CO2 capture performance of calcium-based synthetic sorbent with hollow core-shell structure under calcium looping conditions

A novel calcium-based synthetic CO2 sorbent with hollow core-shell structure was prepared by a carbon microsphere template route where carbide slag, alumina cement and glucose were employed as the low-cost calcium precursor, support and carbon source, respectively. The effects of the alumina cement addition, the pre-calcination temperature during the preparation process, the carbon template addition and calcination conditions on CO2 capture performances of the calcium-based synthetic sorbents were studied during calcium looping cycles. The synthetic sorbent containing 5 wt.% alumina cement possesses the highest CO2 capture capacity during calcium looping cycles, which is mainly composed of CaO and Ca12Al14O33. The CO2 capture capacities of the synthetic sorbent under mild and severe calcination conditions can retain 0.37 and 0.29 g/g after 20 cycles, which are 57% and 99% higher than those of carbide slag under the same conditions, respectively. The synthetic sorbent possesses a hollow micro-sphere morphology with a nano-structured shell and meso-porous structure, which decreases the diffusion resistance of CO2. Periodic density functional theory (DFT) calculations are used to explain why Ca12Al14O33 can effectively retard both agglomeration and sintering of the synthetic sorbent. The hollow core-shell model is proposed to explain the CO2 capture mechanism of the synthetic sorbent. For the same CO2 capture efficiency, the energy consumption in the calciner using the synthetic sorbent is much lower than those using carbide slag and natural limestone. This work designs a good method to prepare the hollow sphere-structured synthetic sorbents with high CO2 capture capacity and provides a promising way to integrate efficient CO2 capture with the utilization of industrial waste

Large Language Models for Travel Behavior Prediction

Travel behavior prediction is a fundamental task in transportation demand management. The conventional methods for travel behavior prediction rely on numerical data to construct mathematical models and calibrate model parameters to represent human preferences. Recent advancement in large language models (LLMs) has shown great reasoning abilities to solve complex problems. In this study, we propose to use LLMs to predict travel behavior with prompt engineering without data-based parameter learning. Specifically, we carefully design our prompts that include 1) task description, 2) travel characteristics, 3) individual attributes, and 4) guides of thinking with domain knowledge, and ask the LLMs to predict an individual's travel behavior and explain the results. We select the travel mode choice task as a case study. Results show that, though no training samples are provided, LLM-based predictions have competitive accuracy and F1-score as canonical supervised learning methods such as multinomial logit, random forest, and neural networks. LLMs can also output reasons that support their prediction. However, though in most of the cases, the output explanations are reasonable, we still observe cases that violate logic or with hallucinations

Drug-induced anaphylaxis in China: a 10 year retrospective analysis of the Beijing Pharmacovigilance Database

Background Few studies on the causes of drug-induced anaphylaxis (DIA) in the hospital setting are available. Objective We aimed to use the Beijing Pharmacovigilance Database (BPD) to identify the causes of DIA in Beijing, China. Setting Anaphylactic case reports from the BPD provided by the Beijing Center for Adverse Drug Reaction Monitoring. Method DIA cases collected by the BPD from January 2004 to December 2014 were adjudicated. Cases were analyzed for demographics, causative drugs and route of administration, and clinical signs and outcomes. Main outcome measure Drugs implicated in DIAs were identified and the signs and symptoms of the DIA cases were analyzed. Results A total of 1189 DIA cases were analyzed. The mean age was 47.6 years, and 732 (61.6%) were aged from 18 to 59 years. A total of 627 patients (52.7%) were females. There was a predominance of cardiovascular (83.8%) followed by respiratory (55.4%), central nervous (50.1%), mucocutaneous (47.4%), and gastrointestinal symptoms (31.3%). A total of 249 different drugs were involved. DIAs were mainly caused by antibiotics (39.3%), traditional Chinese medicines (TCM) (11.9%), radiocontrast agents (11.9%), and antineoplastic agents (10.3%). Cephalosporins accounted for majority (34.5%) of antibiotic-induced anaphylaxis, followed by fluoroquinolones (29.6%), beta-lactam/beta-lactamase inhibitors (15.4%) and penicillins (7.9%). Blood products and biological agents (3.1%), and plasma substitutes (2.1%) were also important contributors to DIAs. Conclusion A variety of drug classes were implicated in DIAs. Patients should be closely monitored for signs and symptoms of anaphylaxis when medications are administered especially with antibiotics, TCM, radiocontrast and antineoplastic agents

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial attacks, as it will directly affect the query efficiency. Recent works have attempted to utilize gradient priors to facilitate score-based methods to obtain better results. However, these gradient priors still suffer from the edge gradient discrepancy issue and the successive iteration gradient direction issue, thus are difficult to simply extend to decision-based methods. In this paper, we propose a novel Decision-based Black-box Attack framework with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent gradient prior and time-dependent prior into the gradient estimation procedure. First, by leveraging the joint bilateral filter to deal with each random perturbation, DBA-GP can guarantee that the generated perturbations in edge locations are hardly smoothed, i.e., alleviating the edge gradient discrepancy, thus remaining the characteristics of the original image as much as possible. Second, by utilizing a new gradient updating strategy to automatically adjust the successive iteration gradient direction, DBA-GP can accelerate the convergence speed, thus improving the query efficiency. Extensive experiments have demonstrated that the proposed method outperforms other strong baselines significantly.Comment: Accepted by IJCAI 202

Boosting Few-Shot Text Classification via Distribution Estimation

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.Comment: Accepted to AAAI 202

Use of Epinephrine in Patients with Drug-Induced Anaphylaxis: An Analysis of the Beijing Pharmacovigilance Database

Few studies assessing the use of epinephrine in drug-induced anaphylaxis (DIA) in the hospital setting are available. We utilized the Beijing Pharmacovigilance Database (BPD) to evaluate the appropriateness of epinephrine for DIA management

Empower the Science of Organ Donation by Multidisciplinary Collaboration

Inter-discipline is formed by the interpenetration and integration of multiple disciplines, which has become a notable trend involving interdisciplinary activities and a combination of research and development. Learned from experience worldwide, the management mode for organ donation and procurement activities varies among countries, but the core of the disciplinary construction of organ donation remains the same. The theoretical basis and practice of organ donation is not purely a matter of coordination, but its ground of knowledge is built upon multidisciplinary integration and its implementation relies on a joint-effort approach and requires collaboration of multiple teams. From the sociological viewpoint, organ donation represents the gift of life for transplant patients, which founds the key element in enhancing the harmony of society. While, from a practical perspective, its professionalism has been widely recognized by the international medical community. As a complex medical and social act, organ donation is a medical-centered subject with sociological, humanistic, ethical, psychologic, and juristic attributes. This chapter will provide an overview of how multidisciplinary collaboration empowers the science of organ donation, followed by the summary of recent efforts taken in China in pursuit of this goal as an example

Internet gaming disorder and aggression: A meta-analysis of teenagers and young adults

Background and aimsInternet gaming disorder (IGD) and aggression (AG) are widespread phenomena around the world. Numerous studies have explored the relationship between the two but findings from such studies are inconsistent. The meta-analysis aimed to evaluate the relationship between IGD and AG as well as identify the variables moderating the relationship.MethodStudies investigating the relationship between IGD and AG were searched using selected terms to identify studies published from 1999 to 2022 on CNKI, Wanfang Data, Chongqing VIP Information Co., Ltd. (VIP), Baidu scholar, ProQuest dissertations, Taylor & Francis, Springer, Web of Science, Google Scholar, Elsevier Science (Science Direct), EBSCO, and PsycINFO. The identified studies were pooled and analyzed.ResultsA total of 30 samples comprising 20,790 subjects were identified. Results showed that there was a moderate relationship between IGD and AG (r = 0.300, 95%CI [0.246, 0.353]). Moderator analysis revealed that the relationship between IGD and AG was moderated by the region, age, and survey year.ConclusionThis meta-analysis indicated that people with a higher level of IGD might show more aggression, and people with more aggression might have a higher level of IGD. The correlation coefficient between IGD and AG was significantly higher in Asia than in Europe, higher in primary school than in middle school and university, and higher by increasing year. Overall, our findings provide a basis for developing prevention and intervention strategies against IGD and AG.Systematic review registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022375267, 42022375267