Research on model predictive control strategy of three-level dual-active bridge DC-DC converter by Junrui Wang, Lu Tan, Jin Li, Changjiang Ji

“…Through the switching conduction mode of the three-level DAB converter, the working conditions of the inductor current in each mode are analyzed, and the range of zero-voltage turn-on of all switches of the three-level DAB under single phase shift (SPS) modulation is derived. The spatial equation of state is established according to each mode, the purpose of fast response is achieved through the cost function, and finally the predictive control strategy of the three-level DAB model with zero voltage turn-on is obtained discretically. …”
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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

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Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory by Yizhen Sun, Junyou Yang, Donghui Zhao, Moses Chukwuka Okonkwo, Jianmin Zhang, Shuoyu Wang, Yang Liu

“…The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. …”
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Evaluating the Uncertainty and Predictive Performance of Probabilistic Models Devised for Grade Estimation in a Porphyry Copper Deposit by Raymond Leung, Alexander Lowe, Arman Melkumyan

“…Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. …”
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Predicting species distributions in the open ocean with convolutional neural networks by Morand, Gaétan, Joly, Alexis, Rouyer, Tristan, Lorieul, Titouan, Barde, Julien

“…These findings show the adequacy of deep learning for species distribution modelling in the open ocean. Additionally, this purely correlative model was then analysed with explicability tools to understand which variables had an influence on the model’s predictions. …”
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Evaluation of the relationships between physico-chemical parameters and the abundance of Vibrio spp. in blue crabs (Callinectes sapidus) and seawater from the Maryland Coastal Bays by Jasmine Smalls, John Jacobs, Howard Townsend, Paulinus Chigbu, Salina Parveen

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Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals by Yazeed Alkhrijah, Yazeed Alkhrijah, Shehzad Khalid, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Muhammad Zubair, Haya Aldossary, Aamir Anwar, Saad Arif

“…The output of these classifiers is then fed into the model-agnostic meta learner ensemble classifier with LSTM as the base classifier for the final prediction of interictal and preictal states.ResultsThe proposed methodology is trained and tested on the publicly available CHB-MIT dataset while achieving 99.34% sensitivity, 98.67% specificity, and a false positive alarm rate of 0.039.DiscussionThe proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems.…”
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GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer by Oumeima Thaalbi, Moulay A. Akhloufi

“…<i>Background:</i> Histopathological images are often used to diagnose breast cancer and have shown high accuracy in classifying cancer subtypes. Prediction of gene expression from whole-slide images and spatial transcriptomics data is important for cancer treatment in general and breast cancer in particular. …”
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Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures by Srinivasagan N. Subhashree, C. Igathinathane, John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo, Kevin Sedivec

“…The developed methodology of RFE for feature selection and RF for biomass yield modeling is recommended for biomass and hay forage yield prediction.…”
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Prediction of Surface Water Temperature and Its Spatial-Temporal Variation Characteristics of 11 Main Lakes in Yunnan&#x2013;Guizhou Plateau by Shanshan Xu, Kun Yang, Xiaolu Zhou, Jie Zhang, Chenghao Yang, Yi Luo

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Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting by Na Hu, Dafang Zhang, Kun Xie, Wei Liang, Meng-Yen Hsieh

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Impacts of Climate Change on the Spatial Distribution and Habitat Suitability of <i>Nitraria tangutorum</i> by Lianxing Li, Zhiqing Jia, Lingxianzi He, Dong Han, Qiankun Yang, Jialuo Li, Pingyi Zhou

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Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City by Naser Shafiei Sabet, Faranak Feyzbabaei cheshmeh sefidi

“…After land use detection and its changes, the trend of these changes was predicted in 2050 using the automatic cell model and Markov chain due to its high ability to detect spatial-spatial component changes.Results and discussion: Results indicated that the growth and development of urbanization in this metropolis have led to the city's expansion in this area. …”
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Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths by Tarek Alahmad, Miklós Neményi, Anikó Nyéki

“…Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. …”
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Ecological risk assessment and prediction of riparian zones in the Jiangsu section of the Yangtze River from a spatiotemporal perspective by Zihan Zhu, Cheng Zhang, Yangyang Lu, Jian Ye, Guohua Fang, Changran Sun, Yun Yang

“…Focusing on the Yangtze River riparian zone in Jiangsu Province, an ecological risk assessment system with 20 indicators was developed based on a systematic analysis of the ecological risk exposure–response process. The temporal and spatial characteristics of ecological risks in the riparian zone from 2003 to 2023 were analysed using the ecological risk composite index model, Moran index, and Getis-Ord Gi* cold hotspot analysis method, while ecological risks for 2028 and 2033 were predicted using the Grey–Markov chain and PLUS models. …”
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Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S. by Lifeng Yuan, Kenneth J Forshay

“…However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. …”
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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

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Precise Spatial Prediction of Rice Seedlings From Large-Scale Airborne Remote Sensing Data Using Optimized Li-YOLOv9 by Jayakrishnan Anandakrishnan, Arun Kumar Sangaiah, Hendri Darmawan, Nguyen Khanh Son, Yi-Bing Lin, Mohammed J. F. Alenazi

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Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china by Qimeng Ren, Ming Sun

“…By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. …”
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Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.Methods Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. …”
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