Aiming at the issues of traditional ant colony algorithm (ACO) in mobile robot path planning, including initial search blindness, susceptibility to local optima, and slow convergence, this paper proposes a multi-strategy improved ant colony algorithm (MS-ACO). Firstly, dynamic non-uniform distribution of initial pheromones is implemented by integrating the repulsive field from artificial the potential field method. Secondly, the heuristic information is enhanced to improve global search capability while constraining unnecessary path turns. Thirdly, an improved pheromone update strategy is developed by adopting distinct updating mechanisms for different evolutionary phases. Finally, dynamic parameter adaptation is achieved through optimized weight coefficients and volatility coefficients that coordinate with the pheromone update strategy, better aligning with the iterative characteristics of ant colony optimization. Experimental results demonstrate that MS-ACO effectively addresses the limitations of traditional ACO. Under identical experimental conditions, it achieves a 30.4% reduction in path length, 37.8% decrease in pathfinding time, and 71% fewer turns compared to conventional methods, verifying the feasibility and superiority of the proposed algorithm.

As an ambivalent symbol of America’s relationship with the natural world of wild things, the bear acquired new importance in the years of Theodore Roosevelt’s presidency (1901–1909). This article offers an interpretation of the significance of the bear at this time, looking at outdoor sportswriting and the cultural response to Roosevelt’s own bear-hunting exploits in that context. It finds two contrasting ideas of the bear, which appears both as a ferocious beast and as a bearskin trophy, a symbol of nature’s uncontrollable power and also a consumer object. Bear-hunting stories, it is proposed here, thus bridged two worlds: that of wild nature and that of human modernity. This, it suggests, was also the essential cultural function of Theodore Roosevelt’s public persona. Serving as president while assuming the unofficial role of bear-hunter-in-chief, and then becoming indelibly associated with sentimentalized cartoon or teddy bears, his image blurred the distinctions between the White House and the Rocky Mountains, modern life and the natural world. It is suggested in this way that the symbolism of the bear enabled Americans to navigate a way into the twentieth century, avoiding a hard choice between industrial modernity and wild nature by retaining a cultural space for both.

Mieko Shiomi (b. 1938) is a pioneering Japanese artist, composer, and performer, known for her lifelong practice that crosses the boundaries between music, visual art, and performance, which has evolved fluidly across cultures, media, and technologies. She co-founded a leading postwar Japanese experimental music collective, Group Ongaku, and later joined Fluxus at the invitation of George Maciunas, which led her to spend a year in New York from 1964–65. This article presents 11 images from an extended interview with Shiomi, conducted by composers Chikako Morishita and Akiko Yamane during their visit to her home in Osaka in January 2020, as part of the filming of the documentary Shadow Piece. In the interview, Shiomi reflects on her childhood, education, career, and family life, and discusses the creation and reception of works such as Endless Box, Mirror Piece, and Spatial Poem. The conversation, originally in Japanese, was translated into English by Kiyo Kamisawa and the author.

This paper introduces the general ideas for parametric integral stochastic orders, with which a continuum of parametric functions are defined as a bridge between different classes of non-parametric functions. This approach allows one to identify a parametric function class over which two given random variables may violate the non-parametric stochastic order with specific patterns. The parameter used to name the parametric function class also measures the ratio of dominance violation for the corresponding non-parametric stochastic orders. Our framework, expanding the domain of stochastic orders, covers the existing studies of almost stochastic dominance. This leads to intuitive explanations and simpler proofs of existing results and their extensions.