Search Results (5,316)

The emergence of mass personalized production has increased the adaptability and intelligence requirements of welding robots. To address the challenges associated with mass personalized production, this paper proposes a novel knowledge-driven framework for intelligent welding process planning in cloud robotics systems. This framework integrates cloud-edge-end collaborative computing with ontology-based knowledge representation to enable efficient welding process optimization. A hierarchical knowledge-based architecture was developed using the SQLite 3.38.0, Redis 5.0.4, and HBase 2.1.0 tools. The ontology models formally define the welding tasks, resources, processes, and results, thereby enabling semantic interoperability across heterogeneous systems. A hybrid knowledge evolution method that combines cloud-based welding simulation and transfer learning is presented as a means of achieving inexpensive, efficient, and intelligent evolution of welding process knowledge. Experiments demonstrated that, with respect to pure cloud-based solutions, edge-based knowledge bases can reduce the average response time by 86%. The WeldNet-152 model achieved a welding parameter prediction accuracy of 95.1%, while the knowledge evolution method exhibited a simulation-to-reality transfer accuracy of 78%. The proposed method serves as a foundation for significant enhancements in the adaptability of welding robots to Industry 5.0 manufacturing environments. Full article

Relativistic jets from AGN are an important driver of feedback in galaxies. They interact with their environments over a wide range of physical scales during their lifetime, and an understanding of these interactions is crucial for unraveling the role of supermassive black holes in shaping galaxy evolution. The impact of such jets has been traditionally considered in the context of heating large-scale environments. However, in the last few decades, there has been additional focus on the immediate impact of jet feedback on the host galaxy itself. In this review, we outline the development of various numerical simulations from the onset of research on jets to the present day, where sophisticated numerical techniques have been employed to study jet feedback, including a range of physical processes. The jets can act as important agents of energy injection into a host’s ISM, as confirmed in both observations of multi-phase gas as well as in simulations. Such interactions have the potential to impact the kinematics of the gas as well as star formation. We summarize recent results from simulations of jet feedback on kpc scales and outline the broader implications for observations and galaxy evolution. Full article

To accurately grasp the land and ecological dynamics in the Henan section of the Yellow River Basin (YRB) and provide detailed local data for the ecological protection of the YRB, this article takes the Henan segment within the YRB as the research area, explores the spatio-temporal evolution of land use (LU) and landscape ecological risks (LERS), and predicts LU and LERS under various scenarios in the future based on the PLUS model. We found that: (1) From 2000 to 2020, object types in research area were given priority with cultivated land, forest land, and construction land, with construction land and cultivated land experiencing the largest changes of 5.71% and −6.34%, respectively. Changes in other land types varied within a ±3% range. The expansion of construction land principally encroached upon cultivated land, indicating significant urban sprawl. (2) The high-ecological-risk areas were clustered in the area centered in Zhengzhou, and the low-ecological-risk areas were distributed in the edge of the study area. As risk levels increased, the risk center gradually shifted towards the central regions, particularly around Luoyang and at the junction of Luoyang, Zhengzhou, and Jiaozuo. (3) The LU status in 2030 was projected using the PLUS model under three varied scenarios. The Kappa coefficient of the model was 0.81, and the overall accuracy was about 88.13%. Cultivated land, forest land, and construction land still accounted for the main part, and the area of cultivated land and construction land changed significantly. Based on this analysis of LERS prediction, the distribution of risk levels in different scenarios was different, but in general, high-ecological-risk areas and higher-ecological-risk areas accounted for the main part, while the study area’s edges were where low-ecological-risk zones were situated. Research can offer scientific and technological support for the sensible utilization and administration of resources, along with the protection of the ecological environment and regional sustainable development. Full article

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

With the increasing attention to China’s ecological environment protection and the prominence of lake water environment problems, the impact of soil erosion on lake ecosystems has become an important research topic for regional sustainable development. Based on the CiteSpace bibliometric method, this study systematically analyzed 225 research articles on the impact of soil erosion on the water environment of lakes in China in the core collection of Web of Science from 1998 to 2025, aiming to reveal the research hotspots, evolution trends and regional differences in this field. The results show that China occupies a dominant position in this field (209 papers), and the Chinese Academy of Sciences is the core research institution (93 papers). The research hotspots show obvious policy-driven characteristics, which are divided into slow start periods (1998–2007), accelerated growth periods (2008–2015), explosive growth periods (2016–2020) and stable development periods (2021–2025). A keyword cluster analysis identified nine main research directions, including sedimentation effect (#0 cluster), soil loss (#2 cluster) and nitrogen and phosphorus migration (#11 cluster) in the Three Gorges Reservoir area. The study found that the synergistic effects of climate change and human activities (such as land use change) are becoming a new research paradigm, and the Yangtze River Basin, the Loess Plateau and the Yunnan–Guizhou Plateau constitute the three core research areas (accounting for 72.3% of the total literature). Future research should focus on a multi-scale coupling mechanism, a climate resilience assessment and an ecological engineering effectiveness verification to support the precise implementation of lake protection policies in China. This study provides a scientific basis for the comprehensive management of the soil erosion–lake water environment system, and also contributes a Chinese perspective to the sustainable development goals (SDG6 and SDG15) of similar regions in the world. Full article

Identifying the impact of climate change and changes in underlying surface conditions on river runoff changes is critical for sustainable water resource use and watershed management in arid regions. The Kriya River is not only a key support for water resources in the arid environment of the Tarim Basin, but also a solid foundation for the survival and development of agricultural oases. In this study, the Kriya River Basin in Xinjiang, China, was taken as the research object, and the Mann–Kendall, Sen’s Slope, Cumulative Sum, and other methods were used to systematically analyze the temporal evolution law and multi-modal characteristics of runoff in the basin. Based on the Budyko hydrothermal coupling equilibrium equation, the contribution of temperature, evaporation, and the underlying surface to runoff variation was quantitatively interpreted. The study found that the annual runoff depth of the Kriya River Basin has shown a significant positive evolution trend in the past 60 years, with an increase rate of 0.5189 mm/a (p ≤ 0.01). Through the identification of mutation points, the runoff time series of the Kriya River was divided into the base period 1957–1999 and the change period 2000–2015. Without considering the supply of snowmelt runoff, the contribution rate of precipitation to runoff change was 75.23%, followed by the change in underlying surface (23.08%), and the potential evapotranspiration was only 1.69%. The results of this study provide a good scientific reference for water resources management and environmental governance in the Kriya River Basin. Full article

Electric vehicle (EV) charging has become a key factor in grid integration, impact analysis, and the development of intelligent charging strategies. However, the rapid rise in EV adoption poses challenges for charging infrastructure and grid stability due to the inherently variable and uncertain charging behavior. Limited access to high-resolution, location-specific data further hinders accurate modeling, emphasizing the need for reliable, privacy-preserving tools to forecast EV-related grid impacts. This study introduces a comprehensive methodology to emulate real-world EV charging behavior using a real-time simulation environment. A physics-based EV charger model was developed on the Typhoon HIL platform, incorporating detailed electrical dynamics and control logic representative of commercial chargers. Simulation outputs, including active power consumption and state-of-charge evolution, were validated against field data captured via phasor measurement units, showing strong alignment across all charging phases, including SOC-dependent current transitions. Quantitative validation yielded an MAE of 0.14 and an RMSE of 0.36, confirming the model’s high accuracy. The study also reflects practical BMS strategies, such as early charging termination near 97% SOC to preserve battery health. Overall, the proposed real-time framework provides a high-fidelity platform for analyzing grid-integrated EV behavior, testing smart charging controls, and enabling digital twin development for next-generation electric mobility. Full article

The Anthropocene, marked by rapid and extensive environmental changes, poses distinct evolutionary pressures and opportunities for species adaptation. Insects, among the most diverse and resilient taxa, exhibit notable dietary plasticity and the ability to convert low-value biomass—such as agro-industrial and urban waste—into usable nutrients. This review explores how these traits serve as adaptive strategies, enabling insects to thrive and expand into novel, human-altered habitats. We examine the evolution of insect nutritional requirements and how alternative diets influence physiological, behavioral, and reproductive traits, ultimately enhancing resilience to anthropogenic stressors. The capacity of insects to metabolize diverse substrates not only supports their role in food security and circular economy initiatives but also provides valuable insights into detoxification pathways and metabolic flexibility in environments rich in xenobiotics. By synthesizing key studies, we highlight the pivotal role insects play in redefining ecosystem functions under human influence. This review underscores the intersection of nutritional and evolutionary biology in understanding insect success in the Anthropocene, emphasizing the importance of nutritional knowledge for both ecological research and applied insect farming systems. Full article

The increasing integration of autonomous electric vehicles (EVs) into Intelligent Transportation Systems (ITSs) needs rigorous mechanisms to ensure their safe and effective operation in dynamic environments. The reliability of such vehicles depends not only on their internal capabilities but also on the suitability and safety of the environments in which they operate. This paper introduces a formal modelling framework that captures independently the dynamic evolution of the environmental context and the EV agent using multi-parameter transition systems. Two distinct models are defined: the Context Transition System (CTS), which models changes in environmental states, and the Agent Transition System (ATS), which captures the internal state evolution of the EV. Safety and liveness properties are formally specified in Computation Tree Logic (CTL) and verified using the nuXmv model checker. The framework is validated through two representative use cases: a dynamic urban delivery zone and an autonomous electric delivery vehicle. The results highlight the framework’s effectiveness in detecting unsafe conditions, verifying mission objectives, and supporting the reliable deployment of EVs in ITS. Full article

The advancement of efficient energy conversion and storage technologies is fundamentally linked to the development of electrochemical systems, including fuel cells, batteries, and electrolyzers, whose performance depends on key electrocatalytic reactions: hydrogen evolution (HER), oxygen evolution (OER), oxygen reduction (ORR), carbon dioxide reduction (CO₂RR), and nitrogen reduction (NRR). Beyond catalyst design, the electrolyte microenvironment significantly influences these reactions by modulating charge transfer, intermediate stabilization, and mass transport, making electrolyte engineering a powerful tool for enhancing performance. This review provides a comprehensive analysis of how fundamental electrolyte properties, including pH, ionic strength, ion identity, and solvent structure, affect the mechanisms and kinetics of these five reactions. We examine in detail how the electrolyte composition and individual ion contributions impact reaction pathways, catalytic activity, and product selectivity. For HER and OER, we discuss the interplay between acidic and alkaline environments, the effects of specific ions, interfacial electric fields, and catalyst stability. In ORR, we highlight pH-dependent activity, selectivity, and the roles of cations and anions in steering 2e⁻ versus 4e⁻ pathways. The CO₂RR and NRR sections explore how the electrolyte composition, local pH, buffering capacity, and proton sources influence activity and the product distribution. We also address challenges in electrolyte optimization, such as managing competing reactions and maximizing Faradaic efficiency. By comparing the electrolyte’s effects across these reactions, this review identifies general trends and design guidelines for enhancing electrocatalytic performance and outlines key open questions and future research directions relevant to practical energy technologies. Full article

The rapid evolution of the metaverse is driving the development of new digital design tools that integrate Computer-Aided Design (CAD) and Building Information Modeling (BIM) technologies. Core technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are increasingly combined with BIM to enhance collaboration and innovation in design and construction workflows. However, current BIM–VR integration often remains limited to isolated tasks, lacking persistent, multi-user environments that support continuous project collaboration. This study proposes a BIM-based Virtual World (VW) framework that addresses these limitations by creating an immersive, real-time collaborative platform for the Architecture, Engineering, and Construction (AEC) industry. The system enables multi-user access to BIM data through avatars, supports direct interaction with 3D models and associated metadata, and maintains a persistent virtual environment that evolves alongside project development. Key functionalities include interactive design controls, real-time decision-making support, and integrated training capabilities. A prototype was developed using Unreal Engine and supporting technologies to validate the approach. The results demonstrate improved interdisciplinary collaboration, reduced information loss during design iteration, and enhanced stakeholder engagement. This research highlights the potential of BIM-based Virtual Worlds to transform AEC collaboration by fostering an open, scalable ecosystem that bridges immersive environments with data-driven design and construction processes. Full article

Deep space exploration missions face technical challenges such as long-distance communication delays and high-precision autonomous positioning. Traditional ground-based telemetry and control as well as inertial navigation schemes struggle to meet mission requirements in the complex environment of deep space. As a vision-based autonomous navigation technology, image-based navigation enables spacecraft to obtain real-time images of the target celestial body surface through a variety of onboard remote sensing devices, and it achieves high-precision positioning using stable terrain features, demonstrating good autonomy and adaptability. Craters, due to their stable geometry and wide distribution, serve as one of the most important terrain features in deep space image-based navigation and have been widely adopted in practical missions. This paper systematically reviews the research progress of deep space image-based navigation technology, with a focus on the main sources of remote sensing data and a comprehensive summary of its typical applications in lunar, Martian, and asteroid exploration missions. Focusing on key technologies in image-based navigation, this paper analyzes core methods such as surface feature detection, including the accurate identification and localization of craters as critical terrain features in deep space exploration. On this basis, the paper further discusses possible future directions of image-based navigation technology in response to key challenges such as the scarcity of remote sensing data, limited computing resources, and environmental noise in deep space, including the intelligent evolution of image navigation systems, enhanced perception robustness in complex environments, hardware evolution of autonomous navigation systems, and cross-mission adaptability and multi-body generalization, providing a reference for subsequent research and engineering practice. Full article

Long and continuous lacustrine sediments in Southwest China provide exceptional records of the Indian summer monsoon (ISM) evolution. Rock magnetic and environmental magnetic methods have significant roles in these lacustrine studies. However, lacustrine sedimentary environments are complex and magnetic mineral signatures can be altered by post-depositional processes. This study applies isothermal remanent magnetization (IRM) component unmixing methods to lacustrine sediments from the Heqing core, to identify and quantify magnetic mineral components. We analyzed 104 samples based on lithological variations and magnetic susceptibility (χ) to examine the composition of magnetic minerals and their relative contributions. Three distinct magnetic components were identified in IRM component unmixing results: a low-coercivity detrital component, a medium-coercivity authigenic component, and a hard magnetic component. Based on rock magnetic results, the medium-coercivity component was attributed to greigite. These components exhibit stratigraphic trends that reflect changes in paleoenvironmental conditions. The medium-coercivity component shows an upwards decrease, indicating a significant change in ISM science at about 1.8 Ma. The study highlights the importance of considering post-depositional processes when interpreting magnetic mineral signatures in lacustrine sediments. The CLG model, combined with conventional rock magnetic analyses, provides a rapid approach for characterizing magnetic assemblages in weakly magnetic sediments. Full article

This study systematically analyzes the influencing factors and optimization strategies of foam stability and performance for CO₂-soluble foaming agents in high-temperature and high-pressure (HTHP) complex reservoir environments. By constructing a HTHP experimental system and utilizing dynamic foam testing, interfacial tension analysis, and microscopic observation of liquid films, the effects of chemical factors (e.g., pH, foaming agent concentration, stabilizer synergy) and physical factors (e.g., temperature, pressure) on foam behavior are investigated. The results show that the nonionic surfactant E-1312 exhibits optimal foam performance in neutral to mildly alkaline environments. The foam performance tends to saturate at around 0.5% concentration. High pressure enhances the foam stability, whereas elevated temperature significantly reduces the foam lifetime. Moreover, the addition of nano-sized foam stabilizers such as silica (SiO₂) can significantly delay liquid film drainage and strengthen interfacial mechanical properties, thereby improving foam durability. This study further reveals the key mechanisms of CO₂-soluble foaming agents in terms of interfacial behavior, liquid film evolution, and foam formation in porous media, providing theoretical guidance and optimization pathways for the molecular design and field application of CO₂ foam flooding technology. Full article

As an important coal-producing region in China, open-pit coal mining in Shaoyang, Hunan Province, has a significant impact on the ecological environment. This study focuses on the three major open-pit mining areas in the city, utilizing remote sensing data from 1998 to 2024. By calculating the normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC), and combining climate factors such as temperature and precipitation with Net Primary Productivity (NPP), this study analyzes the spatiotemporal evolution characteristics of vegetation cover and carbon sinks, and explores the impact of climate and environmental policies on vegetation recovery. The study employed trend analysis and autoregressive integrated moving average (ARIMA) model predictions, which showed that vegetation cover in the mining areas decreased overall from 1998 to 2011, gradually recovered after 2011, and reached a relatively high level by 2024. Changes in carbon sinks were consistent with the trends in vegetation cover. Spatially, the north mining area experienced the most severe vegetation degradation in the early stages, the middle area recovered earliest, and the south area had the fastest vegetation cover recovery rate. Climate factors had a certain influence on vegetation recovery, but precipitation, temperature, and FVC showed no significant correlation. The study indicates that vegetation recovery in mining areas is jointly influenced by mining intensity, climate conditions, and policy interventions, with geological environment management policies in Hunan mining areas playing a key role in promoting vegetation recovery. Full article