A knowledge graph based remanufacturing equipment resource modeling method. [version 2; peer review: 1 approved, 2 approved with reservations] by Yuyao Guo, Lei Wang, Xuhui Xia, Binyuan Zhang, Xinlan Liu

“…By analyzing the categories of remanufacturing equipment resources and integrating resource information such as man, machine, material, method, and environment, a unified remanufacturing equipment information model and service capability information model were constructed. …”
Get full text

Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…Furthermore, multiple machine learning (ML) algorithms, including both traditional and EL models, are employed to develop optimized predictive models for the flexural ultimate capacity of reinforced UHPC specimens derived from the established database. …”
Get full text

Improved food recognition using a refined ResNet50 architecture with improved fully connected layers by Pouya Bohlol, Soleiman Hosseinpour, Mahmoud Soltani Firouz

“…ResNet50 with a specific dense layer was the best development version of ResNet50. This model with Adam optimizer, 10−3 initial learning rate, batch size 4, and image size 340 × 640 could recognize various foods with 97.25% accuracy and 0.2 loss. …”
Get full text

Statistical learning-driven parameter tuning in injection molding using modified simplex method by Pongchanun Luangpaiboon, Walailak Atthirawong, Anucha Hirunwat, Pasura Aungkulanon

“…The optimization of product parameters, including length and standard deviation, is a persistent challenge, despite the fact that injection molding is a critical manufacturing technique. …”
Get full text

Mapping the air temperature in China from time-normalized MODIS land surface temperature data via zone-based stacking ensemble models by Yan Xin, Yongming Xu, Xudong Tong, Yaping Mo, Yonghong Liu, Shanyou Zhu

“…Then, the whole study area was divided into subzones, and nine base models were developed in each zone using machine learning (ML) methods to estimate Ta. …”
Get full text

Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting by Wenting Zhao, Haoran Xu, Peng Chen, Juan Zhang, Jing Li, Tingting Cai

“…This paper proposes a hybrid approach combining traditional time series models (ARIMA) with machine learning models (SVR). …”
Get full text

Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors by Qing Wang, Ke Shao, Zhibo Cai, Yingpu Che, Haochong Chen, Shunfu Xiao, Ruili Wang, Yaling Liu, Baoguo Li, Yuntao Ma

“…Two years of data covering 185 sugar beet varieties were used to train a developed stacked Long Short-Term Memory (LSTM) model, which was compared with traditional machine learning approaches. …”
Get full text

EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis by Zhi-Yang Zhao, Chang-Ling Huang, Tong-Min Wang, Shi-Hao Zhou, Lu Pei, Wen-Hui Jia, Wei-Hua Jia

“…Initially, it utilizes a signal decomposition module to decompose and reconstruct the input end-motif profiles, thereby generating multiple regular subsequences that optimize the subsequent modeling process. Subsequently, both a machine learning module and a deep learning module are employed to improve the accuracy of cancer diagnosis. …”
Get full text

Detecting soil mixing, grain size distribution, and clogging potential of tunnel excavation face by classification-regression algorithms using EPBM operational data by Sharmin Sarna, Marte Gutierrez

“…Thus, it is crucial to identify properties of the tunnel excavation face, such as clay-sand mixed conditions, grain size distributions, and clogging potential along the whole alignment beside the few borehole locations to attain optimally efficient EPBM operation. Therefore, this paper presents the development of machine learning prediction models (i.e., classifiers and regressors) to estimate the properties of the tunnel excavation face using EPBM operational data collected during the tunneling operation as input features. …”
Get full text

Artificial Intelligence Applications in Pediatric Craniofacial Surgery by Lucas M. Harrison, Ragan L. Edison, Rami R. Hallac

“…Artificial intelligence is rapidly transforming pediatric craniofacial surgery by enhancing diagnostic accuracy, improving surgical precision, and optimizing postoperative care. Machine learning and deep learning models are increasingly used to analyze complex craniofacial imaging, enabling early detection of congenital anomalies such as craniosynostosis, and cleft lip and palate. …”
Get full text

Personalized dose reduction strategies for biologic disease-modifying antirheumatic drugs for treating axial spondyloarthritis: a clinical and economic evaluation with predictive m... by Bui Hai Binh, Nguyen Thi Thu Phuong, Vu Thi Thanh Hang, Ngo Thi Thuc Nhan, Nguyen Thi Nhu Hoa, Hoang Van Dung

“…Conclusions In this cohort, bDMARD dose reduction was associated with preserved clinical outcomes and lower costs, suggesting it may be a viable strategy for selected patients under close clinical supervision. Predictive modeling provided actionable insights to optimize personalized treatment strategies, balancing efficacy and economic sustainability. …”
Get full text

Determining parameters of surface defects in the base metal of pipelines using results of complex diagnostics by N.V. Krysko, S.V. Skrynnikov, N.A. Shchipakov, D.M. Kozlov, A.G. Kusyy

Get full text

Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…Precision agriculture has emerged as a revolutionary technology for tackling global food security issues by optimizing crop yield and resource management. Incorporating artificial intelligence (AI) within agricultural practices has fundamentally transformed the discipline by facilitating sophisticated data analysis, predictive modeling, and automation. …”
Get full text

Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection by Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza

“…This work contributes to ongoing efforts in optimizing decomposition methods for predictive modeling in energy management, opening new avenues for improving shipboard power grid efficiency.…”
Get full text

Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts? by Rana Zia Ur Rehman, Christopher Buckley, Maria Encarna Mico-Amigo, Cameron Kirk, Michael Dunne-Willows, Claudia Mazza, Jian Qing Shi, Lisa Alcock, Lynn Rochester, Silvia Del Din

Get full text

Non-invasive methods to assess seed quality based on ultra-weak photon emission and delayed luminescence by Adriano Griffo, Stefanie Sehmisch, Frédéric Laager, Andrea Pagano, Alma Balestrazzi, Anca Macovei, Andreas Börner

Get full text

Finite-size effects in molecular simulations: a physico-mathematical view by Benedikt M. Reible, Carsten Hartmann, Luigi Delle Site

“…The corresponding criterion based on this theorem is complementary to those existing in the literature, and it can be applied to both classical and quantum systems.The need for accurate and physically consistent results of current simulations is enormously increased by the use of simulation data in machine learning procedures. Physically inconsistent data, produced by simulations of insufficient size, results in a substantial error in the modeling procedure that propagates further into the study of several other systems or larger scales beyond the molecular one. …”
Get full text

Research on thermoforming process of WE43 rare earth magnesium alloy plate by Hongjiao Qiao, Sheng Lei, Shaojie Cui, Xia Li, Xinru Qin, Houmin Wang

Get full text

High-throughput platform for label-free sorting of 3D spheroids using deep learning by Claudia Sampaio da Silva, Claudia Sampaio da Silva, Julia Alicia Boos, Jonas Goldowsky, Manon Blache, Noa Schmid, Tim Heinemann, Christoph Netsch, Francesca Luongo, Stéphanie Boder-Pasche, Gilles Weder, Alba Pueyo Moliner, Roos-Anne Samsom, Ary Marsee, Kerstin Schneeberger, Ali Mirsaidi, Bart Spee, Thomas Valentin, Andreas Hierlemann, Vincent Revol

“…We use machine learning to classify spheroids based on their bioprinting compatibility. …”
Get full text

Artificial Intelligence in Advancing Algal Bioactive Ingredients: Production, Characterization, and Application by Bingbing Guo, Xingyu Lu, Xiaoyu Jiang, Xiao-Li Shen, Zihao Wei, Yifeng Zhang

“…This review examines the multidimensional mechanisms by which AI enables and optimizes these processes: (1) AI-powered predictive models, integrated with machine learning algorithms (MLAs), Industry 4.0, and other advanced digital systems, support real-time monitoring and control of intelligent bioreactors, allowing for accurate forecasting of cultivation yields and market demand. (2) AI facilitates in-depth analysis of gene regulatory networks and key metabolic pathways, enabling precise control over the biosynthesis of targeted compounds. (3) AI-based spectral imaging and image recognition techniques enable rapid and reliable identification, classification, and quality assessment of active components. (4) AI accelerates the transition from mass production to the development of personalized medical and functional nutritional products. …”
Get full text