Recent Advances in Surface Functionalized 3D Electrocatalyst for Water Splitting by Nadira Meethale Palakkool, Mike P. C. Taverne, Owen Bell, Jonathan D. Mar, Vincent Barrioz, Yongtao Qu, Chung‐Che Huang, Ying‐Lung Daniel Ho

“…Future research directions include exploring new materials for 3D printing and alternative electrocatalysts alongside leveraging theoretical and machine‐learning approaches to accelerate the development of competitive materials for water electrolysis.…”
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Abnormal Operation Detection of Automated Orchard Irrigation System Actuators by Power Consumption Level by Shahriar Ahmed, Md Nasim Reza, Md Rejaul Karim, Hongbin Jin, Heetae Kim, Sun-Ok Chung

“…Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications.…”
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Advances in the pilot point inverse method: Où En Sommes-Nous maintenant? by White, Jeremy, Lavenue, Marsh

“…The paper ends with newly developed applications of the PPM, given modern machine learning capabilities, and some foreshadowing as to where the PPM might evolve.…”
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Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review by Kholoud Elnaggar, Mostafa M. El-Gayar, Mohammed Elmogy

“…By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. …”
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Soil carbon-food synergy: sizable contributions of small-scale farmers by Toshichika Iizumi, Nanae Hosokawa, Rota Wagai

“…Methods We applied random forest machine learning models to global gridded datasets on crop yield (wheat, maize, rice, soybean, sorghum and millet), soil, climate and agronomic management practices from the 2000s (n = 1808 to 8123). …”
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Real-Time Plant Health Detection Using Deep Convolutional Neural Networks by Mahnoor Khalid, Muhammad Shahzad Sarfraz, Uzair Iqbal, Muhammad Umar Aftab, Gniewko Niedbała, Hafiz Tayyab Rauf

“…In the twenty-first century, machine learning is a significant part of daily life for everyone. …”
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Skin Microbiota: Mediator of Interactions Between Metabolic Disorders and Cutaneous Health and Disease by Magdalini Kreouzi, Nikolaos Theodorakis, Maria Nikolaou, Georgios Feretzakis, Athanasios Anastasiou, Konstantinos Kalodanis, Aikaterini Sakagianni

“…For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. …”
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Unleashing the potential of chatbots in mental health: bibliometric analysis by Qing Han, Chenyang Zhao

“…High-frequency terms such as “ChatGPT”, “machine learning”, and “large language models” underscore the current state of research, highlighting the cutting-edge advancements and frontiers in this field.ConclusionsThis study provides an in-depth analysis of the most prominent countries, institutions, publications, collaboration status, and research topics associated with utilization of chatbots in mental health over the last decade. …”
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Enhancing Tropical Cyclone Risk Assessments: A Multi-Hazard Approach for Queensland, Australia and Viti Levu, Fiji by Jane Nguyen, Michael Kaspi, Kade Berman, Cameron Do, Andrew B. Watkins, Yuriy Kuleshov

“…This study develops an integrated methodology for TC multi-hazard risk assessment that utilises the following individual assessments of key TC risk components: a variable enhanced bathtub model (VeBTM) for storm surge-driven hazards, a random forest (RF) machine learning model for rainfall-induced flooding, and indicator-based indices for exposure and vulnerability assessments. …”
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Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine by Yi Guo, Hua Xu, Fei Wang, Jie Xu, Jiang Bian, Robert Lucero, Mattia Prosperi

“…In stage 3, a computer-based, real-time Delphi survey will be performed to consolidate the PRECOG checklist, involving experts in causal inference, epidemiology, statistics, machine learning, informatics and protocols/standards. …”
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mmPrivPose3D: A dataset for pose estimation and gesture command recognition in human-robot collaboration using frequency modulated continuous wave 60Hhz RaDARMendeley Data by Nima Roshandel, Constantin Scholz, Hoang-Long Cao, Milan Amighi, Hamed Firouzipouyaei, Aleksander Burkiewicz, Sebastien Menet, Felipe Ballen-Moreno, Dylan Warawout Sisavath, Emil Imrith, Antonio Paolillo, Jan Genoe, Bram Vanderborght

“…Our dataset serves as a fundamental resource for developing machine learning algorithms to improve the accuracy of pose estimation and gesture recognition using RaDAR data.…”
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Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees by Dorota Wilk-Kołodziejczyk, Aleksandra Nowotny, Izabela Krzak, Adam Tchórz, Krzysztof Jaśkowiec, Marcin Małysza, Adam Bitka, Mirosław Głowacki, Marzanna Książek, Łukasz Marcjan

“…The available (experimental) data make it possible to unequivocally identify belonging to one of these groups. The use of machine learning methods to recognize the relationships between the physical parameters of particles helps to improve the analysis process. …”
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Massively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS) by Zhi Weng, Jiangxue Li, Yi Wu, Xuehao Xiu, Fei Wang, Xiaolei Zuo, Ping Song, Chunhai Fan

“…Specifically, even a ~ 1 ×  sequencing depth, with the combination of machine learning, results in an acceptable decoding accuracy of ~80%. …”
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Multilevel Precision-Based Rational Design of Chemical Inhibitors Targeting the Hydrophobic Cleft of Apical Membrane Antigen 1 (AMA1) by Umashankar Vetrivel, Shalini Muralikumar, B Mahalakshmi, K Lily Therese, HN Madhavan, Mohamed Alameen, Indhuja Thirumudi

“…Furthermore, binding free energy calculations of these two compounds also revealed a significant affinity to AMA1. Machine learning approaches also predicted these two compounds to possess more relevant activities. …”
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Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT by Chong Jiang, Chunjun Qian, Qiuhui Jiang, Hang Zhou, Zekun Jiang, Yue Teng, Bing Xu, Xin Li, Chongyang Ding, Rong Tian

“…These features, along with handcrafted radiomics, were utilized to construct a radiomic signature (R-signature) using automatic machine learning in the training and internal validation cohort. …”
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Dynamic Analysis of <i>Spartina alterniflora</i> in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite by Huiying Li, Guoli Cui, Haojie Liu, Qi Wang, Sheng Zhao, Xiao Huang, Rong Zhang, Mingming Jia, Dehua Mao, Hao Yu, Zongming Wang, Zhiyong Lv

“…The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, <i>S. alterniflora</i> expanded rapidly, increasing from 4055.06 hm² to 6105.50 hm², primarily in tidal flats and water bodies. …”
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Smart Driving Hardware Augmentation by Flexible Piezoresistive Sensor Matrices with Grafted‐on Anticreep Composites by Kaifeng Chen, Hua Yang, Ang Wang, Linsen Tang, Xin Zha, Ndeutala Selma Iita, Hong Zhang, Zhuoxuan Li, Xinyu Wang, Wei Yang, Shaoxing Qu, Zongrong Wang

“…The recognition of sitting postures is achieved by two 12 × 12 matrices facilitated by machine learning, which prompts the potential for the augmentation of smart driving.…”
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The Accuracy of the NSQIP Universal Surgical Risk Calculator Compared to Operation-Specific Calculators by Mark E. Cohen, PhD, Yaoming Liu, PhD, Bruce L. Hall, MD, PhD, MBA, FACS, Clifford Y. Ko, MD, MS, MSHS, FACS

“…For the N-RC, a cohort of 5,020,713 NSQIP patient records were randomly divided into 80% for machine learning algorithm training and 20% for validation. …”
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Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots by Fei Tan, Xiuwen Gao, Hao Cang, Nianyi Wu, Ruoyu Di, Jingkun Yan, Chengkai Li, Pan Gao, Xin Lv

“…This study proposes an early detection method for cotton wilt disease using hyperspectral imaging and recurrence plots (RP) combined with machine learning techniques. First, spectral curves were collected and analyzed under three conditions of cotton plants: healthy, asymptomatic, and symptomatic. …”
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Fetal-BET: Brain Extraction Tool for Fetal MRI by Razieh Faghihpirayesh, Davood Karimi, Deniz Erdogmus, Ali Gholipour

“…Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. …”
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