The optimization of processes associated with artificial insemination (AI) is of great importance for the success of the pig industry. Over the last two decades, great reproductive performance has been achieved, making further significant progress limited. Optimizing the AI program, however, is essential to the pig industry\'s sustainability. Thus, the aim is not only to reduce the number of sperm cells used per estrous sow but also to improve some practical management in sow farms and boar studs to transform the high reproductive performance to a more efficient program. As productivity is mainly influenced by the number of inseminated sows, guaranteeing a constant breeding group and with healthy animals is paramount. In the AI studs, all management must ensure conditions to the health of the boars. Some strategies have been proposed and discussed to achieve these targets. A constant flow of high-quality and well-managed breeding groups, quality control of semen doses produced, more reliable technology in the laboratory routine, removal of less fertile boars, the use of intrauterine AI, the use of a single AI with control of estrus and ovulation (fixed-time AI), estrus detection based on artificial intelligence technologies, and optimization regarding the use of semen doses from high genetic-indexed boars are some strategies in which improvement is sought. In addition to these new approaches, we must revisit the processes used in boar studs, semen delivery network, and sow farm management for a more efficient AI program. This review discusses the challenges and opportunities in adopting some technologies to achieve satisfactory reproductive performance and efficiency.

Artificial intelligence (AI) technology represents a disruptive innovation that has garnered significant interest among researchers for its potential applications in ecological and environmental management. While many studies have investigated the impact of AI on carbon emissions, relatively few have delved into its relationship with air pollution. This study sets out to explore the causal mechanisms and constraints linking AI technologies and air pollution, using provincial panel data collected from 2007 to 2020 in China. Furthermore, this study examines the distinct pathways through which AI technology can ameliorate air pollution and reduce carbon emissions. The findings reveal the following key insights: (1) AI technologies have the capacity to significantly reduce air pollution, particularly in terms of PM2.5 and SO2 levels. (2) AI technologies contribute to enhanced air quality by facilitating adjustments in energy structures, improving energy efficiency, and strengthening digital infrastructure. Nonetheless, it is important to note that adjusting the energy structure remains the most practical approach for reducing carbon emissions. (3) The efficacy of AI in controlling air pollution is influenced by geographical location, economic development level, level of information technology development, resource dependence, and public attention. In conclusion, this study proposes novel policy recommendations to offer fresh perspectives to countries interested in leveraging AI for the advancement of ecological and environmental governance.

The biomedical literature is a vast and invaluable resource for biomedical research. Integrating knowledge from the literature with biomedical data can help biological studies and the clinical decision-making process. Efforts have been made to gather information from the biomedical literature and create biomedical knowledge bases, such as KEGG and Reactome. However, manual curation remains the primary method to retrieve accurate biomedical entities and relationships. Manual curation becomes increasingly challenging and costly as the volume of biomedical publications quickly grows. Fortunately, recent advancements in Artificial Intelligence (AI) technologies offer the potential to automate the process of curating, updating, and integrating knowledge from the literature. Herein, we highlight the AI capabilities to aid in mining knowledge and building the knowledge base from the biomedical literature.

This paper explores how the robotic process automation (RPA) can benefit financial applications. To fully exploit RPA technologies potential will empower higher education and finance, which makes a better future together. The mechanism of RPA to mimic the process of human thinking in solving financial problems was discussed. Important technologies, challenges from cooperativeness, responsiveness and interconnectedness were explored. Exploration of automation technologies for COVID-19 prevention will reduce virus transmission, which empowers society governance, higher education and finance.