Hasil untuk "Physics"

B. Bransden, C. Joachain

D. Brewer, C. Gorter, W. Halperin et al.

David. Lamp

C. Will

R. Feynman, R. Leighton, M. Sands

S. Brown, S. Singer

M. B. Pour-El, J. Richards

A. Faessler, D. Wilkinson

T. Nagatani

K. Riley, M. P. Hobson, S. J. B. Frontmatter et al.

S. Ramakrishna

Bhubanjyoti Bhattacharya

E. Akkermans, G. Montambaux

L. R. Hilden, K. Morris

P. Avseth, T. Mukerji, G. Mavko

T. Dauxois, M. Peyrard

G. Hunter, E. Weeks

As one increases the concentration of a colloidal suspension, the system exhibits a dramatic increase in viscosity. Beyond a certain concentration, the system is said to be a colloidal glass; structurally, the system resembles a liquid, yet motions within the suspension are slow enough that it can be considered essentially frozen. For several decades, colloids have served as a valuable model system for understanding the glass transition in molecular systems. The spatial and temporal scales involved allow these systems to be studied by a wide variety of experimental techniques. The focus of this review is the current state of understanding of the colloidal glass transition, with an emphasis on experimental observations. A brief introduction is given to important experimental techniques used to study the glass transition in colloids. We describe features of colloidal systems near and in glassy states, including increases in viscosity and relaxation times, dynamical heterogeneity and ageing, among others. We also compare and contrast the glass transition in colloids to that in molecular liquids. Other glassy systems are briefly discussed, as well as recently developed synthesis techniques that will keep these systems rich with interesting physics for years to come.

W. Adams, K. Perkins, Noah S. Podolefsky et al.

The Colorado Learning Attitudes about Science Survey (CLASS) is a new instrument designed to measure student beliefs about physics and about learning physics. This instrument extends previous work by probing additional aspects of student beliefs and by using wording suitable for students in a wide variety of physics courses. The CLASS has been validated using interviews, reliability studies, and extensive statistical analyses of responses from over 5000 students. In addition, a new methodology for determining useful and statistically robust categories of student beliefs has been developed. This paper serves as the foundation for an extensive study of how student beliefs impact and are impacted by their educational experiences. For example, this survey measures the following: that most teaching practices cause substantial drops in student scores; that a student's likelihood of becoming a physics major correlates with their ``Personal Interest'' score; and that, for a majority of student populations, women's scores in some categories, including ``Personal Interest'' and ``Real World Connections,'' are significantly different from men's scores.

P. Chabert, N. Braithwaite

Xiaofang Li, Chunli Lei, Xiang Bai et al.

Condition monitoring of rotating machinery is critical for ensuring industrial safety and operational reliability. As a core component of intelligent diagnostic systems, domain adaptation methods have achieved notable progress in mechanical fault diagnosis. However, most existing approaches presume a fully shared label space between source and target domains, limiting their effectiveness under partial domain adaptation scenarios commonly encountered in industrial practice. In addition, they often struggle with classification uncertainty near decision boundaries. To address these challenges, this paper proposes a Selective Adversarial Augmentation Network (SAAN) for cross-domain rolling bearing fault diagnosis with partial label space alignment. The proposed framework designs a multi-level feature extraction module to enhance transferable feature representation and a Balanced Augmentation Selective Adversarial Module (BASAM) to dynamically balance class distributions and selectively filter irrelevant source classes, thereby mitigating negative transfer and achieving fine-grained class alignment. Furthermore, an uncertainty suppression mechanism is put forth to reinforce classifier boundaries by minimizing the impact of ambiguous samples. Comprehensive experiments conducted on public and proprietary bearing datasets demonstrate that SAAN consistently surpasses state-of-the-art benchmarks in diagnostic accuracy and robustness, providing an effective solution for practical applications under class-imbalanced and variable operating conditions.