Background: The COVID-19 has caused a sizeable global outbreak and has been declared as a public health emergency of international concern. Sufficient evidence shows that temperature has an essential link with respiratory infectious diseases. The objectives of this study were to describe the exposure-response relationship between ambient temperature, including extreme temperatures, and mortality of COVID-19. Methods: The Poisson distributed lag non-linear model (DLNM) was constructed to evaluate the non-linear delayed effects of ambient temperature on death, by using the daily new death of COVID-19 and ambient temperature data from January 10 to March 31, 2020, in Wuhan, China. Results: During the period mentioned above, the average daily number of COVID-19 deaths was approximately 45.2. Poisson distributed lag non-linear model showed that there was a non-linear relationship (U-shape) between the effect of ambient temperature and mortality. With confounding factors controlled, the daily cumulative relative death risk decreased by 12.3% (95% CI [3.4, 20.4%]) for every 1.0 °C increase in temperature. Moreover, the delayed effects of the low temperature are acute and short-term, with the most considerable risk occurring in 5–7 days of exposure. The delayed effects of the high temperature appeared quickly, then decrease rapidly, and increased sharply 15 days of exposure, mainly manifested as acute and long-term effects. Sensitivity analysis results demonstrated that the results were robust. Conclusions: The relationship between ambient temperature and COVID-19 mortality was non-linear. There was a negative correlation between the cumulative relative risk of death and temperature. Additionally, exposure to high and low temperatures had divergent impacts on mortality.

Background: At the end of 2019, the outbreak of coronavirus disease 2019 (COVID-19) severely damaged and endangered people’s lives. The public health emergency management system in China has played an essential role in handling the response to the outbreak, which has been appreciated by the World Health Organization and some countries. Hence, it is necessary to conduct an overall analysis of the development of the health emergency management system in China. This can provide a reference for scholars to aid in understanding the current situation and to reveal new research topics.Methods: We collected 2247 international articles from the Web of Science database and 959 Chinese articles from the China National Knowledge Infrastructure database. Bibliometric and mapping knowledge domain analysis methods were used in this study for temporal distribution analysis, cooperation network analysis, and co-word network analysis.Results: The first international article in this field was published in 1991, while the first Chinese article was published in 2005. The research institutions producing these studies mainly existed in universities and health organizations. Developed countries and European countries published the most articles overall, while eastern China published the most articles within China. There were 52 burst words for international articles published from 1999–2018 and 18 burst words for Chinese articles published from 2003–2018. International top-ranked articles according to the number of citations appeared in 2005, 2007, 2009, 2014, 2015, and 2016, while the corresponding Chinese articles appeared in 2003, 2004, 2009, and 2011.Conclusions: There are differences in the regional and economic distribution of international and Chinese cooperation networks. International research is often related to timely issues mainly by focusing on emergency preparedness and monitoring of public health events, while China has focused on public health emergencies and their disposition. International research began on terrorism and bioterrorism, followed by disaster planning and emergency preparedness, epidemics, and infectious diseases. China considered severe acute respiratory syndrome as the starting research background and the legal system construction as the research starting point, which was followed by the mechanism, structure, system, and training abroad for public health emergency management.

Background: The self-reported KIDSCREEN questionnaires are ideal for capturing children’s and adolescents’ health-related quality of life (HRQoL) and have gained worldwide popularity. Responding to demands for the concise nature of KIDSCREEN among Chinese Mainland researchers and practitioners, this study aimed to evaluate the psychometric properties of the Mandarin Chinese self-reported KIDSCREEN-27 (KS-27) and KIDSCREEN-10 index (KS-10), which are short versions of the self-reported KIDSCREEN-52 (KS-52). Methods: This study reanalyzed the validation dataset of the Mandarin Chinese self-reported KS-52. The dataset originated from a cross-sectional survey conducted in Weifang City, the Chinese Mainland, from October to November 2016. Adolescents aged 11–17 years (N = 4385) were surveyed, and sub-samples (N = 841) were retested. Statistical analyses were conducted on the feasibility, item and dimension properties, reliability, and validity of the Mandarin Chinese self-reported KS-27 and KS-10. Results: Indirect evidence from the Small Group Pilot Survey indicated that the Mandarin Chinese self-reported KS-27 and KS-10 can be completed on average in less than 12.33 mins. Their response rate exceeded 90% regardless of the unit- and item (dimension)-level. The psychometric properties of items and dimensions were likewise found to be satisfactory. Internal consistency was robust with inter-item and item-total correlations (0.173–0.873, 0.422–0.786), Cronbach’s alpha (0.786–0.881), Guttman’s lambda-2 and - 6 (0.807–0.889, 0.829–0.896), and McDonald’s omega (0.725–0.886). Test–retest reliability at both item- and dimension-level was excellent, with intraclass correlation coefficients (ICCs) of (0.590–0.696, 0.785–0.842); standard error of measurements (SEMs) of (0.352–0.635, 0.949–1.949). Confirmatory factor analysis (CFA) confirmed their five- and one-dimensional structures, albeit with slight modifications. Moreover, the multi-group confirmatory factor analysis (MG-CFA) substantiated their configural and metric invariance across gender and grade groups. Convergent validity was robust, with stronger correlations observed with comparable dimensions of the Mandarin Chinese self-reported PedsQL™ 4.0, while discriminant validity was evident with low correlations observed with less comparable dimensions. The known-group validity was mainly supported by medium to large effect sizes concerning differences in socioeconomic status (η² = 0.07–0.17, Cohen’s d = 0.55–1.03) and mental health status (η² = 0.09–0.40, Cohen’s d = 0.73–1.83). The Mandarin Chinese self-reported KS-52 served as the criterion; the ICCs between the Mandarin Chinese self-reported KS-27 and KS-10 and their comparable dimensions were stronger, indicating robust criterion validity. Conclusions: The Mandarin Chinese self-reported KS-27 and KS-10 demonstrated excellent psychometric properties, indicating their good potential for measuring HRQoL for children and adolescents in the Chinese Mainland.

Agent Oriented Programming (AOP) is a recent promising software paradigm that brings concepts from the theories of artificial intelligence into the mainstream realm of distributed systems, and yet it is rather difficult to find a successful application of agent oriented system (specifically) when large-scale systems are considered. When adopting an agent-oriented approach to solve a problem, there are a number of domain independent issues that must always be solved, such as how to model agent behavior to predict future action and how to allow agents to communicate rather than expecting developers to develop this core infrastructure themselves. In our paper, we address several problems that exist in a socialized e-learning environment and provide solutions to these problems through smart and collaborative agent behavior modeling which learn and adapt themselves through prior experiences, thereby assisting in successful implementation of this large scale e-learning system. In this paper, the author (s) proposes an implementation of a complete distributed e-learning system based on Collaborative filtering (CF) method. The system has intelligent collaborative filtering based tutoring system (ICFTS) capabilities that allow contents, presentation and navigation to be adapted according to the learner's requirements. In order to achieve that development, two concepts were put together: multi-agent systems and data mining techniques (specifically, the ARM algorithm). All the implementation code is developed using MATLAB GUI environment. To our best knowledge, very few literatures discusses a portion of e-learning environment using adaptive software agents, but none of the current literatures addresses a complete implementation of their learning system in detail. The goal of the paper is to implement one such multi-agent based e-learning system which learns from its prior user experiences on top of an agent-oriented middleware that provides the domain-independent infrastructure, allowing the developers to focus on building the key logic behind it. In this system, the agents follow an adaptive cognitive learning approach, where the agent learns through user behaviors via a collaborative filtering technique, or experiencing and then processing and remembering the information in an e-learning environment. The paper will utilize agent (a piece of code) based environment in our e-learning system using ARM. The paper follows a learning approach based cognitive domain of Bloom's Taxonomy such as Analyze, Evaluate, Create, Apply, understand and remember.

The Symptom Checklist (SCL) developed by the Health Behaviour in School‑aged Children (HBSC) study is widely used to capture the psychosomatic complaints (PSC) of non-clinical children and adolescents. Although its psychometric properties have been well established internationally, the performance of the Mandarin Chinese version remains unclear. This study evaluates the Mandarin Chinese HBSC-SCL’s psychometric properties, develops its norm, and creates the corresponding scoring algorithm. Data were collected from a two-wave cross-sectional survey conducted between June 20 and July 11, 2022, across eight Chinese Human Geography Regions (CHGRs). The sample included 3290 junior secondary school students, obtained through convenience sampling (first wave) and multistage, stratified, cluster sampling (second wave). The surveys were administered anonymously in the school setting, using a paper-and-pencil, self-administered questionnaire. The Mandarin Chinese HBSC-SCL’s unidimensionality was verified using confirmatory factor analysis (CFA), and its psychometric properties were comprehensively evaluated using the partial credit model (PCM) of the Rasch measurement method. Based on the above scientific evidence, the population-based norm and norm-referenced scoring algorithm were developed and created. The CFA confirmed that the HBSC-SCL can be considered unidimensional in the Chinese Mainland. Evidence-based on the six features of the Rasch model indicated that the Mandarin Chinese HBSC-SCL has satisfactory psychometric properties. All 5-category rating scales of eight items appropriately differentiated the students’ PSC and demonstrated strong goodness-of-fit. This version also exhibited good unidimensionality and local independence. The Rasch model generated two kinds of reliability indicators, with the item indicators performing well. The person-item map demonstrated acceptable person and item matches, and provided new perspectives for future improvements. Additionally, no substantial uniform differential item functioning (UDIF) was detected across 13 groups (e.g., survey waves, gender, chronological age). The Mandarin Chinese HBSC-SCL demonstrates satisfactory psychometric properties and performs well in the Chinese Mainland context. It provides concise self-reported PSC measures for junior secondary school students, potentially applicable to a broader Chinese-speaking population. Its ease of administration, scoring, and interpretation makes it suitable for routine school monitoring, large-scale population surveys, and clinical applications. Additionally, the population-based norm and norm-referenced scoring algorithm support the broader application of this version and offer new insights for interpreting PSC sum scores.