Valorization of fish processing industry waste for biodiesel production: Opportunities, challenges, and technological perspectives
The fish industry is one of the fastest-growing industries in the world, which generates
massive waste, and its unsafe disposal may pose severe health and environmental hazards …
massive waste, and its unsafe disposal may pose severe health and environmental hazards …
[HTML][HTML] Modeling and optimization of anaerobic digestion technology: Current status and future outlook
T Kegl, ET Jiménez, B Kegl, AK Kralj, M Kegl - Progress in Energy and …, 2025 - Elsevier
Anaerobic digestion (AD) is an important technology that can be engaged to produce
renewable energy and valuable products from organic waste while reducing the net …
renewable energy and valuable products from organic waste while reducing the net …
Machine learning models for ecofriendly optimum design of reinforced concrete columns
CO2 emission is one of the biggest environmental problems and contributes to global
warming. The climatic changes due to the damage to nature is triggering a climate crisis …
warming. The climatic changes due to the damage to nature is triggering a climate crisis …
Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin …
D Wang, J Liu, Q Luan, W Shao, X Fu… - Meteorological …, 2023 - Wiley Online Library
Projecting precipitation changes is essential for researchers to understand climate change
impacts on hydrological cycle. This study projected future precipitation over the Hanjiang …
impacts on hydrological cycle. This study projected future precipitation over the Hanjiang …
[HTML][HTML] Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
Z Lou, X Lu, S Li - Agronomy, 2024 - mdpi.com
Accurate and timely prediction of crop yields is crucial for ensuring food security and
promoting sustainable agricultural practices. This study developed a winter wheat yield …
promoting sustainable agricultural practices. This study developed a winter wheat yield …
[HTML][HTML] Prediction of Rock Fracture Pressure in Hydraulic Fracturing with Interpretable Machine Learning and Mechanical Specific Energy Theory
X Zhuang, Y Liu, Y Hu, H Guo, BH Nguyen - Rock Mechanics Bulletin, 2024 - Elsevier
Hydraulic fracturing stimulation technology is essential in the oil and gas industry. However,
current techniques for predicting rock fracture pressure in hydraulic fracturing face significant …
current techniques for predicting rock fracture pressure in hydraulic fracturing face significant …
The State of Art in Machine Learning Applications in Civil Engineering
Abstract Machine learning (ML) is one of the methods used by the artificial intelligence
approach. Machine learning is used to teach machines how to handle data more efficiently …
approach. Machine learning is used to teach machines how to handle data more efficiently …
No country for old men (or women): The impact of migration on pension funding adequacy and sustainability
Retirement security is of paramount importance to working people. Adequate retirement
income is also a leading concern for private and public pension systems. Pension funding …
income is also a leading concern for private and public pension systems. Pension funding …
A Quantitative Analysis of the Impact of European Promotion Banks on the Competition of EU Countries through Linear Conventional and Machine Learning Methods
C Agiropoulos - SPOUDAI Journal of Economics and Business, 2023 - spoudai.org
In recent years, there has been a growing demand for European Promotional Banks to
become more efficient and effective in their activities. Their role is to support small and …
become more efficient and effective in their activities. Their role is to support small and …
Relevance, Redundancy, and Regularization: Penalized Regression and the Quest for the ℓ₀ Quasi-Norm
JM Chen - Available at SSRN 4188299, 2022 - papers.ssrn.com
The vector of a linear model's coefficients represents the clearest guide to causal inference.
Collinearity among variables, however, undermines the interpretation of that model. A wildly …
Collinearity among variables, however, undermines the interpretation of that model. A wildly …