Tea Consumption and Diabetes: A Comprehensive Pharmacological Review of Black, White, Green, Oolong, and Pu-erh Teas by Ochuko L. Erukainure, Chika I. Chukwuma, Jennifer Nambooze, Satyajit Tripathy, Veronica F. Salau, Kolawole Olofinsan, Akingbolabo D. Ogunlakin, Osaretin A. T. Ebuehi, Jeremiah O. Unuofin

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Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring by Dejen Teklu Asfha, Abdul Halim Abdul Latiff, Daniel Asante Otchere, Bennet Nii Tackie-Otoo, Ismailalwali Babikir, Muhammad Rafi, Zaky Ahmad Riyadi, Ahmad Dedi Putra, Bamidele Abdulhakeem Adeniyi

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Tracking Star-forming Cores as Mass Reservoirs in Clustered and Isolated Regions Using Numerical Passive Tracer Particles by Shingo Nozaki, Hajime Fukushima, Kazuki Tokuda, Masahiro N. Machida

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Toward a Secure 5G-Enabled Internet of Things: A Survey on Requirements, Privacy, Security, Challenges, and Opportunities by Shams Forruque Ahmed, Md. Sakib Bin Alam, Shaila Afrin, Sabiha Jannat Rafa, Samanta Binte Taher, Maliha Kabir, S. M. Muyeen, Amir H. Gandomi

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CNN-Based End-to-End CPU-AP-UE Power Allocation for Spectral Efficiency Enhancement in Cell-Free Massive MIMO Networks by Yoon-Ju Choi, Ji-Hee Yu, Seung-Hwan Seo, Seong-Gyun Choi, Hye-Yoon Jeong, Ja-Eun Kim, Myung-Sun Baek, Young-Hwan You, Hyoung-Kyu Song

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Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model by Metin Akay, Yong Du, Cheryl L. Sershen, Minghua Wu, Ting Y. Chen, Shervin Assassi, Chandra Mohan, Yasemin M. Akay

“…Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). …”
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Ultraslow serotonin oscillations in the hippocampus delineate substates across NREM and waking by Claire Cooper, Daniel Parthier, Jeremie Sibille, John J Tukker, Nicolas Tritsch, Dietmar Schmitz

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An Advanced Approach Toward Pedestrian Safety Analysis and Prediction: Advances in Multisensor Data Fusion and GIS Techniques by Jie Tang, Asad Khan, Sheheryar Azam, Umer Khalil, Nuaman Ejaz, Muhammad Afzal, Yahia Said, Dmitry E. Kucher

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Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning by Ting Li, Oliver Miguel Lopez Valencia, Matthew F. McCabe

“…The performance of discrete wavelet transformation(DWT) and Savitzky–Golay filtering was compared for processing the NDVI time series. When evaluated against a reference dataset, the approach achieved a maximum accuracy of 93.38% with DWT level 1 decomposition using the “haar” wavelet function. …”
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Biological heart and brain ageing in subjects with cardiovascular diseases by Elizabeth Mcavoy, Elizabeth Mcavoy, Elizabeth Mcavoy, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert

“…The significant differences observed in disease groups, while lacking a strong correlation between the BAG and HAG, questions the generalizability of the heart-brain axis theory with respect to age gap biomarkers, suggesting potentially heterogeneous aging processes of the two systems that warrant further investigation in future work.…”
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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability by Abdennabi Morchid, Abdennacer Elbasri, Zahra Oughannou, Hassan Qjidaa, Rachid El Alami, Badre Bossoufi, Saleh Mobayen, Pawel Skruch

“…These results show not only the performance of the proposed model in terms of accuracy but also its computational efficiency, outperforming some of the studies mentioned. …”
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Characterizing the microstructural transition at the gray matter-white matter interface: Implementation and demonstration of age-associated differences by Joan Y Song, Roman Fleysher, Kenny Ye, Mimi Kim, Molly E Zimmerman, Richard B Lipton, Michael L Lipton

“…Conclusion: The inverse association of slope magnitude with age indicates loss of the sharp GWI transition in aging, which is consistent with processes such as dendritic pruning, axonal degeneration, and inflammation. …”
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Early diagnosis of lung cancer using a sensor gas analysis complex: case report by E. O. Rodionov, D. E. Kulbakin, D. V. Podolko, E. V. Obkhodskaya, A. V. Obkhodskiy, S. V. Miller, A. A. Mokh, V. I. Sachkov, A. S. Popov, V. I. Chernov

“…Currently, low-dose computed tomography (LDCT) is the only screening test that reduces the risk of death from lung cancer. …”
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Constructing segmentation method for wheat powdery mildew using deep learning by Hecang Zang, Hecang Zang, Congsheng Wang, Qing Zhao, Qing Zhao, Jie Zhang, Jie Zhang, Junmei Wang, Guoqing Zheng, Guoqing Zheng, Guoqiang Li, Guoqiang Li

“…Compared with other mainstream deep learning methods U-Net, PSPNet, DeepLabV3+, and Swin-Unet, the proposed RSE Swin-Unet method can detect wheat powdery mildew and stripe rust image in a challenging situation and has good computer vision processing and performance evaluation effects. …”
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YOLOv8-GABNet: An Enhanced Lightweight Network for the High-Precision Recognition of Citrus Diseases and Nutrient Deficiencies by Qiufang Dai, Yungao Xiao, Shilei Lv, Shuran Song, Xiuyun Xue, Shiyao Liang, Ying Huang, Zhen Li

“…Furthermore, we introduced important features through the Global to Local Spatial Aggregation module (GLSA), focusing on crucial image details to enhance both the accuracy and robustness of the model. This study processed the collected images, resulting in a dataset of 1102 images. …”
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Impact of environmental pollution on human health: Investigating the role of Polycyclic Aromatic Hydrocarbons in pediatric osteosarcoma by Mingxian Xu, Yu Xu, Yaping Huang, Jixiang Shi, Lifeng Yin, Jian Tu, Junqiang Yin, Changye Zou

“…Background: Polycyclic aromatic hydrocarbons (PAHs), widely emitted through industrial processes and vehicular exhaust, are recognized environmental carcinogens. …”
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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

“…Sensitivity, specificity, predictive positive value (PPV), predictive negative value (PNV), F-score, and area under the curve (AUC) were computed on unseen data to assess model performance. …”
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Regionally extensive ejecta layer of the Australasian tektite strewn field: the MIS 20/19 large meteorite impact in mainland South-East Asia by Paul A. Carling, Toshihiro Tada, Ryuji Tada, Wickanet Songtham, Alan J. Cresswell, David C. W. Sanderson, Naomi Porat, Jaroon Duangkrayom, Stephen E. Darby, Praphas Chansom

“…Tektites were co-deposited with the ejecta and not introduced by surface processes reworking the deposits. Similar models may be applicable outside of mainland South-East Asia, wherever other large impacts are suspected to have occurred.…”
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Surface-based morphometry of the cerebral cortex in cognitive impairments of varying severity in patients with age-related cerebral small vessel disease by Elena I. Kremneva, Larisa A. Dobrynina, Kamila V. Shamtieva, Victoria V. Trubitsyna, Zukhra S. Gadzhieva, Angelina G. Makarova, Maria M. Tsypushtanova, Marina V. Krotenkova

“…The assessment included the analysis of signs of cerebral small vessel disease based on the results of magnetic resonance imaging with the computation of general cerebral small vessel disease index and processing T1 multiplanar reconstruction images by surface-based morphometry to quantify general and regional brain parameters, including the thickness of the cerebral cortex. …”
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Does Proprioceptive Impairment Affect Feedforward Motor Control? A Cross-Sectional Study on Patients With Brain Damage by Nicola Vale, Anna Righetti, Enrico Martini, Michele Boldo, Nicola Bombieri, Nicola Smania

“…We divided the sample according to the lesion site in participants with lesions in areas involved in the somatosensory information processing (n = 12, age <inline-formula> <tex-math notation="LaTeX">$= 62.4\pm 13.6$ </tex-math></inline-formula> years, female = 5) and participants with lesions in the cerebellum or cerebellar peduncle (n = 8, age <inline-formula> <tex-math notation="LaTeX">$= 64.3\pm 13.9$ </tex-math></inline-formula> years, female = 1). …”
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