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A hybrid deep learning and fuzzy logic framework for feature-based evaluation of english Language learners
Green construction with sustainable foam mortar utilizing recycled polyethylene terephthalate waste for enhanced thermal insulation and durability properties
Evaluating the role of renewable energy natural resources and globalization in environmental quality in OIC countries
Abstract Climate change has intensified environmental challenges globally, with greenhouse gas emissions rising more rapidly in Organization of Islamic Cooperation (OIC) countries than the global average. Over half of the OIC member states face high climate vulnerability due to limited institutional and technological capacity for mitigation and adaptation. This study makes a novel contribution by analyzing the drivers of environmental quality in 36 OIC countries from 1996 to 2020 using the load capacity factor (LCF), a holistic indicator that balances ecological demand and biocapacity supply. Unlike prior studies that focus narrowly on CO₂ emissions, LCF provides a broader measure of sustainability. The analysis is stratified by income levels (high, upper-middle, and lower-middle income) to uncover income-specific dynamics. The CS-ARDL approach was employed to estimate both short- and long-run relationships. The findings reveal that economic expansion improves environmental quality in high-income but degrades it in upper- and lower-middle-income groups. Renewable energy consumption consistently enhances environmental sustainability across all income groups, while natural resource dependence undermines it. Economic globalization improves environmental quality in high-income but has adverse effects in middle-income economies. Notably, the rule of law emerges as a strong and consistent driver of environmental improvement across all groups. These findings underscore the need for income-specific strategies that prioritize renewable energy investment, promote sustainable natural resource management, strengthen institutional quality, and integrate environmental objectives into globalization and trade policies.
Cognitive efficiency in VR simulated natural indoor environments examined through EEG and affective responses
Median effective concentration of ropivacaine for preoperative analgesia under ultrasound-guided supra-inguinal fascia Iliaca compartment block in hip fracture patients
Discovery of hyde C1 a broad spectrum antimicrobial peptide derived from chicory
Multi-stage fusion of local and global features for few-shot image classification
The role and mechanism of transcription factor KLF4 in regulating mitochondrial damage and apoptosis by activating chondrocyte autophagy in osteoarthritis
Sleep quality by clinical training status among medical students and its associated factors: a cross-sectional study in Da Nang, Vietnam
Shear strength and deformation characteristics of corn husk fiber-reinforced loess
Transformer based spinal vertebrae localization and scoliosis curvature classification
Feasibility of rTMS combined with dexmedetomidine in chronic insomnia disorder
Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy
Anticancer efficacy of albumin nanoparticles co-loaded with silver nanoparticles and 5FU in animal model of colon cancer
Structural uncertainty in mapping Euro-Atlantic atmospheric rivers obscures understanding of associated meteorological extremes
Abstract Understanding uncertainties in meteorological extremes induced by Atmospheric river (AR) structural uncertainties can help to develop effective strategies to mitigate AR induced hazards and adapt to changing climate conditions. As a first step, this study examines the statistical relationship between AR structural uncertainty and the characterisation of associated meteorological extremes over the Euro-Atlantic region, using long-term historical data from ECMWF Reanalysis v5 (ERA5) during 1940 to 2022. Leveraging the Bayesian AR detection (BARD), a form of statistical machine learning model in the Toolkit for Extreme Climate Analysis (TECA), we examine the impact of structural uncertainties in AR dimensions on daily precipitation (wet), wind speeds (windy), and temperature (warm/cold) anomalies and extremes over Europe, the UK and Scandinavia. A large spread in the aggregated detected AR probabilities (ARP) spatially and temporally led to differences in ARs’ attributes, such as frequency, integrated water vapour transport (IVT; intensity), and their impact on weather parameters, anomalies and extremes at selected probability thresholds across space and time. The magnitude of AR impacts and associated meteorological phenomena over land varies based on the chosen deciles (dividing ARP into ten equal parts with a 0.1 increase) of ARPs, along with the default threshold from the model ( $$ARP \ge 0.67$$ ). AR intensities and landfalling area are increasing over the study period, irrespective of the selected ARP. The effects of AR structural uncertainties are more prominent over inland Europe and Scandinavia than over coastal Europe and the UK. The physical and meteorological phenomena underlying these results require further exploration to understand the impact of landfalling ARs on land.