This research presents a novel conceptual framework for inclusive education by integrating Internet of Things (IoT)-driven real-time environmental and behavioral monitoring with adaptive teaching strategies. Unlike traditional methods, our model leve...
The paper proposes an improved A* Algorithm based on historical AIS data for the multi-objective optimisation of ship weather routes, explicitly focusing on optimising voyage distance, economic costs, and emission costs within Sulphur Emission Contro...
In this study, we present a methodology to solve the multi-period ambulance relocation problem based on historical data. We present a methodology to convert historical data in latitude–longitude coordinates into cell-based network data. Then, w...
Nonlocal models are ubiquitous in all branches of science and engineering, with a rapidly expanding range of mathematical and computational applications due to the ability of such models to capture effects and phenomena that traditional models cannot...
Neurodegenerative diseases such as Alzheimer’s (AD) are associated with the propagation and aggregation of toxic proteins. In the case of AD, it was Alzheimer himself who showed the importance of both amyloid beta (
Low-dimensional nanostructures have many advantages when used in sensors compared to the traditional bulk materials, in particular in their sensitivity and specificity. In such nanostructures, the motion of carriers can be confined from one, two, or...
Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properti...
Despite growing recognition of the strategic value of data-driven decision making (DDM) in contemporary organizations, the mechanisms and boundary conditions through which DDM is associated with sustainable innovation performance (SIP) remain theoret...
To overcome the limited accuracy of existing street vitality assessments under dense occlusion and their lack of dynamic, multi-source data fusion, this study proposes an integrated dynamic model that couples an enhanced YOLOv11 with heterogeneous sp...
The suspension system of the Electromagnetic Suspension (EMS) maglev train is crucial for ensuring safe operation. This article focuses on data-driven modeling and control optimization of the suspension system. By the Extended Dynamic Mode Decomposit...
As wheelchair technology evolves and embraces a more prominent role in assistive technology, the onset of intelligent control systems necessitates a comprehensive review from an engineering perspective. In this work, we analyze the development and th...
Extracorporeal organ perfusion, in which organs are preserved in an isolated, ex vivo environment over an extended time-span, is a concept that has led to the development of numerous alternative preservation protocols designed to better maintain orga...
Maritime transportation companies operate in highly volatile environments, where data-driven decision-making is critical to navigating fluctuating freight revenue, fuel and transit costs, and dynamic trade-related policies. This study addresses the l...
Traditional control of robotic systems relies on the availability of an exact model, which assumes complete knowledge of the robot’s parameters and all dynamic effects. However, this idealized scenario rarely holds in practice, as real-world in...
We are living in a data-driven society. Big Data and the Internet of Things are popular terms. Governments, universities and the private sector make great investments in collecting and storing data and also extracting new knowledge from these data ba...
Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulatio...
With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper...
To address the issues of parameter setting, reliance on human experience, and the limitations of traditional model-driven control methods in handling complex nonlinear dynamics in the tin smelting industrial process, this paper proposes a data-driven...
This paper studies the containment control problem of heterogeneous multi-agent systems (MASs) with multiple leaders. The follower agent dynamics are assumed to be unknown and nonlinear. First, each follower is transformed into an incremental data de...
This paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques fr...
In today’s rapidly evolving technological landscape, effective management of information technology (IT) projects is paramount for organizational success. However, ensuring sustainability in IT project management remains a persistent challenge....
Data-driven exoskeletons offer the potential for adaptive augmentation of human mobility. Yet their widespread adoption is hindered by labor-intensive biomechanical data collection and manual tuning. Herein, this study presents a highly efficient syn...
Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real...
Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especiall...
Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem...
Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory&rsqu...
This study proposes a robust data-driven control framework, LSTM-MPC, designed to enhance the velocity stabilization of Autonomous Underwater Vehicles (AUVs) operating under stochastic marine disturbances. Traditional control methods often struggle w...
Accurate data-driven identification and long-term forecasting of surface ship motions in realistic ocean environments are essential for advanced autonomous navigation and predictive control systems. However, robust system identification in such condi...
The safety of workers in modular construction remains a concern due to the dynamic hazardous work environments and unawareness of the potential proximity of equipment. To avoid potential contact collisions and to provide a safe workplace, workers&rsq...
With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach...
Salinity in estuarine environments has been traditionally simulated using process-based models. More recently, data-driven models including artificial neural networks (ANNs) have been developed for simulating salinity. Compared to process-based model...
The reconstruction of fine-scale information from sparse data measured at irregular locations is often needed in many diverse applications, including numerous instances of practical fluid dynamics observed in natural environments. This need is driven...
The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation...
With the expansion of electric vehicle (EV) applications into unstructured sandy terrains such as deserts, accurately characterizing tire–sand dynamic interactions is essential for enhancing off-road performance. However, traditional terramecha...
Accurate runoff prediction in complex slope catchments remains challenging due to terrain heterogeneity and dynamic rainfall interactions. This study conducts a systematic comparison between a physics-based Two-Dimensional Slope Hydrodynamic Model (T...
Accurate dynamic state reconstruction in complex multi-node coupled systems is critical for ensuring operational stability and reliability. However, this task is highly challenging due to spatially sparse measurement sensors, strong dynamic coupling...
Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems...
Emotional design is increasingly recognised within landscape architecture, particularly in the context of sustainable and user-centred environments; however, systematic and data-driven approaches that translate users’ emotional responses into c...
This paper addresses the challenge of degradation prediction in proton-exchange membrane fuel cells (PEMFCs). Traditional methods often struggle to balance accuracy and complexity, particularly under dynamic operational conditions. To overcome these...
One of the challenging problems in robot navigation is efficient and safe planning in a highly dynamic environment, where the robot is required to understand pedestrian patterns in the environment, such as train station. The rapid movement of pedestr...
In this paper, a data-driven system identification method is presented based on the data fusion of a dynamic model and flight test data. The dynamic model is built by a combination of nonlinear auto-regressive networks (NARX) and the steady-state mod...
Continual learning (CL) is a key technology for enabling data-driven autonomous guidance systems to operate stably and persistently in complex and dynamic environments. Its core goal is to enable the model to continuously learn new scenarios and task...
A hybrid real-time path planning method has been developed that employs data-driven target UAV trajectory tracking methods. It aims to autonomously manage the distributed operation of multiple UAVs in dynamically changing environments. The target tra...
Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a signi...
Local scour is a phenomenon of sediment erosion and transport caused by the dynamic interaction between water flow and seabed sediment, posing a serious threat to the safety of marine engineering structures such as cross-sea bridges and offshore wind...
The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that...
The transition toward Industry 5.0 requires safety management systems that are not only intelligent and data-driven but also human-centric, resilient, and aligned with sustainable operations. However, conventional safety risk assessment in steel manu...
This comprehensive review critically examines current methodologies and optimization strategies for designing indoor thermal environments in educational buildings amid the challenges of global climate change and energy demands. The paper evaluates ex...
Accurate state estimation for quadrotors under wind-induced disturbances remains a critical challenge in dynamic outdoor environments. Existing model-based and data-driven approaches often struggle with real-time adaptation and catastrophic forgettin...
This paper presents a multi-source data-driven hybrid path planning framework that integrates global A* search with local Deep Q-Network (DQN) optimization to address complex terrestrial routing challenges. By fusing ASTER GDEM terrain data with Open...
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