How to solve small sample size problems in time-series soil organic carbon mapping: New insights from the Third Law of Geography by Jingzhe Wang, Zipeng Zhang, Yankun Wang, Cheng-Zhi Qin, Xiangyue Chen, Yinghui Zhang, Zhongwen Hu

“…In comparison to the model utilizing only a limited data sample, the S1-1980 s model, achieved a coefficient of determination (R2) of 0.04 and a root mean square error (RMSE) of 2.47 Kg C m−2. …”
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Mental fatigue of operating room nurses and its relationship with missed perioperative nursing care: a descriptive-analytical study by Vahid Rahmani, Valerie L. Marsh, Ebrahim Aliafsari Mamaghani, Ali Soleimani, Maedeh Alizadeh, Omid Zadi, Nasrin Aghazadeh

“…Abstract Introduction Mental fatigue is a psychological condition characterised by diminished alertness, impaired cognitive functioning, heightened error rates, and overall decreased performance. missed perioperative nursing care diminishes patient safety and increases the likelihood of adverse incidents. …”
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Consistency of the orbital chronologies derived for Vostok and EPICA DC ice cores based on the dependence of ice air content on local insolation by V. A. Khomyakova, N. A. Tebenkova, V. Ya. Lipenkov, D. Raynaud

“…Comparison of the TAC timescales with the optimized chronologies AICC2012 and AICC2023 for the Vostok and EDC cores showed that their discrepancy, as a rule, does not exceed 2 ka, which is consistent with both the standard error of the TAC­based dating method (±2.1 ka) and the standard errors of the AICC2012 (±1.9…4.8 ka) and AICC2023 (±0.8…2.6 ka) reference chronologies themselves. …”
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Hyperspectral Remote Sensing Combined with Ground Vegetation Surveys for the Study of the Age of Rodent Mounds by Hao Qi, Xiaoni Liu, Tong Ji, Chenglong Ma, Yafei Shi, Guoxing He, Rong Huang, Yunjun Wang, Zhuoli Yang, Dong Lin

“…Utilizing a combination of vegetation indices and hyperspectral data to determine the age of rodent mounds aims to provide a better method for extracting rodent hazard information. …”
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A Prediction Model of Stable Warfarin Doses in Patients After Mechanical Heart Valve Replacement Based on a Machine Learning Algorithm by Bowen Guo, Cong Chen, Junhang Jia, Jubing Zheng, Yue Song, Taoshuai Liu, Kui Zhang, Yang Li, Ran Dong

“…Comprehensive clinical and genetic data were collected, and patients were divided into training and validation cohorts at an 8:2 ratio through random division. …”
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Advances of Observation and Study on Tall-object Lightning in Guangzhou over the Last Decade by Lü Weitao, Chen Lüwen, Ma Ying, Qi Qi, Wu Bin, Jiang Ruijiao

“…Hundreds of tall-object lightning flashes have been captured during 2009-2018.For the lightning attachment process, tall-object will play the role of "magnifier":The TOLOG high-speed optical observation discovers the connection of the downward negative leader to the lateral surface of the upward connecting leader for the first time, and shows two basic types of the leader connection behaviors during the attachment process in negative cloud-to-ground lightning; the fine structure of negative stepped leader in natural lightning at close distance is revealed by using high-speed video records; the 2D/3D propagation characteristics of downward and upward leaders are analyzed; and the striking distances of lightning flashes to tall-object with different heights are also estimated.Tall-object plays an "amplifier" role on lightning electromagnetic field:Statistical analysis of the TOLOG data show that the magnetic field peak values induced by the first return stroke of lightning flashes to objects higher than 200 m is 2.4 times of that of lightning flashes to objects lower than 200 m; the higher the tall-object is, the larger the lightning location system inferred peak current of strokes are recorded in the vicinity of the tall-object; and the numberical simulation of the tall-object on electromagnetic field of lightning return stroke also show that the height of tall-object has significant enhancing effects.Tall-object is the "hot spot" of downward and upward lightning:Attraction effects of tall-object on downward lightning can protect other objects near tall-object from lightning strikes; the upward negative lightning from the tall-object can be triggered by the return stroke, the continuing current or the discharging process in cloud of positive cloud-to-ground lightning; and in upward lightning, abrupt extension is found at the positive end of the recoil leader which propagates bidirectionally.Using data of the TOLOG, the detection efficiency, the location error and the systematic bias of lightning location systems in Guangdong are evaluated, showing that the observation area of the TOLOG can be used as a "calibration field" for ground-based or space-based lightning monitoring system.…”
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Psychometric validation of the Physician Well-Being Index-Expanded (ePWBI) among physician educators in Hong Kong by Linda Chan, Paul Po Ling Chan, Xiaoai Shen, Emma Victoria Marianne Bilney, Tai Pong Lam, Julie Yun Chen, George L. Tipoe, Fraide A. Ganotice

“…CFA results indicated good data fit to the a priori model: Comparative Fit Index=0.99, Tucker–Lewis Index=0.99, Standardized Root Mean Square Residual=0.05, and Root Mean Square Error of Approximation=0.02 [90% CI 0.00–0.05]. …”
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Optical correction of hyperopia in school-aged children: a scoping review protocol by Su-Hsun Liu, Tawna L Roberts, Gayathri Srinivasan, Kristen L Kerber, Vivian M Manh, Kristine Huang, Alexandra Williamson, Soumen Sadhu, Morgan C Ollinger, Zahra Tajbakhsh, Jennifer H Fisher, Nathan L Cheung, Jasmine Junge, Kevin C H Chan, Jameel Rizwana Hussaindeen, Patrick Simard, Kelsey R Trast, Christina E Morettin, Samantha Krueger, Augustine N Nti, Debora M Lee Chen

“…We will search Cochrane CENTRAL, Embase.com and PubMed. Examples of data to be extracted include population demographics, visual acuity, study-specific definitions for refractive error, treatment regimens for optical correction, vision and vision-related functional outcomes and QoL (general or vision-related) as quantified by validated instruments.Ethics and dissemination Informed consent and Institutional Review Board approval will not be required, as this scoping review will only use published data. …”
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Research on Hybrid Architecture Neural Networks for Time Series Prediction by Fujin Zhuang, Xiao Chen, Punyaphol Horata, Khamron Sunat

“…This multi-module collaborative architecture effectively processes multi-scale features of time series data while providing model interpretability. Through comparative analysis of various optimization algorithms’ convergence performance and prediction accuracy, this study found that the AdamW optimizer, with its effective weight decay mechanism and adaptive learning rate, demonstrated superior performance in training stability and generalization capability, with MSE and R2 metrics outperforming traditional optimizers. …”
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Assessment of pan coefficient performance: A comparative study of empirical and model-driven approaches using a hill-climbing-based alternating model tree and MOORA by Saad Javed Cheema, Aitazaz A. Farooque, Mehdi Jamei, Khabat Khasravi, Farhat Abbas, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal

“…However, this is quite challenging due to variations in climate change and the deep non-linearity of meteorological data. Intensive experiments for pan evaporation (Epan) were conducted to develop a model, which includes hill-climbing based BestFirst-ClassifierSubsetEval (BF), alternating model tree (AMT), and multi-objective optimization by ratio analysis (MOORA). …”
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Complete spatiotemporal quantification of cardiac motion in mice through multi-view magnetic resonance imaging and super-resolution reconstruction by Tanmay Mukherjee, Maziyar Keshavarzian, Elizabeth M. Fugate, Vahid Naeini, Amr Darwish, Jacques Ohayon, Kyle J. Myers, Dipan J. Shah, Diana Lindquist, Sakthivel Sadayappan, Roderic I. Pettigrew, Reza Avazmohammadi

“…The effects of SRR on CMR quality were verified in all mice through image metrics, namely, root mean squared error (MSE) and structural similarity index. Strain calculations were validated against an in silico heart model phantom through MSE analysis, followed by investigations of strain accuracy and reproducibility for all mice using MSE and coefficient of variation analyses. …”
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Sexual and reproductive health (SRH) knowledge of women: a cross-sectional study among the women experienced abortion in urban slums, Dhaka, Bangladesh by Md Arif Billah, Kamrun Nahar Koly, Farzana Begum, Shakera Naima, Quazi Suraiya Sultana, Tithi Rani Sarker, Elvina Mustary, Md. Mahbubul Haque, Daniel Reidpath, Syed Manzoor Ahmed Hanifi

“…Methods We adopted a cross-sectional survey among the reproductive-aged women who experienced any kind of abortion from July 2020 to January 2022 living in the UHDSS sites, applying a predefined interviewer-assisted survey questionnaire. Data were analyzed using descriptive statistics (i.e., mean, standard error, and 95% confidence interval (CI)) for continuous and percentage distribution for categorical variables. …”
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Effect of intermediate airway management on ventilation parameters in simulated paediatric out-of-hospital cardiac arrest: a multicentre randomised crossover trial by Loric Stuby, Laurent Bourgeois, Jean-Marie Tinembart, Elisa Mühlemann, David Thurre, Johan N. Siebert, Laurent Suppan

“…Each team employed one of the airway management strategies. Data was automatically collected by a high-fidelity manikin. …”
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A novel approach for downscaling land surface temperature from 30 m to 10 m using land features multi-interaction by Alfred Homère Ngandam Mfondoum, Sofia Hakdaoui, Ali Mihi, Ibrahima Diba, Mesmin Tchindjang, Luc Beni Moutila, Frederic Chamberlain Lounang Tchatchouang

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Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding by Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

“…The findings demonstrated that the ensemble learning models, specifically random forest and gradient boosting decision tree regression, delivered the best overall performance, with a coefficient of determination (R2) ranging from 0.40 to 0.80 and a mean absolute error (MAE) between 0.11 and 4.36. The shapley additive explanations (SHAP) heatmap used for feature importance analysis revealed that, although the key predictors of weaning performance varied across models, this study newly identified lactation duration, birth litter weight, parity, and backfat thickness on the 7th day of lactation (L.d7BF) as consistently important features across different models. …”
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Postpartum maternal bonding scale: Development and validation in a low- and middle- income country setting. by Bushra Khan, Seyi Soremekun, Waqas Hameed, Bilal Iqbal Avan

“…After multiple rounds of expert review and cognitive pretesting, a 30-item tool was selected for field testing. Using data from 310 postpartum women, we examined the tool's structure through exploratory (EFA) and confirmatory factor analysis (CFA), leading to a refined 12-item tool. …”
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Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice by Shufang Liu, Sara C. Humphreys, Kevin D. Cook, Kip P. Conner, Ana R. Correia, Alex W. Jacobitz, Melissa Yang, Ronya Primack, Marcus Soto, Rupa Padaki, Mariusz Lubomirski, Richard Smith, Marissa Mock, Veena A. Thomas

“…The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. …”
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Development and validation of a single latent variable self-reported periodontal disease scale based on the disease’s common signs and symptoms in Saudi adults by Yasmine N. Alawaji, Mohamed H. Alqasoumi, Saleh N. Alwatban, Abdulaziz M. Halwani, Lamya A. Aljnoubi, Bayan K. Alshehri, May K. Alenezi

“…The final scale’s goodness of fit was acceptable as indicated by the Root Mean Square Error of Approximation (RMSEA) = 0.078, upper bound of the RMSEA 90% CI = 0.093, and the Standardized Root Mean Square (SRMR) = 0.059. …”
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Intelligent Environmental Health Risk Assessment System for the Elderly in Cold Regions Based on Artificial Intelligence Integrated Development Environment by Tianheng ZHANG, Yao FU, Jian GAO, Huanran XUE

“…The system performs best in predicting responses in the activity area (SBP mean root error: 4.8 mmHg; accuracy rate: 91.2%), with slightly higher error rates in street area, where the accuracy rate is still maintained above 88.5%. …”
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Estimation and evaluation of iron reserves in the eastern area of Eileh1 mine, Razavi Khorasan province by Hamid Esmati Daroneh, Maryam Gholamzadeh

“…However, due to the detrimental effects of uncertainty on investment risk, it is essential to utilize the most effective estimation method grounded in precise data analysis techniques to minimize estimation error. …”
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