Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers by Mariah Batool, Oluwafemi Sanumi, Jasna Jankovic

“…This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way, establishing a foundational knowledge base for further discussion. …”
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A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning by Lina Pang, Qingxia Sun, Wenyue Wang, Mingjie Song, Ying Wu, Xin Shi, Xiaonan Shi

“…Prognostic signatures were developed using various machine learning algorithms based on marker genes linked to the identified cell subpopulations. …”
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A Comparison of Recent Global Time-Series Land Cover Products by Peilin Li, Yan Wang, Chisheng Wang, Lin Tian, Meijiao Lin, Siyao Xu, Chuanhua Zhu

“…The results indicate that while datasets exhibit spatial consistency, significant discrepancies exist in land cover classification, with each dataset demonstrating varying levels of accuracy depending on the environmental context and land cover type. …”
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Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning by Huai Huang, Guangqin Ren, Shanghui Sun, Zhi Li, Yongtian Zheng, Lijuan Dong, Shaoliang Zhu, Xiaosheng Zhu, Wenyu Jiang

“…The Cox proportional hazards model assessed the impact of WPR on mortality outcomes, while Kaplan-Meier survival curves and restricted cubic spline (RCS) analysis further validated the findings. …”
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Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China by Zhengwei Liang, Jia Du, Weilin Yu, Kaizeng Zhuo, Kewen Shao, Weijian Zhang, Cangming Zhang, Jie Qin, Yu Han, Bingrun Sui, Kaishan Song

“…The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. …”
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Enhancing needle puncture detection using high-pass filtering and diffuse reflectance by Rachael L’Orsa, Rachael L’Orsa, Rachael L’Orsa, Rachael L’Orsa, Anupam Bisht, Linhui Yu, Kartikeya Murari, Kartikeya Murari, Kartikeya Murari, Garnette R. Sutherland, Garnette R. Sutherland, David T. Westwick, Katherine J. Kuchenbecker

“…Data collection occurred while two experimenters inserted a bevel-tipped percutaneous needle into an ex vivo porcine rib section simulating human chest anatomy. …”
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Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects by Maryam Yeganegi, Mahsa Danaei, Sepideh Azizi, Fatemeh Jayervand, Reza Bahrami, Seyed Alireza Dastgheib, Heewa Rashnavadi, Ali Masoudi, Amirmasoud Shiri, Kazem Aghili, Mahood Noorishadkam, Hossein Neamatzadeh

“…Advancements in DL algorithms, including Oct-U-Net and PAICS, have achieved recall and precision rates of 0.93 and 0.96, respectively, in identifying fetal abnormalities. …”
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Advances and challenges in immunotherapy in head and neck cancer by Hazem Aboaid, Taimur Khalid, Abbas Hussain, Yin Mon Myat, Rishi Kumar Nanda, Ramaditya Srinivasmurthy, Kevin Nguyen, Daniel Thomas Jones, Jo–Lawrence Bigcas, Kyaw Zin Thein

“…Future research should focus on refining biomarker-driven treatment algorithms, developing rational immunotherapy combinations, and leveraging tumor microenvironment modifications to enhance therapeutic efficacy.…”
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Comprehensive Transcriptome Sequencing and Analysis of <i>Euspira gilva</i>: Insights into Aquaculture and Conservation by Zhixing Su, Jiayuan Xu, Xiaokang Lv, Xuefeng Song, Yanming Sui, Benjian Wang, Xiaoshan Wang, Bianbian Zhang, Baojun Tang, Liguo Yang

“…Predictive analysis of coding DNA sequences (CDS) resulted in 1340 BLAST comparisons, while ESTScan predicted 840. Further, 530 long non-coding RNAs (lncRNAs) were identified through the application of the CPC2, CNCI, Pfam, and PLEK algorithms. …”
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Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Badr Aljabri, Leen Al‐Omran, Duminda N. Wijeysundera, Ori D. Rotstein, Thomas F. Lindsay, Charles de Mestral, Muhammad Mamdani, Graham Roche‐Nagle, Mohammed Al‐Omran

“…We developed machine learning algorithms to predict outcomes following TEVAR and complex EVAR. …”
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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In medical imaging, deep learning algorithms, especially those trained with genetic algorithms, have demonstrated immense promise to improve the accuracy of pneumonia and COVID-19 diagnosis from chest X-rays. …”
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Comparison of Raman spectroscopy with mass spectrometry for sequence typing of Acinetobacter baumannii strains: a single-center study by Suling Liu, Ni Zhang, Jiawei Tang, Chong Chen, Weisha Wang, Jingfang Zhou, Long Ye, Xiaoli Chen, ZhengKang Li, Liang Wang

“…., Acinetobacter baumannii. While accurate in identifying A. baumannii strains, current typing methods are often impractical in clinical settings due to their time-consuming nature. …”
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FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations by Pengfei Zhou, Weiqing Min, Chaoran Fu, Ying Jin, Mingyu Huang, Xiangyang Li, Shuhuan Mei, Shuqiang Jiang

“…This bridges the gap between general AI capabilities and the specialized needs of the food sector while also inspiring domain-specific LLM methods across other disciplines. …”
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Hyperspectral imaging for precision nitrogen management: A comparative exploration of two methodological approaches to estimate optimal nitrogen rate in processing tomato by Vito Aurelio Cerasola, Francesco Orsini, Giuseppina Pennisi, Gaia Moretti, Stefano Bona, Francesco Mirone, Jochem Verrelst, Katja Berger, Giorgio Gianquinto

“…PLSR outperformed the other algorithms in estimating N uptake (Relative Root Mean Square Error, RRMSE=21.8 %), while SVR better estimated NNI (RRMSE=10.2 %) and direct biomass (RRMSE=19.4 %). …”
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Exploring ribosome biogenesis in lung adenocarcinoma to advance prognostic methods and immunotherapy strategies by Zipei Song, Yuheng Wang, Miaolin Zhu, Pengpeng Zhang, Zhihua Li, Xin Geng, Xincen Cao, Jianan Zheng, Jianwei Tang, Liang Chen

“…Employing various machine learning algorithms, a ribosome biogenesis-related signature (RBS) was constructed and compared to 140 published LUAD prognostic models. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Additionally, 14 machine learning algorithms were trained and validated using SMOTE-balanced data and five-fold cross-validation. …”
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Non-Celiac Villous Atrophy—A Problem Still Underestimated by Katarzyna Napiórkowska-Baran, Paweł Treichel, Adam Wawrzeńczyk, Ewa Alska, Robert Zacniewski, Maciej Szota, Justyna Przybyszewska, Amanda Zoń, Zbigniew Bartuzi

“…Various immune pathways are involved, such as autoimmune deregulation and chronic inflammatory responses, while drug-induced and environmental factors further complicate its clinical picture. …”
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Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosisResearch in context by Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V. Omar, Anita Suresh, Timothy C. Rodwell, Rebecca E. Colman

“…For fluoroquinolones (moxifloxacin and levofloxacin), the MTBDRsl LPA and ONT had similar sensitivities (94.3% and 92.7%, and 94.8% and 93.9%, respectively), while GenoScreen outperformed both (97.3% and 96.6%). …”
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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

“…Our results demonstrate that EfficientNet-B0 achieved a classification accuracy of 72 %, while NASNetMobile attained 81 %. Notably, DenseNet121 outperformed these models with an accuracy of 99 %. …”
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Opportunities, Challenges, and Future Directions for Generative Artificial Intelligence in Library Information Literacy Education: A Scoping Review by Fan YUAN, Jia LI

“…This methodological approach enabled a thorough exploration of current practices while identifying critical gaps in existing research. …”
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