Spectral Variation and Corresponding Changing Mechanism of Suspended Particulate Material Absorption in Poyang Lake during Flood Periods by Yuandong Wang, Xibin You, Lianfang Yu, Lihong Meng, Xiangming Xu, Guangxu Liu

“…Evidence also presented that the nonlinear dependency of specific phytoplankton particulate absorption on pigment concentration for various trophic statuses in different periods could be unstable due to relative contributions of the package effect and accessory pigments; this could bring uncertainties to the parameterization of optical models and remote sensing algorithms proposed for accurate applications in lake water environment monitoring.…”
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Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Mette Sagbakken, Ahmed Ghannam, Fuad H. Abuadas, Joel Somerville, Joel Somerville, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair

“…These efficiencies allowed nursing teams to devote more time to direct patient care and were associated with reductions in burnout and improved workplace morale.Nursing perceptionsAcross practice settings, nursing students and practicing nurses broadly welcomed AI’s ability to streamline workflows and support decision-making, recognizing its potential to elevate patient care and professional practice.Ethical implicationsSimultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.Framework and recommendationsThe Nursing AI Integration Roadmap (NAIIR) was developed, emphasizing transformational education, advanced clinical integration, ethical governance, robust organizational infrastructure, participatory design, and rigorous economic evaluation. …”
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Continuous heart rate measurements in patients with cardiac disease: Device comparison and development of a novel artefact removal procedure by Paulien Vermunicht, Katsiaryna Makayed, Christophe Buyck, Lieselotte Knaepen, Juan Sebastian Piedrahita Giraldo, Sebastiaan Naessens, Wendy Hens, Emeline Van Craenenbroeck, Kris Laukens, Lien Desteghe, Hein Heidbuchel

“…Photoplethysmography (PPG)-based HR data provides near-continuous data, spanning longer periods, but improved algorithms to filter unreliable data are needed. …”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. …”
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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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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. …”
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MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area by Jiaxin Lu, Liangzhi Li, Junfeng Wang, Ling Han, Zhaode Xia, Hongjie He, Zongfan Bai

“…In addition, pixel-level K-Nearest Neighbor (KNN), Random Forest (RF) and SVM classification algorithms, as well as deep-learning models including Resnet50 (Res50), Efficientnet_B8 (Effi_B8), and Vision Transformer (ViT) were chosen for a comparative analysis. …”
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Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort by Kyndaron Reinier, Harpriya S. Chugh, Audrey Uy-Evanado, Elizabeth Heckard, Marco Mathias, Nichole Bosson, Vinicius F. Calsavara, Piotr J. Slomka, David A. Elashoff, Alex A.T. Bui, Sumeet S Chugh

“…Conclusions: The OSCAR cohort will provide a large, diverse dataset and adjudicated SCA outcomes to facilitate the derivation and testing of risk prediction models for incident SCA.…”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are prone to various artifacts such as noise amplification and boundary artifact. …”
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Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinomaResearch in context by Bolin Song, Amaury Leroy, Kailin Yang, Tanmoy Dam, Xiangxue Wang, Himanshu Maurya, Tilak Pathak, Jonathan Lee, Sarah Stock, Xiao T. Li, Pingfu Fu, Cheng Lu, Paula Toro, Deborah J. Chute, Shlomo Koyfman, Nabil F. Saba, Mihir R. Patel, Anant Madabhushi

“…Importantly, SMuRF outperformed unimodal models derived from radiology or pathology alone. …”
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The risks of telehealth in radiation oncology: challenges and mitigation strategies by Sotiri Stathakis, Niko Papanikolaou, Jessica Ashford, Jason Stephens, Aaron LaRose

“…The integration of telehealth into radiation oncology represents a significant evolution in healthcare delivery, driven by the potential for enhanced patient accessibility, convenience, and improved multidisciplinary collaboration and operational efficiency. …”
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Sedimentary microfacies prediction based on multi-point geostatistics under the constraint of INPEFA curve by Xudong Wang, Zicheng Yang, Xibao Liu, Chengyuan Yuan

“…High-order compatibility algorithms constrained the simulations, with iterative human-computer interaction refining sedimentary microfacies models for three sand groups. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…However, further studies are required to address challenges such as algorithmic bias, data privacy, and regulatory oversight. …”
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Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review by Hein Minn Tun, Hanif Abdul Rahman, Lin Naing, Owais Ahmed Malik

“…ConclusionsThe findings highlight the need for explainable AI models, comprehensive training, stakeholder involvement, and human-centered design to foster health care workers’ trust in AI-CDSSs. …”
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Using Pleiades Satellite Imagery to Monitor Multi-Annual Coastal Dune Morphological Changes by Olivier Burvingt, Bruno Castelle, Vincent Marieu, Bertrand Lubac, Alexandre Nicolae Lerma, Nicolas Robin

“…Among different potential sources of errors, shadow areas due to the steepness of the dune stoss slope and crest, along with planimetric errors that can also occur due to the steepness of the terrain, remain the major causes of errors still limiting accurate enough volumetric change assessment. However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography.…”
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FGFR2 identified as a NETs-associated biomarker and therapeutic target in diabetic foot ulcers by Linrui Dai, Shunli Rui, Mengling Yang, Shiyan Yu, Qingqing Chen, Hongyan Wang, Bo Deng, Liling Deng, Wei Hao, Xiaohua Wu, David G. Armstrong, Zhidong Cao, Xiaodong Duan, Wuquan Deng

“…LASSO logistic regression and Random Forest algorithms were applied to the NETDEGs to select key feature genes. …”
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Ethical Foundations of AI-Driven Avatars in the Metaverse for Innovation and User Privacy by Ammar Almomani, Ahmad Al-Qerem, Mohammad Alauthman, Amjad Aldweesh, Samer Aoudi, Said A. Salloum

“…Finally, we explore future directions, including integrating brain computer interfaces (BCIs), emotion detection, quantum-resistant cryptography, and international governance models. Validation through case study analysis demonstrates significant improvements in privacy protection metrics, substantial reduction in identity theft vulnerabilities, and marked enhancement in user autonomy controls compared to existing frameworks. …”
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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…Conserving energy and improving energy efficiency in cloud servers is a major challenge. …”
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Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

“…We compared the predictive performance of these different machine learning algorithms to identify the most effective one. The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic>²; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7&#x0025;, 73.1&#x0025;, and 63.1&#x0025;, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. …”
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