Perspectives on water quality analysis emphasizing indexing, modeling, and application of artificial intelligence for comparison and trend forecasting by Rijurekha Dasgupta, Subhasish Das, Gourab Banerjee, Asis Mazumdar

“…Using statistical operations and soft computing techniques have been done by researchers to combat the subjectivity error in indexing. …”
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Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review by José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, Eduardo Maio, Eduardo Maio, Eduardo Maio, Pedro Afonso, Pedro Afonso, Samuel Encarnação, Samuel Encarnação, Samuel Encarnação, Guilherme F. Machado, Guilherme F. Machado, Ryland Morgans, Tiago M. Barbosa, Tiago M. Barbosa, António M. Monteiro, António M. Monteiro, Pedro Forte, Pedro Forte, Pedro Forte, Ricardo Ferraz, Ricardo Ferraz, Luís Branquinho, Luís Branquinho, Luís Branquinho, Luís Branquinho

“…This systematic review aims to examine and map out the current state of research on AI-based tactical behavior, collective dynamics, and movement patterns in football. …”
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Machine learning model for predicting corneal stiffness and identifying keratoconus based on ocular structures by Longhui Li, Yifan Xiang, Xi Chen, Duoru Lin, Lanqin Zhao, Jun Xiao, Zhenzhe Lin, Jianyu Pang, Xiaotong Han, Lixue Liu, Yuxuan Wu, Zhenzhen Liu, Jingjing Chen, Jing Zhuang, Keming Yu, Haotian Lin

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Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study by M. Zouzoua, S. Bastin, F. Lohou, M. Lothon, M. Chiriaco, M. Jome, C. Mallet, L. Barthes, G. Canut

“…<p>This study proposes using a data-driven statistical model to freeze errors due to differences in environmental forcing when evaluating surface turbulent heat fluxes from weather and climate numerical models with observations. …”
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Study on carbon emissions of a small hydropower plant in Southwest China by Caihong Tang, Yiling Leng, Pengyu Wang, Jian Feng, Shanghong Zhang, Yujun Yi, Hui Li, Shaoliang Tian

“…The uncertainty was evaluated using the error propagation method. Following analysis, suggestions for carbon footprint reduction measures were proposed. …”
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Multistep PV power forecasting using deep learning models and the reptile search algorithm by Sameer Al-Dahidi, Hussein Alahmer, Bilal Rinchi, Abdullah Bani-Abdullah, Mohammad Alrbai, Osama Ayadi, Loiy Al-Ghussain

“…These findings support the broader adoption and further benchmarking of TFT future research.…”
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Enhancing Quality Control: A Study on AI and Human Performance in Flip Chip Defect Detection by Wannisa Cheamsiri, Anuchit Jitpattanakul, Paisarn Muneesawang, Konlakorn Wongpatikaseree, Narit Hnoohom

“…The model serves as a valuable tool for failure analysis (FA) engineers working with Chip-on-Wafer (CoW) products by enhancing inspection precision and accuracy, save time and costs, reduce human error, and ensure reliability. The dataset, provided by an electronics manufacturing service provider in Thailand, and is divided into four categories: good bump, head-in-pillow (HIP) defect, non-wetting defect, and solder void defect. …”
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Influence of ultrasonication during soaking on water absorption and Softness characteristics in the cooking process of cowpea by Sholeh Rostamirad, K.G. Duodu, J.P. Meyer, M. Sharifpur

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Unmanned aerial sprayers: evaluating platform configurations and flight patterns for effective chemical applications in modern vineyards by M. Jacob Schrader, Dattatray G. Bhalekar, Ramesh K. Sahni, Lav R. Khot

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Machine learning-aided hybrid technique for dynamics of rail transit stations classification: a case study by Ahad Amini Pishro, Shiquan Zhang, Alain L’Hostis, Yuetong Liu, Qixiao Hu, Farzad Hejazi, Maryam Shahpasand, Ali Rahman, Abdelbacet Oueslati, Zhengrui Zhang

“…Additionally, a neural network achieving high accuracy on labeled data enhances the model’s predictive capabilities for unlabeled instances. The research demonstrates high accuracy, with the Mean Squared Error (MSE) for regression models (Multiple Linear Regression (MLR), Deep-Learning Neural Network (DNN), and K-Nearest Neighbor (KNN)) remaining below 0.012, while the neural networks used for station classification achieve 100% accuracy across seven time intervals and 98.15% accuracy for the eighth, ensuring reliable ridership forecasts and classification outcomes. …”
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Exploring <i>γ</i>-Ray Flares from High-Redshift Blazar B3 1343+451 at GeV Energies by Xiongfei Geng, Yang Liu, Gang Cao, Jing Fan, Xiongbang Yang, Nan Ding, Minghu Gao, Yehui Yang, Zhijie Zhang

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V˙O2 linear-onset kinetics spanning steady- and non-steady-state exercise by Robert Robergs, Robert Robergs, Robert Robergs, Bridgette O’Malley, Bridgette O’Malley, Anais Dewilde, Shaun D’Auria, Ales Krouzecky

“…Anomalies exist to question the validity of this method, as they show the initial (∼1 min) of this V˙O2 response is linear.MethodsFourteen highly endurance trained subjects (12 males, 2 females) completed a ramp incremental cycling protocol, as well as 8 different constant load trials at 43 to 148 % of their critical power (CP).ResultsFor the initial five exercise bouts, the linear fit of the initial segment was significantly more accurate (lower standard error of estimates; SE) compared to the mono-exponential fit (p &lt; 0.001). …”
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Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks by Zahraa Ismail, Ahmet Sait Alali, Ahmad Muhammad, Mahmoud Ashraf, Sameh O. Abdellatif

“…Furthermore, the enhanced LSTM architecture, featuring deeper layers, dropout for regularization, and batch normalization, demonstrated improved stability and training speed, leading to a test Mean Absolute Error (MAE) of 0.0354 and an R2 value of 0.9991, indicating near-perfect predictive accuracy. …”
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SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

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Unraveling Plastic Pollution in Protected Terrestrial Raptors Using Regurgitated Pellets by Chloe Wayman, Francisca Fernández-Piñas, Irene López-Márquez, Rocío Fernández-Valeriano, Juan José Iglesias-Lebrija, Fernando González-González, Roberto Rosal, Miguel González-Pleiter

“…The concentrations (mean ± standard error of the mean) were 2.39 ± 0.39 MPs/pellet and 5.16 ± 0.72 AFs/pellet. …”
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Vegetable Commodity Organ Quality Formation Simulation Model (VQSM) in Solar Greenhouses by Chen Cheng, Liping Feng, Chaoyang Dong, Xianguan Chen, Feiyun Yang, Lu Wu, Jing Yang, Chengsen Zhao, Guoyin Yuan, Zhenfa Li

“…These relationships are mainly presented through linear functions, exponential functions, logarithmic function, and logical functions. (2) The normalized root mean square error (NRMSE) of the cucumber quality model ranges from 1.13% to 29.53%, and the NRMSE of the celery quality model ranges from 1.63% to 31.47%. (3) Based on two kinds of normalization methods, the average NRMSE of the VQSM model is 13.72%, demonstrating a relatively high level of accuracy in simulation. …”
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Characterizing wind, wave, and Stokes drift interactions in the upper ocean during Typhoon Doksuri using the COAWST model by Yaoyao Han, Changsheng Zuo, Zhizu Wang, Yucui Wang, Chengchen Tao, Xu Zhang, Juncheng Zuo

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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

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Assessing the association between tourism and the alcohol urban environment in Barcelona: a cross-sectional study by Xisca Sureda, Manuel Franco, Susanna Puigcorbé, Joan R Villalbí, Marina Bosque-Prous, Ester Teixidó-Compañó, Montserrat Bartroli, Albert Espelt

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BrYOLO-Mamba: A Approach to Efficient Tracheal Lesion Detection in Bronchoscopy by Yuejiao Cao, Jianzhong Zhang, Ruibing Zhuo, Jin Zhao, Yanting Dong, Tanzhen Liu, Hui Zhao

“…However, manual analysis of bronchoscopic images is both time-consuming and labor-intensive, with a higher risk of human error, particularly due to physician fatigue. Although artificial intelligence has made significant progress in enhancing the accuracy and efficiency of medical image diagnosis, current systems still struggle to effectively capture long-range dependencies and fine-grained features in complex medical images. …”
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