A model for evaluating the performance of compulsory education inputs in ethnic areas in China.

A scientific performance evaluation model is necessary to establish a performance evaluation index system for compulsory education in ethnic areas and to conduct objective and impartial evaluations. After conducting theoretical analysis and reviewing literature, it was determined that existing educational input performance evaluation models are general and fail to reflect the unique characteristics of compulsory education development in ethnic areas of China. Therefore, this study intends to improve their self-adaptability and degree of fit. Based on the features of China's ethnic areas and the current situation of compulsory education development, a trinity evaluation model of compulsory education input performance in ethnic regions was constructed using the classical performance evaluation theoretical framework. This model includes the "implementation topic - target concept - performance dimension." The government is the main organization responsible for organizing and implementing the entire performance evaluation, with publicness and responsiveness as the value idea of evaluation. The "4E″ of enough, equity, efficiency, and effectiveness are the evaluation objectives, and input, allocation, output, and effect are the dimensions of the building of the performance evaluation index system. The "4E″ evaluation objectives are integrated into the performance evaluation dimensions and index system. The reconstructed theoretical model of performance evaluation combines universality and specificity, highlights the dual attributes of "tool-value," realizes the organic combination of internal and external performance evaluation, illustrates the overall performance evaluation process and ensures objective, fair, and accurate performance evaluation results. It provides useful guidelines for further optimizing compulsory education investment policies and promoting high-quality and well-balanced compulsory education in China's ethnic areas.

Impact of industry-university-research collaboration and convergence on economic development: Evidence from chengdu-chongqing economic circle in China.

Collaboration among industry, universities, and research is crucial for building an innovative nation. Although industry-university-research collaborative innovation (IURCI) and time-space convergence can drive innovation, increase productivity, and spur economic development, their effects on the regional economy have not been thoroughly examined in existing literature. Therefore, this study investigates the impact of industry-university-research collaborative innovation (IURCI) and time-space convergence on economic development in China. Specifically, we focus on local-level cities in the Chengdu-Chongqing Economic Circle (CCEC) and construct an evaluation index system and time-space convergence model to measure the effects of IURCI and time-space convergence on economic development from 2007 to 2021. Our findings indicate that the efficacy of IURCI on economic development in China follows an inverted U-shaped curve, meaning that the marginal impact of IURC may decrease as more creative funds are deployed. Furthermore, the positive marginal effect of inventive talent input may decrease when it surpasses a certain value in an open innovation environment. The spatiotemporal convergence of collaborative innovation and development levels of IURCI in the CCEC shows significant differences. Regionally, the development level of IURCI in different regions exhibits significant differences in state and speed of convergence. In the southern Sichuan urban agglomeration, the collaborative innovation level of industry, education, and research follows an evolutionary process from convergence to divergence and then to convergence. Policymakers should pay close attention to the spatial effect of high-level development of regional IURCI and promote regions with higher development to drive regions with relatively weak development levels.

Big data-driven public health policy making: Potential for the healthcare industry.

The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.

Airborne fungi in Universiti Sains Malaysia: knowledge, density and diversity.

Airborne fungi are among common contaminants in indoor and outdoor environments, leading to poor indoor air quality (IAQ), and to some extent, implicate health risks to humans worldwide. In Malaysia, fungal contamination in institutional buildings is rarely documented although these places are frequently visited by many. This study was conducted to assess the density and diversity of airborne fungi in Universiti Sains Malaysia (USM) main campus, Penang. A total of 11 sampling sites were assessed. Fungi were collected by using Andersen Single Stage Impact Air Sampler N-6 and MEA plates. Two separate trials, namely Trial 1 and Trial 2, were conducted in 2008 and 2019, respectively. The recovered fungi were identified up to the genus level-based morphological features. A survey involving 400 respondents among USM staff and students in relation to fungal contamination in indoor air environment was also conducted to evaluate the knowledge on indoor fungi among USM community. The densities of indoor air fungi in Trial 1 were higher; ranging from 81 to 1743 CFU/m3, exceeding the recommended level set by the Malaysia Industry Code of Practice (MCPIAQ) in some sampling sites, compared to that of in Trial 2 where the densities ranged from 229 to 699 CFU/m3. A total of 154 isolates and 230 isolates of airborne fungi were recovered in Trial 1 and Trial 2, respectively. In total, 11 fungal genera were identified in both trials, and three genera were predominant: Aspergillus, Penicillium, and Cladosporium. The survey also revealed that knowledge of IAQ among staff and students was limited and that they were unaware of fungal contamination and IAQ. A continuous and wide-spread awareness should be implemented at USM main campus for safer and healthier indoor air environments, particularly university students where productivity and efficiency are of the utmost importance.