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China has become the low‐cost manufacturing center of the world. The purpose of this paper is to provide an in‐depth analysis of a new manufacturing strategy for China's future manufacturing sector which is trying to transform into a new low‐carbon development paradigm. It also aims to discuss the empirical implications and policy suggestions.

The paper is based on the broad reviewing of the relative government documents and press reports in China, the USA, and Japan. The authors also conducted case research of China's remanufacturing practices.

The high energy consumption and emissions of China's manufacturing sector results from the widespread use of obsolete production equipment, which can produce low‐cost products with a severely negative environmental effect. The remanufacturing strategy can upgrade existing production equipment to improve production efficiency, which will be a more practical paradigm for China's future manufacturing sector.

The previous relative low‐carbon policies in China are mainly associated with the administrative measures, such as direct “command‐control”, which have paid little attention to the practical development paradigm. This paper first provides a framework for understanding and practical evidence for the remanufacturing strategy as a new low‐carbon paradigm for the giant manufacturing sector of the world's largest developing country.

Facial expression provides abundant information for social interaction, and the analysis and utilization of facial expression data are playing a huge driving role in all areas of society. Facial expression data can reflect people's mental state. In health care, the analysis and processing of facial expression data can promote the improvement of people's health. This paper introduces several important public facial expression databases and describes the process of facial expression recognition. The standard facial expression database FER2013 and CK+ were used as the main training samples. At the same time, the facial expression image data of 16 Chinese children were collected as supplementary samples. With the help of VGG19 and Resnet18 algorithm models of deep convolution neural network, this paper studies and develops an information system for the diagnosis of autism by facial expression data.

The facial expression data of the training samples are based on the standard expression database FER2013 and CK+. FER2013 and CK+ databases are a common facial expression data set, which is suitable for the research of facial expression recognition. On the basis of FER2013 and CK+ facial expression database, this paper uses the machine learning model support vector machine (SVM) and deep convolution neural network model CNN, VGG19 and Resnet18 to complete the facial expression recognition.

In this study, ten normal children and ten autistic patients were recruited to test the accuracy of the information system and the diagnostic effect of autism. After testing, the accuracy rate of facial expression recognition is 81.4 percent. This information system can easily identify autistic children. The feasibility of recognizing autism through facial expression is verified.

The CK+ facial expression database contains some adult facial expression images. In order to improve the accuracy of facial expression recognition for children, more facial expression data of children will be collected as training samples. Therefore, the recognition rate of the information system will be further improved.

This research uses facial expression data and the latest artificial intelligence technology, which is advanced in technology. The diagnostic accuracy of autism is higher than that of traditional systems, so this study is innovative. Research topics come from the actual needs of doctors, and the contents and methods of research have been discussed with doctors many times. The system can diagnose autism as early as possible, promote the early treatment and rehabilitation of patients, and then reduce the economic and mental burden of patients. Therefore, this information system has good social benefits and application value.

The primary driver of future aviation has recently been sustainability. The rapid development of radically new, disruptive technologies and solutions should be regularly evaluated to maintain the desired trends in sustainable aviation. The purpose of this research can be listed as follows: (i) to propose a sustainable performance index and methodology (ii) to evaluate the new technologies and solutions, and (iii) apply them to evaluate the effect of technologies and solutions under development.

This paper introduces a total sustainable performance index for evaluating the sustainability; demonstrates its applicability to future development processes; recognizes the supporting new technologies and solutions by implementing their identification, evaluation and selection processes; and defines the major trends and drivers maintaining the sustainability of the future aviation.

This study has resulted in a proposed new “total sustainable performance index,” and methodology of identifying key drivers that allow defining the technology and solution-driven trends, and defines the major trends and listed technologies and solutions that may have a determining role in given trends.

There are dilemmas on taking into account the positive effects of aviation on the economy and society that may overwrite the costs and limited information about the foresight on new technologies and solutions.

It depends on access to required inputs.

Two-way effects of solid expectations of society on the possible greening of aviation can be listed as the social implication of this research.

The proposed “total sustainability performance index” totally evaluates sustainability, including a penalty, considering the policy (regulation) and interest of future generations.