Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China by Qinggang Meng, Xiaolan Chen, Hui Wang, Wanfang Shen, Peixin Duan, Xinyue Liu

“…The distribution patterns, transition probabilities, and driving mechanisms of the CCD of PCCR are explored using kernel density estimation, Markov chains, and geographically and temporally weighted regression (GTWR). …”
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Spatial-temporal distribution and evolution of medical and health talents in China by Lei Zhang, Jie Tang, Qianqian Zhou, Yu Song, Dayong Zhang

“…Furthermore, the regional dynamic distribution of MHT was analyzed using kernel density estimation diagrams. The spatial autocorrelation of MHT was assessed through global and local Moran’s I, and the spatial gap and decomposition of MHT were analyzed using the Dagum Gini coefficient. …”
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Estimation the pH of CO2-saturated NaCl solutions using gene expression programming: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Parirokh Ebrahimi, Moein Kafi, Hamed Nikravesh, Yousef Kazemzadeh

“…A cumulative frequency plot of absolute relative error showed all data points had errors below 0.102, with over 90 % below 0.0615. Kernel density estimation plots confirmed the model generally underestimated pH values. …”
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Geo-behavioural predictors of diagnosed hypertension in Igbo Ora Area, Oyo State, Nigeria by Olalekan J. Taiwo, Joshua O. Akinyemi, Ayodeji Adebayo, Oluwafemi A. Popoola, Rufus O. Akinyemi, Onoja M. Akpa, Paul Olowoyo, Akinkunmi P. Okekunle, Ezinne O. Uvere, Omotolani Titilayo Ajala, Chukwuemeka Nwimo, Olayinka J. Adebajo, Adewale E. Ayodele, Ayodeji Salami, Oyedunni S. Arulogun, Olanrewaju Olaniyan, Richard W. Walker, Carolyn Jenkins, Bruce Ovbiagele, Mayowa Owolabi

“…Spatial patterns were analyzed using the Nearest Neighbour Statistic, Ripley K Function, and Kernel Density Estimation, while Binomial logistic regression identified predictors. …”
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Identification of the primary pollution sources and dominant influencing factors of soil heavy metals using a random forest model optimized by genetic algorithm coupled with geodet... by Tong Liu, Mingshi Wang, Mingya Wang, Qinqing Xiong, Luhao Jia, Wanqi Ma, Shaobo Sui, Wei Wu, Xiaoming Guo

“…Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation. Geodetector and a genetic algorithm optimized random forest model (GA-RF) were employed to quantify the dominant factors and precisely identify pollution sources. …”
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Status and annual change of vulnerability among middle-aged and elderly people with cardiometabolic multimorbidity in a division of Xinjiang Production and Construction Corps, 2017... by Ziqi WANG, Jieting CHEN, Mei ZHANG, Mingxia JING

“…The entropy weighting method was used to dynamically evaluate the vulnerability level of the CMM population from 2017 to 2021. Kernel density estimation was used to analyze the spatial and temporal evolution characteristics of the vulnerability level of the CMM population in different community types. …”
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Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints by Fatemeh Nosrat, Cem Dede, Lucas B. McCullum, Raul Garcia, Abdallah S.R. Mohamed, Jacob G. Scott, James E. Bates, Brigid A. McDonald, Kareem A. Wahid, Mohamed A. Naser, Renjie He, Aysenur Karagoz, Amy C. Moreno, Lisanne V. van Dijk, Kristy K. Brock, Jolien Heukelom, Seyedmohammadhossein Hosseinian, Mehdi Hemmati, Andrew J. Schaefer, Clifton D. Fuller

“…The MDP parameters were derived from a dataset comprising 52 HNC patients treated between 2007 and 2013. Kernel density estimation was used to smooth the sample distributions. …”
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Alteração da paisagem do município de Candeias do Jamari em Rondônia: uma análise temporal dos anos de 2007 a 2020. by Sheila Castro dos Santos, Siane Cristhina Pedroso Guimarães, Hélio Marques de Lira

“…The methodological basis is a qualitative and quantitative approach of the data and is anchored in satellite images and maps of hot spots produced from a set of data (2007, 2011, 1015, 2019 and 2020), where densities of the areas of study were calculated using the kernel density estimator, present in the Spatial Analyst extension of the free software QGIS 2.8.9 WIEN. …”
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A reliance on human habitats is key to the success of an introduced predatory reptile. by Tom Major, Lauren Jeffrey, Guillem Limia Russel, Rebecca Bracegirdle, Antonio Gandini, Rhys Morgan, Benjamin Michael Marshall, John F Mulley, Wolfgang Wüster

“…We investigated snake home range size using Autocorrelated Kernel Density Estimators (AKDE). Using AKDE-weighted Habitat Selection Functions we identified preferences for habitat features in a mosaic of habitats, and we used Integrated Step Selection Functions to further explore how these features influence movement. …”
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Effects of emigration on home range and behavior of Atlantic spotted dolphins (Stenella frontalis) by Brittini A. Hill, Denise L. Herzing, Jeremy M. Bono

“…We examined the home ranges for LBB residents on LBB (2005–2012), LBB immigrants on GBB (2013–2020), and GBB residents on GBB (2013–2020). Fixed kernel density estimators revealed LBB immigrant dolphins maintained significantly larger home ranges (34–43%) and core areas (42–94%) than both LBB and GBB residents. …”
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Tigers in their new territory: intraspecific interactions among the reintroduced tiger population in Panna Tiger Reserve, Central India by Supratim Dutta, Ramesh Krishnamurthy

“…To investigate spatial interactions, we used Autocorrelated Kernel Density Estimator methods. To capture dynamic interactions at different scales, we calculated Benhamou’s Movement Interaction index and a Correlation Coefficient (Cr) at two critical distance thresholds (Δ500m and 100m). …”
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Space use of polar bears (Ursus maritimus) in Davis Strait in relation to sea ice and harp seals (Pagophilus groenlandicus) by Larissa Thelin, Evan Richardson, Garry Stenson, Erik Hedlin, Andrew E. Derocher

“…Polar bears (n = 18) tracked in 1991–2001 for 7–12 months had a mean 95% minimum convex polygon (MCP) home range size of 108 146 km2 (standard error of the mean (SE) = 18 252 km2) and a mean 95% kernel density home range size (kernel density estimate (KDE)) of 76 863 km2 (SE = 12 260 km2). …”
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Cluster Candidates with Massive Quiescent Galaxies at z ∼ 2 by Tomokazu Kiyota, Makoto Ando, Masayuki Tanaka, Alexis Finoguenov, Sadman Shariar Ali, Jean Coupon, Guillaume Desprez, Stephen Gwyn, Marcin Sawicki, Rhythm Shimakawa

“…We adopt a Gaussian kernel density estimate with two different spatial scales (10″ and 60″) to draw a density map of massive ( $\mathrm{log}({M}_{* }/{M}_{\odot })\gt 10.5$ ) and quiescent ( $\mathrm{log}(\mathrm{sSFR}\,[{\mathrm{yr}}^{-1}])\lt -10$ ) galaxies at z  ∼ 2. …”
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