Phasic and periodic change of drought under greenhouse effect by Yang Li, Zhicheng Zheng, Yaochen Qin, Haifeng Tian, Zhixiang Xie, Peijun Rong

“…Second, the local water budget is mainly affected by surface evapotranspiration and precipitation. …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…Each of these approaches incorporates advanced algorithms and sensor technologies for cluttered orchard environments. …”
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Age and Saving Lives in Crisis Standards of Care: A Multicenter Cohort Study of Triage Score Prognostic Accuracy by Michael Hermsen, MD, MS, Patrick G. Lyons, MD, MS, Govind Persad, JD, PhD, Alice F. Bewley, MS, Chengsheng Mao, PhD, Kaveri Chhikara, MS, Anoop Mayampurath, PhD, Matthew Churpek, MD, PhD, Monica E. Peek, MD, MPH, MSc, Yuan Luo, PhD, William F. Parker, MD, PhD

“…Incorporating age into prioritization algorithms could save more lives in a crisis scenario.…”
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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

“…It is divided into two main types: acute, which stems from early cell growth ab- normalities and involves rapid immature cell proliferation, and chronic, which progresses more slowly due to a blockage in the later stages of the cell life cycle. …”
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Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning by Shang S, Wei M, Lv H, Liang X, Lu Y, Tang B

“…The SHAP analysis showed that CRP, BMI, NT-ProBNP, D-dimer, and GSP were the main influencing factors for death.Conclusion: This study successfully constructed a prediction model for the in-hospital death risk of advanced elderly patients with HF, and the XGB model exhibited excellent prediction performance. …”
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The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT by Krzysztof Wach, Cong Doanh Duong, Joanna Ejdys, Rūta Kazlauskaitė, Pawel Korzynski, Grzegorz Mazurek, Joanna Paliszkiewicz, Ewa Ziemba

“…Next, we grouped the identified threats into clusters to summarize the seven main threats we see. In our opinion they are as follows: (i) no regulation of the AI market and urgent need for regulation, (ii) poor quality, lack of quality control, disinformation, deepfake content, algorithmic bias, (iii) automation-spurred job losses, (iv) personal data violation, social surveillance, and privacy violation, (v) social manipulation, weakening ethics and goodwill, (vi) widening socio-economic inequalities, and (vii) AI technostress. …”
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Study of the interval width of features for improving forecasting efficiency of the transport flow intensity by I. N. Pugachev, N. G. Sheshera, D. Yе. Grigorov

“…Logicaland statistical validity of the selection automation of interval rages are the main feature of this method. This is necessary not only for grouping features, but also for increasing their value in a joint analysis. …”
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Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO by Rayyan Tariq Khan, Petra Pokorna, Jan Stourac, Simeon Borko, Adam Dobias, Joan Planas-Iglesias, Stanislav Mazurenko, Ihor Arefiev, Gaspar Pinto, Veronika Szotkowska, Jaroslav Sterba, Jiri Damborsky, Ondrej Slaby, David Bednar

“…Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. …”
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Estimating root zone soil moisture in farmland by integrating multi-source remote sensing data based on the water balance equation by Xuqian Bai, Shuailong Fan, Ruiqi Li, Tianjin Dai, Wangye Li, Sumeng Ye, Long Qian, Lu Liu, Zhitao Zhang, Haorui Chen, Haiying Chen, Youzhen Xiang, Junying Chen, Shikun Sun

“…The model is developed based on the soil water balance equation and incorporates multi-source remote sensing data. A random forest algorithm is employed as the core predictive framework. …”
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Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning by Chenbo Yang, Chenbo Yang, Meichen Feng, Juan Bai, Hui Sun, Rutian Bi, Lifang Song, Chao Wang, Yu Zhao, Wude Yang, Lujie Xiao, Meijun Zhang, Xiaoyan Song

“…The main results were as follows: For the 3 types of FOD, GL-FOD was suitable for analyzing the change process of the original spectral curve towards the integer-order derivative spectral curve. …”
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POSSIBILITIES OF CARIES PROGNOSIS IN CHILDREN OF SCHOOL-AGE ACCORDING TO DATA GAINED FROM THEM AND THEIR PARENTS QUESTIONNAIRE by L.F. Kaskova, T.B. Mandziuk, L.P. Ulasevych, L.D. Korovina, M.A. Sadovski

“… Caries in children is one of the main dental problems that scientists are trying to solve [1, 2, 3]. …”
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Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. …”
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A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis, many patients have not undergone screening with this technique. …”
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Selecting of global phenological field observations for validating coarse AVHRR-derived forest phenology products based on spatial heterogeneity and temporal consistency by Qi Shao, Chao Huang, Yuanjun Xiao, Li Liu, Weiwei Liu, Ran Huang, Chang Zhou, Wei Weng, Jingfeng Huang

“…Global phenological field observations play a crucial role in validating remote sensing products and algorithms. However, due to the spatial mismatch and scale effect between the field observations and the pixels of remote sensing phenology products, a direct comparison often leads to scale errors and increased uncertainty. …”
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Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin by Qingqin Bai, Lei Wang, Yangzong Cidan

“…The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. …”
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Continual Learning With Neuromorphic Computing: Foundations, Methods, and Emerging Applications by Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique

“…., sparse spike-driven operations and bio-plausible learning rules) for improving energy efficiency and performance, thereby enabling efficient CL algorithms (e.g., unsupervised learning approach) executed in dynamically-changed environments with resource-constrained computing systems. …”
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In-Hospital Stroke in a Multidisciplinary Hospital by S. S. Petrikov, G. R. Ramazanov, S. B. Binnatova, M. V. Tikhomirova

“…Systemic thrombolytic therapy (sTLT) is the main method of treating patients with IS. However, patients with in-hospital stroke may have a large number of contraindications to this type of therapy. …”
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Immune training enhances anti-viral responses and improves outcomes in Pax5 −/+ mice susceptible to chronic infection by Zhe Lu, Olivia Stencel, Wei Liu, Eleni Vasileiou, Haifeng C Xu, Piyush Pandey, Paweł Stachura, Abdelrahman Elwy, Anastassia Tsombal, Ann-Sophie Mai, Franziska Auer, Mina N F Morcos, Maximilian Seidl, Sarah Koziel, Peter-Martin Bruch, Sascha Dietrich, Sarah Elitzur, Gunther Hartmann, Karl S Lang, Stefan Janssen, Ute Fischer, Sanil Bhatia, Philipp A Lang, Arndt Borkhardt, Julia Hauer, Aleksandra A Pandyra

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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

“…Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. …”
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