Optimization of non-smooth functions via differentiable surrogates. by Shikun Chen, Zebin Huang, Wenlong Zheng

“…These models are commonly used to predict outputs based on a combination of fixed parameters and adjustable variables. …”
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Are treatment plans optimized on the basis of acuros XB dose calculation robust against anatomic changes during online adaptive radiotherapy for lung cancer regarding dose homogene... by Khouya Aymane, Santiago Alina, Ringbaek Toke, Guberina Nika, Guberina Maja, Gauler Thomas, Lübcke Wolfgang, Zylka Waldemar, Pöttgen Christoph, Stuschke Martin

“…Abstract Introduction The Acuros XB dose calculation algorithm implements advanced modelling of lateral electron transport, making dose distributions sensitive to density changes between source and subsequent CT. …”
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Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery by Yakun Zhang, Mengxin Guan, Libo Wang, Xiahua Cui, Yafei Wang, Peng Li, Shaukat Ali, Fu Zhang

“…Three spectral characteristic variables selection methods, including correlation coefficient analysis, stepwise regression, and spectral index analysis, were used to determine the spectral characteristic variables that are closely related to the soybean canopy nitrogen content. The predictive models for soybean canopy nitrogen content based on spectral characteristic variables were established using a multiple linear regression algorithm. …”
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Identification of biomarkers for Laryngeal squamous cell carcinoma through Mendelian randomization and integrated bioinformatics analysis by Pengjian Zhang, Weizhang Chen, Kai Chen, Yuanhang Ye, Weijun Wu

“…The differences in immune landscapes between high-risk and low-risk groups were assessed using the CIBERSORT algorithm, and drug sensitivity was analyzed using the “oncoPredict” package. …”
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Building occupancy type classification and uncertainty estimation using machine learning and open data by Tom Narock, J. Michael Johnson, Justin Singh-Mohudpur, Arash Modaresi Rad

“…Multiple ML algorithms are compared. We address strategies to handle significant class imbalance and introduce Bayesian neural networks to handle prediction uncertainty. …”
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Scientists yet to consider spatial correlation in assessing uncertainty of spatial averages and totals by Alexandre M.J.-C. Wadoux, Gerard B.M. Heuvelink

“…The remote sensing data typically serve as covariates to deliver spatially explicit information using machine learning algorithms. Often the uncertainty associated with the maps is also quantified, for instance by prediction error variance maps or by maps of the lower and upper limits of a prediction interval. …”
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A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network by Lihua Bi, Wenjiao Zhang, Xiangfei Zhang, Canlin Li

“…Subsequently, the output of the light enhancement network is input into the segmentation network to obtain the final segmentation prediction. Experimental results show that the proposed method achieves mean Intersection over Union (mIoU) values of 59.4% on the Dark Zurich-test dataset, outperforming other segmentation algorithms for nighttime driving-scenes.…”
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Comprehensive Analysis of Phenotypic Traits in Chinese Cabbage Using 3D Point Cloud Technology by Chongchong Yang, Lei Sun, Jun Zhang, Xiaofei Fan, Dongfang Zhang, Tianyi Ren, Minggeng Liu, Zhiming Zhang, Wei Ma

“…By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. …”
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LSTM-MM: Efficient LSTM-Based Mobility Management for Power Inspection Vehicles in Smart Grids by Xiaoyan Wang, Xianfeng Xie, Shengnan Zhao

“…This scheme leverages Software-Defined Networking (SDN) technology to centrally manage vehicle communication and network traffic, integrating trajectory prediction algorithms to forecast the future movements of power inspection vehicles, optimizing route planning, and improving operational efficiency and network performance. …”
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Bounds on the initial macroporosity of sea ice pressure ridges by Sönke Maus

“…The proposed macroporosity prediction can be useful for modeling the consolidation and property evolution of sea ice ridges and rubble fields and for improving thickness redistribution algorithms in sea ice models.…”
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Inertial Subrange Optimization in Eddy Dissipation Rate Estimation and Aircraft-Dependent Bumpiness Estimation by Zhenxing Gao, Qilin Zhang, Kai Qi

“…In current vertical wind-based eddy dissipation rate (EDR) estimation algorithms based on flight data, the inertial subrange is determined empirically. …”
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Enabling disaggregation of Asian American subgroups: a dataset of Wikidata names for disparity estimation by Qiwei Lin, Derek Ouyang, Cameron Guage, Isabel O. Gallegos, Jacob Goldin, Daniel E. Ho

“…We show that these data, when combined with public geography-race distributions to predict subgroup membership, outperform existing deterministic name lists in key prediction settings, and enable critical Asian disparity assessments.…”
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Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria. by Juho Rousu, Daniel D Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes

“…We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. …”
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Radiomics in glioma: emerging trends and challenges by Zihan Wang, Lei Wang, Yinyan Wang

“…Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune‐related biomarkers and characterizing immune responses within tumors. …”
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Artificial intelligence in diabetology by V. V. Klimontov, V. B. Berikov, O. V. Saik

“…Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. …”
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Monthly Electricity Generatıon Forecast in Solar Power Plants with LSTM by Ali Hakan Isık, Ömer Çetin

“…In the study, predictions and analyses were made using the LSTM (Long Short-Term Memory) method, which is one of the artificial neural networks. …”
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Research Progress of Finite Element Technology in Wood Processing by Maosen Wang, Lin Yang

“…It highlights challenges such as model accuracy and algorithm optimization, suggesting that continuous improvements in FEA models and algorithms can further enhance processing efficiency and product quality. …”
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Artificial Intelligence—What to Expect From Machine Learning and Deep Learning in Hernia Surgery by Robert Vogel, Björn Mück

“…The term AI incorporates various technologies including ML, Neural Networks (NN), and DL. Classical ML algorithms depend on structured, labeled data for predictions, requiring significant human oversight. …”
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A review of machine learning approaches for the discovery of thermoelectric materials by Övgü C. Yelgel, Celal Yelgel

“…Recent progress in computational infrastructure, along with the advent of data-centric approaches such as machine learning (ML), has transformed the landscape of thermoelectric research. ML algorithms, trained on comprehensive datasets including experimental measurements, crystallographic data, and density functional theory (DFT) results can predict key TE metrics, such as the figure of merit (ZT), with remarkable speed and accuracy. …”
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Computational Estimation of Football Player Wages by Yaldo L., Shamir L.

“…Experimental results using data from 6,082 players show that the Pearson correlation between the predicted and actual salary of the players is ~0.77 (p < .001). …”
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