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Exposure to biomass-burning particulate matter (PM) is associated with various adverse health effects, including respiratory and cardiovascular conditions, cancer, and systemic effects. Multiple mechanisms underlying PM toxicity components derived from biomass burning elicit harmful effects, such as reactive oxygen species (ROS) generation, inflammation, genotoxicity, and tissue-specific damage. Specific compounds or families of compounds present in biomass-burning PM, such as polyaromatic hydrocarbons (PAHs) and their derivatives, have been identified as key contributors to the observed health effects. Their roles in oxidative stress, DNA damage, and cell death have been elucidated in various organs, such as the lungs, liver, kidneys, and brain, providing valuable insights into the systemic biological influence of biomass-burning-related diseases. Current knowledge of the impact of biomass burning highlights the imperative need for further research to understand the health implications of this environmental challenge and the importance of mitigating the adverse effects of increased exposure to biomass-burning pollution to protect the well-being of exposed populations worldwide. This review focuses on the crucial roles of oxidative stress and inflammation in mediating health effects, triggered by exposure to biomass-burning aerosols. It examines various aspects of the health-related impacts of biomass-burning emissions, particularly those from PM components. The review highlights the health consequences on exposed populations, emphasizing specific biochemical responses, contributions to toxicity mechanisms, tissue-specific effects, and the families of compounds responsible for these effects. [ABSTRACT FROM AUTHOR]

Rheum tanguticum is one of the primary rhubarb species used for food and medicinal purposes, and it has recently been gaining more attention and recognition. This research represents the first attempt to use stable isotopes and elemental analysis via IRMS to identify the geographical origin of Rheum tanguticum. A grand total of 190 rhubarb samples were gathered from 38 locations spread throughout the provinces of Gansu, Sichuan, and Qinghai in China. The carbon content showed a decreasing trend in the order of Qinghai, followed by Sichuan, and then Gansu. Nitrogen content was notably higher, with Qinghai and Sichuan displaying similar levels, while Gansu had the lowest nitrogen levels. Significant differences were noted in the δ13C (−28.9 to −26.5‰), δ15N (2.6 to 5.6‰), δ2H (−120.0 to −89.3‰), and δ18O (16.0‰ to 18.8‰) isotopes among the various rhubarb cultivation areas. A significant negative correlation was found between %C and both longitude and humidity. Additionally, δ13C and δ15N isotopes were negatively correlated with longitude, and δ15N showed a negative correlation with humidity as well. δ2H and δ18O isotopes exhibited a strong positive correlation with latitude, while significant negative correlations were observed between δ2H and δ18O isotopes and temperature, precipitation, and humidity. The LDA, PLS-DA, and k-NN models all exhibited strong classification performance in both the training and validation sets, achieving accuracy rates between 82.1% and 91.7%. The combination of stable isotopes, elemental analysis, and chemometrics provides a reliable and efficient discriminant model for accurately determining the geographical origin of R. tanguticum in different regions. In the future, the approach will aid in identifying the geographical origin and efficacy of rhubarb in other studies. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) is widely applied due to its fast identification speed and non-contact detection. However, the identification process of RFID tags is susceptible to interference from other tags and environmental factors, resulting in inaccurate identification data. To overcome these problem, this paper proposes an improved sensing data cleaning scheme for object localization in edge computing environment. In tag level data cleaning, we use adaptive sliding window and further consider dynamic tags and read rate in continuous reading cycle to adjust the window size timely and appropriately. In the reader level data cleaning, we estimate the tag number based on Chebyshev's inequality through Markov chain for cyclic control and optimize different time slot lengths to improve the recognition rate. We build an edge computing environment and combine the proposed tag-level cleaning method and reader-level cleaning method to form a comprehensive RFID data cleaning process. Comparative experimental results show that the RFID data cleaning method proposed in this paper can effectively reduce redundant and missing data and improve the accuracy of tag recognition. [ABSTRACT FROM AUTHOR]

The world faces the ongoing challenge of terrorism and extremism, which threaten the stability of nations, the security of their citizens, and the integrity of political, economic, and social systems. Given the complexity and multifaceted nature of this phenomenon, combating it requires a collective effort, with tailored methods to address its various aspects. Identifying the terrorist organization responsible for an attack is a critical step in combating terrorism. Historical data plays a pivotal role in this process, providing insights that can inform prevention and response strategies. With advancements in technology and artificial intelligence (AI), particularly in military applications, there is growing interest in utilizing these developments to enhance national and regional security against terrorism. Central to this effort are terrorism databases, which serve as rich resources for data on armed organizations, extremist entities, and terrorist incidents. The Global Terrorism Database (GTD) stands out as one of the most widely used and accessible resources for researchers. Recent progress in machine learning (ML), deep learning (DL), and natural language processing (NLP) offers promising avenues for improving the identification and classification of terrorist organizations. This study introduces a framework designed to classify and predict terrorist groups using bidirectional recurrent units and self-attention mechanisms, referred to as BiGRU-SA. This approach utilizes the comprehensive data in the GTD by integrating textual features extracted by DistilBERT with features that show a high correlation with terrorist organizations. Additionally, the Synthetic Minority Over-sampling Technique with Tomek links (SMOTE-T) was employed to address data imbalance and enhance the robustness of our predictions. The BiGRU-SA model captures temporal dependencies and contextual information within the data. By processing data sequences in both forward and reverse directions, BiGRU-SA offers a comprehensive view of the temporal dynamics, significantly enhancing classification accuracy. To evaluate the effectiveness of our framework, we compared ten models, including six traditional ML models and four DL algorithms. The proposed BiGRU-SA framework demonstrated outstanding performance in classifying 36 terrorist organizations responsible for terrorist attacks, achieving an accuracy of 98.68%, precision of 96.06%, sensitivity of 96.83%, specificity of 99.50%, and a Matthews correlation coefficient of 97.50%. Compared to state-of-the-art methods, the proposed model outperformed others, confirming its effectiveness and accuracy in the classification and prediction of terrorist organizations. [ABSTRACT FROM AUTHOR]

Time management is the study to use time scientifically by deploying skills, techniques and means, and maximizing time value to help individuals or organizations efficiently complete tasks and achieve goals. University professor as a body is an important force in teaching and research. In order to ensure high-quality teaching, productive research, we should establish appropriate service systems to help professors save time and improve efficiency. Time management is the main point of penetration in this research. The responsibilities and missions of professors in universities are the main points of concern. The responsibilities for universities to improve the quality of time management for professors were analyzed. Suggestions and counter measures used to economize time and to improve efficiency for professors with better service were proposed. Through introducing the concept and methods of time management for professor-service into the system, one can greatly improve the service quality, optimize professors' time allocation, increase work efficiency.

The current experiments examined the creation of nonbelieved true and false memories after imagining bizarre and familiar actions using the imagination inflation procedure (Goff & Roediger, 1998). In both experiments, participants took part in three sessions. In Session 1, participants had to perform or imagine simple familiar actions (e.g., "stir the water with the spoon") and bizarre actions (e.g., "balance the spoon on your nose"). A day later, participants needed to imagine simple actions of which some were new actions, and some were old actions that appeared in the first session. After a week, the participants completed a recognition task. For those actions that were correctly or incorrectly remembered as having been performed, the participant was challenged that the action was not performed in order to evoke nonbelieved true and false memories. In general, we found that the imagination inflation procedure can successfully induce participants to produce nonbelieved memories. In Study 1, we successfully induced nonbelieved memories for bizarre actions, although in general nonbelieved memory rates were low. In Study 2, more participants formed nonbelieved memories for bizarre actions than for familiar actions. Also, we found that especially belief was more susceptible to revision when memories were challenged than recollection. In two experiments, we showed that nonbelieved memories can successfully be induced for both familiar and bizarre actions.