Air quality forecasting and environmental health research at urban and regional scales depend on the combination of measurements from heterogeneous sensor networks, yet the construction of integrated multi-source datasets is rarely described or relea...
With the increasing scale of industrial devices and the growing complexity of multi-source heterogeneous sensor data, traditional methods struggle to address challenges in fault detection, data security, and trustworthiness. Ensuring tamper-proof dat...
Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditio...
Future Internet ecosystems increasingly rely on distributed and intelligent data infrastructures, yet evidence of their evolution is fragmented across scholarly publications, patents, open-source repositories, and technology news. This study proposes...
Accurate vineyard mapping is important for agricultural resource monitoring, land-use management, and inventory updating in arid regions. However, vineyard identification in Xinjiang, China, is challenged by fragmented parcels, exposed soil backgroun...
In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definit...
Large and complex ring forgings are key structural parts of the aerospace field, and their quality is closely related to the reliability of aerospace vehicles. However, high-quality production of aerospace ring forgings faces many problems, such as t...
In the manufacturing process of optical lenses, the lack of monitoring and detection of the central processing process leads to low processing efficiency and difficulty in ensuring product consistency. We propose a digital twin system for alignment p...
This study focuses on establishing a novel heuristic algorithm for life-cycle performance evaluation. Special attention is given to decision-making algorithms for concrete-filled steel tubular (CFST) arch bridge maintenance. The main procedure is dev...
24 October 2019
Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mappin...
Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and v...
Accurate path travel time prediction is often hindered by sparse and heterogeneous traffic data. This paper proposes FusionODE-TT, a novel model designed to address these challenges by modeling traffic as a continuous-time process. The model features...
Machine learning (ML) has become an increasingly important tool in concrete engineering which has significantly altered the method of prediction and optimization of concrete properties, enabling more efficient, accurate, and sustainable processes. Ho...
Due to the rapid rise of China’s coastal economic zone, the urbanization of the surrounding islands has accelerated. Intensive disturbance caused by human activities and frequent changes in land types have resulted in the continuous erosion of...
Integrated logistics support (ILS) is of great significance for maintaining equipment operational capability in the whole lifecycle. Numerous segments and complex product objects exist in the process of equipment ILS, which gives ILS data multi-sourc...
The digital twin (DT) approach has risen in popularity for applications in many industrial process managements. By applying the “Shipyard 4.0” digital transformation trend, the ship industry is developing techniques able to reduce risks b...
Lakes are essential components of the terrestrial water cycle. Their size and quantity reflect natural climate change and anthropogenic activities in time. Lakes on the Inner-Mongolian Plateau (IMP) have experienced significant changes in recent deca...
Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative mann...
Selective laser melting (SLM) is a key technology in metal additive manufacturing (AM), but the widespread presence of porosity defects in fabricated parts significantly degrades mechanical performance and limits practical applications. Machine learn...
Multi-source heterogeneous data in discrete manufacturing shop floors, including vibration signals, equipment logs, visual monitoring data, and handwritten production reports, exhibit significant differences in modality and semantic representation. T...
To address the technical limitations of traditional pavement performance prediction models in capturing temporal features and analyzing multi-factor coupling, this study proposes a Bayesian Optimization Dual-Layer Feature Fusion Model (BO-DLFF). The...
Land surface temperature (LST) and its diurnal variation are critical for understanding the surface energy balance and water cycle processes. Traditional diurnal temperature cycle (DTC) models are widely used to reconstruct continuous temperature seq...
Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate...
To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to imp...
Accurate mapping of soil total nitrogen (STN) is fundamental for advancing sustainable and precision soil management. While digital soil mapping (DSM) has increasingly relied on Earth observation (EO) data, the potential of various synthetic aperture...
Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, th...
Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a regional NPP study in southwest China, with a 33-year time span...
Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack...
The response of Arctic vegetation to climate warming exhibits pronounced spatial heterogeneity, driven partly by widespread permafrost degradation. However, the role of thermokarst lake development in mediating vegetation-climate interactions remains...
Smart cities generate vast, heterogeneous data streams from transportation networks, energy grids, environmental sensors, and public services, yet the semantic fragmentation of these data silos prevents urban operators from deriving actionable, cross...
Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges s...
The integration of the industrial Internet and big data analytics is reshaping enterprise decision-making models and providing new momentum for the transformation and upgrading of traditional manufacturing industries. In this study, a decision suppor...
Storm surges are catastrophic marine disasters that pose severe threats to coastal populations, making the rapid extraction of key information from multi-source texts critical for effective emergency response. However, existing extraction methods oft...
The rational development of urban underground space resources (UUSRs) is especially crucial for alleviating “urban diseases”, and it is of great significance for exploring the appropriateness of urban underground space (UUS) development u...
Stricter requirements on nutrient removal in wastewater treatment are being imposed by rapid urbanization and tightening water-quality standards. Despite their excellent solid–liquid separation and effective biological treatment, MBRs in conven...
Riverine dissolved oxygen (DO) dynamics are critical to aquatic ecosystem health, yet global understanding has long been constrained by sparse, temporally discontinuous, and heterogeneous monitoring data. Conventional statistical and process-based ap...
The structure of the grain-and-oil-food-supply chain has the characteristics of complexity, cross-regionality, a long cycle, and numerous participants, making it difficult to maintain the safety of supply. In recent years, some phenomena have emerged...
Feature selection is essential for high-dimensional multi-source feature analysis, particularly in Internet of Things (IoT) environments characterized by data heterogeneity, redundancy, and noise. To address the need to balance classification perform...
Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedbac...
Groundwater-storage anomalies (GWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) mission provide valuable information for regional groundwater monitoring. Improving the spatial representation and temporal continuity of GRACE-deri...
Integrating complementary information from many available technologies is a question of growing interest among the Cultural Heritage community, due to the complexity of the cultural assets under study and of their contexts. Recently, this need has pu...
28 May 2022
The grain and oil food supply chain has a complex structure, long turnover cycles, and many stakeholders, so it is challenging to maintain the security of this supply chain. A reliable traceability system for the whole grain and oil food supply chain...
Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thou...
The quantitative study of the relationship between material composition, microstructure and properties is of great importance for the improvement in material properties. In this study, the continuous data of elemental composition, recrystallization,...
Soil organic carbon (SOC) is essential for ecosystem stability and long-term carbon storage in alpine grasslands, yet the relative importance and interactions of hydrothermal and biotic controls remain poorly understood at regional scales. In this st...
Spatial predictions of forest biomass at regional scale in forests are critical to evaluate the effects of management practices across environmental gradients. Although multi-source remote sensing combined with machine learning has been widely applie...
Correct prediction of potential miRNA–disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the...
To meet the growing demand for secure and reliable image protection in digital communication, this paper proposes a novel image encryption framework that addresses the challenges of high plaintext sensitivity, resistance to statistical attacks, and k...
Rapid urbanization and persistent road-safety challenges have increased the demand for intelligent traffic management systems capable of supporting traffic monitoring, incident analysis, and short-term forecasting. Existing artificial intelligence so...
Soil water models are increasingly required to support irrigation, drought assessment and sustainable water management, yet physical, artificial intelligence (AI)-based and hybrid approaches differ in process representation, data demand and transfera...
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