Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production by Feofilovs Maksims, Zaeemi Majid, Cappelli Andrea, Romagnoli Francesco

Get full text

The analysis of cascade refrigeration machine cycles efficiency using carbon dioxide by A. S. Khrekin, I. V. Baranov, A. A. Nikitin

Get full text

Machine-learning unravels spatial shifting in homogeneous rainfall subregions in Central Africa under global warming by Alain T Tamoffo, Fernand L Mouassom, Torsten Weber

Get full text

Technological Scheme for Utilizing Exhaust Gas Heat from a Self-Propelled Machine by N. M. Ivanov, A. B. Ivannikov, A. I. Dusantaev, G. M. Krochta, V. A. Krum

Get full text

Complex Approach to the Conversion of Existing Refrigeration Systems to A2L Group Refrigerants by Serhii M. Molskyi, Oleksandr S. Molskyi, Anna L. Vorontsova

“…Modern requirements for refrigeration equipment include the cessation of the use of systems with refrigerants that destroy the ozone layer, as well as a gradual reduction in the use of refrigerants with a high impact on global warming. The current task is to replace an environmentally unacceptable refrigerant with a neutral refrigerant for ozone and with a low global warming potential. …”

Soft Computing Solutions for Reducing the Carbon Footprint of Fly Ash Based Concrete. Advances in Civil Engineering by Awoyera, Paul O., Adetola, Joshua, Nayeemuddin, Mohammed, Mewada, Hiren, George Fadugba, Olaolu

“…The construction industry significantly contributes to environmental degradation,with many structures exhibiting high carbon footprints throughout their construction processes and lifespans.Activities such as cement hydration and other commoncon-struction practices substantially influence environmental conditions overtime,necessitating a critical evaluation of material and design choices.This study reported the environmental impact of fly ash(FA),which is largely used to enhance concrete strength.A prediction of two end point indicators,that is,global warming potential(GWP)and CO2 emission using soft computing methods are presented,which are particularly effective for handling complex,non linear relationships in environmental data.To achieve this, two machine learning approaches,the random forest(RF)and decision tree(DT)models,are employed to assess the environ- mental impact of structural materials and designs.Two data sets were obtained from reputable databases,including ResearchGate, Science Direct, Semantic Scholar,and Mendeley Data.The models are trained to explore the potential for optimizing structural designs and material selection stominimize environmental impacts.Feature importance is analyzed using Shapley values,providing insights into the most influential factors affecting GWP and CO2 emission Model performance is evaluated using R2 and root mean square error(RMSE) metrics. …”
Get full text

Machine learning integration in thermodynamics: Predicting CO2 mixture saturation properties for sustainable refrigeration applications by Carlos G. Albà, Ismail I.I. Alkhatib, Lourdes F. Vega, Fèlix Llovell

“…The need for sustainable alternatives in refrigeration has grown as Europe enforces mandates on avoiding high global warming potential (GWP) refrigerants. CO₂-based refrigerants have emerged as a promising choice in response, distinguished by its low GWP and reduced flammability, compared to formulated hydrofluoroolefins, thus offering a safer and sustainable solution in the context of next generation drop-in refrigerants. …”
Get full text

Enhanced Satellite Monitoring of Dryland Vegetation Water Potential Through Multi‐Source Sensor Fusion by J. Du, J. S. Kimball, J. S. Guo, S. A. Kannenberg, W. K. Smith, A. Feldman, A. Endsley

“…Abstract Drylands are critical in regulating global carbon sequestration, but the resiliency of these semi‐arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical‐Infrared (IR) and microwave remote sensing observations to quantify plant‐to‐stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. …”
Get full text

Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach by Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee

Get full text

Global Predictability of Marine Heatwave Induced Rapid Intensification of Tropical Cyclones by Soheil Radfar, Ehsan Foroumandi, Hamed Moftakhari, Hamid Moradkhani, Gregory R. Foltz, Alex Sen Gupta

“…Building upon this, we propose an ensemble machine learning model for RI forecasting based on storm and MHW characteristics. …”
Get full text

An Intelligent Long Short-Term Memory-Based Machine Learning Model for the Potential Assessment of Global Hydropower Capacity in Sustainable Energy Transition and Security by Muhammad Amir Raza, Abdul Karim, Mohammed Alqarni, Mahmoud Ahmad Al-Khasawneh, Touqeer Ahmed Jumani, Mohammed Aman, Muhammad I. Masud

Get full text

An observational laboratory study to evaluate an anesthetic gas adsorber without anesthetic gas scavenging system by Katja Nickel, Christian Thoben, Christiane E. Beck, Jan Karsten, Terence Krauß, Alexander Nitschke, Moritz Hitzemann, Stefan Zimmermann, Sebastian Heiderich

Get full text

Estimation for Reduction Potential Evaluation of CO₂ Emissions from Individual Private Passenger Cars Using Telematics by Masahiro Mae, Ziyang Wang, Shoma Nishimura, Ryuji Matsuhashi

Get full text

Transition state structure detection with machine learningś by Yitao Si, Yiding Ma, Tao Yu, Yifan Wu, Yingzhe Liu, Weipeng Lai, Zhixiang Zhang, Jinwen Shi, Liejin Guo, Oleg V. Prezhdo, Maochang Liu

“…By applying the method to typical bi-molecular hydrogen abstraction reactions involving hydrofluorocarbons, hydrofluoroethers, and hydroxyl radicals—reactions critical in atmospheric fluoride degradation and global warming potential evaluation, yet extremely challenging to model, we achieve transition state optimizations with an impressive, verified success rate of 81.8% for hydrofluorocarbons and 80.9% for hydrofluoroethers. …”
Get full text

Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions by Fhulufhelo Walter Mugware, Caston Sigauke, Thakhani Ravele

“…The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. …”
Get full text

Enhanced ensemble learning-based uncertainty and sensitivity analysis of ventilation rate in a novel radiative cooling building by Majid Mohsenpour, Mohsen Salimi, Atieh Kermani, Majid Amidpour

Get full text

Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics by Pankaj Lal Sahu, Sukumaran Sandeep, Hariprasad Kodamana

“…Abstract Machine Learning Weather Prediction (MLWP) models have recently demonstrated remarkable potential to rival physics‐based Numerical Weather Prediction (NWP) models, offering global weather forecasts at a fraction of the computational cost. …”
Get full text

Accelerated photonic design of coolhouse film for photosynthesis via machine learning by Jinlei Li, Yi Jiang, Bo Li, Yihao Xu, Huanzhi Song, Ning Xu, Peng Wang, Dayang Zhao, Zhe Liu, Sheng Shu, Juyou Wu, Miao Zhong, Yongguang Zhang, Kefeng Zhang, Bin Zhu, Qiang Li, Wei Li, Yongmin Liu, Shanhui Fan, Jia Zhu

“…Abstract Controlling the suitable light, temperature, and water is essential for plant photosynthesis. While greenhouses/warm-houses are effective in cold or dry climates by creating warm, humid environments, a cool-house that provides a cool local environment with minimal energy and water consumption is highly desirable but has yet to be realized in hot, water-scarce regions. …”
Get full text

Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning by Nayomi Fernando, Lasantha Seneviratne, Nisal Weerasinghe, Namal Rathnayake, Yukinobu Hoshino

“…Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. …”
Get full text

A machine learning approach to identifying climate change drivers in Africa’s bioenergy sector by Adusei Bofa, Temesgen Zewotir

“…Shape Additive Explanation (SHAP) analysis further revealed that bagasse consumption negatively correlates with temperature changes, suggesting its potential as a sustainable energy alternative. Conversely, charcoal consumption, charcoal production, and solid biofuel production exhibited a warming effect, emphasizing their role in exacerbating climate change. …”
Get full text