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A comprehensive review of carbon sequestration and its assessment techniques using remote sensing and geospatial methods
Get PDFGlobal warming has elevated carbon sequestration as a critical strategy for mitigating climate change, while enhancing sustainability in productivity. Agricultural land use systems contribute substantially to CO2 emissions due to crop residues, shifting cultivation practices, low-biomass crops, land degradation, and deforestation. The significant rise in CO2 emissions over the past thirty years is associated with burning fossil fuels, leading to substantial environmental changes, including global warming. Remote sensing (RS) and Geographic Information Systems (GIS) are advanced geospatial technologies that facilitate the rapid evaluation of terrestrial carbon stock over extensive regions. An integrated RS-GIS approach for carbon stock estimation and precision carbon management is a time and cost-effective strategy for implementing appropriate management at local and regional scales. The paper reviews various remote sensing (RS) methodologies for evaluating carbon sequestration (CS), focusing on various land ecosystems associated with vegetative indices and biomass that address carbon stocking. It explores associated challenges, opportunities, and emerging trends, examining conventional and RS techniques while highlighting their limitations and current and developing methodologies while identifying the key RS variables essential for representing predictors of carbon sequestration. This also highlights the importance of geospatial tools in evaluating different community services. The paper evaluates several approaches and sensors, such as optical, RADAR, and LiDAR-based RS, commonly used for biomass estimation and CS assessment. The paper concludes by emphasizing the need for further research to bridge gaps and address challenges in implementing these new strategies for precision carbon management. Overall, geospatial technologies are valuable tools for accurate carbon sequestration estimation, particularly in remote and challenging terrains, and benefit the research communities focused on the carbon cycle, remote sensing, climate change elucidation, and global climate changes
Changes in energy homeostasis, gut peptides, and gut microbiota in Emiratis with obesity after bariatric surgery
Get PDFBackground Obesity is a growing health concern worldwide, including United Arab Emirates. Bariatric surgery is an effective treatment option, with to date unclear weight loss mechanisms. In this prospective study, we explored post-bariatric surgery changes in energy homeostasis, gut peptides, hormones, and gut microbiota. Method We recruited 19 Emirati adults who were planning to undergo sleeve gastrectomy (SG). We assessed the energy requirements using 24-hour diet recalls, indirect calorimetry for resting energy expenditure (REE), and a questionnaire for appetite. Anthropometrics included body mass index (BMI), waist circumference, waist-to-height ratio, fat mass, fat-free mass, and percentage of body fat. Gut peptides, including peptide YY (PYY), glucagon-like peptide-1/2 (GLP-1/2), ghrelin (GHR), cholecystokinin (CCK), insulin, and leptin, were quantified using ELISA. Gut microbiota composition at phylum and genus levels, including the Firmicutes/Bacteroidetes (F/B) ratio and alpha (α) and beta (β) diversity, was determined by sequencing amplicons of the V3-V4 region of the 16S rRNA at baseline and three months post-surgery. Comparisons used paired sample T-test, Wilcoxon, and McNemar test. QIIME 2 was used to identify taxa and their relative abundance; subsequent analyses were done in R for (α) and (β) diversity (package qiime2R) and Wilcoxon signed-rank test in R for differences in microbiota at phylum and genus levels. We conducted Spearman correlation analyses between genera and energy homeostasis, appetite, anthropometrics, hormones, and gut peptides. Results At three months post-SG, energy intake, appetite, all anthropometric indices, insulin, leptin, and GLP-1 significantly decreased; PYY and GHR significantly increased, and REE was stable. β-diversity of the gut microbiota and its composition at phylum and genus levels significantly changed post-surgery, yet F/B remained constant. Energy intake, BMI, and appetite negatively correlated with several taxa that significantly increased post-SG. Conclusion Gut peptides, hormones, and microbiota change partly account for bariatric surgery’s weight-loss benefits. Understanding these alterations can inform personalized interventions targeting obesity
Digital Transformation of Education: An Integrated Framework for Metaverse, Blockchain, and AI-Driven Learning
Get PDFThe integration of Metaverse, Blockchain, and Artificial Intelligence (AI) has the potential to revolutionize the educational landscape by providing immersive, secure, and personalized learning environments. This study proposes a conceptual framework that combines these technologies to address the key challenges faced by contemporary education systems, including accessibility, engagement, security, and personalization. The Metaverse serves as the immersive platform, offering virtual classrooms, interactive simulations, and gamified learning experiences. Blockchain provides the foundation for secure and transparent academic records, enabling tamper-proof credential verification and decentralized data management. AI enhances the educational experience by powering adaptive learning systems, predictive analytics, and intelligent tutoring systems that personalize content delivery and identify at-risk students. This framework aims to foster a more inclusive, efficient, and student-centered learning ecosystem. Practical use cases demonstrate how the integration of these technologies can improve STEM education, medical training, credentialing systems, and inclusive learning environments. However, the implementation of these technologies presents challenges related to infrastructure costs, regulatory compliance, and ethical considerations in AI decision-making. Future research should explore the empirical validation of this framework, scalability issues, and strategies for overcoming adoption barriers to fully realize the transformative potential of these technologies in education
Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems
Get PDFThis research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during such conditions. To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. The models were evaluated across multiple forecasti24 hoursons (1, 6, 12, and 24 hours), demonstrating that including dust-related variables significantly enhances prediction accuracy, particularly for short-term forecasts. Temporal and seasonal analyses revealed that dust events, most frequent in the late afternoon and early spring, correlate with substantial drops in solar power output. The LSTM model consistently outperformed the others, achieving a Mean Absolute Error (MAE) of 0.018034 for a 1-hour horizon when dust features were included. Statistical tests confirmed that dust events significantly affect forecasting accuracy, reinforcing the importance of dust-related features for reliable predictions. This research contributes to optimizing PV power generation in challenging environments, supporting sustainable energy systems and decarbonization efforts. It also offers insights for further model refinement and the inclusion of additional environmental variables
Finite-Difference Approximation for Explaining Neural Network Predictions in Financial Credit Evaluation
No full textFinancial credit evaluation is an essential process that enables lenders to assess the creditworthiness of applicants. While deep learning (DL) models have shown high predictive accuracy in financial risk assessment, their lack of interpretability limits trust and regulatory compliance. Explainable AI (XAI) methods such as SHAP and LIME offer feature attribution, but they suffer from instability and reliance on external data. To address these challenges, this paper introduces the One-sided Linear Gradient Approximation (OLGA), a deterministic and computationally efficient explainability method based on finite-difference approximations and direct perturbation. OLGA provides direct attributions without requiring background data or extensive sampling, making it scalable for high-dimensional financial datasets. We compare OLGA against SHAP and LIME using key explanation metrics, including infidelity, sparsity, and sensitivity, across four deep learning architectures: Multi-Layer Perceptron, Convolutional Neural Network, Transformer, and Autoencoder. Experimental results indicate that OLGA achieves competitive fidelity and provides computational efficiency and stability compared to LIME and SHAP
Health Care and Medical System for Early Detection of Lung Cancer Using Integrated Intelligent Techniques
No full textIn recent years, non-communicable diseases, particularly cancer, have seen a significant rise in prevalence, with lung cancer posing a major challenge in diagnostics and detection. Timely identification of lung cancer can potentially improve public health outcomes and save lives by enabling physicians to provide targeted treatments. Accurate classification of lung cancer using medical imaging data can reduce mortality rates associated with the disease. Despite significant advancements in convolutional neural networks (CNN) for lung cancer detection, predicting lung cancer remains challenging due to the complexity of CT scans. Many models face challenges related to insufficient labeled data and overfitting, which hinder accuracy. We introduced the 4DCNN model to address these issues, with and without data augmentation. Our model outperformed previous approaches, achieving exceptional results with an accuracy of 99.05%, precision of 97.55%, recall of 98.51%, and F1-score of 97.99% in classifying Normal vs. Adenocarcinoma. The developed lung cancer diagnostic model also demonstrated superior performance across three additional lung cancer classes when all performance indicators were evaluated
Safeguarding of Intangible Cultural Heritage in the UAE: Preserving Emirati Crafts Through Digital Innovation
No full textThe preservation of intangible cultural heritage is essential for maintaining cultural diversity and fostering intercultural dialogue, particularly in the face of globalization. In the United Arab Emirates (UAE), traditional crafts such as Al Sadu, Al Khos, Al Talli, and Sea Crafts embody national identity and craftsmanship. However, modernization and shifting lifestyle preferences have led to a decline in the transmission of these skills, putting them at risk of fading from Emirati culture. This research explores strategies for safeguarding traditional Emirati crafts by integrating modern digital tools to engage younger generations effectively. The study highlights the significance of traditional crafts as carriers of historical and cultural narratives, emphasizing the need for innovative approaches to make them more accessible. It proposes the use of mobile applications, interactive learning platforms, and craft kits as contemporary solutions for cultural preservation. By leveraging technology, these crafts can be revitalized, ensuring intergenerational knowledge transfer and sustaining local artisanship. Ultimately, this research seeks to bridge the gap between tradition and modernity by fostering a renewed appreciation for intangible heritage among Emirati youth. Through digital engagement and community-driven initiatives, this study aims to contribute to the sustainable preservation of cultural traditions in an evolving social landscape
Beyond objectivity: interventionist journalism and professional role performance in Global South media landscape
No full textThis study investigates journalistic interventionism within the diverse media landscape of the Global South. Journalism in these regions sometimes takes an interventionist approach. Understanding this approach necessitates moving beyond Western-centric paradigms that may overlook Global South media’s distinctive historical, political, economic, and sociocultural circumstances. The primary objective of this research is to understand the manifestation of the interventionist journalistic role performance across various Global South countries, examining its overall prominence and the influential factors that affect interventionist role deployment. The study examines interventionist journalism in 16 Global South nations using quantitative content analysis of 59,391 news items from 153 media outlets based on the operationalization framework to measure journalistic role performance within news content. The findings reveal a regional pattern in journalistic role performance, with Latin American journalism displaying a more interventionist orientation. The results further showed a significant negative relationship between sociopolitical constraint and interventionist journalistic role performance, suggesting that levels of interventionist journalism decrease as sociopolitical constraints increase. Results also illuminate how national contexts, economic development levels, and political and press freedom influence interventionist journalistic role performance. These findings have significant implications for media organizations and policymakers, highlighting the need for adaptive strategies considering how organizational and contextual factors shape interventionist-driven journalistic practices
Building the Belt and Road Initiative in the Arab World: China’s Middle East Math
No full textThis book analyses the expansion of Chinese power and influence in the Middle East and North Africa (MENA) through Belt and Road Initiative (BRI) projects undertaken in the “1+ 2+ 3 cooperation pattern.” Under this framework, 1 represents energy, 2 represents infrastructure and trade and investment, and 3 represents nuclear energy, space satellites, and renewable energy. Taken together these elements indicate the trajectory of China’s MENA presence, and by examining each element in different states across the region, this book provides empirical evidence of which states or subregions are most important for Beijing’s regional ambitions, as well as a better understanding of which sectors are being developed. The book will draw substantial interest from a wide range of readers including academics in the field of Chinese foreign policy, international relations, international political economy, foreign policy analysis, and area studies. Professionals in the corporate world will also be engaged and governmental practitioners and non-government agencies will also find it an important resource
