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Prioritization of technological skills in China has led to scarce resources for art education. In this study, we tested whether personality traits were associated with creative learning and creative thinking skills, and whether these aspects of creativity were linked with academic achievement. We considered self-efficacy and 21st-century skills as mediating and moderating factors.

498 art school coaches were recruited from 12 Chinese universities. Coaches reported on their students’ Big Five personality traits, creative thinking skills, creative learning and self-efficacy, 21st-century skills and academic achievement. Data were analyzed with partial least squares structural equation modeling.

High openness, low conscientiousness, high extraversion and high agreeableness were associated with creative thinking skills, while high openness, low conscientiousness, high agreeableness and low neuroticism were associated with creative learning. Creative thinking and learning skills were both positively associated with academic achievement. Self-efficacy partially mediated the association between creative thinking skills and academic achievement. No moderation effects were identified.

Findings imply that art education would benefit from deeper consideration of individual differences and the promotion of learning environments conducive to creativity.

Industrial land renewal is a significant constituent of urban environment and sustainable development. Most implementation in planning of renewal of industrial land has been mainly conducted at the site level of industrial zones or parks and the larger scale of township planning deserve further attention in China. To fill this gap, this paper aims to investigate the implementation of industrial land renewal for a whole urbanized area under the township master planning.

This study introduces a progressive approach to identify and evaluate the renewal of industrial land in township master planning to move toward a more practical understanding of industrial transition. The authors chose a typical industrialized town, Lijia in Changzhou City, under the development model of “Southern Jiangsu” to explain the measurement and assessment framework to identify and evaluate the renewable industrial land. Synthesizing the idea of sustainable development, the authors investigated the renewable industrial land with an econometric model including multiple-indexes of economic, social and ecological aspects, field observations and depth interviews.

The analysis demonstrated the spatial heterogeneity and complex generous structure of industrial land renewal in developing countries. It pointed out the major responsibility of enterprises as main industrial land users and indispensable responsibility of government and society. Following the idea of organic concentration and avoiding one-size-fits-all kind of deal, the master planning of Lijia emphasized the connection of industrial land and the combination of market force, social force and government regulation.

With original data and discussion, the authors provide more scientific renewal strategies for planners in sustainable development.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

The construction collapse is one of the most serious accidents since it has several attributes (e.g. accident type and consequence) and its occurrence involves various kinds of causal factors (e.g. human factors). The impact of causal factors on construction collapse accidents and the interrelationships among causal factors remain poorly explored. Thus, the purpose of this paper is to use association rule mining (ARM) for cause analysis of construction collapse accidents.

An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored.

Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition.

This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents.

This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.

Building on the theory of brand psychological ownership, this paper aims to explore the mediating role of brand psychological ownership in the relationship between brand personality (innocence/coolness) and consumers’ preferences, as well as identify the boundary conditions of this relationship.

To test the hypotheses, a series of four experiments were conducted in Wuhan, a city in southern China, using questionnaires administered at two universities and two supermarkets. Hypotheses were tested using PLS-SEM in SmartPLS 4.

The results indicate that brand personality, specifically the dimensions of innocence and coolness, has a significant impact on consumers’ brand preferences. Brands with a cool personality are preferred over those with an innocent personality. Moreover, the relationship between brand personality and consumers’ brand preferences is moderated by power motivation and identity centrality.

This study contributes to the literature by differentiating between brand personality of innocence and coolness as two separate constructs and proposing brand psychological ownership as a mechanism through which brand personality affects brand preferences. The study’s samples were drawn from universities and supermarkets in southern China, providing evidence for the significant moderating effects of power motivation and identity centrality on consumers’ brand preferences.

The purpose of this study is to detect and reconstruct a fault in pitot probe and static ports, which are components of the air data system in commercial aircrafts, without false alarm and no need for pitot-static measurements. In this way, flight crew will be prevented from flying according to incorrect data and aircraft accidents that may occur will be prevented.

Real flight data collected from a local airline was used to design the relevant system. Correlation analysis was performed to select the data related to the airspeed and altitude. Fault detection and reconstruction were carried out by using adaptive neural fuzzy inference system and artificial neural networks, which are machine learning methods. MATLAB software was used for all the calculations.

No false alarm was detected when the fault test following the fault modeling was carried out at 0–2 s range by filtering the residual signal. When the fault was detected, fault reconstruction process was initiated so that system output could be achieved according to estimated sensor data.

The presented alternative analytical redundant airspeed and altitude calculation scheme could be used when the pitot-static system contains any fault condition.

Instead of using the methods based on hardware redundancy, the authors designed a new system within the scope of this study. Fault situations that may occur in pitot probes and static ports are modeled and different fault scenarios that can be encountered in all flight phases have been examined.