The global economic trends and the winds of technological change have elevated the status of integration between industry and education for innovation and entrepreneurship to that of being a national strategic priority of China. However, for a long time prior to that, the many differences between the industrial and educational systems have caused a rift between education for innovation and entrepreneurship and professional education, a profound disconnection between professional education and the local industries, and the subsequent disinterest of entrepreneurial mentors. In this paper, we analyze the status of education for innovation and entrepreneurship in Chinese technical universities. It is pointed out that technical universities should deepen the integration between the industry and education for innovation and entrepreneurship in order to mitigate the imbalance between the supply side of the higher education talent training and the demand side of industrial development. It is also argued that technical universities should change their talent training paradigm, which includes a makeover of the organizational structure and of the curricular system, as well as make amends in the innovation ecosystem with respect to the organization of incubation platforms and of teacher–student teams, in order to promote national and regional economic development, as well as social progress. A method to evaluate the performance of the education for innovation and entrepreneurship in Chinese technical universities, based on specific performance indicators including patents filled, publications, awards in competitions, and acquired funding and on certain non-specific ones including organizational arrangements and satisfaction rates, is presented and then applied to the specific case of the Changzhou Institute of Technology.

We examine the effectiveness and sustainability of the distance teacher education program established by the University of Education, Winneba, Ghana, by investigating the differences in the academic performance of students who are trained in the teacher education program via traditional and distance education modes, respectively, from 2011 to 2015. Close attention is paid to the factors that affect the academic performance of students in the distance mode. Our findings confirm that traditional mode students perform better than their distance mode counterparts in terms of cumulative GPAs. Gender and economic demographics of distance study centers are found to affect the academic performance of distance education students significantly. The policy implications of these findings are discussed and directions of further action are outlined.

The growing demand for meat products has made quality and safety assessment a priority throughout production, processing, storage, distribution, and consumption. Meat quality encompasses sensory attributes, physicochemical properties, nutritional composition, microbial status, freshness, and safety-related characteristics. Conventional sensory, physicochemical, and microbiological methods provide reliable reference measurements, but they are generally time-consuming, labor-intensive, destructive, and unsuitable for real-time or online inspection. This review summarizes recent advances in non-destructive technologies for meat quality assessment, including spectroscopy, imaging and machine vision, odor and taste sensing, ultrasonic and electrical methods, low-field nuclear magnetic resonance, and multi-source information fusion. After outlining the fundamental principles and representative applications of each technology, this review further compares the types of sensing information they provide, the quality attributes they are suitable for assessing, their application scenarios, practical limitations, and model validation requirements. Current evidence indicates that these technologies have considerable potential for rapid screening, process monitoring, quality visualization, and intelligent evaluation. However, practical deployment still requires reliable reference measurements, representative calibration datasets, robust models, external validation, and standardization across instruments and production conditions. Future research should prioritize sensor miniaturization, calibration transfer, standardized datasets, explainable intelligent evaluation, and practically deployable multi-source fusion.