Nationwide integration of AI into the contemporary art sector has taken place since government AI regulations in 2023 to promote AI use. China’s AI integration into industry is ‘ahead’ of other countries, meaning that other countries can learn from these creative professionals. Consequently, contemporary visual artists have devised arts-led sustainable AI solutions to overcome global AI concerns. They are now putting these solutions into practice to maintain their jobs, arts forms, and industry. This paper draws on 30 interviews with contemporary visual artists, and a survey with 118 professional artists from across China between 2023 and 2024. Findings show that 87% use AI and 76% say AI is useful and they will continue to use AI into the future. Findings show professionals have had time to find DIY, bottom-up solutions to AI concerns, including (1) building strong authorship practices, identity, and brand, (2) showing human creativity and inner thinking, (3) gaining a balanced independent position with AI. They want AI regulations to liberalise and promote AI use so they can freely experiment and develop AI. These findings show how humans are directing the use of AI, altering current narratives on AI-led impacts on industry, jobs, and human creativity.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Urban morphologies in the global south are shaped by a complex interplay of historical imprints, from colonial legacies and ethnic tensions to waves of modernization and decolonization efforts. This study delves into the urban morphology of Hangzhou during the late 19th and early 20th centuries, unraveling its transformative patterns steered by a convergence of spatial politics, economic forces, and cultural dynamics. Drawing upon a unique blend of historical map restoration techniques, we unearth pivotal morphological nuances that bridge Hangzhou’s transition from its pre-modern fabric to its modern-day urban layout. We uncover key shifts such as the movement from intricate street layouts to systematic grids, the strategic integration of public spaces like West Lakeside Park, and the city’s evolving urban epicenter mirroring its broader socio-political and economic narratives. These insights not only spotlight Hangzhou’s distinct urban journey in the context of ethnic conflicts, Western influences, and decolonization drives but also underscore the value of context-sensitive urban morphological research in the global south. Our findings emphasize the criticality of synergizing varied methodologies and theoretical perspectives to deepen our comprehension of urban transitions, sculpt place identities, and invigorate public imagination in global urban planning.

Researchers from all over the world have been working tirelessly to combat the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) COVID-19 pandemic since the World Health Organization (WHO) proclaimed it to be a pandemic in 2019. Expanding testing capacities, creating efficient medications, and creating safe and efficient COVID-19 (SARS CoV-2) vaccinations that provide the human body with long-lasting protection are a few tactics that need to be investigated. In clinical studies, drug delivery techniques, including nanoparticles, have been used since the early 1990s. Since then, as technology has advanced and the need for improved medication delivery has increased, the field of nanomedicine has recently seen significant development. PNPs, or polymeric nanoparticles, are solid particles or particulate dispersions that range in size from 10 to 1000 nm, and their ability to efficiently deliver therapeutics to specific targets makes them ideal drug carriers. This review article discusses the many polymeric nanoparticle (PNP) platforms developed to counteract the recent COVID-19 pandemic-related severe acute respiratory syndrome coronavirus (SARS-CoV-2). The primary subjects of this article are the size, shape, cytotoxicity, and release mechanism of each nanoparticle. The two kinds of preparation methods in the synthesis of polymeric nanoparticles have been discussed: the first group uses premade polymers, while the other group depends on the direct polymerization of monomers. A few of the PNPs that have been utilized to combat previous viral outbreaks against SARS-CoV-2 are also covered.

Air pollution in Jakarta has become a severe concern in the last four months. IQAir, in August 2023, revealed that the level of air pollution had reached 161 points on the Air Pollution Standard Index (APSI). The negative impact on society has placed air pollution as a concern for environmental safety and survival in danger. This condition will encourage the development of a national policy agenda to integrate environmental welfare through various energy efficiency channels. This research analyzes the relationship between air pollutant elements that can reduce air quality. The analysis includes pollutant intensity measured by APSI per unit of pollutant as a measure of efficiency. The aim is to observe energy use, which causes an increase in pollutant levels. This research utilizes dynamic system modeling to produce relationships between parameters to produce factors that cause pollution. The parameters used are motorized vehicles, waste burning in landfills, industry, and power plants. The results of historical behavioral tests and statistical suitability tests show that the behavior is suitable for the short and long term. The simulation results show that the pollution level will worsen by the end of 2027, a hazardous condition for society. The optimistic scenario simulation model proposes immediate counter-measures to reduce pollution to 45.01, the ideal condition. To accelerate improvements in air quality, the Government can plan policies to reduce the use of coal by power plants and industry, as well as the use of electric motorized vehicles, resulting in an ideal reduction in pollution by 2024. In conclusion, pollution can be reduced effectively if the Government firmly implements policies to maintain that air quality remains stable below 50 points.