Al-Qanṭara

Transitivity and Urgency in UNEP’s COP28 Climate Discourse: A Systemic Functional Analysis of Grammatical Metaphor

2025-12-21T07:16:21+00:00

To respond to the climate crisis, it is not enough to produce scientific evidence; the way climate action is written and spoken about also shapes how urgent and unavoidable it feels. In this study we analyse the official COP28 Production Gap Report 2023 book, treating it as a key example of UNEP’s climate discourse. Using a Systemic Functional Linguistics approach,we clause-coded the entire report for process types, participant roles, circumstantial elements and four kinds of grammatical metaphor (nominalisation, verbalisation, adverbialisation and rank-shifting). The analysis shows that relational and material processes work together to present climate change both as a fixed state of affairs and as an unfolding course of action. Carriers and Actors are the most frequent participant roles, while purpose-, location-, manner- and time-related circumstances repeatedly tie actions to specific goals, places and deadlines such as “by 2030”. Dense patterns of nominalisation and adverbialisation, often combined with rank-shifting, compress long causal chains into compact targets and pathways, and distribute agency across governments, sectors and abstract entities like “systems” or “trajectories”. Taken together, these linguistic resources create an “action grammar of urgency” that makes rapid transition appear necessary, time-bound and administratively manageable. The findings highlight how the language of the COP28 book itself can support more persuasive and responsible climate communication, and offer practical cues for policymakers and authors who draft similar reports.

Emotion-Aware Healthcare Chatbots Using Multimodal Deep Learning and Natural Language Understanding

2025-11-28T05:28:22+00:00

The advent of modern technologies like Artificial Intelligence (AI), Internet of Things (IoT) and Deep Learning (DL) has ushered in a transformative era in healthcare, offering innovative solutions towards personalized healthcare by enhancing the quality of various medical services. Our proposed methodology involves the development of a BERT-based medical chatbot, leveraging cutting-edge deep learning technology to significantly enhance healthcare communication and accessibility. The traditional challenges faced by medical chatbots, such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to offer personalized feedback, are addressed through the utilization of Bidirectional Encoder Representations from Transformers (BERT). The performance metrics of our chatbot underscores its effectiveness. With an accuracy of 98%, the chatbot ensures a high level of precision in handling medical queries. The precision score of 97% attests to the accuracy and reliability of its responses. The AUC-ROC score of 97% indicates the chatbot's exceptional ability to predict specific diseases based on user queries and symptoms, showcasing its robust predictive power. Furthermore, a recall of 96% demonstrates the chatbot's capability to avoid missing cases in medical diagnoses, ensuring comprehensive coverage of potential conditions. The F1 score of 98% showcases the chatbot's proficiency in delivering accurate and personalized healthcare information, striking a harmonious balance between precision and recall. Our BERT-based medical chatbot not only addresses the limitations of traditional approaches but also achieves a remarkable performance with high accuracy, precision, predictive power, and comprehensive coverage, making it a valuable tool for advancing the quality of healthcare services.

A Study on the Empowering Effect of Digital Economy Industrial Policies on Common Prosperity in China's Urban and Rural Areas

2025-12-04T18:01:36+00:00

Guided by the goal of common prosperity, China's urban-rural income gap has narrowed continuously but remains significantly wide. With the intensive implementation of policies such as the "Overall Plan for Building a Digital China," the digital economy has emerged as a new engine driving urban-rural integration. Using panel data from 283 prefecture-level cities in China from 2012 to 2023, this study empirically analyzes the intrinsic relationship and operational mechanisms between digital economy industrial policies and common prosperity in urban and rural areas. The results indicate that the enabling effect of digital economy industrial policies on urban-rural common prosperity exhibits significant regional and digital transformation heterogeneity. Specifically, these policies demonstrate stronger enabling effects in the eastern, central, and northeastern regions and areas with high levels of digital transformation. Mechanism analysis indicates that digital economy industrial policies drive shared prosperity through two pathways: stimulating entrepreneurial activity and enhancing resource allocation efficiency. Based on these findings, this study proposes a three-pronged policy approach: optimizing the digital industrial ecosystem through dual measures, channeling entrepreneurial vitality to precisely nurture the fertile ground of shared prosperity, and pursuing optimal resource allocation solutions through coordinated efforts. These recommendations aim to provide theoretical insights for advancing shared prosperity between urban and rural areas.

Pioneering Smart Leadership: Integrating AI Analytics to Transform STEM Education Management for the Future

2025-12-17T02:48:38+00:00

The mixed-methods study explored how integrating artificial intelligence (AI), innovative leadership practices, leadership competencies, and ethical governance influences management outcomes in STEM education within higher education institutions in Pakistan. A quantitative survey was conducted with 380 respondents to assess institutional readiness and the effectiveness of AI-enabled management practices. Descriptive analyses revealed generally positive perceptions across all constructs, with mean scores ranging from 3.72 to 3.88 on a five-point scale. All measurement scales demonstrated high internal consistency (α = .88–.93), and confirmatory factor analysis indicated an excellent model fit (CFI = .95, TLI = .94, RMSEA = .054), confirming strong construct validity. Correlation results showed significant, moderate-to-strong positive associations among all variables (r = .45–.66), indicating that practical AI usage and leadership practices are aligned with improved STEM management outcomes. Independent t-tests revealed no significant gender differences, whereas ANOVA results showed significant differences by job position. This result suggests that administrators and department heads perceive AI-enabled outcomes more positively than faculty members. Multiple regression analysis demonstrated that AI integration, visionary leadership, leadership competence, and ethical governance significantly predicted STEM management outcomes, explaining 35% of the variance. Structural equation modeling further supported the hypothesized relationships, with visionary leadership (β = .32) and AI integration (β = .30) identified as the strongest contributors. The findings emphasize the crucial role of AI-enabled and ethically grounded leadership in enhancing STEM education management. The study highlights the importance of building institutional capacity, promoting data-literate leadership, and establishing robust governance frameworks to optimize the impact of AI-driven decision-making in STEM environments.