António Tapiés is considered a master artist in Spain with the same influence as Picasso, Miró and Dali. Throughout his artistic career, Antoni Tapiés has formed distinctive and personal artistic expression characteristics and forms of expression in his continuous development and changes. The development and aesthetic habits of contemporary art have also been influenced by his work. When we combine his creative background and learning process, we can more accurately analyze his mysterious artistic characteristics and unique forms of expression. When we combine the background and study process of Antoni Tapiés, we can more accurately analyze his mysterious artistic expressions and unique forms of expression.

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.

Lean (also referred to as the Toyota Production System, TPS) is considered to be a radical alternative to the traditional method of mass production and batching principles for maximising operational efficiency, quality, speed and cost. Many hospitals inspired from lean manufacturing to develop their process. They had many improvements in their process. Hospitals reduced their patient waiting times, defects, wastes related to inventory, staff movement and patient transportation by implementing. This study utilizes scientometric and bibliometric tools to analyze visually the literature published in the field of medical lean manufacturing from 2009 to 2023. The relevant articles published from 2009 to 2023 were retrieved from the Web of Science Core Collection, VOSviewer and R software were used for bibliometric analysis and visualization. The number of publications related to the research has been increasing year by year before 2021, and then showed a downward trend, including 418 articles from 64 countries and regions, 743 institutions, 198 journals, and 1766 authors. The United States, Italy, and England are the main publishing countries in this research field. The journal “International Journal of Lean Six Sigma” published the most papers (n = 21) about lean manufacturing in medicine, the author with the most publications is Teeling SP, and the most influential author is Improta G. The top three keywords are “Healthcare”, “Quality improvement” and “Management”. This study provides a comprehensive bibliometric analysis of lean manufacturing in medicine, which can help researchers understand the current research hotspots in this field, explore potential research directions, and identify future development trends.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

Human settlement patterns in the South are clearly inequitable and dysfunctional, with tenure insecurity remaining a significant issue. Consequently, there has been a dramatic increase in housing demand driven by rising household sizes and accelerated urbanization. Local governments have a clear mandate to ensure socio-economic development and promote democracy, which necessitates ongoing consultations and renegotiations with citizens. This paper critically examines the de-densification of informal settlements as a pivotal strategy to enhance the quality of life for citizens, all while maintaining essential social networks. Governments must take decisive action against pandemics by transforming spaces into liveable settlements that improve livelihoods. A qualitative method was employed, analyzing data drawn from interviews to gain insights into individual views, attitudes, and behaviors regarding the improvement of livelihoods in informal settlements. The study utilized a simple random sampling technique, ensuring that every individual in the population selected had an equal opportunity for inclusion. Semi-structured interviews were conducted with twenty community members in Cornubia, alongside discussions with three officials from eThekwini Municipality and KwaZulu Natal (KZN) Provincial Department of Human Settlements. Data was analyzed using thematic analysis, and the findings hold substantial benefits for the most disadvantaged citizens. Therefore, municipalities have an obligation to transform urban areas by reducing inequality, bolstered by national government policy, to achieve a resilient, safe, and accessible urban future. The evidence presented in this paper underscores that local governments, through municipalities, must prioritize de-densifying informal settlements in response to pandemics or hazards. It is vital to leverage community-driven initiatives and reinforce networks within these communities. The paper calls for the establishment of a socially centered government through the District Development Model (DDM), emphasizing socio-economic transformation as a pathway to enhance community quality of life.

This study focuses on the improvement strategy of information technology application ability of science education teachers and students under the background of informatization. Firstly, the current status of informatization of science education and the importance of the information technology application ability of teacher training students are analyzed. Subsequently, the promotion strategies were discussed, including curriculum design and implementation, teacher training and development, provision of practice environment and conditions, and construction of evaluation mechanisms. These strategies are expected to systematically improve the information technology application ability of teacher training students and provide effective support for the development of science education. However, these strategies also need to be tried and refined in practice to adapt to the development needs of information technology and science education.