Search Results (1,076)

Microplastics are found in Earth’s atmosphere, lithosphere, hydrosphere, pedosphere, and ecosphere. While there is a growing interest and need to solve this grand challenge in both the academic and policy realms, few have engaged with academics, policymakers, and community partners to co-identify the problem, co-design research, and co-produce knowledge in tackling this issue. Using a convergence research framework, we investigated the perception of microplastic pollution among different end users, delivered educational materials to K-12 teachers and practitioners, and identified key sampling points for assessing environmental microplastic concentrations in the Columbia River Basin, United States. Three community partner workshops identified regional issues and concerns associated with microplastic pollution and explored potential policy intervention strategies. The stakeholder survey, co-designed with community partners, identified varying perceptions around microplastic pollution across educators, government employees, non-profit employees, and industry practitioners. Pre- and post-test results of teacher workshops show increases in participants’ knowledge after taking a four-week summer class with the knowledge being translated to their students. Community partners also helped develop a unique passive sampling plan for atmospheric deposition of microplastics using synoptic moss samples and provided freshwater samples for microplastic quantification across the basin. Our study drew three major lessons for successfully conducting convergence environmental research—(1) communication and trust building, supported by the use of key-informants to expand networks; (2) co-creation through collaboration, where partners and students shaped research and education to enhance impact; and (3) change-making, as project insights were translated into policy discussions, community outreach, and classrooms. Full article

Efficient cooling and heat recovery systems are becoming increasingly critical in large-scale commercial and industrial facilities, especially with the rising demand for sustainable energy solutions. Traditional air-conditioning and refrigeration systems often dissipate significant amounts of waste heat, which remains underutilized. This study addresses the challenge of harnessing low-potential waste heat from such systems to support fifth-generation district heating and cooling (5GDHC) networks, particularly in moderate-temperate regions like Flanders, Belgium. To evaluate the technical and economic feasibility of waste heat recovery, a methodology is developed that integrates established performance metrics—such as the energy efficiency ratio (EER), power usage effectiveness (PUE), and specific cooling demand (kW/t)—with capital (CapEx) and operational expenditure (OpEx) assessments. Empirical correlations, including regression analysis based on manufacturer data and operational case studies, are used to estimate equipment sizing and system performance across three operational modes. The study includes detailed modeling of data centers, cold storage facilities, and large supermarkets, taking into account climatic conditions, load factors, and thermal capacities. Results indicate that average cooling loads typically reach 58% of peak demand, with seasonal coefficient of performance (SCOP) values ranging from 6.1 to a maximum of 10.3. Waste heat recovery potential varies significantly across building types, with conversion rates from 33% to 68%, averaging at 59%. In data centers using water-to-water heat pumps, energy production reaches 10.1 GWh/year in heat pump mode and 8.6 GWh/year in heat exchanger mode. Despite variations in system complexity and building characteristics, OpEx and CapEx values converge closely (within 2.5%), demonstrating a well-balanced configuration. Simulations also confirm that large buildings operating above a 55% capacity factor provide the most favorable conditions for integrating waste heat into 5GDHC systems. In conclusion, the proposed approach enables the scalable and efficient integration of low-grade waste heat into district energy networks. While climatic and technical constraints exist, especially concerning temperature thresholds and equipment design, the results show strong potential for energy savings up to 40% in well-optimized systems. This highlights the viability of retrofitting large-scale cooling systems for dual-purpose operation, offering both environmental and economic benefits. Full article

Recent advances in large language models (LLMs) have triggered rapid growth in AI-powered educational agents, yet researchers and practitioners still lack a consolidated view of how these systems are engineered and validated. To address this gap, we conducted a systematic literature review of 82 peer-reviewed and industry studies published from January 2023 to February 2025. Using a four-phase protocol, we extracted and coded them along six groups: technical and pedagogical frameworks, tutoring systems, assessment and feedback, curriculum design, personalization, and ethical considerations. Synthesizing these findings, we propose design principles that link technical choices to instructional goals and outline safeguards for privacy, fairness, and academic integrity. Across all domains, the evidence converges on a key insight: hybrid human–AI workflows, in which teachers curate and moderate LLM output, outperform fully autonomous tutors by combining scalable automation with pedagogical expertise. Limitations in the current literature, including short study horizons, small-sample experiments, and a bias toward positive findings, temper the generalizability of reported gains, highlighting the need for rigorous, long-term evaluations. Full article

Concrete-printing robots have become an advanced technology in the construction industry that allows the creation of complex structures, while saving materials and shortening construction time compared to traditional methods. With the structure of a concrete 3D printing robot using a concrete extruder with a screw, this mechanism provides stable flow of concrete, and less pressure fluctuation. However, using a large mass extruder changes the inertia of the joint and the mass coefficient of the arm when the mass changes, leading to a position error. With the high demands for precision and stability in the operation of 3D concrete printing robots, advanced control methods have become essential to ensure trajectory tracking and robustness in complex real-world environments. This study provides a sliding mode controller with an error and integral, and derivatives are introduced into the sliding surface to improve the stability of the robot without chattering. The controller exhibits fast convergence times and small trajectory tracking errors, at less than 0.1 mm. Simulation results show that this controller is suitable for concrete 3D printing applications, and the controller exhibits fast and good responses to continuously changing extruder mass. This enables the robot to track the expected trajectory with high accuracy. Full article

This work presents a novel fully automated computational framework for optimizing profile extrusion dies, aiming to achieve balanced flow at the die flow channel outlet while minimizing total pressure drop. The framework integrates non-isothermal, non-Newtonian flow modeling in OpenFOAM with a geometry parameterization routine in FreeCAD and a Bayesian optimization algorithm from Scikit-Optimize. A custom solver was developed to account for temperature-dependent viscosity using the Bird–Carreau–Arrhenius model, incorporating viscous dissipation and a novel boundary condition to replicate the thermal regulation used in the experimental process. For optimization, the die flow channel outlet cross-section is discretized into elemental sections, enabling localized flow analysis and establishing a convergence criterion based on the total objective function value. A case study on a tire tread die demonstrates the framework’s ability to iteratively refine internal geometry by adjusting key design parameters, resulting in significant improvements in outlet velocity uniformity and reduced pressure drop. Within the searching space, the results showed an optimal objective function of 0.2001 for the best configuration, compared to 0.7333 for the worst configuration, representing an enhancement of 72.7%. The results validate the effectiveness of the proposed framework in navigating complex design spaces with minimal manual input, offering a robust and generalizable approach to extrusion die optimization. This methodology enhances process efficiency, reduces development time, and improves final product quality, particularly for complex and asymmetric die geometries commonly found in the automotive and tire manufacturing industries. Full article

In the context of Industry 4.0, the transformative integration of intelligent digitalization, sustainable practices, and cross-sectoral industrial convergence within manufacturing systems constitutes a strategic imperative for addressing global climate emergencies and accelerating resource scarcity. Using panel data from 279 prefecture-level cities and the multiperiod difference-in-differences (DID) approach, this study investigates the impact of intelligent manufacturing on urban green innovation efficiency and its heterogeneous effects. The results reveal that intelligent manufacturing significantly enhances urban green innovation efficiency through scale agglomeration, economic agglomeration, and talent agglomeration, with robustness confirmed by propensity score matching DID tests and double machine learning. In addition, financial development has a nonlinear moderating effect on this relationship. When financial development surpasses the critical threshold of 3.817, its positive moderating effect becomes significantly enhanced. Heterogeneity analyses demonstrate that the benefits of intelligent manufacturing are more pronounced in the middle and western regions, noncentral cities, and cities with advanced industrial intelligence, robust digital finance ecosystems, or stringent environmental regulations. These findings provide novel insights into the dynamic mechanisms through which intelligent manufacturing fosters green innovation, offering policymakers in developing economies a framework to tailor regional strategies, optimize governance systems, and harness intelligent manufacturing as a catalyst for sustainable, innovation-driven growth. Full article

Background: The maritime industry is undergoing a profound transformation to meet global decarbonization goals. As Industry 4.0 advanced digital technologies are increasingly integrated into shipping operations, the role of the human element is evolving significantly. This intersection of decarbonization, digitalization, and human element/workforce transformation lays the foundation for more structured initiatives such as Green Digital Shipping Corridors (GDSCs), a strategic solution to scale zero-emission, smart maritime routes. Methods: This paper explores the interconnected roles of decarbonization, digitalization, and human capital development through a systematic literature review. It examines how these pillars converge in the implementation of GDSCs, drawing on academic and industry sources to identify challenges and opportunities in workforce readiness, policy integration, and technological adoption. Results: The findings underscore the necessity of coordinated action across the three pillars, particularly highlighting the importance of structured training programs, cross-sector collaboration, and standardized regulations. GDSCs are presented as an applied framework to align these transitions, enabling scalable, digitally enabled, low-emission maritime routes. Conclusions: There is a significant gap in current research that holistically connects the human factor with technological and environmental imperatives in the context of maritime transformation. This paper addresses that gap by introducing GDSCs as a strategic outcome of integrated change, providing actionable insights for policymakers, industry leaders, and educators aiming to advance sustainable shipping. Full article

This paper presents an improved methodology for optimizing the fed-batch fermentation process of xylitol production, aiming to maximize the final concentration in a bioreactor co-fed with xylose and glucose. Xylitol is a valuable sugar alcohol widely used in the food and pharmaceutical industries, and its microbial production requires precise control over substrate feeding strategies. The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. Unlike traditional optimization approaches based on piecewise parameterization, which produce discontinuous control profiles and require post-processing, this method generates smooth profiles directly applicable to real systems. Additionally, it significantly reduces mathematical complexity compared to strategies that combine Fourier series with orthonormal polynomials while maintaining similar optimization results. The methodology achieves good results in xylitol production using only eight parameters, compared to at least twenty in other approaches. This dimensionality reduction improves the robustness of the optimization by decreasing the likelihood of convergence to local optima while also reducing the computational cost and enhancing feasibility for implementation. The results highlight the potential of this strategy as a practical and efficient tool for optimizing nonlinear multivariable bioprocesses. Full article

Welding robots are essential in modern manufacturing, providing high precision and efficiency in welding processes. To optimize their performance and minimize errors, accurate simulation of their behavior is crucial. This paper presents a novel approach to enhance the simulation of welding robots using the Virtual, Augmented, and Mixed Reality (VAM) simulation platform. The VAM platform offers a dynamic and versatile environment that enables a detailed and realistic representation of welding robot actions, interactions, and responses. By integrating VAM with existing simulation techniques, we aim to improve the fidelity and realism of the simulations. Furthermore, to accelerate the learning and optimization of the welding robot’s behavior, we incorporate deep reinforcement learning (DRL) techniques. Specifically, DRL is utilized for task offloading and trajectory planning, allowing the robot to make intelligent decisions in real-time. This integration not only enhances the simulation’s accuracy but also improves the robot’s operational efficiency in smart manufacturing environments. Our approach demonstrates the potential of combining advanced simulation platforms with machine learning to advance the capabilities of industrial robots. In addition, experimental results show that ANFIS achieves higher accuracy and faster convergence compared to traditional control strategies such as PID and FLC. Full article

This study proposes a novel fuzzy disturbance observer (FDO)-augmented adaptive nonsingular terminal sliding mode control (NTSMC) framework for multi-joint robotic manipulators, addressing critical challenges in trajectory tracking precision and disturbance rejection. Unlike conventional disturbance observers requiring prior knowledge of disturbance bounds, the proposed FDO leverages fuzzy logic principles to dynamically estimate composite disturbances—including unmodeled dynamics, parameter perturbations, and external torque variations—without restrictive assumptions about disturbance derivatives. The control architecture achieves rapid finite-time convergence by integrating the FDO with a singularity-free terminal sliding manifold and an adaptive exponential reaching law while significantly suppressing chattering effects. Rigorous Lyapunov stability analysis confirms the uniform ultimate boundedness of tracking errors and disturbance estimation residuals. Comparative simulations on a 2-DOF robotic arm demonstrate a 97.28% reduction in root mean square tracking errors compared to PD-based alternatives and a 73.73% improvement over a nonlinear disturbance observer-enhanced NTSMC. Experimental validation on a physical three-joint manipulator platform reveals that the proposed method reduces torque oscillations by 58% under step-type disturbances while maintaining sub-millimeter tracking accuracy. The framework eliminates reliance on exact system models, offering a generalized solution for industrial manipulators operating under complex dynamic uncertainties. Full article

This study examines the relationship between technological innovation and economic development in the Yangtze River economic belt context. Specifically, the study employs the SBM-GML model to assess the efficiency of industrial green technology innovation across 110 prefecture-level cities between 2006 and 2022. The study also employs exploratory spatial data analysis (ESDA) and the Spatio-temporal transition method to analyze the spatial evolution pattern of the GML index of industrial green technology innovation. In addition, the study investigates the convergence mechanism using absolute and conditional β convergence models. The findings reveal that the GML index of industrial green technology innovation in the Yangtze River Economic Belt exhibits an upward trend, and technological progress is a key driver. Moreover, the spatial and temporal transition of the GML index of industrial green technology innovation shows substantial spatial dependence and solid spatial stability. The study also finds regional heterogeneity in the absolute and conditional β convergence characteristics and their influencing factors. Considering regional differences, the results suggest differentiated policy recommendations to promote the coordinated development of industrial green technological innovation efficiency in the Yangtze River Economic Belt. The study contributes to the literature on the relationship between technological innovation and economic development, highlighting the importance of spatial considerations and regional heterogeneity in promoting sustainable economic growth. Full article

The dip angle and thickness of coal seams are key geological determinants in mine system engineering. Roadways excavated in steeply inclined or thick coal seams typically exhibit significant deformation, with the combined geological configuration of steeply inclined thick seams thus presenting heightened support demands. Therefore, taking the 1502 level roadway in the Dayuan Coal Industry—situated in a steeply inclined thick coal seam—as an engineering case, mechanical models of roadways with different cross-sectional shapes are established, and the deformation and failure mechanisms of surrounding rock under different coal seam dip angles are analyzed. Based on this analysis, an asymmetric support technology scheme is proposed, followed by surrounding rock deformation monitoring and a support effectiveness evaluation. Key findings include the following: (1) in steeply inclined thick coal seam roadways with different cross-sectional shapes, the stress distribution and plastic zone development of surrounding rock follow a descending sequence, inclined roof trapezoidal section > rectangular section > arched section. Among these, the arched section is identified as the optimal roadway cross-sectional shape for this engineering context. (2) The stress-concentration area in the arch roadway aligns with the inclined direction of the coal seam, forming asymmetric stress concentration patterns. Specifically, as the coal seam dip angle increases, stress increases at the arch shoulder of the upper sidewall and the wall foundation of the lower sidewall. Concurrently, such stress concentration induces shear failure in the surrounding rock, which serves as the primary mechanism causing asymmetric deformation and failure in steeply inclined thick coal seam roadways. (3) In the 1502 level roadway, the asymmetric support technology with dip-oriented reinforcement was implemented. Compared to the original support scheme, roof deformation and sidewall convergence decreased by 46.17% and 46.8%, respectively. The revealed failure mechanisms of steeply inclined thick coal seam roadways and the proposed asymmetric support technology provide technical and engineering references for roadway support in similar mining conditions. Full article

The rise of 6G-enabled smart industries necessitates secure, adaptive, and interpretable anomaly detection frameworks capable of operating under dynamic, adversarial, and resource-constrained environments. This study presents SAFEL-IoT, a novel Secure Adaptive Federated Learning framework with integrated explainability, specifically designed for anomaly detection in Industrial Internet-of-Things (IIoT) systems under Industry 5.0 paradigms. SAFEL-IoT introduces a dynamic aggregation mechanism based on temporal model divergence, a hybrid encryption scheme combining partial homomorphic encryption with differential privacy, and an interpretable anomaly scoring pipeline leveraging SHapley Additive exPlanations (SHAP) values and temporal attention mechanisms. Extensive experimentation on the SKAB industrial dataset demonstrates that SAFEL-IoT achieves a superior F1 score of 0.93, reduces training time to 63.7 s, and maintains explanation fidelity with only a 0.15 explanation error. Communication efficiency is improved by 70.3% through 6G network slicing, while detection latency remains below 12 ms across 100 distributed edge clients. Further analysis shows a 41.7% improvement in drift robustness and a 68.9% reduction in false positives compared to traditional federated learning baselines. Theoretical convergence guarantees, scalability under large node deployments, and resilience against adversarial attacks validate SAFEL-IoT as a comprehensive and practical solution for secure, explainable, and scalable anomaly detection in next-generation industrial ecosystems. Full article

Nickel (Ni) catalysts have numerous applications in the chemical industry, but they are susceptible to sulfurization, with their sulfurized structures and underlying formation mechanisms remaining unclear. Herein, density functional theory (DFT) combined with the particle swarm optimization (PSO) algorithm is employed to investigate the low-energy structures and formation mechanisms of sulfide phases on Ni(111) surfaces, especially under high-sulfur-coverage conditions where traditional DFT calculations fail to reach convergence. Using ($\sqrt{3}\times \sqrt{3}$ ) Ni(111) slab models, we identify a sulfurization limit, finding that each pair of deposited sulfur atoms can sulfurize one layer of three Ni atoms at most (Ni:S = 3:2), with additional sulfur atoms penetrating deeper layers until saturation. Under typical reactive adsorption desulfurization conditions, the ab initio thermodynamics analysis indicates that Ni₃S₂ is the most stable sulfide phase, consistent with sulfur K-edge XANES data. Unsaturated phases, including Ni₃S, Ni₂S, and Ni₉S₅, represent intermediate states towards saturation, potentially explaining the diverse Ni sulfide compositions observed in experiments. Full article

Technological evolution has driven the integration of computing devices in various domains, giving rise to heterogeneous and dynamic intelligent environments; together with market pressure, these pose challenges in formulating an architecture that takes advantage of contextual knowledge. In terms of architectural design, we are witnessing a transition from a centralised, monolithic view of systems to a decentralised view that incorporates the vertical and horizontal dimensions of the production environment. Therefore, this review aimed to (i) identify the requirements, (ii) find out about the representation models and context inference techniques, and (iii) identify architectural technologies, norms, models, and standards. The results observed in 25 articles made it possible to identify interoperability, automation, and decision-making as convergence points and observe the adoption of ontologies as a research area for context representation. In contrast, the discussion of context inference techniques remains open. Finally, this study presents recommendations for the design of a context-aware systems architecture that incorporates the principles of Industry 4.0 and facilitates the development of applications. Full article