develop effective anticancer therapies. Full article

Multi-population mean-field game is a critical subclass of mean-field games (MFGs). It is a theoretically feasible multi-agent model for simulating and analyzing the game between multiple heterogeneous populations of interacting massive agents. Due to the factors of game complexity, dimensionality disaster and disturbances should be taken into account simultaneously to solve the multi-population high-dimensional stochastic MFG problem, which is a great challenge. We present CA-Net, a coupled alternating neural network approach for tractably solving multi-population high-dimensional MFGs. First, we provide a universal modeling framework for large-scale heterogeneous multi-agent game systems, which is strictly expressed as a multi-population MFG problem. Next, we generalize the potential variational primal–dual structure that MFGs exhibit, then phrase the multi-population MFG problem as a convex–concave saddle-point problem. Last but not least, we design a generative adversarial network (GAN) with multiple generators and multiple discriminators—the solving network—which parameterizes the value functions and the density functions of multiple populations by two sets of neural networks, respectively. In multi-group quadcopter trajectory-planning numerical experiments, the convergence results of HJB residuals, control, and average speed show the effectiveness of the CA-Net algorithm, and the comparison with baseline methods—cluster game, HJB-NN, Lax–Friedrichs, ML, and APAC-Net—shows the progressiveness of our solution method. Full article

Multi-buoy sonar systems achieve target localization by receiving broadband Linear Frequency Modulation signals emitted from the transmitter. Accurate estimations of the parameters of Linear Frequency Modulation signals significantly enhance the localization accuracy. Linear Frequency Modulation signals can be focused into the fractional domain through Fractional Fourier Transform, but this increases the computational complexity. In marine environments, the low signal-to-noise ratio and multipath effects degrade the parameter estimation accuracy further. To address these issues, this paper proposes a fast estimation algorithm based on the Fractional Fourier Transform and a Gradient Subtraction-Average-Based Optimizer. This algorithm leverages the impulsive characteristics of Linear Frequency Modulation signals after Fractional Fourier Transform transformation, using the Fractional Fourier Transform as the fitness function. The Gradient Subtraction-Average-Based Optimizer algorithm includes three enhancement strategies: chaotic mapping initialization, a Golden Sine Algorithm, and an adaptive t-distribution variational operator. The simulation results demonstrate that the Gradient Subtraction-Average-Based Optimizer algorithm improves the issues of low diversity in the search agents, imbalanced global and local search capabilities, and susceptibility to local optima. A comparative analysis and statistical testing confirm that under a low signal-to-noise ratio and multipath effect conditions, the Gradient Subtraction-Average-Based Optimizer algorithm not only ensures real-time parameter estimation but also improves the estimation accuracy. The results of the parameter estimation provide reliable data support for subsequent target localization. Full article

Background/Objectives: Although the use of radiation-sensitizing agents has been shown to enhance the effect of radiation on tumor cells, the blood–brain barrier (BBB) impedes these agents from reaching brain tumor sites when provided systemically. Localized methods of sensitizer delivery, utilizing hydrogels, have the potential to bypass the blood–brain barrier. This study examined the ability of photochemical internalization (PCI) of hydrogel-released bleomycin to enhance the growth-inhibiting effects of radiation on multi-cell glioma spheroids in vitro. Methods: Loaded fibrin hydrogel layers were created by combining thrombin, fibrinogen, and bleomycin (BLM). Supernatants from these layers were collected, combined with photosensitizer, and added to F98 glioma spheroid cultures. Following light (PCI) and radiation treatment, at increasing dosages, spheroid growth was monitored for 14 days. Results: PCI of released BLM significantly reduced the radiation dose required to achieve equivalent efficacy compared to radiation or BLM + RT alone. Both immediate and delayed RT delivery post-BLM-PCI resulted in similar degrees of growth inhibition. Conclusions: Non-degraded BLM was released from the fibrin hydrogel. PCI of BLM synergistically increased the growth-inhibiting effects of radiation treatment compared to radiation and BLM, as well as radiation acting as a single treatment. Full article

Public transit systems are critical to the quality of urban life, and enhancing their efficiency is essential for building cost-effective and sustainable smart cities. Historically, researchers sought reinforcement learning (RL) applications to mitigate bus bunching issues with holding strategies. Nonetheless, these attempts often led to oversimplifications and misalignment with the goal of reducing the total time passengers spent in the system, resulting in less robust or non-optimal solutions. In this study, we introduce a novel setting where each bus, supervised by an RL agent, can appropriately form aggregated policies from three strategies (holding, skipping station, and turning around to serve the opposite direction). It’s difficult to learn them all together, due to learning complexity, we employ domain knowledge and develop a gradually expanding action space curriculum, enabling agents to learn these strategies incrementally. We incorporate Long Short-Term Memory (LSTM) in our model considering the temporal interrelation among these actions. To address the inherent uncertainties of real-world traffic systems, we impose Domain Randomization (DR) on variables such as passenger demand and bus schedules. We conduct extensive numerical experiments with the integration of synthetic and real-world data to evaluate our model. Our methodology proves effective, enhancing bus schedule reliability and reducing total passenger waiting time by over 15%, thereby improving bus operation efficiency and smoothering operations of buses that align with sustainable goals. This work highlights the potential of robust RL combined with curriculum learning for optimizing public transport in smart cities, offering a scalable solution for real-world multi-agent systems. Full article

Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination. Full article

The logistics and transportation sectors are struggling to manage empty containers (ECs), resulting in unused resources, inefficiencies, and increased CO₂ emissions. The Physical Internet (PI) concept provides an opportunity to improve container sharing and transportation by intelligently organising logistics resources. This paper shows how PI principles can address the EC problem in truck transportation. The objective is to reduce CO₂ emissions with improved space-sharing strategies. The problem is formulated and solved using a two-stage optimisation approach (2Stage-Opt) to optimise container motion. The validity of the 2Stage-Opt solutions is tested using a developed multi-agent system simulation (MASS) model to replicate the behaviour of real multi-modal hubs. This approach is evaluated using a real-world case study from a multi-modal logistics centre in the north of France. The results indicate that utilising PI-container solutions offers significant sustainability benefits, especially in reducing the number of trucks used in the simulation and the CO₂ emissions from ECs. Full article

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences. Full article

Collective migration underlies key developmental and disease processes in vertebrates. Mathematical models describing collective migration can shed light on emergent patterns arising from simple mechanisms. In this paper, a mathematical model for collective migration is given for arbitrary numbers and types of individuals using principles outlined as a set of assumptions, such as the assumed preference for individuals to be “close but not too close" to others. The model is then specified to the case of two species with arbitrary numbers of individuals in each species. A particular form of signal response is used that may be parameterized based on experiments involving two or three agents. In its simplest form, the model describes two species of individuals that emit distinct signals, distinguishes between them, and exhibits responses unique to the type by moving according to signal gradients in various planar regions, a situation described as "mixotaxis". Beyond this simple form, initial conditions and boundary conditions are altered to simulate specific, additional in vitro as well as in vivo dynamics. The behaviors that were specifically accounted for include motility, directed migration, and a functional response to a signal. Ultimately, the paper’s results highlight the ability of a single framework for signal and response to account for patterns seen in multi-species systems, in particular the emergent self-organization seen in the embryonic development of placodal cells, which display chase-and-run behavior, flocking behavior, herding behavior, and the splitting of a herd, depending on initial conditions. Numerical experiments focus around the primary example of neural crest and placodal cell “chase-and-run” and “flocking” behaviors; the model reproduces the separation of placodal cells into distinct clumps, as described in the literature for neural crest and placodal cell development. This model was developed to describe a heterogeneous environment and can be expanded to capture other biological systems with one or more distinct species. Full article

Accurately predicting the long-term trajectories of agents in complex traffic environments is crucial for the safety and effectiveness of autonomous driving systems. This paper introduces DBN-MACTraj, a probabilistic model that takes historical trajectories and surrounding lane information as inputs to generate a distribution of predicted trajectory combinations for all agents. DBN-MACTraj consists of two main components: a single-agent probabilistic model and a multi-agent risk-averse sampling algorithm. The single-agent model utilizes a dynamic Bayesian network, which incorporates the driver’s maneuvering decisions along with information about surrounding lanes. The multi-agent sampling algorithm simultaneously generates predictions for all agents, using a risk potential field model to filter out samples that may lead to traffic accidents. Ultimately, this results in a probability distribution of the combinations of long-term trajectories. Experimental evaluations on the nuScenes dataset demonstrate that DBN-MACTraj delivers competitive performance in trajectory prediction compared to other state-of-the-art approaches. Full article

During the evolution of the RNA World, compartments, which were fragments of space surrounded by a primitive lipid membrane, had to have emerged. These led eventually to the formation of modern cellular membranes. Inside these compartments, another process had to take place—switching from RNA to DNA as a primary storage of genetic information. The latter part needed a handful of enzymes for the DNA to be able to perform its function. A natural question arises, i.e., how the concentration of all vital molecules could have been kept in check without modern cellular mechanisms. The authors propose a theory on how it could have worked during early stages, using only short RNA molecules, which could have emerged spontaneously. The hypothesis was analysed mathematically and tested against different scenarios by using computer simulations. Full article