The causation conundrum in climate change litigation has long plagued the legal and scientific communities. This article explores the role of climate attribution theory in solving the loss and damage causation puzzle in climate change litigation. First, it describes the limitations of traditional causation theories in climate change litigation and analyzes the performance of emerging theories, such as the “substantial contribution” theory and the “market share” theory, in addressing this issue. The paper then evaluates the application of climate attribution theory in actual litigation through specific case studies and puts forward a series of policy recommendations. These include strengthening funding and support for climate attribution research, establishing a platform for interdisciplinary cooperation, developing a unified standard of proof, promoting public and judicial education, and promoting the improvement of the international legal framework. Finally, the paper points out the main problems and limitations in the application of climate attribution theory and proposes key directions for future research. The paper posits that by fostering continuous scientific research and enhancing the legal framework, climate attribution theory will assume a more prominent role in climate change litigation and facilitate the process of global climate governance.

With the continuous development of science and technology, network technology has been applied to various fields, and the education model of universities has also made innovations with the application of network technology. In ideological and political education in universities, influenced by traditional educational models and other factors, the quality of education is uneven, and the learning effectiveness of students needs to be improved. Therefore, integrating network technology and innovating teaching methods in ideological and political education in universities is very important. Conducting online ideological and political education in universities can enhance students' interest in learning, while also helping them develop good moral qualities and providing assistance for their future development. This article focuses on the research goal of ideological and political education models in universities, exploring the importance and methods of integrating online ideological and political education in universities, hoping to provide some help for relevant universities.

This paper proposes an incentive model to involve communities and industries in effectively managing coastal waste in Makassar, Indonesia. The model seeks to incentivize stakeholders to invest in waste management solutions and enable public stakeholders to monitor and evaluate the progress of waste management activities. The model actively encourages participation from all stakeholders and builds upon existing efforts to promote environmental accountability. The proposed model includes several key components. It focused on public and private partnerships that should be fostered to coordinate stakeholder approaches and provide capital investment. It also focused on a financial reward scheme that should be adopted to incentivize businesses and individuals that invest in waste management initiatives. Performance bonus awards and tax incentives are proposed as possible incentive schemes. Lastly, a regulatory framework should be developed to ensure environmental standards are met and regulated. The framework should include regular reporting and auditing requirements and the implementation of penalties for those who fail to comply. The proposed incentive model seeks to engage stakeholders in effectively managing coastal waste in Makassar, Indonesia, through public and private incentive schemes.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.

The discourse on advocacy planning involving actors has not explicitly addressed the question of who the actor advocate planner is and how an actor can become an advocate planner. This paper attempts to exploring the actor advocate planner in the context of Regional Splits as, employing social network analysis as a research tool. This research employs an exploratory, mixed-methods approach, predominantly qualitative in nature. The initial phase entailed the investigation and examination of qualitative data through the acquisition of information from interviews with key stakeholders involved in Regional Splits, including communities, non-governmental organizations (NGOs), governmental entities, and political parties. The subsequent phase utilized quantitative techniques derived from the findings of the qualitative analysis, which were then analysis into the Gephi application. The findings indicate that the Regional Splits the Presidium Community represents civil society and political parties serve as crucial advocate planners, facilitating connections between disparate actors and promoting Regional Splits through political parties.