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32 pages, 2102 KiB  
Article
D* Lite and Transformer-Enhanced SAC: A Hybrid Reinforcement Learning Framework for COLREGs-Compliant Autonomous Navigation in Dynamic Maritime Environments
by Tianqing Chen, Yamei Lan, Yichen Li, Jiesen Zhang and Yijie Yin
J. Mar. Sci. Eng. 2025, 13(8), 1498; https://doi.org/10.3390/jmse13081498 - 4 Aug 2025
Abstract
Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently [...] Read more.
Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article
(This article belongs to the Special Issue Motion Control and Path Planning of Marine Vehicles—3rd Edition)
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44 pages, 6212 KiB  
Article
A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction
by Ali Mirzaei and Amir Aghsami
Math. Comput. Appl. 2025, 30(4), 83; https://doi.org/10.3390/mca30040083 (registering DOI) - 3 Aug 2025
Viewed by 32
Abstract
Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework [...] Read more.
Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R2 of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article
(This article belongs to the Section Engineering)
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20 pages, 4467 KiB  
Review
Structuring the Future of Cultured Meat: Hybrid Gel-Based Scaffolds for Edibility and Functionality
by Sun Mi Zo, Ankur Sood, So Yeon Won, Soon Mo Choi and Sung Soo Han
Gels 2025, 11(8), 610; https://doi.org/10.3390/gels11080610 - 3 Aug 2025
Viewed by 44
Abstract
Cultured meat is emerging as a sustainable alternative to conventional animal agriculture, with scaffolds playing a central role in supporting cellular attachment, growth, and tissue maturation. This review focuses on the development of gel-based hybrid biomaterials that meet the dual requirements of biocompatibility [...] Read more.
Cultured meat is emerging as a sustainable alternative to conventional animal agriculture, with scaffolds playing a central role in supporting cellular attachment, growth, and tissue maturation. This review focuses on the development of gel-based hybrid biomaterials that meet the dual requirements of biocompatibility and food safety. We explore recent advances in the use of naturally derived gel-forming polymers such as gelatin, chitosan, cellulose, alginate, and plant-based proteins as the structural backbone for edible scaffolds. Particular attention is given to the integration of food-grade functional additives into hydrogel-based scaffolds. These include nanocellulose, dietary fibers, modified starches, polyphenols, and enzymatic crosslinkers such as transglutaminase, which enhance mechanical stability, rheological properties, and cell-guidance capabilities. Rather than focusing on fabrication methods or individual case studies, this review emphasizes the material-centric design strategies for building scalable, printable, and digestible gel scaffolds suitable for cultured meat production. By systemically evaluating the role of each component in structural reinforcement and biological interaction, this work provides a comprehensive frame work for designing next-generation edible scaffold systems. Nonetheless, the field continues to face challenges, including structural optimization, regulatory validation, and scale-up, which are critical for future implementation. Ultimately, hybrid gel-based scaffolds are positioned as a foundational technology for advancing the functionality, manufacturability, and consumer readiness of cultured meat products, distinguishing this work from previous reviews. Unlike previous reviews that have focused primarily on fabrication techniques or tissue engineering applications, this review provides a uniquely food-centric perspective by systematically evaluating the compositional design of hybrid hydrogel-based scaffolds with edibility, scalability, and consumer acceptance in mind. Through a comparative analysis of food-safe additives and naturally derived biopolymers, this review establishes a framework that bridges biomaterials science and food engineering to advance the practical realization of cultured meat products. Full article
(This article belongs to the Special Issue Food Hydrocolloids and Hydrogels: Rheology and Texture Analysis)
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36 pages, 5053 KiB  
Systematic Review
Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
by Marko Orošnjak, Felix Saretzky and Slawomir Kedziora
Appl. Sci. 2025, 15(15), 8507; https://doi.org/10.3390/app15158507 (registering DOI) - 31 Jul 2025
Viewed by 175
Abstract
Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented [...] Read more.
Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented Systematic Literature Review of studies published between 2013–2024. We identify key enablers—artificial intelligence and machine learning, horizontal and vertical integration, and deep reinforcement learning—that map the functional space of PsM across industrial sectors. The results from our multivariate meta-synthesis uncover three main thematic research clusters, ranging from decision-automation of technical (multi)component-level systems to strategic and organisational-support strategies. Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. Primary reasons include semantic interoperability, real-time optimisation, and deployment scalability. As a response, a structured research agenda is proposed to emphasise hybrid architectures, context-aware prescription mechanisms, and alignment with Industry 5.0 principles of human-centricity, resilience, and sustainability. The review establishes a critical foundation for future advances in intelligent, explainable, and action-oriented maintenance systems. Full article
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18 pages, 1910 KiB  
Article
Hierarchical Learning for Closed-Loop Robotic Manipulation in Cluttered Scenes via Depth Vision, Reinforcement Learning, and Behaviour Cloning
by Hoi Fai Yu and Abdulrahman Altahhan
Electronics 2025, 14(15), 3074; https://doi.org/10.3390/electronics14153074 - 31 Jul 2025
Viewed by 229
Abstract
Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central [...] Read more.
Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article
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20 pages, 10603 KiB  
Article
A Safety-Based Approach for the Design of an Innovative Microvehicle
by Michelangelo-Santo Gulino, Susanna Papini, Giovanni Zonfrillo, Thomas Unger, Peter Miklis and Dario Vangi
Designs 2025, 9(4), 90; https://doi.org/10.3390/designs9040090 (registering DOI) - 31 Jul 2025
Viewed by 141
Abstract
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper [...] Read more.
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s2, comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article
(This article belongs to the Section Vehicle Engineering Design)
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23 pages, 2345 KiB  
Article
From Waste to Biocatalyst: Cocoa Bean Shells as Immobilization Support and Substrate Source in Lipase-Catalyzed Hydrolysis
by Luciana Lordelo Nascimento, Bruna Louise de Moura Pita, César de Almeida Rodrigues, Paulo Natan Alves dos Santos, Yslaine Andrade de Almeida, Larissa da Silveira Ferreira, Maira Lima de Oliveira, Lorena Santos de Almeida, Cleide Maria Faria Soares, Fabio de Souza Dias and Alini Tinoco Fricks
Molecules 2025, 30(15), 3207; https://doi.org/10.3390/molecules30153207 - 30 Jul 2025
Viewed by 161
Abstract
This study reports the development of a sustainable biocatalyst system for free fatty acid (FFA) production from cocoa bean shell (CBS) oil using Burkholderia cepacia lipase (BCL). CBS was explored as both a support material and a reaction substrate. Six immobilized [...] Read more.
This study reports the development of a sustainable biocatalyst system for free fatty acid (FFA) production from cocoa bean shell (CBS) oil using Burkholderia cepacia lipase (BCL). CBS was explored as both a support material and a reaction substrate. Six immobilized systems were prepared using organic (CBS), inorganic (silica), and hybrid (CBS–silica) supports via physical adsorption or covalent binding. Among them, the covalently immobilized enzyme on CBS (ORG-CB) showed the most balanced performance, achieving a catalytic efficiency (Ke) of 0.063 mM−1·min−1 (18.6% of the free enzyme), broad pH–temperature tolerance, and over 50% activity retention after eight reuse cycles. Thermodynamic analysis confirmed enhanced thermal resistance for ORG-CB (Ed = 32.3 kJ mol−1; ΔH‡ = 29.7 kJ mol−1), while kinetic evaluation revealed that its thermal deactivation occurred faster than for the free enzyme under prolonged heating. In application trials, ORG-CB reached 60.1% FFA conversion from CBS oil, outperforming the free enzyme (49.9%). These findings validate CBS as a dual-function material for enzyme immobilization and valorization of agro-industrial waste. The results also reinforce the impact of immobilization chemistry and support composition on the operational and thermal performance of biocatalysts, contributing to the advancement of green chemistry strategies in enzyme-based processing. Full article
(This article belongs to the Special Issue Biotechnology and Biomass Valorization)
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24 pages, 1508 KiB  
Article
Genomic Prediction of Adaptation in Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) Hybrids
by Felipe López-Hernández, Diego F. Villanueva-Mejía, Adriana Patricia Tofiño-Rivera and Andrés J. Cortés
Int. J. Mol. Sci. 2025, 26(15), 7370; https://doi.org/10.3390/ijms26157370 - 30 Jul 2025
Viewed by 260
Abstract
Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, [...] Read more.
Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article
(This article belongs to the Special Issue Plant Breeding and Genetics: New Findings and Perspectives)
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15 pages, 3232 KiB  
Article
Residual Flexural Behavior of Hybrid Fiber-Reinforced Geopolymer After High Temperature Exposure
by Yiyang Xiong, Ruiwen Jiang, Yi Li and Peipeng Li
Materials 2025, 18(15), 3572; https://doi.org/10.3390/ma18153572 - 30 Jul 2025
Viewed by 213
Abstract
Cement-based building materials usually exhibit weak flexural behavior under high temperature or fire conditions. This paper develops a novel geopolymer with enhanced residual flexural strength, incorporating fly ash/metakaolin precursors and corundum aggregates based on our previous study, and further improves flexural performance using [...] Read more.
Cement-based building materials usually exhibit weak flexural behavior under high temperature or fire conditions. This paper develops a novel geopolymer with enhanced residual flexural strength, incorporating fly ash/metakaolin precursors and corundum aggregates based on our previous study, and further improves flexural performance using hybrid fibers. The flexural load–deflection response, strength, deformation capacity, toughness and microstructure are investigated by a thermal exposure test, bending test and microstructure observation. The results indicate that the plain geopolymer exhibits a continuously increasing flexural strength from 10 MPa at 20 °C to 25.9 MPa after 1000 °C exposure, attributed to thermally induced further geopolymerization and ceramic-like crystalline phase formation. Incorporating 5% wollastonite fibers results in slightly increased initial and residual flexural strength but comparable peak deflection, toughness and brittle failure. The binary 5% wollastonite and 1% basalt fibers in geopolymer obviously improve residual flexural strength exposed to 400–800 °C. The steel fibers show remarkable reinforcement on flexural behavior at 20–800 °C exposure; however, excessive steel fiber content such as 2% weakens flexural properties after 1000 °C exposure due to severe oxidation deterioration and thermal incompatibility. The wollastonite/basalt/steel fibers exhibit a positive synergistic effect on flexural strength and toughness of geopolymers at 20–600 °C. Full article
(This article belongs to the Section Construction and Building Materials)
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8 pages, 1177 KiB  
Proceeding Paper
Quadruped Robot Locomotion Based on Deep Learning Rules
by Pedro Escudero-Villa, Gustavo Danilo Machado-Merino and Jenny Paredes-Fierro
Eng. Proc. 2025, 87(1), 100; https://doi.org/10.3390/engproc2025087100 - 30 Jul 2025
Viewed by 166
Abstract
This research presents a reinforcement learning framework for stable quadruped locomotion using Proximal Policy Optimization (PPO). We address critical challenges in articulated robot control—including mechanical complexity and trajectory instability by implementing a 12-degree-of-freedom model in PyBullet simulation. Our approach features three key innovations: [...] Read more.
This research presents a reinforcement learning framework for stable quadruped locomotion using Proximal Policy Optimization (PPO). We address critical challenges in articulated robot control—including mechanical complexity and trajectory instability by implementing a 12-degree-of-freedom model in PyBullet simulation. Our approach features three key innovations: (1) a hybrid reward function (Rt=0.72 · eΔCoGt + 0.25 · vt  0.11 · τt) explicitly prioritizing center-of-gravity (CoG) stabilization; (2) rigorous benchmarking demonstrating Adam’s superiority over SGD for policy convergence (68% lower reward variance); and (3) a four-metric evaluation protocol quantifying locomotion quality through reward progression, CoG deviation, policy loss, and KL-divergence penalties. Experimental results confirm an 87.5% reduction in vertical CoG oscillation (from 2.0″ to 0.25″) across 1 million training steps. Policy optimization achieved −6.2 × 10−4 loss with KL penalties converging to 0.13, indicating stable gait generation. The framework’s efficacy is further validated by consistent CoG stabilization during deployment, demonstrating potential for real-world applications requiring robust terrain adaptation. Full article
(This article belongs to the Proceedings of The 5th International Electronic Conference on Applied Sciences)
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9 pages, 1792 KiB  
Proceeding Paper
A Comparative Analysis of the Impact Behavior of Honeycomb Sandwich Composites
by Yasir Zaman, Shahzad Ahmad, Muhammad Bilal Khan, Babar Ashfaq and Muhammad Qasim Zafar
Mater. Proc. 2025, 23(1), 3; https://doi.org/10.3390/materproc2025023003 - 29 Jul 2025
Viewed by 177
Abstract
The increasing need for materials that are both lightweight and strong in the aerospace and automotive sectors has driven the extensive use of composite sandwich structures. This study examines the impact response of honeycomb sandwich composites fabricated using the vacuum-assisted resin transfer molding [...] Read more.
The increasing need for materials that are both lightweight and strong in the aerospace and automotive sectors has driven the extensive use of composite sandwich structures. This study examines the impact response of honeycomb sandwich composites fabricated using the vacuum-assisted resin transfer molding (VARTM) technique. Two configurations were analyzed, namely carbon–honeycomb–carbon (CHC) and carbon–Kevlar–honeycomb–Kevlar–carbon (CKHKC), to assess the effect of Kevlar reinforcement on impact resistance. Charpy impact testing was conducted to evaluate energy absorption, revealing that CKHKC composites exhibited significantly superior impact resistance compared to CHC composites. The CKHKC composite achieved an average impact strength of 70.501 KJ/m2, which is approximately 73.8% higher than the 40.570 KJ/m2 recorded for CHC. This improvement is attributed to Kevlar’s superior toughness and energy dissipation capabilities. A comparative assessment of impact energy absorption further highlights the advantages of hybrid Kevlar–carbon fiber composites, making them highly suitable for applications requiring enhanced impact performance. These findings provide valuable insights into the design and optimization of high-performance honeycomb sandwich structures for impact-critical environments. Full article
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28 pages, 3144 KiB  
Review
Artificial Intelligence-Driven and Bio-Inspired Control Strategies for Industrial Robotics: A Systematic Review of Trends, Challenges, and Sustainable Innovations Toward Industry 5.0
by Claudio Urrea
Machines 2025, 13(8), 666; https://doi.org/10.3390/machines13080666 - 29 Jul 2025
Viewed by 612
Abstract
Industrial robots are undergoing a transformative shift as Artificial Intelligence (AI)-driven and bio-inspired control strategies unlock new levels of precision, adaptability, and multi-dimensional sustainability aligned with Industry 5.0 (energy efficiency, material circularity, and life-cycle emissions). This systematic review analyzes 160 peer-reviewed industrial robotics [...] Read more.
Industrial robots are undergoing a transformative shift as Artificial Intelligence (AI)-driven and bio-inspired control strategies unlock new levels of precision, adaptability, and multi-dimensional sustainability aligned with Industry 5.0 (energy efficiency, material circularity, and life-cycle emissions). This systematic review analyzes 160 peer-reviewed industrial robotics control studies (2023–2025), including an expanded bio-inspired/human-centric subset, to evaluate: (1) the dominant and emerging control methodologies; (2) the transformative role of digital twins and 5G-enabled connectivity; and (3) the persistent technical, ethical, and environmental challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the study employs a rigorous methodology, focusing on adaptive control, deep reinforcement learning (DRL), human–robot collaboration (HRC), and quantum-inspired algorithms. The key findings highlight up to 30% latency reductions in real-time optimization, up to 22% efficiency gains through digital twins, and up to 25% energy savings from bio-inspired designs (all percentage ranges are reported relative to the comparator baselines specified in the cited sources). However, critical barriers remain, including scalability limitations (with up to 40% higher computational demands) and cybersecurity vulnerabilities (with up to 20% exposure rates). The convergence of AI, bio-inspired systems, and quantum computing is poised to enable sustainable, autonomous, and human-centric robotics, yet requires standardized safety frameworks and hybrid architectures to fully support the transition from Industry 4.0 to Industry 5.0. This review offers a strategic roadmap for future research and industrial adoption, emphasizing human-centric design, ethical frameworks, and circular-economy principles to address global manufacturing challenges. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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52 pages, 3733 KiB  
Article
A Hybrid Deep Reinforcement Learning and Metaheuristic Framework for Heritage Tourism Route Optimization in Warin Chamrap’s Old Town
by Rapeepan Pitakaso, Thanatkij Srichok, Surajet Khonjun, Natthapong Nanthasamroeng, Arunrat Sawettham, Paweena Khampukka, Sairoong Dinkoksung, Kanya Jungvimut, Ganokgarn Jirasirilerd, Chawapot Supasarn, Pornpimol Mongkhonngam and Yong Boonarree
Heritage 2025, 8(8), 301; https://doi.org/10.3390/heritage8080301 - 28 Jul 2025
Viewed by 574
Abstract
Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework [...] Read more.
Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article
(This article belongs to the Special Issue AI and the Future of Cultural Heritage)
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18 pages, 1040 KiB  
Article
A TDDPG-Based Joint Optimization Method for Hybrid RIS-Assisted Vehicular Integrated Sensing and Communication
by Xinren Wang, Zhuoran Xu, Qin Wang, Yiyang Ni and Haitao Zhao
Electronics 2025, 14(15), 2992; https://doi.org/10.3390/electronics14152992 - 27 Jul 2025
Viewed by 282
Abstract
This paper proposes a novel Twin Delayed Deep Deterministic Policy Gradient (TDDPG)-based joint optimization algorithm for hybrid reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems in Internet of Vehicles (IoV) scenarios. The proposed system model achieves deep integration of sensing and [...] Read more.
This paper proposes a novel Twin Delayed Deep Deterministic Policy Gradient (TDDPG)-based joint optimization algorithm for hybrid reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems in Internet of Vehicles (IoV) scenarios. The proposed system model achieves deep integration of sensing and communication by superimposing the communication and sensing signals within the same waveform. To decouple the complex joint design problem, a dual-DDPG architecture is introduced, in which one agent optimizes the transmit beamforming vector and the other adjusts the RIS phase shift matrix. Both agents share a unified reward function that comprehensively considers multi-user interference (MUI), total transmit power, RIS noise power, and sensing accuracy via the CRLB constraint. Simulation results demonstrate that the proposed TDDPG algorithm significantly outperforms conventional DDPG in terms of sum rate and interference suppression. Moreover, the adoption of a hybrid RIS enables an effective trade-off between communication performance and system energy efficiency, highlighting its practical deployment potential in dynamic IoV environments. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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26 pages, 8400 KiB  
Article
Conceptual Design of a Hybrid Composite to Metal Joint for Naval Vessels Applications
by Man Chi Cheung, Nenad Djordjevic, Chris Worrall, Rade Vignjevic, Mihalis Kazilas and Kevin Hughes
Materials 2025, 18(15), 3512; https://doi.org/10.3390/ma18153512 - 26 Jul 2025
Viewed by 318
Abstract
This paper describes the development of a new hybrid composite for the metal joints of aluminium and glass fibre composite adherents. The aluminium adherend is manufactured using friction stir-formed studs that are inserted into the composite adherend in the through-thickness direction during the [...] Read more.
This paper describes the development of a new hybrid composite for the metal joints of aluminium and glass fibre composite adherents. The aluminium adherend is manufactured using friction stir-formed studs that are inserted into the composite adherend in the through-thickness direction during the composite manufacturing process, where the dry fibres are displaced to accommodate the studs before the resin infusion process. The materials used were AA6082-T6 aluminium and plain-woven E-glass fabric reinforced epoxy, with primary applications in naval vessels. This joining approach offers a cost-effective solution that does not require complicated onsite welding. The joint design was developed based on a simulation test program with finite element analysis, followed by experimental characterisation and validation. The design solution was analysed in terms of the force displacement response, sequence of load transfer, and characterisation of the joint failure modes. Full article
(This article belongs to the Section Advanced Composites)
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