Article
Mathematics, Applied
Qiaohua Liu, Chuge Li, Yimin Wei
Summary: This paper discusses the multidimensional mixed least squares-total least squares (MTLS) problem in the context of regression models, signal processing, and space coordinate transformation. It proves that the MTLS problem is equivalent to a weighted total least squares problem in the limit sense, and provides perturbation analysis and explicit condition number formulae for the MTLS problem. Tight and computable upper bounds for the condition numbers are also given. The numerical experiments demonstrate the tightness of the condition numbers and upper bounds in evaluating the forward errors.
APPLIED NUMERICAL MATHEMATICS
(2022)
Article
Mathematics, Applied
Pingping Zhang, Qun Wang
Summary: This paper investigates the MLSSTLS problem that combines MTLS and STLS problems, providing explicit expressions of the solution under certain conditions, conducting perturbation analysis and examining the condition numbers of the solution. Numerical experiments are performed to demonstrate the validity of the results, which can be connected to previously published findings on MTLS and STLS problems.
NUMERICAL ALGORITHMS
(2022)
Article
Chemistry, Analytical
Yuchen Fan, Zhenqi Shi, Shengli Ma, Sayyeda Zeenat Anwer Razvi, Yige Fu, Tao Chen, Jason Gruenhagen, Kelly Zhang
Summary: In this study, a modeling approach based on UV spectra was developed to quantify the loading of nucleic acid cargos in LNPs in-situ. The method showed similar predictive performance as more complicated experimental approaches and significantly saved analytical time and efforts.
ANALYTICAL CHEMISTRY
(2022)
Article
Mathematics, Applied
Baolei Wei, Naiming Xie
Summary: This study investigates the parameter estimation of grey system models from noisy observations, and finds that nonlinear least squares has multiple advantages over the conventional integral matching method in terms of accuracy and robustness.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2021)
Article
Statistics & Probability
Hao Chen, Lanshan Han, Alvin Lim
Summary: In this paper, the classical linear mixed effects (LME) models are extended to allow for sign constraints on its overall coefficients. A symmetric doubly truncated normal (SDTN) distribution is proposed for the random effects, instead of the unconstrained normal distribution. Likelihood-based approaches are developed to estimate the unknown model parameters using the approximation of its exact distribution. Simulation studies show that the proposed constrained model improves real-world interpretations and achieves satisfactory performance on model fits compared to the existing model.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Article
Acoustics
Nezih Topaloglu, Cevat V. Karadag
Summary: A linear regression based SDOF resonator parameter extraction method is proposed, which outperforms other methods using amplitude FRF.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Engineering, Electrical & Electronic
Cody Mazza-Anthony, Bogdan Mazoure, Mark Coates
Summary: This paper introduces two novel estimators based on the OWL norm for estimating sparse structured precision matrices, which can simultaneously identify groups of related edges and control sparsity. The ccGOWL estimator shows good computational efficiency and accuracy in both synthetic data and real-world applications, demonstrating its efficacy in gene network analysis and econometrics.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Mathematics, Applied
Lingling He, Xiaoqin Li, Yan Shen, Qiuyue Wu
Summary: In this paper, we investigate the partially linear regression model based on asymptotically almost negatively associated (AANA) random variables. Convergence results for the parametric least squares estimator and nonparametric weighted estimator are obtained under weak conditions. The results extend the corresponding ones for negatively associated (NA) errors to AANA errors and the selection of design points and weight functions is discussed. Simulation experiments are conducted to demonstrate the performance of the obtained results.
JOURNAL OF MATHEMATICAL INEQUALITIES
(2023)
Article
Engineering, Multidisciplinary
Baolei Wei
Summary: Parameter estimation is a crucial step in grey system models for time series modeling and forecasting. This study presents a separable grey system model that encompasses both linear and nonlinear models with separable structural parameters. Three least squares-based strategies are proposed for estimating structural parameters and initial conditions. Nonlinear least squares outperforms the other two strategies, especially in scenarios with large time intervals and high noise levels. Real-world applications demonstrate the effectiveness of the proposed method in forecasting failure times of products and traffic flows.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Environmental
Thomas Krumpolc, D. W. Trahan, D. A. Hickman, L. T. Biegler
Summary: Applications of fixed-effects models for kinetic parameter estimation assume independence among batches, but biased residuals often exist in multiple longitudinal batch experiments with time series data. Nonlinear mixed-effects models provide an alternative approach to address the two types of random experimental variation resulting from longitudinal experiments: measurement error for each data point and random batch-to-batch variation. In our case study, implementing a mixed-effects model using nonlinear programming for a batch reactor system yields parameter estimates with less bias compared to a fixed-effects model. Additionally, the Bayesian notion of probability shares is applied to discriminate between several candidate mixed-effects models, demonstrating the ability to elucidate additional model information when fixed-effects models are inappropriate.
CHEMICAL ENGINEERING JOURNAL
(2022)
Article
Statistics & Probability
Marianne Menictas, Gioia Di Credico, Matt P. Wand
Summary: This article presents streamlined mean field variational Bayes algorithms for fitting linear mixed models with crossed random effects. It introduces a hierarchy of relaxations of the mean field product restriction, providing faster computing alternatives with diminished inferential accuracy.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2023)
Article
Mathematics, Applied
Baasansuren Jadamba, Akhtar A. Khan, Fabio Raciti, Miguel Sama
Summary: This paper develops a stochastic approximation approach for estimating the flexural rigidity within the framework of variational inequalities. The nonlinear inverse problem is analyzed as a stochastic optimization problem using an energy least-squares formulation. A stochastic variational inequality is solved by a stochastic auxiliary problem principle-based iterative scheme, which satisfies the necessary and sufficient optimality condition for the optimization problem. The convergence analysis for the proposed iterative scheme is given under general conditions on the random noise. Detailed computational results demonstrate the feasibility and efficacy of the proposed methodology.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Chemistry, Applied
Hiago de O. Gomes, Roseni da S. Cardoso, Jose Galberto M. da Costa, Vitor P. Andrade da Silva, Crisiana de A. Nobre, Raimundo N. Pereira Teixeira, Ronaldo F. do Nascimento
Summary: This study aimed to validate the analytical curves of a chromatography method for identifying and quantifying pesticide residues in banana pulp. Using a weighted least squares regression, the study confirmed heteroscedasticity for all compounds and found high correlation coefficients for the pesticides. The detection and quantification limits were below the maximum limits stipulated by regulatory legislation.
Article
Automation & Control Systems
Zhe Li, Xun Wang, Uwe Kruger
Summary: Kernel Partial Least Squares (KPLS) is an effective nonlinear modeling technique used in control engineering applications, capable of handling small sample sizes and noisy, highly correlated variable sets. By mapping input variables to a feature space, it produces an optimal prediction model for process output variables. However, the computational intensity of the procedure can be a challenge, especially when dealing with large data sets. The proposed Efficient Kernel Partial Least Squares (EKPLS) aims to reduce computational complexity significantly compared to the traditional approach.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Engineering, Chemical
Lin Xuan You, Junghui Chen
Summary: To achieve desired product qualities, monitoring the operational status is necessary. Traditional multivariate statistical process control techniques are not suitable for monitoring unevenly distributed process data. This study proposes the use of a multilocal partial least-squares (ML-PLS) model to monitor a wide operation process.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2022)
Article
Forestry
Maren Granstrom, Mindy S. Crandall, Laura S. Kenefic, Aaron R. Weiskittel
Summary: This study compared the effects of different silvicultural systems and harvesting methods on the quality and value of mixed, northern conifer stands in Maine, USA. Findings showed that selection systems and shelterwood systems resulted in higher tree quality and stand value, while commercial clearcutting and fixed diameter-limit cutting led to lower tree quality and residual stand value.
CANADIAN JOURNAL OF FOREST RESEARCH
(2022)
Article
Ecology
Garret T. Dettmann, David W. MacFarlane, Philip J. Radtke, Aaron R. Weiskittel, David L. R. Affleck, Krishna P. Poudel, James Westfall
Summary: Estimating tree leaf biomass for multiple tree species is challenging, especially when limited data is available for some of the species of interest. In this study, models of varying complexity were formulated using a national database of observations to estimate tree leaf biomass for any species across the continental United States. The most complex model, incorporating eight predictors, performed the best and explained a significant amount of the variation in leaf mass. The models can be applied to new species or species with limited leaf biomass data available.
ECOLOGICAL APPLICATIONS
(2022)
Article
Environmental Sciences
Erica R. Bigio, Thomas W. Swetnam, Christopher H. Baisan, Christopher H. Guiterman, Yegor K. Kisilyakhov, Sergey G. Andreev, Eduard A. Batotsyrenov, Alexander A. Ayurzhanaev
Summary: This study uses dendrochronology to analyze fire activity in Siberia over the past 400 years. It finds that the frequency of fires has varied, with agricultural burning and regional drought being major contributors. Although fire frequencies increased in the 20th century, the relationship between fires and climate weakened, suggesting that human-caused ignitions may override climate drivers.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Review
Forestry
Donald A. Falk, Philip J. van Mantgem, Jon E. Keeley, Rachel M. Gregg, Christopher H. Guiterman, Alan J. Tepley, Derek J. N. Young, Laura A. Marshall
Summary: Ecosystems exhibit dynamic responses to environmental variation, such as reorganization, persistence, and recovery. Resilience, the ability of ecosystems to recover or adapt following disturbance, results from multiple mechanisms operating across different levels. The ability of persistence, recovery, and reorganization are essential components of ecosystem resilience in the face of changing environmental conditions.
FOREST ECOLOGY AND MANAGEMENT
(2022)
Editorial Material
Biodiversity Conservation
Anthony W. D'Amato, Christopher W. Woodall, Aaron R. Weiskittel, Caitlin E. Littlefield, Lara T. Murray
Summary: This study presents a relative frequency distribution of observed annual mortality expressed in aboveground carbon across different forest types and site classes in the US. The results summarize the conditions of plots that meet or do not meet the California Air Resources Board standards based on basal area, as well as the conditions of plots falling within or outside optimum relative density levels.
GLOBAL CHANGE BIOLOGY
(2022)
Article
Ecology
Caitlin Andrews, Jane R. Foster, Aaron Weiskittel, Anthony W. D'Amato, Erin Simons-Legaard
Summary: This study assessed the bioclimatic distribution of spruce-fir forests in the North American Acadian Forest Region using new species distribution models that incorporated historical data and seasonal climate interactions. The results showed that including historical data and considering seasonal climate interactions improved the prediction of species distributions and provided more accurate forecasts.
Article
Ecology
Christopher H. Guiterman, Rachel M. Gregg, Laura A. E. Marshall, Jill J. Beckmann, Phillip J. van Mantgem, Donald A. Falk, Jon E. Keeley, Anthony C. Caprio, Jonathan D. Coop, Paula J. Fornwalt, Collin Haffey, R. Keala Hagmann, Stephen T. Jackson, Ann M. Lynch, Ellis Q. Margolis, Christopher Marks, Marc D. Meyer, Hugh Safford, Alexandra Dunya Syphard, Alan Taylor, Craig Wilcox, Dennis Carril, Carolyn A. F. Enquist, David Huffman, Jose Iniguez, Nicole A. Molinari, Christina Restaino, Jens T. Stevens
Summary: This study examines vegetation type conversion (VTC) in the western United States and finds that high-severity fires are the main driver of VTC in semi-arid coniferous forests. The study also reveals the extent and complexity of ecological reorganization across the region, with forests converting to shrubland and chaparral and sagebrush areas converting to non-native grasses. The study emphasizes the need for better management strategies and research agendas to address the challenges posed by VTC.
Article
Ecology
Ellis Q. Margolis, Christopher H. Guiterman, Raphael D. Chavardes, Jonathan D. Coop, Kelsey Copes-Gerbitz, Denyse A. Dawe, Donald A. Falk, James D. Johnston, Evan Larson, Hang Li, Joseph M. Marschall, Cameron E. Naficy, Adam T. Naito, Marc-Andre Parisien, Sean A. Parks, Jeanne Portier, Helen M. Poulos, Kevin M. Robertson, James H. Speer, Michael Stambaugh, Thomas W. Swetnam, Alan J. Tepley, Ichchha Thapa, Craig D. Allen, Yves Bergeron, Lori D. Daniels, Peter Z. Fule, David Gervais, Martin P. Girardin, Grant L. Harley, Jill E. Harvey, Kira M. Hoffman, Jean M. Huffman, Matthew D. Hurteau, Lane B. Johnson, Charles W. Lafon, Manuel K. Lopez, R. Stockton Maxwell, Jed Meunier, Malcolm North, Monica T. Rother, Micah R. Schmidt, Rosemary L. Sherriff, Lauren A. Stachowiak, Alan Taylor, Erana J. Taylor, Valerie Trouet, Miguel L. Villarreal, Larissa L. Yocom, Karen B. Arabas, Alexis H. Arizpe, Dominique Arseneault, Alicia Azpeleta Tarancon, Christopher Baisan, Erica Bigio, Franco Biondi, Gabriel D. Cahalan, Anthony Caprio, Julian Cerano-Paredes, Brandon M. Collins, Daniel C. Dey, Igor Drobyshev, Calvin Farris, M. Adele Fenwick, William Flatley, M. Lisa Floyd, Ze'ev Gedalof, Andres Holz, Lauren F. Howard, David W. Huffman, Jose Iniguez, Kurt F. Kipfmueller, Stanley G. Kitchen, Keith Lombardo, Donald McKenzie, Andrew G. Merschel, Kerry L. Metlen, Jesse Minor, Christopher D. O'Connor, Laura Platt, William J. Platt, Thomas Saladyga, Amanda B. Stan, Scott Stephens, Colleen Sutheimer, Ramzi Touchan, Peter J. Weisberg
Summary: This study investigates fire regimes in North American forests using tree-ring fire scars and reveals important patterns and trends. The study also finds that modern fires are burning in similar climate spaces as historical fires, but disproportionately in warmer regions.
Article
Telecommunications
Sonia Naderi, Kenneth Bundy, Thayer Whitney, Ali Abedi, Aaron Weiskittel, Alexandra Contosta
Summary: Intelligent management of power and spectrum is crucial for creating wireless sensor networks with high reliability and longevity. This study focuses on accurate monitoring of forest ecosystems using high spatio-temporal resolution. By utilizing artificial intelligence and machine learning, the researchers developed a low-cost and power efficient system for large-scale monitoring.
INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS
(2022)
Article
Forestry
Bishnu Hari Wagle, Aaron R. Weiskittel, Anil R. Kizha, John-Pascal Berrill, Anthony W. D'Amato, David Marshall
Summary: This study quantified the long-term influences of thinning treatments on spruce-fir forests in northeastern North America. The findings suggest that early commercial thinning (CT) combined with pre-commercial thinning (PCT) can have long-term benefits in terms of tree size, merchantable volume, and financial value. However, light thinning in stands without PCT can maximize average merchantable stem size without compromising total stand value.
FOREST ECOLOGY AND MANAGEMENT
(2022)
Article
Forestry
Pete Bettinger, Krista Merry, Songlin Fei, Aaron Weiskittel, Zhao Ma
Summary: Key components of a digital forestry program, including digital tools, databases, and decision-support systems, are crucial in modern forest management. A survey of registered foresters from five US states revealed that private landowners are less likely to use digital technologies compared to those working for forestry organizations. Both private landowners and forestry organizations desire geographic information systems and smartphone applications as important technologies.
JOURNAL OF FORESTRY
(2023)
Article
Multidisciplinary Sciences
Christopher I. Roos, Christopher H. Guiterman, Ellis Q. Margolis, Thomas W. Swetnam, Nicholas C. Laluk, Kerry F. Thompson, Chris Toya, Calvin A. Farris, Peter Z. Fule, Jose M. Iniguez, J. Mark Kaib, Christopher D. O'Connor, Lionel Whitehair
Summary: This study investigates the fire-climate relationships over a 400-year period in the traditional territories of three different Indigenous cultures using a network of 4824 fire-scarred trees. The findings suggest that Indigenous fire management weakens the fire-climate relationships at local and landscape scales, but this effect does not scale up to the entire region.
Article
Forestry
Bishnu Hari Wagle, Aaron R. Weiskittel, John-Pascal Berrill, Anil R. Kizha, Anthony W. D'Amato, David Marshall
Summary: This study examined the long-term effects of pre-commercial and commercial thinning on the growth of balsam fir and red spruce in spruce-fir forests. The results showed that thinning treatments significantly increased the growth of balsam fir in stands without pre-commercial thinning, while the growth of red spruce was more moderate. In stands with pre-commercial thinning, both species showed similar growth responses. Thinning treatments effectively reduced mean height diameter ratios and improved growth efficiency.
FOREST ECOLOGY AND MANAGEMENT
(2023)
Article
Forestry
David Ray, Robert Seymour, Shawn Fraver, John-Pascal Berrill, Laura Kenefic, Nicole Rogers, Aaron Weiskittel
Summary: Stand density management is crucial for achieving diverse silvicultural objectives. Decision-support tools in this domain range from expert opinion to sophisticated computer models. Graphical frameworks like density management diagrams and stocking guides are well established and balance quantitative rigor with user accessibility. Advances in statistical modeling and data availability are overcoming limitations in developing reliable charts, encouraging more widespread use. The adoption of relative density based on stand density index is proposed as a logical metric for linking different formats.
JOURNAL OF FORESTRY
(2023)
Article
Forestry
Cen Chen, John Kershaw Jr, Aaron Weiskittel, Elizabeth McGarrigle
Summary: In this study, a new multistage modeling approach for individual tree mortality predictions was developed and compared with a conventional approach. The results showed that the multistage approach outperformed the conventional approach, with better depiction of observed mortality and smaller differences in predicted and observed numbers of dead trees.