Search Results (89)

Compliant mechanisms are often utilized in precise positioning systems but have not been thoroughly examined in vibration-aided fine CNC machining. This study aims to develop a new 02-DOF flexure stage for vibration-aided fine CNC milling. A hybrid displacement amplifier, featuring a two-lever mechanism, two Scott–Russell mechanisms, and a parallel leading mechanism, was integrated into a symmetric perpendicular series configuration to create an innovative design. The pseudo-rigid body model (PRBM), Lagrangian approach, finite element analysis (FEA), and Firefly optimization algorithm were employed to develop, verify, and optimize the quality response of the new positioner. The PRBM and Lagrangian methods were used to construct an analytical model, while finite element analysis was used to validate the theoretical solution. The primary natural frequency results from theoretical and FEM methods were 318.16 Hz and 308.79 Hz, respectively. The difference between these techniques was 3.04%, demonstrating a reliable modelling strategy. The Firefly optimization approach applied mathematical equations to enhance the key design factors of the mechanism. The prototype was then built, revealing an error of 7.23% between the experimental and simulated frequencies of 331.116 Hz and 308.79 Hz, respectively. The specimen was subsequently mounted on the fabricated optimization positioner, and vibration-assisted fine CNC milling was performed at 100–1000 Hz. At 400 Hz, the specimen achieved ideal surface roughness with a Ra value of 0.187 µm. The developed design is a potential structure that generates non-resonant frequency power for vibration-aided fine CNC milling. Full article

Optimized earth-rock allocation is key in the construction of large-scale navigation channel projects. This paper analyzes the characteristics of a large-scale navigation channel project and establishes an earth-rock allocation system in phases and categories without a transit field. Based on the physical characteristics of the earthwork and stonework used to design a differentiated transport strategy, a synergistic optimization model is built with economic and ecological benefits. As a solution, this paper proposes a sustainable earth-rock allocation optimization method that integrates the improved ant colony algorithm and firefly algorithm, and establishes a two-stage hybrid optimization framework. The application of the Pinglu Canal Project shows that ant colony optimization via division and cooperation combined with the firefly algorithm reduces the transportation cost by 0.128% compared with traditional ant colony optimization; improves the stability by 57.46% (standard deviation) and 59.09% (coefficient of variation) compared with ant colony optimization through division and cooperation; and effectively solves the problems of precocious convergence and local optimization of large-scale earth-rock allocation. It is used to successfully construct an earth-rock allocation model that takes into account the efficiency of the project and the protection of the ecological system in a dynamic environment. Full article

Terahertz metamaterial dual-band absorbers are used for multi-target detection and high-sensitivity sensing in complex environments by enhancing information that reflects differences in the measured substances. Traditional design processes are complex and time-consuming. Machine learning-based methods, such as neural networks and deep learning, require a large number of simulations to gather training samples. Existing design methods based on single-objective optimization often result in uneven multi-objective optimization, which restricts practical applications. In this study, we developed a metamaterial absorber featuring a circular split-ring resonator with four gaps nested in a “卍” structure and used the Multi-Objective Firefly Algorithm based on Multiple Cooperative Strategies to achieve fast optimization of the absorber’s structural parameters. A comparison revealed that our approach requires fewer iterations than the Multi-Objective Particle Swarm Optimization and reduces design time by nearly half. The absorber designed using this method exhibited two resonant peaks at 0.607 THz and 0.936 THz, with absorptivity exceeding 99%, indicating near-perfect absorption and quality factors of 31.42 and 30.08, respectively. Additionally, we validated the absorber’s wave-absorbing mechanism by applying impedance-matching theory. Finally, we elucidated the resonance-peak formation mechanism of the absorber based on the surface current and electric-field distribution at the resonance frequencies. These results confirmed that the proposed dual-band metamaterial absorber design is efficient, representing a significant step toward the development of metamaterial devices. Full article

Nature-inspired metaheuristics have proven effective for addressing complex structural optimization challenges where traditional deterministic or gradient-based methods often fall short. This study investigates the feasibility and benefits of embedding three prominent metaheuristic algorithms, the Genetic Algorithm (GA), the Firefly Algorithm (FA), and the Group Search Optimizer (GSO) embedded into the ANSYS Parametric Design Language (APDL). The performance of each optimizer was assessed in three case studies. The first two are spatial truss structures, one comprising 22 bars and the other 25 bars, commonly used in structural optimization research. The third is a planar 15-bar truss in which member sizing and internal topology were simultaneously refined using a Discrete Topology (DT) variable method. For both the FA and the GSO, enhanced ranger-movement strategies were implemented to improve exploration–exploitation balance. Comparative analyses were conducted to assess convergence behavior, solution quality, and computational efficiency across the different metaheuristics. The results underscore the practical advantages of a fully integrated APDL approach, highlighting improvements in execution speed, workflow automation, and overall robustness. This work not only provides a comprehensive performance comparison of GA, FA, and GSO in structural optimization tasks, but it can also be considered a novelty in employing native APDL routines for metaheuristic-based finite element analysis. Full article

Cloud computing faces growing challenges in energy consumption due to the increasing demand for services and resource usage in data centers. To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. The proposed method, RLVMP, classifies VMs as sensitive or insensitive and dynamically allocates resources to minimize energy consumption while ensuring compliance with service level agreements (SLAs). Experimental results using the CloudSim simulator, adapted with data from Microsoft Azure, show that our model significantly reduces energy consumption. Specifically, under the lr_1.2_mmt strategy, our model achieves a 5.4% reduction in energy consumption compared to PABFD, 12.8% compared to PSO, and 12% compared to genetic algorithms. Under the iqr_1.5_mc strategy, the reductions are even more significant: 12.11% compared to PABFD, 15.6% compared to PSO, and 18.67% compared to genetic algorithms. Furthermore, our model reduces the number of live migrations, which helps minimize SLA violations. Overall, the combination of Q-learning and the Firefly algorithm enables adaptive, SLA-compliant VM placement with improved energy efficiency. Full article

The current evaluation models for earth-rock dam compaction quality seldom incorporate parameter uncertainty considerations. Additionally, the existing models frequently demonstrate constrained prediction accuracy and generalization capabilities. To resolve these issues, we present an intelligent evaluation method for the compaction quality of earth-rock dams that explicitly accounts for parameter uncertainty. The method utilizes a dynamic inertia weight, an adaptive factor, and a differential evolution strategy to enhance the search capability of the firefly algorithm. Furthermore, the random forest (RF) algorithm’s Ntree and Mtry parameters are adaptively optimized through the improved firefly algorithm (IFA) to develop a dam compaction quality prediction model. This model aims to reveal the complex nonlinear mapping relationship between input influencing factors, such as compaction parameters, material source parameters, and meteorological factors, and the compaction quality. The proposed model improves the prediction accuracy, generalization ability, and robustness. The improved firefly optimization-based random forest (IFA-RF) is applied in practical engineering projects, and the results validate that this method can reliably and accurately predict the compaction quality of earth-rock dam construction in real time (R = 0.90107, MSE = 0.0000602, p = 0.000) and thereby guide remedial measures to ensure engineering safety and quality compliance. Full article

This study develops a novel Lyrebird Optimization Algorithm (LOA), a technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In a two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied to handle load frequency control (LFC) by optimizing the parameters of a Proportional–Integral–Derivative controller with a filter (PIDn). This study incorporates generation rate constraints (GRCs). The efficiency of the provided LOA-PIDn is evaluated through simulations under various disturbance scenarios and is compared against other well-established optimization techniques, including the Ziegler–Nichols (ZN), genetic algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Firefly Approach (FA), hybridized FA and pattern search (hFA–PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, and Teaching–Learning-Based Optimizer (TLBO) IDD/PIDD controllers. The results demonstrate the LOA’s ability to minimize the integral of time multiplied by absolute error (ITAE) and achieve significantly lower settling times for the two-area frequencies and transferred power variances in comparison with other methods. The comprehensive comparison and the inclusion of real-world constraints validate the LOA as a robust and effective tool for addressing complex optimization challenges in modern power systems. Full article

Background/Objectives: The endosomal escape of lipid nanoparticles (LNPs) is crucial for efficient mRNA-based therapeutics. Here, we present a cationic polymeric micelle (cPM) as a safe and potent co-delivery system with enhanced endosomal escape capabilities. Methods: We synthesized a cationic and ampholytic di-block copolymer, poly (poly (ethylene glycol)_4-5 methacrylate_a-co-hexyl methacrylate_b)_X-b-poly(butyl methacrylate_c-co-dimethylaminoethyl methacrylate_d-co-propyl acrylate_e)_Y (p(PEG_4-5MA_a-co-HMA_b)_X-b-p(BMA_c-co-DMAEMA_d-co-PAA_e)_Y), via reversible addition–fragmentation chain transfer polymerization. The cPMs were then formulated using the synthesized polymer by the dispersion–diffusion method and characterized by dynamic light scattering (DLS) and cryo-transmission electron microscopy (CryoTEM). The membrane-destabilization activity of the cPMs was evaluated by a hemolysis assay. We performed an in vivo functional assay of firefly luciferase (Fluc) mRNA using two of the most commonly studied LNPs, SM102 LNP and Dlin-MC3-DMA LNPs. Results: With a particle size of 61.31 ± 0.68 nm and a zeta potential of 37.76 ± 2.18 mV, the cPMs exhibited a 2–3 times higher firefly luciferase signal at the injection site compared to the control groups without cPMs following intramuscular injection in mice, indicating the high potential of cPMs to enhance the endosomal escape efficiency of mRNA-LNPs. Conclusions: The developed cPM, with enhanced endosomal escape capabilities, presents a promising strategy to improve the expression efficiency of delivered mRNAs. This approach offers a novel alternative strategy with no modifications to the inherent properties of mRNA-LNPs, preventing any unforeseeable changes in formulation characteristics. Consequently, this polymer-based nanomaterial holds immense potential for clinical applications in mRNA-based vaccines. Full article

This study innovatively develops a multi-objective Markal-Macro model, which simultaneously considers three objectives: minimizing carbon emissions from energy consumption, minimizing carbon emissions from production processes, and maximizing societal welfare. Based on the Cobb–Douglas production function, we construct a production function of carbon emission and use it as a coupling equation of the Markal-Macro model (Markal is the abbreviation of market allocation, and Macro is the abbreviation of macroeconomy). This enables the coupling of the endogenous variables of carbon emissions and those related to maximizing societal welfare. By collecting relevant data on energy consumption, production outputs, and key economic indicators, five different scenarios are established. To enhance the computational efficiency of the simulation, we introduce a Firefly Algorithm into the penalty function method. The objective of our simulation is to explore the optimal carbon peak pathway for China. The results indicate that under the baseline scenario, China can achieve its carbon peak by 2029, with the peak value reaching approximately 12.5 billion tons of carbon dioxide. Finally, based on the simulation results, this study provides specific policy recommendations for China’s carbon peak pathway, addressing aspects such as industrial structure, energy consumption structure, the share of clean energy, economic growth targets, and the growth of emission reduction expenditures, while considering regional five-year plans and regional carbon peak strategies. From the aspect of the practical contributions, this article not only provides a set of methods for policymakers to make the Carbon Peak Implementation Plan but also offers an optimal path to improve the sustainable development for China. Full article

Rapid technology advances have made managing charging loads and optimizing routes for electric vehicle (EV) fleets, especially in cities, increasingly important. IoT sensors in EV charging stations and cars enhance prediction and optimization algorithms with real-time data on charging behaviors, traffic, vehicle locations, and environmental factors. These IoT data enable the GNN-ViGNet hybrid deep learning model to anticipate electric vehicle charging needs. Data from 400,000 IoT sensors at charging stations and vehicles in Texas were analyzed to identify EV charging patterns. These IoT sensors capture crucial parameters, including charging habits, traffic conditions, and other environmental elements. Frequency-Aware Dynamic Range Scaling and advanced preparation methods, such as Categorical Encoding, were employed to improve data quality. The GNN-ViGNet model achieved 98.9% accuracy. The Forecast Accuracy Rate (FAR) and Charging Load Variation Index (CLVI) were introduced alongside Root-Mean-Square Error (RMSE) and Mean Square Error (MSE) to assess the model’s predictive power further. This study presents a prediction model and a hybrid Coati–Northern Goshawk Optimization (Coati–NGO) route optimization method. Routes can be real-time adjusted using IoT data, including traffic, vehicle locations, and battery life. The suggested Coati–NGO approach combines the exploratory capabilities of Coati Optimization (COA) with the benefits of Northern Goshawk Optimization (NGO). It was more efficient than Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), reducing the journey distance to 511 km. The hybrid strategy converged more quickly and reached optimal results in 100 rounds. This comprehensive EV fleet management solution enhances charging infrastructure efficiency, reduces operational costs, and improves fleet performance using real-time IoT data, offering a scalable and practical solution for urban EV transportation. Full article

Background: Preclinical cell tracking is enhanced with a multimodal imaging approach. Bioluminescence imaging (BLI) is a highly sensitive optical modality that relies on engineering cells to constitutively express a luciferase gene. Magnetic particle imaging (MPI) is a newer imaging modality that directly detects superparamagnetic iron oxide (SPIO) particles used to label cells. Here, we compare BLI and MPI for imaging cells in vitro and in vivo. Methods: Mouse 4T1 breast carcinoma cells were transduced to express firefly luciferase, labeled with SPIO (ProMag), and imaged as cell samples after subcutaneous injection into mice. Results: For cell samples, the BLI and MPI signals were strongly correlated with cell number. Both modalities presented limitations for imaging cells in vivo. For BLI, weak signal penetration, signal attenuation, and scattering prevented the detection of cells for mice with hair and for cells far from the tissue surface. For MPI, background signals obscured the detection of low cell numbers due to the limited dynamic range, and cell numbers could not be accurately quantified from in vivo images. Conclusions: It is important to understand the shortcomings of these imaging modalities to develop strategies to improve cellular detection sensitivity. Full article

The Lunarminer framework explores the use of biomimetic swarm robotics, inspired by the division of labor in leafcutter ants and the synchronized flashing of fireflies, to enhance lunar water ice extraction. Simulations of water ice extraction within Shackleton Crater showed that the framework may improve task allocation, by reducing the extraction time by up to 40% and energy consumption by 31% in scenarios with high ore block quantities. This system, capable of producing up to 181 L of water per day from excavated regolith with a conversion efficiency of 0.8, may allow for supporting up to eighteen crew members. It has demonstrated robust fault tolerance and sustained operational efficiency, even for a 20% robot failure rate. The framework may help to address key challenges in lunar resource extraction, particularly in the permanently shadowed regions. To refine the proposed strategies, it is recommended that further studies be conducted on their large-scale applications in space mining operations at the Extraterrestrial Environmental Simulation (EXTERRES) laboratory at the University of Adelaide. Full article

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO. Full article

With the increasing popularity of Android smartphones, malware targeting the Android platform is showing explosive growth. Currently, mainstream detection methods use static analysis methods to extract features of the software and apply machine learning algorithms for detection. However, static analysis methods can be less effective when faced with Android malware that employs sophisticated obfuscation techniques such as altering code structure. In order to effectively detect Android malware and improve the detection accuracy, this paper proposes a dynamic detection model for Android malware based on the combination of an Improved Zebra Optimization Algorithm (IZOA) and Light Gradient Boosting Machine (LightGBM) model, called IZOA-LightGBM. By introducing elite opposition-based learning and firefly perturbation strategies, IZOA enhances the convergence speed and search capability of the traditional zebra optimization algorithm. Then, the IZOA is employed to optimize the LightGBM model hyperparameters for the dynamic detection of Android malware multi-classification. The results from experiments indicate that the overall accuracy of the proposed IZOA-LightGBM model on the CICMalDroid-2020, CCCS-CIC-AndMal-2020, and CIC-AAGM-2017 datasets is 99.75%, 98.86%, and 97.95%, respectively, which are higher than the other comparative models. Full article

To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal control theory to solve this problem. Firstly, a mathematical model for the wind turbine is established and linearized. Then, each optimal sub-controller is designed for different wind speed conditions by optimal theory. Subsequently, a back-propagation neural network is utilized to learn the variation pattern of controller parameters with respect to wind speed. Finally, real-time changes in wind speed are applied to evaluate and adjust controller parameters using the trained back-propagation neural network. The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. The simulation results show that the rotor speed overshoot of the optimal controller under the step wind speed is the smallest, only 0.05 rad/s. Under other wind speed conditions, the rotor speed range fluctuates around 4.35 rad/s, and the fluctuation size is less than 0.2 rad/s, which is much smaller than the fluctuation range of other controllers. It can be seen that the back-propagation optimal controller can ensure the stability of the rotor speed above the rated wind speed. At the same time, it has better control accuracy compared to other controllers. Full article