Yang-En University

The global carbon–climate system is a highly complex and dynamic network characterized by multiple feedback loops between interconnected components. Addressing the risks of climate change requires active intervention across these components (Atmospheric level, Surface ocean, and Terrestrial biosphere). Consequently, this research introduces a new mathematical fuzzy control theory to explore how control mechanisms, incorporating both open and closed–loop, can help guide the carbon–climate system toward more stable and sustainable levels. First, a fuzzy mathematical generalization as a compartmental dynamical model is proposed for a formal analysis of closed–loop control strategies for climate regulation. Second, the challenge of managing carbon–climate dynamics is reframed as a network congestion control problem, incorporating critical concepts to highlight gaps in current scientific approaches to climate feedback management. Third, an algorithm based on an implicit open–loop control assumption, incorporating the need for continuous adjustments when discrepancies arise between targets and actual measurements, is introduced. Additionally, taking into account nonlinear behavior and feedback from an international carbon monitoring system, the authors show how the task of regulating the global carbon cycle may be viewed as an abstracted network congestion problem using a reduced complexity model. Finally, a simulation scenario demonstrating how closed–loop control could be developed to more effectively manage the carbon–climate structure is presented.

Effective early warning systems are crucial for mitigating the risks of landslides in vulnerable coastal areas. With the increasing frequency of coastal landslides, precise monitoring solutions are essential for timely interventions. This review addresses real-time monitoring by fiber-optic technologies, which offer high precision, rapid data acquisition, and adaptability, making them a promising solution. Fiber-optics provide continuous, real-time data for early detection of ground movements, unlike traditional methods that struggle with spatial and temporal coverage. Fiber-optic sensors cover large areas and maintain a high frequency, allowing for a detailed assessment of landslide behavior. This review highlights successful implementations of fiber-optic monitoring in both lab and field settings, showcasing their versatility and effectiveness. This review demonstrates the significant contributions of fiber-optic technologies in improving landslide monitoring and risk management. By enabling timely and accurate data collection, these sensors enhance early warning systems and trigger proactive responses to protect lives and infrastructure. Additionally, their use aids in understanding landslides, developing effective mitigation strategies, and land use planning, ultimately strengthening the resilience 22 of coastal communities.

In response to the limited accuracy of the original UNET in image segmentation, this paper presents innovative optimizations to the loss function, resulting in a significant enhancement of UNET's segmentation accuracy. Through experiments conducted on a retinal vascular dataset, UNET demonstrates outstanding segmentation performance. To address the pronounced imbalance between foreground and background pixels in remote sensing road images, we introduce Dice loss to the UNET model, thereby proposing DC-UNET. Evaluation on the Road_segmentation_ideal dataset reveals that DC-UNET achieves an average improvement of 1.06% in the test set crossover ratio compared to the original model. This research not only enhances the accuracy of road segmentation but also amplifies the practicality and utility of deep learning models in real-world applications such as road detection.

With the advent of globalization in recent years, the status of foreign languages in international exchanges and cooperation has become increasingly prominent. To meet the development needs, China's education has also been undergoing reforms accordingly, and the status of English writing in English teaching has been gradually rising. Therefore, conducting relevant research on foreign language writing anxiety (FLWA) is significance for foreign language writing teaching. The research theme of this paper is the causes of FLWA and the strategies to alleviate writing anxiety. This study combines a range of research techniques to gather information on the reasons behind students' writing anxiety and investigate potential solutions. The research finds that multiple factors affect writing anxiety and multiple methods can relieve anxiety. Meanwhile, it also points out the limitations of the research and future directions. This paper points out that the causes of writing anxiety mainly include the cognitive and emotional factors of the writers, such as metacognition, writing self-efficacy, self-confidence, self-esteem, learning outcomes.

Abstract China Taiwan Strait needs to be connected by sea-crossing channels. The ideal model is to build a permanent dam in Taiwan Strait to connect China Mainland and Taiwan Island. The basic idea is to firstly select the endpoints of Mainland side and Taiwan side, determine the dam direction and channel location, then divide the sea area within the dam channel into functional sections such as port base, breeding base, dam community, tourism base, cultural and educational base, scientific research base, industrial park, wind power base, marine cemetery area, garbage landfill area, military base, etc. The military base and other functional sections with national attributes are under the management, development and use of the military and government. Wind power base, breeding base, tourism base, marine cemetery and other functional sections with market attributes are authorized to enterprises for development and operation by bidding. In the process of developing and utilizing the sea area and benefiting from them, each unit should complete the phase construction task of strait dam according to the planning, so as to solve the investment problem. The seabed foundation, seabed piles and abandoned structural parts that have reached the service life of each functional section become the dam structural parts. The dam foundation and main structure are hardened and rocky by the semi-natural and semi-artificial sedimentation consolidation method according to the construction period of 300~500 years, so as to achieve the permanent stability of the dam at low cost. At the same time, the difficulty of the project is reduced. The appropriate dam channel options are North Dam (Pingtan-Xinzhu), Middle Dam (Quanzhou-Zhanghua) and South Dam (Jinmen-Penghu-Jiayi), with effective volumes of about 710 million, 680 million and 890 million m3 respectively.

In this paper, we establish a new compressed sensing model, specifically the $\ell_1-\ell_G$-minimization model. We derive the necessary and sufficient conditions for the recovery of fixed sparse vectors using the $\ell_1-\ell_G$ local minimization model. Building upon this foundation, we further obtain its equivalent condition.

The flood in Pakistan has become one of the severe natural disasters in the country that has killed millions of people and made a wide impact on the economy, social life, and environment of the region. However, while the focus was mostly on the ground response, human emotions, and sentiments, which were shared on social media, known as dark social media data, were left untapped. In this research, federated querying involving the proposed approach EHDSAF (Enhanced Hybrid Dark Social Analytical Framework) is employed to aggregate and analyze unstructured social media data on the 2010 flood disaster. Thus, using the proposed approach, we reveal the unconscious emotions of people in different regions and aged within Pakistan. The study shows the ability to consistently monitor the emotional status of the population, differences in positive, neutral, and negative attitudes depending on the region and age category. These findings suggest a more profound understanding of the process of communication of the experiences and reactions during the crisis and provide the information that may prove useful for strategizing the future disaster management and the carrying out of humanitarian interventions.

Comment on "Transformation of educational introspection: evaluation of the current situation

In the data on the outcomes of higher education employment transformation, there is high correlation among multiple independent variables, leading to multicollinearity. If multicollinearity exists, it can complicate the establishment of predictive analysis models and the interpretation of relationships between variables for higher education employment transformation outcomes. The employment issues of college graduates have become a focal point of social concern. These issues not only reflect the employment status of college graduates but also indirectly indicate the quality of higher education and the level of talent cultivation. Therefore, a prediction method for the effectiveness of higher education employment transformation based on multi-source information technology is proposed. According to the theory of multi-source information technology, an employment data classification framework is constructed to integrate multi-source information. The matching operator function output values are calculated, weights are determined, and the final college student information collection results are output. The standard error between employment unit types and salary ranges is analyzed, clustering training samples are trained, and the distance from each employment information sample to the centroid is calculated to obtain preprocessing results of the transformation outcomes, thus avoiding the impact of multicollinearity on the results of higher education employment transformation. A model structure for analyzing factors influencing higher education employment is established, allowing for an understanding of the characteristics of different types of educational resources and enabling the prediction of the effectiveness of higher education employment transformation. Experimental results show that this method can accurately monitor the dynamic employment situation of university students. After applying this method, the standard error and probability error values are significantly reduced, proving to be significantly effective in predicting the outcomes of higher education employment transformation.