High temperature warning indicators play a pivotal role in meteorological departments, serving as crucial criteria for issuing warnings that guide both social production and daily life. Despite their importance, limited studies have explored the relationship between different global warming levels and changes in high temperature warning indicators. In this study, we analyze data from 2,419 meteorological stations over China and utilize the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine historical changes in high temperature warning indicators used by the China Meteorological Administration. We evaluate model performance and estimate future changes in these indicators using an annual cycle bias correction method. The results indicate that since 1961, the number of high temperature days (TX35d and TX40d) and length of season (TX40d and TX40l) with daily maximum temperature reaching or exceeding 35°C and 40°C have increased over China. The intensity of high temperatures (TXx) has strengthened and the geographical extent affected by high temperatures has expanded. In 2022, the occurrence of 40°C high temperatures surges, with Eastern China experiencing a two-day increase in TX40d and an extended seasonal length in TX40l by over five days. While CMIP6 models have underestimated the high temperature indictors associated with 35°C during historical periods, notable difference is not observed between the models and observations for TX40d and TX40l, given their rare occurrence. However, future projections, after bias correction, indicate that the increasing trends for 35°C and 40°C high temperature days and length of season become more pronounced than the raw projection, suggesting a more severe increase than that anticipated originally. As global warming intensifies, the high temperature days and length of season are projected to increase non-linearly, while the intensity of high temperatures is expected to increase linearly. For every 1°C increase in global temperature, the intensity is projected to rise by approximately 1.4°C. The impact of high temperatures is expanding, with the major hotspot for China located in the eastern and northwestern regions. Under 5°C global warming, certain regions in China may experience prolonged extreme high temperatures. For instance, 40°C high temperature days in areas like North China and the Yangtze River Basin could increase by about 32 d, and the length of season could extend by approximately 100 d.
High temperature warning indicators play a pivotal role in meteorological departments, serving as crucial criteria for issuing warnings that guide both social production and daily life. Despite their importance, limited studies have explored the relationship between different global warming levels and changes in high temperature warning indicators. In this study, we analyze data from 2,419 meteorological stations over China and utilize the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine historical changes in high temperature warning indicators used by the China Meteorological Administration. We evaluate model performance and estimate future changes in these indicators using an annual cycle bias correction method. The results indicate that since 1961, the number of high temperature days (TX35d and TX40d) and length of season (TX40d and TX40l) with daily maximum temperature reaching or exceeding 35°C and 40°C have increased over China. The intensity of high temperatures (TXx) has strengthened and the geographical extent affected by high temperatures has expanded. In 2022, the occurrence of 40°C high temperatures surges, with Eastern China experiencing a two-day increase in TX40d and an extended seasonal length in TX40l by over five days. While CMIP6 models have underestimated the high temperature indictors associated with 35°C during historical periods, notable difference is not observed between the models and observations for TX40d and TX40l, given their rare occurrence. However, future projections, after bias correction, indicate that the increasing trends for 35°C and 40°C high temperature days and length of season become more pronounced than the raw projection, suggesting a more severe increase than that anticipated originally. As global warming intensifies, the high temperature days and length of season are projected to increase non-linearly, while the intensity of high temperatures is expected to increase linearly. For every 1°C increase in global temperature, the intensity is projected to rise by approximately 1.4°C. The impact of high temperatures is expanding, with the major hotspot for China located in the eastern and northwestern regions. Under 5°C global warming, certain regions in China may experience prolonged extreme high temperatures. For instance, 40°C high temperature days in areas like North China and the Yangtze River Basin could increase by about 32 d, and the length of season could extend by approximately 100 d.
The omnipresence of the digital ecosystem makes it increasingly important in our societies, which implies that the analysis and study of the digital battlefield in political elections is also becoming more necessary to protect our democracies. Previous literature showed the existence of information operations around the world, designed to manipulate the political perception of citizens, and therefore, the electoral results. This paper examines the Twitter conversation around #14F 2021 Catalonia regional elections, which had special significance due to the pandemic situation and the highly polarized scenario around Catalonia and Spain, using tools and techniques from Big Data Analytics and Artificial Intelligence. The results obtained show that the conversation existed inside robust echo chambers within each political party community, which became even more powerful if parties are unified into political affinity blocks. Also, focusing on the analysis related to the social bot presence, a significant quantity of results showed a higher presence of social bots in VOX party community compared to the rest of communities. This study corroborates other existing studies regarding the Catalan and Spanish scenario on the presence of echo chambers and on the existence of social bots with their tendency to basically amplify content; it also uncovers the lack of existence of cross-conversation between the independentist and unionist political block claimed in other studies.
In response to the overdose crisis, a collaborative group of two community-based organizations, a health authority and a research institute in Vancouver, Canada, implemented a pilot community-based drug checking (CBDC) intervention for sexual and gender minority (SGM) men. This study identified key factors that influenced the implementation of the CBDC intervention, including opportunities and challenges.
We conducted semi-structured interviews with seven pertinent parties involved in the CBDC, including policymakers, researchers and representatives from community-based organizations. These interviews were coded and analyzed using domains and constructs of the Consolidated Framework for Implementation Research.
While drug-related stigma was identified as a challenge to deliver drug checking services, participants described the context of the overdose crisis as a key facilitator to engage collaboration between relevant organizations (e.g., health authorities, medical health officers, community organizations) to design, resource and implement the CBDC intervention. The implementation of the CBDC intervention was also influenced by SGM-specific needs and resources (e.g., lack of information about the drug supply). The high level of interest of SGM organizations in providing harm reduction services combined with the need to expand drug checking into community spaces represented two key opportunities for the CBDC intervention. Here, SGM organizations were recognized as valued partners that fostered a broader culture of harm reduction. Participants’ emphasis that knowing the composition of one’s drugs is a “right to know”, particularly in the context of a highly contaminated illicit drug market, emerged as a key implementation factor. Lastly, participants emphasized the importance of involving SGM community groups at all stages of the implementation process to ensure that the CBDC intervention is appropriately tailored to SGM men.
The context of the overdose crisis and the involvement of SGM organizations were key facilitators to the implementation of a drug checking intervention in SGM community spaces. This study offers contextualized understandings about how SGM knowledge and experiences can contribute to implement tailored drug checking interventions.
In recent years climate prediction has obtained more attention to mitigate the impact of natural disasters caused by climatic variability. Efficient and effective climate prediction helps palliate negative consequences and allows favourable conditions for managing the resources optimally through proper planning. Due to the environmental, geopolitical and economic consequences, forecasting of atmospheric and oceanic parameters still results in a challenging task. An efficient prediction technique named Sea Lion Autoregressive Deer Hunting Optimization-based Deep Recurrent Neural Network (SLArDHO-based Deep RNN) is developed in this research to predict the oceanic and atmospheric parameters. The extraction of technical indicators makes the devised method create optimal and accurate prediction outcomes by employing the deep learning framework. The classifier uses more training samples and this can be generated by augmenting the data samples using the oversampling method. The atmospheric and the oceanic parameters are considered for the prediction strategy using the Deep RNN classifier. Here, the weights of the Deep RNN classifier are optimally tuned by the SLArDHO algorithm to find the best value based on the fitness function. The devised method obtains minimum mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) of 0.020, 0.142, and 0.029 for the All India Rainfall Index (AIRI) dataset.
Effective science communication is necessary for engaging the public in scientific discourse and ensuring equitable access to knowledge. Training doctoral students in science communication will instill principles of accessibility, accountability, and adaptability in the next generation of scientific leaders, who are poised to expand science’s reach, generate public support for research funding, and counter misinformation. To this aim, we provide a guide for implementing formal science communication training for doctoral students.