An Objective and Subjective Evaluation of Masticatory Efficiency in Periodontal Patients Before and After Basic Periodontal Therapy: A Case Series Study by María José Moya-Villaescusa, Claudia López-Lisón, José María Montoya-Carralero, Alfonso Jornet-García, Arturo Sánchez-Pérez

“…Methods: A prospective, longitudinal, case series study was carried out in 42 periodontal patients treated at the University Odontology Clinic. Masticatory efficiency before and after basic periodontal treatment was assessed using both objective (HueCheck Gum test) and subjective (Quality of Masticatory Function Questionnaire: QMFQ) methods. …”
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A novel boundary meshfree method with two kinds of interpolations for spatially varying coefficient orthotropic thermal transfer by Jing Ling, Dongsheng Yang, Chengpeng Liu, Wei Gong

“…Four examples are computed including or excluding variable thermal sources. The proposed method's reliability and precision have been confirmed for thermal transfer in orthotropic media with spatially varying coefficients. …”
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Long-Term Teleconnections Between Global Circulation Patterns and Interannual Variability of Surface Air Temperature over Kingdom of Saudi Arabia by Abdullkarim K. Almaashi, Hosny M. Hasanean, Abdulhaleem H. Labban

“…The empirical orthogonal function (EOF) method is employed for analyzing SAT due to its effectiveness in extracting dominant patterns of variability during the winter (DJF) and summer (JJA) seasons. …”
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An Analytical Approximation of the Stress Function for Conical Flywheels by Miguel Garcia, Onofre Orozco-López, Jesús Uribe-Chavira, Andrés Blanco-Ortega

“…Starting from a stress function, the model was simplified under the assumption of a small-thickness gradient, allowing the derivation of a closed-form solution. …”
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On empirical Bayes estimation of multivariate regression coefficient by R. J. Karunamuni, L. Wei

“…We investigate the empirical Bayes estimation problem of multivariate regression coefficients under squared error loss function. In particular, we consider the regression model Y=Xβ+ε, where Y is an m-vector of observations, X is a known m×k matrix, β is an unknown k-vector, and ε is an m-vector of unobservable random variables. …”
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VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning by Haohai You, Hao Wang, Zhanchen Wei, Chunguang Bi, Lijuan Zhang, Xuefang Li, Yingying Yin

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Characterizing the effects of age, puberty, and sex on variability in resting-state functional connectivity in late childhood and early adolescence by Kelly A. Duffy, Andrea Wiglesworth, Donovan J. Roediger, Ellery Island, Bryon A. Mueller, Monica Luciana, Bonnie Klimes-Dougan, Kathryn R. Cullen, Mark B. Fiecas

“…We found decreased variability in global efficiency across the age range, and increased variability within the frontolimbic network driven primarily by those assigned female at birth (AFAB). …”
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Functional Analysis of Thyme Production in Denizli Province by Arif Semerci, Enver Ken

“…Among the variables in the production function, the variables with marginal efficiency coefficient above 1 were determined as the production area (3.20) and the fertilizer factor (1.33). …”
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Morpho-functional groups as an efficient tool for monitoring and management of the Billings reservoir (São Paulo, Brazil) by Ana Carolina Peixoto Chamizo, Cacilda Thais Janson Mercante, Munique de Almeida Bispo Moraes, Clóvis Ferreira do Carmo, Matheus Barbosa Herbst de Oliveira, João Alexandre Saviolo Osti

“…This research applied the Morphology-Based Functional Groups (MBFGs) combined with classical approaches, such as community descriptor species and phytoplankton classes in the Billings reservoir. …”
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An Improvement of Least Squares Theory: Theory of Least p-Variances Approximation and p-Uncorrelated Functions by Mohammad Masjed-Jamei

“…In this direction, some new concepts, such as p-covariances with respect to a fixed variable, p-correlation coefficients with respect to a fixed variable, and p-uncorrelatedness with respect to a fixed variable, are defined in order to establish least p-variance approximations. …”
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The constrained-disorder principle defines the functions of systems in nature by Yaron Ilan

“…The Constrained Disorder Principle (CDP) defines all systems in nature by their degree of inherent variability. Per the CDP, the intrinsic variability is mandatory for their proper function and is dynamically changed based on pressures. …”
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Efficiency of structural brain networks mediates age-associated differences in executive functioning in older adults by Geraldine Rodríguez-Nieto, Geraldine Rodríguez-Nieto, Caroline Seer, Caroline Seer, Hamed Zivari Adab, Hamed Zivari Adab, Antonio Jimenez-Marin, Sima Chalavi, Sima Chalavi, Amirhossein Rasooli, Amirhossein Rasooli, Jesús M. Cortés, Jesús M. Cortés, Jesús M. Cortés, Stefan Sunaert, Stephan P. Swinnen, Stephan P. Swinnen

“…Further regional efficiency analyses identified the nodes that contributed to the mediation effect of local efficiency.DiscussionThese results shed light on the shared variability among the integrity of structural brain networks and EF at older age. …”
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Research on Variable Circulation Design Method and Internal Flow Characteristic of the Axial Flow Pump by Xuewei Yu, Qili Gan, Zifan Ling, Jiahui Gong, Jiajia Tang, Lijian Shi

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Deformation Characteristic Analysis and Engineering Application of a Variable Cross-sectional Flexible Pin by Chen Hengjian, Xiao Shuhong

“…The deflection equation and slope-deflection equation of the variable cross-sectional flexible pin are obtained by using the Green's function method. …”
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Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson’s Disease Mobility Assessments by Rana M. Khalil, Lisa M. Shulman, Ann L. Gruber-Baldini, Sunita Shakya, Jeffrey M. Hausdorff, Rainer von Coelln, Michael P. Cummings

“…Although the total duration remains relatively consistent between trials (intraclass correlation coefficient (ICC) = 0.62 to 0.95), greater variability is seen in subtask duration and sensor-derived measures, reflected in machine learning performance and statistical differences. …”
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Neural Network Method of Controllers’ Parametric Optimization with Variable Structure and Semi-Permanent Integration Based on the Computation of Second-Order Sensitivity Functions... by Serhii Vladov, Lukasz Scislo, Nina Szczepanik-Ścisło, Anatoliy Sachenko, Victoria Vysotska

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A Hierarchical Archimedean Copula (HAC) model for climatic variables: an application to Kenyan data by Kevin Otieno, Linda Chaba, Linda Chaba, Evans Omondi, Evans Omondi, Collins Odhiambo, Collins Odhiambo, Bernard Omolo, Bernard Omolo

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Evaluation of the Variability of Micro and Macro Spray Parameters as a Function of Sampling Time Using a Laser Doppler Analyzer by Dariusz Lodwik, Mariusz Koprowski

“…In the first stage of the research, the variability of the coefficients characterizing the spray spectrum as a function of variable measurement time was analyzed. …”
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Analysis of effects of meteorological variables on dengue incidence in Bangladesh using VAR and Granger causality approach by Md. Jamal Hossain, Nazia Sultana, Anwesha Das, Fariea Nazim Jui, Md. Kamrul Islam, Md. Mijanoor Rahman, Mohammad Mafizur Rahman

“…BackgroundDengue fever is a serious public health issue in Bangladesh, where its incidence rises with the monsoon. Meteorological variables are believed to be responsible factors among others. …”
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Addressing Challenges in Simulating Inter–Annual Variability of Gross Primary Production by Ranit De, Shanning Bao, Sujan Koirala, Alexander Brenning, Markus Reichstein, Torbern Tagesson, Michael Liddell, Andreas Ibrom, Sebastian Wolf, Ladislav Šigut, Lukas Hörtnagl, William Woodgate, Mika Korkiakoski, Lutz Merbold, T. Andrew Black, Marilyn Roland, Anne Klosterhalfen, Peter D. Blanken, Sara Knox, Simone Sabbatini, Bert Gielen, Leonardo Montagnani, Rasmus Fensholt, Georg Wohlfahrt, Ankur R. Desai, Eugénie Paul‐Limoges, Marta Galvagno, Albin Hammerle, Georg Jocher, Borja Ruiz Reverter, David Holl, Jiquan Chen, Luca Vitale, M. Altaf Arain, Nuno Carvalhais

“…Both models simulated hourly GPP and were parameterized for (a) each site–year, (b) each site with an additional constraint on IAV (CostIAV), (c) each site, (d) each plant–functional type, and (e) globally. This was followed by forward runs using calibrated parameters, and model evaluations using Nash–Sutcliffe efficiency (NSE) as a model‐fitness measure at different temporal scales across 198 eddy‐covariance sites representing diverse climate–vegetation types. …”
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