Search Results (3,374)

Collaborative robots (cobots) play a crucial role in modern industry by ensuring safe and efficient human–robot interaction. However, traditional control methods struggle with uncertainty handling and adaptability to dynamic environments. This review explores the application of fuzzy logic as a promising approach for cobot control. The article discusses the fundamental principles of fuzzy logic, its advantages over classical methods, and successful case studies. It analyzes current research, including hybrid methods combining fuzzy logic with machine learning and evolutionary algorithms. The paper also highlights existing challenges and potential future research directions. The conclusions emphasize the potential of fuzzy logic to enhance cobot adaptability and reliability in real-world conditions. Full article

The Line Balancing Problem (LBP) is a classic optimization topic in production management, aiming to improve efficiency through task allocation. With the transformation of the manufacturing industry towards intelligence, customization, and sustainability, its research scope has been significantly expanded. This study systematically reviews the recent research progress and proposes the C|H|V|E framework to analyze the LBP in four dimensions: (i) extension of the core line problem; (ii) horizontal integration with shop-floor decision-making; (iii) vertical coordination with enterprise-level operations; and (iv) extension of the value from efficiency improvement to sustainability and resilience enhancement. The review focuses on emerging trends, including artificial intelligence and data-driven approaches, digital twin-based optimization, flexible human-machine collaboration, and system integration across the lifecycle and circular economy. This paper provides a systematic overview of the current state of LBP research and explains how it continues to expand its boundaries by incorporating knowledge from new fields. Full article

Green hydrogen has the potential to contribute to the decarbonization of the fossil fuel industry, and its development is expected to increase in the coming years. The social dynamics among the various actors in the green hydrogen sector are studied to understand their public perception. Using the technological innovation system research approach for the stakeholder analysis and the qualitative thematic analysis method for the interviews with experts, this study presents an overview of the actors in the green hydrogen sector and their relations in Luxembourg. As a central European country with strategic political and geographic relevance, Luxembourg offers a timely case for analyzing public perception before the large-scale implementation of green hydrogen. Observing this early stage allows for future comparative insights as the national hydrogen strategy progresses. Results show high expectations for green hydrogen in mobility and industry, but concerns persist over infrastructure costs, safety, and public awareness. Regional stakeholders demonstrate a strong willingness to collaborate, recognizing that local public acceptance still requires effort, particularly in areas such as clear and inclusive communication, sharing knowledge, and fostering trust. These findings provide practical insights for stakeholder engagement strategies and theoretical contributions to the study of social dynamics in sustainability transitions. Full article

Architectural design stands as a highly knowledge-intensive field, with designers serving as the linchpin for its premium development. China’s construction industry is now navigating a transitional phase of slower growth, where a misalignment in designer capabilities significantly obstructs the nation’s shift from being a mere “construction giant” to becoming a true “construction powerhouse”. Based on the spatial mismatch model and Geodetector, this study empirically analyzes the mismatch relationship among designers and its influencing factors using panel data from 31 provinces in China from 2013 to 2023, and proposes strategies for cultivating architectural design talents. Findings reveal that China’s architectural designers exhibit spatial supply imbalance, and complex trends in designer allocation-simultaneous growth and decline coexist. China exhibits diverse types of architect mismatch: 22.58% of regions are in a state of Positive Mismatch, and 12.90% experience Negative Mismatch. In over one-third of regions, the architectural design talent market can no longer self-correct architect mismatch through market mechanisms, urgently requiring collaborative intervention policies from governments, design associations, and enterprises to address architect supply–demand governance. For a smooth transition during the transformation and upgrading of the construction and design industries, the architectural design talent market should accommodate frictional designer mismatch. The contribution of designer mismatch varies significantly, with factors such as innovation, industrial structure, and fiscal self-sufficiency exerting more direct influence, while other factors play indirect roles through dual-factor enhancement effects and nonlinear enhancement effects. The insights from the analysis results and conclusions for future designer cultivation include fostering an interdisciplinary teaching model for designers through university–enterprise collaboration, enhancing education in AI and intelligent construction literacy, and establishing an intelligent service platform for designer supply–demand matching to promptly build a new differentiated and precise designer supply system. Full article

This work presents a task-oriented framework for optimizing robotic surface finishing to improve efficiency and ensure feasibility under realistic kinematic and geometric constraints. The approach combines surface subdivision, optimal placement of the workpiece, and region-based toolpath planning to adapt machining strategies to local surface characteristics. A novel time evaluation criterion is introduced that improves our previous kinematic approach by incorporating dynamic aspects. This advancement enables a more realistic estimation of machining time, providing a more reliable basis for optimization and path planning. The framework determines both the optimal position of the workpiece and the subdivision of its surface into regions systematically, enabling machining directions and speeds to be adapted to the geometry of each region. The methodology was validated on several semi-complex surfaces through simulation and experimental trials with collaborative robotic manipulators. The results demonstrate that improved region-based optimization leads to machining time reductions of 9–26% compared to conventional single-direction machining strategies. The most significant improvements were achieved for larger, more complex geometries and denser machining paths, confirming the method’s industrial relevance. These findings establish the framework as a practical solution for reducing cycle time in specific robotic surface finishing tasks. Full article

Antibiotics are essential for treating bacterial and fungal infections in plants, animals, and humans. Their widespread use in agriculture and the food industry has significantly enhanced animal health and productivity. However, extensive and often inappropriate antibiotic use has driven the emergence and spread of antimicrobial resistance (AMR), a global health crisis marked by the reduced efficacy of antimicrobial treatments. Recognized by the World Health Organization (WHO) as one of the top ten global public health threats, AMR arises when certain bacteria harbor antimicrobial resistance genes (ARGs) that confer resistance that can be horizontally transferred to other bacteria, accelerating resistance spread in the environment. AMR poses a significant global health challenge, affecting humans, animals, and the environment alike. A One-Health perspective highlights the interconnected nature of these domains, emphasizing that resistant microorganisms spread across healthcare, agriculture, and the environment. Recent scientific advances such as metagenomic sequencing for resistance surveillance, innovative wastewater treatment technologies (e.g., ozonation, UV, membrane filtration), and the development of vaccines and probiotics as alternatives to antibiotics in livestock are helping to mitigate resistance. At the policy level, global initiatives including the WHO Global Action Plan on AMR, coordinated efforts by (Food and Agriculture Organization) FAO and World Organisation for Animal Health (WOAH), and recommendations from the O’Neill Report underscore the urgent need for international collaboration and sustainable interventions. By integrating these scientific and policy responses within the One-Health framework, stakeholders can improve antibiotic stewardship, reduce environmental contamination, and safeguard effective treatments for the future. Full article

In a global industrial landscape where the digital economy accounts for over 40% of total output, cloud computing technology is reshaping business models at a compound annual growth rate of 19%. This trend has led to an increasing number of cloud computing tasks requiring timely processing. However, most computational tasks are latency-sensitive and cannot tolerate significant delays. This has led to the urgent need for researchers to address the challenge of effectively scheduling cloud computing tasks. This paper proposes a hybrid SAO and RIME optimizer (HSAO) for global optimization and cloud task scheduling problems. First, population initialization based on ecological niche differentiation is proposed to enhance the initial population quality of SAO, enabling it to better explore the solution space. Then, the introduction of the soft frost search strategy and hard frost piercing mechanism from the RIME optimization algorithm enables the algorithm to better escape local optima and accelerate its convergence. Additionally, a population-based collaborative boundary control method is proposed to handle outlier individuals, preventing them from clustering at the boundary and enabling more effective exploration of the solution space. To evaluate the effectiveness of the proposed algorithm, we compared it with 11 other algorithms using the IEEE CEC2017 test set and assessed the differences through statistical analysis. Experimental data demonstrate that the HSAO algorithm exhibits significant advantages. Furthermore, to validate its practical applicability, we applied HSAO to real-world cloud computing task scheduling problems, achieving excellent results and successfully completing the scheduling planning of cloud computing tasks. Full article

Industrial microorganisms have a wide range of applications in biomanufacturing, energy production, environmental protectionpharmaceutical development, etc. Synthetic biology has revolutionised the field of industrial microorganisms by designing, constructing and optimising biological systems. The aim of this study is to discuss the key technologies of synthetic biology in industrial microorganisms and their application prospects. Gene editing technology, one of the core tools of synthetic biology, enables researchers to precisely modify microbial genomes to optimise their metabolic pathways or introduce new functions. Metabolic engineering, as an important direction for the application of synthetic biology in industrial microorganisms, enables the efficient synthesis of target products by optimising and reconstructing the metabolic pathways of microorganisms. The development of high-throughput screening and automated platforms has enabled large-scale gene editing and metabolic engineering experiments. The application of synthetic genomics promises to develop microbes with highly customised functions. However, there are still many challenges in this field, and future research still requires interdisciplinary collaboration to drive the application of synthetic biology in industrial microorganisms to new heights. Full article

As the potential of e-aircraft in short-range routes becomes more prominent, the question of how to rationally plan airport electric infrastructure and efficiently produce it has become a key issue in the aviation industry’s efforts to move towards electrification. In this paper, we propose and construct a three-layer optimization model for determining the size of airport electric infrastructure, which is solved collaboratively at the three levels of strategic, tactical, and operational layers, in order to construct an optimization algorithm to minimize the construction and operation costs of electric infrastructure, and at the same time to ensure that flights are not delayed by the influence of electric power supply. Specifically, Stage-1 considers infrastructure sizes; Stage-2 assigns a binary charge–swap decision per turnaround under no-delay policy; Stage-3 schedules power under time-of-use tariffs and outputs a feasible day plan and daily cost. In order to verify the effectiveness of this paper’s algorithm, this paper conducts case studies and algorithm validation on actual flight data. The results show that the proposed model can significantly reduce the overall airport operating costs while ensuring normal flight operations. Full article

Driven by the global energy transition and the pursuit of dual carbon goals (carbon peaking and carbon neutrality), the innovation network of the new energy vehicle (NEV) industry, composed of enterprises, universities, and research institutes, has become a key driver of sustainable industrial development. The evolution of this network is jointly shaped by both supply chain networks (SCNs) and executive networks (ENs), representing formal and informal relational structures, respectively. To systematically explore these dynamics, this study analyzes panel data from Chinese A-share-listed NEV firms covering the period 2003–2024. Employing social network analysis (SNA) and Quadratic Assignment Procedure (QAP) regression, we investigate how SCNs and ENs influence the formation and structural evolution of innovation networks. The results reveal that although all three networks exhibit sparse connectivity, they differ substantially in their structural characteristics. Moreover, both SCNs and ENs have statistically significant positive effects on innovation network development. Building on these findings, we propose an integrative policy framework to strategically enhance the innovation ecosystem of China’s NEV industry. This study not only provides practical guidance for fostering collaborative innovation but also offers theoretical insights by integrating formal and informal network perspectives, thereby advancing the understanding of multi-network interactions in complex industrial systems. Full article

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation and significantly influence the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, and research funding decisions. This study utilizes machine learning techniques and builds four different classifiers, i.e., random forest, support vector machines, naïve bayes, and logistic regression, to predict star scientists in the field of artificial intelligence while highlighting features related to their success. The analysis is based on publication data collected from Scopus from 2000 to 2019, incorporating a diverse set of features such as gender, ethnic diversity, and collaboration network structural properties. The random forest model achieved the best performance with an AUC of 0.75. Our results confirm that star scientists follow different patterns compared to their non-star counterparts in almost all the early-career features. We found that certain features, such as gender and ethnic diversity, play important roles in scientific collaboration and can significantly impact an author’s career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, betweenness centrality, research impact indicators, and weighted degree centrality. Our approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers. Full article

The aviation industry is responsible for approximately 2–3% of worldwide CO₂ emissions and is increasingly subjected to demands for the attainment of net-zero emissions targets by the year 2050. Traditional fossil jet fuels, which exhibit lifecycle emissions of approximately 89 kg CO₂-eq/GJ, play a substantial role in exacerbating climate change, contributing to local air pollution, and fostering energy insecurity. In contrast, Sustainable Aviation Fuels (SAFs) derived from renewable feedstocks, including biomass, municipal solid waste, algae, or through CO₂- and H₂-based power-to-liquid (PtL) represent a pivotal solution for the immediate future. SAFs generally accomplish lifecycle greenhouse gas (GHG) reductions of 50–80% (≈20–30 kg CO₂-eq/GJ), possess reduced sulfur and aromatic content, and markedly diminish particulate emissions, thus alleviating both climatic and health-related repercussions. In addition to their environmental advantages, SAFs promote energy diversification, lessen reliance on unstable fossil fuel markets, and invigorate regional economies, with projections indicating the creation of up to one million green jobs by 2030. This comprehensive review synthesizes current knowledge on SAF sustainability advantages compared to conventional aviation fuels, identifying critical barriers to large-scale deployment and proposing integrated solutions that combine technological innovation, supportive policy frameworks, and international collaboration to accelerate the aviation industry’s sustainable transformation. Full article

In conventional logistics optimization problems, an objective function describes the relationship between parameters. However, in many industrial practices, such a relationship is unknown, and only observational data is available. The objective of the research is to use machine learning-based regression models to uncover patterns in the warehousing dataset and use them to generate an accurate objective function. The models are not only suitable for prediction, but also for interpreting the effect of input variables. This data-driven approach is consistent with the automated, intelligent systems of Industry 4.0, while Industry 5.0 provides opportunities for sustainable, flexible, and collaborative development. In this research, machine learning (ML) models were tested on a fictional dataset using Automated Machine Learning (AutoML), through which Light Gradient Boosting Machine (LightGBM) was selected as the best method (R² = 0.994). Feature Importance and Partial Dependence Plots revealed the key factors influencing storage performance and their functional relationships. Defining performance as a cost indicator allowed us to interpret optimization as cost minimization, demonstrating that ML-based methods can uncover hidden patterns and support efficiency improvements in warehousing. The proposed approach not only achieves outstanding predictive accuracy, but also transforms model outputs into actionable, interpretable insights for warehouse optimization. By combining automation, interpretability, and optimization, this research advances the practical realization of intelligent warehouse systems in the era of Industry 4.0. Full article

Critical metals constitute essential raw materials for clean energy transition, making their policy evolution highly significant for global resource governance. Analyzing policy texts from China (1973–2024), this study develops a three-dimensional analytical framework—Instrument Type, Policy Objective, and Implementation Domain—integrated with social network analysis to investigate the characteristics and drivers of policy evolution. Findings indicate that China’s critical metal governance paradigm has shifted from securing resource supply to pursuing sustainability goals. Policy instruments have transitioned from authority-based dominance to diversified combinations, while the policy network, centered on the Ministry of Industry and Information Technology (MIIT) and the National Development and Reform Commission (NDRC), exhibits increasingly frequent interdepartmental collaboration. The evolution is shown to stem from the dynamic interdependence between policy instruments and network structures. This research provides theoretical and practical insights for optimizing critical metals governance systems. Full article

Industrial environments are complex, making the monitoring of gauge meters challenging. This is especially true in confined spaces, underground, or at high altitudes. These difficulties underscore the need for intelligent solutions in the inspection and monitoring of plant assets, such as gauge meters. In this study, we plan to integrate unmanned ground vehicles and unmanned aerial vehicles to address the challenge, but the integration of these heterogeneous systems introduces additional complexities in terms of coordination, interoperability, and communication. Our goal is to develop a multi-domain robotic swarm system for industrial mapping and asset monitoring. We created an experimental setup to simulate industrial inspection tasks, involving the integration of a TurtleBot 2 and a QDrone 2. The TurtleBot 2 utilizes simultaneous localization and mapping (SLAM) technology, along with a LiDAR sensor, for mapping and navigation purposes. The QDrone 2 captures high-resolution images of meter gauges. We evaluated the system’s performance in both simulation and real-world environments. The system achieved accurate mapping, high localization, and landing precision, with 84% accuracy in detecting meter gauges. It also reached 87.5% accuracy in reading gauge indicators using the paddle OCR algorithm. The system navigated complex environments effectively, showcasing the potential for real-time collaboration between ground and aerial robotic platforms. Full article