Article
Engineering, Environmental
J. E. Ruprecht, I. P. King, K. A. Dafforn, S. M. Mitrovic, A. J. Harrison, S. C. Birrer, S. L. Crane, W. C. Glamore
Summary: Widespread wastewater pollution poses a major obstacle to the sustainable management of freshwater and coastal marine ecosystems. The study emphasizes the importance of calibrating aquatic ecosystem response models with net growth rates of biological functional groups. Improved data collection and modeling efforts are needed to better address the release of nutrients into the natural environment.
Article
Nuclear Science & Technology
Riccardo Cocci, Guillaume Damblin, Alberto Ghione, Lucia Sargentini, Didier Lucor
Summary: This paper presents a methodology called Bayesian calibration for the development, validation, and uncertainty quantification of closure laws in thermal-hydraulic system codes. It introduces a robust and reliable assessment, selection, and uncertainty quantification of physical models by tuning parameters and selecting the best-suited model based on statistical indicators. The paper also discusses the application of this methodology to condensation heat transfer correlations.
ANNALS OF NUCLEAR ENERGY
(2022)
Review
Oceanography
Abhinav Gupta, Pierre F. J. Lermusiaux
Summary: Predictive dynamical models for marine ecosystems have significant uncertainty due to sparse measurements and limited understanding. A Bayesian model learning methodology is developed to interpolate and discover new models from noisy, sparse, and indirect observations, while estimating state variable fields and parameter values. This methodology addresses high-dimensional and multidisciplinary dynamics by using state augmentation and the computationally efficient Gaussian Mixture Model - Dynamically Orthogonal filter.
PROGRESS IN OCEANOGRAPHY
(2023)
Article
Computer Science, Artificial Intelligence
Kate Duffy, Thomas J. Vandal, Weile Wang, Ramakrishna R. Nemani, Auroop R. Ganguly
Summary: Numerical models based on physics are the best tools for generating insights and predictions in Earth system modeling. However, the need for higher model resolutions exceeds the capabilities of current computers, leading to the development of surrogate models. Recent successes of machine learning methods, particularly deep learning, suggest that they can capture the complex structures and processes in Earth systems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Mechanical
Omid Sedehi, Costas Papadimitriou, Lambros S. Katafygiotis
Summary: This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. The framework considers the mismatch between modal parameters and the variability of structural parameters, and applies Expectation-Maximization (EM) strategies to compute optimal weights for updating structural parameters based on modal properties.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Psychology, Mathematical
Donald R. Williams, Stephen R. Martin, Philippe Rast
Summary: This study discusses the importance of measurement reliability in psychology, introducing hierarchical models and the concept of individual-level ICC. Findings reveal heterogeneous within-person variance in cognitive inhibition tasks, leading to significant differences in individual reliability, with traditional indices potentially masking individual variations.
BEHAVIOR RESEARCH METHODS
(2022)
Article
Geosciences, Multidisciplinary
Dean S. Oliver
Summary: The choice of a prior model has a significant impact on the data assimilation capability. The hierarchical approach is more robust against model misspecification. This paper discusses three methods for sampling from the posterior for hierarchical parameterizations and applies them to specific problems.
MATHEMATICAL GEOSCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Saubhagya S. Rathore, Grace E. Schwartz, Scott C. Brooks, Scott L. Painter
Summary: In this study, a Bayesian joint-fitting scheme is proposed to calibrate the entire biogeochemical model at once by simultaneously fitting all available datasets using the MCMC method. The joint fitting of datasets allows for complete uncertainty propagation and parameter estimates informed by all available data.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Ecology
Hanna M. McCaslin, Abigail B. Feuka, Mevin B. Hooten
Summary: Bayesian hierarchical models play a crucial role in ecology, but can be computationally intensive. Recursive Bayesian computing and transformation-assisted RB methods help improve the efficiency and interpretability of Bayesian models, reducing computation time for fitting complex ecological statistical models.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Engineering, Electrical & Electronic
Rubinder Nagi, Xun Huan, Yu Christine Chen
Summary: This paper proposes an analytically tractable Bayesian method to infer parameters in power system dynamic models based on noisy measurements. The method bypasses the need for system model simulations and improves scalability for large-scale power systems.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Automation & Control Systems
Shailesh Garg, Souvik Chakraborty
Summary: VB-DeepONet is a Bayesian operator learning framework that addresses the challenges faced by the deterministic DeepONet architecture. It provides better resistance against overfitting, improved generalization, and allows for the quantification of predictive uncertainty. The results from various mechanics problems demonstrate the effectiveness of VB-DeepONet in uncertainty quantification.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Marine
Antti Solonen, Ramona Maraia, Sebastian Springer, Heikki Haario, Marko Laine, Olle Raty, Jukka-Pekka Jalkanen, Matti Antola
Summary: Mathematical models for ships' consumption play a crucial role in assessing CO2 emissions and optimizing vessel operations. This paper presents a hierarchical Bayesian approach that fits a single model over multiple vessels, exploiting similarities in vessel characteristics. By borrowing information from similar ships, the approach improves parameter estimation and enables prediction of vessel behavior based on characteristics alone. The model is tested using real data from 64 ships and shows higher accuracy compared to existing methods.
Article
Computer Science, Artificial Intelligence
Hui Lan, Ziquan Liu, Janet H. Hsiao, Dan Yu, Antoni B. Chan
Summary: This article proposes a novel HMM-based clustering algorithm, which clusters HMMs through their densities and priors, and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Energy & Fuels
Carmen Maria Calama-Gonzalez, Phil Symonds, Giorgos Petrou, Rafael Suarez, Angel Luis Leon-Rodriguez
Summary: This paper presents the application of a Bayesian calibration approach to improve the energy efficiency of buildings, showing that the combination of sensitivity analysis and Bayesian calibration techniques can enhance the agreement between on-site measurements and simulated outputs.
Article
Computer Science, Artificial Intelligence
Wentao Fan, Lin Yang, Nizar Bouguila
Summary: This paper proposes an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data, introduces a closed-form optimization algorithm based on collapsed variational Bayes inference, and demonstrates the merits of the proposed models through experiments on gene expression data clustering and depth image analysis.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Fisheries
Chen Zhang, Michael T. Brett, Jens M. Nielsen, George B. Arhonditsis, Ashley P. Ballantyne, Jackie L. Carter, Jacob Kann, Dorthe C. Muller-Navarra, Daniel E. Schindler, Jason D. Stockwell, Monika Winder, David A. Beauchamp
Summary: Emerging evidence suggests that zooplankton production is affected by climate change and eutrophication, which could have broad implications for food-web dynamics and fisheries production. A resource-based model developed in this study shows that seasonal variation in resource availability and quality greatly influences zooplankton production, providing important insights for understanding the biophysical control of zooplankton under a changing climate.
CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES
(2022)
Article
Environmental Sciences
Lewis A. Molot, David C. Depew, Arthur Zastepa, George B. Arhonditsis, Susan B. Watson, Mark J. Verschoor
Summary: Studies suggest that high concentrations of nitrate can mitigate cyanobacteria populations in eutrophic systems and that reduced surficial sediments precede the formation of large cyanobacteria populations. Remedial improvements to wastewater treatment plant oxidation capacity may inadvertently contribute to high nitrate concentrations and thus mitigate cyanobacteria populations. Lowering nitrate concentrations could result in earlier formation of cyanobacteria populations in the summer.
JOURNAL OF GREAT LAKES RESEARCH
(2022)
Article
Meteorology & Atmospheric Sciences
Lamees Shah, Carlos Alberto Arnillas, George Arhonditsis
Summary: Forecasts of increased frequency of meteorological extremes have significant implications for biotic communities, terrestrial and aquatic environments, ecosystem services, and societal prosperity. Canada is expected to experience greater warming rates than other regions, and changes in meteorological extremes vary across the country. Large-scale atmospheric oscillations have a discernible impact on air temperature and humidity variables, but less influence on relative humidity, wind speed, and precipitation variability. The observed trends of air temperature, humidity, and wind speed extremes have profound effects on the phenology of ecosystems and human experience of weather.
WEATHER AND CLIMATE EXTREMES
(2022)
Editorial Material
Ecology
George Arhonditsis
ECOLOGICAL INFORMATICS
(2022)
Article
Environmental Sciences
Alexey Neumann, E. Agnes Blukacz-Richards, Ratnajit Saha, Carlos Alberto Arnillas, George B. Arhonditsis
Summary: This study examines the ability of a SPARROW-based model to assess regional P export coefficients for nutrient mitigation and watershed management. Multi-agency water quality data were collected to overcome limitations in monitoring stations. A Bayesian hierarchical framework was used to estimate nutrient loading during different flow regimes. Agriculture and urban runoff were identified as major non-point sources, with different contributions during dry and wet years. The study highlights the importance of mitigating urban non-point sources and controlling agricultural runoff.
JOURNAL OF GREAT LAKES RESEARCH
(2023)
Article
Engineering, Civil
Ali Saber, Vincent Y. S. Cheng, George B. Arhonditsis
Summary: Understanding the drivers of water level variability in large water bodies is crucial for developing proactive mitigation plans. By analyzing the influence of climate oscillations on Lake Huron-Michigan's water budget, it was found that these oscillations had a stronger impact on water levels after 1980. Furthermore, after removing atmospheric effects, changes were observed in runoff and river flow rates, indicating the effects of human activities on the regional water cycle.
JOURNAL OF HYDROLOGY
(2023)
Article
Ecology
Felicity J. Ni, George B. Arhonditsis
Summary: Mercury (Hg) sequestration by phytoplankton and subsequent consumption by herbivorous zooplankton can mediate the transfer of mercury to higher trophic levels. This study introduces two prey species to a predator-prey system to investigate the effects of different prey items on zooplankton assemblages. The results show that the nutritional quality of prey is a major driver of predator-prey relationships, with higher nutritional quality leading to prey-dominated food webs. The study also suggests that the homeostatic rigidity of the predator can help cope with toxic exposure.
ECOLOGICAL INFORMATICS
(2023)
Article
Environmental Sciences
Yuko Shimoda, Haibin Cai, Yasasi Fernando, Akunne Okoli, Zhuowei Xu, Marten Koops, Timothy B. Johnson, George B. Arhonditsis
Summary: Food web theory predicts that oligotrophication can lead to a decline in fisheries, but emerging evidence suggests that more complex trophic interactions can lead to ecosystem responses that deviate from theoretical predictions, especially in shallow littoral zones. Two end-to-end modelling strategies were used in this study to characterize potential food web structural shifts and overall ecosystem productivity in response to oligotrophication. The analysis suggests that reduction in phosphorus levels may not necessarily trigger a significant decline in fish biomass in the studied bay.
JOURNAL OF GREAT LAKES RESEARCH
(2023)
Article
Environmental Sciences
Carlos Alberto Arnillas, Roya Abedi, Camilla Parzanini, Ursula Strandberg, Michael T. Arts, Satyendra P. Bhavsar, George B. Arhonditsis
Summary: We investigated the relationship between biochemical and morphometric traits in sixteen fish species from the Canadian waters of the Laurentian Great Lakes. The study focused on the correlations between fish length, condition factor, lipid content, and polyunsaturated fatty acid (PUFA) composition in the dorsal muscle tissue. Linear relationships were found to be better representations of the correlations. Interspecific and among lakes differences accounted for most of the variability in fatty acid composition.
JOURNAL OF GREAT LAKES RESEARCH
(2023)
Article
Biology
A. O. Achieng, G. B. Arhonditsis, N. Mandrak, C. Febria, B. Opaa, T. J. Coffey, F. O. Masese, K. Irvine, Z. M. Ajode, K. Obiero, J. E. Barasa, B. Kaunda-Arara
Summary: Africa is facing extensive biodiversity loss due to environmental changes, lack of data and resources, as well as insufficient capacity to implement conservation measures. The lack of harmonized indicators and databases further hinders effective policies and monitoring. The article emphasizes the importance of establishing monitoring programs to inform evidence-based decisions for ecosystem conservation and restoration in Africa.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2023)
Article
Ecology
Amanda L. L. Loder, Sophia A. A. Zamaria, George B. B. Arhonditsis, Sarah A. A. Finkelstein
Summary: Freshwater marsh restoration is a potential natural climate solution, but the preservation of organic carbon in marsh soils over policy-relevant timescales is uncertain. Comparative analysis of undrained reference marsh, passively restored marsh, and actively restored marshes in Canada showed that the reference site had the highest organic carbon accumulation and mass, while restored wetlands had variable organic carbon masses. Passive restoration generated high rates of organic carbon accumulation in low-lying sites with appropriate substrate and hydrology. Active restoration measures may promote organic carbon preservation, especially in fine-grained soil. The selection of restoration sites should consider substrate, topographic gradient, and hydrology for maximizing carbon sequestration.
RESTORATION ECOLOGY
(2023)
Article
Environmental Sciences
Ursula Strandberg, George Arhonditsis, Petri Kesti, Jussi Vesterinen, Jussi S. Vesamaeki, Sami J. Taipale, Paula Kankaala
Summary: Shallow littoral areas in lakes are important habitats for diverse invertebrate and vertebrate species, and their abundance, diversity, and nutritional quality are influenced by various environmental factors. Lake typology, habitat, water chemistry, and latitude all play a significant role in determining the taxon richness, abundance, and content of polyunsaturated fatty acids (PUFAs) in littoral macroinvertebrate communities. Understanding these relationships is crucial for maintaining the ecological balance and functioning of lakes.
Editorial Material
Ecology
Falk Huettmann, George Arhonditsis
ECOLOGICAL INFORMATICS
(2023)
Article
Ecology
Haibin Cai, Yuko Shimoda, Jingqiao Mao, George B. Arhonditsis
Summary: With the development of computational power, complex mathematical models have been developed to explicitly represent the functional diversity of biotic communities and multiple biogeochemical cycles. In this study, a novel multi-pronged sensitivity analysis (SA) framework was proposed, integrating advanced statistical and machine learning techniques. The framework was applied to examine competition patterns and structural shifts among multiple functional phytoplankton and zooplankton groups in a complex aquatic biogeochemical model. The results showed the influential parameters for recreating plankton community dynamics during different seasons, and discussed the importance of ML-based SA framework in understanding parametric interactions in complex mathematical models.
ECOLOGICAL INFORMATICS
(2023)
Article
Engineering, Civil
M. Georgina Kaltenecker, Carl P. J. Mitchell, E. Todd Howell, George Arhonditsis
Summary: The shape and evolution of a concentration-discharge (C-Q) relationship can provide valuable insights into hydrological pathways, biogeochemical production and uptake, and the impact of catchment characteristics on export dynamics. This study uses statistical models to establish linkages between watershed attributes and water quality constituents in Ontario, Canada. The results reveal enriching behavior for several constituents, while others show source limitation or dilution patterns. The study highlights the interplay between anthropogenic stressors and biogeochemical processes, which is crucial for understanding nonpoint-source pollution.
JOURNAL OF HYDROLOGY
(2023)
Article
Ecology
Florian Lecorvaisier, Dominique Pontier, Benoit Soubeyrand, David Fouchet
Summary: Research has found that the use of vaccines that do not entirely block pathogen transmission may lead to the evolution of more virulent strains. High vaccine coverage favors the emergence and prevalence of avirulent strains, and competition between strains is crucial for the eradication of toxigenic strains when these vaccines are used.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Monica E. Barros, Ana Arriagada, Hugo Arancibia, Sergio Neira
Summary: The stock biomass of carrot prawn in the south-central area of Chile has decreased in the past 12 years, mainly due to fishing mortality. Predation mortality has been less studied and quantified, so it is important to estimate and compare predation and fishing mortality to understand their effects on fishing stocks. A food web model was built to analyze the biomass changes and evaluate the relative contribution of different mortality factors. The results showed that predation mortality was the main component of total mortality for carrot prawns and yellow prawns.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Shubham Krishna, Victoria Peterson, Luisa Listmann, Jana Hinners
Summary: This study incorporated viral dynamics into an ecosystem model to investigate the effects of viruses on ecosystem dynamics under current and future climatic conditions. The results showed that the presence of viruses increased nutrient retention in the upper water column, leading to a reduction in phytoplankton biomass and transfer of biomass to higher trophic levels.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Zahra Dehghan Manshadi, Parastoo Parivar, Ahad Sotoudeh, Ali Morovati Sharifabadi
Summary: This study demonstrates the importance of strategies such as limiting built-up areas, preserving green spaces, and protecting water resources on the urban carrying capacity in arid and semi-arid regions. Implementing a combination of policies aimed at enhancing urban green spaces and regulating water demand is found to be the most effective in terms of health and urban carrying capacity.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Shay S. Keretz, Daelyn A. Woolnough, Todd J. Morris, Edward F. Roseman, David T. Zanatta
Summary: This study surveyed native freshwater mussels in the St. Clair-Detroit River system and found 14 live unionids representing 9 species. However, the model used to predict their presence in the main channels was not successful. The study also revealed characteristic differences between the St. Clair and Detroit Rivers.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Zhengrong Zhang, Xuemei Li, Xinyu Liu, Kaixin Zhao
Summary: This study examines land use change in the Chinese Tianshan mountainous region using system dynamics and patch-generating land use simulation models. The results show an expansion in forest and construction land, a decline in grassland area, and an increase in cultivated land area from 2005 to 2020. By 2040, unused land, grassland, and water are expected to decrease while other land types increase, with construction land showing the most significant increase. The study provides insights for future ecological and environmental management in the region.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Amira Khelifa, Nadjia El Saadi
Summary: This paper develops an agent-based model to study malaria disease transmission, taking into account the interactions between hosts, vectors, and aquatic habitats, as well as their geographical locations. The simulation results highlight the significant role of aquatic habitats in infection transmission and disease persistence, and demonstrate the effectiveness of eliminating these habitats in limiting disease transmission.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Guillaume Peron
Summary: The theory for movement-based coexistence between species often overlooks small-scale, station-keeping movements. However, at this scale, there are many instances where positive correlations exist between species traits that are expected to be negatively correlated based on current theory. Through simulations, the researcher presents a counter-example to demonstrate that functional tradeoffs are not a necessary condition for movement-based coexistence. This study highlights the significance of species-specific space use patterns under the time allocation tradeoff hypothesis.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Sandra Y. Mendiola, Nicole M. Gerardo, David J. Civitello
Summary: Research on the use of insect microbial symbionts as a means of controlling the spread of insect vectors and the pathogens they carry has made significant progress in the last decade. This study focused on the relative importance of simultaneous effects caused by a symbiont called Caballeronia spp. on the ability of squash bugs to transmit phytopathogenic Serratia marcescens. The researchers found that infection with Caballeronia significantly reduced pathogen titers and cleared S. marcescens in bugs, thus reducing the vectoring potential of these pests. The study also showed that maximizing symbiont prevalence in the vector population is crucial for effectively mitigating plant infections.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Shirui Hao, Dongryeol Ryu, Andrew W. Western, Eileen Perry, Heye Bogena, Harrie Jan Hendricks Franssen
Summary: This study investigates the sensitivity of model yield prediction to uncertainties in model parameters and inputs using the Sobol' method. The results show that yield is more sensitive to changes in water availability and nitrogen availability, depending on soil, management, and weather conditions.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Nitika Mundetia, Devesh Sharma, Aditya Sharma
Summary: This study focused on assessing groundwater sustainability using different modeling approaches in a river basin in Rajasthan, India. The results showed a decrease in future groundwater recharge and emphasized the need for better management and conservation practices to achieve sustainable development goals.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Sukdev Biswas, Sk Golam Mortoja, Ritesh Kumar Bera, Sabyasachi Bhattacharya
Summary: Bacteria play a crucial role in regulating the nutrient cycle of ecosystems, and maintaining a thriving bacterial population is essential for the sustainability of these environments. This study introduces the concept of cooperation as a group defense mechanism employed by bacteria and incorporates it into the functional response, offering a more comprehensive understanding of the complex tritrophic food chain dynamics. The results highlight the importance of a balance between strong group defense and moderate cooperation for bacteria sustainability and overall system stability.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
D. Z. M. Le Gouvello, S. Heye, L. R. Harris, J. Temple-Boyer, P. Gaspar, M. G. Hart-Davis, C. Louro, R. Nel
Summary: This study modeled the dispersal pathways and compared potential dispersal corridors of different sea turtle species in the Western Indian Ocean. The results showed that ocean currents play a major role in driving dispersal, with species and years exhibiting differences in dispersal patterns. Active swimming had little influence on dispersal during the first year.
ECOLOGICAL MODELLING
(2024)
Review
Ecology
Yingying Duan, Haina Rong, Gexiang Zhang, Sergey Gorbachev, Dunwu Qi, Luis Valencia-Cabrera, Mario J. Perez-Jimenez
Summary: Computing models are an effective way to study population dynamics of endangered species like giant pandas. This paper proposes a unified framework and conducts a comprehensive survey of computing models for giant panda ecosystems. Multi-factor computing models are more suitable for studying giant panda ecosystems.
ECOLOGICAL MODELLING
(2024)
Article
Ecology
Samantha Lai, Theophilus Zhi En Teo, Arief Rullyanto, Jeffery Low, Karenne Tun, Peter A. Todd, Siti Maryam Yaakub
Summary: Understanding the exchange of genetic material among populations in the marine environment is crucial for conservation efforts. Agent-based models are increasingly used to predict dispersal pathways, including for seagrasses. This study highlights the importance of considering both sexual propagules and asexual vegetative fragments when evaluating seagrass connectivity.
ECOLOGICAL MODELLING
(2024)