Article
Microbiology
Ahmed Abdel-Hadi, Bader Alshehri, Mohammed Waly, Mohammed Aboamer, Saeed Banawas, Mohammed Alaidarous, Manikandan Palanisamy, Mohamed Awad, Alaa Baazeem
Summary: This study aimed to generate predictive models for growth, sporulation, and OTA production of Aspergillus ochraceus group under abiotic climatic variables. The models showed high accuracy and reliable agreement between predicted and observed data, providing valuable information for predicting fungal growth and OTA contamination. The effects of abiotic climatic variables on growth, sporulation, and OTA production have been effectively defined, resulting in validated and adequately predicted models for strains within A. ochraceus group.
Article
Engineering, Environmental
Kezhen Wang, Rajith Mukundan, Rakesh K. Gelda
Summary: Predictive models of disinfection byproducts in treated drinking water have been widely used to guide operational decisions. This study proposes a two-component statistical approach to predict the formation potentials of DBPs in source water streams and successfully demonstrates its applicability in two water sources of the New York City water supply system.
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
(2023)
Article
Economics
Zhuan Pei, David S. Lee, David Card, Andrea Weber
Summary: In this paper, we propose a polynomial order selection procedure based on the asymptotic mean squared error of the local regression RD estimator, which performs well in large sample sizes typically found in empirical RD applications. This procedure can be easily extended to fuzzy regression discontinuity and regression kink designs.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Environmental Sciences
Shangqin Liu, Xizhi Zhao, Fuhao Zhang, Agen Qiu, Liujia Chen, Jing Huang, Song Chen, Shu Zhang
Summary: This study proposes a method using multi-source spatial variables and Multiscale Geographically Weighted Regression (MGWR) to downscale NPP-VIIRS nighttime lights (NTL) data. The results show that the spatial resolution of the data is improved after downscaling, and the MGWR-based downscaling results are more accurate than other algorithms.
Article
Computer Science, Artificial Intelligence
Dan Wang, Xiubin Zhu, Witold Pedrycz, Adam Gacek, Aleksander Sobotnicki, Zhiwu Li
Summary: A novel approach is proposed to automatically determine the positions of characteristic points in bioimpedance curves. This method predicts the positions through a linear combination of parameters and demonstrates substantial improvement in prediction accuracy compared to the baseline method, as shown in experimental studies. The proposed method is also robust to noise.
APPLIED SOFT COMPUTING
(2023)
Article
Energy & Fuels
Darya Pyatkina, Tamara Shcherbina, Vadim Samusenkov, Irina Razinkina, Mariusz Sroka
Summary: This study aims to assess the efficiency of cash flow management at power supply companies in the CIS countries. The use of polynomial regression models can accurately predict future cash flow trends and highlights the importance of cash flow synchronization and consistency for financial stability.
Article
Environmental Sciences
Yichen Wu, Xuebin Xu, Colin P. R. McCarter, Nan Zhang, Mohamed A. Ganzoury, James Michael Waddington, Charles-Francois de Lannoy
Summary: The study found that burning peat can lead to a large amount of pollutants leaching into water, exceeding water quality standards from various countries, especially including COD, TN, TP, and phenols, etc. Different heating temperatures of peat result in variations in the composition of leached substances, and the study calls for more monitoring and treatment of fire-affected peatlands.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Remote Sensing
Yang Ye, Linyan Huang, Qiming Zheng, Chenxin Liang, Baiyu Dong, Jinsong Deng, Xiuzhen Han
Summary: A feasible framework is proposed in this study to downscale the NTL imagery of the Suomi National Polar-orbiting Partnership using geographically weighted regression method and multi-source spatial variables, which leads to an improvement in data quality and accuracy.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Green & Sustainable Science & Technology
Taeho Bong, Jin-Kyu Kang, Viviane Yargeau, Hye-Lim Nam, Sang-Hyup Lee, Jae-Woo Choi, Song-Bae Kim, Jeong-Ann Park
Summary: By comparing the adsorption capacities of different carbon materials for geosmin and 2-methylisoborneol, it was found that powdered activated carbon C-PAC had the highest maximum adsorption capacity in both distilled water and river water. Multiple linear regression (MLR) and deep neural network (DNN) models were effective in predicting the removal efficiency of C-PAC for GSM and MIB.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Automation & Control Systems
Carl Emil Eskildsen, Tormod Naes, Peter B. Skou, Lars Erik Solberg, Katinka R. Dankel, Silje A. Basmoen, Jens Petter Wold, Siri S. Horn, Borghild Hillestad, Nina A. Poulsen, Mette Christensen, Theo Pieper, Nils Kristian Afseth, Soren B. Engelsen
Summary: This paper discusses the concept of the cage of covariance in analytical chemistry, where indirect relationships between response and explanatory variables may affect the calibration of multivariate models. It highlights the importance of considering use of the models and validity of indirect relationships in future samples. Additionally, it explores the hidden role of interfering compounds in calibration models and the potential consequences of strong covariance relationships between analyte estimates and interfering compounds.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Engineering, Chemical
S. Chehreh Chelgani, H. Nasiri, A. Tohry, H. R. Heidari
Summary: Undoubtedly, hydrocyclones are crucial in powder technology and have a significant impact on process efficiency in plants. However, there is a lack of industrial-scale modeling of hydrocyclones, which can be used to train operators and reduce scale-up errors and lab costs. This study proposes a novel approach using conscious lab (CL) and explainable artificial intelligence (XAI) to fill this gap. The interactions between hydrocyclone variables were explored using the SHapley Additive exPlanations (SHAP) method and a new machine-learning model, CatBoost. The SHAP-CatBoost model successfully captured all the relationships and achieved higher accuracy in predicting O-80 and K-80 compared to other conventional AI methods.
Article
Forestry
Anil Koirala, Cristian R. Montes, Bronson P. Bullock
Summary: Site-index models based on dominant height and age are commonly used to measure site productivity in forest plantations, however, reliable estimates of dominant height over time are crucial. Factors contributing to forest site productivity are complex, making tree height only a general proxy indicator of these factors. Incorporating environmental variables, particularly climatic water balance components, can lead to more consistent estimates. This study aims to evaluate changes in dominant height estimates of forest stands based on water balance components, leading to improved precision in dominant height estimates for loblolly pine in the southeast US.
FOREST ECOLOGY AND MANAGEMENT
(2021)
Article
Chemistry, Physical
Keya Fu, Dexin Zhu, Yuqi Zhang, Cheng Zhang, Xiaodong Wang, Changji Wang, Tao Jiang, Feng Mao, Xiaobo Meng, Hua Yu
Summary: This study explores the relationship between grain size and tensile strength in aluminum alloys by compiling a comprehensive dataset and utilizing machine learning models. By integrating hardness as a feature variable, a more robust predictor of tensile strength is obtained. Polynomial regression is also used to derive a mathematical relationship between tensile strength, alloy composition, and grain size.
Article
Green & Sustainable Science & Technology
Praanjal Nasery, Ahmed Aziz Ezzat
Summary: Accurate estimation of wind power curves is crucial for various wind farm operations. Existing methods typically use environmental variables to construct the relationship between wind and power. This paper proposes a method that integrates yaw misalignment as an additional input, resulting in improved accuracy. The proposed method uses a local-regression-based approach to reconstruct the relationship between yaw and power, conditional on environmental variables. Testing on real data from two onshore wind turbines in France demonstrates significant improvements compared to existing models.
Article
Computer Science, Theory & Methods
Ben Sherwood, Shaobo Li
Summary: This paper explores model selection and estimation for quantile regression with a known group structure in the predictors. It introduces a new estimation method and validates its effectiveness through simulation and empirical results.
STATISTICS AND COMPUTING
(2022)
Article
Engineering, Civil
Alvaro Ossandon, Balaji Rajagopalan, William Kleiber
Summary: The semi-Bayesian hierarchical modeling framework combines generalized extreme value distribution and Gaussian multivariate process to analyze precipitation extremes over a large domain. By conducting space-time frequency analysis of seasonal maximum precipitation, the model captures historical variability well and has wide applications in natural resources and infrastructure management.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Alvaro Ossandon, Balaji Rajagopalan, Upmanu Lall, J. S. Nanditha, Vimal Mishra
Summary: The novel BHNM model leverages the spatial dependence induced by river network topology and hydrometeorological variables to improve ensemble forecasts of daily streamflow, demonstrating high skill in predicting monsoon period streamflow in Central India. Incorporating upstream flow information and precipitation as covariates allows for modeling spatial correlation of flows with parsimony. The validation results show that the BHNM model outperforms a null-model of generalized linear regression, highlighting its reliability and skillfulness in streamflow predictions.
WATER RESOURCES RESEARCH
(2021)
Article
Archaeology
Adam W. Schneider, Emily C. Gill, Balaji Rajagopalan, Guillermo Algaze
Summary: The climate of the western Indian Ocean during the third millennium BCE played a crucial role in the development of a vast maritime commercial network, with the Indian Summer Monsoon and eastward-blowing trade winds intensifying during that time period. This climate change facilitated southern Mesopotamian polities to shift their resource procurement efforts towards the Persian Gulf and points south and east, enabling them to efficiently import high-bulk metal ores and export low-value, high-bulk agricultural and pastoral goods on a large scale. Additionally, some coastal Arabian and Indus Valley participants may have utilized bulk imports from Mesopotamia to mitigate the effects of drought around 2200 BCE.
JOURNAL OF MARITIME ARCHAEOLOGY
(2021)
Article
Engineering, Environmental
Sarah A. Baker, Balaji Rajagopalan, Andrew W. Wood
Summary: In the Colorado River Basin, climate forecasts are incorporated into ensemble streamflow prediction through variations on a weighted approach, utilizing k-nearest neighbors technique. The study finds that climate-informed forecasts add greater skill in late winter and early spring, with disaggregated-basin use of climate forecasts slightly improving skill over the basin-wide method at most lead times.
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
(2021)
Article
Environmental Sciences
Karl Rittger, Mitchell Krock, William Kleiber, Edward H. Bair, Mary J. Brodzik, Thomas R. Stephenson, Balaji Rajagopalan, Kat J. Bormann, Thomas H. Painter
Summary: Snow not only provides water for nearly 2 billion people, but also influences wildlife resource selection and behavior of many species. Mapping snow cover extent using current satellite data is challenging due to its highly variable nature. Scientists are developing new techniques to accurately map snow cover on a daily basis for various applications such as analyzing regional energy budgets and validating global and regional snow cover models.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Astronomy & Astrophysics
Benjamin D. Abel, Balaji Rajagopalan, Andrea J. Ray
Summary: This study examines the sources and pathways for summer rainfall in the southeast Prairie Pothole Region using the HYSPLIT model. Land is found to be the primary moisture source, with moisture recycling playing a crucial role in precipitation generation. The Great Plains Low-Level Jet/Maya Express is the most significant moisture pathway.
EARTH AND SPACE SCIENCE
(2022)
Article
Meteorology & Atmospheric Sciences
J. S. Nanditha, Balaji Rajagopalan, Vimal Mishra
Summary: Floods in India during the summer monsoon are caused by extreme precipitation and high antecedent soil moisture. The Narmada River basin experiences most high-flow events in August and September, which are related to low mean sea level pressure, strong winds, and high water vapor flux. The Arabian Sea is the primary moisture source for flood-producing storms, but the north Indian plain also contributes during the mid and late monsoon season. Understanding these combined factors can assist in developing early flood warning systems for Indian river basins.
Article
Engineering, Civil
Erin Towler, David Woodson, Sarah Baker, Ming Ge, James Prairie, Balaji Rajagopalan, Seth Shanahan, Rebecca Smith
Summary: This study presents a framework for incorporating mid-term temperature predictions into streamflow forecasting and operational projections, showing marginal improvements in streamflow forecast accuracy with WeighESP. The improvements are more pronounced for recent hindcast dates, but limitations exist in achieving the desired time lead.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2022)
Article
Environmental Sciences
Alvaro Ossandon, Balaji Rajagopalan, Amar Deep Tiwari, Thomas Thomas, Vimal Mishra
Summary: This study demonstrates the utility of a Bayesian hierarchical model combination (BHMC) framework in generating skillful and reliable real-time daily ensemble streamflow forecast and peak flow in the Narmada River basin in India. The framework combines information from multiple sources as predictors and improves the forecast skill by 40% compared to the raw deterministic forecast. It provides sharp and reliable streamflow forecast ensembles for short lead times and can be useful in emergency and disaster preparedness.
Article
Geosciences, Multidisciplinary
J. B. Wycech, B. Rajagopalan, P. H. Molnar, E. Gill, T. M. Marchitto
Summary: Wetter conditions in the African Sahel during the Pliocene, possibly due to warm North Atlantic sea-surface temperatures, played an important role in hominid evolution. The reconstructed Pliocene North Atlantic SSTs showed overall warming compared to the comparison period, with the most extreme warming occurring in specific regions. The warming pattern resembled that of the modern warm phase of the Atlantic multidecadal oscillation.
PALEOCEANOGRAPHY AND PALEOCLIMATOLOGY
(2022)
Article
Multidisciplinary Sciences
Fangfang Yao, Ben Livneh, Balaji Rajagopalan, Jida Wang, Jean-Francois Cretaux, Yoshihide Wada, Muriel Berge-Nguyen
Summary: This study finds that over the past few decades, approximately 53% of the largest 1,972 global lakes have experienced significant declines in water volume. The volume loss in natural lakes is primarily attributed to climate warming, increasing evaporative demand, and human water consumption, while sedimentation dominates storage losses in reservoirs. It is estimated that around one-quarter of the world's population resides in a basin of a drying lake, emphasizing the importance of incorporating climate change and sedimentation impacts into sustainable water resources management.
Article
Environmental Sciences
Alvaro Ossandon, Balaji Rajagopalan, William Kleiber
Summary: We develop a Bayesian hierarchical modeling framework for flood risk attributes using generalized extreme value and Poisson distributions, with non-stationary parameters, and Gaussian copulas for capturing spatial dependence. The best covariates are selected using the WAIC. The framework enables the forecast of flood risk attributes at multiple gauges with useful long lead skill.
WATER RESOURCES RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Fangfang Yao, J. Toby Minear, Balaji Rajagopalan, Chao Wang, Kehan Yang, Ben Livneh
Summary: In nearly all reservoirs, storage capacity is lost due to sediment accumulation, and the sedimentation rates are poorly understood. In this study, a novel approach is proposed to estimate reservoir sedimentation rates and storage capacity losses using satellite images and water level data. The approach is validated on eight reservoirs in the United States and shows good accuracy in estimating the bathymetry and sedimentation rates.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Meteorology & Atmospheric Sciences
Jia Wei, Weiqing Han, Weiguang Wang, Lei Zhang, Balaji Rajagopalan
Summary: By analyzing observational data and conducting model experiments, this study finds that the heatwave intensity in China experienced a significant increase during the transition period of 1993-2000, and this intensification remains robust in northern and western regions even after removing the warming trend. The combined impacts of ENSO, AMO, and IOD explain a substantial portion of the observed heatwave intensification in specific regions. The results emphasize the importance of concurrent phase transitions of decadal climate modes in regulating heatwaves.
NPJ CLIMATE AND ATMOSPHERIC SCIENCE
(2023)
Article
Geosciences, Multidisciplinary
Alvaro Ossandon, Manuela I. Brunner, Balaji Rajagopalan, William Kleiber
Summary: The study aims to implement a space-time model to predict seasonal streamflow extremes, considering nonstationarity and spatiotemporal dependence. Results indicate that the model can capture space-time variability in extreme streamflow well and skill increases as lead time decreases.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)