Titlebook: Numerical and Phyical Aspects of Aerodynamic Flow III; Tuncer Cebeci Conference proceedings 1986 Springer-Verlag New York Inc. 1986 develo

曲解

David E. Edwards,James E. Carterns in the community. To achieve the Sustainable Development Goal, whose slogan is ‘No one will be left behind’, it is essential to shed light on individual children and take the aforementioned necessary measures.

嚴(yán)重傷害

M. Drela,M. Giles,W. T. Thompkins Jr.viding individualised responses and recommendations based on regional characteristics. Moreover, implementing an automatic promotion system could reduce repetition and dropout further. However, it is essential to discuss the provision of remedial measures for academically underperforming children.

Customary

鎮(zhèn)壓

R. Michel,E. Coustols,D. Arnalel poverty risks in households using smart meters, Consumer Access Device (CAD) data, and machine learning. By detecting Activities of Daily Living (ADLS) through household appliance usage, it is possible to spot the early signs of financial difficulty and identify when support packages are required.

extrovert

H. K. Cheng,C. J. Leeement the TRUSTIE environment to support the construction of software ecosystem. We illustrate the framework and key technologies and present typical application practices in both proprietary companies and online communities.

任意

笨重

D. V. Roscoe,H. J. Gibeling,H. McDonald,S. J. Shamroth

Keratectomy

mension linear-information-distribution. Section 4.4 demonstrates the benefit of information distribution for probability distribution estimation. In section 4.5, we construct a fuzzy relation matrixes with the method of information distribution. In section 4.6, we discuss approximate inference based on information distribution.

高度表

Tuncer Cebeci,J. H. Whitelawmension linear-information-distribution. Section 4.4 demonstrates the benefit of information distribution for probability distribution estimation. In section 4.5, we construct a fuzzy relation matrixes with the method of information distribution. In section 4.6, we discuss approximate inference based on information distribution.

amphibian