A geometric Brownian motion car-following model: towards a better understanding of capacity drop

investigating the feasibility of a proposed model for geometric design of deployable arch structures

deployable scissor type structures are composed of the so-called scissor-like elements (sles), which are connected to each other at an intermediate point through a pivotal connection and allow them to be folded into a compact bundle for storage or transport. several sles are connected to each other in order to form units with regular polygonal plan views. the sides and radii of the polygons are...

I-34: NRY Haplotype Analysis: towards A Better Understanding of The Genetic Basis of Spermatogenic Failure

It has been established that the Y chromosome carries genes required for spermatogenesis and male fertility. For many decades worldwide screening for gene identification has been conducted in research laboratories. However, it has been a difficult process in identifying such genes (i.e. causative mutations) which could explain the phenotypic variation and could be potentially used as markers fo...

a tripartite model of efl teachers attributions, burnout, and ‎self-regulation: towards the prospects of effective teaching

همطالعه حاضر به ارائه مدلی برای آموزش موثر زبان انگلیسی ‏می پردازد. مدل حاضر از سه عامل تاثیر گذار در کارایی ‏تدریس معلمان زبان انگلیسی بهره می برد. این سه عامل شامل ‏سبکهای اسنادی، خود تنطیمی و فرسودگی شغلی معلمان ایرانی ‏زبان انگلیسی می باشد. رساله مورد نظر درچهار فاز طراحی ‏شده است: فاز اول شامل طراحی و رواسازی پرسشنامه سبکهای ‏اسنادی معلمان زبان انگلیسی و فاز دوم شامل استفاده از ‏این پرسشنا...

1 Geometric Brownian motion

where X(t) = σB(t) + μt is BM with drift and S(0) = S0 > 0 is the intial value. We view S(t) as the price per share at time t of a risky asset such as stock. Taking logarithms yields back the BM; X(t) = ln(S(t)/S0) = ln(S(t))− ln(S0). ln(S(t)) = ln(S0) +X(t) is normal with mean μt + ln(S0), and variance σ2t; thus, for each t, S(t) has a lognormal distribution. As we will see in Section 1.4: let...

Understanding Driver Car-Following Behavior Using a Fuzzy Logic Car-Following Model