Complex multiscale systems are ubiquitous in many areas. This research expository article discusses the development and applications of a recent information-theoretic framework as well as novel reduced-order nonlinear modeling strategies for understa...
Traffic flow prediction is a key issue in intelligent transportation systems. The growing trend in data disclosure has created more potential sources for the input for predictive models, posing new challenges to the prediction of traffic flow in the...
Fibre-reinforced composite materials are used in structural applications in marine, offshore, and oil and gas industries due to their light weight and excellent mechanical properties. However, the exposure of such materials to water leads to environm...
The purpose of this study was to develop an integrated control strategy for multiscale crystallization processes. An image analysis method using a deep learning neural network is used to measure the fine-scale information of the crystallization proce...
Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower acc...
Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian wh...
The real-time, precise monitoring of physiological signals such as intracranial pressure (ICP) and arterial blood pressure (BP) holds significant clinical importance. However, traditional methods like invasive ICP monitoring and invasive arterial blo...
To overcome the limitations of current link prediction methods in effectively leveraging topological information and node importance, this paper introduces a new model called AMPS (Adaptive Multi-scale Potential-enhanced Path Similarity). The model i...
As the world is changing, mainly due to climate change, extreme events such as floods and droughts are becoming more frequent and severe. Considering this, the predictive modeling of flow in small mountain catchments that are particularly vulnerable...
Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and de...
Sea surface temperature (SST) serves as a fundamental driver of ocean–atmosphere interactions and global climate variability, exhibiting strong nonstationarity, multiscale dynamics, and cross-variable coupling. However, current deep learning mo...
Computational prediction of molecular structures of amyloid fibrils remains an exceedingly challenging task. In this work, we propose a multi-scale modeling procedure for the structure prediction of amyloid fibrils formed by the association of ACC1-1...
The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired...
This paper proposes Feature fusion and Dilated causal convolution model for Bearing Remaining useful life Prediction (FDBRP), an integrated framework for accurate Remaining Useful Life (RUL) prediction of rolling bearings that combines three key inno...
Sea surface temperature (SST) is a crucial factor that affects global climate and marine activities. Predicting SST at different temporal scales benefits various applications, from short-term SST prediction for weather forecasting to long-term SST pr...
Service lifetimes of polymers and polymer composites are impacted by environmental ageing. The validation of new composites and their environmental durability involves costly testing programs, thus calling for more affordable and safe alternatives, a...
Calibrating the Lattice Discrete Particle Model (LDPM), a mesoscale framework for simulating concrete, is computationally and experimentally demanding because it requires data across multiple material scales. This study examines how machine learning...
Soil–groundwater pollution is a complex environmental phenomenon formed by the coupling of multiple processes. Due to the concealment of pollution, the persistence of harm, and the complexity of the system, soil–groundwater pollution has...
High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong...
The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to...
This study develops a multi-scale fiber-reinforced cementitious composite (MFRC) by hybridizing calcium carbonate whisker (CW), polyvinyl alcohol (PVA) fiber, and steel fiber. The interfacial micromechanical properties between steel fiber/matrix and...
This paper studies a MuJoCo-based locomotion framework that couples an adaptive-frequency central pattern generator (AFCO-CPG) with single rigid-body dynamics model predictive control (MPC) for the RENS Q1 quadruped with elastic parallel knee joints....
Capturing complex temporal dependencies across multiple scales remains a fundamental challenge in time series forecasting. Transformer-based models have achieved impressive performance on sequence tasks, but vanilla designs often struggle to integrat...
Exploration for hidden ore bodies is important and requires new prospecting methods. However, predicting the presence of deep hidden mineral resources is difficult because of the large prospective area and the numerous factors influencing underground...
Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid pred...
As a nonlinear dynamic method for complexity measurement of time series, multiscale entropy (MSE) has been successfully applied to fault diagnosis of rolling bearings. However, the MSE algorithm is sensitive to the predetermined parameters and depend...
RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing m...
Accurate wind power forecasting is crucial for modern power systems, yet most deep learning models neglect spatial relationships between turbines. We propose GSTAformer, a graph-guided spatio-temporal model capturing both spatial and temporal depende...
Parking space availability forecasting is challenging due to noise contamination, non-stationary dynamics, and complex multi-scale temporal dependencies. This study proposes a wavelet-enhanced Autoformer with dynamic multi-kernel decomposition for pa...
Greenhouse gases (GHGs) significantly shape global climate systems by driving temperature rises, disrupting weather patterns, and intensifying environmental imbalances, with direct consequences for human life, including rising sea levels, extreme wea...
Predicting human future motion holds significant importance in the domains of autonomous driving and public safety. Kinematic features, including joint coordinates and velocity, are commonly employed in skeleton-based human motion prediction. Neverth...
Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to e...
Fuel cell electric vehicles (FCEVs) require energy management strategies that can balance hydrogen economy, battery utilization, component protection, and real-time control under varying driving conditions. This paper proposes an Adaptive-Modality De...
Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relations...
The multiscale coupling characteristics of the kinematic interface behavior of mechanical transmission systems are the core factors affecting system accuracy and lifetime. In this paper, we propose an innovative framework to achieve multiscale modeli...
The fatigue behavior of metal specimens is influenced by defects, material properties, and loading. This study aims to establish a multi-scale fatigue crack growth model that describes physically short crack (PSC) and long crack (LC) behavior. The mo...
The method based on convolution neural networks (CNNs) has been widely developed and applied to residual life prediction, and many excellent results have been achieved. However, CNN models can only learn feature information relative to size, and it i...
Online prediction of key parameters (e.g., process indices) is essential in many industrial processes because online measurement is not available. Data-based modeling is widely used for parameter prediction. However, model mismatch usually occurs owi...
To address the limitations of CNNs and RNNs in handling complex operating conditions, multi-scale degradation patterns, and long-term dependencies—with attention mechanisms often failing to highlight key degradation features—this paper pr...
Accurate daily runoff prediction is fundamental to understanding runoff dynamics and supporting water resources management. However, runoff prediction in snow-influenced alpine catchments remains challenging because complex hydrometeorological and cr...
This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequen...
In this study, an experimental adiabatic temperature rise (ATR) measurement and a multi-scale analytical approach were investigated for concrete containing Class F fly ash (FA). A half-meter (0.5 m) insulated cube was developed to measure the ATR. AT...
Currently, improving runway utilization under operational safety constraints has become a critical concern for small and medium airports. Existing research focuses primarily on landing-phase runway occupation time, while predictive studies on the tak...
Accurate multi-step satellite cloud-image prediction remains challenging because cloud fields exhibit displacement, deformation, and local appearance changes over time. This study proposes M-SimVP, a multi-channel prediction framework built on SimVP....
Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode...
Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble...
Groundwater is an essential water resource and plays a crucial role, especially in areas with limited surface water availability. However, the exacerbation of groundwater droughts, fueled by phenomena such as climate change, urbanization, and industr...
This paper presents the development and update of a multi-scale yield prediction model for processing tomatoes. The study was carried out under the EU-funded programme “Support to Development of a Rural Business Information System (RBIS)”...
A multiscale modeling approach is proposed to investigate the mechanical properties of carbon fiber/silicon carbide (C/SiC) composites fabricated by chemical vapor infiltration (CVI) process. First, reactive molecular dynamics simulations are conduct...
Radar precipitation nowcasting remains challenging because a model must not only represent the overall motion trends of large-scale precipitation systems, but also capture the fine-grained structural variations of localized strong echo regions while...
of 47