The learner's acceptance of e‐learning systems has received extensive attention in prior studies, but how their experience of using e‐learning systems impacts on their behavioural intention to reuse those systems has attracted limited research. As the applications of e‐learning are still gaining momentum in developing countries, such as China, it is necessary to examine the relationships between e‐learners' experience and perceptions and their behavioural intention to reuse, because it is argued that system reuse is an important indicator of the system's success. Therefore, a better understanding of the multiple factors affecting the e‐learner's intention to reuse could help e‐learning system researchers and providers to develop more effective and acceptable e‐learning systems. Underpinned by the information system success model, technology acceptance model and self‐efficacy theory, a theoretical framework was developed to investigate the learner's behavioural intention to reuse e‐learning systems. A total of 280 e‐learners were surveyed to validate the measurements and proposed research model. The results demonstrated that e‐learning service quality, course quality, perceived usefulness, perceived ease of use and self‐efficacy had direct effects on users' behavioural intention to reuse. System functionality and system response have an indirect effect, but system interactivity had no significant effect. Furthermore, self‐efficacy affected perceived ease of use that positively influenced perceived usefulness. Practitioner Notes: What is already known about this topic Internet has provided great opportunities for continuous education through e‐learning in rural areas of China. Many factors affect initial e‐learning adoption. Limited attentions have been paid to examine the relationships between e‐learners' experience and perceptions and their behavioural intention to reuse e‐learning systems, which is an important indicator of the system's success. What this paper adds A first attempt to study how e‐learners' experience and perceptions affect their intention to reuse e‐learning systems in the context of rural education in China. Proposed and validated a hybrid model underpinned by information systems success model, technology acceptance model and self‐efficacy theory. An effective model to explain the relationships between contributing factors and their effect on behavioural intention of users to reuse an e‐learning system. Identified key factors and the extent of their impact on e‐learning system reuse that will enable e‐learning providers to be more focused with their effort and work more effectively on future improvement. Implications for practice and/or policy It is essential for system designers to use appropriate system features and functions that can effectively facilitate the teaching and learning processes and outcomes. As the course quality significantly affects the learner's intention to reuse an e‐learning system, pedagogical principles, including principles of developing and structuring the course content, should be employed in the development and evaluation of relevant curriculum. E‐learning content developed for learners in rural areas of China must be practical and relevant to learners' personal development goals and work requirements, so the learners can benefit from the learning outcomes and improve their work performance eventually. A perfectly designed e‐learning system will only work well with appropriate e‐learning service. Human interaction as an important part of services must not be underestimated and eliminated. As perceived ease of use has a strong impact on e‐learning success, e‐learning providers in China must consider the low education level of people living in rural areas and take effective measures to improve the ease of use.

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data technologies, has become one of the most pressing issues. In recent years, there has been an unprecedented increase in food waste, which has had a negative impact on economic growth in many countries. Food waste has also caused serious environmental problems. Agricultural production, post-harvest handling, and storage, as well as food processing, distribution, and consumption, can all lead to food wastage. This wastage is primarily caused by inefficiencies in the food supply chain and a lack of information at each stage of the food cycle. In order to minimize such effects, the Internet of Things, big data-based systems, and various management models are used to reduce food waste in food supply chains. This paper provides a comprehensive review of IoT and big data-based food waste management models, algorithms, and technologies with the aim of improving resource efficiency and highlights the key challenges and opportunities for future research.

Nitrite and nitrate are widely found in various water environments but the potential toxicity of nitrite and nitrate poses a great threat to human health. Recently, many methods have been developed to detect nitrate and nitrite in water. One of them is to use graphene-based materials. Graphene is a two-dimensional carbon nano-material with sp² hybrid orbital, which has a large surface area and excellent conductivity and electron transfer ability. It is widely used for modifying electrodes for electrochemical sensors. Graphene based electrochemical sensors have the advantages of being low cost, effective and efficient for nitrite and nitrate detection. This paper reviews the application of graphene-based nanomaterials for electrochemical detection of nitrate and nitrite in water. The properties and advantages of the electrodes were modified by graphene, graphene oxide and reduced graphene oxide nanocomposite in the development of nitrite sensors are discussed in detail. Based on the review, the paper summarizes the working conditions and performance of different sensors, including working potential, pH, detection range, detection limit, sensitivity, reproducibility, repeatability and long-term stability. Furthermore, the challenges and suggestions for future research on the application of graphene-based nanocomposite electrochemical sensors for nitrite detection are also highlighted.