Hybrid closed-loop systems for managing blood glucose levels in type 1 diabetes: a systematic review and economic modelling by Asra Asgharzadeh, Mubarak Patel, Martin Connock, Sara Damery, Iman Ghosh, Mary Jordan, Karoline Freeman, Anna Brown, Rachel Court, Sharin Baldwin, Fatai Ogunlayi, Chris Stinton, Ewen Cummins, Lena Al-Khudairy

“…The system includes a combination of real-time continuous glucose monitoring from a continuous glucose monitoring device and a control algorithm to direct insulin delivery through an insulin pump. …”
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Dispatch Decisions and Emergency Medical Services Response in the Prehospital Care of Status Epilepticus by Robert P. McInnis, Andrew J. Wood, Courtney L. Shay, Anna A. Haggart, Remle P. Crowe, Elan L. Guterman

“…However, it is unclear whether dispatch algorithms accurately identify those patients having a seizure-related medical emergency and how dispatch algorithms influence what prehospital resources are allocated for the encounter. …”
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A QSAR-based application for the prediction of lethal blood concentration of new psychoactive substances by Tarcisio Correa, Jéssica Sales Barbosa, Thiara Vanessa Barbosa da Silva, Thiala Soares Josino da Silva Parente, Danielle de Paula Magalhães, Wanderley Pinheiro Holanda Júnior

“…Utilising linear regression and multilayer perceptron algorithms, the models was trained using data from the existing literature. …”
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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…Estimating cotton N requirements is crucial, as growers often apply excessive N, exceeding the amount needed for maximum yield. By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). …”
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Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning by Min Zhang, Xinlei Xu, Junji Ou, Zengguang Zhang, Fangzheng Chen, Lijie Shi, Bin Wang, Meiqin Zhang, Liang He, Xueliang Zhang, Yong Chen, Kelin Hu, Puyu Feng

“…The resulting dataset captures over 50% of the yield variability at the county scale, demonstrating superior accuracy compared to publicly available datasets with reductions in Root Mean Square Error (RMSE) ranging from 0.18 to 0.60 t/ha. …”
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Резолюция Экспертного совета «Диагностика и лечение больных со сладжем в желчном пузыре» by V. T. Ivashkin, Alexey V. Okhlobystin, Dmitry S. Bordin, M. F. Osipenko, E. Ya. Seleznyova, Yu. O. Shulpekova, S. R. Abdulkhakov, O. P. Alexseyeva, I. G. Bakulin, L. G. Vologjhanina, V. B. Grinevich, N. V. Bakulina, T. A. Kolesova, N. V. Korochanskaya, I. V. Putintseva, A. S. Sarsenbayeva, N. S. Yazenok

“…Advisory council activity provided the algorithm of diagnosis and treatment of biliary sludge. …”
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Corneal stromal mapping characteristics in normal corneas using anterior segment SD-OCT by Mohammad Abusamak, Mohammad Abusamak, Sara Mazen Issa, Amal F. Alomari, Husam A. Alsalamat, Husam A. Alsalamat, Nour S. Haj Ali, Abdallah Izmegna, Mais Shawashreh, Mahmoud Abu Samak, Talal M. Abusamak, Talal M. Abusamak

“…AS SD-OCT, employing novel algorithms, proves vital in refractive surgery planning and corneal disease diagnosis. …”
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Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC–MS-based metabolomics and machine learning by Kaiqi Cheng, Kaiqi Cheng, Jingzhe Xiao, Jingyuan He, Rongguang Yang, Jinjin Pei, Jinjin Pei, Wengang Jin, Wengang Jin, A. M. Abd El-Aty, A. M. Abd El-Aty

“…The 38 features selected by LASSO enabled clear differentiation of onion types via PCA, UMAP, and k-means clustering. Among the four machine learning models tested, the random forest model achieved the highest classification accuracy (1.00). …”
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Optimization of PV parameters under varied environmental conditions: A hybrid secant–Newton-Raphson method by Brahim El Fahmi, Driss Saadaoui, Imade Choulli, Khalid Assalaou, Ismail Abazine, Mustapha Elyaqouti, El hanafi Arjdal, Mohammed Agdam, Yassine El aidi Idrissi

“…The Newton-Raphson technique is then applied to iteratively solve the I-V equation to finalize parameter estimation, while minimizing the root mean square error (RMSE) between modeled and measured data. …”
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Optimization of heat and mass transfer in chemically radiative nanofluids using Cattaneo-Christov fluxes and advanced machine learning techniques by Shazia Habib, Saleem Nasir, Zeeshan Khan, Abdallah S. Berrouk, Waseem Khan, Saeed Islam

“…This functionality empowers specialists to oversee the progression of optimization, identify convergence patterns, and adjust algorithms to achieve superior results, thereby making a remarkable contribution to heat transfer and fluid dynamics.…”
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Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency by Jiaqi Zhang, Xiaocheng Zhou, Xueping Liu, Xiaoqin Wang, Guojin He, Youshui Zhang

“…A high degree of congruence was observed between Landsat-8 OLI and Sentinel-2 MSI sensor reflectance products, with root mean square error values consistently below 0.05. …”
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Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population by Yu-Xin Guo, Jun-Long Lan, Wen-Qing Bu, Yu Tang, Di Wu, Hui Yang, Jia-Chen Ren, Yu-Xuan Song, Hong-Ying Yue, Yu-Cheng Guo, Hao-Tian Meng

“…The regression model performed best, with mean absolute errors (MAE) of 1.45 years (under 18) and 3.51 years (aged 18 and above), providing relatively precise age predictions. …”
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A Robust Framework for Bamboo Forest AGB Estimation by Integrating Geostatistical Prediction and Ensemble Learning by Lianjin Fu, Qingtai Shu, Cuifen Xia, Zeyu Li, Hailing He, Zhengying Li, Shaoyang Ma, Chaoguan Qin, Rong Wei, Qin Xiang, Xiao Zhang, Yiran Zhang, Huashi Cai

“…Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. …”
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Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat by Yu Han, Jiaxue Zhang, Yan Bai, Zihao Liang, Xinhui Guo, Yu Zhao, Meichen Feng, Lujie Xiao, Xiaoyan Song, Meijun Zhang, Wude Yang, Guangxin Li, Sha Yang, Xingxing Qiao, Chao Wang

“…Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. …”
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SSCD-YOLO: Semi-Supervised Cross-Domain YOLOv8 for Pedestrian Detection in Low-Light Conditions by Fangliang Cao, Kai Yan, Hongliang Chen, Zhen Wang, Yunliang Du, Zekang Zheng, Kefan Li, Baozhu Qi, Mingjia Wang

“…The overall detection performance of SSCD-YOLO on both LLVIP and M3FD datasets surpasses other comparative algorithms while meeting real-time detection requirements.…”
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AI-Driven Mapping System for Smart Parking Management Applications Using an INS-GNSS-Solid-State LiDAR-Monocular Camera Fusion Engine Empowered by HD Maps by Kai-Wei Chiang, Syun Tsai, Jou-An Chen, Surachet Srinara, Meng-Lun Tsai, Chih-Yun Hsieh, Jyun-Yang Hung, Chalermchon Satirapod, Naser El-Sheimy

“…When applied to license plate localization in two experimental scenarios, the mean performance error was approximately 0.48 and 0.62 m.…”
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Cardiovascular risk prediction models in people living with human immunodeficiency virus under antiretroviral therapy in northern mexico by Arguiñe I. Urraza-Robledo, Francisco C. López-Márquez, Faviel F. González-Galarza, Domingo Pere-Pedrol, María E. Gutiérrez-Pérez, Ana P. Roiz-Bollain y Goytia, Pablo Ruiz-Flores, Fanny K. Segura-López, Alberto A. Miranda-Pérez

“…We used three well-established algorithmic models for assessing cardiovascular risk: Framingham (10-year period), Data Collection on Adverse Events of Anti-HIV Drugs Study (D: A: D) reduced, and full (5-year period). …”
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Hybrids versus parental species: insights from wing phenotype similarities and differences in triatomine insects by Álvaro Lara, María Laura Hernández, María Laura Hernández, César A. Yumiseva, Mario J. Grijalva, Mario J. Grijalva, Anita G. Villacís

“…Discriminant analysis was more effective for distinguishing parental groups than with hybrids. The K-means algorithm successfully classified the parental species and hybrid groups, although with low assignment percentages and a different number of groups than expected.DiscussionThe smaller wing size in hybrid offspring may indicate lower fitness, potentially due to genetic effects or reduced viability. …”
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Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements by L. Oberhagemann, L. Oberhagemann, M. Billing, W. von Bloh, M. Drüke, M. Drüke, M. Forrest, S. P. K. Bowring, J. Hetzer, J. Ribalaygua Batalla, K. Thonicke

“…We resolve these issues by correcting the implementation of the Rothermel model and implementing a new live grass moisture parametrization, in addition to several other improvements, including a multi-day fire spread algorithm, and evaluate these changes in the European domain. …”
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Simultaneous and Proportional Myoelectric Control of Multiple Degrees of Freedom in Individuals With Chronic Hemiparesis by Caleb J. Thomson, W. Caden Hamrick, Jakob W. Travis, Michael D. Adkins, Patrick P. Maitre, Steven R. Edgely, Jacob A. George

“…We collected data from seven hemiparetic patients and systematically explored the root mean squared error (RMSE) of kinematic predictions for various degrees of freedom. …”
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