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Search Results (893)

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Keywords = applied reinforcement learning

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22 pages, 1398 KB  
Article
A Bibliometric Analysis of the Trends in UAV Research Using the Bibliometrix R-Tool
by Tibor Guzsvinecz and Judit Szűcs
Appl. Sci. 2025, 15(21), 11305; https://doi.org/10.3390/app152111305 (registering DOI) - 22 Oct 2025
Abstract
We present a bibliometric analysis of unmanned aerial vehicle (UAV) research that replaces simple keyword filtering with a context-aware, two-tier pipeline. Records from Web of Science and Scopus (198,152 total) were harmonized and de-duplicated in three stages (DOI, normalized title, blockwise Jaro–Winkler), yielding [...] Read more.
We present a bibliometric analysis of unmanned aerial vehicle (UAV) research that replaces simple keyword filtering with a context-aware, two-tier pipeline. Records from Web of Science and Scopus (198,152 total) were harmonized and de-duplicated in three stages (DOI, normalized title, blockwise Jaro–Winkler), yielding 129,124 unique items. To separate UAV work from entomology using overlapping vocabulary (e.g., swarm), we first applied rule-based weak labels with explicit UAV and insect regex families and a UAV context rule for “swarm,” then trained an elastic-net logistic regression on TF–IDF features and tuned the decision threshold to meet a high-precision target on a held-out split. The final corpus comprises 129,099 UAV records. Beyond lexical inventories, a keyword co-occurrence timeline shows reinforcement learning increasingly aligned with path planning and collision avoidance, while constraints such as energy and communication persist. A co-authorship network reveals bridging authors that connect guidance/control, perception, and communication subfields. The results show how UAV research is organized around central scientific problems and identify persistent obstacles such as energy efficiency, communication reliability, and robust decision-making in dynamic conditions. Full article
(This article belongs to the Section Materials Science and Engineering)
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14 pages, 412 KB  
Review
The Role of Artificial Intelligence in Exercise-Based Cardiovascular Health Interventions: A Scoping Review
by Asterios Deligiannis, Panagiota Sotiriou, Pantazis Deligiannis and Evangelia Kouidi
J. Funct. Morphol. Kinesiol. 2025, 10(4), 409; https://doi.org/10.3390/jfmk10040409 - 21 Oct 2025
Abstract
Background: As cardiovascular medicine advances rapidly, the integration of artificial intelligence (AI) has garnered increasing attention. Although AI has been widely adopted in diagnostics, risk prediction, and decision support, its application in exercise-based cardiovascular rehabilitation is still limited, representing a new and promising [...] Read more.
Background: As cardiovascular medicine advances rapidly, the integration of artificial intelligence (AI) has garnered increasing attention. Although AI has been widely adopted in diagnostics, risk prediction, and decision support, its application in exercise-based cardiovascular rehabilitation is still limited, representing a new and promising research frontier. Objective: This scoping review aimed to identify and analyze original studies that have applied AI to exercise-based interventions designed to improve cardiovascular outcomes. Methods: Following the PRISMA-ScR guidelines, PubMed, Scopus, Web of Science, Embase, and IEEE Xplore were searched for articles published between January 2015 and August 2025. Eligible studies were peer-reviewed by human research employing AI (machine learning or deep learning) to deliver, adapt, or monitor an exercise intervention with cardiovascular outcomes. Reviews, diagnostic-only studies, protocols without data, and animal studies were excluded. Non-original works (reviews, protocols), animal studies, and purely diagnostic applications were excluded, ensuring a strict focus on AI applied within exercise interventions. Data extraction focused on study design, AI method, exercise modality, outcomes, and findings. Results: From 2183 records, nine studies met the inclusion criteria (two RCTs, feasibility pilots, and validation studies). Designs included feasibility pilots, randomized controlled trials (RCTs), and validation studies. AI applications encompassed adaptive step goals, reinforcement learning for engagement, coaching apps, machine learning–based exercise prescription, and continuous monitoring (e.g., VO2 estimation). These AI methods, such as machine learning and reinforcement learning, were used to personalize exercise interventions and improve cardiovascular outcomes. Reported outcomes included blood pressure reduction, improved adherence, increased daily steps, improvement in VO2max, continuous physiological monitoring, and enhanced diagnostic accuracy. Conclusions: Findings demonstrate that AI has the potential to significantly enhance cardiovascular rehabilitation. It can personalize exercise prescriptions, enhance adherence, and facilitate safe monitoring in home settings. However, the evidence base remains preliminary, with very few RCTs and substantial methodological heterogeneity. Future research must prioritize large-scale clinical trials, explainable AI, and equitable implementation strategies to ensure clinical translation. Full article
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26 pages, 3460 KB  
Article
Classification and Clustering of Fiber Break Events in Thermoset CFRP Using Acoustic Emission and Machine Learning
by Richard Dela Amevorku, David Amoateng-Mensah, Manoj Rijal and Mannur J. Sundaresan
Sensors 2025, 25(20), 6466; https://doi.org/10.3390/s25206466 - 19 Oct 2025
Viewed by 204
Abstract
Carbon Fiber-Reinforced Polymer (CFRP) composites, widely used across industries, exhibit various damage mechanisms depending on the loading conditions applied. This study employs a structural health monitoring (SHM) approach to investigate the three primary failure modes, fiber breakage, matrix cracking, and delamination, in thermoset [...] Read more.
Carbon Fiber-Reinforced Polymer (CFRP) composites, widely used across industries, exhibit various damage mechanisms depending on the loading conditions applied. This study employs a structural health monitoring (SHM) approach to investigate the three primary failure modes, fiber breakage, matrix cracking, and delamination, in thermoset quasi-isotropic CFRPs subjected to quasi-static tensile loading until failure. Acoustic emission (AE) signals acquired from an experiment were leveraged to analyze and classify these real-time signals into the failure modes using machine learning (ML) techniques. Due to the extensive number of AE signals recorded during testing, manually classifying these failure mechanisms through waveform inspection was impractical. ML, alongside ensemble learning, algorithms were implemented to streamline the classification, making it more efficient, accurate, and reliable. Conventional AE parameters from the data acquisition system and feature extraction techniques applied to the recorded waveforms were implemented exclusively as classification features to investigate their reliability and accuracy in classifying failure modes in CFRPs. The classification models exhibited up to 99% accuracy, as depicted by evaluation metrics. Further studies, using cross-correlation techniques, ascertained the presence of fiber break events occurring in the bundles as the thermoset CFRP composite approached failure. These findings highlight the significance of integrating machine learning into SHM for the early detection of real-time damage and effective monitoring of residual life in composite materials. Full article
(This article belongs to the Special Issue Sensing and Machine Learning Control: Progress and Applications)
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29 pages, 1101 KB  
Article
Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators
by Yuling Liang, Mengjia Xie, Juan Zhang, Zhongyang Ming and Zhiyun Gao
Actuators 2025, 14(10), 506; https://doi.org/10.3390/act14100506 - 19 Oct 2025
Viewed by 124
Abstract
This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode. Firstly, a modified Hamilton–Jacobi–Isaac (HJI) equation is formulated, and then [...] Read more.
This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode. Firstly, a modified Hamilton–Jacobi–Isaac (HJI) equation is formulated, and then the worst-case disturbance policy and the asymmetric saturation optimal control signal can be obtained. Secondly, the multivariate probabilistic collocation method (MPCM) is used to evaluate the value function at designated sampling points. The purpose of introducing the MPCM is to simplify the computational complexity of stochastic dynamic programming (SDP) methods. Furthermore, the DET mode is utilized to solve the SDP problem to reduce the computational burden on communication resources. Finally, the Lyapunov stability theorem is applied to analyze the stability of time-varying systems, and the simulation shows the feasibility of the designed method. Full article
(This article belongs to the Special Issue Advances in Intelligent Control of Actuator Systems)
14 pages, 539 KB  
Article
Contribution to Sustainable Education: Co-Creation Citizen Science Project About Monitoring Species Distribution and Abundance on Rocky Shores
by Ana Teresa Neves, Diana Boaventura and Cecília Galvão
Sustainability 2025, 17(20), 9198; https://doi.org/10.3390/su17209198 - 16 Oct 2025
Viewed by 169
Abstract
Citizen science is not only a participatory means of contributing to scientific knowledge but also an effective approach to addressing a wide range of societal challenges. Integrating citizen science with sustainability entails leveraging public engagement in scientific research to promote sustainable practices and [...] Read more.
Citizen science is not only a participatory means of contributing to scientific knowledge but also an effective approach to addressing a wide range of societal challenges. Integrating citizen science with sustainability entails leveraging public engagement in scientific research to promote sustainable practices and advance the United Nations 2030 Agenda for Sustainable Development Goals (SDGs). The degree of public participation can influence the learning outcomes achieved. This study investigated the benefits and limitations of a co-creation citizen science approach implemented in a school context for monitoring species distribution on rocky shores, aligned with SDGs 4, 13, and 14. A mixed-methods design was applied, combining questionnaires administered to students (n = 100); participant observations of students, teachers, and researchers; and the analysis of observations submitted by one class (C2) to the iNaturalist platform. Students recorded 21 valid observations representing 13 different taxa, and developed skills such as critical thinking, problem-solving, collaboration, and interpersonal communication. They also recognised the potential of co-creation as a means of addressing scientific questions. However, teachers reported constraints in implementing the project, notably the breadth of the school curriculum and the lack of local support. This study reinforces the potential of co-creation citizen science projects to foster sustainable education. Full article
(This article belongs to the Special Issue Challenges and Future Trends of Sustainable Environmental Education)
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18 pages, 2728 KB  
Article
Monthly Power Outage Maintenance Scheduling for Power Grids Based on Interpretable Reinforcement Learning
by Wei Tang, Xun Mao, Kai Lv, Zhichen Cai and Zhenhuan Ding
Energies 2025, 18(20), 5454; https://doi.org/10.3390/en18205454 - 16 Oct 2025
Viewed by 202
Abstract
This paper proposes an interpretable optimization method for power grid outage scheduling based on reinforcement learning. An outage scheduling optimization model is proposed, considering the convergence of power flow calculation, voltage violations, and operational economic behavior as objectives, while considering constraints such as [...] Read more.
This paper proposes an interpretable optimization method for power grid outage scheduling based on reinforcement learning. An outage scheduling optimization model is proposed, considering the convergence of power flow calculation, voltage violations, and operational economic behavior as objectives, while considering constraints such as simultaneous outage constraints, mutually exclusive constraints, and maintenance windows. Key features of the outage schedule are selected based on Shapley values to construct a Markov optimization model for outage scheduling. A deep reinforcement learning agent is established to optimize the outage schedule. The proposed method is applied to the IEEE-39 and IEEE-118 bus system for validation. Experimental results show that the proposed method outperforms existing algorithms in terms of voltage violation, total power losses, and computational time. The proposed method eliminates all voltage violations and reduces active power losses up to 5.7% and computation time by 6.8 h compared to conventional heuristic algorithms. Full article
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19 pages, 4546 KB  
Article
LiDAR Dreamer: Efficient World Model for Autonomous Racing with Cartesian-Polar Encoding and Lightweight State-Space Cells
by Myeongjun Kim, Jong-Chan Park, Sang-Min Choi and Gun-Woo Kim
Information 2025, 16(10), 898; https://doi.org/10.3390/info16100898 - 14 Oct 2025
Viewed by 345
Abstract
Autonomous racing serves as a challenging testbed that exposes the limitations of perception-decision-control algorithms in extreme high-speed environments, revealing safety gaps not addressed in existing autonomous driving research. However, traditional control techniques (e.g., FGM and MPC) and reinforcement learning-based approaches (including model-free and [...] Read more.
Autonomous racing serves as a challenging testbed that exposes the limitations of perception-decision-control algorithms in extreme high-speed environments, revealing safety gaps not addressed in existing autonomous driving research. However, traditional control techniques (e.g., FGM and MPC) and reinforcement learning-based approaches (including model-free and Dreamer variants) struggle to simultaneously satisfy sample efficiency, prediction reliability, and real-time control performance, making them difficult to apply in actual high-speed racing environments. To address these challenges, we propose LiDAR Dreamer, a novel world model specialized for LiDAR sensor data. LiDAR Dreamer introduces three core techniques: (1) efficient point cloud preprocessing and encoding via Cartesian Polar Bar Charts, (2) Light Structured State-Space Cells (LS3C) that reduce RSSM parameters by 14.2% while preserving key dynamic information, and (3) a Displacement Covariance Distance divergence function, which enhances both learning stability and expressiveness. Experiments in PyBullet F1TENTH simulation environments demonstrate that LiDAR Dreamer achieves competitive performance across different track complexities. On the Austria track with complex corners, it reaches 90% of DreamerV3’s performance (1.14 vs. 1.27 progress) while using 81.7% fewer parameters. On the simpler Columbia track, while model-free methods achieve higher absolute performance, LiDAR Dreamer shows improved sample efficiency compared to baseline Dreamer models, converging faster to stable performance. The Treitlstrasse environment results demonstrate comparable performance to baseline methods. Furthermore, beyond the 14.2% RSSM parameter reduction, reward loss converged more stably without spikes, improving overall training efficiency and stability. Full article
(This article belongs to the Section Artificial Intelligence)
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29 pages, 2033 KB  
Review
The Intelligentization Process of Agricultural Greenhouse: A Review of Control Strategies and Modeling Techniques
by Kangji Li, Jialu Shi, Chenglei Hu and Wenping Xue
Agriculture 2025, 15(20), 2135; https://doi.org/10.3390/agriculture15202135 - 14 Oct 2025
Viewed by 434
Abstract
With the increasing demand for sustainable food production, the facility agriculture is progressively developing towards automation and intelligence. Traditional control techniques such as PID, fuzzy logic, and model predictive control have been widely applied in greenhouse planting for years. Existing greenhouse management systems [...] Read more.
With the increasing demand for sustainable food production, the facility agriculture is progressively developing towards automation and intelligence. Traditional control techniques such as PID, fuzzy logic, and model predictive control have been widely applied in greenhouse planting for years. Existing greenhouse management systems still face challenges such as limited adaptability to fluctuating outdoor climates, and difficulties in maintaining both productivity and cost-effectiveness. Recently, with the development of greenhouse systems towards comprehensive environmental perception and intelligent decision-making, a large number of intelligent control and modeling technologies have provided new opportunities for the technological update of greenhouse management systems. This review systematically summarizes recent progress in greenhouse regulation and crop growth control technologies, emphasizing applications of intelligent techniques, involving adaptive strategies, neural networks, and reinforcement learning. Special attention is given to how these methods improve system robustness and control performance in terms of environmental stability, crop productivity, and energy efficiency, which are key performance indicators of greenhouse systems. Their advantages over conventional strategies in agricultural greenhouse systems are also analyzed in detail. Furthermore, the integration of intelligent technologies with greenhouse system modeling is examined, covering both greenhouse environmental models and crop growth models. The strengths and weaknesses of different techniques, such as mechanism, computational fluid dynamics (CFD), and data-driven models, are analyzed and discussed in terms of accuracy, computational cost, and applicability. Finally, future challenges and research opportunities are discussed, emphasizing the need for real-time adaptability, sustainability, and cluster intelligence. Full article
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25 pages, 3060 KB  
Article
Curiosity-Driven Exploration in Reinforcement Learning: An Adaptive Self-Supervised Learning Approach for Playing Action Games
by Sehar Shahzad Farooq, Hameedur Rahman, Samiya Abdul Wahid, Muhammad Alyan Ansari, Saira Abdul Wahid and Hosu Lee
Computers 2025, 14(10), 434; https://doi.org/10.3390/computers14100434 - 13 Oct 2025
Viewed by 308
Abstract
Games are considered a suitable and standard benchmark for checking the performance of artificial intelligence-based algorithms in terms of training, evaluating, and comparing the performance of AI agents. In this research, an application of the Intrinsic Curiosity Module (ICM) and the Asynchronous Advantage [...] Read more.
Games are considered a suitable and standard benchmark for checking the performance of artificial intelligence-based algorithms in terms of training, evaluating, and comparing the performance of AI agents. In this research, an application of the Intrinsic Curiosity Module (ICM) and the Asynchronous Advantage Actor–Critic (A3C) algorithm is explored using action games. Having been proven successful in several gaming environments, its effectiveness in action games is rarely explored. Providing efficient learning and adaptation facilities, this research aims to assess whether integrating ICM with A3C promotes curiosity-driven explorations and adaptive learning in action games. Using the MAME Toolkit library, we interface with the game environments, preprocess game screens to focus on relevant visual elements, and create diverse game episodes for training. The A3C policy is optimized using the Proximal Policy Optimization (PPO) algorithm with tuned hyperparameters. Comparisons are made with baseline methods, including vanilla A3C, ICM with pixel-based predictions, and state-of-the-art exploration techniques. Additionally, we evaluate the agent’s generalization capability in separate environments. The results demonstrate that ICM and A3C effectively promote curiosity-driven exploration in action games, with the agent learning exploration behaviors without relying solely on external rewards. Notably, we also observed an improved efficiency and learning speed compared to baseline approaches. This research contributes to curiosity-driven exploration in reinforcement learning-based virtual environments and provides insights into the exploration of complex action games. Successfully applying ICM and A3C in action games presents exciting opportunities for adaptive learning and efficient exploration in challenging real-world environments. Full article
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21 pages, 1647 KB  
Article
UAV-Centric Privacy-Preserving Computation Offloading in Multi-UAV Mobile Edge Computing
by Chao Gao, Dawei Wei, Keying Li and Wenjin Liu
Drones 2025, 9(10), 701; https://doi.org/10.3390/drones9100701 - 12 Oct 2025
Viewed by 219
Abstract
Unmanned aerial vehicles (UAVs) offer high mobility, cost-effectiveness and flexible deployment, but their limited computing and battery resources constrain their development. Mobile edge computing (MEC) can alleviate these constraints by computation offloading. Although reinforcement learning (RL) has recently been applied to optimize offloading [...] Read more.
Unmanned aerial vehicles (UAVs) offer high mobility, cost-effectiveness and flexible deployment, but their limited computing and battery resources constrain their development. Mobile edge computing (MEC) can alleviate these constraints by computation offloading. Although reinforcement learning (RL) has recently been applied to optimize offloading strategies, using raw UAV data poses a risk of privacy leakage. To address this issue, we design a privacy-preserving RL-based offloading approach that applies local differential privacy (LDP) to perturb decision trajectories. We theoretically derive the O(M/ϵ) regret bound and achieve (ϵ,δ)-LDP for the perturbation mechanism. Finally, we evaluate the efficiency of the proposed approach through experiments. Full article
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17 pages, 2807 KB  
Article
Genome-Wide Inference of Essential Genes in Dirofilaria immitis Using Machine Learning
by Túlio L. Campos, Pasi K. Korhonen, Neil D. Young, Sunita B. Sumanam, Whitney Bullard, John M. Harrington, Jiangning Song, Bill C. H. Chang, Richard J. Marhöfer, Paul M. Selzer and Robin B. Gasser
Int. J. Mol. Sci. 2025, 26(20), 9923; https://doi.org/10.3390/ijms26209923 - 12 Oct 2025
Viewed by 220
Abstract
The filarioid nematode Dirofilaria immitis is the causative agent of heartworm disease, a major parasitic infection of canids, felids and occasionally humans. Current prevention relies on macrocyclic lactone-based chemoprophylaxis, but the emergence of drug resistance highlights the need for new intervention strategies. Here, [...] Read more.
The filarioid nematode Dirofilaria immitis is the causative agent of heartworm disease, a major parasitic infection of canids, felids and occasionally humans. Current prevention relies on macrocyclic lactone-based chemoprophylaxis, but the emergence of drug resistance highlights the need for new intervention strategies. Here, we applied a machine learning (ML)-based framework to predict and prioritise essential genes in D. immitis in silico, using genomic, transcriptomic and functional datasets from the model organisms Caenorhabditis elegans and Drosophila melanogaster. With a curated set of 26 predictive features, we trained and evaluated multiple ML models and, using a defined threshold, we predicted 406 ‘high-priority’ essential genes. These genes showed strong transcriptional activity across developmental stages and were inferred to be enriched in pathways related to ribosome biogenesis, translation, RNA processing and signalling, underscoring their potential as anthelmintic targets. Transcriptomic analyses suggested that these genes are associated with key reproductive and neural tissues, while chromosomal mapping revealed a relatively even genomic distribution, in contrast to patterns observed in C. elegans and Dr. melanogaster. In addition, initial evidence suggested structural variation in the X chromosome compared with a recently published D. immitis assembly, indicating the importance of integrating long-read sequencing with high-throughput chromosome conformation capture (Hi-C) mapping. Overall, this study reinforces the potential of ML-guided approaches for essential gene discovery in parasitic nematodes and provides a foundation for downstream validation and therapeutic target development. Full article
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23 pages, 1688 KB  
Article
NR-U Network Load Balancing: A Game Theoretic Reinforcement Learning Approach
by Yemane Teklay Seyoum, Syed Maaz Shahid, Tho Minh Duong, Sungmin Kim and Sungoh Kwon
Electronics 2025, 14(20), 3986; https://doi.org/10.3390/electronics14203986 - 11 Oct 2025
Viewed by 207
Abstract
In this paper, we propose a load-aware, load-balancing procedure for fifth-generation (5G) New Radio-Unlicensed (NR-U) networks in order to address performance degradation and resource inefficiencies caused by load imbalance. Load imbalances frequently occur in NR-U networks due to factors such as the dynamic [...] Read more.
In this paper, we propose a load-aware, load-balancing procedure for fifth-generation (5G) New Radio-Unlicensed (NR-U) networks in order to address performance degradation and resource inefficiencies caused by load imbalance. Load imbalances frequently occur in NR-U networks due to factors such as the dynamic spectrum, user mobility, and varying traffic demand. To tackle these challenges, a load-aware, load-balancing procedure utilizing game theoretic reinforcement learning (GT-RL) is introduced. For load awareness, an extended System Information Block (SIB) is incorporated within the framework of 5G wireless networks. The load-balancing problem is addressed as a game theoretic cost-minimization task combining conditional offloading with reinforcement learning traffic-steering to dynamically distribute loads. Reinforcement learning applies a game theoretic policy to move users from overloaded cells to less congested cells that best serve their needs. Analytically, the proposed method is proven to spread the network load toward equilibrium. The proposed method is validated through simulations that show the effectiveness of its load balancing. The proposed method achieved better performance than previous work by attaining lower load variances while achieving higher throughput and greater quality of service satisfaction. Especially under high-load dynamics, the proposed method achieved an 8% gain in UE satisfaction with QoS and a 7.61% gain in network throughput compared to existing RL-based approach, whereas compared to the non-AI approaches, UE QoS satisfaction and the network throughput were enhanced by more than 15%. Full article
(This article belongs to the Special Issue Advanced Control Strategies and Applications of Multi-Agent Systems)
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16 pages, 2721 KB  
Article
Compressive Strength Prediction of Green Concrete with Recycled Glass-Fiber-Reinforced Polymers Using a Machine Learning Approach
by Pouyan Fakharian, Reza Bazrgary, Ali Ghorbani, Davoud Tavakoli and Younes Nouri
Polymers 2025, 17(20), 2731; https://doi.org/10.3390/polym17202731 - 11 Oct 2025
Viewed by 509
Abstract
Fiber-reinforced polymer (FRP) materials are increasingly used in the construction and transportation industries, generating growing volumes of waste. This study applied a machine learning model to predict the compressive strength of eco-friendly concrete incorporating recycled glass-fiber-reinforced polymer (GFRP) waste. Based on 119 laboratory [...] Read more.
Fiber-reinforced polymer (FRP) materials are increasingly used in the construction and transportation industries, generating growing volumes of waste. This study applied a machine learning model to predict the compressive strength of eco-friendly concrete incorporating recycled glass-fiber-reinforced polymer (GFRP) waste. Based on 119 laboratory mixes, the model achieved a good prediction accuracy (R2 = 0.8284 on the test set). The analysis indicated that compressive strength tends to decrease at higher GFRP dosages, with relatively favorable performance observed at low contents. The two most influential factors were the water-to-cement ratio and the total GFRP content. The physical form of the recycled material was also important: powders and fibers generally showed positive effects, while coarse aggregate replacement was less effective. This machine learning-based approach offers preliminary quantitative guidance on mix design with GFRP waste and highlights opportunities for reusing industrial by-products in more sustainable concretes. Full article
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17 pages, 2195 KB  
Article
Collision-Free Robot Path Planning by Integrating DRL with Noise Layers and MPC
by Xinzhan Hong, Qieshi Zhang, Yexing Yang, Tianqi Zhao, Zhenyu Xu, Tichao Wang and Jing Ji
Sensors 2025, 25(20), 6263; https://doi.org/10.3390/s25206263 - 10 Oct 2025
Viewed by 446
Abstract
With the rapid advancement of Autonomous Mobile Robots (AMRs) in industrial automation and intelligent logistics, achieving efficient and safe path planning in dynamic environments has become a critical challenge. These environments require robots to perceive complex scenarios and adapt their motion strategies accordingly, [...] Read more.
With the rapid advancement of Autonomous Mobile Robots (AMRs) in industrial automation and intelligent logistics, achieving efficient and safe path planning in dynamic environments has become a critical challenge. These environments require robots to perceive complex scenarios and adapt their motion strategies accordingly, often under real-time constraints. Existing methods frequently struggle to balance efficiency, responsiveness, and safety, especially in the presence of continuously changing dynamic obstacles. While Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) have each shown promise in this domain, they also face limitations when applied individually—such as high computational demands or insufficient environmental exploration. To address these challenges, we propose a hybrid path planning framework that integrates an optimized DRL algorithm with MPC. We replace the Actor’s output with a learnable noisy linear layer whose mean and scale parameters are optimized jointly with the policy via backpropagation, thereby enhancing exploration while preserving training stability. TD3 produces stepwise control commands that evolve into a short-horizon reference trajectory, while MPC refines this trajectory through constraint-aware optimization to ensure timely obstacle avoidance. This complementary process combines TD3′s learning-based adaptability with MPC’s reliable local feasibility. Extensive experiments conducted in environments with varying obstacle dynamics and densities demonstrate that the proposed method significantly improves obstacle avoidance success rate, trajectory smoothness, and path accuracy compared to traditional MPC, standalone DRL, and other hybrid approaches, offering a robust and efficient solution for autonomous navigation in complex scenarios. Full article
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25 pages, 2714 KB  
Article
Evaluating Municipal Solid Waste Incineration Through Determining Flame Combustion to Improve Combustion Processes for Environmental Sanitation
by Jian Tang, Xiaoxian Yang, Wei Wang and Jian Rong
Sustainability 2025, 17(19), 8872; https://doi.org/10.3390/su17198872 - 4 Oct 2025
Viewed by 298
Abstract
Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic [...] Read more.
Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article
(This article belongs to the Section Waste and Recycling)
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