Prevalence of prolonged transitional neonatal hypoglycemia and associated factors in Ethiopia: A systematic review and meta-analysis. by Solomon Demis Kebede, Amare Kassaw, Tigabu Munye Aytenew, Kindu Agmas, Demewoz Kefale

“…Heterogeneity among the studies was assessed using a forest plot, I2 statistics, and Egger's test. Data extraction was conducted from May 20 to May 27, 2023, for studies published since 2020. …”
Get full text

Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study by Adriana Ivanescu, Simona Popescu, Adina Braha, Bogdan Timar, Teodora Sorescu, Sandra Lazar, Romulus Timar, Laura Gaita

“…With the use of machine learning, the patients were assessed and categorized as having one of the three main types of cataracts: cortical (CC), nuclear (NS), and posterior subcapsular (PSC). A Random Forest Classification algorithm was employed to predict the incidence of different associations of cataracts (1, 2, or 3 types). …”
Get full text

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). …”
Get full text

Assessing the Direct Impact of Typhoons on Vegetation Canopy Structure and Photosynthesis by Yaoyao Zheng, Simin Zhan, Zaichun Zhu, Sen Cao, Jiana Chen, Pengjun Zhao, Weimin Wang, Ranga B. Myneni

“…This study proposes a novel framework for quantifying typhoons’ immediate and long-term impacts on vegetation canopy structure and photosynthesis. We developed random forest models based on satellite-observed leaf area index (LAI) and environmental data during typhoon-free periods to simulate LAI under non-typhoon conditions. …”
Get full text

Classification of Periodic Variable Stars from TESS by Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu

“…We used 19 parameters including period, physical parameters, and light-curve (LC) parameters to classify periodic variable stars into 12 subtypes using the random forest method. Pulsating variable stars and eclipsing binaries are distinguished mainly by period, LC parameters, and physical parameters. …”
Get full text

Uncovering mercury accumulation and the potential for bacterial bioremediation in response to contamination in the Singalila National Park by Sukanya Acharyya, Soumya Majumder, Sudeshna Nandi, Arindam Ghosh, Sumedha Saha, Malay Bhattacharya

“…Abstract Several recent investigations into montane regions have reported on excess mercury accumulation in high-altitude forest ecosystems. This study explored the Singalila National Park, located on the Singalila ridge of the Eastern Himalayas, revealing substantial mercury contamination. …”
Get full text

Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China by Tiantian Tang, Yifan Wu, Yujie Li, Lexi Xu, Xinyi Shi, Haitao Zhao, Guan Gui

“…We developed and compared five deterministic precipitation forecasting models, including elastic net regression (ENR), support vector regression, random forest, extreme gradient boosting, and light gradient boosting to provide forecasts with lead times ranging from 0 to 5 months at a spatial resolution of 0.5<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>. …”
Get full text

Prevalence of non-vaccine high-risk HPV cervical infections in vaccinated women: a systematic review and meta-analysis by Glauciane Resende do Nascimento, Ana Carolina da Silva Santos, Nayara Nascimento Toledo Silva, Nathalia Sernizon Guilmarães, Angélica Alves Lima, Wendel Coura-Vital

“…Intervention effects were expressed as prevalence ratios (PR). Forest plots were used to visualize vaccination effects. …”
Get full text

Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin by Ren Xu, Nengcheng Chen, Yumin Chen, Zeqiang Chen

“…Support vector machine for regression (SVR) was superior to multilayer perceptron (MLP) and random forest (RF). The downscaling results based on the BMA ensemble simulation and SVR models were regarded as the best performing overall (PCC, RMSE, and Rbias were 0.82, 35.07, mm and −5.45%, respectively). (3) Based on BMA and SVR models, the projected precipitations show a weak increasing trend on the whole under RCP4.5 and RCP8.5. …”
Get full text

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. …”
Get full text

Association of plasma biomarkers of Alzheimer’s pathology and neurodegeneration with gait performance in older adults by Farwa Ali, Jeremy A. Syrjanen, Dan J. Figdore, Walter K. Kremers, Michelle M. Mielke, Clifford R. Jack, David S. Knopman, Prashanthi Vemuri, Jonathan Graff-Radford, B. Gwen Windham, Leland R. Barnard, Ronald C. Petersen, Alicia Algeciras-Schimnich

Get full text

Narrow Dietary Niche With High Overlap Between Snow Leopards and Himalayan Wolves Indicates Potential for Resource Competition in Shey Phoksundo National Park, Nepal by Sandesh Lamichhane, Bikram Shrestha, Bhumi Prakash Chaudhary Tharu, Raj Kumar Koirala, Bishnu Prasad Bhattarai, Pratigyan Poudel, Binaya Adhikari, Gopal Khanal

Get full text

Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
Get full text

Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors by Daitlin Esmee Huisman, MD, Erik Wouter Ingwersen, MD, Joanna Luttikhold, MD, PhD, Gerrit Dirk Slooter, MD, PhD, Geert Kazemier, MD, PhD, Freek Daams, MD, PhD, LekCheck Study Group, Audrey Jongen, Carlo V. Feo, Simone Targa, Hidde M. Kroon, Emmanuel A. G. L. Lagae, Aalbert K. Talsma, Johannes A. Wegdam, Bob van Wely, Dirk J. A. Sonneveld, Sanne C. Veltkamp, Emiel G. G. Verdaasdonk, Rudi M. H. Roumen, Freek Daams

“…This study included patients who underwent colorectal surgery in 14 different hospitals between January 2016 and December 2020. Two distinct random forest models were developed: one solely based on preoperative variables (preoperative prediction model [PP model]) and the other incorporating both preoperative and intraoperative variables (intraoperative prediction model [IP model]). …”
Get full text

Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing by Yirui Zhang, Shouhang Du, Linye Zhu, Tianzhuo Guo, Xuesong Zhao, Junting Guo

“…The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. …”
Get full text

Childhood neurodivergent traits, inflammation and chronic disabling fatigue in adolescence: a longitudinal case–control study by Neil A Harrison, Hugo D Critchley, Jessica Eccles, Kevin A Davies, Lisa Quadt, Rod Bond, Jenny Csecs

Get full text

Cooperative Overbooking-Based Resource Allocation and Application Placement in UAV-Mounted Edge Computing for Internet of Forestry Things by Xiaoyu Li, Long Suo, Wanguo Jiao, Xiaoming Liu, Yunfei Liu

“…Due to the high mobility and low cost, unmanned aerial vehicle (UAV)-mounted edge computing (UMEC) provides an efficient way to provision computing offloading services for Internet of Forestry Things (IoFT) applications in forest areas without sufficient infrastructure. Multiple IoFT applications can be consolidated into fewer UAV-mounted servers to improve the resource utilization and reduce deployment costs with the precondition that all applications’ Quality of Service (QoS) can be met. …”
Get full text

Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission by Aleksandr Šabanovič, Jonas Matijošius, Dragan Marinković, Aleksandras Chlebnikovas, Donatas Gurauskis, Johannes H. Gutheil, Artūras Kilikevičius

“…In this paper, a random forest machine learning model developed to predict particulate concentrations post-cleaning demonstrated robust performance (MAE = 0.49 P/cm³, <i>R</i>² = 0.97). …”
Get full text

The emerging role of blood-based biomarkers in early detection of colorectal cancer: A systematic review by Faris Shweikeh, Yuhao Zeng, Abdur Rahman Jabir, Erica Whittenberger, Saurav P. Kadatane, Yuting Huang, Mohamad Mouchli, Dani Ran Castillo

Get full text

Evaluation of Four Multiple Imputation Methods for Handling Missing Binary Outcome Data in the Presence of an Interaction between a Dummy and a Continuous Variable by Sara Javadi, Abbas Bahrampour, Mohammad Mehdi Saber, Behshid Garrusi, Mohammad Reza Baneshi

“…MI methods included using predictive mean matching with an interaction term in the imputation model in MICE (MICE-interaction), classification and regression tree (CART) for specifying the imputation model in MICE (MICE-CART), the implementation of random forest (RF) in MICE (MICE-RF), and MICE-Stratified method. …”
Get full text