Bircham International University is a private, unaccredited institution of distance-learning higher education. It is registered in Spain and Delaware, and formerly operated from the Bahamas.
Assessing earthquake-induced landslide (EQIL) susceptibility is essential for hazard mitigation in mountainous regions. While background hydrological variations influence slope stability, long-term mean soil moisture is rarely incorporated into deep learning-based landslide susceptibility mapping (LSM). This study proposes a hybrid Convolutional Neural Network and Swin Transformer (CNN-SwinT) framework that integrates long-term mean soil moisture as a static covariate to represent persistent background moisture conditions. The model couples the local spatial feature extraction of CNNs with the hierarchical contextual representation of Swin Transformers to capture multi-scale spatial dependencies. Using Minxian County of China as the study area, thirteen conditioning factors were selected via multicollinearity and information gain ratio analyses. The dataset was split into training (70%) and validation (30%) sets. Performance comparison against standalone CNN and SwinT models revealed that the hybrid CNN-SwinT achieved the highest accuracy (0.856) and AUC (0.95), with predicted high-susceptibility zones closely aligning with historical inventories. However, these reported metrics reflect a random, spatially non-independent split, and spatial block cross-validation is recommended for future operational deployment. The results demonstrate that incorporating long-term mean soil moisture provides critical complementary hydrological information that enhances predictive performance. These findings indicate that the proposed hybrid framework is reliable and effective for high-resolution EQIL susceptibility mapping.
Meta learning carries the hope of adaptation by Artificial Intelligence (AI) agents for novel tasks. However current meta-learning is not able to meet the need of generalising across diverse task distributions as well as the need for meeting sufficient learning dynamics. Here in this paper we propose Meta-Artificial General Intelligence (Meta-AGI). Meta-AGI is a hierarchical meta-learning framework developed to meet the requirements in generalization and efficiency which is achieved by the combination of three key components including Hierarchical Task Decomposition (HTD), Adaptive Meta-Optimization (AMO) and Structured Experience Replay (SER). In this paper we have provided theoretical analysis establishing generalization bounds and convergence rates as well as demonstrating superior performance across globally accepted multiple benchmarks consisting of Meta-World, Omniglot, Mini-ImageNet and MuJoCo. MetaAGI demonstrated a 15% improvement in few-shot accuracy on Meta-World by reducing the error rate from 31.8% to20.5% and a $\mathbf{2 0 \%}$ reduction in sample complexity which allowed faster learning with fewer data points and thus lowering computational overhead by approximately $10 \%$. These results established MetaAGI as the foundational framework for generalizable artificial intelligence. Meta-AGI demonstrated exceptional results in real-world applications showcasing its capacity beyond theoretical advances. In industrial robotics our Meta-AGI achieved a $\mathbf{4 0 \%}$ reduction in robot programming time and $65 \%$ improvement in task adaptation. In healthcare implementation our Meta-AGI showed92% diagnostic accuracy across diverse medical imaging tasks and in educational applications it demonstrated $\mathbf{4 5 \%}$ faster student learning rates. These results establish our Meta-AGI as the foundational framework for applied generalizable artificial intelligence.
Artificial intelligence continues to struggle with efficiently adapting to new tasks and generalizing across diverse domains. This paper introduces HierarchAGI, a novel framework designed to enhance meta-learning through a combination of structured task decomposition, adaptive optimization, and knowledge integration. By incorporating Multi-level Task Structure Analysis (MTSA), Dynamic Meta-Learning Optimization (DMLO), and Integrated Knowledge Consolidation (IKC), HierarchAGI effectively addresses the core limitations of conventional meta-learning approaches. Empirical evaluations on standard benchmarks-including Meta-World, Omniglot, Mini-ImageNet, and Mu-JoCo-demonstrate the superiority of HierarchAGI, yielding a 17 % improvement in few-shot learning accuracy and a 22 % increase in data efficiency. Beyond benchmark performance, real-world applications underscore its transformative potential. In manufacturing, HierarchAGI enhances robotic task flexibility by 70 %, enabling seamless adaptation to new production requirements. In healthcare, it achieves 94% accuracy in cross-specialty medical image analysis, significantly improving diagnostic precision. In education, its integration with adaptive learning platforms leads to a 50 % improvement in personalized learning outcomes, offering more effective and tailored instruction. These advancements establish HierarchAGI as a substantial step toward building more adaptive, efficient, and generalizable AI systems, with wide-ranging applications across multiple industries.
Introduction: Pregnant women’s satisfaction is a crucial indicator for the continuous improvement of health services, particularly in the management of antenatal care. Objective: The study aimed to explore the experiences of pregnant women who had received antenatal care at Notsè Hospital in the Haho Health Zone for at least six months. It sought to assess their satisfaction, identify aspects of care that met their expectations, and highlight areas needing improvement based on their perceptions. Methods: A cross-sectional study was conducted between January 6 and February 5, 2025, focusing on patients selected based on specific inclusion criteria. The multi-attribute model developed by Erin was utilised to assess satisfaction, with this model dividing satisfaction into six key dimensions: speed, competence, courtesy, comfort, fairness of treatment, and adequacy of expectations. Results: The pregnant women expressed overall satisfaction with certain aspects of their care, particularly with regard to the perception of fairness, with 96% reporting that they felt treated equitably. Furthermore, the pregnant women expressed appreciation for specific services, including dietary advice (66.7%), the provision of free medications (85.1%), and the monitoring of biological constants (87.2%). However, the findings also exposed areas that merited attention. Satisfaction with provider competence was notably low, with only 24.5% feeling confident in their caregivers' abilities. Furthermore, courtesy was identified as a salient issue, with only 31.4% of respondents reporting feelings of kindness and respect during their interactions. Waiting times remained a challenge, with only 54.9% satisfied with the speed of service, while 58.8% of respondents expressed concerns about confidentiality. Conclusion: Improving the quality of antenatal care at Notsè Hospital requires a systematic review of providers' practices, focusing on (i) reducing delays (waiting time), (ii) respecting confidentiality, and (iii) strengthening the interpersonal relationship. An approach that takes into account the socio-cultural specificities of patients is recommended to optimize clinical outcomes and beneficiary satisfaction.
