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Juan Ma, Qiang Yang, Mingzhi Zhang, Yao Chen, Wenyi Zhao, Chengyu Ouyang and Dongping Ming
Accurately predicting landslide deformation based on monitoring data is key to successful early warning of landslide disasters. Landslide displacement?time curves offer an intuitive reflection of the landslide motion process and deformation predictions o...
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Enrico Santangelo, Angelo Del Giudice, Simone Figorilli, Simona Violino, Corrado Costa, Marco Bascietto, Simone Bergonzoli and Claudio Beni
The autonecrotic tomato line V20368 (working code IGSV) spontaneously develops necrotic lesions with acropetal progression in response to an increase in temperature and light irradiation. The process is associated with the interaction between tomato and ...
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Andreas F. Gkontzis, Sotiris Kotsiantis, Georgios Feretzakis and Vassilios S. Verykios
Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving...
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Nathan Eskue
This paper provides a detailed review of a digital thread for composite aerospace components. The current state of the digital thread continues to progress and at an ever-accelerating rate due to advancements in supporting technologies such as AI, data c...
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Ulises Manuel Ramirez-Alcocer, Edgar Tello-Leal, Gerardo Romero and Bárbara A. Macías-Hernández
In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources sinc...
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Wenchong Tian, Yuting Liu, Jun Xie, Weizhong Huang, Weihao Chen, Tao Tao and Kunlun Xin
The accurate simulation of the dynamics of the anaerobic?anoxic?oxic (A2O) process in the biochemical reactions in wastewater treatment plants (WWTPs) is important for system prediction and optimization. Previous studies have used real-time monitoring da...
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Juseong Lee, Mihaela Mitici, Henk A. P. Blom, Pierre Bieber and Floris Freeman
The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We i...
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Abubakar Ahmad Musa, Adamu Hussaini, Weixian Liao, Fan Liang and Wei Yu
Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the ...
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Lan Wang, Mingjiang Xie, Min Pan, Feng He, Bing Yang, Zhigang Gong, Xuke Wu, Mingsheng Shang and Kun Shan
Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new ho...
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Peng Lyu, Jeongik Min and Juwhan Song
A non-invasive automatic broiler weight estimation and prediction method based on a machine learning algorithm was developed to address the issue of high labor costs and stress responses caused by the traditional broiler weighing method in large-scale br...
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