Journals
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Journal of Intelligent and Autonomous Control
Articles

Federated Learning-Enabled Security Enhancement for Distributed Autonomous Control Systems Against Malicious Attacks

Distributed Autonomous Control Systems (DACs) are widely used in safety-critical fields like intelligent transportation and industrial automation, yet face growing threats from Byzantine attacks, data poisoning and jamming that may cause catastrophic failures. Federated learning (FL) addresses DACs’ privacy and communication issues but lacks dedicated security mechanisms for its training and deployment phases. This paper proposes the Federated Secure Learning Framework (FSLF), integrating Byzantine-resilient aggregation, attack-aware adversarial training and cryptographic communication validation to balance security and privacy. It filters malicious model updates, generates attack-specific perturbations for robust training and detects tampered communication data. Experiments on multi-UAV tracking, CAV platoon control and robot manipulation show FSLF achieves 92.3% Byzantine attack detection rate, 15.7% lower Avg-RMSE under attacks and 0.6% data leakage rate, boosting the security and reliability of FL-enabled DACs in adversarial environments.

Journal of Intelligent and Autonomous Control
Articles

Edge Intelligence and Digital Twin Synergy for Low-Latency Autonomous Control in Dynamic Cyber-Physical Systems

Cyber-Physical Systems (CPS) are evolving towards higher autonomy and real-time responsiveness, posing stringent demands on low-latency decision-making and dynamic environmental adaptation. Traditional cloud-centric control architectures suffer from inevitable network delays and bandwidth bottlenecks, limiting their applicability in time-critical scenarios. This paper proposes a novel synergistic framework integrating Edge Intelligence (EI) and Digital Twin (DT) for autonomous control systems. By deploying lightweight AI models at the network edge and establishing high-fidelity virtual replicas of physical assets, the framework enables real-time perception, local decision-making, and predictive control. This study elaborates on the architectural design, communication protocols, and security mechanisms of the proposed framework. Through experimental validation on smart grid load regulation and autonomous mobile robot navigation, the results demonstrate that the EI-DT synergy reduces end-to-end latency by over 60% and improves control stability by 30% compared to cloud-based approaches. This research provides a viable solution for latency-sensitive and dynamic autonomous control applications.

Journal of Intelligent and Autonomous Control
Articles

AI-Driven Adaptive Optimization for Autonomous Control Systems: Advances, Challenges, and Industrial Applications

Corresponding Author: Li Wei; Email: Abstract: Autonomous control systems are increasingly integrated into industrial production, smart cities, and robotics, demanding higher adaptability to complex and dynamic environments. This study explores the application of artificial intelligence (AI) technologies, including deep reinforcement learning and fuzzy logic, in adaptive optimization of autonomous control systems. It analyzes recent advances, addresses key challenges such as real-time response and robustness, and verifies effectiveness through industrial case studies. The results show AI-driven strategies significantly improve control precision and system adaptability. This paper provides insights for future research on intelligent autonomous control, promoting its sustainable development in diverse fields.

Journal of Intelligent and Autonomous Control
Articles

Federated Learning-Driven Collaborative Protection of Privacy and Security in Distributed Autonomous Control Systems

Distributed Autonomous Control Systems (DACs), the core infrastructure of modern industrial production, intelligent transportation and emergency response, face dual risks of data privacy leakage and malicious attacks due to their open communication and distributed collaboration. Traditional separate protection schemes cause resource conflicts and performance trade-offs, failing to meet high-reliability demands in safety-critical scenarios. This paper proposes the FL-PSCP framework driven by federated learning, integrating local differential privacy, secure multi-party computation, abnormal detection and trusted node authentication. It designs privacy-enhanced federated aggregation, federated multi-dimensional attack detection and dynamic trust evaluation. Experiments on smart grid control, multi-robot rescue and autonomous ship navigation show FL-PSCP raises attack detection rate by 19.3% on average, cuts privacy leakage risk by 78.5% and keeps average control error within 5%, providing an integrated privacy and security protection solution for DACs and boosting their safe application in complex environments.

Journal of Intelligent and Autonomous Control
Articles

Optimization of Autonomous Navigation Systems in Unknown Environments Based on Improved Reinforcement Learning Algorithms

Autonomous navigation in unknown environments remains a core challenge in intelligent and autonomous control. This study proposes an improved deep reinforcement learning (DRL) algorithm, namely Adaptive Reward Shaping Deep Q-Network (ARS-DQN), to enhance the navigation performance of autonomous agents. The ARS-DQN optimizes the reward function by integrating environmental exploration progress and collision avoidance safety, addressing the over-exploration and sparse reward problems of traditional DRL algorithms. Comparative experiments with DQN, Double DQN, and Dueling DQN are conducted in simulated unknown environments with different complexity levels. Results show that the ARS-DQN reduces navigation time by 18.3%–25.7% and collision rate by 32.1%–41.5% compared to baseline algorithms. The proposed algorithm also exhibits strong robustness to environmental dynamic changes. This research provides a feasible solution for improving the adaptability and reliability of autonomous navigation systems in unknown environments.

Future Foods and Technologies
Articles

R&D and Application of 3D Printing Technology in Functional Plant-Based Foods

The rising consumer focus on health promotion and disease prevention has made functional foods a core global food industry trend. Plant-based functional foods, abundant in polyphenols, probiotics, and dietary fiber, have unique health benefits, and 3D printing—with its flexible, precise manufacturing capabilities—unlocks new avenues for personalized products. This study reviews 3D printing’s R&D and application in functional plant-based foods, systematically analyzing functional ingredient integration into printable inks, printing process optimization for bioactive substance retention, and functional efficacy evaluation of printed outputs. Key challenges are addressed, including functional ingredient-printability compatibility, bioactive stability during printing and storage, and functional evaluation standardization. Innovative solutions (microencapsulation, intelligent parameter regulation, multi-dimensional evaluation systems) are proposed, alongside case studies targeting gut health, immune regulation, and anti-aging. This review offers a comprehensive reference for researchers and industry practitioners, advancing the innovation and industrialization of 3D-printed functional plant-based foods.

Future Foods and Technologies
Articles

Shelf Life Extension Technologies and Quality Maintenance of 3D-Printed Plant-Based Foods

The rapid development of 3D printing technology has promoted the industrialization process of personalized plant-based foods, but the short shelf life and easy quality deterioration of 3D-printed products have become key bottlenecks restricting their commercialization. This study systematically reviews the shelf life limiting factors of 3D-printed plant-based foods, focuses on the application of shelf life extension technologies (including modified atmosphere packaging, active packaging, non-thermal processing, and formulation optimization), and explores the mechanisms of these technologies in maintaining the nutritional quality, sensory properties, and microbial safety of 3D-printed plant-based foods. The interactions between 3D printing processes and shelf life extension technologies are analyzed, and the existing challenges and future development directions are discussed. Research results show that the combination of multiple shelf life extension technologies can synergistically improve the storage stability of 3D-printed plant-based foods. This review provides theoretical support and technical reference for solving the quality maintenance problems of 3D-printed plant-based foods during storage, and promotes the sustainable development of the 3D-printed plant-based food industry.

Future Foods and Technologies
Articles

Sensory Characteristics Optimization and Consumer Acceptance of 3D-Printed Plant-Based Foods: A Multidimensional Evaluation

3D printing holds promise for personalized sustainable plant-based foods, yet commercialization is hindered by suboptimal sensory quality and uncertain consumer acceptance. This study optimized sensory traits (texture, flavor, color) of 3D-printed plant-based foods and assessed consumer acceptance via multidimensional methods. Chickpea protein isolate and oat β-glucan formed core ink materials, supplemented with sensory modifiers. Single-factor and response surface methodology optimized printing parameters (nozzle diameter, temperature, filling density). Evaluations included professional sensory panel tests, 200-participant hedonic/WTP surveys, and electronic nose/tongue analyses. Optimal products matched traditional plant-based meat analog texture, reached 82% real meat flavor similarity, and had natural color. 78% of consumers liked the product, with WTP ($5.2±0.3/100g) comparable to commercial alternatives. This work offers theoretical and technical support for 3D-printed plant-based food commercialization.

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    Dear Colleagues in the Academic Community,   Transportation Development Research is a newly established academic journal aimed at providing scholars with a platform for exchanging and sharing research achievements. Currently, we are seeking an experienced and dedicated scholar to serve as the inaugural Editor-in-Chief of our journal.   Responsibilities and Expectations:   Lead the academic direction and development strategy of the journal. Oversee the review process and select suitable reviewers to ensure the quality of submitted manuscripts. Collaborate with the editorial team to drive the day-to-day operations and development of the journal. Actively engage with the academic community to enhance the visibility and impact of the journal.   Qualifications:   Demonstrated excellence in the relevant field with a strong academic track record. Substantial academic and peer-review experience. Enthusiasm for advancing the development of the journal and the ability to lead a team. Active participation in academic discourse with a strong academic reputation.   Application Process:   Interested individuals are invited to submit their resumes and a letter of application for the Editor-in-Chief position to tdr@bilpub.com.   We look forward to your contributions in building Transportation Development Research into an outstanding academic journal. Thank you for your attention and support.   Best Regards,   Editorial Team Transportation Development Research

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    Welcome to our newly created journal! We are looking for editorial board members and editors-in-chief, and we invite scholars to nominate themselves or suggest suitable candidates. If you are interested, please download the Scholar Information Form, fill it out and submit it to the editorial office. It is worth mentioning that we will process manuscripts for the first issue of the journal free of charge and completely waive the article processing fee. Authors are kindly requested to refer to our author guidelines to ensure proper and consistent article formatting, the link to the guidelines is here: https://ojs.bilpub.com/index.php/cl/about/submissions . If you have any questions or need further advice, please send them to the editorial office at: cl@bilpub.com. Classical Literature Editorial Team

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