Tether Force Estimation Airborne Kite Using Machine Learning Methods by Akarsh Gupta, Yashwant Kashyap, Panagiotis Kosmopoulos

“…Through a series of controlled field experiments and the application of classical machine learning techniques, we achieved significant improvements in tether force estimation. Our XGBoost model, for example, demonstrated a notable reduction in error in predicting the tether force that can be extracted at a particular location, with a root mean square error of 52.3 Newtons and a mean absolute error of 32.1 Newtons, coupled with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> error, which measures the proportion of variance explained by the model, achieved an impressive value of 0.93. …”
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Comprehensive Validation of MODIS-Derived Instantaneous Air Temperature and Daily Minimum Temperature at Nighttime by Wenjie Zhang, Jiarui Zhao, Wenbin Zhu, Yunbo Kong, Bingcheng Wan, Yilan Liao

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An explainable AI-based blood cell classification using optimized convolutional neural network by Oahidul Islam, Md Assaduzzaman, Md Zahid Hasan

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Strain and Dimension Effects on the Threshold Voltage of Nanoscale Fully Depleted Strained-SOI TFETs by Yu-Chen Li, He-Ming Zhang, Shu-lin Liu, Hui-Yong Hu

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Selection of clones and parents in full-sibling families of Jatropha curcas by Joseilton Faria Silva, Silvia Nietsche, Samy Pimenta, Marlon Cristian Toledo Pereira, Alcinei Místico Azevedo, Renata Aparecida Neres Faria, Joseane Faria da Silva Souza, Gabriela Cristina Alves Custódio

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Smoking Functions as a Negative Regulator of IGF1 and Impairs Adipokine Network in Patients with Rheumatoid Arthritis by Malin C. Erlandsson, Roberto Doria Medina, Sofia Töyrä Silfverswärd, Maria I. Bokarewa

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Identifying performance patterns in professional mixed martial arts: An exploratory data approach by Muhammad Qasthalani, Ahmad Maulana, Bonita Amelia

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Lost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data by Ahmad Azadivash

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Approaches to evaluate nutrition of minerals in food by Xuan Wang, Yifan He, Qian Gao, Dong Yang, Jianfen Liang

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Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis by Ahmed Alfakeeh, Mhd Saeed Sharif, Abin Daniel Zorto, Thiago Pillonetto

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Nutrient transport by overland sheet flow on sites containing swine slurry by John E. Gilley

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Smart cities and smart health: Innovations in home medical devices for efficient healthcare delivery by Taoyang Cai, Jingfei Ma, Shangjie Ge-Zhang

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Survey on multi-agent reinforcement learning methods from the perspective of population by XIANG Fengtao, LUO Junren, GU Xueqiang, SU Jiongming, ZHANG Wanpeng

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Free vibration of beams using stochastic finite element method considering three-dimensional randomness of material properties by Diem Dang Nguyen, Hien Duy Ta

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EVALUATING THE MOLECULAR INTERACTION OF SAMBUCUS PLANT BIOACTIVE COMPOUNDS TOWARD TNF-R1 AND TRAIL-R1/R2 AS POSSIBLE ANTI-CANCER THERAPY BASED ON TRADITIONAL MEDICINE: THE BIOINFOT... by WIRA EKA PUTRA, MUHAIMIN RIFA’I

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Heterogeneity in meta-analyses: an unavoidable challenge worth exploring by Geun Joo Choi, Hyun Kang

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Progressive feature enrichment and ensemble learning for enhanced rice seed purity classification by Minh-Dung Le, Van-Giap Le, Thi-Thu-Hong Phan

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Simulating ecosystem carbon dioxide fluxes and their associated influencing factors for a restored peatland by H. He, I. B. Strachan, N. T. Roulet

“…After 14 years of restoration, the peatland has a mean <span class="inline-formula">CO₂</span> uptake rate similar to pristine sites but with a much larger interannual variability, and in dry years, the restored peatland can switch back to a temporary <span class="inline-formula">CO₂</span> source. The model predicts a moderate reduction in <span class="inline-formula">CO₂</span> uptake but still a reasonable sink under future climate change conditions if the peatland is ecologically and hydrologically restored. …”
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Compression properties, processing maps, and microstructure evolution of rapidly solidified ZK60 alloy by Zhenshuai Li, Shuai Bao, Chao Yang, Yungui Chen

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DELTa: Dynamic Energy-and-Latency-Aware Task Scheduling for Fog-Cloud Paradigm by Abhijeet Mahapatra, Rosy Pradhan, Santosh K. Majhi, Kaushik Mishra

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