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As1the most revolutionary technology in cell therapy, stem cell technology will give birth to a series of new biotechnology, drive the development of the pharmaceutical industry, and lead the future of biological economy. Guangdong province has a good foundation for stem cell R&D and has set up a project named "Stem Cell and Tissue Engineering Technology" to make breakthroughs in core technologies of stem cell research. In this paper, it is of great significance to compile the industrial technology roadmap and clarify the key points of stem cell and tissue engineering industry through the research to provide reference for the decision-making in Guangdong province.

Magnesium slag is a kind of industrial waste with the similar chemical composition to silicate cements, which is of hydration activity to some extent. However, the hydration activity of magnesium slag is much lower than that of traditional silicate cements. Through the method of carbonization, this experiment enhanced the hydration activity of magnesium slag, so that the strength of magnesium slag products can meet the requirements of structures together with certain mechanical and physical properties as well.

Magnesium slag is an industrial waste residue produced in magnesium smelting. Its chemical composition is similar to Portland cement and has potential activity. After the magnesium slag is foamed, it can be made into a new type of lightweight material, and its forming, mechanical and physical properties were closely related to dry material formula, water-cement ratio and foaming agent amount. In this paper, experiments were carried out to find the ingredients and processes suitable for the forming of foamed magnesium slag, making it have certain mechanical and physical properties at the same time.

This study takes students from higher vocational colleges in SX City as the research object to study the relationship between parental parenting styles and employment anxiety. The study used the Parenting Style Scale and the Employment Anxiety Scale, and conducted a questionnaire survey on the research subjects, using four-point and five-point scales respectively for scoring. The results confirmed that both the father’s and mother’s parenting styles have a significant impact on employment anxiety, among which the father’s and mother’s parenting styles have varying degrees of impact on employment anxiety in different dimensions. The research conclusion states that parents should educate their children in a more reasonable and scientific way, or with warm educational techniques so that students in higher vocational colleges can have a better mental state to face the fierce competition. At the same time, negative education methods must be avoided in order to better build students’ self-confidence and alleviate factors such as lack of confidence in anxiety. Finally, it is suggested that parents need to consider their children’s ideas more and not impose their own hospitals on their children too much, and that higher vocational students should learn how to alleviate their friends’ employment anxiety.

Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.