Abstract The runoff coefficient (RC) is widely used despite requiring to know the effective contributing area, which cannot be known a priori . In previous work, we defined length (RL), difficult measure. This work aimed define minimum RL (mRL), quantitative and easy proxy of RL, for use in pilot study on biocrusts Tabernas Desert, Spain. We show that RC decreases according hyperbola when area ...

Electronics industry is one of the fastest growing industries in the world. In India also, there are high turnovers and growing demand of electronics product especially after post liberalization in early nineties. These products generate e-waste which has become big environmental issue. Industries can handle these e-waste and product returns efficiently by developing reverse logistics (RL) syst...

Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing to deliver data-intensive solutions achieving higher levels performance than traditional systems. Within Traffic Signal Control (TSC), this has allowed emergence Machine Learning (ML) based Among group, Reinforcement (RL) approaches have performed particularly well. Given lack industry standa...

Environmental concern and consumer rights have entailed e-tailers to adopt better strategies to facilitate product returns from customers. As the demand for reverse logistics (RL) continues to grow, little is known about what motivates e-tailers to enhance their RL capabilities and about the role RL capabilities plays in enabling e-tailers to achieve better customer satisfaction and economic pe...

A radio labeling of a graph G is an assignment of pairwise distinct, positive integer labels to the vertices of G such that labels of adjacent vertices differ by at least 2. The radio labeling problem (RL) consists in determining a radio labeling that minimizes the maximum label that is used (the so-called span of the labeling). RL is a well-studied problem, mainly motivated by frequency assign...

Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the problems have a very large number of states. We present an empirical study of (i) the effect of step-sizes (learning rules) in the convergence of RL algorithms, (ii) stochastic shortest paths in solving average reward probl...

Purpose – The purpose of this study is to investigate how the use of information technology (IT) and supply chain management initiatives (information sharing and collaboration) impact a company’s performance in reverse logistics (RL). Design/methodology/approach – A survey based on a previous exploratory research and literature review was sent out to 600 US companies having substantial activiti...

Objectives of this study were: (a) to compare live weight (LW) and zoometric measures (ZM) of local goats in two locations, (b) to fit the best regression equation for goat LW prediction using ZM. LW, body length (BL), trunk length (TL), withers height (WH), hearth girth (HG), rump width (RW), rump length (RL), head length (HL), head width (HW), and ear length (EL) were measured in 318 Local do...

This paper describes compound reinforcement learning (RL) that is an extended RL based on the compound return. Compound RL maximizes the logarithm of expected double-exponentially discounted compound return in returnbased Markov decision processes (MDPs). The contributions of this paper are (1) Theoretical description of compound RL that is an extended RL framework for maximizing the compound r...