The Emergence of 5-Year-Olds’ Behavioral Difficulties: Analyzing Risk and Protective Pathways in the United Kingdom and Germany

This study aimed to advance our understanding of 5-year-olds’ behavioral difficulties by modeling and testing both mediational protective and risk pathways simultaneously. Drawing on two national samples from different Western European countries—the United Kingdom (13,053) and Germany (2,022), the proposed model considered observed sensitive parental interactive behaviors and tested child vocabulary as protective pathways connecting parental education with children’s behavioral outcomes; the risk pathways focused on negative parental disciplinary practices linking (low) parental education, parental distress, and children’s difficult temperament to children’s behavioral difficulties. Further, the tested model controlled for families’ income as well as children’s sex and formal child care attendance. Children with comparatively higher educated parents experienced more sensitive interactive behavior, had more advanced vocabulary, and exhibited fewer behavioral difficulties. Children with a comparatively higher level of difficult temperament or with parents who suffered from distress tended to experience more negative disciplinary behavior and exhibited more behavioral difficulties. Additionally, children’s vocabulary skills served as a mechanism mediating the association between parental education and children’s behavioral difficulties. Overall, we found similar patterns of results across the United Kingdom and Germany with both protective and risk pathways contributing simultaneously to children’s behavioral development. The findings suggest that promoting parents’ sensitive interactive behaviors, favorable disciplinary practices, and child’s vocabulary skills have potential for preventing early behavioral difficulties.

The Expenses Needed for the Effect of Restricted Group Sandplay on Interpersonal Sensitivity in College Students

This study aims to investigate the intervention effect of group sandplay on the interpersonal sensitivity of college students and analyze the relationship between the theme and interactive behavior characteristics and the intervention effect of group sandplay especially during the period of COVID-19. Sixty college students were randomly assigned to the experimental group (group sandplay) or the control group (neutral task interventions). The results showed that the interpersonal sensitivity level of the experimental group was significantly lower than that of the control group. For the experimental group, the variation in the interpersonal sensitivity level was significantly negatively correlated with the change in warm, supportive behavior during group sandplay interaction. These findings suggest that group sandplay is effective in improving the interpersonal sensitivity level of college students, and this effect can be positively predicted by warm and supportive interaction behaviors in group sandplay.

The application of graph theoretical analysis to complex networks in medical malpractice: Lessons learned from China (Preprint)

BACKGROUND Studies have shown that hospitals or physicians with multiple malpractice claims are more likely to be involved in new claims; this finding indicates that medical malpractice may be clustered by institutions. OBJECTIVE We aimed to identify common factors that contribute to developing interventions to reduce future claims and patient harm. METHODS This study implemented a null hypothesis whereby malpractice claims are random events—attributable to bad luck with random frequency. As medical malpractice is a complex issue, thus, this study applied the complex network theory, which provided the methodological support for understanding interactive behavior in medical malpractice. Specifically, this study extracted the semantic network in 6610 medical litigation records (unstructured data) obtained from a public judicial database in China; they represented the most serious cases of malpractice in the country. The medical malpractice network of China (MMNC) was presented as a knowledge graph; it employs the International Classification of Patient Safety from the World Health Organization as a reference. RESULTS We found that the MMNC was a scale-free network: the occurrence of medical malpractice in litigation cases was not random, but traceable. The results of the hub nodes revealed that orthopedics, obstetrics and gynecology, and emergency department were the three most frequent specialties that incurred malpractice; inadequate informed consent work constituted the most errors. Non-technical errors (e.g. inadequate informed consent) showed a higher centrality than technical errors. CONCLUSIONS Hospitals and medical boards could apply our approach to detect hub nodes that are likely to benefit from interventions; doing so could effectively control medical risks. CLINICALTRIAL Not applicable

Recovery of Magnesium from Ferronickel Slag to Prepare Magnesium Oxide by Sulfuric Acid Leaching

This paper provides a technical approach for efficiently recovering Mg from ferronickel slag to produce high-quality magnesium oxide (MgO) by using the sulfuric acid leaching method under atmospheric pressure. The leaching rate of magnesium is 84.97% after a typical one-step acid leaching process, which is because Mg in FNS mainly exists in the forsterite (Mg2SiO4) phase, which is chemically stable. In order to increase the leaching rate, a two-step acid leaching process was proposed in this work, and the overall leaching rate reached up to 95.82% under optimized conditions. The response surface methodology analysis for parameter optimization and Mg leaching rules revealed that temperature was the most critical factor affecting the Mg leaching rate when the sulfuric acid concentration was higher than 2 mol/L, followed by acid leaching time. Furthermore, interactive behavior also existed between the leaching temperature and leaching time. The leaching kinetics of magnesium from FNS followed a shrinkage-nuclear-reaction model with composite control, which were chemically controlled at lower temperatures and diffusion controlled at higher temperatures; the corresponding apparent activation energy was 19.57 kJ/mol. The leachate can be used to obtain spherical-like alkali magnesium carbonate particles with diameters of 5–10 μm at 97.62% purity. By using a further calcination process, the basic magnesium carbonate can be converted into a light magnesium oxide powder with a particle size of 2–5 μm (MgO content 94.85%), which can fulfill first-level quality standards for industrial magnesium oxide in China.

Research on Real-time Sensing Technology of Aerospace Interactive Behavior Based on Infrared Array Force Tactile

Aerospace robots can replace astronauts to complete dangerous work in the expanded space. How to efficiently and reliably interact with robots in complex and harsh space environment has become an urgent problem and technical challenge. Based on the infrared array force tactile technology, an infrared point force touch detection system is developed to realize the intention sensing of aerospace human-robot interaction. Based on multipoint positioning technology combined with image method and global combined optimal tracking method, the control software is designed, and the effective human-robot interaction in the process of cooperation is realized. Finally, the touch panel test effect of the system is given. This research can provide a theoretical basis for the practical application of aerospace human-robot interaction.

Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

Symbiotic Organisms Search (SOS) is a global optimization algorithm inspired by the natural synergy between the organisms in an ecosystem. The interactive behavior among organisms in nature simulated in SOS consists of mutualism, commensalism, and parasitism strategies to find the global optimum solution in the search space. The SOS algorithm does not require a tuning parameter, which is usually used to balance explorative and exploitative search by providing posterior sampling of the model parameters. This paper proposes an improvement of the Modified SOS (MSOS) algorithm, called IMSOS, to enhance exploitation along with exploration strategies via a modified parasitism vector. This improves the search efficiency in finding the global minimum of two multimodal testing functions. Furthermore, the algorithm is proposed for solving inversion problems in geophysics. The performance of IMSOS was tested on the inversion of synthetic and field data sets from self-potential (SP) and vertical electrical sounding (VES) measurements. The IMSOS results were comparable to those of other global optimization algorithms, including the Particle Swarm Optimization, the Differential Evolution and the Black Holes Algorithms. IMSOS accurately determined the model parameters and their uncertainties. It can be adapted and can potentially be used to solve the inversion of other geophysical data as well.

Crowd intelligence evolution based on complex network

Purpose In recent years, with the increase in computing power, artificial intelligence can gradually be regarded as intelligent agents and interact with humans, this interactive network has become increasingly complex. Therefore, it is necessary to model and analyze this complex interactive network. This paper aims to model and demonstrate the evolution of crowd intelligence using visual complex networks. Design/methodology/approach This paper uses the complex network to model and observe the collaborative evolution behavior and self-organizing system of crowd intelligence. Findings The authors use the complex network to construct the cooperative behavior and self-organizing system in crowd intelligence. Determine the evolution mode of the node by constructing the interactive relationship between nodes and observe the global evolution state through the force layout. Practical implications The simulation results show that the state evolution map can effectively simulate the distribution, interaction and evolution of crowd intelligence through force layout and the intelligent agents’ link mode the authors proposed. Originality/value Based on the complex network, this paper constructs the interactive behavior and organization system in crowd intelligence and visualizes the evolution process.

An improved deep forest model for prediction of e-commerce consumers’ repurchase behavior

As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model’s overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.