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Nuclear response to the climate challenge?:The sustainability argument in the European Union taxonomy debate
No full textIn the fight against climate change, nuclear energy as a sustainable energy source has gained interest. The European Union’s taxonomy is a significant initiative because it de- fines at a European level which economic activities can be considered sustainable, and because it gave citizens, businesses, environmental organizations and research institutes the opportunity to provide feedback on certain parts of the taxonomy during two public feedback periods. The aim of our research is to explore how respondents argue about the sustainability of nuclear energy. Our research confirms that those arguing for the inclusion of nuclear energy in the taxonomy stress the low-carbon nature of nuclear energy production and focus on the economic dimension of sustainability. Contrastingly, those arguing for the exclusion of nuclear energy highlight the unsolved problem of ra- dioactive waste disposal and the environmental dimension of sustainability. The social dimension of sustainability is neglected in the arguments justifying both positions.<br/
How Technological and Natural Consumption Experiences Impact Consumer Well-Being: The Role of Consumer Mindfulness and Fatigue
No full textA Novel Enhanced SOC Estimation Method for Lithium-Ion Battery Cells Using Cluster-Based Lstm Models and Centroid Proximity Selection
No full textIn line with the global mission in achieving the net zero target through deployment of renewable energy technologies and electrifying the transportation sector; precise and adaptable State of Charge (SOC) estimation for Lithium-ion batteries has emerged as a critical need. This study introduces an innovative SOC estimation method for Lithium-ion batteries, featuring a novel Cluster-Based Learning Model (CBLM) that integrates K-Means and Fuzzy C-Means clustering with the predictive capabilities of Long Short-Term Memory (LSTM) networks. A key innovation of this research is the application of the CBLM to SOC estimation, paired with a dynamic SOC estimation process enabled by a centroid proximity selection mechanism. This approach significantly enhances the precision and adaptability of SOC predictions under varying operational conditions. Comprehensive evaluations demonstrate the model&#38;apos;s superior performance, with reductions in Root Mean Square Error (RMSE) to as low as 0.65% and Mean Absolute Error (MAE) to 0.51%, reducing state-of-art benchmark model errors by margins of 61.8% and 68.5% respectively. Additionally, the maximum error exhibited a notable decrease, emphasizing the model&#38;apos;s reliability in worst-case-scenarios. These findings set new benchmarks in battery management systems and expands the potential of machine learning applications in energy storage, offering an advanced tool for real-time SOC estimation with enhanced accuracy and reliability
Does Oil Spin the Commodity Wheel? Quantile Connectedness with a Common Factor Error Structure across Energy and Agricultural Markets
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Comprehensive Review and Meta-Analysis of Machine Learning Applications in Screening for Diabetic Retinopathy Analysis
No full textThe leading cause: diabetic retinopathy global blindness, affects 10% to 24% of individuals with type 1 or type 2 diabetes in primary care. Early detection using deep learning methods is critical for timely intervention and preserving vision. Our comprehensive review, including a meta-analysis, evaluates the effectiveness of these algorithms in DR detection. This study extensively assessed machine learning’s diagnostic accuracy in identifying diabetic retinopathy across diverse cases using color fundus images, aiming to pinpoint the most advanced ML strategy available. We extensively reviewed relevant literature from January 2015 to December 2022, utilizing EMBASE, PubMed, Google Scholar, and Scopus. Following PRISMA guidelines, we focused on machine learning-based study designs. Two authors independently assessed articles for inclusion based on predefined criteria, and data were collected using a standardized form. The meta-analysis reveals strong performance of machine learning in detecting diabetic retinopathy in color fundus photos, indicating readiness for clinical use. However, caution is advised due to methodological limitations in some earlier studies, such as lack of external validation and potential biases in participant selectio
Prediction of Health Indicators Based on Exercise, Sleep, and Water Intake: Data-Based Machine Learning Approach
No full textThis project uses machine learning algorithms to build health status prediction models by analyzing multi-dimensional health behavior data such as exercise frequency, type, sleep duration, and water intake. The authors collected datasets on health indicators and further explored how these lifestyle factors collectively influence health indicator values. The health data collected is used to calculate the target variable “health value,” which is the column that distinguishes whether an individual is healthy or not, based on comparing the mean value of the column, with 1 and 0 indicating healthy or unhealthy. By analyzing the data and machine learning algorithms, they discovered the essential and inseparable relationship between these health indicators and an individual's health status. They optimized the model to improve the accuracy of the prediction. They also confirmed the generalization ability and performance of the model by giving the optimized model new health data and obtaining health values, as well as whether it is healthy. The results of this project highlight the importance of integrating data on lifestyle habits for health prediction to give data support to the development of a scientific basis for personalized health management strategies and health promotion interventions
