The lay of the land: Associations between environmental features and personality.

OBJECTIVE: Personality traits cluster across countries, regions, cities, and neighborhoods. What drives the formation of these clusters? Ecological theory suggests that physical locations shape humans' patterns of behaviors and psychological characteristics. Based on this theory, we examined whether and how differential land-cover relates to individual personality. METHOD: We followed a preregistered three-pronged analysis approach to investigate the associations between personality (N = 2,690,878) and land-cover across the United States. We used eleven land-cover categories to classify landscapes and tested their association with personality against broad physical and socioeconomic factors. RESULTS: Urban areas were positively associated with openness to experience and negatively associated with conscientiousness. Coastal areas were positively associated with openness to experience and neuroticism but negatively associated with agreeableness and conscientiousness. Cultivated areas were negatively associated with openness. Landscapes at the periphery of human activity, such as shrubs, bare lands, or permanent snows, were not reliably associated with personality traits. CONCLUSIONS: Bivariate correlations, multilevel, and random forest models uncovered robust associations between landscapes and personality traits. These findings align with ecological theory suggesting that an individual's environment contributes to their behaviors, thoughts, and feelings.

Aesthetics++: Refining Graphic Designs by Exploring Design Principles and Human Preference.

During the creation of graphic designs, individuals inevitably spend a lot of time and effort on adjusting visual attributes (e.g., positions, colors, and fonts) of elements to make them more aesthetically pleasing. It is a trial-and-error process, requires repetitive edits, and relies on good design knowledge. In this work, we seek to alleviate such difficulty by automatically suggesting aesthetic improvements, i.e., taking an existing design as the input and generating a refined version with improved aesthetic quality as the output. This goal presents two challenges: proposing a refined design based on the user-given one, and assessing whether the new design is better aesthetically. To cope with these challenges, we propose a design principle-guided candidate generation stage and a data-driven candidate evaluation stage. In the candidate generation stage, we generate candidate designs by leveraging design principles as the guidance to make changes around the existing design. In the candidate evaluation stage, we learn a ranking model upon a dataset that can reflect humans' aesthetic preference, and use it to choose the most aesthetically pleasing one from the generated candidates. We implement a prototype system on presentation slides and demonstrate the effectiveness of our approach through quantitative analysis, sample results, and user studies.

Enumeration optimization of open pit production scheduling based on mobile capacity search domain.

The optimization of open pit mine production scheduling is not only a multistage decision-making problem but also involves space-time dynamic action among multiple factors, which makes it difficult to optimize production capacity, mining sequence, mining life, and other factors simultaneously in optimizing design. In addition, the production capacity is disorderly expanded, the calculation scale is large, and the optimization time is long. Therefore, this article designs a mobile capacity search domain method to improve computing efficiency without omitting the optimal production capacity. At the same time, taking the maximum net present value as the objective function, an enumeration method is used to optimize the possible paths in different capacity domains and calculate the infrastructure investment and facility idle cost required to meet the maximum production capacity on each possible path to control the disorderly expansion and violent fluctuation of production capacity. The research shows that the open pit mine production scheduling optimization algorithm proposed in this article can not only realize the simultaneous optimization of the three elements of production capacity, mining sequence, and mining life but also improve the computing efficiency by 200 times. Furthermore, the production capacity fluctuation is less than 1.4%. The mining life of the mine is extended by 13 years, and the overall economic benefit is increased by 18%.