Improved estimation of forage nitrogen in alpine grassland by integrating Sentinel-2 and SIF data by Yongkang Zhang, Jinlong Gao, Dongmei Zhang, Tiangang Liang, Zhiwei Wang, Xuanfan Zhang, Zhanping Ma, Jinhuan Yang

“…In this study, we integrates SIF products from TanSat and Orbiting Carbon Observatory-2 (OCO-2) satellites, Sentinel-2 Multi-Spectral Instrument (MSI) data with derived vegetation indices, and field observations across phenological stages (green-up stage, vigorous growth stage, and senescence stage) in northeastern Tibetan Plateau alpine grasslands to develop support vector machine (SVM), gaussian process regression (GPR), and artificial neural network (ANN) models for regional-scale forage nitrogen estimation. …”
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Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach by Yu CS, Wu JL, Shih CM, Chiu KL, Chen YD, Chang TH

“…Random forest, support vector machine (SVM), Adaboost, and logistic regression had better overall performances with areas under the receiver operating characteristic curve (AUROCs) of > 0.87. …”
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Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation paramet... by Xin-Yue Song, Xin-Peng Xie, Wen-Jing Xu, Yu-Jia Cao, Bin-Miao Liang

“…We applied several supervised ML algorithms and feature selection strategies to distinguish between DN and DA, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Naive Bayes (BAYES), K-Nearest Neighbors (KNN), SelectKBest, Recursive Feature Elimination with Cross-Validation (RFECV), and SelectFromModel. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…The MCCL model was compared to other machine learning and deep learning models, including LSTM, Random Forest (RF), CNN, Artificial Neural Network (ANN), Support Vector Machine (SVM) and partial least squares regression (PLSR). …”
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Comparative analysis of Egypt's malaria elimination strategies and implementation science: Pathways to achieve malaria-free status for other African countries. by Chukwuka Elendu, Dependable C Amaechi, Rhoda C Elendu, Sehajmeet Kaur Saggi, Tochi C Elendu, Emmanuel C Amaechi, Ijeoma D Elendu, Kanishk Dang, Opeyemi P Amosu, Omoyelemi F Idowu, Toluwanimi S Oseni

“…Economic evaluations and process analyses provided additional insights into cost-effectiveness and operational efficiency.…”
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Screening and identification of protein 29 of Echinococcus granulosus interacting molecules by Zailing Shang, Fei Qiao, Yaning Li, Xuelin Ma, Mingxia Wang, Wenji Yang, Tianyu He, Haixia Ma, Yana Wang, Yana Wang

“…However, the function of Eg.P29 remains unknown. During the process life, protein is commonly with other proteins to form a complex network of interactions to play the function. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Quality assessments were performed using the ROBIS and AMSTAR 2 tools to evaluate risk of bias and methodological rigor.ResultsAmong the 27 reviews, traditional machine learning approaches—random forests, support vector machines, gradient boosting, and logistic regression—dominated tasks from antigen discovery and epitope prediction to supply‑chain optimization. …”
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Deep Learning Shield Attitude Prediction Model Based on Grey Correlation Analysis by Ke MAN, Zongxu LIU, Yan SHANG, Zhifei SONG, Xiaoli LIU, Bao SU

“…The DWT–LSTM–SVR stacking ensemble prediction model was obtained by optimally weighting the processed data after prediction by two single models separately. …”
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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…Classification models were developed using supervised machine learning algorithms (decision tree, support vector machines, k-nearest neighbor, and neural networks) and evaluated through cross-validation. …”
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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

“…Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). …”
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Role of Lysogenic Phages in the Dissemination of Antibiotic Resistance Genes Applied in the Food Chain by Rafael Dorighello Cadamuro, Mariana Alves Elois, Giulia Von Tönnemann Pilati, Beatriz Pereira Savi, Leonardo Pessi, Yasmin Ferreira Souza Hoffmann Jempierre, David Rodríguez-Lázaro, Gislaine Fongaro

“…These findings demonstrate the potential for lysogenic phages to act as vectors for resistance gene dissemination, posing risks to public health. …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…This highly weathered geological and pedological occurrence makes the site ideal for studying the pedogenetic process of lateralization and the spatial variability of chemical elements. …”
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Data of vegetation structure metrics retrieved from airborne laser scanning surveys for European demonstration sitesZenodo by W. Daniel Kissling, Wessel Mulder, Jinhu Wang, Yifang Shi

“…The 35 rasterized LiDAR metrics (GeoTIFF files, 10 m resolution) from all sites, including Comino, as well as the corresponding site boundary shapefiles (geospatial vector format), are provided in a Zenodo repository. …”
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NNHMC: An Efficient Stokes Inversion Method Using a Neural Network (NN) Model Combined with the Hamiltonian Monte Carlo (HMC) Algorithm by Chong Xu, JinLiang Wang, Hao Li, ZiYao Hu, XianYong Bai, JiaBen Lin, Hui Liu, ZhenYu Jin, KaiFan Ji

“…By applying the Levenberg–Marquardt algorithm or training a neural network (NN) model, the magnetic field vector can be quickly inferred from the Stokes profile but lacks reliable and statistically well-defined confidence intervals for parameters. …”
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Understanding one health challenges in marginalized urban settings: A patient and public involvement (PPI) approach from the CHIP consortium activities across four global cities by Logan Manikam, Darlington David Faijue, Kalpita Shringarpure, Margi Sheth, Pam Factor-Livak, Priti Parikh, Hector Altamirano-Medina, Dewi Nur Aisyah, Radhika Sharma, Hemant Chaturvedi, Kaushik Sarkar, Rajib Dasgupta, Nancy Hiu Lan Leung, Pradeep Kumar Srivastava, Monica Lakhanpaul

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Knowledge-data-driven power flow calculation for lowvoltage active distribution network considering gray data by LIU Siliang, ZHENG Zenan, ZHANG Yongjun, YI Yingqi, CHI Yuquan

“…The input-output feature vectors of the deep learning model are constructed based on the DistFlow model, where the head-end node voltage, photovoltaic (PV) output, and load power at user nodes serve as input features, and the voltage magnitude at user nodes is the output feature. …”
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The Surface Water and Ocean Topography Mission (SWOT) Prior Lake Database (PLD): Lake Mask and Operational Auxiliaries by Jida Wang, Claire Pottier, Cécile Cazals, Marjorie Battude, Yongwei Sheng, Chunqiao Song, Md Safat Sikder, Xiao Yang, Linghong Ke, Manon Delhoume, Marielle Gosset, Rafael Reis Alencar Oliveira, Manuela Grippa, Félix Girard, George H. Allen, Xiangtao Xu, Xiaolin Zhu, Sylvain Biancamaria, Laurence C. Smith, Jean‐François Crétaux, Tamlin M. Pavelsky

“…The PLD will be recursively improved throughout the mission lifetime and serves as a critical framework for organizing, processing, and interpreting SWOT observations over lacustrine environments with fundamental significance to lake system science.…”
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Advancing Grapevine Disease Detection Through Airborne Imaging: A Pilot Study in Emilia-Romagna (Italy) by Virginia Strati, Matteo Albéri, Alessio Barbagli, Stefano Boncompagni, Luca Casoli, Enrico Chiarelli, Ruggero Colla, Tommaso Colonna, Nedime Irem Elek, Gabriele Galli, Fabio Gallorini, Enrico Guastaldi, Ghulam Hasnain, Nicola Lopane, Andrea Maino, Fabio Mantovani, Filippo Mantovani, Gian Lorenzo Mazzoli, Federica Migliorini, Dario Petrone, Silvio Pierini, Kassandra Giulia Cristina Raptis, Rocchina Tiso

“…This pattern, confirmed by spatial autocorrelation and non-parametric tests, likely reflects increased vector activity and environmental stress at the vineyard margins. …”
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Prevalence of Trypanosoma Cruzi antibodies in blood donors from the Sao Paulo State, Brazil, between 2012 and 2014 by Svetoslav Nanev Slavov, Katia Kaori Otaguiri, Mariana Tomazini Pinto, Vanderléia Bárbaro Valente, Eugênia Maria Amorim Ubiali, Dimas Tadeu Covas, Simone Kashima

“…Though natural transmission by insect vectors has been controlled, there is significant risk of T. cruzi transmission by blood transfusion in non-endemic regions, generally due to immigration processes from endemic areas. …”
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